MOLECULAR MARKERS OF LYMPH NODE METASTASES IN ORAL CANCER
Frank Karel Johan Leusink The research in this thesis was performed at the departments of Oral & Maxillofacial Surgery and Pathology, University Medical Center Utrecht, the Netherlands, and at the departments of Pathology and Otolaryngology-Head and Neck Surgery, Section Tumor Biology, VU University Medical Center, Amsterdam, the Netherlands.
The research was financially supported by the Dutch Cancer Society (KWF Kankerbestrijding) Grant No. KUN 2006-3675 and by the Dutch Society of Oral & Maxillofacial Surgery (NVMKA) BOOA Research Grant 2009.
No commercial funding was received to perform this research.
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Cover design Karel J. Leusink | kareljleusink.blogspot.nl
ISBN: 978-94-6233-502-8 © Copyright 2016, F.K.J. Leusink
All rights reserved. No part of this book may be reproduced in any form, by print, photocopy, electronic data transfer or any other means, without prior permission of the author. MOLECULAR MARKERS OF LYMPH NODE METASTASES IN ORAL CANCER
Moleculaire markers voor lymfklier metastasen van mondkanker
[met een samenvatting in het Nederlands]
Proefschrift
ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus, prof. dr. G.J. van der Zwaan, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op dinsdag 31 januari 2017 des middags 2.30 uur
door
Frank Karel Johan Leusink geboren 7 april 1979 te Hengelo (O.) Promotoren: Prof. dr. P.J. van Diest Prof. dr. R. Koole
Copromotoren: Dr. S.M. Willems Dr. R.J.J. van Es CONTENTS
Chapter 1. General introduction and outline of the thesis 7
Chapter 2. Lymphatic vessel density and lymph node metastasis in head and neck 21 squamous-cell carcinoma: a systematic review
Chapter 3. Validation of a gene expression signature for assessment of lymph node 43 metastasis in oral squamous-cell carcinoma
Chapter 4. Novel diagnostic modalities for the clinically node-negative neck in oral 63 squamous-cell carcinoma
Chapter 5. Tumor biological determinants of locoregional recurrence of non-HPV 83 head and neck squamous-cell carcinoma
Chapter 6. Nodal metastasis and survival in oral cancer: Association with protein 101 expression of SLPI, not with LCN2, TACSTD2, or THBS2
Chapter 7. The co-expression of kallikrein 5 and kallikrein 7 associates with poor 121 survival in non- HPV oral squamous-cell carcinoma
Chapter 8. Cathepsin K associates with lymph node metastasis and poor prognosis 145 in oral squamous-cell carcinoma
Chapter 9. General discussion 165
Chapter 10. Future perspectives 177
Chapter 11. Summary 185
Chapter 12. Nederlandse samenvatting 195
Chapter 13. List of publications 205
Chapter 14. Dankwoord 209
CHAPTER 1
General introduction and outline of the thesis Chapter 1
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
8 General introduction and outline of the thesis
GENERAL INTRODUCTION R1 1 R2 Head and neck squamous-cell carcinoma R3 Head and neck squamous-cell carcinoma (HNSCC) is the seventh most common malignancy R4 globally and accounts for approximately 4% of all malignant tumors.1,2 HNSCC arises in mucosal R5 linings of the oral cavity, oropharynx, nasopharynx, hypopharynx and larynx (figure 1).3 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 Figure 1 | Head and neck cancer regions. This figure illustrates location of oral cavity, pharynx (including the nasopharynx, oropharynx and hypopharynx) R27 and larynx. (source: 2012, Terese Winslow LLC) R28 R29 The research in this thesis focuses on two subgroups of HNSCC: first, tumors originating in R30 the oral cavity (oral squamous-cell carcinomas, OSCC) and second, tumors originating in the R31 oropharynx (oropharyngeal squamous-cell carcinomas, OPSCC). Risk factors include tobacco R32 smoking, betel nut chewing, excessive alcohol consumption, and human papillomavirus (HPV) R33 infection,4-6 although the etiologic role of HPV in OSCC is unclear.7 Together, OSCC and OPSCC R34 account for two thirds of the worldwide incidence of HNSCC with an estimation of 400.000 cases R35 and 223.000 deaths in 2008.8 R36 R37 R38 R39
9 Chapter 1
R1 Oral squamous-cell carcinoma R2 With approximately 265.000 new cases annually, OSCC has the highest incidence of all HNSCCs. R3 In The Nether1lands, the incidence is around 1.100 cases and this has slowly been rising over R4 the last two decades.9,10 For OSCC the male to female ratio is 1.6 and reflects the distribution of R5 smoking between both sexes. Subsites of the oral cavity include the lips, the anterior two-thirds R6 of the tongue, the floor of the mouth, the gums, the lining inside the cheeks and lips, the hard R7 palate and the retromolar trigone. Symptoms can consist of a white or red patch or an ulcer on R8 the mucosal lining of the mouth, a mucosal swelling of the jaw that causes dentures to fit poorly R9 or become uncomfortable and unusual bleeding or pain in the mouth.3 R10 R11 Staging of the tumor and the neck is performed by clinical examination (i.e. palpation) and R12 imaging, but accuracy of these techniques to detect small lymph node metastases (LNM) is R13 limited. In general, 30–40% of patients will have occult nodal disease and will develop clinically R14 detectable LNM when the neck is left untreated.11 R15 R16 Surgery is the preferred modality for local treatment of the primary OSCC. Adjuvant treatment R17 (re-resection, radiotherapy, or chemotherapy) may be indicated based on adverse tumor features R18 diagnosed at histopathological examination. For regional treatment of the node-positive neck R19 (cN+) a therapeutic neck dissection (TND) is indicated, but for the clinically node-negative neck R20 (cN0) the choice is either elective neck dissection (END) or watchful waiting (WW) followed by R21 TND in patients who develop manifest metastases. R22 R23 Oropharyngeal squamous-cell carcinoma R24 OPSCC occurs with an annual estimated worldwide incidence of 136.000 cases.8,9 Subsites of R25 the oropharynx include the base of tongue, the tonsils, the soft palate and the posterior and R26 lateral walls of the oropharynx. HPV is reported as a causative factor with prevalence rates varying R27 between 15-90%.12 In The Netherlands the prevalence rate of HPV in OPSCC is around 25% and R28 has increased in the last two decades,13 in contrast to the HPV prevalence rate in OSCC which is R29 reported to be around 3%.14 R30 R31 Treatment of early OPSCC (T1-T2) can be curatively performed effectively with primary radiotherapy R32 or surgery.15 More advanced OPSCC (T3-4 or node positive) calls for multimodal therapy consisting R33 of chemoradiation or primary radiotherapy.16 Although patients with a HPV-positive tumor usually R34 present with a smaller primary tumor, and show more often nodal disease at time of diagnosis16-17 R35 they respond better to therapy and therefore have a better outcome compared to patients with R36 HPV-negative OPSCC.17 However, clinical management for the HPV-positive OPSCC has not R37 changed. Therefore, as in OSCC, the most important factor for management and prognosis in R38 HPV-positive as well as HPV-negative OPSCC is regional LNM. R39
10 General introduction and outline of the thesis
“To treat or not to treat?” - The dilemma of the clinically node-negative neck R1 The presence of regional neck metastases is a major determinant of both prognosis and treatment 1 R2 decisions in patients with OSCC.20 However, the low sensitivity of currently available diagnostic R3 modalities is a problem, because a fairly high proportion (30–40%) of LNM are left undetected in R4 this population. These metastases will develop into overt neck disease during follow-up. R5 R6 A policy has been in place during the last decades to treat the neck even when the tumor has R7 been classified as cN0, indicating no disease is detectable in the neck21 on physical examination R8 and imaging. This strategy prevents disease in the neck becoming more advanced once previously R9 occult metastases become clinically apparent or are detected late during follow-up. Thus, 60–70% R10 of patients receive unnecessary treatment, which in the case of elective neck dissection (END) R11 encompasses a surgical procedure potentially causing disfigurement and associated morbidity.22-25 R12 R13 The alternative approach of watchful waiting (WW) entails careful monitoring of the neck (eg, by R14 ultrasound-guided fine-needle aspiration cytology during follow-up).26,27 Treatment (therapeutic R15 neck dissection) will be given only to patients who develop manifest metastasis, which usually R16 arises within 1–2 years. Although some authors report evidence showing convincingly that END R17 should be preferred over WW,28 others doubt the generalizability of these results and question R18 whether they can be implemented worldwide.29 Consensus—partly based on an often cited R19 but outdated decision analysis model—suggests that elective neck treatment is indicated when R20 the chance of occult nodal disease exceeds 20%.30 Hence, increased accuracy to ascertain the R21 metastatic status of the neck will result in fewer unnecessary elective treatment of the neck. R22 R23 Currently, the neck is staged by palpation and different imaging techniques, including CT, MRI, R24 PET/CT, and ultrasound, which are more accurate than palpation alone.31-33 Morphological and R25 size criteria are the main determinants of specificity of imaging techniques, whereas sensitivity R26 is limited by the detection threshold. Up to a third of nodal metastases in patients with oral R27 SCC are smaller than the 3 mm detection threshold that limits sensitivity of available imaging R28 techniques,34 and occult neck disease therefore remains a relevant issue. In a meta-analysis of R29 assessment of cervical LNM,32 sensitivity of PET/CT was only 50% for patients who were node- R30 negative on palpation, whereas specificity was 87%. The performance of ultrasound, MRI, and R31 CT was equally disappointing. Later research has shown similar results.35 R32 R33 The limitation of imaging techniques to detect small metastatic deposits has led to a search for R34 additional characteristics or biomarkers assessable on the primary tumor to predict nodal disease. R35 Histopathological or molecular features of the primary tumor can predict the presence of nodal R36 metastases in the individual patient, irrespective of the actual size of the tumor.36 Measures such R37 as perineural invasion, vascular invasion, tumor border (infiltrative or pushing), tumor thickness, R38 R39
11 Chapter 1
R1 and depth of invasion have been studied extensively for such correlations. Only depth of invasion R2 is consistently associated with the presence of nodal metastases.37 However, definitions and R3 methods to measure vary (suggested cutoffs range from 1,5 - 10 mm) and consensus when to R4 recommend elective neck dissection is lacking.37-39 R5 R6 Thus, the dilemma in current clinical management of the neck is the choice between possible R7 undertreatment of 30–40% of patients with occult metastases and overtreatment of the R8 remaining 60–70%. Personalized management of the cN0 neck, especially in patients with oral R9 SCCs, would benefit greatly from staging techniques that add accuracy to the assessment of R10 nodal disease, particularly when these methods are non-invasive and are not or only minimally R11 dependent on size of metastases. R12 R13 New diagnostic modalities for the clinically node-negative neck R14 Sentinel lymph-node biopsy R15 In an attempt to more reliably select lymph nodes that potentially contain metastases, sentinel R16 lymph-node (SLN) assessment, which is used extensively in melanoma and breast cancer, has R17 been introduced. The SLN is likely to be the first lymph node to harbor metastases and can be R18 used to provide information on the remainder of the nodal basin. It is usually identified by peri- R19 tumoral injection of radioactive colloid and blue dye. Preoperative lymphoscintigraphy (figure R20 1A), intra-operative visualization of blue coloration, and intra-operative radionuclide detection R21 with a gamma probe (figure 1B) allow identification of the SLN(s). After surgical removal, the R22 node or nodes are studied meticulously by histopathological examination, using step sectioning R23 and immunohistochemistry (figure 1C). R24 R25 If the SLN contains metastatic tumor cells, treatment of the neck is recommended, usually in a R26 second procedure.40 The SLN procedure is deemed more precise than imaging procedures and less R27 invasive than END. Moreover, it is associated with significantly less postoperative morbidity and R28 better shoulder function compared with END,41-42 however paralysis of the marginal mandibular R29 branch of the facial nerve has also been observed in the SLN biopsy procedure. R30 R31 Overall, negative predictive values of SLN biopsy procedures are generally above 88%.43-45 In R32 the American NCCN guidelines, the UK NICE guidelines as well as the guidelines of the Dutch R33 Head and Neck Society, SLN biopsy is already mentioned as an alternative for END.46-48 However, R34 this technique does require experience and is currently recommended only for centers with the R35 necessary facilities and expertise.29 R36 R37 R38 R39
12 General introduction and outline of the thesis
R1 1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 Figure 2 | Sentinel lymph-node biopsy procedure. (A) Lymphoscintigram showing injection site (large white area) and SLN (small white area). (B) Gamma probe- R17 guided SLN biopsy. (C) A micrometastasis (red color) is depicted by immunohistochemistry using antibodies R18 against cytokeratin. (source: 2012, Derrek Heuveling, Remco de Bree and Elisabeth Bloemena) R19 Molecular diagnosis and tumor profiling R20 Besides histological variables, many single molecular markers have been studied to predict the R21 presence of nodal metastasis. In view of the complexity of the metastatic process, no consistent R22 and clinically useful correlations have been noted for any markers36. Gene-expression profiling R23 with DNA microarrays and next generation sequencing approaches (RNAseq) signal a new era of R24 tumor classification and prognostication. RNA isolated from the tumor specimen can be used to R25 ascertain expression levels of all genes simultaneously in one experiment (fig 3). R26 R27 This process has led to novel classifications of lymphomas, breast cancer, and HNSCC.49-51 R28 Furthermore, prognostic profiles have been identified in many tumor types, including breast cancer R29 and HNSCC.52,53 With regard to HNSCC, previous studies identified a gene expression signature R30 for distinguishing metastasizing (N+) from N0 OSCC and N0 oropharynx SCC (OPSCC).54,55 R31 Although promising (negative predictive value of 100%), the independent validation cohort in R32 that study was small (n = 22), with all samples (N = 104) derived from a single clinical center.54 R33 Reanalysis of the entire data set identified more genes with predictive power.55 Before applying R34 such a LNM predicting profile in clinical practice, a study is needed to validate this signature in a R35 large multicenter patient cohort. R36 R37 R38 R39
13 Chapter 1 Chapter 1
R1 Figure 1.6 | Microarray procedure. R2 DNA microarrays are generated by fixation of a collection of DNA probes R3 on a glass surface. Each feature on R4 the microarray represents one human gene. For the analyzed samples, R5 i.e. primary tumors, RNA is isolated R6 and subsequently labeled with a R7 fluorescent group, such as cy5 (red) or cy3 (green). The sample target R8 is combined with a reverse labeled R9 reference target and hybridized on a DNA microarray. After quantification R10 of the sample and reference signal R11 channels on the array, a sample– R12 reference ratio is calculated by which genes can be identified that R13 are higher or lower expressed in the R14 analyzed samples compared to the reference. By combining the results R15 of multiple samples, a specific set R16 of genes can be identified that is R17 able to discriminate the analyzed samples in specific groups. R18 Figure 3 | Microarray procedure and identification of a gene expression signature. DNA microarrays are generated by grid-wise fixation of a collection of DNA probes on a glass surface. Each R19 probe represents onewhereby human gene. thousands From the of analyzed DNA probes samples, that i.e. representanalyzed tumors, genes RNAare fixatedis isolated on and a glass surface and subsequently labeled hybridizedwith a fluorescent against group, fluorophore-labeled such as cy5 (red) or cy3 RNA (green). targets The sample from target various is combined sources (111-113) (Fig. R20 with a reverse labeled reference target and hybridized on a DNA microarray. After quantification of the R21 sample and reference1.6). signal The channels power on of the DNA array, microarray a sample reference technology ratio is is calculated that the by simultaneous which genes measurement of can be identified thatthousands are higher ofor lower genes expressed will uncover in the analyzed higher-order samples organization compared to the in reference. complex By gene transcription R22 combining the results of multiple samples, a specific set of genes can be identified that is able to discriminate R23 the analyzed samplesregulation. in specific groupsThe more (i.e. N0genes vs N+). and (source: biological 2005, samplesPaul Roepman) are studied, the more obvious underlying biological properties or processes become in complex gene expression behavior. The gene R24 expression data generated by microarrays should therefore not be considered a collection of R25 separate “Northerns” but as a composite snapshot of gene expression in the analyzed cells or R26 OUTLINE OF THIS tissues.THESIS Thorough analysis of the complex gene expression data will be the most critical issue R27 in microarray studies to identify underlying biological patterns and mechanism (114-116). R28 The aim of the work in this thesis was to investigate additional modalities to improve assessment R29 of the cN0 neck inDiagnostic patients with gene early expression OSCC. This signatures has been achieved by applying different R30 approaches with regardIn to cancer detecting research, occult DNA metastases. microarrays have initially been used for molecular classification R31 of tumors into known or newly identified subtypes (117-120). In a landmark study, van’t R32 The aim of each individualVeer et alstudy. have was used to discover gene expression one or more profiling molecular to biomarkers predict the associated clinical outcome of breast R33 with LNM or prognosiscancer to provide patients new (61). insight Van’t into Veer and andthe ability colleagues to distinguish identified between a 70-gene different signature that could R34 underlying disease distinguishphenotypes, between with the goalgood of prognosis providing optionsand poor to prognosischange and patient improve groups current based on the gene expression profiles of primary breast tumors, where classical predictors such as histological R35 management in each patient. grade or lymph node status were less successful (61, 78). Since this study, numerous other R36 This thesis started with general aspects of diagnosis and prognosis of OSCC and OPSCC and the gene expression signatures have been identified that are associated with poor outcome of R37 dilemma about the cN0 neck in patients with early OSCC. cancer patients (77, 121-124). Besides prognosis for survival, molecular signatures have R38 been designed for prediction of specific treatment response (125-127), recurrence rate (128, R39
19 14 General introduction and outline of the thesis
The density of the lymphatic vessels (LVD), both intratumoral and peritumoral, as a variable used R1 for the appraisal of the tumor related lymphatic system is described; LVD has been associated to 1 R2 the presence of nodal metastasis in various malignancies. In chapter 2 a systematic review of the R3 literature is presented about the association of both intratumoral and peritumoral LVD to nodal R4 spread in HNSCC, although OSCC was the main primary malignancy studied. R5 R6 The second study described in chapter 3 is the multicenter validation of a previously published R7 diagnostic gene signature that predicts LNM in OSCC. In light of the imperfect clinical examination, R8 which is the current standard and is especially suboptimal for determining the absence of LNM, R9 a concurrent and independent diagnosis based on expression profiling of the primary tumor can R10 greatly improve the matching of patients with the appropriate treatment. R11 R12 Chapter 4 consists of a review of recent developments in staging of the neck, including expression R13 profiling and sentinel node biopsy, and suggests a new staging algorithm to incorporate both R14 methods, to optimize management of the cN0 neck in patients with early-stage OSCC. R15 R16 Chapter 5 aims to identify tumor characteristics that are associated with the development of R17 locoregional recurrences and a poor prognosis and to reveal their biological basis. With gene R18 expression microarray analysis the biological basis of the determinants of locoregional recurrence R19 was established. R20 R21 Four of the most predictive genes of the LNM predicting signature described in chapter 2 are R22 secretory leukocyte protease inhibitor (SLPI), lipocalin-2 (LCN2), thrombospondin-2 (THBS2), and R23 tumor-associated calcium signal transducer 2 (TACSTD2). In chapter 6 their protein expression is R24 correlated with LNM and survival. R25 R26 Chapter 7 tries to reveal the importance of the balance between the kallikrein 5 and 7 proteins R27 and their inhibitor serine protease inhibitor kazal type 5 with regard to LNM. These three genes R28 are included in the LNM predicting signature described in chapter 2. R29 R30 In chapter 8 the role of the protease cathepsin K is described and its value as a single marker R31 predicting LNM and survival. R32 R33 Finally, the results obtained in the current thesis are discussed in chapter 9, followed by some R34 future perspectives in chapter 10, and a summary in chapter 11. R35 R36 R37 R38 R39
15 Chapter 1
R1 REFERENCES R2 1. Ferlay J, Shin HR, Bray F, et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int R3 J Cancer 2010; 127: 2893. R4 2. Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011; 61: 69. R5 3. http://www.cancer.gov/types/head-and-neck/head-neck-fact-sheet R6 4. Blot WJ, McLaughlin JK, Winn DM, et al. Smoking and drinking in relation to oral and pharyngeal cancer. Cancer Res 1988; 48: 3282-3287. R7 5. Argiris A, Karamouzis MV, Raben D, et al. Head and neck cancer. Lancet 2008; 371: 1695-709. R8 6. Ang KK, Harris J, Wheeler R, et al. Human papillomavirus and survival of patients with oropharyngeal R9 cancer. N Engl J Med 2010; 363: 24-35. R10 7. Lingen MW, Xiao W, Schmitt A, et al. Low etiologic fraction for high-risk human papillomavirus in oral cavity squamous cell carcinomas. Oral Oncol 2013; 49: 1-8. R11 8. Chaturvedi AK, Anderson WF, Lortet-Tieulent J, et al. Worldwide trends in incidence rates for oral R12 cavity and oropharyngeal cancers. J Clin Oncol 2013; 31: 4550. R13 9. Braakhuis BJ, Visser O, Leemans CR. Oral and oropharyngeal cancer in the Netherlands between 1989 R14 and 2006: Increasing incidence, but not in young adults. Oral Oncol 2009; 45:e85-e89. 10. Dutch cancer figures [Homepage on the Internet] 2016; cited August 28, 2016. Available from: http:// R15 www.cijfersoverkanker.nl. R16 11. Flach GB, Tenhagen M, de Bree R, et al. Outcome of patients with early stage oral cancer managed by R17 an observation strategy towards the N0 neck using ultrasound guided fine needle aspiration cytology: No survival difference as compared to elective neck dissection. Oral Oncol 2013; 49: 157-64. R18 12. Marur S, D’Souza G, Westra WH, et al. HPV-associated head and neck cancer: a virus-related cancer R19 epidemic. Lancet Oncol 2010; 11: 781-9. R20 13. Rietbergen MM, Leemans CR, Bloemena E et al. Increasing prevalence rates of HPV attributable R21 oropharyngeal squamous cell carcinomas in the Netherlands as assessed by a validated test algorithm. Int J Cancer 2013; 132: 1565-71. R22 14. Combes JD, Franceschi S. Role of human papillomavirus in non-oropharyngeal head and neck cancers. R23 Oral Oncol 2014; 50: 370-9. R24 15. Cohan DM, Popat S, Kaplan SE, et al. Oropharyngeal cancer: current understanding and management. Curr Opin Otolaryngol Head Neck Surg 2009; 17: 88-94. R25 16. Lanza L, Rizzi L, Durso D, et al. Integrated treatment in locally advanced carcinoma of the oropharynx. R26 J Surg Oncol 2000; 74: 75-8. R27 17. Gillison ML, D’Souza G, Westra W, et al. Distinct risk factor profiles for human papillomavirus type R28 16-positive and human papillomavirus type 16-negative head and neck cancers. J Natl Cancer Inst 2008 19; 100: 407-20. R29 18. Schwartz SR, Yueh B, McDougall JK, et al. Human papillomavirus infection and survival in oral squamous R30 cell cancer: a population-based study. Otolaryngol Head Neck Surg 2001; 125: 1-9. R31 19. Ang KK, Harris J, Wheeler R, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med 2010; 363: 24-35. R32 20. Argiris A, Karamouzis MV, Raben D, et al. Head and neck cancer. Lancet 2008; 371: 1695-709. R33 21. Ferlito A, Silver CE, Rinaldo A. Elective management of the neck in oral cavity squamous carcinoma: R34 current concepts supported by prospective studies. Br J Oral Maxillofac Surg 2009; 47: 5-9. R35 22. van Wilgen CP, Dijkstra PU, van der Laan BF, et al. Morbidity of the neck after head and neck cancer therapy. Head Neck 2004; 26: 785-91. R36 23. van Wouwe M, de Bree R, Kuik DJ, et al. Shoulder morbidity after non-surgical treatment of the neck. R37 Radiother Oncol 2009; 90: 196-201. R38 24. Bradley PJ, Ferlito A, Silver CE, et al. Neck treatment and shoulder morbidity: still a challenge. Head R39 Neck 2011; 33: 1060-67.
16 General introduction and outline of the thesis
25. Speksnijder CM, van der Bilt A, Slappendel M, et al. Neck and shoulder function in patients treated for oral malignancies: a 1-year prospective cohort study. Head Neck 2013; 35: 1303-13. R1 26. Nieuwenhuis EJ, Castelijns JA, Pijpers R, et al. Wait-and-see policy for the N0 neck in early-stage oral 1 R2 and oropharyngeal squamous cell carcinoma using ultrasonography-guided cytology: is there a role for R3 identification of the sentinel node? Head Neck 2002; 24: 282-89. R4 27. Rodrigo JP, Shah JP, Silver CE, et al. Management of the clinically negative neck in early-stage head and neck cancers after transoral resection. Head Neck 2011; 33: 1210-19. R5 28. D’Cruz AK, Vaish R, Kapre N, et al. Elective versus Therapeutic Neck Dissection in Node-Negative Oral R6 Cancer. N Engl J Med 2015; 373: 521-9. R7 29. de Bree R, van den Brekel MWM. Elective neck dissection versus observation in the clinically node negative neck in early oral cancer: Do we have the answer yet? Oral Oncol 2015; 51: 963-65. R8 30. Weiss MH, Harrison LB, Isaacs RS. Use of decision analysis in planning a management strategy for the R9 stage N0 neck. Arch Otolaryngol Head Neck Surg 1994; 120: 699-702. R10 31. de Bondt RB, Nelemans PJ, Hofman PA, et al. Detection of lymph node metastases in head and neck R11 cancer: a meta-analysis comparing US, USgFNAC, CT and MR imaging. Eur J Radiol 2007; 64: 266-72. 32. Kyzas PA, Evangelou E, Denaxa-Kyza D, et al. 18F-fluorodeoxyglucose positron emission tomography R12 to evaluate cervical node metastases in patients with head and neck squamous cell carcinoma: a meta- R13 analysis. J Natl Cancer Inst 2008; 100: 712-20. R14 33. Ng SH, Yen TC, Chang JT, et al. Prospective study of [18F] fluorodeoxyglucose positron emission tomography and computed tomography and magnetic resonance imaging in oral cavity squamous cell R15 carcinoma with palpably negative neck. J Clin Oncol 2006; 24: 4371-76. R16 34. Buckley JG, MacLennan K. Cervical node metastases in laryngeal and hypopharyngeal cancer: a R17 prospective analysis of prevalence and distribution. Head Neck 2000; 22: 380-85. R18 35. Liao LJ, Lo WC, Hsu WL, et al. Detection of cervical lymph node metastasis in head and neck cancer patients with clinically N0 neck-a meta-analysis comparing different imaging modalities. BMC Cancer R19 2012; 12: 236. R20 36. Takes RP, Rinaldo A, Rodrigo JP, et al. Can biomarkers play a role in the decision about treatment of the clinically negative neck in patients with head and neck cancer? Head Neck 2008; 30: 525-38. R21 37. Melchers LJ, Schuuring E, van Dijk BA, et al. Tumour infiltration depth >/=4 mm is an indication for an R22 elective neck dissection in pT1cN0 oral squamous cell carcinoma. Oral Oncol 2012; 48: 337-42. R23 38. Pentenero M, Gandolfo S, Carrozzo M. Importance of tumor thickness and depth of invasion in nodal R24 involvement and prognosis of oral squamous cell carcinoma: a review of the literature. Head Neck 2005; 27: 1080-91. R25 39. Huang SH, Hwang D, Lockwood G, et al. Predictive value of tumor thickness for cervical lymph-node R26 involvement in squamous cell carcinoma of the oral cavity: a meta-analysis of reported studies. Cancer R27 2009; 115: 1489-97. 40. Alkureishi LW, Burak Z, Alvarez JA, et al. Joint practice guidelines for radionuclide lymphoscintigraphy R28 for sentinel node localization in oral/oropharyngeal squamous cell carcinoma. Ann Surg Oncol 2009; R29 16: 3190-210. R30 41. Murer K, Huber GF, Haile SR, et al. Comparison of morbidity between sentinel node biopsy and elective neck dissection for treatment of the n0 neck in patients with oral squamous cell carcinoma. Head Neck R31 2011; 33: 1260-64. R32 42. Schiefke F, Akdemir M, Weber A, et al. Function, postoperative morbidity, and quality of life after R33 cervical sentinel node biopsy and after selective neck dissection. Head Neck 2009; 31: 503-512. R34 43. Civantos FJ, Zitsch RP, Schuller DE, et al. Sentinel lymph node biopsy accurately stages the regional lymph nodes for T1–T2 oral squamous cell carcinomas: results of a prospective multi-institutional trial. R35 J Clin Oncol 2010; 28: 1395-400. R36 44. Govers TM, Hannink G, Merkx MA, et al. Sentinel node biopsy for squamous cell carcinoma of the oral cavity and oropharynx: a diagnostic meta-analysis. Oral Oncol, 2013; 49: 726-732. R37 R38 R39
17 Chapter 1
45. Flach GB, Bloemena E, Klop WMC, et al. Sentinel lymph node biopsy in clinically N0 T1–T2 staged oral R1 cancer: The Dutch multicenter trial. Oral Oncol 2014; 50: 1020-1024. R2 46. NCCN Guidelines Version 1.2016. http://oralcancerfoundation.org/wp-content/uploads/2016/09/ R3 head-and-neck.pdf Accesses on November 9, 2016 R4 47. NICE Guidelines. [NG36]1.3.5 Published date February 2016 https://www.nice.org.uk/guidance/NG36/ chapter/recommendations. Accessed on November 9, 2016 R5 48. Dutch Guideline Head and Neck Tumors. https://www.nvmka.nl/sites/www.nvmka.nl/files/Richtlijn%20 R6 Hoofd-halstumoren%202015.pdf Accesses on November 9, 2016 R7 49. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403: 503-11. R8 50. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumors. Nature 2000; 406: R9 747-52. R10 51. Chung CH, Parker JS, Karaca G, et al. Molecular classification of head and neck squamous cell R11 carcinomas using patterns of gene expression. Cancer Cell 2004; 5: 489-500. 52. van’t Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of R12 breast cancer. Nature 2002; 415: 530-36. R13 53. Chung CH, Parker JS, Ely K, et al. Gene expression profiles identify epithelial-to-mesenchymal transition R14 and activation of nuclear factor-κB signaling as characteristics of a high-risk head and neck squamous cell carcinoma. Cancer Res 2006; 66: 8210-18. R15 54. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases R16 from primary head and neck squamous cell carcinomas. Nat Genet 2005; 37: 182-86. R17 55. Roepman P, Kemmeren P, Wessels LF, et al. Multiple robust signatures for detecting lymph node R18 metastasis in head and neck cancer. Cancer Res 2006; 66: 2361-66. R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
18 General introduction and outline of the thesis
R1 1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
19
CHAPTER 2
Lymphatic vessel density and lymph node metastasis in
head and neck squamous-cell carcinoma:
a systematic review
E Koudounarakis F.K.J. Leusink S.M. Willems P.A. Bhairosing M.W.M. van den Brekel C.L. Zuur
Accepted in European Journal of Surgical Oncology Chapter 2
R1 ABSTRACT R2 R3 INTRODUCTION R4 The density of the lymphatic vessels (LVD), both intratumoral and peritumoral, is a parameter R5 used for the appraisal of the tumor related lymphatic system and has been associated to R6 the presence of nodal metastasis in various malignancies. This study constitutes the first R7 systematic review of the literature on the association of both intratumoral and peritumoral R8 LVD to nodal spread in head and neck squamous cell carcinoma (HNSCC). R9 R10 METHODS R11 A systematic database search of Pubmed and Embase was conducted to identify original R12 articles addressing the association of LVD to lymphatic metastasis in patients with HNSCC. R13 R14 RESULTS R15 The database search yielded 151 articles. Among these, 28 original studies were eligible R16 for inclusion. Oral cancer was the main primary malignancy studied, but also patients with R17 laryngeal, hypopharyngeal and oropharyngeal carcinoma were included. R18 R19 CONCLUSION R20 The majority of the articles reported significantly higher values of LVD in the group of R21 patients with nodal metastasis compared to the non-metastatic group, suggesting that LVD R22 can be potentially used as an additional tool for the prediction of occult metastasis in cases R23 of HNSCC. R24 R25 KEYWORDS R26 Lymphatic vessel density; lymphangiogenesis; head and neck squamous cell carcinoma; R27 lymph node metastasis; biomarkers R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
22 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
INTRODUCTION R1 R2 Squamous-cell carcinoma of the head and neck accounts for less than 5% of the human malignant R3 tumours.1 Lymph node metastasis can be an early event in disease progression and is associated R4 with a 50% decrease in survival, being one of the most important independent prognostic R5 factors.2 Prompt diagnosis of occult nodal metastasis holds a crucial role in tumor staging and R6 treatment planning. The area of the head and neck features a rich lymphatic network, including R7 approximately three hundred out of the total eight hundred lymph nodes of the human body.3 R8 Physical examination and various imaging techniques, including ultrasound along with guided R9 fine needle aspiration cytology (US-FNAC), computed tomography (CT) and magnetic resonance R10 imaging (MRI), are commonly used for the detection of metastasis in regional lymph nodes. The R11 sensitivity of US-FNAC in the N0 neck has been reported to range between 48-73%, whereas R12 the corresponding values for CT and MRI are 40-83% and 55-80%, respectively.4,5,6Additionally, R13 US is highly performer dependent, while CT and MRI rely on morphological criteria based on size R14 and contrast enhancement. The use of positron emission tomography CT (PET-CT) in detecting R15 metastasis in the clinically negative neck has shown a sensitivity of 30-79%.7,8 Currently, the R16 sentinel node procedure provides the best sensitivity and specificity for the detection of occult R17 neck metastasis. However, it is an invasive technique that complicates future neck dissection and R18 has the disadvantage of any surgical intervention. Its role and prognostic impact has been well R19 established in N0 melanomas but is also increasingly used in early staged oral carcinomas in the R20 head and neck region.9 R21 R22 Whereas imaging and sentinel node biopsies aim at early detection, during the last decades, a R23 significant amount of research has focused on the biological information harbored in the primary R24 tumor that predicts the chance of neck node metastasis. Many immunohistochemical markers, R25 gene expression profiles but also features like depth of infiltration, invasive front and perineural R26 growth have been shown to correlate with the risk of metastasis.10,11,12 In this context, tumor R27 lymphangiogenesis, the formation of new lymphatics related to malignant disease, has become a R28 new field of studying the lymphatic dissemination of various neoplasms.13 The initial concept of R29 lymphatic metastasis was that tumor cells spread solely through preexisting, peritumoral lymphatic R30 vessels that serve as passive channels.14 However, the emergence of a number of lymphatic vessel R31 markers has provided new insights into the active process of developing a tumor-associated R32 lymphatic vasculature, analogous to the process of neovascularization. The first markers identified R33 include the homeobox transcription factor Prox-1, the lymphatic vessel endothelial hyaluronan R34 receptor-1 (LYVE-1), desmoplakin, podoplanin and vascular endothelial growth factor receptor-3 R35 (VEGFR-3), offering the advantage to differentiate between the presence of blood and lymphatic R36 vessels.15 Moreover, research has revealed a number of growth factors and chemokines that R37 are involved in the process of tumor lymphangiogenesis and provide additional information to R38 R39
23 Chapter 2
R1 understand the active role of malignant cells in the actual development of a pathological lymphatic R2 vasculature, which serves as a conduit for regional spread.16, Interestingly, structural changes have R3 been also observed in non-metastatic sentinel nodes, as demonstrated in mouse models with skin R4 cancer, combined with the overexpression of the lymphangiogenic growth factor VEGF-C.17 It is R5 assumed that this “remodeling” creates a favorable environment for the advent and growth of R6 the tumor cells.18 R7 R8 Nowadays, studies oriented to the prognostic role of tumor lymphangiogenesis in various R9 neoplasms, including head and neck cancer, use immunohistochemistry (IHC) to quantify the R10 density of lymphatic vessels inside the tumor mass, as well as in the peritumoral area in close R11 proximity to the tumor margin. The main interest has been engrossed in the correlation of R12 the lymphatic vessel density (LVD) to regional spread of HNSCC, in an attempt to discover an R13 additional tool to estimate the risk of occult metastasis. The purpose of the present systematic R14 review is to assess the robustness of the correlation of intratumoral and peritumoral LVD to lymph R15 node metastasis. R16 R17 R18 MATERIALS AND METHODS R19 R20 Search strategy R21 A systematic search was conducted with the help of a medical information specialist in the PubMed R22 and Embase databases for original articles published until the 5th of July 2016. Search terms R23 used included “lymphangiogenesis“, “lymphatic vessel density”, “head and neck neoplasms”, R24 “squamous cell carcinoma” and “lymph node metastasis” their synonyms in title and abstract R25 (Supplemental File 1). Two authors (EK and FKL) independently screened all titles and abstracts R26 of the retrieved search for selection using predefined inclusion and exclusion criteria that are R27 reported below. Subsequently, full text of relevant studies was screened for a more detailed R28 selection. Citations and references of selected articles and reviews were checked to identify R29 potentially missed relevant studies. Discordant judgments were resolved by consensus discussion. R30 R31 Study selection R32 Inclusion criteria R33 Articles were selected on the basis of (1) original full text publication with detailed description R34 of used methods, (2) original reports containing data on the assessment of LVD in HNSCC and R35 its subsites (3) studies performed in human primary tumor treated surgically, (4) containing a R36 correlation between LVD and lymph node metastasis. R37 R38 R39
24 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
Exclusion criteria R1 Exclusion criteria were (1) duplicate articles that contained all or some of the original publication R2 data, (2) reviews, book chapters, case reports, oral and poster presentations (3) experimental R3 studies on animal models (4) articles that included subsites other than the head and neck region, R4 (5) languages other than English. R5 R6 Data extraction and analysis R7 Using a standardized data extraction form we extracted first author, year of publication, sample R8 size, tumor location, distribution of average age, endothelial marker, LVD assessment method R9 and outcome in terms of established regional metastasis (through neck dissection, sentinel node R10 procedure, or US-FNAC). Data extraction was performed independently by 2 authors (EK and FKL) R11 and disagreements were resolved by discussion. R12 R13 Critical appraisal R14 Using the Quality in Prognostic Studies (QUIPS) tool, the included articles were evaluated for R15 the risk of bias. According to the QUIPS tool, risk of bias was scored as low, moderate or high R16 for the following six items: sufficient study population, study attrition, adequate measurement R17 of prognostic factor and outcome of interest, account of potential confounding factors and R18 appropriate statistical analysis.19 Studies scoring low risk of bias on three or more items were R19 considered to be of ‘high’ methodological quality, while studies scoring high risk of bias on three R20 or more items were considered to be of ‘low’ methodological quality. Articles with high and/or low R21 risk of bias in less than three items were classified as of “moderate” quality. The methodological R22 quality of the studies was appraised by two reviewers (EK and FKL) and disagreements on scoring R23 were resolved by discussion. R24 R25 R26 RESULTS R27 R28 Search results R29 Combined PubMed and Embase searches retrieved 151 articles (Figure 1). After removing R30 duplicates and screening titles and abstracts, 63 articles remained. The remaining articles R31 underwent full text screening for formal review. Twenty-eight articles met the inclusion criteria R32 and were eligible for further analysis.20-47 The main reason for exclusion was the absence of R33 correlation between LVD and lymphatic metastasis. The characteristics of the included studies are R34 summarized in Table 1. R35 R36 R37 R38 R39
25 Chapter 2
R1 R2 p=0.02) p=0.002) R3 p=0.013) S (p<0.001) S (p=0.001) S (p< 0.001) PT, p=0.004) PT, PT, p<0.001) PT, S (p<0.0001) S (IT, p<0.01; S (IT, CI:1.16-2.05) CI:1.48-2.20) S (IT, p<0.001; S (IT, p=0.009) S (IT, S (La, p=0.001) S (OP, p=0.027) S (OP, S (IT, p=0.028; PT, p=0.028; PT, S (IT, R4 p=0.015) PT in HP, S (p<0.05; OR:1.73, S (IT in OC, p=0.032; S (p<0.001; OR:1.80, R5 R6 R7 I-IV I-IV I-IV I-IV I-IV I-IV I-IV I-IV I-IV S (p<0.0001; OR:10.50) I-IV I-IV I-IV II-IV
R8 in larynx) III-IV (T3-T4 R9 R10 R11 R12 method method method present/absent T1-T2 recurrence, S (locoregional R13 present/absent T1-T2 recurrence, S (regional vessels per field R14 R15 IT Chalkley counting IT Chalkley counting PT PT
R16 IT-PT vessels per field
R17 Studied area Counting method Stage to LNM of LVD Correlation R18 R19 LYVE-1 LYVE-1 LYVE-1 LYVE-1 R20 Marker R21 Podoplanin Not defined vessels per field R22 R23 R24 Biopsy Podoplanin IT-PT vessels per field Biopsy Resection Podoplanin IT-PTResection vessels per field Podoplanin IT-PTResectionResection vessels per field Podoplanin Podoplanin Resection IT-PT IT-PTResection Podoplanin vessels per field Not defined vessels per field Chalkley counting Resection Podoplanin Resection IT-PT Podoplanin vessels per field Resection Resection Resection Podoplanin IT Specimen Lymphatic
R25 Not defined Podoplanin IT-PTNot defined vessels per field Podoplanin R26 R27 La La La To To Biopsy-Resection LYVE-1 Not defined vessels per field To OC OC OC OC OC OC OC
R28 Tumor La (16) La (38) HP (33) HP (34) OP (23) OP (18) location OP (62) R29 R30 40 54 86 60 62 43 84 55 70 OC (31) R31 110 160 138 101 168 La (73) 105 OC (15) patients R32 Number of 61 25 39 R33 27 30 26 29 34 43 20 32 R34 42 23 44 33 35 R35 38 R36 Lin (2011) R37 First Author Baek (2009) Zhao (2008) Frech (2009) Frech Kono (2013) Audet (2005) Zhang (2011) Chung (2010) Beasley (2002) Sugiura (2009) Sasahira (2010) Miyahara (2007)
R38 (2007) Siriwardena Munoz-Guerra (2004) Bolzoni Villaret (2013) Bolzoni Villaret Mashhadiabbas (2012)
R39 to lymph node metastasis. 1 | Characteristics of the included studies and correlation Table
26 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
R1 NS NS
= 0.285) R2 p p=0.02) p=0.002) p=0.013) p=0.767) OR:11.32) CI:1.3-8.1) R3 S (p<0.001) S (p=0.001) PT p=0.729) S (p< 0.003) S (p< 0.001) PT, p=0.004) PT, PT, p<0.001) PT, p=0.023) PT, S (p<0.0001) S (IT, p<0.01; S (IT, NS (p=0.608) PT, p=0.007) PT, CI:1.16-2.05) CI:1.48-2.20) S (IT, p=0.02) NS ( S (IT, p<0.001; S (IT, p=0.009) S (IT, p=0.002; S (IT, S (IT, p<0.001; S (IT, S (La, p=0.001) S (OP, p=0.027) S (OP, NS (IT p=0.083; S (IT, p=0.028; PT, p=0.028; PT, S (IT, PT in HP, p=0.015) PT in HP, NS (PT p=0.722; IT R4 S (p<0.05; OR:1.73, S (p<0.001; OR:3.2, S (IT in OC, p=0.032; S (p<0.001; OR:1.80, R5 R6 R7 I-IV I-IV I-IV I-IV I-IV I-IV I-IV I-IV I-IV I-IV I-IV I-IV p=0.01; p=0.003; PT, S (IT, I-IV I-IV I-IV I-IV I-IV S (p<0.0001; OR:10.50) I-IV I-IV I-IV I-IV I-IV I-IV II-IV
in larynx) R8 III-IV (T3-T4 R9 R10 R11 R12 method method method method present/absent T1-T2 recurrence, S (locoregional present/absent T1-T2 recurrence, S (regional R13 vessels per field vessels per field vessels per field R14 R15 IT Chalkley counting IT Chalkley counting PT PT
IT-PT vessels per field IT-PT vessels per field R16
Studied area Counting method Stage to LNM of LVD Correlation R17 R18 R19 LYVE-1 LYVE-1 LYVE-1 LYVE-1 LYVE-1 Marker R20 Podoplanin Not defined vessels per field Podoplanin Not defined vessels per field R21 R22 R23 R24 Biopsy Podoplanin IT-PT vessels per field Biopsy Biopsy Resection Podoplanin IT-PTResection vessels per field Podoplanin IT-PTResectionResection vessels per field Podoplanin Podoplanin Resection IT-PT IT-PTResection Podoplanin vessels per field Not defined vessels per field Chalkley counting Resection Podoplanin Resection IT-PT Podoplanin vessels per field Resection Resection Resection PodoplaninResection IT-PTResection Podoplanin vessels per field Podoplanin IT IT-PTResection vessels per field Podoplanin III-IV (T3-T4) IT-PT vessels per field Resection Resection Podoplanin IT-PT vessels per field Resection Podoplanin IT Resection Podoplanin IT-PT vessels per field Specimen Lymphatic Not defined Podoplanin Not defined Chalkley counting Not defined Podoplanin IT-PTNot defined vessels per field Podoplanin R25 Biopsy -Resection IT-PT LYVE-1 vessels per field R26 R27 La La La To To Biopsy-Resection LYVE-1 Not defined vessels per field To To To OC OC OC OC OC OC OC Resection-LN Podoplanin OC Biopsy-Resection IT-PT Podoplanin vessels per field PT OC OC La (9) HP (8) Tumor Tumor R28 La (16) La (38) La (54) La (22) HP (33) HP (34) OP (23) OP (18) OP (31) OP (17) OP (19) OP (12) OC (15) location HP (16) OP (62) OC (22) tonsil (4) R29 R30 40 54 86 60 62 43 84 55 70 OC (31) 4548 OC (28) 52 OC (29) OC (18) 81 LL (50) 36 50 60 La (33) 30 110 160 138 101 168 La (73) 105 OC (15) 104 HP(18) 120 La (104) 109 R31 patients Number of R32 61 45 31 36 25 39 40 27
24 R33 30 31
21 26 37 29 34 43 46 20 41 28 32 42 R34 47 23 44 33 35 22 38 R35 R36 Lin (2011) First Author R37 Ding (2014) Baek (2009) Zhao (2008) Frech (2009) Frech Kono (2013) Kyzas (2005) Audet (2005) Zhang (2011) Okada (2010) Chung (2011) Chung (2010) Franchi (2004) Beasley (2002) Sugiura (2009) Sasahira (2010) Koskinen (2005) Miyahara (2007) De Sousa (2012) Watanabe (2013) Watanabe
Siriwardena (2007) Siriwardena R38 Longatto Filho (2007) Munoz-Guerra (2004) Bolzoni Villaret (2013) Bolzoni Villaret Bolzoni Villaret (2010) Bolzoni Villaret Mashhadiabbas (2012) Hinojar-Gutierez (2010) Hinojar-Gutierez Garcia-Garracedo (2010) Garcia-Garracedo
Table 1 | Characteristics of the included studies and correlation to lymph node metastasis. 1 | Characteristics of the included studies and correlation Table R39
27 Chapter 2
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 Figure 1 | Flowchart of included and excluded studies. R22 R23 Quality assessment R24 According to the QUIPS evaluation tool, the quality of the included studies ranged from low to R25 high. Eight studies were of high quality, whereas six and fourteen articles were classified as of R26 low and moderate quality, respectively (Table 2). The main reasons for lower scoring included the R27 absence of control for confounding factors and logistic regression analysis, as well as the lack R28 of cut-off values of the predictive factor. In some studies, the method of evaluating the lymph R29 node status at presentation and during the follow-up period was not reported. Additionally, R30 non-blinded prognostic factor assessment also led to poor scoring. However, the majority of R31 the studies described thoroughly the procedure of immunostaining evaluation. No studies were R32 excluded based on methodological quality. R33 R34 R35 R36 R37 R38 R39
28 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
R1 R2 Low Low Low Low Low Low High High High High High High High High R3 Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Quality level R4 R5 R6 R7 R8 and reporting
Statistical analysis R9 R10 R11
Study R12
confounding R13 R14 R15 R16
Outcome R17 measurement R18 R19 R20 R21
measurement R22 Prognostic factor Prognostic R23 R24 R25 Study attrition R26 R27 R28 ●● ●●●● ●●● ●●● ●● ●●● ●● ●●● ●● ● ●●●● ●●● ● ●● ●● ●●● ●●● ●●● ●● ●●● ● ●● ●● ●●● ● ●● ●● ● ● ● ●● ● ●● ●● ●●● ●●● ●● ●● ●● ●● ●● ●●● ●● ●●● ● ●● ●● ●● ● ● ●● ● ● ●●● ●● ● ●●● ● ●●● ●●● ●● ● ● ● ● ● ●●● ●● ● ●●● ● ● ●●●● ●● ●●●●● ●●● ●●●● ●●● ● ●●●●● ●●● ●● ● ● ●●● ●●● ●● ●● ●●● ●●● ●●●● ● ●●● ● ●● ●●● ●● ● ●●● ●●●● ●● ●● ●● ● ●●● ●● ●●● ● ●●● ●● ● ● ●●● ●● ●●● ● ●●● ●● ● ● ●● ●● ● ● ● ● ●● ●●● ●● ● ●●● ●●● ●●● ● ● ●● ●●● ●●● ● ●●● ●● ●●● ● ●●● ●●●
Study R29
participation R30 R31 45 36 25 39 40 27 24 R32 30 31 21 26 37 29 34 43 46 20 41 28 32 42
47 R33 23 44 33 35 22 38 R34 R35 Study Lin (2011) Ding (2014)
Baek (2009) R36 Zhao (2008) Frech (2009) Frech Kono (2013) Kyzas (2005) Audet (2005) Zhang (2011) Okada (2010) Chung (2011) Chung (2010) Franchi (2004) Beasley (2002) Sugiura (2009) Sasahira (2010) Koskinen (2005) Miyahara (2007) De Sousa (2012) Watanabe (2013) Watanabe
Siriwardena (2007) Siriwardena R37 Longatto Filho (2007) Munoz-Guerra (2004) Bolzoni Villaret (2010) Bolzoni Villaret Bolzoni Villaret (2013) Bolzoni Villaret Mashhadiabbas (2012) Hinojar-Gutierez (2010) Hinojar-Gutierez Garcia-Garracedo (2010) Garcia-Garracedo R38 Table 2 | Quality assessment of the included studies using QUIPS. R39
29 Chapter 2
R1 Study characteristics R2 Patients’ characteristics R3 The number of patients included across all studies ranged from 30 to 168, while the mean age R4 of the patients was between 45.3 to 65.2 years. Among the included articles, 15 dealt only with R5 oral SCC and, moreover, 5 of these just with tongue cancer.20-34 Three studies encompassed R6 solely laryngeal SCC, whereas the rest referred to HNSCC from various anatomical sites, in R7 particular oral cavity, oropharynx, larynx and hypopharynx.35-47 In two papers the results were R8 limited to advanced (stage III-IV) disease and in another two only tumors with early T stage were R9 included.27,39,40,42 The tissue specimens consisted either of completely resected tumors or biopsy R10 material and formalin-fixed paraffin-embedded specimens were used in all studies. The follow-up R11 period ranged from 1 to 143 months. R12 R13 Biomarkers R14 In order to outline the lymphatic endothelium, immunohistochemistry using the polyclonal anti- R15 LYVE-1 antibody was performed in six studies.22,29,37,42-44 Podoplanin, traced by the monoclonal R16 D2-40 antibody, was used as a lymphatic marker in all other studies. Staining with the proliferative R17 marker Ki-67, combined with a lymphatic marker, was performed in 4 studies to identify potential R18 proliferative activity in the tumor-associated lymphatic vessels.26,33,43,47 Additional staining with a R19 blood vessel endothelial marker, such as CD31, CD34 or CD105, was also performed in several R20 studies in order to evaluate the blood vessel density. The association to the growth factor receptor R21 VEGFR-3 was reported in only one of the articles.21 R22 R23 LVD assessment R24 In most of the studies both intratumoral and peritumoral lymphatics were assessed. In four papers R25 only the intratumoural lymphatic vessels were studied.27,36,42,43 On the contrary, in three articles R26 the peritumoral lymphatics were exclusively quantified.23,31,39 Although there is no universally R27 accepted definition of the peritumoral area, it was reported as the region within 500 μm from R28 the tumor margin in all but one study, in which it was defined as within 1 mm.30 In all reports, R29 evaluation of the LVD was performed in the areas with the highest lymphatic vessel density R30 (“hot spots”), as this was estimated at low magnification. Scoring of the lymphatic vessels was R31 accomplished with the Chalkley point overlap morphometric technique in four studies.35,41-43 R32 This method involves the use of an eyepiece graticule containing 25 randomly positioned dots, R33 which is rotated until the maximum number of points is on or within the vessels. Thus, instead R34 of counting the individual vessel, the overlaying dots are counted, reflecting the relative area R35 occupied by the lymphatic vasculature. This technique allows for rapid analysis with a relatively R36 low interobserver variability.48 In the rest of the articles, LVD was assessed as the number of R37 lymphatic vessels per high magnification field and in two papers as present or absent.27,39 Mean R38 or median values of LVD from 2 to 10 high magnification fields were used as cut-off values. Apart R39
30 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
from nodal metastasis, lymphatic vessel counts were also related to other clinical parameters, R1 such as the tumor size, grade, gender and age, in most of the papers. R2 R3 Correlations to LVD R4 Morphological features of tumor-associated lymphatic vessels R5 A few papers provided morphological details on the intratumoral and peritumoral lymphatic R6 vasculature. Lymphatic vessels were clearly distinguished from the adjacent blood vessels, due to R7 the highly specific endothelial markers (anti-LYVE-1, D2-40). Beasley et al described intratumoral R8 lymphatic vessels with a distinctive reticular architecture within sheets of tumor cells in SCCs R9 with a pushing margin and in areas containing leukocyte infiltration in tumors with an invasive R10 margin.43 In accordance with the findings of other authors, these vessels featured an immature R11 morphology, comprising two or three endothelial cells, with miniscule, ill-defined lumina R12 that could be easily discriminated from the wider peritumoral lymphatics with conventional R13 architecture.20,21,27,38 Furthermore, peritumoral lymphatics appeared more abundant than the R14 intratumoral vessels. Decreased perimeter of the latter vessels was, interestingly, associated to R15 higher risk for distant metastasis, according to one study.40 Positive staining with the proliferative R16 marker Ki-67 was observed only in a few small intratumoral lymphatics, but no dividing nuclei R17 were found in the peritumoral area. The distribution of the proliferative marker supports the R18 presence of newly formed intratumoral lymph vessels and not only pre-existing lymphatics.43,47 R19 A potential interconnection between intratumoral and peritumoral lymphatics in favor of tumor R20 lymphatic spread, was also assumed possible in these articles. R21 R22 LVD and lymph node metastasis in head and neck cancer R23 In five studies that included a mix of SCC from various sites of the head and neck region, a strong R24 association of high LVD values to regional dissemination was demonstrated.36,40,41,45,46 Franchi et al R25 reported mean intratumoral LVD values of 25.4±16.1 vessels per field for the N0 versus 45.7±19 R26 vessels per field for the N+ group of patients, whereas values of peritumoral LVD were 39±8.9 R27 and 51.8±12.8 vessels per field, respectively.46 In another article, a 3.2-fold increased risk for R28 lymphatic metastasis was exhibited in patients with increased counts of intratumoral lymphatic R29 vessels.36 On the contrary, in a study conducted by Koskinen et al on tumors from various subsites R30 of the head and neck, a median LVD value of 7 vessels per field was found, but the correlation R31 between LVD and N status was not significant.37 R32 R33 LVD and lymph node metastasis in oral cancer R34 A significant correlation to regional dissemination was observed in 12 studies that included R35 oral carcinomas.20,23,25-27,29,30,32-34,44,47 Two of these studies scored highly according to the quality R36 assessment tool and four were judged as of low methodological level. In particular, according R37 to Chung et al, this association was found only for intratumoral LVD (mean value: 16.2 vessels R38 R39
31 Chapter 2
R1 per field), which was additionally correlated to an increased rate of extracapsular spread of SCC, R2 20 Although multivariate analysis identified intratumoral LVD as the only variable significantly R3 correlated to regional spread in the study of Chung et al, this was not proven by Frech et al, R4 who showed tumor site as the most important predictive factor for nodal involvement. In 6 R5 other series, both intratumoral and peritumoral LVD were found to be related to higher rates R6 of regional dissemination, whereas higher nodal stage was also connected to higher lymphatic R7 vessel counts.25,26,29,30,32,34 Mean LVD values of 14.9±2.86 and 34.8±2.51 vessels per field were R8 calculated, according to Miyahara et al, for the N0 and N+ group, respectively.26 Similar values R9 were found in another study regarding tongue cancer (15.97±0.67 vessels per field for the R10 N0 group versus 29.38±0.57 vessels per field for the N+ group).32 Odds ratios for lymphatic R11 dissemination were calculated in only 3 studies, with values ranging from 1.73 to 10.50 for cases R12 with high LVD.23,32,34 R13 R14 The relationship to regional relapse after surgical treatment of the neck in oral cancer was assessed R15 only in a limited number of studies. An increased number of intratumoral lymphatic vessels was R16 shown to be associated to regional, as well as local, recurrence, in contrast to the peritumoral R17 LVD.27,33 Sasahira et al demonstrated such an association only for cases of local relapse, which is R18 in disagreement to the findings of another study.24,29 R19 R20 In six studies, no statistically significant association was found between higher LVD values in the R21 primary tumor and nodal metastasis or regional recurrence.21,22,24,28,31,43 Only one of these studies R22 was considered of high methodological quality.22 It is also worth noting that, according to one R23 paper, tumor LVD tended to decrease with higher stages of nodal involvement, although no R24 significant differences of LVD were found in relationship to N status.31 R25 R26 LVD and lymph node metastasis in oropharyngeal and hypopharyngeal cancer R27 In cases of oropharyngeal and hypopharyngeal SCC, Frech et al did not find any significant R28 correlation between intratumoral LVD and nodal disease, though there was a strong association R29 of high peritumoral LVD to lymph node status in hypopharyngeal SCC (p= 0.015).44 Additionally, R30 another research did not demonstrate an association of increased intratumoral LVD to lymph R31 node metastasis in oropharyngeal and hypopharyngeal cancer, in contrast to the results of a R32 report considering oropharyngeal carcinoma.42,43 In all these studies, a polyclonal antibody against R33 LYVE-1 was used in immunostaining, but the latter two performed counting with the Chalkley R34 point grid. R35 R36 LVD and lymph node metastasis in laryngeal cancer R37 Regarding laryngeal SCC, all three studies that encountered solely larynx as the primary site, R38 demonstrated a significant association of increased LVD to regional metastasis, using podoplanin R39
32 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
as a lymphatic marker.35,37,38 Additionally, Audet et al found that high counts of intratumoral R1 lymphatics are related to nodal metastasis in laryngeal carcinoma (p= 0.001), but not in R2 oropharyngeal or hypopharyngeal cancer.42 Baek et al reported a median LVD value of 4 (using R3 the Chalkley counting method) and showed an important correlation of high LVD to lymph node R4 metastasis at presentation in supraglottic SCC of the larynx (p< 0.001).35 A significant association R5 (p= 0.011) to regional relapse was also shown. However, in the logistic regression analysis model, R6 VEGF-C appeared to be the strongest independent predictive factor for regional recurrence R7 (p=0.001), when compared to T stage, N status and LVD, with an odd ratio of 20.56. Lin et al R8 reported median values of 0 (range 0-26) and 4 (range 0-32) vessels per field for intratumoral R9 and peritumoral LVD, respectively.38 Locoregional recurrence was not correlated to LVD according R10 to the latter article. In contrast, Bolzoni Villaret et al demonstrated a significant correlation of R11 increased LVD to locoregional relapse (p=0.013).39 Nonetheless, no correlation was reported in R12 one paper that included a limited number (sixteen) of cases with laryngeal carcinoma.,43 R13 R14 LVD in relationship to other parameters R15 In a small number of articles an association between LVD values and primary tumor site was R16 demonstrated. De Sousa et al found that a high peritumoral LVD was significantly associated R17 to tumors localized in the tongue and floor of the mouth in comparison to other subsites of R18 the oral cavity.21 This is in accordance with the findings of three other researchers studying oral R19 SCC.23,26,27 Miyahara et al reported a mean value of 28.3±12.5 for tongue SCC versus 16.5±5.6 R20 vessels per field for SCC from other sites of the oral cavity26. However, it is not confirmed by other R21 studies, that did not find any correlation between LVD and oral subsites.24,25,33 These differences R22 can be explained on the basis of the lower number of cases in these studies. Furthermore, other R23 authors demonstrated a different density of lymphatic vessels in different subsites of the head R24 and neck region. In particular, Franchi et al found an increased mean value (44.9±28.7 vessels R25 per field) of intratumoral LVD in SCC of the oropharynx, compared to SCC of the oral cavity R26 (26.2±16.4 vessels per field) and larynx (31.5±18.0 vessels per field).46 Similarly, in another R27 article, higher mean LVD values were observed for pharyngeal in comparison to laryngeal tumor R28 sites.45 An interesting finding is that a considerable proportion of glottic tumors (approximately R29 45%) displayed intratumoral lymphatics, even in T1 stage, though the glottic area is known for R30 its absent lymphatic network and T1 glottic cancers rarely metastasize.45 Moreover, Kyzas et al R31 reported higher intratumoral LVD for oral cavity and laryngeal SCC in comparison to lip cancer, R32 but not significantly higher values for peritumoral LVD.47 R33 R34 Three authors showed a correlation of high LVD to moderate/poorly differentiated oral carcino- R35 mas. This was also reported in one of the studies dealing with laryngeal cancer.25,28,42 Regarding R36 the T stage, 5 authors reported a strong relationship of advanced T stage to increased intratumoral R37 LVD in oral cancer and one study reported a similar finding in laryngeal and hypopharyngeal R38 cancer.20,21,26,29,41,45 R39
33 Chapter 2
R1 Some studies demonstrated also a correlation of increased LVD to survival. In a study on R2 supraglottic laryngeal SCC, patients with increased LVD showed significantly shorter disease- R3 free and overall survival rates, a result that was confirmed by Garcia-Carracedo et al not only for R4 laryngeal but also for pharyngeal carcinomas.38,45 Multivariate analysis using a Cox proportional R5 hazards model demonstrated intratumoral LVD as an independent prognostic factor for both R6 overall and disease-free survival with a relative risk of 3.22 and 3.08 (p=0.008 and 0.005), R7 respectively.38 In a similar logistic regression model for disease free survival, Baek et al found a R8 hazards ratio of 1.10 in patients with high LVD (p=0.904).35 In that report, multivariate analysis R9 highlighted VEGF-C expression as the strongest prognostic factor for disease-free survival with a R10 hazards ratio of 13.62 (p=0.006). Furthermore, there was a strong association between increased R11 VEGF-C expression and positive N status, as well as regional recurrence, but not with LVD values. R12 Disease-free survival was also strongly affected by high LVD in patients with oral carcinoma R13 according to four articles.26,27,29,33 Multivariate analysis performed by Zhao et al demonstrated R14 intratumoral LVD as the only independent prognostic factor for cumulative survival in cases of R15 oral SCC (p< 0.001), whereas N status was the strongest predictor of disease-free survival.33 R16 In another paper, intratumoral LVD was the most important prognostic factor for disease-free R17 survival, but nodal status was not incorporated in the Cox hazards model. 27 Kyzas et al found that R18 elevated intratumoral LVD was an independent factor of overall survival (p= 0.004) in cases of R19 oral cavity and laryngeal SCC.47 Hazard ratios, though, are not provided in the latter two articles. R20 In the rest of the papers, either no association to survival rates was found or the relationship was R21 not assessed. R22 R23 R24 DISCUSSION R25 R26 Recent advances in the field of specific endothelial-type antibodies have provided valuable tools R27 in studying the lymphatic vasculature and its prognostic implications. The monoclonal D2-40 R28 antibody, against podoplanin, has been found to delineate an increased number of intratumoral R29 lymphatics in comparison to other markers, demonstrating a sensitivity and specificity of 98.8% R30 and 97.3%, respectively.49,50 In addition, extensive analyses in many different laboratories have R31 revealed that LYVE-1 is a reliable marker for distinguishing lymphatic vessels from blood vessels in R32 a range of different human cancers.51-53 R33 R34 In 2006, the first international consensus on the methodology of lymphangiogenesis quantification R35 in solid tumors was established, providing guidelines for the assessment of the lymphatic vessel R36 counts with the use of specific markers and the expression of lymphangiogenic growth factors.60 R37 According to these recommendations, studies on LVD should fulfill the following criteria: a. R38 double immunostaining with the D2-40 and Ki-67 monoclonal antibodies, in order to detect R39
34 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
the presence of lymphatic vessels and proliferating lymphatic endothelial cells; b. “hot spot” R1 selection at low magnification in intratumoral and/or peritumoral areas; c. quantification with R2 the use of the Chalkley point graticule; d. counting both the proliferating and non-proliferating R3 lymphatic endothelial cells; and e. independent evaluation by two pathologists. However, none R4 of the studies, included in this review, adhered completely to the above recommendations, even R5 those published after 2006. As detailed above, combined immunostaining with the proliferative R6 marker Ki-67 and a lymphatic marker, which allows accurate detection of proliferating endothelial R7 lymphatic cells, was performed in just four of the included studies.26,33,43,47 In these studies, Ki-67 R8 was mainly detected in the endothelial cells of the intratumoral lymphatic vessels, suggesting R9 that these are proliferating new vessels and not preexisting lymphatics. However, the proliferative R10 marker was only used to detect the presence of proliferating endothelial cells, and not to count R11 these cells, as recommended by the consensus. Regarding the counting method, only four R12 articles reported the use of the Chalkley point graticule.35,41-43 LYVE-1 was the sole endothelial R13 marker used in six studies, out of which two did not show any important correlation to regional R14 metastasis. Although, the D2-40 antibody is considered the most reliable marker of the lymphatic R15 endothelium, differences in the correlations between studies using the antibody against R16 podoplanin or LYVE-1 were not observed in the current review, since a limited number of studies R17 using the latter antibody were included. Additionally, while most researchers selected three “hot R18 spots” for the assessment of LVD, in three studies only two “hot spots” were used.31,37,39 Another R19 issue is the evidence of regional disease. A neck dissection is not always performed in cases of R20 HNSCC and US-FNAC or imaging are often the modality of staging the neck. Therefore, the R21 presence of lymphatic metastasis may have been underestimated, contributing to the absence R22 of association between LVD and nodal dissemination in some cases. This might work both ways R23 and makes it difficult to calculate a risk factor associated with LVD. Multivariate analysis was not R24 included in most of the articles and, thus, the relationship between LVD and nodal disease was R25 not fully assessed. R26 R27 Although the vast majority of the studies included in the review showed a significant correlation R28 between high LVD and the presence of regional involvement, adoption of the recommended R29 guidelines in future research will ensure a better comparison between the different studies R30 from a methodological standpoint. In addition, there is an implication of a different effect of R31 intratumoral and peritumoral LVD on regional disease and, thus, it would be optimal to study R32 separately the correlation of these two parameters to lymphatic metastasis. This, however, is only R33 possible in surgical resection specimens, which is difficult for some tumors, such as early stage R34 larynx, nasopharynx and oropharynx, where most often a tissue biopsy is available. Furthermore, R35 newly formed lymphatics, detected by dual staining with a lymphatic marker and the proliferative R36 marker Ki-67, should be counted, according to the recommendations of the consensus. The R37 current review also shows that there are differences in baseline levels of LVD at different (sub) R38 sites in the head and neck, and it is thus not justified to analyze all these (sub)sites as one group. R39
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R1 Reports on other types of malignancy support the significant correlation of LVD to nodal metastasis. R2 A meta-analysis on the predictive value of LVD in melanoma, revealed a strong association of R3 increased peritumoral LVD with lymph node metastasis.55 However, a similar relationship was R4 not proven for intratumoral LVD. High peritumoral LVD was also found to be associated to R5 nodal status in gastric and breast carcinoma.56,57 Nevertheless, another article on early gastric R6 cancer, indicated a strong correlation between increased intratumoral LVD values and lymphatic R7 metastasis.58 Such a correlation was also found in a study on papillary thyroid carcinoma.59 It is R8 worth noting that heterogeneity is commonly found in the methodology (for example type of R9 antibody used, counting method, number of “hot spots” selected) and the reported values of R10 LVD in studies on other tumor sites. R11 R12 In conclusion, there is strong evidence that LVD is closely associated to lymphatic spread of HNSCC. R13 In this review, most of the included studies had a mix of clinically N+ and N0 patients. However, R14 in order to obtain quantitative data on prediction of occult metastasis in HNSCC, further research R15 should focus on the value of LVD as a predictive marker in the clinically T1-2N0 group for each R16 distinct tumor site. R17 R18 R19 CONFLICT OF INTEREST R20 R21 The authors of this manuscript have no conflicts of interest to disclose and there has been no R22 financial support for this research study. R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
36 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
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38 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
43. Beasley NJ, Prevo R, Banerji S, et al. Intratumoral lymphangiogenesis and lymph node metastasis in head and neck cancer. Cancer Res 2002; 62: 1315-20. R1 44. Frech S, Hörmann K, Riedel F, et al. Lymphatic vessel density in correlation to lymph node metastasis in R2 head and neck squamous cell carcinoma. Anticancer Res 2009; 29: 1675-79. R3 45. Garcia-Carracedo D, Rodrigo JP, Astudillo A, et al. Prognostic significance of lymphangiogenesis in R4 pharyngolaryngeal carcinoma patients. BMC Cancer 2010; 10:416. 46. Franchi A, Gallo O, Massi D, et al. Tumor lymphangiogenesis in head and neck squamous cell carcinoma: R5 a morphometric study with clinical correlations. Cancer 2004; 101: 973-78. R6 47. Kyzas PA, Geleff S, Batistatou A, et al. Evidence for lymphangiogenesis and its prognostic implications R7 in head and neck squamous cell carcinoma. J Pathol 2005; 206: 170-77. R8 48. Vermeulen PB, Gasparini G, Fox SB, et al. Second international consensus on the methodology and criteria of evaluation of angiogenesis quantification in solid human tumours. Eur J Cancer 2002; 38: R9 1564-79. R10 49. Dadras SS, Paul T, Bertoncini J, et al. Tumor lymphangiogenesis: a novel prognostic indicator for R11 cutaneous melanoma metastasis and survival. Am J Pathol 2003; 162: 1951-60. 50. Kane SV, Gupta M, Kakade AC, et al. Depth of invasion is the most significant histological predictor of R12 subclinical cervical lymph node metastasis in early squamous carcinomas of the oral cavity. Eur J Surg R13 Oncol 2006; 32: 795-803. R14 51. Wetterwald A, Hoffstetter W, Cecchini MG, et al. Characterization and cloning of the E11 antigen, a marker expressed by rat osteoblasts and osteocytes. Bone 1996; 18: 125-32. R15 52. Breiteneder-Geleff S, Soleiman A, Kowalski H, et al. Angiosarcomas express mixed endothelial R16 phenotypes of blood and lymphatic capillaries: podoplanin as a specific marker for lymphatic R17 endothelium. Am J Pathol 1999; 154: 385-94. R18 53. Kahn HJ, Bailey D, Marks A. Monoclonal antibody D2-40, a new marker of lymphatic endothelium, reacts with Kaposi’s sarcoma and a subset of angiosarcomas. Mod Pathol 2002; 15: 434-40. R19 54. Van der Auwera I, Cao Y, Tille JC, et al. First international consensus on the methodology of R20 lymphangiogenesis quantification in solid human tumours. Br J Cancer 2006; 95: 1611-25. R21 55. Pastushenko I, Vermeulen PB, Carapeto FJ, et al. Blood microvessel density, lymphatic microvessel density and lymphatic invasion in predicting melanoma metastases: systematic review and meta- R22 analysis. Br J Dermatol 2014; 170:66-77. R23 56. Wang XL, Fang JP, Tang RY, et al. Different significance between intratumoral and peritumoral lymphatic R24 vessel density in gastric cancer: a retrospective study of 123 cases. BMC Cancer 2010; 10: 299. 57. Kandemir NO, Barut F, Bektas S, et al. Can lymphatic vascular density be used in determining metastatic R25 spreading potential of tumor in invasive ductal carcinomas? Pathol Oncol Res 2012; 18: 253-62. R26 58. Gao P, Zhou GY, Zhang QH, et al. Clinicopathological significance of peritumoral lymphatic vessel R27 density in gastric carcinoma. Cancer Lett 2008, 263: 223-30. R28 59. Hall FT, Freeman JL, Asa SL, et al. Intratumoral lymphatics and lymph node metastases in papillary thyroid carcinoma. Arch Otolaryngol Head Neck Surg 2003, 129: 716-19 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 SUPPLEMENTARY FILES R2 R3 Supplementary Table 1 | Search strategy for Pubmed. R4 Pubmed R5 (“Carcinoma, Squamous Cell”[Mesh:NoExp] OR squamous cell carcinoma* [tiab] OR squamous carcinoma* [tiab] OR epidermoid carcinoma* [tiab] OR Planocellular R6 carcinoma* [tiab] OR “Carcinoma, squamous cell of head and neck” [Supplementary R7 Concept]) AND (“Head and Neck Neoplasms”[MeSH:NoExp] OR ((head [tiab] OR neck [tiab] OR UADT [tiab] OR aerodigestive tract [tiab] OR facial [tiab] OR mouth R8 [tiab] OR gingival [tiab] OR lip [tiab] OR lips [tiab] OR palatal [tiab] OR Salivary Gland* R9 [tiab] OR parotid [tiab] OR Sublingual Gland* [tiab] OR Submandibular Gland [tiab] OR tongue [tiab] OR Otorhinolaryn* [tiab] OR ear [tiab] OR ears [tiab] OR Auricular R10 [tiab] OR laryn* [tiab] OR nose [tiab] OR nasal [tiab] OR Paranasal Sinus [tiab] OR R11 #1 Maxillary Sinus [tiab] OR pharyn* [tiab] OR Hypopharyn* [tiab] OR Nasopharyn* [tiab] Domain OR Oropharyn* [tiab] OR Tonsillar [tiab] OR tonsil* [tiab]) AND (neoplasm* [tiab] OR R12 tumor* [tiab] OR tumour* [tiab] OR cancer* [tiab] OR malign* [tiab] OR oncolog* R13 [tiab] OR carcinom* [tiab] OR carcinogenes* [tiab] OR oncogenes* [tiab]))) OR oral R14 neoplasm* [tiab] OR oral tumor* [tiab] OR oral tumour* [tiab] OR oral cancer* [tiab] OR oral malign* [tiab] OR oral oncolog* [tiab] OR oral carcinom* [tiab] OR R15 oral carcinogens* [tiab] OR oral oncogenes* [tiab] OR ((oral cavit* [tiab] OR oral R16 squamous cell [tiab]) AND (neoplasm* [tiab] OR tumor* [tiab] OR tumour* [tiab] OR cancer* [tiab] OR malign* [tiab] OR oncolog* [tiab] OR carcinom* [tiab] OR R17 carcinogenes* [tiab] OR oncogenes* [tiab]) R18 (“Lymphangiogenesis”[Mesh] OR Lymphangiogenes* [tiab] OR lymphatic vessel R19 densit* [tiab]) AND (“Lymphatic Metastasis”[Mesh] OR Lymphatic Metastas* [tiab] #2 OR lymph node metastas* [tiab]) Determinant R20 #3 #1 AND #2 Final search R21 R22 R23 Supplementary Table 2 | Search strategy for Embase. R24 Embase R25 (“head and neck cancer”/ OR face cancer/ OR “head and neck carcinoma”/ OR “head and neck squamous cell carcinoma”/ OR head cancer/ OR lip cancer/ OR mouth R26 cancer/ OR neck cancer/ OR nose cancer/ OR paranasal sinus cancer/ OR pharynx R27 cancer/ OR salivary gland cancer/ OR tongue cancer/ OR tonsil cancer/) OR (head OR neck OR UADT OR (aerodigestive adj1 tract) OR facial OR mouth OR gingival OR lip R28 OR lips OR palatal OR (Salivary adj1 Gland*) OR parotid OR (Sublingual adj1 Gland*) R29 OR (Submandibular adj1 Gland) OR tongue OR Otorhinolaryn* OR ear OR ears OR R30 #1 Auricular OR laryn* OR nose or nasal or (Paranasal adj1 Sinus) or (Maxillary adj1 Sinus) Domain or pharyn* or Hypopharyn* or Nasopharyn* or Oropharyn* or Tonsillar or tonsil* or R31 oral) adj3 (neoplasm* or tumor* or tumour* or cancer* or malign* or oncolog* or R32 carcinom* or carcinogenes* or oncogenes*)).ti,ab.AND (squamous cell carcinoma/ exp) OR (squamous adj1 cell) OR squamous OR epidermoid OR Planocellular) adj1 R33 carcinoma*).ti,ab). R34 (Lymphangiogenes* OR (lymphatic adj1 vessel adj1 densit*)).ti,ab.OR R35 Lymphangiogenesis/exp AND ((Lymphatic adj1 Metastas*) OR (lymph adj1 node #2 Determinant R36 adj1 metastas*)).ti,ab OR Lymphatic Metastasis/exp) R37 #3 #1 AND #2 Final search R38 R39
40 Lymphatic vessel density and lymph node metastasis in head and neck squamous-cell carcinoma
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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CHAPTER 3
Validation of a gene expression signature for the assessment of lymph node metastasis
in oral squamous-cell carcinoma
F.K.J. Leusink* S.R. van Hooff* P. Roepman R.J. Baatenburg de Jong E.J.M. Speel M.W.M. van den Brekel M.L.F. van Velthuysen P.J. van Diest R.J.J. van Es M.A.W. Merkx J.A. Kummer C.R. Leemans E. Schuuring J.A. Langendijk M. Lacko M.J. De Herdt J.C. Jansen R.H. Brakenhoff P.J. Slootweg R.P. Takes* F.C.P. Holstege*
* Equal contribution
Journal of Clinical Oncology 2012;30: 4104-10 Chapter 3
R1 ABSTRACT R2 R3 INTRODUCTION R4 Current assessment of lymph node metastasis in patients with head and neck squamous- R5 cell carcinoma is not accurate enough to prevent overtreatment. The aim of this study R6 was validation of a gene expression signature for distinguishing metastasizing (N+) from R7 nonmetastasizing (N0) squamous-cell carcinoma of the oral cavity (OSCC) and oropharynx R8 (OPSCC) in a large multicenter cohort, using a diagnostic DNA microarray in a Clinical R9 Laboratory Improvement Amendments/International Organization for Standardization– R10 approved laboratory. R11 R12 METHODS R13 A multigene signature, previously reported as predictive for the presence of lymph node R14 metastases in OSCC and OPSCC, was first re-evaluated and trained on 94 samples using R15 generic, whole-genome, DNA microarrays. Signature genes were then transferred to a R16 dedicated diagnostic microarray using the same technology platform. Additional samples R17 (n = 222) were collected from all head and neck oncologic centers in the Netherlands and R18 analyzed with the diagnostic microarray. Human papillomavirus status was determined by R19 real-time quantitative polymerase chain reaction. R20 R21 RESULTS R22 The negative predictive value (NPV) of the diagnostic signature on the entire validation cohort R23 (n = 222) was 72%. The signature performed well on the most relevant subset of early-stage R24 (cT1-T2N0) OSCC (n = 101), with an NPV of 89%. R25 R26 CONCLUSION R27 Combining current clinical assessment with the expression signature would decrease the R28 rate of undetected nodal metastases from 28% to 11% in early-stage OSCC. This should be R29 sufficient to enable clinicians to refrain from elective neck treatment. A new clinical decision R30 model that incorporates the expression signature is therefore proposed for testing in a R31 prospective study, which could substantially improve treatment for this group of patients. R32 R33 R34 R35 R36 R37 R38 R39
44 Validation of a gene expression signature for the assessment of lymph node metastasis in oral squamous-cell carcinoma
INTRODUCTION R1 R2 Each year, nearly 500,000 people are diagnosed with head and neck squamous cell cancer R3 (HNSCC),1,2 making it the sixth most common cancer worldwide. Cervical lymph node metastasis R4 occurs frequently in these patients and is a major determinant of prognosis and treatment planning. R5 Accurate lymph node staging is therefore crucial. Current preoperative clinical assessment of the R6 lymph nodes by physical examination and imaging is suboptimal. Depending on the techniques R7 used, in approximately 20% to 40% of all patients with HNSCC, nodal metastases remain R8 undetected during diagnostic work-up.3-6 Establishing optimal treatment of patients who are R9 clinically assessed as having nonmetastasizing (N0) disease is therefore challenging.7 R10 R11 If no nodal metastasis is detected, the preferred treatment in many centers is to electively treat R12 the neck, in addition to treating the primary tumor in all patients. An alternative is to refrain from R13 additional neck treatment, closely observe the patient (watchful waiting), and only treat the necks R14 of patients who develop clinically apparent metastases during follow-up. Whichever strategy is R15 chosen, suboptimal treatment may be given. Most patients with HNSCC are treated by surgery, R16 (chemo)radiotherapy, or combinations thereof. The primary treatment of oral cavity squamous R17 cell cancer (OSCC) is surgery, in most cases followed by (chemo)radiotherapy on indication. Neck R18 dissection is therefore also the most frequent mode for treatment of the neck in OSCC. In the case R19 of elective neck dissection, the 60% to 80% of patients who are clinically assessed as N0 (cN0) R20 and are indeed free of metastases receive unnecessary treatment of the neck, causing morbidity, R21 including shoulder dysfunction, pain, lymphedema, contour changes, and lower lip paresis, even R22 in more conservative types of selective neck dissection.8,9 Conversely, watchful waiting results in R23 undertreatment of the 20% to 40% of patients with occult metastases, which may result in an R24 unfavorable prognosis in the case of delayed treatment. R25 R26 Because there is no conclusive evidence to indicate which approach is best, the management R27 of patients who are clinically assessed as having N0 disease is one of the major issues in the R28 management of HNSCC and OSCC in particular, which is reflected in the different approaches R29 between centers.10,11 Treatment of the N0 neck, both surgically and by radiotherapy, would R30 greatly benefit from improved preoperative assessment of lymph node metastasis, which is the R31 goal of this study. R32 R33 Gene expression profiling has been shown to be useful for diagnosis and prognosis of several R34 cancers.12 With regard to HNSCC, a previous study identified a gene expression signature for R35 distinguishing metastasizing (N) from N0 OSCC and N0 oropharynx SCC (OPSCC).13 Although R36 promising, the independent validation cohort in that study was small (n=22), with all samples R37 (N=104) derived from a single clinical center. Reanalysis of the entire data set identified more R38 R39
45 Chapter 3
R1 genes with predictive power.14 In the current study, this predictive gene set was first re-evaluated R2 using a different microarray platform on 94 samples, transferred to a dedicated diagnostic R3 microarray, and subsequently tested in a large, multicenter patient cohort (n=222). On the basis R4 of the results, a new clinical decision model that incorporates the gene expression signature is R5 proposed for prospective analysis of early-stage OSCC management. R6 R7 R8 METHODS R9 R10 Platform Transition Previously, a 102-gene profile,13 and later, a more comprehensive set R11 of genes,14 were reported as having predictive value for assessment of lymph node status in R12 OSCC and OPSCC. These were identified on self-made, whole-genome, DNA oligonucleotide R13 microarrays using a pool of tumor samples as common reference. The current study consisted R14 of two stages (Fig 1). In the first stage (platform transition), a more generic diagnostic tool was R15 developed for application in a Clinical Laboratory Improvement Amendments/International R16 Organization for Standardization (CLIA/ISO) –approved laboratory that also performs a US Food R17 and Drug Administration– cleared microarray test. Platform transition was achieved by adopting R18 commercially available technology and by determining classification settings for the new platform R19 on the first cohort as a whole, or on a subset for which improved assessment of metastasis is most R20 relevant (Appendix Fig A1, online only). The second stage consisted of independent multicenter R21 validation of the resulting diagnostic array on a second cohort (Fig 1). R22 R23 Samples from patients R24 Fresh frozen primary tumor samples of OSCC (86% of all samples) and OPSCC were collected R25 from patients with no previous oncologic history. Because the original signature was developed R26 on samples of both OSCC and OPSCC, both were also included here. In the Data Supplement, the R27 characteristics of each patient are described individually, and these data are linked to individual R28 array data. Clinical assessment and histologic examination consisted of palpation, computed R29 tomography and/ or magnetic resonance imaging, and ultrasound with ultrasound-guided, fine- R30 needle aspiration cytology. The reference standard for nodal status was considered positive in the R31 case of cytologically or histologically proven metastases and negative in the case of no detected R32 metastasis in the neck dissection specimen and/or during follow-up of at least 2 years. Because R33 the selection of patients was based on the availability of frozen material, the number of patients R34 who did not receive a neck dissection in this study population reflects clinical practice in the R35 participating centers. Some centers have adopted a watchful waiting strategy with respect to the R36 neck in patients with cT1N0 tumors. Nine such patients were included in the validation cohort R37 and three in the platform transition cohort. R38 R39
46 Validation of a gene expression signature for the assessment of lymph node metastasis in oral squamous-cell carcinoma
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 Figure 1 | Schematic representation of the different stages of this study. Previous studies resulted in discovery of a comprehensive multigene signature.13,14 In this study, the platform R24 transition cohort (n = 94) was used to transfer this signature to a generic diagnostic platform. The resulting R25 diagnostic microarray with trained settings was then validated on the multicenter validation cohort (n = 222). The results of this validation are incorporated in a clinical decision model (see Fig 3). R26 R27 Two separate cohorts were studied. The first, the platform transition cohort, consisted of 94 R28 tumors that were collected from patients who were treated in the University Medical Center R29 Utrecht (Utrecht, the Netherlands) between 1998 and 2003. The second, the multicenter R30 validation cohort, consisted of 222 tumors that were collected from patients who were treated R31 in one of the eight centers cooperating in the Dutch Head and Neck Society between 2000 R32 and 2007. Table 1 describes the clinical characteristics of both cohorts. Table 2 describes the R33 distribution across the participating centers. The study was approved by the institutional review R34 boards of the participating centers. R35 R36 R37 R38 R39
47 Chapter 3
Table 1 | Characteristics of patients in the two study cohorts* R1 Characteristic Platform Transition Cohort Validation Cohort P† R2 (n = 94) (n = 222) R3 No. % No. % R4 Age .66 R5 Years 63 62 SD 11 11 R6 Age group .53 R7 ≤ 40 1 1 6 3 R8 41-55 25 27 48 22 R9 56-69 39 42 107 48 ≥ 70 29 31 61 27 R10 Location of primary tumor .16 R11 Oral cavity 85 90 187 84 R12 Oropharynx 9 11 35 16 R13 Clinical T stage .002 T1 13 14 38 17 R14 T2 37 39 109 49 R15 T3 11 12 41 18 R16 T4 33 35 34 15 R17 Clinical N status .21 R18 Positive 41 44 79 36 Negative 53 56 143 64 R19 Pathologic N status .27 R20 Positive 54 57 112 50 R21 Negative 40 43 110 50 R22 Abbreviation: SD, standard deviation. R23 *Percentages may not total 100 because of rounding. †P values were calculated with the use of Mann-Whitney test for continuous variables and Fisher’s exact test R24 for categorical variables. R25 R26 R27 Table 2 | Participating medical centers and their sample contributions to the multicenter validation set R28 Medical Center Name Abbreviation No. of Samples R29 Netherlands Cancer Institute–Antoni van Leeuwenhoek NKI-AvL 64 R30 Radboud University Nijmegen Medical Center RUNMC 50 R31 University Medical Center Utrecht UMCU 49 R32 Vrije Universiteit Medical Center VUMC 18 Maastricht University Medical Center MUMC 16 R33 University Medical Center Groningen UMCG 13 R34 Erasmus Medical Center Erasmus MC 7 R35 Leiden University Medical Center LUMC 5 R36 R37 R38 R39
48 Validation of a gene expression signature for the assessment of lymph node metastasis in oral squamous-cell carcinoma
Analysis of gene expression R1 Frozen tumor samples were sectioned at the participating institutions, aliquoted in Trizol (Life R2 Technologies, Frederick, MD), and sent to Agendia laboratories (Amsterdam, the Netherlands) for R3 expression profile analysis. Tumor percentage (at least 50%) was assessed on hematoxylin and R4 eosin–stained sections taken in parallel. RNA isolation, amplification, and labeling were performed R5 as previously described.15 Tumor sample RNA was labeled as Cy3, and reference RNA was labeled R6 Cy5. As a reference, the Universal Human Reference RNA (Agilent Technologies, Santa Clara, CA) R7 was used. Samples from the platform transition cohort were hybridized on full-genome Agilent R8 arrays that included the originally identified gene expression signature13,14 in replicate. Samples R9 from the multicenter validation cohort were hybridized on dedicated diagnostic eight-pack R10 arrays that represented this signature. Raw fluorescence intensities were quantified using Agilent R11 Feature Extraction software and imported into R/Bioconductor (http://www.bioconductor.org/) R12 for normalization (loess normalization using the limma package) and additional analysis. Because R13 of global differences between the cell line reference RNA and tumor sample RNA, the expression R14 values of all genes were centered using the unweighted aggregate mean per gene of pN and R15 pN0 samples from the platform transition cohort. More information on these procedures and R16 array designs is given in the Appendix (online only) and Data Supplement. Microarray data has R17 been deposited in the National Center for Biotechnology Information Gene Expression Omnibus16 R18 under accession number GSE30788. Gene Signature Readout Analysis of the originally identified R19 comprehensive gene set,13,14 with updated genome annotation and alongside the probes available R20 on the manufacturers’ arrays, resulted in 732 unique probes that represent 696 genes. All analyses R21 are based on this 732-probe set. Signature readout was through a mean-based classification,13 R22 on the basis of an updated risk profile that was derived from the mean expression profile of the R23 signature in all patients with lymph node– positive disease (pN) in the platform transition set. This R24 mean expression profile was used as the Nrisk profile for all subsequent analyses. Patients with R25 a cosine correlation coefficient (ie, signature score) of ≤ - 0.256 were classified as N0; patients R26 with a signature score of > -0.256 were classified as N. The threshold was based on the subgroup R27 for which additional nodal assessment is most crucial (early-stage [cT1-T2N0] OSCC) and set to R28 achieve a negative predictive value (NPV) of at least 90%, with the highest possible specificity R29 (Fig A1D). For the multicenter validation, the same procedure was used: the signature score R30 was calculated using the Nrisk profile and the threshold determined on the platform transition R31 cohort was applied. To determine whether any center-specific bias existed, the signature scores R32 for patients from individual centers were compared with the signature scores of patients from R33 the remaining centers. This was performed for patients assessed as pN0 and pN separately using R34 a Mann-Whitney test. R35 R36 R37 R38 R39
49 Chapter 3
R1 Detection of human papillomavirus R2 Detection of human papillomavirus (HPV16) was performed by realtime quantitative polymerase R3 chain reaction with 100 ng of total RNA using the iScriptcDNAKit (Bio-Rad, Veenendaal, the R4 Netherlands)17 with polymerase chain reactions and controls as described.18 R5 R6 R7 RESULTS R8 R9 Transition to a dedicated diagnostic platform R10 Previously reported gene signatures13,14 were based on self-made microarrays using a common R11 reference pool of samples, both with limited availability. To perform analyses with CLIA/ISO- R12 approvable settings, commercially available microarrays and commercially available reference R13 material were adopted (see Methods). A platform transition step was therefore required to R14 determine classification settings with these new technologic features before independent R15 multicenter validation (Fig 1). The platform transition cohort consisted of 94 tumor samples R16 (Table 1). First, the performance of the most comprehensive, previously reported gene set was R17 re-evaluated on whole-genome arrays. No gene set was found (data not shown) that performed R18 better; this was expected, given that this signature is a compilation of many different, robustly R19 performing gene sets.14 This signature was therefore applied for additional analyses. Signature R20 scores for the platform transition cohort are depicted in Figure 2A. To complete the transition to a R21 diagnostic microarray (Fig 1), an optimal classification threshold was sought (see Methods). Figure R22 A1 shows the trade-off between NPV (pN0 as percentage of all N0 predictions) versus specificity, R23 compared with positive predictive value versus sensitivity, when choosing a cutoff. Because no R24 threshold could be found that was sufficiently optimal for all patients (Fig A1C), the threshold R25 was based on those patients for whom an improved N0 prediction would be most useful, that is, R26 patients with early-stage (cT1-T2N0) OSCC,who are most frequently treated with neck dissection R27 (Fig 2B and Fig A1D). This threshold yields an NPV of 92% for this group (Fig 2B; Table 3). The R28 NPV for the entire platform transition cohort is 75% (Fig 2A; Table 3). The result of the platform R29 transition stage is therefore a set of genes with predictive expression patterns as determined from R30 previous studies,13,14 with settings trained for application in an independent validation study using R31 a diagnostic microarray (Fig 1). R32 R33 R34 R35 R36 R37 R38 R39
50 Validation of a gene expression signature for the assessment of lymph node metastasis in oral squamous-cell carcinoma
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26
Figure 2 | Signature scores of the study cohorts and subgroups. R27 Gold triangles indicate patients with pN disease. Blue squares indicate patients with pN0 disease. The dotted R28 line indicates the signature score threshold ( 0.256), below which patients are classified as N0 and above which patients are classified as N. (A) Signature scores for all patients in the platform transition cohort. R29 (B) Signature scores for patients with early-stage (cT1-T2N0) oral cavity squamous cell cancer (OSCC) in R30 the platform transition cohort. (C) Signature scores for all patients in the validation cohort. (D) Signature scores for patients with early-stage (cT1-T2N0) OSCC in the validation cohort. (E) Distribution of signature R31 scores for patients in the validation cohort from all centers combined, the three centers with the highest sample contribution (Netherlands Cancer Institute [NKI-AvL], Radboud University Nijmegen Medical Center R32 [RUNMC], and University Medical Center Utrecht [UMCU]), and the aggregate of the five other centers R33 (Erasmus Medical Center, Leiden University Medical Center, Vrije Universiteit Medical Center, University Medical Center Groningen, and Maastricht University Medical Center). The blue boxplots show the signature R34 score distributions for patients with pN0 disease; the gold boxplots show the signature score distributions for R35 patients with pN disease. The rectangular box represents the interquartile range (IQR); the black horizontal line within the box represents the median. The upper whisker extends to the highest observed value within R36 1.5 IQR of the upper quartile; the lower whisker extends to the lowest observed value within 1.5 IQR of the lower quartile. Observations outside of the maximum ranges of the whiskers are shown as individual points. R37 The horizontal dotted line indicates the signature score threshold, above which patients are classified as N and R38 below which patients are classified as N0. R39
51 Chapter 3
R1 R2 NA† NA† NA†
R3 Clinical Assessment 101 73 72 101 73 R4 72 R5
R6 OSCC (n = 101)
R7 Gene Signature Validation Cohort, cT1-T2N0 Validation 101 56 55 24 28 44 32 73 65 86 24 37 36 32 R8 89 R9 R10 R11 Clinical
R12 Assessment 222 177 80 73 112 95 104 110 79 65 73 92 143 104 73 R13
R14 All (n = 222) Validation Cohort, Validation
R15 Gene Signature 28.8 to 46.6 88.6 to 97.5 33.0 to 55.2 78.0 to 91.0 56.0 to 73.4 68.5 to 94.3 R16 50.6 to 65.4 84.4 to 96.5 26.2 to 49.1 222 137 62 96 112 37 41 110 165 86 96 58 57 41 72 R17 R18 R19 NA† NA† NA†
R20 Clinical R21 Assessment 39 24 62 39 24 62 R22 R23 cT1-T2N0 OSCC (n = 39)
R24 Gene Platform Transition Cohort, Platform Transition R25 Signature 39 25 64 14 15 46 11 24 27 93 14 52 12 11 92 R26 R27 R28
R29 Clinical R30 Assessment
R31 All (n = 94) R32 64 73 68 78 4954 37 54 38 90 15 36 40 40 74 41 91 69 49 37 66 90 20 53 15 36 75 68 Gene
R33 Cohort, Platform Transition Signature R34 R35 R36 TP TP TN TN PPV, % PPV, TP + FP NPV, % NPV, TP + FN TN + FP TP + TN TN + FN 95% CI* 58.1-76.6 68.2 to 84.9 48.4 to 77.3 45.9 to 75.1 55.2 to 67.9 74.0 to 84.5 45.7 to 64.8 62.9 to 80.1 95% CI* 24.2-53.0 76.9 to 96.0 27.9 to 64.9 95% CI* 80.1-96.0 55.3 to 79.3 70.2 to 99.7 95% CI* 54.9-76.0 77.5 to 96.1 34.0 to 69.3 R37 95% CI* 53.1-88.8 54.5 to 78.9 64.6 to 99.6 45.9 to 75.1 59.8 to 82.2 64.9 to 79.4 74.7 to 95.6 62.9 to 80.1 Accuracy, % Accuracy, Sensitivity, % Sensitivity, Specificity, % Specificity,
R38 TP + FP TN FN 94 94 Abbreviations: FN, false negative; FP, positive; NA, not applicable; NPV, negative predictive value; OSCC, oral cavity squamous cell cancer; PPV, positive predictive value; TN, true negative; TP, positive. *CIs were calculated using the Wilson method. of TPs and FPs. the presence subset contains only N0 clinical assessments, precluding †The clinically relevant R39 and clinical assessment 3 | Performance metrics of the gene signature Table
52 Validation of a gene expression signature for the assessment of lymph node metastasis in oral squamous-cell carcinoma
Independent validation on multicenter cohort R1 To validate the signature-based classification, 222 samples were collected from eight institutions R2 (Tables 1 and 2). Gene expression was analyzed on the dedicated diagnostic array and classified on R3 the basis of the settings derived from the platform transition cohort (Figs 2C and 2D). Performance R4 is summarized in Table 3. The NPV of the signature on the entire validation cohort was 72% (Fig R5 2C; Table 3). This result is similar to the NPV that was achieved by previous clinical assessment R6 (73%; Table 3). Combining clinical assessment with the signature outcome results in an NPV R7 of 89% for patients who have early-stage (cT1-T2N0) OSCC (Fig 2D; Table 3). No significant R8 outliers between the signature scores from the different centers were detected for patients with R9 and without lymph node metastasis (Mann-Whitney, P > .05; Fig 2E). This indicates that the R10 signature performs similarly across clinical centers. Importantly, the independent validation shows R11 that the signature-based prediction has a significant added value when applied in combination R12 with current clinical assessment, increasing the NPV to 89% for early-stage OSCC. R13 R14 Clinical decision model incorporating the gene expression signature R15 On the basis of these results, the following clinical decision model is proposed (Fig 3A). A patient R16 with early-stage (cT1-T2N0) OSCC can be analyzed by gene expression profiling. If applied to R17 resection material, this would imply a two-stage procedure: resection of the primary tumor first R18 and a subsequent neck dissection in patients with a high-risk profile. This would be analogous to R19 sentinel node procedures19,20 and could cause some delay in treatment and logistical challenges. R20 More optimally, the test could be performed on biopsy material that was obtained at initial R21 diagnosis. This would not alter current timing but first requires a separate evaluation of the R22 similarity in signature outcomes for such biopsies. On the basis of the additional information R23 provided by the signature, a choice of treatment could be made between elective neck dissection R24 when the signature-based prediction is N+, or watchful waiting when the prediction is N0. R25 Current elective treatment with a neck dissection of level I to III results in overtreatment of 72% R26 of patients (Fig 3B, left side). The proposed scheme would prevent overtreatment in almost R27 half of these patients by replacing neck dissection with watchful waiting (Fig 3B, right side). In R28 addition to enhancing the effectiveness of the surgical intervention, this projected improvement R29 will yield significant benefits in terms of morbidity and costs for a large number of patients who R30 are currently being overtreated. R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 Figure 3 | Incorporation of the gene signature in a clinical decision model. R18 (A) Clinical decision model that includes the readout of the gene signature. This model is proposed for R19 patients with early-stage (cT1-T2N0) oral cavity squamous cell cancer (OSCC). In the event of a positive classification (N) on the basis of the gene signature, elective neck dissection of level I-III is proposed. For R20 patients with a negative classification (N0), watchful waiting is proposed. (B) The consequences of this model R21 on the basis of the validation cohort. Currently, all patients with early-stage (cT1-T2N0) OSCC are treated with elective neck dissection of level I-III, causing overtreatment in 73 patients (first bar). In the model involving R22 gene signature readout, overtreatment is limited to 41 patients (second bar), and 32 patients now receive the most appropriate treatment (third bar). The group of patients that are actually N and who receive elective R23 neck dissection of level I-III (indicated by gold color) are labeled as receiving mostly appropriate treatment R24 here, given that in subclinical or limited metastatic disease, the metastases are usually confined to level I-III. R25 R26 R27 DISCUSSION R28 R29 Previous studies have indicated that gene expression profiling can potentially help predict lymph R30 node status in HNSCC and OSCC, in particular.21-23 Such a signature13,14 is completely independently R31 verified in this large multicenter study that was performed in a CLIA/ISO–certified laboratory using R32 a diagnostic array platform. The results indicate that the signature should be prospectively tested R33 and applied alongside current clinical assessment to identify a subgroup of patients with OSCC R34 for whom a watchful waiting strategy would be appropriate. OSCC is predominantly treated R35 surgically.7 In patients with advanced tumors (cT3-T4), for whom the probability of cervical lymph R36 node metastases is relatively high and for whom free flap reconstruction of the neck is often R37 required, most head and neck surgeons opt for elective neck dissection.24,25 The decision model R38 (Fig 3A) does not propose that this approach be changed. The issue of elective neck dissection R39
54 Validation of a gene expression signature for the assessment of lymph node metastasis in oral squamous-cell carcinoma
versus watchful waiting is most relevant for early-stage (cT1T2N0) OSCC.24,25 In these patients R1 elective neck dissection is commonly performed. Treatment of the cN0 neck in early-stage OSCC R2 is one of most heavily debated subjects in head and neck oncology. Although it can be argued R3 that performing elective neck dissection provides prognostic information and could identify R4 patients with pN disease at an early stage, this strategy has not been convincingly proven to be R5 superior as regards survival and control of neck disease.7 The procedure also comes with a price.3 R6 Currently, approximately 60% to 80% of these patients unnecessarily undergo a procedure R7 that has potentially serious associated morbidity, even in the often performed selective neck R8 dissections.8,9 Improving the NPV of lymph node staging such that a watchful waiting strategy R9 becomes more appropriate would therefore improve the quality of life for a significant number R10 of patients with OSCC. Reduction in operating time and improved effectiveness of surgical R11 treatment are additional likely advantages. R12 R13 Factors that require consideration when judging the combined 89% NPV for early-stage (cT1- R14 T2N0) OSCC that is reported here include the incidence of occult metastases in general, as well R15 as HPV infection.26 The false prediction rate of N0 is dependent on the prevalence of occult R16 metastases in the population studied (Bayes’ theorem). Within the validation cohort, 28% of the R17 patients with early-stage (cT1-T2N0) OSCC show occult metastases. This is in line with the level of R18 incidence reported in other studies (20% to 40%)3-6 and indicates that 89% is a realistic estimate R19 of performance in this group of patients. R20 R21 HPV infection represents a distinct etiologic factor for HNSCC, with different tumor characteristics R22 and prognosis.27-29 In studies that distinguish between SCC in the oral cavity (OSCC) and R23 oropharynx (OPSCC), the incidence of HPV in OSCC is less than 4%,30 which indicates that this R24 is unlikely to influence the results reported here. To confirm this, HPV incidence was determined R25 (see Methods) and was found to be 4% in OSCC samples of the validation cohort and not at R26 all present in any of the patients with early-stage (cT1-T2N0) OSCC. HPV incidence therefore R27 does not interfere with the proposed clinical decision model (Fig 3A), which only includes early- R28 stage (cT1-T2N0) OSCC. OPSCC exhibits a higher incidence of HPV26-30 but was excluded from R29 the clinical decision model because these tumors are predominantly treated nonsurgically in the R30 Netherlands; therefore, elective neck dissection is less commonly performed in such patients. R31 Performance of the signature on OPSCC and HPV-positive subgroups from the entire validation R32 cohort was nevertheless analyzed separately and demonstrated poorer performance (Appendix R33 Table A1, online only), in agreement with a distinct etiology and higher rate of occult metastasis. R34 R35 Is a combined NPV of 89% for early stage OSCC sufficient to warrant watchful waiting (Fig R36 3)? Feasibility of watchful waiting is strongly linked to the probability of occult metastases, the R37 frequency and sensitivity of follow-up, and the success rate of salvage therapy. Decision analysis R38 R39
55 Chapter 3
R1 techniques have recommended watchful waiting below risk thresholds varying from 17% to R2 44%.5,31 Such thresholds are much higher than the 11% risk of occult metastases that is projected R3 by the proposed clinical decision model, arguing in favor of implementation of the expression R4 signature alongside current clinical assessment. R5 R6 A logical next step is implementation of the gene expression signature prospectively. The CLIA/ R7 ISO-approvable features that have been incorporated here have been adopted with this goal in R8 mind. The expression signature is not the only relevant development. A prospective study may R9 also benefit from inclusion of sentinel lymph node detection procedures.19,20 Performing such R10 procedures on patients with a positive expression signature could further reduce the number of R11 unnecessary neck treatments, while at the same time restricting the disadvantages of sentinel R12 node procedures to a smaller group of higher-risk patients (Fig 3B, middle). Regardless of whether R13 these different procedures can be combined, the results presented here indicate that a signature R14 for predicting lymph node metastasis in OSCC, after application in a prospective study, may R15 usefully be implemented in a clinical setting. R16 R17 R18 AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST R19 R20 Although all authors completed the disclosure declaration, the following author(s) and/or an R21 author’s immediate family member(s) indicated a financial or other interest that is relevant to R22 the subject matter under consideration in this article. Certain relationships marked with a “U” R23 are those for which no compensation was received; those relationships marked with a “C” were R24 compensated. For a detailed description of the disclosure categories, or for more information R25 about ASCO’s conflict of interest policy, please refer to the Author Disclosure Declaration and the R26 Disclosures of Potential Conflicts of Interest section in Information for Contributors. Employment R27 or Leadership Position: Paul Roepman, Agendia (C) Consultant or Advisory Role: None Stock R28 Ownership: None Honoraria: None Research Funding: None Expert Testimony: None Other R29 Remuneration: None R30 R31 R32 AUTHOR CONTRIBUTIONS R33 R34 Conception and design: Paul Roepman, J. Alain Kummer, Ruud H. Brakenhoff, Piet J. Slootweg, R35 Robert P. Takes, Frank C.P. Holstege Provision of study materials or patients: Piet J. Slootweg R36 Collection and assembly of data: Sander R. van Hooff, Frank K.J. Leusink, Paul Roepman, Robert R37 J. Baatenburg de Jong, Ernst-Jan M. Speel, Michiel W.M. van den Brekel, Marie-Louise F. van R38 Velthuysen, Paul J. van Diest, Robert J.J. van Es, Matthias A.W. Merkx, J. Alain Kummer, Ed R39
56 Validation of a gene expression signature for the assessment of lymph node metastasis in oral squamous-cell carcinoma
Schuuring, Johannes A. Langendijk, Martin Lacko, Maria J. De Herdt, Jeroen C. Jansen, Ruud H. R1 Brakenhoff, Piet J. Slootweg, Robert P. Takes, Frank C.P. Holstege Data analysis and interpretation: R2 Sander R. van Hooff, Frank K.J. Leusink, Paul Roepman, C. Rene´ Leemans, Piet J. Slootweg, R3 Robert P. Takes, Frank C.P. Holstege Manuscript writing: All authors Final approval of manuscript: R4 All authors R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 REFERENCES R2 1. Argiris A, Karamouzis MV, Raben D, et al. Head and neck cancer. Lancet 2008; 371: 1695-1709. R3 2. Kamangar F, Dores GM, Anderson WF. Patterns of cancer incidence, mortality, and prevalence across R4 five continents: Defining priorities to reduce cancer disparities in different geographic regions of the R5 world. J Clin Oncol 2006; 24: 2137-2150. 3. Borgemeester MC, van den Brekel MW, van Tinteren H, et al: Ultrasound-guided aspiration cytology R6 for the assessment of the clinically N0 neck: Factors influencing its accuracy. Head Neck 2008; 30: R7 1505-1513. R8 4. Ganly I, Patel S, Shah J. Early stage squamous cell cancer of the oral tongue: Clinicopathologic features affecting outcome. Cancer 2012; 118: 101-111. R9 5. Okura M, Aikawa T, Sawai NY, et al. Decision analysis and treatment threshold in a management for R10 the N0 neck of the oral cavity carcinoma. Oral Oncol 2009; 45: 908-911. R11 6. Wensing BM, Merkx MA, De Wilde PC, et al. Assessment of preoperative ultrasonography of the neck R12 and elective neck dissection in patients with oral squamous cell carcinoma. Oral Oncol 2010; 46:87-91. 7. Rodrigo JP, Shah JP, Silver CE, et al. Management of the clinically negative neck in early-stage head and R13 neck cancers after transoral resection. Head Neck 2010; 33: 1210-1219. R14 8. Bradley PJ, Ferlito A, Silver CE, et al. Neck treatment and shoulder morbidity: Still a challenge. Head R15 Neck 2011; 33:1060-1067. R16 9. van Wilgen CP, Dijkstra PU, van der Laan BF, et al. Morbidity of the neck after head and neck cancer therapy. Head Neck 2004; 26: 785-791. R17 10. Dünne AA, Folz BJ, Kuropkat C, et al. Extent of surgical intervention in case of N0 neck in head and R18 neck cancer patients: An analysis of data collection of 39 hospitals. Eur Arch Otorhinolaryngol 2004; R19 261: 295-303. 11. Werning JW, Heard D, Pagano C, et al. Elective management of the clinically negative neck by R20 otolaryngologists in patients with oral tongue cancer. Arch Otolaryngol Head Neck Surg 2003; 129: R21 83-88. R22 12. van’t Veer LJ, Bernards R. Enabling personalized cancer medicine through analysis of gene expression patterns. Nature 2008; 452: 564-570. R23 13. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases R24 from primary head and neck squamous cell carcinomas. Nat Genet 2005; 37: 182-186. R25 14. Roepman P, Kemmeren P, Wessels LF, et al. Multiple robust signatures for detecting lymph node R26 metastasis in head and neck cancer. Cancer Res 2006; 66: 2361-2366. 15. Glas AM, Floore A, Delahaye LJ, et al. Converting a breast cancer microarray signature into a R27 highthroughput diagnostic test. BMC Genomics, 2006; 7: 278. R28 16. National Center for Biotechnology Information: Gene expression omnibus. http://www.ncbi.nlm.nih R29 .gov/geo/query/acc.cgi?tokenpvwflwuyuyucglm&ac cGSE30788 R30 17. Shi W, Kato H, Perez-Ordonez B, et al. Comparative prognostic value of HPV16 E6 mRNA compared with in situ hybridization for human oropharyngeal squamous carcinoma. J Clin Oncol 2009; 27: 6213- R31 6221. R32 18. Hafkamp HC, Speel EJ, Haesevoets A, et al. A subset of head and neck squamous cell carcinomas R33 exhibits integration of HPV 16/18 DNA and overexpression of p16INK4A and p53 in the absence of mutations in p53 exons 5-8. Int J Cancer 2003; 107: 394-400. R34 19. Alkureishi LW, Ross GL, Shoaib T, et al. Sentinel node biopsy in head and neck squamous cell cancer: R35 5-year follow-up of a European multicenter trial. Ann Surg Oncol 2010; 17: 2459-2464. R36 20. Civantos FJ, Zitsch RP, Schuller DE, et al. Sentinel lymph node biopsy accurately stages the regional lymph nodes for T1–T2 oral squamous cell carcinomas: Results of a prospective multiinstitutional trial. R37 J Clin Oncol 2010; 28: 1395-1400. R38 R39
58 Validation of a gene expression signature for the assessment of lymph node metastasis in oral squamous-cell carcinoma
21. Chung CH, Parker JS, Karaca G, et al. Molecular classification of head and neck squamous cell carcinomas using patterns of gene expression. Cancer Cell 2004; 5: 489-500. R1 22. O’Donnell RK, Kupferman M, Wei SJ, et al. Gene expression signature predicts lymphatic metastasis in R2 squamous cell carcinoma of the oral cavity. Oncogene 2005; 24: 1244-1251. R3 23. Warner GC, Reis PP, Jurisica I, et al. Molecular classification of oral cancer by cDNA microarrays identifies R4 overexpressed genes correlated with nodal metastasis. Int J Cancer 2004; 110: 857-868. 24. Bar Ad V, Chalian A. Management of clinically negative neck for the patients with head and neck R5 squamous cell carcinomas in the modern era. Oral Oncol 2008; 44:817-822. R6 25. Genden EM, Ferlito A, Silver CE, et al. Contemporary management of cancer of the oral cavity. Eur Arch R7 Otorhinolaryngol 2010; 267: 1001-1017. R8 26. D’Souza G, Kreimer AR, Viscidi R, et al. Casecontrol study of human papillomavirus and oropharyngeal cancer. N Engl J Med 2007; 356: 1944-1956. R9 27. Ang KK, Harris J, Wheeler R, et al. Human papillomavirus and survival of patients with oropharyngeal R10 cancer. N Engl J Med 2010; 363: 24-35. R11 28. Chung CH, Gillison ML. Human papillomavirus in head and neck cancer: Its role in pathogenesis and clinical implications. Clin Cancer Res 2009; 15: 6758-6762. R12 29. Leemans CR, Braakhuis BJ, Brakenhoff RH. The molecular biology of head and neck cancer. Nat Rev R13 Cancer 2011; 11: 9-22. R14 30. Psyrri A, Gouveris P, Vermorken JB. Human papillomavirus-related head and neck tumors: Clinical and R15 research implication. Curr Opin Oncol 2009; 21: 201-205. 31. Song T, Bi N, Gui L, et al. Elective neck dissection or “watchful waiting”: Optimal management strategy R16 for early stage N0 tongue carcinoma using decision analysis techniques. Chin Med J (Engl) 2008; 121: R17 1646-1650. R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 SUPPORTING INFORMATION R2 R3 Supporting methods R4 Arrays The whole-genome array was based on the standard Agilent whole-genome high- R5 density (4 × 44K) array (Agilent Technologies, Santa Clara, CA), with default Agilent probes for R6 all genes. The array (ID 015866) was designed in 2007 using the best (at that time) updated R7 standard Agilent human genome probe set and the Agilent eArray database and interface R8 (http://www.earray.com). The full-genome array was custom made for Agendia (Amsterdam, R9 the Netherlands) to also include the presence of additional probes that represented previously R10 published signature probes in replicate. These probes were selected by mapping probe sequences R11 from the original studies and replicating standard Agilent probes for these genes. Of the 825 R12 previously published predictive gene probes, 732 unique probes representing 696 unique genes R13 could be unambiguously mapped in this way and were therefore applied throughout this study. R14 Other than the full-genome array, a dedicated diagnostic array was designed (high-density eight- R15 pack) that contained, in addition to Agilent’s default control probe set, all probes necessary for a R16 diagnostic readout of the gene signatures (5× replicate) alongside a set of normalization probes. R17 The normalization probes were selected from the full-genome array by identifying the most stably R18 expressed genes across all samples that were profiled for the platform transition step, covering R19 a complete range of expression levels. These probes were also transferred to the dedicated R20 diagnostic array. The Data Supplement contains sequence information and probe mapping R21 information for all signature probes (for both arrays). R22 R23 Analysis of gene expression R24 Unless otherwise stated, the procedures described here refer to both the full-genome (44K) R25 and diagnostic (eight-pack) arrays. Raw fluorescence intensities were quantified using Agilent R26 Feature Extraction software (v. 9.5) and imported into R/Bioconductor (http:// www.r-project.org R27 and http://www.bioconductor.org). Normalization was performed with the limma Bioconductor R28 package v. 2.5.21 in R (v. 2.12.0) using the normalizeWithinArrays function with a loess span of R29 0.4 and including local background subtraction. Normalization of the full-genome expression R30 data was performed in a global fashion to include all noncontrol probes on the array. For the R31 dedicated diagnostic arrays the normalization probe set was used. Individual spot measurements R32 with missing or negative values were dismissed from additional analysis. Replicate spots (with a R33 positive value) were averaged, resulting in a single value for every probe. Because of replication, R34 all of the signatures probes could be read out. After normalization, a centering procedure was R35 introduced to increase the dynamic range of the signature score. Given that the reference RNA R36 is derived from cell lines, the difference in expression levels is much larger between sample and R37 reference than between pN0 and pN+ samples. This compresses the range of the signature scores. R38 Centering was therefore performed to alleviate this compression of values. The transformation is R39
60 Validation of a gene expression signature for the assessment of lymph node metastasis in oral squamous-cell carcinoma
a modified version of mean centering, that is, an unweighted aggregate mean centering. After R1 normalization, the expression values of all probes were centered using the unweighted aggregate R2 mean per probe of pN+ and pN0 samples from the platform transition cohort. In other words, R3 for every probe, the mean expression of all pN+ samples (n = 54) as well as all pN0 samples (n R4 = 40) was calculated. Next, the mean of the pN+ and pN0 means (the unweighted aggregate R5 mean) was calculated. The resulting unweighted aggregate mean for a particular probe was then R6 subtracted from the individual expression values of this probe in the platform transition and the R7 multicenter validation cohorts. The aggregate mean was not recalculated for the multicenter R8 validation cohort, thereby avoiding the introduction of unwarranted bias. R9 R10 Supporting tables R11 Due to size the tables are not shown here. Supplementary tables 1, 2 & 3 can be found at: http:// R12 jco.ascopubs.org/content/suppl/2012/11/15/JCO.2011.40.4509.DC1/Online_table_1.xls http:// R13 jco.ascopubs.org/content/suppl/2012/11/15/JCO.2011.40.4509.DC1/Online_table_2.xls http:// R14 jco.ascopubs.org/content/suppl/2012/11/15/ JCO.2011.40.4509.DC1/Online_table_3.xls R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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Novel diagnostic modalities for assessment
of the clinically node-negative neck in
oral squamous-cell carcinoma
F.K.J. Leusink R.J.J. van Es R. de Bree R.J. Baatenburg de Jong S.R. van Hooff F.C.P. Holstege P.J. Slootweg R.H. Brakenhoff R.P. Takes
Lancet Oncology 2012; 13: e554-61 Chapter 4
R1 ABSTRACT R2 R3 Oral squamous-cell carcinomas arise in mucosal linings of the oral cavity and frequently R4 metastasize to regional lymph nodes in the neck. The presence of nodal metastases is a R5 determinant of prognosis and clinical management. The neck is staged by palpation and R6 imaging, but accuracy of these techniques to detect small metastases is low. In general, 30– R7 40% of patients will have occult nodal disease and will develop clinically detectable lymph- R8 node metastases when the neck is left untreated. The choice at present is either elective R9 treatment or careful observation followed by treatment of the neck in patients who develop R10 manifest metastases. These unsatisfying therapeutic options have been the subject of debate R11 for decades. Recent developments in staging of the neck, including expression profiling and R12 sentinel lymph-node biopsy, will allow more personalized management of the neck. R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
64 Novel diagnostic modalities for assessment of the clinically node-negative neck in oral squamous-cell carcinoma
INTRODUCTION R1 R2 Head and neck squamous-cell carcinoma (HNSCC) is a common cancer worldwide.1 Risk factors R3 include tobacco smoking, betel nut chewing, excessive alcohol consumption, and human R4 papillomavirus (HPV) infection.2,3 Of all HNSCCs, oral squamous-cell carcinoma (SCC) has the R5 highest incidence. Most tumors in oral-cavity subsites have a comparable high propensity to R6 metastasize to regional lymph nodes in the neck. Findings of recent studies seem to refute R7 the traditional belief that maxillary SCCs have a lower rate of metastasis to the neck.4–6 The R8 presence of regional neck metastases is a major determinant of both prognosis and treatment R9 decisions in patients with oral SCC.7 However, the low sensitivity of currently available diagnostic R10 modalities is a problem, because a fairly high proportion (30–40%) of lymph node metastases are R11 left undetected in this population. These metastases will develop into overt neck disease during R12 follow-up. R13 R14 A policy is in place to treat the neck even when the tumor has been classified clinically as node- R15 negative (cN0), indicating no disease is detectable in the neck.8 This strategy prevents disease R16 in the neck becoming more advanced once previously occult metastases become clinically R17 apparent or are detected late during follow-up. Thus, 60–70% of patients receive unnecessary R18 treatment, which in the case of neck dissection encompasses a surgical procedure potentially R19 causing disfigurement and associated morbidity.9–11 The alternative approach of watchful waiting R20 entails careful monitoring of the neck (eg, by ultrasound-guided fine-needle aspiration cytology R21 during follow-up).12,13 Treatment will be given only to patients who develop manifest metastasis, R22 which usually arises within 1–2 years. Although no evidence shows convincingly that one strategy R23 should be preferred over the other, consensus— partly based on an often cited but outdated R24 decision analysis model—suggests that elective neck treatment is indicated when the chance of R25 occult nodal disease exceeds 20%.14 Hence, increased accuracy to ascertain the metastatic status R26 of the neck will result in fewer unnecessary elective treatments of the neck. R27 R28 Currently, the neck is staged by palpation and different imaging techniques, including CT, MRI, R29 PET/CT, and ultrasound, which are more accurate than palpation alone.15–17 Morphological and R30 size criteria are the main determinants of specificity of imaging techniques, whereas sensitivity R31 is limited by the detection threshold. Up to a third of nodal metastases in patients with oral R32 SCC are smaller than the 3 mm detection threshold that limits sensitivity of available imaging R33 techniques,18 and occult neck disease therefore remains a relevant issue. In a meta-analysis of R34 PET/CT for assessment of cervical lymph-node metastasis,16 sensitivity of PET/CT was only 50% for R35 patients who were node-negative on palpation, whereas specificity was 87%. The performance R36 of ultrasound, MRI, and CT was equally disappointing. Later research has shown similar results.19 R37 R38 R39
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R1 The limitation of imaging techniques to detect small metastatic deposits has led to a search for R2 additional characteristics or biomarkers assessable on the primary tumor to predict nodal disease. R3 Histopathological or molecular features of the primary tumor can predict the presence of nodal R4 metastases in the individual patient, irrespective of the actual size of the tumour.20 Measures such R5 as perineural invasion, vascular invasion, tumor border (infiltrative or pushing), tumor thickness, R6 and depth of invasion have been studied extensively for correlations. Only tumor thickness is R7 associated consistently with the presence of nodal metastases.21 However, suggested cutoff s for R8 thickness range from 1·5 mm to 10 mm, and consensus to recommend elective neck dissection is R9 missing.21–24 One reason could be that definitions and methods to measure tumor thickness vary R10 in reports. Moreover, tumor thickness is usually ascertained on histopathological examination of R11 the resection specimen, implying a second-stage surgical procedure when elective neck dissection R12 is indicated. Measurement of tumor thick ness before surgery, using an intraoral ultrasound R13 probe, could be more promising in this respect.24 R14 R15 Besides histological variables, many single molecular markers have been studied to predict the R16 presence of nodal metastasis. In view of the complexity of the metastatic process, no consistent R17 and clinically useful correlations have been noted for any markers.20 R18 R19 Thus, the dilemma in current clinical management of the neck is the choice between possible R20 undertreatment of 30–40% of patients with occult metastases and overtreatment of the R21 remaining 60–70%. Personalized management of the clinically node-negative (cN0) neck, R22 especially in patients with oral SCCs, would benefit greatly from staging techniques that add R23 accuracy to assessment of nodal disease, particularly when these methods are not or are minimally R24 dependent on size of the metastasis. R25 R26 Methods are being developed to diagnose or predict occult metastases in the neck. In this R27 Personal View, we restrict ourselves to discussion of the two most promising techniques, which R28 are arguably ready for clinical implementation and have a very different but complementary R29 nature: gene-expression profiling and sentinel lymph-node biopsy. We describe various aspects R30 of these two approaches and suggest a new staging algorithm to incorporate both methods, to R31 optimize management of the cN0 neck in patients with early-stage oral SCC. R32 R33 R34 SEARCH STRATEGY AND SELECTION CRITERIA R35 R36 We searched PubMed for studies published in the English language from 2000, until June, 2012, R37 with the terms: “head and neck cancer”, “head and neck squamous cell carcinomas”, “oral R38 cavity squamous cell carcinomas”, “lymph node metastasis”, “occult lymph node metastasis”, R39
66 Novel diagnostic modalities for assessment of the clinically node-negative neck in oral squamous-cell carcinoma
“staging techniques”, “sentinel lymph node”, “molecular biology”, “predictive markers”, R1 “expression profiling”, and “management of the N0 neck”. One article published before 2000 R2 was used as a historical reference. Selection was based on novelty and relevance to the scope of R3 this Personal View. R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 Figure 1 | Sentinel lymph-node procedure (A) Lymphoscintigram showing injection site (large white area) and sentinel lymph node (small white area). R36 (B) Gamma probe-guided sentinel lymph-node biopsy. (C) A micrometastasis (red colour) is depicted by R37 immunohistochemistry using antibodies against cytokeratin. R38 R39
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R1 MOLECULAR DIAGNOSIS AND TUMOR PROFILING R2 R3 Gene-expression profiling with DNA microarrays and next generation sequencing approaches R4 (RNAseq) signal a new era of tumor classification and prognostication. RNA isolated from the R5 tumor specimen can be used to ascertain expression levels of all genes simultaneously in one R6 experiment. This process has led to novel classifications of lymphomas, breast cancer, and R7 HNSCC.25–27 Furthermore, prognostic profiles have been identified in many tumor types, including R8 breast cancer and HNSCC.28,29 Roepman and colleagues30,31 used gene-expression profiling to R9 stage the clinically N0 neck in HNSCC patients and, using RNA from the primary tumor specimen, R10 identified particular profiles that could predict N-stage. R11 R12 In a multicenter validation study, undertaken at all head and neck oncological centers in the R13 Netherlands,32 the expression profile to predict lymph-node metastasis was transferred to a R14 diagnostic platform to facilitate clinical implementation. Subsequently, the profile was validated R15 with an independent series of 222 samples of oral and oropharyngeal SCC. Although the array R16 platform was changed, the profile predicted N-stage as expected. For the group of cT1–T2N0 or R17 early-stage oral SCCs (n=101), a negative predictive value of 89% was recorded (table 1). For R18 these cases the issue of elective neck treatment is most relevant, because early stage oral SCC R19 is treated by transoral surgery and, thus, there is no need to enter the neck for excision of the R20 primary tumor. Some cancers in the validation series were HPV-positive, and the profile worked R21 less well with HPV-positive tumours.32 However, HPV occurs rarely in tumors of the oral cavity, R22 with a prevalence estimated at less than 5%.3,34 Hence, in patients with early-stage (cT1–T2N0) R23 oral SCC, gene-expression profiling might reduce greatly the number of unnecessary elective R24 neck dissections, allowing more personalized treatment. R25 R26 Improvements can still be made, however. N-status was ascertained by routine histopathological R27 examination as reference standard, whereas detection of micrometastases (0·2–2 mm) can be R28 increased by stepped serial sectioning and immunohistochemistry of all lymph nodes. Such R29 extensive immunohistopathological examination allows detection of 5–58% (mean 20%) of R30 metastases35 that escape routine histological detection. Recently, we showed that the predictive R31 power of the profiling approach rises further when histopathological assessment of all lymph R32 nodes is scrutinized (unpublished data). Another improvement of the gene-expression profiling R33 approach might be noted with analysis of multiple biopsy samples. R34 R35 Findings of next-generation sequencing studies indicate that intratumor heterogeneity is present R36 in several cancer types, and this heterogeneity can sometimes be linked to particular areas of R37 the tumor specimen.36–39 Intratumor genetic heterogeneity might not necessarily be reflected in R38 global gene-expression profiles, but further studies are needed because the predictive power of R39 gene-expression profiles in early-stage oral SCC could be affected by genetic heterogeneity.
68 Novel diagnostic modalities for assessment of the clinically node-negative neck in oral squamous-cell carcinoma
Table 1 | Patient characteristics and performance metrics of two compared studies *, ** R1 Characteristics GEP study (Van Hooff) SNB study (Alkureishi) P Value † cT1-2N0 (N=101) cT1-2N0 (N=134) R2 Tumours included from 2000-2007 1998-2002 R3 Follow-up – yr > 5 > 5 R4 Participating centres – no. 8 6 R5 Age – yr±SD 62 61 NA R6 Location Primary Tumor – no. (%) 0.001 oral cavity 101 (100) 122 (91) R7 oropharynx 0 (0) 12 (9) R8 Sub-location Primary Tumor – no. (%) 0.010 R9 Floor of mouth 22 (22) 42 (31) R10 Anterior tongue 58 (57) 50 (37) Other sites 21 (21) 42 (31) R11 Clinical T stage – no. (%) <0.001 R12 T1 24 (24) 75 (56) R13 T2 77 (76) 59 (44) R14 Pathological N status – no. (%) 0.32 positive 28 (28) 46 (34) R15 negative 73 (72) 88 (65) R16 Performance metrics R17 NPV % (tn/tn+fn) 89 (33/36) 95 (88/92) R18 Sensitivity % (tp/tp+fn) 86 (24/28) 91 (42/46) R19 * Percentages may not total 100 because of rounding. ** NPV denotes negative predictive value, tp true positive, tn true negative, and fn false negative. R20 † The positive predictive value and the specificity are dependent on the number of false positive samples. R21 Since SNB assessment precludes the presence of false positives, the positive predictive value and the specificity are left out of this comparison. R22 R23 Encouragingly, the highest predictive power for gene-expression profiling was seen in the R24 clinically relevant group of cT1–T2N0 oral SCCs. Although this association could be related to R25 the lower prevalence of nodal metastasis in this group, it seems to also make sense from a R26 biological perspective. Early lymphatic dissemination of tumors classified as T1 and T2 might R27 be the direct result of intrinsic tumor properties reflected in specific gene-expression profiles, R28 whereas in advanced cancers, destruction of anatomical barriers and invasive growth is likely to R29 become an important factor associated with lymphatic metastasis as well. R30 R31 Gene-expression profiling is best undertaken on fresh or frozen tumor samples; arguably, this R32 strategy might restrict applicability, because biopsy samples or surgical specimens are routinely R33 formalin-fixed and paraffin-embedded (FFPE). However, taking an additional biopsy sample R34 for gene-expression profiling would hardly impose a substantial burden. Moreover, recent R35 developments to extract RNA from FFPE specimens for gene-expression profiling could be R36 helpful.29,40 An alternative option would be to ascertain genome-wide genetic changes in DNA, R37 using a comparative genomic hybridization microarray or next-generation sequencing platform. R38 R39
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R1 Switching from RNA to DNA profiling is not an unrealistic idea; the breast cancer classification R2 ascertained by RNA expression profiling has been reproduced accurately by comparative genomic R3 hybridization microarray DNA profiling,41 suggesting that differences in gene-expression profiles R4 between tumors might be reflected well in the number and type of DNA changes. Whether or R5 not this idea also holds true for staging of the cN0 neck in patients with oral SCC remains to be R6 established. R7 R8 Gene-expression profiles of tumor biopsy samples assessed by microarray hybridization have R9 proven their potential value for N-staging of oral SCC. However, although DNA or protein profiles R10 might be applied likewise, N-stage predictive profiles have not been published, to the best of our R11 knowledge, let alone validated in prospective multicenter studies. R12 R13 R14 SENTINEL LYMPH-NODE BIOPSY R15 R16 In an attempt to more reliably select lymph nodes that potentially contain metastases, sentinel R17 lymph-node assessment, which is used extensively in melanoma and breast cancer, has been R18 introduced. The sentinel lymph node is likely to be the first lymph node to harbor metastasis R19 and can be used to provide information on the rest of the nodal basin. It is usually identified by R20 peritumoral injection of radioactive colloid and blue dye. Preoperative lymphoscintigraphy (figure R21 1A), intra-operative visualization of blue coloration, and intra operative radionuclide detection R22 with a gamma probe (figure 1B) allow identification of the sentinel lymph node. After surgical R23 removal, this node is studied meticulously by histopathological examination, using stepped serial R24 sectioning and immunohistochemistry (figure 1C). If the sentinel lymph node contains metastatic R25 tumor cells, treatment of the neck is recommended, usually in a second procedure.42 R26 R27 The sentinel lymph-node procedure is deemed more precise than imaging procedures and less R28 invasive than elective neck dissection. Moreover, it is associated with significantly less postoperative R29 morbidity and better shoulder function compared with elective neck dissection.43 Current best- R30 practice guidelines for provision of sentinel lymph-node biopsy to patients with early-stage oral R31 SCC have been outlined, which provide a framework for the currently evolving recommendations R32 for its use.42 R33 R34 To safely avoid elective treatment of the neck in as many patients as possible, a high sensitivity R35 and high negative predictive value are both needed. In the American College of Surgeons R36 Oncology Group Z0360 validation study of 140 patients in 25 institutions, sensitivity was 90% R37 and the negative predictive value was 96%; these figures were even better for skilled surgeons.44 R38 However, in this study, standard histopathological examination of the neck-dissection specimen R39
70 Novel diagnostic modalities for assessment of the clinically node-negative neck in oral squamous-cell carcinoma
was used as the gold standard. Therefore, occult micrometastasis might have been missed, R1 contributing to higher figures for sensitivity and negative predictive value. In a meta-analysis of R2 19 pilot studies with a total of 347 oral and oropharyngeal cancer patients, a pooled sensitivity R3 of 92·6% was reported.45 R4 R5 After initial studies to validate the sentinel lymph-node approach in patients with early-stage oral R6 SCC, several prospective observational studies have been reported. In two large single-center R7 studies, sensitivities and negative predictive values of at least 90% were noted.46,47 In these R8 studies, neck dissection was undertaken only when the sentinel lymph node contained metastasis, R9 and a watchful-waiting strategy was followed when the sentinel lymph node was tumor-free. In R10 a European multicenter study33 of 134 patients with cT1/2N0 oral SCC (table 1), 79 patients R11 underwent sentinel lymph-node biopsy as the sole staging method, whereas 55 underwent R12 sentinel lymph-node biopsy followed by elective neck dissection. For the two groups together, R13 using a reference standard of 5 years follow-up after sentinel lymph-node biopsy staging, a R14 sensitivity of 91% and a negative predictive value of 95% were recorded. The better performance R15 seen in patients who underwent both techniques (sensitivity 96%, negative predictive value R16 97%) compared with those who only had sentinel lymph-node biopsy (87%, 94%) can again be R17 accounted for by use of standard histopathological examination of the neck dissection specimen R18 versus 5 years follow-up as a gold standard for metastasis. R19 R20 Of note, both the sentinel lymph-node identification rate and sensitivity were significantly R21 poorer in patients with floor-of-mouth tumors. Peritumoral injection sites might overshine the R22 sentinel lymph node in these patients, resulting in detection failure by the gamma probes.33 The R23 observational multicenter European Sentinel Node Trial (SENT),48 with more than 300 patients R24 enrolled, has completed accrual and long-term follow-up data are awaited. Overall, detection R25 rates, sensitivity, and negative predictive values of sentinel lymph-node biopsy procedures are R26 generally above 90%. R27 R28 Some innovations would improve preoperative imaging of the sentinel lymph node. Hybrid single- R29 photon emission CT with integrated CT (SPECT/CT) can augment visualization of the relation of R30 sentinel lymph nodes to several vital vascular and neural structures, thus enabling safer removal R31 of these nodes. Furthermore, enhanced topographical orientation and delineation of sentinel R32 lymph nodes against surrounding structures might also reduce surgical time. Although SPECT/CT R33 has the potential to detect more sentinel lymph nodes, it still has some diffculties in visualization R34 of nodes in close spatial relation to the injection site.49 Promising preclinical results have been R35 reported of 89Zr-nanocolloidal albumin-based PET/CT lymphoscintigraphy for sentinel lymph- R36 node detection in HNSCC.50 Technical innovations to improve intraoperative localization of R37 sentinel lymph nodes include intraoperative real-time imaging, freehand SPECT, and fluorescence R38 R39
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R1 imaging. Intraoperative real-time imaging with the portable gamma camera provides an overview R2 of all radioactive spots and can show sentinel lymph nodes near the injection site by adjustment R3 of its position. Another advantage might be the certainty it can provide about the completeness R4 and accuracy of sentinel lymph node excision, by showing the remaining activity.51 Freehand R5 SPECT is designed to ascertain the position of the detector relative to the patient, through which R6 three dimensional images are generated. These images provide the surgeon with information R7 about the direction and depth of the sentinel lymph node in relation to the probe.52 R8 R9 The feasibility of near-infrared fluorescence guided sentinel lymph-node detection has been R10 shown in HNSCC, with indocyanine green as fluorescent tracer.53 Other tracers with enhanced R11 optical properties have been tested in HNSCC in preclinical settings.50,54 The real clinical additional R12 value of these techniques has still to be assessed. R13 R14 Developments in detection of metastasis in the sentinel lymph-node specimen have been made. R15 While the biopsy procedure is taking place, immediate frozen section, imprint cytology, or R16 molecular techniques can be done that should reliably show sentinel lymph-node metastasis.55 R17 Furthermore, a rapid automated quantitative reverse transcriptase PCR assay has been described R18 that took about 35 min to complete and had an accuracy of 94·2% for identification of positive R19 and negative nodes, which might be more accurate than intraoperative pathological analysis.56 R20 Thus, the surgical procedure—including neck dissection in case of a positive test for nodal R21 metastasis—could be restricted to one session instead of the two currently needed in sentinel- R22 node procedures. However, this change could interfere with current surgical planning efficiency, R23 because complete neck dissection takes about five times longer to undertake than does sentinel R24 lymph-node biopsy of the neck. These technical developments could raise the rate of sentinel R25 lymph-node detection further and facilitate the procedure, potentially resulting in less morbidity R26 for the patient, reduced operating time, and increased convenience for the head and neck R27 surgeon. R28 R29 TOWARDS A NEW DIAGNOSTIC STRATEGY R30 R31 To compare molecular diagnosis and sentinel lymph node biopsy for staging of the cN0 neck, we R32 selected two studies (table 1). We chose the study by Van Hooff and colleagues32 because it is the R33 only multicenter gene-expression profiling study available. The sentinel lymph node biopsy study R34 by Alkureishi and colleagues33 was selected because it is also a multicenter study. R35 R36 Weighing the list of advantages and disadvantages of both methods (table 2), gene-expression R37 profiling might be judged more favorable. Although sentinel lymph-node biopsy has a good R38 negative predictive value, it remains an invasive surgical procedure with (albeit limited) chances R39
72 Novel diagnostic modalities for assessment of the clinically node-negative neck in oral squamous-cell carcinoma
of associated morbidity, needs injection of radioactive tracers, and has logistical challenges R1 such as additional second-stage surgery in case of detected metastasis. Would a combination R2 of approaches have additional value? Figure 2A outlines our proposed modification to the R3 management strategy for patients with early-stage (cT1–T2) oral SCC. If the neck is staged cN0 R4 by current imaging techniques, use of gene-expression profiling is indicated, with subsequent R5 sentinel lymph-node biopsy if the signature classification is positive (cN+[gep]). A tumor with R6 a negative gene-expression profile would indicate low risk for metastasis and be classified as R7 cN0(gep). Since occult nodal disease is unlikely, this group could be followed up by watchful R8 waiting — eg, surveillance of the neck by ultrasound-guided fine-needle aspiration cytology R9 every 3–4 months during the first year of follow-up. The high-risk group, classified as cN+(gep), R10 would undergo sentinel lymph-node biopsy followed by either watchful waiting in case of R11 pN0(sn) or neck dissection in case of pN+(sn).42 The main disadvantage of current management R12 is overtreatment, because of the policy to treat the neck in all patients. On the basis of data R13 from the multicenter study by Van Hooff and colleagues (n=101),32 72% of patients would be R14 overtreated (table 1, figure 2B). R15 R16 R17 Table 2 | Advantages and disadvantages of gene expression profiling and sentinel lymph node biopsy* R18 Gene expression profiling Sentinel lymph node biopsy R19 Advantages non-invasive when combined with routine minimally invasive R20 biopsy minimal morbidity R21 no morbidity NPV 90-95% NPV (89%) R22 no general anaesthesia R23 no hospitalization R24 Disadvantages at present fresh or frozen specimen required additional 2nd stage surgery in case of costs pN+(sn) R25 hospitalization R26 injection of radioactive tracers labour intensive R27 R28 * NPV denotes negative predictive value, PPV positive predictive value and pN+(sn) pathological positive lymph node by sentinel lymph node analysis. R29 R30 With our proposed strategy, the number of unnecessary treatments of the neck could be R31 eliminated. However, 11% of false-negative results after gene-expression profiling imply that R32 four of 36 patients classified as cN0(gep) are undertreated (figure 2B). Furthermore, a negative R33 predictive value of 95% for the sentinel lymph node procedure indicates that two of 43 patients R34 classified as pN0(sn) are also undertreated. Taken together, 6% of patients could be undertreated R35 with our strategy and will need to undergo second-stage treatment. R36 R37 R38 R39
73 Personal View
because complete neck dissection takes about fi ve times imaging techniques, use of gene-expression profi ling is longer to undertake than does sentinel lymph-node indicated, with subsequent sentinel lymph-node biopsy biopsy of the neck. These technical developments could if the signature classifi cation is positive (cN+[gep]). raise the rate of sentinel lymph-node detection further A tumour with a negative gene-expression profi le would and facilitate the procedure, potentially resulting in less indicate low risk for metastasis and be classifi ed as morbidity for the patient, reduced operating time, and cN0(gep). Since occult nodal disease is unlikely, this increased convenience for the head and neck surgeon. group could be followed up by watchful waiting—eg, surveillance of the neck by ultrasound-guided fi ne-needle Towards a new diagnostic strategy aspiration cytology every 3–4 months during the fi rst To compare molecular diagnosis and sentinel lymph- year of follow-up. The high-risk group, classifi ed as node biopsy for staging of the cN0 neck, we selected two cN+(gep), would undergo sentinel lymph-node biopsy studies (table 1). We chose the study by Van Hooff and followed by either watchful waiting in case of pN0(sn) or colleagues32 because it is the only multicentre gene- neck dissection in case of pN+(sn).42 expression profi ling study available. The sentinel lymph- The main disadvantage of current management is node biopsy study by Alkureishi and colleagues33 was overtreatment, because of the policy to treat the neck in selected because it is also a multicentre study. Weighing all patients. On the basis of data from the multicentre the list of advantages and disadvantages of both methods study by Van Hooff and colleagues (n=101),32 72% of (table 2), gene-expression profi ling might be judged patients would be overtreated (table 1, fi gure 2B). With more favourable. Although sentinel lymph-node biopsy our proposed strategy, the number of unnecessary has a good negative predictive value, it remains an treatments of the neck could be eliminated. However, invasive surgical procedure with (albeit limited) chances 11% of false-negative results after gene-expression of associated morbidity, needs injection of radioactive profi ling imply that four of 36 patients classifi ed as tracers, and has logistical challenges such as additional cN0(gep) are undertreated (fi gure 2B). Furthermore, a second-stage surgery in case of detected metastasis. negative predictive value of 95% for the sentinel lymph- Would a combination of approaches have additional node procedure indicates that two of 43 patients classi- value? Figure 2A outlines our proposed modifi cation to fi ed as pN0(sn) are also undertreated. Taken together, 6% theChapter management 4 strategy for patients with early-stage of patients could be undertreated with our strategy and (cT1–T2) oral SCC. If the neck is staged cN0 by current will need to undergo second-stage treatment.
A B R1 120 Appropriate treatment Patients with early stage (cT1–T2N0) tumour Mostly appropriate treatment R2 in the oral cavity Undertreatment Current Overtreatment R3 100 pN+ R4 Gene-expression profiling 80 R5 Classified cN0 (gep) Classified cN+ (gep) R6 pN0 Proposed model: 60 GEP with or without SLNB R7 pN+
Sentinel lymph-node biopsy of patients Number pN+ R8 40 R9 pN0 pN0 pN0(sn) pN+(sn) 20 R10 pN+ R11 Watchful waiting Watchful waiting (Modified) radical 0 Selective neck Watchful waiting Watchful waiting (Modified) radical neck dissection R12 dissection I–III (GEP) (GEP plus SLNB)
R13 FigureFigure 2: Proposed 2 | Proposeddiagnostic algorithm diagnostic incorporating algorithm gene-signature incorporating prediction and sentinel gene-signature lymph-node biopsy prediction and sentinel R14 (A)lymph-node Clinical decision model biopsy proposed for early-stage (cT1–T2N0) oral squamous-cell carcinoma patients. In case of a negative classifi cation (cN0[gep]) based on the gene(A) signature,Clinical watchful decision waiting model is proposed. proposed For patients for with early-stage a positive classifi (cT1–T2N0) cation (cN+[gep]), oral sentinel-node squamous-cell biopsy is proposed. carcinoma In case patients.of a negative sentinelIn case R15 node (pN0[sn]), watchful waiting is proposed, whereas with a positive sentinel node (pN+[sn]), usually a (modifi ed) radical neck dissection is recommended. (B)of Currently, a negative all early-stage classi patientsfi cation are treated (cN0[gep]) with selective based neck ondissection the gene(level I–III), signature, causing overtreatment watchful in 72%waiting of patients is proposed. (red). The remaining For patients28% of R16 patientswith areceive positive mostly classiappropriatefication treatment (cN+[gep]), (light green). Insentinel-node the proposed model biopsy with gene-signature is proposed. readout In and case sentinel of lymph-node a negative biopsy, sentinel overtreatment node by elective(pN0[sn]), neck dissection watchful does not waiting exist. 94% is of proposed, patients now receive whereas the most with appropriate a positive treatment sentinel (dark green) node and only (pN+[sn]), 6% (yellow) usually will undergo a second-stage(modifi ed) R17 treatment (neck dissection, of which some will also need postoperative irradiation). Numbers of patients for every outcome of this strategy are based on patients enrolledradical in theneck gene-expression dissection profi is ling recommended. validation study by van (B) Hooff Currently, and colleagues all (table early-stage 1).32 Because patients study populations are treatedare not entirely with comparable, selective the neck R18 numbersdissection shown (levelshould be I–III), regarded causing as estimates. overtreatment GEP=gene-expression in 72% profi ling. of SLNB=sentinel patients (red).lymph-node The biopsy. remaining 28% of patients receive R19 mostly appropriate treatment (light green). In the proposed model with gene-signature readout and sentinel www.thelancet.com/oncologylymph-node biopsy, overtreatmentVol 13 December 2012 by elective neck dissection does not exist. 94% of patients now receive e558 R20 the most appropriate treatment (dark green) and only 6% (yellow) will undergo second-stage treatment (neck R21 dissection, of which some will also need postoperative irradiation). Numbers of patients for every outcome of R22 this strategy are based on patients enrolled in the gene-expression profi ling validation study by van Hooff and colleagues (table 1).32 Because study populations are not entirely comparable, the numbers shown should be R23 regarded as estimates. GEP=gene-expression profiling SLNB=sentinel lymph-node biopsy. R24 R25 R26 CONSIDERATIONS AND FUTURE PERSPECTIVES R27 R28 Oral SCC is predominantly treated surgically,13 therefore, the issue of elective neck dissection R29 versus watchful waiting is most relevant for early-stage (cT1–T2N0) tumours.57,58 In patients with R30 advanced tumors (cT3–T4), who have a fairly high probability of cervical lymph-node metastases R31 and often need neck surgery to access the primary tumor or to reconstruct the surgical defect, R32 most head and neck surgeons will opt for elective neck dissection anyway.57,58 Our proposed R33 strategy does not intend to change this policy. R34 R35 Morbidity is a relevant issue with current management of early-stage (cT1–T2N0) oral SCC. By R36 applying our proposed algorithm, oncological safety is not so much pursued by over treatment R37 and its associated unnecessary morbidity but rather by further reduction of the rate of occult R38 metastasis and accurate follow-up. Timely treatment is still possible for patients who are under R39 staged.59
74 Novel diagnostic modalities for assessment of the clinically node-negative neck in oral squamous-cell carcinoma
Decision-analysis techniques recommend watchful waiting below risk thresholds that vary from R1 17% to 44%;14,60,61 thus, we judge the negative predictive values of gene-expression profiling R2 and sentinel lymph-node biopsy acceptable because such thresholds are much higher than the R3 6% risk of occult metastases projected by our proposed algorithm. R4 R5 Although a significant survival difference has not been reported between stringent watchful R6 waiting without neck dissection and elective neck dissection,59,62,63 patients who develop delayed R7 lymph-node metastases can be diagnosed with more advanced disease in the neck, requiring R8 extensive neck dissection or adjuvant (chemo)radiotherapy. With adoption of a watchful-waiting R9 policy, 30–40% of patients will need neck dissection, and under our proposed algorithm this R10 proportion would fall to 6%. R11 R12 In the end, how to weigh the expected 6% of patients needing second-stage (and maybe R13 intensified) treatment against the inevitable morbidity of 60–70% of patients who undergo R14 unnecessary treatment of their neck will remain a matter of subjective judgment. With respect R15 to cost-effectiveness of our proposed algorithm, the price of gene-expression profiles, sentinel R16 lymph-node biopsy procedures, neck dissections, and ultrasound-guided fine-needle aspiration R17 cytology procedures should be weighed against expenses and associated morbidity of elective R18 neck dissections that would take place with current management. Similar to breast cancer, cost- R19 effectiveness depends heavily on costs of operation time, hospital stay,64 rehabilitation programs, R20 and the expected length of sick leave associated with the procedure, which will be reduced R21 substantially with our proposed regimen. Future price developments and cost-comparison studies R22 are awaited to quantify cost precisely. R23 R24 The increase in different prediction profiles and staging modalities will alter future clinical decision R25 making. Categorization of patients into a few prognostic groups—such as TNM-derived stages— R26 will not be sufficient to further improve personalized cancer treatment.65 Several variables will have R27 to be integrated into multivariate risk prediction models, using different statistical approaches.66,67 R28 Another challenge for doctors treating oral SCC will be to communicate these individualized R29 treatment options to the patient. R30 R31 R32 CONCLUSION R33 R34 Currently, physical examination and imaging techniques are the most widely accepted and applied R35 methods for assessment of the neck in oral SCC. Limitations in accuracy of these modalities have R36 not led to altered management of the cN0 neck in early-stage disease. R37 R38 R39
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R1 Combining gene-expression profiling and sentinel lymph-node biopsy in addition to current R2 imaging techniques will further reduce the rate of occult metastasis. In view of the accuracy of R3 these techniques, this rate can be reduced to a level acceptable to allow a wait-and-see policy for R4 the neck in patients with oral SCC classified as T1 and T2. R5 R6 Furthermore, restriction of sentinel lymph-node biopsy to individuals who are classified as node- R7 positive on gene-expression profiling eliminates overtreatment. R8 R9 Prospective clinical trials implementing this staging algorithm are needed to investigate whether R10 gene-expression profiling and sentinel lymph-node biopsy can be combined and whether R11 oncological and functional outcomes in patients with oral SCC will indeed improve. R12 R13 R14 CONTRIBUTORS R15 R16 FKJL, RHB, and RPT designed the report. The literature search was undertaken by FKJL, RHB, RPT, R17 RdB, and RJJvE. Figure 1 was made by RdB and figure 2 by FKJL and SRvH. All authors contributed R18 to interpretation of data and wrote and revised the report. R19 R20 R21 CONFLICT OF INTEREST R22 R23 PJS and FCPH are co-inventors of a patented method (patent owned by University Medical Centre R24 Utrecht) of gene-expression profiling, designed to improve diagnosis of metastases in head and R25 neck squamous-cell carcinoma. No commercial application has been realized to date. All other R26 authors declare that they have no conflicts of interest. R27 R28 R29 ACKNOWLEDGMENTS R30 R31 We thank the Dutch Cancer Society and the Dutch Head and Neck Society for research support. R32 R33 R34 R35 R36 R37 R38 R39
76 Novel diagnostic modalities for assessment of the clinically node-negative neck in oral squamous-cell carcinoma
REFERENCES R1 R2 1. Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011; 61: 69-90. R3 2. Ang KK, Harris J, Wheeler R, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med 2010; 363: 24-35. R4 3. Leemans CR, Braakhuis BJ, Brakenhoff RH. The molecular biology of head and neck cancer. Nat Rev R5 Cancer 2011; 11: 9-22. R6 4. Morris LG, Patel SG, Shah JP, et al. High rates of regional failure in squamous cell carcinoma of the hard R7 palate and maxillary alveolus. Head Neck 2011; 33: 824-30. R8 5. Montes DM, Carlson ER, Fernandes R, et al. Oral maxillary squamous carcinoma: an indication for neck dissection in the clinically negative neck. Head Neck 2011; 33: 1581-85. R9 6. Brown JS, Bekiroglu F, Shaw RJ, et al. Management of the neck and regional recurrence in squamous R10 cell carcinoma of the maxillary alveolus and hard palate compared with other sites in the oral cavity. R11 Head Neck 2012; published online Feb 6. DOI:10.1002/hed.22957. R12 7. Argiris A, Karamouzis MV, Raben D, et al. Head and neck cancer. Lancet 2008; 371: 1695-709. 8. Ferlito A, Silver CE, Rinaldo A. Elective management of the neck in oral cavity squamous carcinoma: R13 current concepts supported by prospective studies. Br J Oral Maxillofac Surg 2009; 47: 5-9. R14 9. van Wilgen CP, Dijkstra PU, van der Laan BF, et al. Morbidity of the neck after head and neck cancer R15 therapy. Head Neck 2004; 26: 785-91. R16 10. van Wouwe M, de Bree R, Kuik DJ, et al. Shoulder morbidity after non-surgical treatment of the neck. Radiother Oncol 2009; 90: 196-201. R17 11. Bradley PJ, Ferlito A, Silver CE, et al. Neck treatment and shoulder morbidity: still a challenge. Head R18 Neck 2011; 33: 1060-67. R19 12. Nieuwenhuis EJ, Castelijns JA, Pijpers R, et al. Wait-and-see policy for the N0 neck in early-stage oral R20 and oropharyngeal squamous cell carcinoma using ultrasonography-guided cytology: is there a role for identification of the sentinel node? Head Neck 2002; 24: 282-89. R21 13. Rodrigo JP, Shah JP, Silver CE, et al. Management of the clinically negative neck in early-stage head and R22 neck cancers after transoral resection. Head Neck 2011; 33: 1210-19. R23 14. Weiss MH, Harrison LB, Isaacs RS. Use of decision analysis in planning a management strategy for the R24 stage N0 neck. Arch Otolaryngol Head Neck Surg 1994; 120: 699-702. R25 15. de Bondt RB, Nelemans PJ, Hofman PA, et al. Detection of lymph node metastases in head and neck cancer: a meta-analysis comparing US, USgFNAC, CT and MR imaging. Eur J Radiol 2007; 64: 266-72. R26 16. Kyzas PA, Evangelou E, Denaxa-Kyza D, et al. 18F-fl uorodeoxyglucose positron emission tomography R27 to evaluate cervical node metastases in patients with head and neck squamous cell carcinoma: a meta- R28 analysis. J Natl Cancer Inst 2008; 100: 712-20. R29 17. Ng SH, Yen TC, Chang JT, et al. Prospective study of [18F] fl uorodeoxyglucose positron emission tomography and computed tomography and magnetic resonance imaging in oral cavity squamous cell R30 carcinoma with palpably negative neck. J Clin Oncol 2006; 24: 4371-76. R31 18. Buckley JG, MacLennan K. Cervical node metastases in laryngeal and hypopharyngeal cancer: a R32 prospective analysis of prevalence and distribution. Head Neck 2000; 22: 380-85. R33 19. Liao LJ, Lo WC, Hsu WL, et al. Detection of cervical lymph node metastasis in head and neck cancer patients with clinically N0 neck-a meta-analysis comparing different imaging modalities. BMC Cancer R34 2012; 12: 236. R35 20. Takes RP, Rinaldo A, Rodrigo JP, et al. Can biomarkers play a role in the decision about treatment of the R36 clinically negative neck in patients with head and neck cancer? Head Neck 2008; 30: 525-38. 21. Melchers LJ, Schuuring E, van Dijk BA, et al. Tumour infi ltration depth >/=4 mm is an indication for an R37 elective neck dissection in pT1cN0 oral squamous cell carcinoma. Oral Oncol 2012; 48: 337-42. R38 R39
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22. Pentenero M, Gandolfo S, Carrozzo M. Importance of tumor thickness and depth of invasion in nodal R1 involvement and prognosis of oral squamous cell carcinoma: a review of the literature. Head Neck R2 2005; 27: 1080-91. R3 23. Huang SH, Hwang D, Lockwood G, et al. Predictive value of tumor thickness for cervical lymph-node involvement in squamous cell carcinoma of the oral cavity: a meta-analysis of reported studies. Cancer R4 2009; 115: 1489-97 R5 24. Lodder WL, Teertstra HJ, Tan IB, et al. Tumour thickness in oral cancer using an intra-oral ultrasound R6 probe. Eur Radiol 2011; 21: 98-106. R7 25. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large B-cell lymphoma identifi ed by gene expression profi ling. Nature 2000; 403: 503-11. R8 26. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature 2000; 406: R9 747-52. R10 27. Chung CH, Parker JS, Karaca G, et al. Molecular classifi cation of head and neck squamous cell R11 carcinomas using patterns of gene expression. Cancer Cell 2004; 5: 489-500. R12 28. van’t Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profi ling predicts clinical outcome of breast cancer. Nature 2002; 415: 530-36. R13 29. Chung CH, Parker JS, Ely K, et al. Gene expression profi les identify epithelial-to-mesenchymal transition R14 and activation of nuclear factor-κB signaling as characteristics of a high-risk head and neck squamous R15 cell carcinoma. Cancer Res 2006; 66: 8210-18. R16 30. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas. Nat Genet 2005; 37: 182-86. R17 31. Roepman P, Kemmeren P, Wessels LF, et al. Multiple robust signatures for detecting lymph node R18 metastasis in head and neck cancer. Cancer Res 2006; 66: 2361-66. R19 32. van Hooff SR, Leusink FK, Roepman P, et al. Validation of a gene expression signature for assessment R20 of lymph node metastasis in oral squamous cell carcinoma. J Clin Oncol 2012; published online Oct 8. DOI:10.1200/JCO.2011.40.4509. R21 33. Alkureishi LW, Ross GL, Shoaib T, et al. Sentinel node biopsy in head and neck squamous cell cancer: R22 5-year follow-up of a European multicenter trial. Ann Surg Oncol 2010; 17: 2459-64. R23 34. Herrero R, Castellsague X, Pawlita M, et al. Human papillomavirus and oral cancer: the International R24 Agency for Research on Cancer multicenter study. J Natl Cancer Inst 2003; 95: 1772-83. 35. Ferlito A, Rinaldo A, Devaney KO, et al. Detection of lymph node micrometastases in patients with R25 squamous carcinoma of the head and neck. Eur Arch Otorhinolaryngol 2008; 265: 1147-53. R26 36. Yachida S, Jones S, Bozic I, et al. Distant metastasis occurs late during the genetic evolution of R27 pancreatic cancer. Nature 2010; 467: 1114-17. R28 37. Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012; 366: 883-92. R29 38. Nik-Zainal S, Alexandrov LB, Wedge DC, et al. Mutational processes molding the genomes of 21 breast R30 cancers. Cell 2012; 149: 979-93. R31 39. Nik-Zainal S, Van Loo P, Wedge DC, et al. The life history of 21 breast cancers. Cell 2012; 149: 994- R32 1007. R33 40. Xie Y, Xiao G, Coombes KR, et al. Robust gene expression signature from formalin-fixed paraffin- embedded samples predicts prognosis of non-small-cell lung cancer patients. Clin Cancer Res 2011; R34 17: 5705-14. R35 41. Smeets SJ, Harjes U, van Wieringen WN, et al. To DNA or not to DNA? That is the question, when it R36 comes to molecular subtyping for the clinic! Clin Cancer Res 2011; 17: 4959-64. R37 42. Alkureishi LW, Burak Z, Alvarez JA, et al. Joint practice guidelines for radionuclide lymphoscintigraphy for sentinel node localization in oral/oropharyngeal squamous cell carcinoma. Ann Surg Oncol 2009; R38 16: 3190-210. R39
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43. Murer K, Huber GF, Haile SR, et al. Comparison of morbidity between sentinel node biopsy and elective neck dissection for treatment of the n0 neck in patients with oral squamous cell carcinoma. Head Neck R1 2011; 33: 1260-64. R2 44. Civantos FJ, Zitsch RP, Schuller DE, et al. Sentinel lymph node biopsy accurately stages the regional R3 lymph nodes for T1–T2 oral squamous cell carcinomas: results of a prospective multi-institutional trial. J Clin Oncol 2010; 28: 1395-400. R4 45. Paleri V, Rees G, Arullendran P, et al. Sentinel node biopsy in squamous cell cancer of the oral cavity and R5 oral pharynx: a diagnostic meta-analysis. Head Neck 2005; 27: 739-47. R6 46. Kovacs AF, Stefenelli U, Seitz O, et al. Positive sentinel lymph nodes are a negative prognostic factor for R7 survival in T1-2 oral/ oropharyngeal cancer-a long-term study on 103 patients. Ann Surg Oncol 2009; 16: 233-39. R8 47. Broglie MA, Haile SR, Stoeckli SJ. Long-term experience in sentinel node biopsy for early oral and R9 oropharyngeal squamous cell carcinoma. Ann Surg Oncol 2011; 18: 2732-38. R10 48. Gurney BAS, Schilling C, Putcha V, et al. Implications of a positive sentinel node in oral squamous cell R11 carcinoma. Head Neck 2012; published online Jan 31. DOI:10.1002/hed.21973. R12 49. Haerle SK, Hany TF, Strobel K, et al. Is there an additional value of SPECT/CT over planar lymphoscintigraphy for sentinel node mapping in oral/oropharyngeal squamous cell carcinoma? Ann R13 Surg Oncol 2009; 16: 3118-24. R14 50. Heuveling DA, Visser GW, Baclayon M, et al. 89Zr-nanocolloidal albumin-based PET/CT lymphoscintigraphy R15 for sentinel node detection in head and neck cancer: preclinical results. J Nucl Med 2011; 52: 1580-84. R16 51. Vermeeren L, Valdes Olmos RA, Klop WM, et al. A portable gamma-camera for intraoperative detection of sentinel nodes in the head and neck region. J Nucl Med 2010; 51: 700-03. R17 52. Heuveling DA, Karagozoglu KH, van Schie A, et al. Sentinel node biopsy using 3D lymphatic mapping R18 by freehand SPECT in early stage oral cancer: a new technique. Clin Otolaryngol 2012; 37: 89-90. R19 53. van den Berg NS, Brouwer OR, Klop WM, et al. Concomitant radio- and fluorescence-guided sentinel R20 lymph node biopsy in squamous cell carcinoma of the oral cavity using ICG-(99m)Tc-nanocolloid. Eur J Nucl Med Mol Imaging 2012; 39: 1128-36. R21 54. Keereweer S, Hutteman M, Kerrebijn JD, et al. Translational optical imaging in diagnosis and treatment R22 of cancer. Curr Pharm Biotechnol 2012; 13: 498-503. R23 55. Vorburger MS, Broglie MA, Soltermann A, et al. Validity of frozen section in sentinel lymph node biopsy R24 for the staging in oral and oropharyngeal squamous cell carcinoma. J Surg Oncol 2012; published online May 14. DOI:10.1002/jso.23156. R25 56. Ferris RL, Xi L, Seethala RR, et al. Intraoperative qRT-PCR for detection of lymph node metastasis in R26 head and neck cancer. Clin Cancer Res 2011; 17: 1858-66. R27 57. Bar Ad V, Chalian A. Management of clinically negative neck for the patients with head and neck R28 squamous cell carcinomas in the modern era. Oral Oncol 2008; 44: 817-22. 58. Genden EM, Ferlito A, Silver CE, et al. Contemporary management of cancer of the oral cavity. Eur Arch R29 Otorhinolaryngol 2010; 267: 1001-17. R30 59. Yuen AP, Ho CM, Chow TL, et al. Prospective randomized study of selective neck dissection versus R31 observation for N0 neck of early tongue carcinoma. Head Neck 2009; 31: 765-72. R32 60. Song T, Bi N, Gui L, et al. Elective neck dissection or “watchful waiting”: optimal management strategy for early stage N0 tongue carcinoma using decision analysis techniques. Chin Med J (Engl) 2008; 121: R33 1646-50. R34 61. Okura M, Aikawa T, Sawai NY, et al. Decision analysis and treatment threshold in a management for R35 the N0 neck of the oral cavity carcinoma. Oral Oncol 2009; 45: 908-11. R36 62. Keski-Santti H, Atula T, Tornwall J, et al. Elective neck treatment versus observation in patients with T1/ T2 N0 squamous cell carcinoma of oral tongue. Oral Oncol 2006; 42: 96-101. R37 R38 R39
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63. Tsang RK, Chung JC, Howe To VS, et al. Efficacy of salvage neck dissection for isolated nodal recurrences R1 in early carcinoma of oral tongue with watchful waiting management of initial N0 neck. Head Neck R2 2011; 33: 1482-85. R3 64. Classe JM, Baff ert S, Sigal-Zafrani B, et al. Cost comparison of axillary sentinel lymph node detection and axillary lymphadenectomy in early breast cancer: a national study based on a prospective multi- R4 institutional series of 985 patients ‘on behalf of the Group of Surgeons from the French Unicancer R5 Federation’. Ann Oncol 2012; 23: 1170-77. R6 65. Takes RP, Rinaldo A, Silver CE, et al. Future of the TNM classification and staging system in head and R7 neck cancer. Head Neck 2010; 32: 1693-711. 66. Gerds TA, Cai T, Schumacher M. The performance of risk prediction models. Biom J 2008; 50: 457-79. R8 67. Stadler WM. Prognosis and prediction in a Facebook world. Cancer 2009; 115: 5368-70. R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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CHAPTER 5
Tumor biological determinants of locoregional
recurrence of non-HPV head and
neck squamous cell carcinoma
F.K.J. Leusink* B.J.M. Braakhuis* W.N. van Wieringen E.Bloemena F. Rustenburg B. Ylstra J.A. Kummer P.J. van Diest Q.J. Voorham S.J. Smeets P. Roepman R. Koole C.R. Leemans R.H. Brakenhoff
* Equal contribution
Submitted Chapter 5
R1 ABSTRACT R2 R3 INTRODUCTION R4 Head and neck squamous cell carcinoma (HNSCC) has still a fairly bad prognosis with more R5 than 50% of patients dying within five years after diagnosis. A considerable part of these R6 deaths is caused by locoregionally recurrent cancer. Aim of this study was to identify tumor R7 characteristics that are associated with the development of locoregional recurrences and a R8 poor prognosis and to reveal their biological basis. R9 R10 METHODS R11 Detailed histopathological tumor characteristics were obtained from 170 SCC of the oral R12 cavity and oropharynx and these features were linked to locoregional recurrence-free survival R13 (LRFS) and overall survival (OS). With gene expression array analysis the biological basis of the R14 determinants of locoregional recurrence was established. All patients had been treated with R15 surgery and 120 with adjuvant radiotherapy, if indicated. R16 R17 RESULTS R18 Perineural growth (OS and LRFS) and non-cohesive invasive growth were correlated with R19 worse prognosis (OS and LRFS). The negative effect of these characteristics on survival was R20 maintained in the group that received post-operative radiotherapy. No association was found R21 for degree of differentiation and bone invasion and presence of dysplasia or tumor at the R22 margins. For the pattern of extensive non-cohesive growth, but not for perineural growth, a R23 differential set of 160 genes was established, that included genes involved in extra-cellular R24 matrix modeling R25 R26 CONCLUSION R27 This study confirms the prognostic value of histological features in HNSCC, and reveals R28 biologically relevant genes of tumors that show extensive non-cohesive growth. R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
84 Tumor biological determinants of locoregional recurrence of non-HPV head and neck squamous cell carcinoma
INTRODUCTION R1 R2 Head and neck squamous cell carcinoma (HNSCC) contributes to approximately 4% of all cancers R3 worldwide.1 These carcinomas are largely caused by tobacco and excessive alcohol consumption, R4 which seem to have a synergistic effect. A subgroup of HNSCCs, particularly those of the R5 oropharynx is caused by infection with high-risk-types of human papilloma virus (HPV).2 There R6 were approximately 130,000 new HNSCC patients in Europe in 2008, more than two-thirds of R7 them with cancer in the oral cavity and pharynx.3 The percentage of European patients dying R8 from SCC of the oral cavity and pharynx (together coined OPSCC) in that year was estimated at R9 45%. Unfortunately, the five-year survival rate of OPSCC has not markedly improved over the last R10 decades and an important explanation for this observation is the development of locoregional R11 recurrences.4 R12 R13 Investigators have tried to unravel the association between histopathological tumor characteristics R14 and the development of locoregionally recurrent cancer and overall survival time. The background R15 of this research is that prognostic features may help to optimize treatment planning. Some R16 publications demonstrate a relation between histopathological characteristics such as the R17 presence of tumor near or in the margins and a shorter locoregional recurrence-free (LRFS) and R18 overall survival (OS).5-7 In addition, perineural invasion,8-10 bone involvement11 12-14 have been R19 associated with worse LRFS and OS. In general, the risk associated with these histopathological R20 characteristics is moderate, for some features the results are inconclusive and sometimes OS and R21 not LRFS is reported.15-17 R22 R23 Gene expression array analysis of HNSCC has also been applied to uncover genes or gene profiles R24 associated with prognosis.18-20 However, the relation between the development of locoregional R25 recurrent cancer and gene expression profiles has previously hardly been addressed. R26 R27 The present study was designed to identify for a large group of OPSCC the histopathological R28 characteristics that are associated with the development of locoregionally recurrent OPSCC. As R29 a next step we aimed to identify genes associated with these characteristics since they may R30 play a regulatory role. Knowledge on the genes underlying key biological processes could R31 improve diagnostic procedures, and accelerate and facilitate the development of a more targeted R32 treatment in OPSCC. R33 R34 R35 R36 R37 R38 R39
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R1 MATERIALS AND METHODS R2 R3 Patients and tumors R4 The study included 170 patients, who had a histopathologically proven HNSCC and were treated R5 by surgery in two large referral University medical centers in The Netherlands University Medical R6 Center Utrecht (UMCU; N=92) and VU University Medical Center (VUmc; N=78) Amsterdam, R7 between 1996 and 2007. Written informed consent was obtained from all patients and both R8 Institutional Review Boards approved the study. Patients had a primary HPV-negative HNSCC R9 in the oral cavity (N= 146) or oropharynx (N=24); 70 patients were female and 100 were male. R10 Because HPV-positive tumors belong to a separate group,21,22 we did not include these in this R11 study. HPV involvement was tested for all carcinomas according to a validated algorithm.2 R12 R13 All were staged according to the classification of the International Union Against Cancer.23 In R14 147 patients a neck dissection was performed allowing pathological neck staging, and in the R15 remaining 23 patients a follow-up period of at least two years was taken to clinically stage the R16 neck. There was a tumor-positive neck in 103 (61%) of the patients. The median age was 61, R17 mean 62 and the range was from 34 to 84 years. At the end of follow-up there were 78 patients R18 alive, with a median survival time of 81 months (range 13-154). Ninety-two patients had died, R19 with a median survival time of 22 months (range 4-119). All patients were treated with curative R20 intent by surgery with (N= 120) or without (N=50) post-operative radiotherapy. Indications for R21 radiotherapy were multiple nodal metastases, extranodal spread and inadequate surgical margins. R22 R23 Histopathology R24 In addition to standard examination of the surgical specimen, detailed histopathological R25 characteristics of each tumor was scored by two experienced pathologists (EB + JAK), according R26 to the guidelines of the Royal College of Pathologists UK (http://www.rcpath.org/publications- R27 media/publications). For this, the routine (formalin fixed, paraffin embedded) slides were used R28 that had been prepared from the surgical specimens. Grading of the differentiation was based R29 on the degree of resemblance of the carcinoma to the normal epithelium. The most aggressively R30 appearing part was scored as well, as moderately or poorly differentiated. The margin status was R31 evaluated and was divided into three groups: tumor-free margins, when the excised carcinoma R32 was > 5 mm from the surgical margin; involved margins when carcinoma was present in or R33 within 1 mm of the margin; and close margins in the remaining cases.24 The pattern of invasive or R34 infiltrative growth was scored as either cohesive: carcinoma composed of broad cohesive sheets R35 of cells, or non-cohesively invasive: carcinomas composed of narrow strands, ‘spidery’ small R36 groups or single cells. A distinction was made between focal and extensive infiltrative growth, R37 based on the extent of the growth pattern. In case of ‘extensive’, the non-cohesive infiltrative R38 growth pattern was the most predominant one. Perineural growth was scored when tumor R39
86 Tumor biological determinants of locoregional recurrence of non-HPV head and neck squamous cell carcinoma
cells were in direct contact with neurons, at or outside the invasive front of the carcinoma, and R1 classified as focal or extensive. Dysplasia in the margins was scored according to the four-tiered R2 WHO categories:25 no dysplasia, mild, moderate and severe. Bone invasion was scored as erosive R3 or infiltrative as described by Slootweg and Muller.26 In our patient cohort there were 91 patients R4 with bone in the surgical specimen. R5 R6 Expression arrays R7 A frozen tumor biopsy taken at the time of surgery was the source tissue for expression analysis. R8 RNA was isolated as described before using TRIzol (Life Technologies, Breda, The Netherlands) R9 according to the manufacturer’s instructions. The samples were analysed per center on the same R10 platform (4x44K from Agilent Technologies, Santa Clara, CA, USA), but with differences. In R11 Utrecht, custom-made full-genome array (made for Agendia (Amsterdam, The Netherlands) was R12 used to also include the presence of additional probes that represented previously published R13 signature probes in replicate. Raw fluorescence intensities were quantified using Agilent Feature R14 Extraction software (v. 9.5) and imported into R/Bioconductor (http://www.r-project.org and R15 http://www.bioconductor.org) Further details of the data analysis in Utrecht can be found in the R16 publication of van Hooff et al.27 R17 R18 As for Amsterdam, array hybridization, using the Agilent Low RNA Input Fluorescent Linear R19 Amplification Kit and 4x44K Whole Human Genome Arrays, was carried out according to the R20 manufacturer (Agilent Technologies, Amstelveen, The Netherlands). 43,371 probes were on the R21 array, covering a total of 30,982 different probes. Adhering to the ceteris paribus principle, gene R22 expression data generated in the VUmc was preprocessed in an identical fashion as those from R23 the UMCU. This comprised: 1) extraction of the median signal from the raw data files, 2) no R24 background correction, and 3) median and loess within-array normalization (as implemented in R25 the Limma-package). R26 R27 Next, the preprocessed VUmc and UMCU data set were combined by limiting both data sets to R28 overlapping probes (using their Agilent probe id’s). Finally, comparibility of the expression data of R29 both hospitals is ensured by a) joint between-array normalization (A-quantile as implemented in R30 the Limma-package), and b) removal of possible batch (i.e. hospital) effects using the Combat- R31 package.28 Ingenuity pathway analysis (IPA from Ingenuity Systems, Redwood City, CA, USA) R32 was used to identify underlying biological processes and pathways. This web-based software R33 program uses an up-to-date database of structured biological and chemical information from the R34 literature. The data are stored at the GEO-internet database as record GSE2621. R35 R36 R37 R38 R39
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R1 Statistical analyis R2 As endpoint overall survival (OS) was calculated, with death (N=97) as event. Locoregional R3 recurrence free survival (LRFS) was determined with the development of a locoregional recurrence R4 (N=33) as event. A locoregional recurrence was defined as tumor regrowth at or near the site of R5 the primary carcinoma within three years after diagnosis of the primary carcinoma.29 R6 R7 Kaplan-Meier curves were plotted and the log-rank test was performed to test for significance. R8 Univariate Cox regression was used to investigate the influence of histopathological parameters R9 on the Hazard Ratios (HR). R10 R11 Differential gene expression with the arrays was assessed using the hierarchical linear model R12 with empirical Bayes estimation of the variance as implemented in Limma, which also allows R13 for the inclusion of possible confounders. The multiplicity problem (many genes are tested) was R14 addressed by application of the Benjamini-Hochberg procedure to the raw p-values to control the R15 FDR (false discovery rate). R16 R17 The association between the expression levels of (a group of) genes and a clinical variable was R18 assessed using the globaltest.30 The globaltest evaluates the association at an aggregated level, R19 averaging the evidence (in favor of an association) over all genes. Hence, it does not pinpoint the R20 genes responsible for an observed association. R21 R22 Frequency distribution was analyzed by chi-square (Pearson’s) correlation analysis. Statistical R23 tests were taken two-sided and differences were considered significant atα =0.05. For statistical R24 analyses SPSS 20.0 for Windows (IBM, Armonk, NY USA) was used. R25 R26 R27 RESULTS R28 R29 Histopathological characteristics and prognosis R30 Patient and tumor characteristics were analyzed for their association with LRFS (table 1) and OS R31 (table 2). With respect to well known risk factors, a significant association was noted for disease R32 stage and lymph node status, in case of LRFS as well as OS. R33 R34 The development of a locoregional recurrence was associated with the presence of perineural R35 growth (focal and extensive) and a pattern of non-cohesive growth ( focal and extensive) (table R36 1). Extensive perineural growth and extensive non-cohesive growth, but not the focal features R37 were associated with a worse OS. The presence of dysplasia or carcinoma in the margins, the R38 degree of differentiation and bone invasion of the tumor were not associated with prognosis. R39
88 Tumor biological determinants of locoregional recurrence of non-HPV head and neck squamous cell carcinoma
Figure 1 shows the LFRS and OS Kaplan-Meier curves stratified for pattern of invasive growth. R1 The difference between the patient groups with extensive non-cohesive and a cohesive growth R2 pattern was stastistically different and most prominent. R3 R4 Table 1 | Association HNSCC characteristics locoregional recurrence free survival (LRFS) R5 Variable Characteristic N HR R6 Value 95% CI P R7 Disease stage I+II 40 1.0 R8 III+IV 130 5.9 1.4-24.8 0.015 R9 Site R10 oropharynx 24 1.0 oral cavity 146 0.8 0.3-2.0 0.646 R11 T-stage R12 1+2 85 1.0 R13 3+4 85 1.7 0.8-3.4 0.151 Lymph node status R14 negative 67 1.0 R15 positive 103 6.2 2.2 0.001 R16 Dysplasia margins no 72 1.0 R17 mild 46 0.9 0.5-1.4 0.555 R18 moderate 27 0.7 0.4-1.3 0.271 R19 severe 25 1.0 0.5-1.7 0.927 Perineural growth R20 no 107 1.0 R21 focal 36 2.3 1.0-5.3 0.041 R22 extensive 22 2.9 1.2-7.2 0.021 Non-cohesive growth R23 no 55 1.0 R24 focal 65 2.9 1.1-8.1 0.037 R25 extensive 50 3.4 1.2-9.5 0.021 Margin status R26 tumor free 105 1.0 R27 close 21 1.4 0.5-3.8 0.512 R28 involved 44 1.5 0.7-3.3 0.256 Differentiation degree R29 well 9 1.0 R30 moderately 122 0.8 0.2-3.4 0.753 R31 poorly 36 13 0.3-6.1 0.701 Bone invasion R32 no invasion 43 1.0 R33 invasion 47 1.3 0.5-2.9 0.583 R34 The hazard ratio (HR) was calculated with the HNSCC without the particular characteristic as the reference R35 (value 1.0). Significant differences are shown by a p-value in bold. Regarding the degree of differentiation R36 the group of well differentiated HNSCC was chosen to be the reference. The analysis of bone invasion was limited to HNSCC that showed bone in the surgical specimen. In 43 patients the tumor was adjacent or close R37 to bone but did not invade. In 47 patients there was a sign of bone invasive growth: in 4 cases this consisted R38 of bone erosion, in the others bone invasion was observed. R39
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R1 A B R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13
R14 R15 Figure 1 | Kaplan-Meier survival analysis for invasive growth. R16 Stratification of invasive growth: extensive (N=50), focal (N=65) and non (N=55). (A) LRFS: the overall p-value is 0.043, and for the pair-wise comparisons these were 0.016 (no vs. extensive), 0.028 (no vs. focal) and R17 0.718 (focal vs. extensive). (B) OS: the overall p-value of 0.004 is shown, for pair-wise comparisons these were R18 0.025 (extensive vs. focal), 0.343 (no vs .focal) and 0.001 (no vs. extensive). The overall log rank p-value was highly significant for OS (p=0.004) and significant for LFRS (p=0.043). R19 In a separate analysis we stratified for treatment to examine the interaction between the histological R20 characteristics and radiotherapy on survival. An extensive non-cohesive growth pattern and extensive R21 perineural growth had a significant negative effect on OS in this separate patient group (table 4). With regard to the development of a locoregional recurrence, an increased risk for extensive perineural growth and non- R22 cohesive growth was observed, but this effect approached statistical significance. The presence of tumor in R23 the margin did not appear to influence the patients’ prognosis. In the patient group treated with surgery no significant effects were observed, possibly related to the relative low number of patients in the groups (data R24 not shown). R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
90 Tumor biological determinants of locoregional recurrence of non-HPV head and neck squamous cell carcinoma
Table 2 | Association between tumor characteristics and overall survival R1 Variable Characteristic N HR Value 95% CI P R2 Disease stage R3 I+II 40 1.0 R4 III+IV 130 3.1 1.6-5.8 <0.000 R5 Site oropharynx 24 1.0 R6 oral cavity 146 1.1 0.7-2.0 0.640 R7 T-stage 1+2 85 1.0 R8 3+4 85 2.1 1.4-3.2 0.001 R9 Lymph node status R10 negative 67 1.0 positive 103 1.5 1.3-1.8 <0.000 R11 Locoregional recurrence R12 negative 137 1.0 R13 positive 33 4.2 2.7-6.7 <0.000 R14 Dysplasia margins no 72 1.0 R15 mild 46 0.8 0.5 -1.3 0.328 R16 moderate 27 0.8 0.4-1.4 0.368 R17 severe 25 1.0 0.6-1.8 0.978 R18 Perineural growth no 107 1.0 R19 focal 36 1.9 0.7-2.0 0.593 R20 extensive 22 2.6 1.5-4.5 0.001 R21 Non-cohesive growth R22 no 55 1.0 R23 focal 65 1.3 0.8-2.2 0.346 extensive 50 2.3 1.4-3.8 0.002 R24 Margin status R25 tumor free 105 1.0 R26 close 21 0.9 0.5-1.8 0.848 R27 involved 44 1.4 0.9-2.3 0.115 Differentiation degree R28 well 109 1.0 R29 moderately 122 1.3 0.5-3.6 0.600 R30 poorly 36 1.4 0.5-4.1 0.546 R31 Bone invasion no invasion 43 1.0 invasion 47 1.1 0.7-1.9 0.590 R32 R33 The HR was calculated with the HNSCC without the particular characteristic as the reference (value 1.0). Significant differences are shown by a p-value in bold (see further details in the legends of table 1). R34 R35 R36 R37 R38 R39
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R1 Kaplan-Meier analysis was also performed for the pattern of invasive growth when stratified R2 according to different stage groups (table 3). For patients with less advanced carcinomas the R3 relation between type of invasion and survival (OS and LRFS) was not statistically significant. R4 Table 3 | Association between extensive non-cohesive gene profile and growth pattern vs. survival. R5 Association Survival type Stage R6 Early All R7 Extensive non-cohesive growth and survival* R8 LRFS 0.250 0.043 OS 0.598 0.004 R9 Extensive non-cohesive gene profile and survival** R10 LRFS 0.544 0.159 R11 OS 0.143 0.126
R12 HNSCC were stratified in two groups: the early stage (stage I and II; N=40), and all HNSCC (N=170). P-values R13 are shown. R14 *overall log-rank p value of the Kaplan-Meier analysis of the three groups **Global test (see materials and methods) with ‘worst pattern of growth’ 160 gene profile. R15 Significant associations (p<0.05) are shown in bold. R16 R17 Table 4 | Association tumor characteristics locoregional recurrence free survival (LRFS) and overall R18 survival (OS) in 120 patients treated with postoperative radiotherapy Variable Characteristic N HR-Value 95% CI P R19 LRFS R20 Perineural growth no 69 R21 focal 28 1.7 0.7-4.1 0.239 R22 extensive 20 2.3 0.9-5.8 0.077 R23 Invasive growth no 33 1.0 R24 focal 49 2.1 0.7-5.8 0.160 R25 extensive 38 2.1 0.7-6.0 0.191 R26 Margin status tumor free 63 1.0 R27 close 17 0.9 0.3-2.8 0.911 involved 40 1.2 0.5-2.6 0.786 R28 OS R29 Perineural growth no 69 R30 focal 28 1.0 0.6-1.9 0.757 R31 extensive 20 2.3 1.3-4.2 0.005 R32 Invasive growth no 33 1.0 R33 focal 49 1.0 0.6-1.8 0.929 extensive 38 1.9 1.0-3.3 0.036 R34 R35 Margin status tumor free 63 1.0 close 17 0.7 0.3-1.5 0.348 R36 involved 40 1.1 0.7-1.8 0.702 R37 The HR was calculated with the carcinomas without the particular characteristic as the reference (value 1.0). R38 Significant differences are shown by a p-value in bold. R39
92 Tumor biological determinants of locoregional recurrence of non-HPV head and neck squamous cell carcinoma
Gene expression arrays R1 The aim was to identify the genes that were associated with characteristics with prognostic value R2 (table 5). As for non-cohesive growth, 160 different transcripts were found to be differentially R3 expressed, when ‘extensive’ was compared with ‘no’ (supplementary table S1 provides the full list R4 of these transcripts). Pathways that stood out were ‘matrix metalloproteinases’ and ‘HIF1alpha R5 signaling’ as determined with IPA. In this list many genes related to the extra-cellular matrix R6 component (matrix metalloproteinases and collagen) can be appreciated. No differentially R7 expressed transcripts were observed with regard to perineural growth, bone invasion, degree of R8 differentiation, the presence of dysplasia and carcinoma in or near the surgical margins and the R9 occurrence of a locoregional recurrence. R10 R11 Table 5 | Genes differentially expressed between clinically &histologically defined patient groups R12 Variable Characteristic Patients (N) Number of different genes R13 Disease stage I+II vs. III+IV 40 vs. 130 2,442 R14 Lymph node status Negative vs. positive 67 vs. 103 1,769 R15 Locoregional recurrence Negative vs positive 137 vs. 33 0 R16
Invasive growth no vs. focal 55 vs. 65 0 R17 no vs. extensive 55 vs. 50 160 R18
Perineural growth No vs. focal 107 vs. 36 0 R19 No vs. extensive 107 vs. 22 0 R20
Bone invasion Negative vs. positive 43 vs. 47 0 R21 R22 Significantly different genes are shown (p-value <0.05 with FDR-correction). The analysis of bone invasion was limited to the HNSCC that had bone in the surgical specimen. R23 R24 The non-cohesive growth associated gene profiles were also tested for prognostic significance. R25 There was no statistically association between the ‘non-cohesive’ gene profile and OS or LRFS, for R26 the whole tumor group as well as for the early stage carcinomas (table 3). R27 R28 R29 DISCUSSION R30 R31 This study examined the prognostic value of several histopathological characteristics of a large R32 series of OPSCC, in particular in relation to locoregional recurrence. Because HPV-positive HNSCC R33 are considered as a different class of OPSCC,31 these were excluded from the study. Disease R34 stage, and some tumor histopathological characteristics were associated with LRFS and OS. A R35 non-cohesive growth pattern was found to be of prognostic significance and moreover, the genes R36 responsible for this phenotype were uncovered. R37 R38 R39
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R1 The present results on the correlation of various histopathological characteristics with LRFS and R2 OS confirm those of others. Most studies find a shorter OS and LRFS to be associated with R3 perineural growth,8-10,16,32 and a non-cohesive growth pattern.14,16,33,34 Nevertheless, there are R4 publications that do not find such a correlation between growth pattern and the development of R5 locoregional recurrences.15 R6 R7 The present study revealed that the expression of a certain gene set differs between tumors that R8 showed non-cohesive, spidery invasive growth compared with those with cohesive growth. The R9 group with extensive non-cohesive growth consists of about one-third of the tumor population R10 in this study, showing that this is a relatively frequent phenomenon. Certain genes involved in R11 the homeostasis of the extracellular matrix were overexpressed in tumors that showed a relatively R12 high level of this non-cohesive growth. It makes sense from the biological point of view that R13 enzymes like matrix metalloproteinases, (MMPs) are upregulated in this relatively aggressive R14 group of OPSCC. Previous immunohistochemical studies also noted a correlation between MMP- R15 expression and poor prognosis and MMP-1, -2, -9 and -13 have most frequently be reported to R16 be of significance.35-37 Our analysis indicates that MMP-13 and -14 stand out most. Furthermore, R17 genes encoding for collagen, e.g. COL5A2 were co-upregulated. It is conceivable that tumor cells R18 synthesize MMPs to enable invasion; the source of collagens is more uncertain and it may be that R19 stromal cells produce these as well. The source cells of collagen overproduction in HNSCC can be R20 stromal as well as tumor cells.38 Nevertheless, the function of other genes in the ‘non-cohesive R21 gene set’, need to be elucidated. R22 R23 The presence of perineural growth was shown to be a poor prognostic factor, but this was R24 not reflected in differences in gene expression between tumor groups with and without this R25 characteristic. A possible explanation is that gene expression is only altered in the relatively small R26 group of cells involved in this process. On the other hand it cannot be excluded that this process R27 is regulated by delicate changes in gene expression. R28 R29 Bone-invasion did not have consequences for prognosis. This is a somewhat unexpected result R30 and is in contrast to an earlier report.39 Apparently, the surgical and radiation treatment is effective R31 in our series and aggressive biological behavior is sufficiently controlled. No genes associated with R32 bone invasion were uncovered, suggesting that no such genes exists or that these are acting very R33 locally, at the interphase of tumor and bone. R34 R35 Another important finding in the present study is that, despite treatment with radiotherapy, R36 extensive perineural and non-cohesive growth retained their association with worse OS. For LRFS, R37 the HR values were as large as observed for OS, but the significance level of 0.05 was just not R38 reached. Extensive perineural and non-cohesive growth had higher prognostic value than for R39
94 Tumor biological determinants of locoregional recurrence of non-HPV head and neck squamous cell carcinoma
tumors with tumor in or near the margin. In fact, there was no association between the presence R1 of tumor or dysplasia in the margin and prognosis (locoregional recurrence or death), and this R2 was found for the total group of patients and for the group that was treated with post-operative R3 radiotherapy. The effectiveness of the radiotherapy is likely the reason that tumor in or near R4 the margins does not lead to a worse prognosis. In this respect the present study confirms the R5 result of others.16,40 The design of our study was not suited to address the question whether R6 radiotherapy is effective or not. In our centers the administration of radiotherapy is indicated R7 when the margins are compromised. The effect of radiotherapy in this respect is considered to R8 be sufficiently proven.5 Thus is seems that our study suggests that extensive perineural growth or R9 extensive non-cohesive growth radiotherapy combined with chemotherapy is indicated. R10 R11 The present study could not reveal a distinct association between the non-cohesive gene set and R12 worse LRFS or OS. Thus, based on our patient set, this gene profile has little potential to predict R13 the prognosis of OPSCC patients. This aspect may be particularly important for patients from R14 whom the tumor is not surgically removed. In a small biopsy, that is taken when chemoradiation R15 is planned as primary therapy, it is hard to assess histopathological characteristics, like pattern R16 of invasion. Expression measurement in such biopsy for instance of MMPs could be useful in R17 this respect. The reason for the weak association between a non-cohesive gene profile and the R18 development of locoregional recurrences is subject of speculation. It is possible that the gene R19 profile is more valid in early stage tumors and that in later stage tumors other processes are R20 involved in tumor recurrence as well. R21 R22 In summary, this study confirms the prognostic value of several histological characteristics in R23 OPSCC, and reveals biologically relevant genes of tumors that show a non-cohesive growth R24 pattern. R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 REFERENCES R2 1. Ferlay J, Soerjomataram I, Ervik M, et al. GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality R3 Worldwide: IARC CancerBase No. 11 [Internet]. Lyon, France: International Agency for Research on R4 Cancer; 2013 Available from: http://globocan iarc fr, accessed on March 6, 2014. R5 2. Rietbergen MM, Leemans CR, Bloemena E, et al. Increasing prevalence rates of HPV attributable oropharyngeal squamous cell carcinomas in the Netherlands as assessed by a validated test algorithm. R6 Int J Cancer 2013; 132: 1565-1571. R7 3. Ferlay J, Parkin DM, Steliarova-Foucher E. Estimates of cancer incidence and mortality in Europe in R8 2008. Eur J Cancer 2010; 46: 765-781. 4. Graveland AP, Braakhuis BJM, Eerenstein SEJ et al. Molecular diagnosis of minimal residual disease in R9 head and neck cancer patients. Cell Oncol 2012; 35: 367-375. R10 5. Woolgar JA. Histopathological prognosticators in oral and oropharyngeal squamous cell carcinoma. R11 Oral Oncol 2006; 42: 229-239. R12 6. Kovacs AF. Relevance of positive margins in case of adjuvant therapy of oral cancer. Int J Oral Maxillofac Surg 2004; 33: 447-453. R13 7. Iyer NG, Nixon IJ, Palmer F, et al. Surgical management of squamous cell carcinoma of the soft palate: R14 Factors predictive of outcome. Head and Neck 2012; 34: 1071-1080. R15 8. Woolgar JA, Rogers S, West CR, et al. Survival and patterns of recurrence in 200 oral cancer patients treated by radical surgery and neck dissection. Oral Oncol 1999; 35: 257-265. R16 9. Fagan JJ, Collins B, Barnes L, et al. Perineural invasion in squamous cell carcinoma of the head and R17 neck. Arch Otolaryngol Head Neck Surg 1998; 124: 637-640. R18 10. Johnston M, Yu E, Kim J. Perineural invasion and spread in head and neck cancer. Expert Rev Anticancer R19 Ther 2012; 12: 359-371. 11. Brown JS, Lowe D, Kalavrezos N, et al. Patterns of invasion and routes of tumor entry into the mandible R20 by oral squamous cell carcinoma. Head Neck 2002; 24: 370-383. R21 12. Bryne M, Koppang HS, Lilleng R, et al. Malignancy grading of the deep invasive margins of oral R22 squamous-cell carcinomas has high prognostic value. J Pathol 1992; 166: 375-381. R23 13. Woolgar JA, Scott J. Prediction of cervical lymph-node metastasis in squamous-cell carcinoma of the tongue floor of mouth. Head Neck 1995; 17: 463-472. R24 14. Okamoto M, Ozeki S, Watanabe T, et al. Cervical lymph-node metastasis in carcinoma of the tongue R25 - correlation between clinical and histopathological findings and metastasis. J Craniomaxillofac Surg R26 1988; 16: 31-34. 15. Janot F, Klijanienko J, Russo A, et al. Prognostic value of clinicopathological parameters in head and R27 neck squamous cell carcinoma: A prospective analysis. Br J Cancer 1996; 73: 531-538. R28 16. Brandwein-Gensler M, Teixeira MS, Lewis CM, et al. Oral squamous cell carcinoma: histologic risk R29 assessment, but not margin status, is strongly predictive of local disease-free and overall survival. Am J Surg Pathol 2005; 29: 167-178. R30 17. Braakhuis BJ, Bloemena E, Leemans CR, et al. Molecular analysis of surgical margins in head and neck R31 cancer: more than a marginal issue. Oral Oncol 2010; 46: 485-491. R32 18. Chung CH, Parker JS, Ely K, et al. Gene Expression Profiles Identify Epithelial-to-Mesenchymal Transition R33 and Activation of Nuclear Factor-{kappa}B Signaling as Characteristics of a High-risk Head and Neck Squamous Cell Carcinoma. Cancer Res 2006; 66: 8210-8218. R34 19. Lohavanichbutr P, Mendez E, Holsinger FC, et al. A 13-gene signature prognostic of HPV-negative R35 OSCC: discovery and external validation. Clin Cancer Res 2013; 19: 1197-1203. R36 20. Jung AC, Job S, Ledrappier S, et al. A poor prognosis subtype of HNSCC is consistently observed across methylome, transcriptome, and miRNome analysis. Clin Cancer Res 2013; 19: 4174-4184. R37 21. Braakhuis BJ, Snijders PJ, Keune WJ, et al. Genetic patterns in head and neck cancers that contain or R38 lack transcriptionally active human papillomavirus. J Natl Cancer Inst 2004; 96: 998-1006. R39
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22. Smeets SJ, Braakhuis BJ, Abbas S, et al. Genome-wide DNA copy number alterations in head and neck squamous cell carcinomas with or without oncogene-expressing human papillomavirus. Oncogene R1 2006; 25: 2558-2564. R2 23. Sobin L, Gospodarowicz M, Wittekind C. TNM Classification of malignant tumours. Seventh edition. R3 TNM classification of malignant tumours, 2009, 7th Edition Wiley Blackwell, Chichester, UK. R4 24. Graveland AP, de Maaker M, Braakhuis BJ, et al. Molecular detection of minimal residual cancer in surgical margins of head and neck cancer patients. Cell Oncol 2009; 31: 317-328. R5 25. Warnakulasuriya S, Reibel J, Bouquot J, et al. Oral epithelial dysplasia classification systems: predictive R6 value, utility, weaknesses and scope for improvement. J Oral Pathol Med 2008; 37: 127-133. R7 26. Slootweg PJ, Muller H. Mandibular invasion by oral squamous cell carcinoma. J Craniomaxillofac Surg 1989; 17: 69-74. R8 27. van Hooff SR, Leusink FKJ, Roepman P, et al. Validation of a gene expression signature for assessment R9 of lymph node metastasis in oral squamous cell carcinoma. J Clin Oncol 2012; 30: 4104-4110. R10 28. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical R11 Bayes methods. Biostatistics 2007; 8: 118-127. 29. Braakhuis BJ, Tabor MP, Leemans CR, et al. Second primary tumors and field cancerization in oral and R12 oropharyngeal cancer: molecular techniques provide new insights and definitions. Head Neck 2002; R13 24: 198-206. R14 30. Goeman JJ, van de Geer SA, de Kort F, et al. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 2004; 20: 93-9. R15 31. Leemans CR, Braakhuis BJ, Brakenhoff RH. The molecular biology of head and neck cancer. Nat Rev R16 Cancer 2011; 11: 9-22. R17 32. Dissanayaka WL, Pitiyage G, Kumarasiri PVR, et al. Clinical and histopathologic parameters in survival R18 of oral squamous cell carcinoma. Oral Surg Oral Med Oral Pathol Oral Radiol 2012; 113: 518-525. 33. Bryne M, Jenssen N, Boysen M. Histological Grading in the Deep Invasive Front of T1 and T2 Glottic R19 Squamous-Cell Carcinomas Has High Prognostic Value. Virchows Arch 1995; 427: 277-281. R20 34. Sinha P, Mehrad M, Chernock RD, et al. Histologic and systemic prognosticators for local control and R21 survival in margin-negative transoral laser microsurgery treated oral cavity squamous cell carcinoma. Head and Neck 2015; 37: 52-63. R22 35. Al-Azri AR, Gibson RJ, Keefe DMK, et al. Matrix metalloproteinases: do they play a role in mucosal R23 pathology of the oral cavity? Oral Diseases 2013; 19: 347-359. R24 36. Jordan RC, Macabeo-Ong M, Shiboski CH, et al. Overexpression of matrix metalloproteinase-1 and -9 mRNA is associated with progression of oral dysplasia to cancer. Clin Cancer Res 2004; 10: 6460-6465. R25 37. Katayama A, Bandoh N, Kishibe K, et al. Expressions of matrix metalloproteinases in early-stage oral R26 squamous cell carcinoma as predictive indicators for tumor metastases and prognosis. Clin Cancer Res R27 2004; 10: 634-640. R28 38. Sok JC, Lee JA, Dasari S, et al. Collagen type XI alpha1 facilitates head and neck squamous cell cancer growth and invasion. Br J Cancer 2013; 109: 3049-3056. R29 39. Shaw RJ, Brown JS, Woolgar JA, et al. The influence of the pattern of mandibular invasion on recurrence R30 and survival in oral squamous cell carcinoma. Head Neck 2004; 26: 861-869. R31 40. Kademani D, Bell RB, Bagheri S, et al. Prognostic factors in intraoral squamous cell carcinoma: The R32 influence of histologic grade. J Oral Maxillofac Surg 2005; 63: 1599-1605. R33 R34 R35 R36 R37 R38 R39
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R1 SUPPLEMENTARY INFORMATION R2 R3 Supplementary Table 1 | Invasive growth differentially expressed transcripts (n=160) HNSCC with extensive invasive growth (N=50) vs. without invasive growth (N=55) R4 A_24_P671842 BX647792 GLIS3 PLAU R5 A_32_P131449 C10orf13 GOT1 PORCN R6 A_32_P20040 C10orf77 GPLD1 PRSS23 R7 A_32_P4349 C20orf103 GPM6B PYCR2 R8 A_32_P52268 C20orf175 GRIA3 RPAP1 AA663883 C21orf63 GRP SEDLP R9 ABCA1 C5orf13 HAS2 SGEF R10 ACO2 C6orf35 HEYL SGIP1 R11 ADAM12 C6orf72 HNT SLC16A14 R12 ADAMTS2 C9orf21 HRBL SLC22A15 AF147398 CACNA1G HSD17B7 SLC25A23 R13 AI263220 CALD1 HSPA12A SLC26A10 R14 AI669361 CD248 IFT20 SLC38A2 R15 AK001903 CEBPA IKIP SMCR7L R16 AK027294 CHN1 IL11 STARD13 AK094629 CLEC11A IL7R SYNE1 R17 AK095300 COL11A1 INHBA SYTL2 R18 AK123704 COL1A2 ITGA1 TBL1X R19 AK124515 COL3A1 KCNE4 TBL1Y R20 AK5 COL4A1 KHK TCF8 ALDH5A1 COL5A1 KIF26B TES R21 ANGPT2 COL5A2 LAMA4 TGFBR1 R22 ANKRD38 COL5A3 LOC284120 THC2280664 R23 ANTXR1 COL6A1 LOC389641 THC2297943 R24 APEX2 COL6A2 LOC541471 THC2302090 R25 APLN E2F2 LOC90499 THC2305868 ARRDC3 ENSA LOXL2 THC2380898 R26 AW068592 ENST00000295249 MGC23280 THC2407148 R27 AW276332 ENST00000312710 MME THC2440022 R28 BC008667 ENST00000330336 MMP1 TIPARP R29 BC024745 ENST00000339446 MMP13 TMCC1 BC037528 ENST00000344415 MMP14 TNFAIP6 R30 BC053363 ENST00000369731 MMP28 TPST1 R31 BC063022 ENST00000381961 MSX2 TWIST1 R32 BC091525 FANCD2 MTMR4 UBE2W R33 BF089603 FAP NID2 UFM1 BI828537 FLJ40869 OLFM2 VAMP3 R34 BM932296 GAS1 OSBPL8 WISP1 R35 BQ066852 GGA1 PAPPA ZBTB43 R36 BX427588 GINS4 PARP1 ZNF469 R37 R38 R39
98 Tumor biological determinants of locoregional recurrence of non-HPV head and neck squamous cell carcinoma
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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CHAPTER 6
Nodal metastasis and survival in oral cancer:
Association with protein expression of SLPI,
not with LCN2, TACSTD2, or THBS2
R. Noorlag P. van der Groep F.K.J. Leusink S.R. van Hooff M.H. Frank S.M. Willems R.J.J. van Es
Head Neck 2015; 37: 1130-6 Chapter 6
R1 ABSTRACT R2 R3 BACKGROUND R4 Gene expression profiling revealed a strong signature predicting lymph node metastases R5 in oral squamous cell carcinoma (OSCC). Four of the most predictive genes are secretory R6 leukocyte protease inhibitor (SLPI), lipocalin-2 (LCN2), thrombospondin-2 (THBS2), and R7 tumor-associated calcium signal transducer 2 (TACSTD2). This study correlates their protein R8 expression with lymph node metastases, overall survival (OS), and disease-specific survival R9 (DSS). R10 R11 METHODS R12 Two hundred twelve patients with OSCC were included for protein expression analysis by R13 immunohistochemistry. R14 R15 RESULTS R16 SLPI expression correlates with lymph node metastases in the whole cohort, not in a R17 subgroup of cT1 to 2N0. SLPI expression correlates with OS (hazard ratio [HR] = 0.61) and R18 DSS (HR = 0.47) in multivariate analysis. LCN2, THBS2, and TACSTD2 show no correlation R19 with lymph node metastases, OS, or DSS. R20 R21 CONCLUSION R22 Although SLPI expression correlates with lymph node metastases, it has no additional value R23 in determining lymph node metastases in early oral cancer. However, it is an independent R24 predictor for both OS and DSS and therefore a relevant prognostic biomarker in OSCC. R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
102 Nodal metastasis and survival in oral cancer: Association with protein expression of SLPI, not with LCN2, TACSTD2, or THBS2
INTRODUCTION R1 R2 Head and neck cancer is the sixth most common malignancy worldwide, of which one-third R3 consists of oral squamous cell carcinoma (OSCC). Its incidence in The Netherlands, being 6.2 per R4 100 000 in 2010, is rising annually. Despite improvements in therapy, the 5-year survival rate has R5 not changed over the past decades and remains approximately 50%.1-3 The prognosis depends R6 on numerous clinical and pathological factors, of which cervical lymph node metastases is a major R7 determinant.4 To perform appropriate treatment, it is therefore pivotal to determine the nodal R8 status of the neck. However, in 30% to 40% of the patients, even optimal imaging is unable to R9 detect nodal disease.5 R10 R11 To improve the negative predictive value for metastasis detection in OSCC, new diagnostic tools, R12 such as molecular diagnosis and tumor profiling, have been developed.[5] Roepman et al6 showed R13 that microarray gene expression profiling could be used to predict lymph node metastases for R14 OSCC. Recently, this gene expression signature has been validated in a multicenter study and R15 focused on the prediction of lymph node metastases in early oral cancer.7 Four of the strongest R16 predictive genes in this signature encode for the proteins lipocalin-2 (LCN2), thrombospondin-2 R17 (THBS2), tumor-associated calcium signal transducer 2 (TACSTD2), and secretory leukocyte R18 protease inhibitor (SLPI). R19 R20 LCN2 participates in carcinogenesis by favoring iron uptake from the extracellular space within R21 the tumor cell, a fundamental process for maintaining neoplastic cell multiplication. Although R22 increased LCN2 plasma levels in patients with OSCC were found, no correlation was established R23 with regional or distant metastases.8 R24 R25 THBS2 suppresses angiogenesis by inhibiting endothelial cell migration, inducing endothelial cell R26 apoptosis and preventing the interaction of growth factors with the cell surface receptors of the R27 endothelial cell.9 In supraglottic cancer, THBS2 gene expression seems inversely correlated with R28 nodal metastases.10 R29 R30 TACSDT2, also known as TROP-2, belongs to a unique family of transmembrane glycoproteins R31 that has a regulatory role in cell-cell adhesion and has a key controlling role in human cancer R32 growth. Tumor development is quantitatively driven by TACSTD2 expression levels in many R33 tumors.11 Fong et al12 correlated increased TACSTD2 expression in OSCC with decreased overall R34 survival (OS), but found no correlation with nodal metastases. R35 R36 SLPI, also known as antileukoproteinase, is a protease inhibitor of neutrophil elastase, cathepsin R37 G, chymotrypsin, and trypsin,13,14 enzymes with extracellular matrix degradative properties, and R38 R39
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R1 associated with cancer development, invasiveness, and progression.15,16 SLPI expression has R2 recently been associated with carcinogenesis and metastasis in various types of cancer, although R3 its role remains controversial. In gastric and prostate cancer, increased SLPI expression is associated R4 with invasiveness, metastases, and a worse survival.17-19 This is in contrast with the reports of SLPI R5 expression in ovarian cancer, in which SLPI expression is associated with decreased tumor growth R6 and fewer nodal metastases.20 In head and neck cancer, SLPI mRNA and protein levels seem to R7 be increased compared to normal tissue.21 Reports correlating SLPI expression with lymph node R8 metastases are contradicting.22, 23 R9 R10 The purpose of this study was to present the correlation of the aforementioned protein expressions R11 with lymph node metastases, OS, and disease specific survival (DSS), and evaluate their potential R12 role as biomarkers for treatment decision and predictors of survival in OSCC. R13 R14 R15 MATERIALS AND METHODS R16 R17 Patient selection R18 Patients with histologically confirmed OSCC, whose primary treatment was by surgery between R19 1996 and 2005 in our institute were included in this study. Patients who had had a synchronous R20 primary tumor or a previous malignancy in the head and neck region were excluded. R21 R22 Two hundred twelve patients were selected on the availability of both representative formaldehyde- R23 fixed, paraffin-embedded tissue blocks and frozen tissue samples of the primary tumor. R24 R25 A dedicated head and neck pathologist examined all hematoxylin-eosin stained slides with special R26 attention to the following pathological characteristics: type of tumor, differentiation grade, R27 infiltration depth, invasive pattern, perineural growth, vasoinvasive growth, extracapsular spread, R28 and bone invasion. R29 R30 A tissue microarray was made of the paraffin-embedded tissue. For each tumor block, 3 central R31 tissue cylinders and 3 tissue cylinders at the tumor front with a diameter of 0.6 mm were punched R32 out, avoiding areas of necrosis, and arrayed in a recipient paraffin block. Normal epithelium from R33 the floor of the mouth, gingiva, and tonsil was incorporated in each block to ensure similarity of R34 staining between the different blocks, as described earlier.24 R35 R36 From each patient, clinical characteristics, clinical TNM classification (based on palpation, R37 ultrasound-guided fine-needle aspiration, MRI or CT, and classified in a multidisciplinary panel), R38 pathological TNM classification, and cause of death were retrieved from the medical records, as R39 listed in Table 1.
104 Nodal metastasis and survival in oral cancer: Association with protein expression of SLPI, not with LCN2, TACSTD2, or THBS2
Table 1 | Baseline characteristics. R1 Variable No. of patients (%) Sex R2 Female 84 (40) R3 Male 128 (60) Age at diagnosis R4 Median (Range) 61 (26-87) R5 Smoking Never 43 (20) R6 Ceased > 1 year 34 (16) Active smoker or ceased < 1 year 133 (63) R7 Missing 2 (1) R8 Alcohol Never 46 (22) R9 Occasionally 49 (23) R10 1-4 U/day 71 (33) ≥ 5 U/day 44 (21) R11 Missing 2 (1) Clinical N-classification R12 cN0 146 (69) R13 cN1-3 66 (31) Clinical T-classification R14 cT1 44 (21) R15 cT2 79 (37) cT3 19 (9) R16 cT4 70 (33) Pathological N-classification R17 pN0 97 (46) R18 pN1-3 115 (54) Pathological T-classification R19 pT1 44 (21) R20 pT2 73 (34) pT3 22 (10) R21 pT4 73 (34) R22 Infiltration depth < 4.0mm 19 (9) R23 ≥ 4.0mm 193 (91) Differentiation grade R24 Good / Moderate 173 (82) R25 Poor / Undifferentiated 39 (18) Vaso-invasion R26 No 39 (18) R27 Yes 169 (80) Missing 4 (2) R28 Bone-invasion R29 No 152 (72) Yes 60 (28) R30 Perineural growth No 122 (57) R31 Yes 80 (38) R32 Missing 10 (5) Invasive pattern R33 Cohesive 44 (21) R34 Non-cohesive 167 (79) Missing 1 (<1) R35 Extra capsular spread R36 No 59 (28) Yes 56 (26) R37 No nodal metastasis 97 (46) High risk HPV status R38 Negative 210 (99) R39 Positive 2 (1)
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R1 Gene expression R2 For a subgroup of 83 tumors, normalized gene expression data were available from an earlier R3 study for which methods has been described in detail earlier.7 In short, frozen tumor samples were R4 sectioned, aliquoted in Trizol (Life Technologies, Frederick, MD), and sent to Agendia Laboratories R5 (Amsterdam, The Netherlands) for expression profile analysis. Tumor areas with a percentage of R6 at least 50% were assessed on hematoxylin-eosin stained sections and taken in parallel; RNA R7 was isolation and amplification. Tumor sample RNA was labeled as Cy3, and reference RNA was R8 labeled Cy5. As a reference, the Universal Human Reference RNA (Agilent Technologies, Santa R9 Clara, CA) was used. Samples were hybridized on full-genome Agilent arrays. Raw fluorescence R10 intensities were quantified using Agilent Feature Extraction software and imported into R/ R11 Bioconductor (http://www.bioconductor.org/) for normalization (loess normalization using the R12 LIMMA package) and additional analysis. R13 R14 Human papillomavirus type 16 analysis R15 Human papillomavirus type 16 (HPV-16) active tumors were determined by p16 R16 immunohistochemistry (IHC) followed by GP 5+/6+ polymerase chain reaction in positive p16 R17 staining, a reliable algorithm for detection of HPV-16 in paraffin-embedded head and neck R18 cancer specimens, as described by Smeets et al.25 R19 R20 Immunohistochemistry R21 IHC was performed on 4-µm thick paraffin sections. The tissue sections were deparaffinized with R22 xylene and rehydrated. Endogenous peroxidase activity was blocked for 15 minutes in a 0.3% R23 hydrogen peroxide phosphate-citrate buffer. Then, tissue sections were washed in water and R24 subsequently subjected to antigen retrieval by boiling the slides in ethylenediaminetetraacetic R25 acid buffer, pH 9.0 (SLPI) or citrate buffer, and pH 6.0 (TACSTD2, LCN2, and THBS2) for 20 R26 minutes. Sections were cooled down within the buffers for 30 minutes. After washing with R27 phosphate-buffered saline (PBS) for 5 minutes, tissue slides were incubated with the primary R28 antibody SLPI (clone 31; HyCult biotechnology, Uden, The Netherlands; dilution 1:50), primary R29 antibody TACSTD2 (AF650, R&D Systems, Oxon, England; dilution 1:50), primary antibody LCN2 R30 (MAB1757; R&D Systems, Oxon, England; dilution 1:50), or primary antibody THBS2 (sc-12313, R31 Santa Cruz Biotechnology, Santa Cruz, CA; dilution 1:50) for 60 minutes. After washing with R32 PBS (3 times), incubation with poly-HRP Goat anti-Mouse/Rabbit/Rat (Brightvision, Immunologic, R33 Duiven, The Netherlands; ready to use) for 30 minutes was followed by washing with PBS (3 R34 times). Slides were then developed with diaminobenzidine for 10 minutes, counterstained with R35 hematoxylin, followed by dehydration, and mounted. R36 R37 Evaluation of immunohistochemical staining R38 A core was considered inadequate/lost when the core contained <5% tumor tissue or when R39 >95% of the core contained no tissue. Patients were only included in the study when one or
106 Nodal metastasis and survival in oral cancer: Association with protein expression of SLPI, not with LCN2, TACSTD2, or THBS2
more tumor cores were available. When two or more cores were available from one patient, the R1 mean (SLPI, THBS2, or LCN2) or maximum (TACSTD2) score was calculated for that patient. R2 R3 The expression of SLPI and THBS2 in the primary tumor was evaluated by scoring the percentage R4 of cytoplasm staining. The percentage of cytoplasm stained was classified as 0 (<5%), 1 (5% to R5 30%), 2 (31% to 75%), or 3 (>75%), see Figure 1.22, 26 Expression of TACSTD2 was evaluated R6 by scoring the staining intensity of the cell membrane as 0 = no, 1 = weak, 2 = moderate, or R7 3 = strong staining. For LCN2 expression, both the intensity (0 = no, 1 = weak, 2 = moderate, or R8 3 = strong) and percentage of cytoplasm staining was scored, multiplying the intensity score with R9 the percentage of staining classified as 1 (≤25%), 2 (26% to 50%), 3 (51% to 75%), or 4 R10 (>75%) was used as a final score for LCN2 expression. Scores ≤3 were interpreted as negative, R11 and scores >3 as positive.27 A dedicated head and neck pathologist (S.W.) and a researcher (R.N.), R12 both blinded to the clinical characteristics of the patients, evaluated the protein expressions R13 independently. Consensus was reached regarding discordant findings. R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 Figure 1 | Scoring system for SLPI, LCN2, TACSTD2 and THBS2. Abbreviations: SLPI, secretory leukocyte protease inhibitor; THBS2, thrombospondin-2; LCN2, lipocalin-2; TACSTD2, tumor-associated calcium signal R38 transducer 2. R39
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R1 Statistical analysis R2 An interrater reliability analysis using the Spearman (for continuous data) and Kappa (for R3 categorical data) statistic was performed to determine consistency of IHC scoring among raters. R4 The Mann–Whitney test was used to determine differences in gene expression between lymph R5 node-positive and lymph node-negative tumors. Receiver operating characteristic (ROC) curve R6 analysis was used to determine cutoff points for the correlation of gene and protein expression R7 and nodal metastases. Correlations between gene expression or protein expression and lymph R8 node metastases were assessed by the chi-square test. R9 R10 OS was defined as the length of the time interval from surgery to death from any cause. DSS R11 was defined as the time interval from surgery to either death because of or as a recurrence of R12 the disease. ROC curve analysis was used to determine cutoff points for protein expression and R13 survival. The OS and the DSS curves were constructed using the Kaplan–Meier method and the R14 log-rank test was used to test for significance. Prognostic value was examined by univariate and R15 multivariate analyses using the Cox proportional hazards regression model. Characteristics with R16 a p < .10 in univariate analysis and potential confounders were included, and the model was R17 created with backward logistic regression. All p values were based on 2-tailed statistical analysis R18 and p < .05 was considered statistically significant. Statistical analysis was performed using the R19 SPSS 20.0 statistical package (SPSS, Chicago, IL). R20 R21 R22 RESULTS R23 R24 Human papillomavirus type 16 analysis R25 Of our 212 tumor samples, 36 showed p16 overexpression on IHC. Of this group, only 2 samples R26 (0.9%) proved to be true HPV-16 positive with polymerase chain reaction, see Table 1. R27 R28 Immunohistochemistry: descriptive analysis R29 A total of 1080 cores (85%) stained with SLPI antibody, 1119 cores (88%) stained with THBS2 R30 antibody, 1077 cores (85%) stained with LCN2 antibody, and 1077 cores (84%) stained with R31 TACSTD2 antibody were available for analysis. There was at least 1 core of each tumor suitable R32 for each staining so no tumors were excluded from analysis. The level of interrater concordance R33 was high, with a Spearman’s rank correlation of 0.975 (p < .001) for continuous data and a Kappa R34 of 0.874 (95% confidence interval, 0.806–0.942; p < .001) for categorical data, scatter plot in R35 Supplemenary Figure 1, online only. The IHC results are given in Table 2. R36 R37 R38 R39
108 Nodal metastasis and survival in oral cancer: Association with protein expression of SLPI, not with LCN2, TACSTD2, or THBS2
Table 2 | Immunohistochemistry descriptive results. R1 Variable SLPI THBS2 LCN2 TACSTD2 No. of cores (%) R2 Tumor 1080 (85) 1119 (88) 1077 (85) 1074 (84) R3 No Tumor 50 (40 86 (7) 68 (5) 73 (6) R4 No Core 142 (11) 67 (5) 127 (10) 125 (10) R5 No. of cores per tumor (212 tumors) R6 6 110 107 109 109 5 50 60 53 51 R7 4 26 20 23 24 R8 3 16 18 16 16 2 8 6 4 9 R9 1 2 1 7 3 R10 Score per tumor (212 tumors) Score Score R11 ≤ 5% 103 24 ≤3 126 0 22 R12 6-30% 90 100 >3 86 1+ 63 31-75% 19 87 2+ 73 R13 > 75% 0 1 3+ 54 R14
Abbreviations: SLPI, secretory leukocyte protease inhibitor; THBS2, thrombospondin-2; LCN2, lipocalin-2; R15 TACSTD2, tumor-associated calcium signal transducer 2 R16 R17 Gene expression and lymph node metastases R18 Analysis of 83 OSCCs show a statistically significant differential gene expression between lymph R19 node-positive and lymph node-negative patients for SLPI (p = .001), LCN2 (p < .001), TACSTD2 R20 (p = .002), and THBS2 (p = .001), see Figure 2. Optimal cutoff points determined with ROC curve R21 analysis (Supplementary Figure 2 and Supplementary Table 1, online only) revealed that gene R22 expression is a significant predictor of lymph node metastasis for all 4 genes. SLPI, LCN2, and R23 TACSTD2 mRNA are downregulated and THBS2 mRNA is upregulated in lymph node-positive R24 patients, see Table 3. R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 Figure 2 | Gene expression and nodal status. Mann-Whitney test, ** p < 0.01, *** p < 0.001. R36 Abbreviations: SLPI, secretory leukocyte protease inhibitor; THBS2, thrombospondin-2; LCN2, lipocalin-2; R37 TACSTD2, tumor-associated calcium signal transducer 2; pN+, pathologic lymph node positive; pN0, pathologic lymph node negative. R38 R39
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Table 3 | Gene expressions correlated with lymph node metastasis. R1 LNM R2 Gene expression No. of patients No Yes p-value R3 SLPI R4 M ≤ 0,130 57 18 (32%) 39 (68%) <0.001 M > 0,130 26 19 (73%) 7 (27%) R5 LCN2 R6 M ≤ -0,360 43 11 (26%) 32 (74%) <0.001 R7 M > -0,360 40 26 (65%) 14 (35%) R8 TACSTD2 R9 M ≤ 0,027 47 14 (30%) 33 (70%) 0.002 M > 0,027 36 23 (64%) 13 (36%) R10 THBS2 R11 M > 0,100 34 7 (79%) 27 (21%) <0.001 R12 M ≤ 0,100 49 30 (61%) 19 (39%)
R13 Abbreviations: SLPI, secretory leukocyte protease inhibitor; THBS2, thrombospondin-2; LCN2, lipocalin-2; R14 TACSTD2, tumor-associated calcium signal transducer 2; M, log2(sample/reference pool). R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 Figure 3 | SLPI expression and survival. Log Rank test, overall survival p = 0.002 disease-specific R29 survival p < 0.001. Abbreviations: SLPI, secretory leukocyte protease inhibitor. R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
110 Nodal metastasis and survival in oral cancer: Association with protein expression of SLPI, not with LCN2, TACSTD2, or THBS2
Table 4 | Biomarkers correlated with lymph node metastasis. R1 Biomarker Whole cohort (212 tumors) cT1-2N0 (101 tumors) expression pN0 pN+ p-value pN0 pN+ p-value R2 SLPI R3 ≤ 30% 83 (43%) 110 (57%) 0.01 55 (63%) 33 (37%) NS R4 > 30% 14 (74%) 5 (26%) 9 (69%) 4 (31%) R5 LCN2 Score ≤ 3 51 (41%) 75 (59%) NS 33 (60%) 22 (40%) NS R6 Score > 3 46 (54%) 40 (46%) 31 (67%) 15 (33%) R7 TACSTD2 R8 0 - 1+ 35 (41%) 50 (59%) NS 31 (65%) 17 (35%) NS R9 2+ - 3+ 62 (49%) 65 (51%) 33 (62%) 20 (38%) R10 THBS2 ≤ 5% 13 (52%) 12 (48%) NS 7 (78%) 2 (22%) NS R11 > 5% 84 (45%) 103 (55%) 57 (62%) 35 (38%) R12
Abbreviations: SLPI, secretory leukocyte protease inhibitor; THBS2, thrombospondin-2; LCN2, lipocalin-2; R13 TACSTD2, tumor-associated calcium signal transducer 2; pN+, pathologic lymph node positive; pN0, R14 pathologic lymph node negative; NS, not significant R15 R16 R17 Table 5 | Cox regression analysis of overall survival. R18 Variable HR (95 % CI) p-value SLPI expression 0.56 (0.39-0.81) 0.002 R19 Multivariate model R20 SLPI expression 0.61 (0.41-0.89) 0.010 R21 Age 1.04 (1.02-1.06) <0.001 cN-classification 3.81 (2.58-5.65) <0.001 R22 Vaso-invasion 1.59 (1.02-2.46) 0.040 R23 Non-cohesive invasive pattern 2.69 (1.53-4.73) 0.001 R24 Bone invasion 1.75 (1.17-2.63) 0.007 R25 Abbreviations: SLPI, secretory leukocyte protease inhibitor; HR, hazard ratio; CI, confidence interval R26 R27 R28 Table 6 | Cox regression analysis of disease-specific survival. R29 Variable HR (95 % CI) p-value SLPI expression 0.43 (0.27-0.67) <0.001 R30 Multivariate model R31 SLPI expression 0.47 (0.29-0.75) 0.002 R32 cN-classification 2.14 (1.38-4.19) 0.002 Extra capsular spread 1.93 (1.10-3.38) 0.022 R33 R34 Abbreviations: SLPI, secretory leukocyte protease inhibitor; HR, hazard ratio; CI, confidence interval R35 R36 R37 R38 R39
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R1 DISCUSSION R2 R3 Biomarkers with diagnostic and prognostic value for determining lymph node metastases and R4 predicting survival in OSCC are crucial for determining treatment planning and possible targets R5 for personalized treatment in the future. Gene expression profiling revealed LCN2, THBS2, R6 TACSTD2, and SLPI as genes with a strong statistically significant differential gene expression R7 between pN+ and pN0 patients with early oral cancer.7 Although the precise function of these R8 genes is yet not fully understood, an explanation might be their joint role in matrix remodeling.28 R9 R10 In our cohort of OSCC, LCN2, THBS2, and TACSTD2 showed no correlation with lymph node R11 metastases or survival on protein expression level despite significant differences in staining R12 between tumors on IHC. There are several reasons for the poor correlations between mRNA R13 and protein expression levels. First, there is the undervalued role of complicated and varied R14 posttranscriptional and translational mechanisms, which are not yet sufficiently defined. Second, R15 proteins differ substantially in degradation and in vivo half-lives.29,30 Finally, both protein and R16 mRNA experiments contain a significant amount of errors and noise that limits our ability to R17 get a clear picture.30 A combination of one or more of these factors may explain these poor R18 correlations, which is in line with several studies that report discrepancies between mRNA and R19 protein correlations with prognostically relevant outcomes.31-33 Therefore, mRNA levels cannot be R20 used as surrogates for corresponding protein levels without validation. R21 R22 To our knowledge, this is the first study that shows the correlation between SLPI expression R23 by IHC and lymph node metastases, OS, and DSS in a large cohort of patients with OSCC. R24 Despite a significant correlation between SLPI protein expression and lymph node metastases R25 in the whole cohort, SLPI expression has no additional diagnostic value as a predictor for lymph R26 node metastases in a subgroup of early cancers, which are clinically lymph node-negative in R27 this cohort of OSCC. Previous studies report different results correlating SLPI expression with R28 lymph node metastases in head and neck squamous cell carcinoma. Westin et al23 found no R29 significant correlation, whereas Cordes et al22 found a strong correlation between lower SLPI R30 protein expression and an increased risk of lymph node metastases (p < .001). However, there R31 are some drawbacks in comparing these studies. First, they did not analyze whether SLPI had R32 additional value as a predictor for lymph node metastases. Second, most cancers in the Cordes R33 study were located in the larynx, oropharynx, and hypopharynx (87.6%). This might explain the R34 difference with our findings in oral cancer, as also for other genes, such as EGFR, pAkt, and PTEN, R35 it is known that its expressions vary between oral and oropharyngeal carcinomas.34 R36 R37 R38 R39
112 Nodal metastasis and survival in oral cancer: Association with protein expression of SLPI, not with LCN2, TACSTD2, or THBS2
Although Won et al34 suggested initially that the difference in HPV-related pathogenesis of the R1 tumors could be the reason for different protein expression in head and neck subsites, a later R2 study by Hoffmann et al35 identified SLPI expression to be an HPV-independent predictor for R3 lymph node metastases in head and neck cancer. In addition, their cohort contained mainly R4 laryngeal and oropharyngeal carcinomas. Another possibility for the discrepancy could be the R5 amount of tumors with moderate/strong immunoreactivity, which, in our cohort, was 9.0% R6 compared to 31.4% in the Cordes cohort.22 As a result, the group of tumors with moderate/ R7 strong immunoreactivity in our study could be too small to have additional value as a predictor R8 for lymph node metastases. R9 R10 We identified SLPI as an independent predictor for OS and DSS in OSCC. Patients with low SLPI R11 protein expression had a worse OS and DSS compared with patients with any SLPI expression, R12 see Supplementary Figures S3 and S4. Earlier studies suggested a role for SLPI expression as a R13 prognostic biomarker in head and neck cancer. Westin et al23 correlated stronger SLPI expression R14 with well-differentiated tumors in a group of 26 head and neck cancers and suggested its use as R15 a prognostic tool, although they found no significant relationship with lymph node metastases R16 and did not correlate its expression with survival. Alkemade et al36 found the same significant R17 correlation between SLPI expression and tumor differentiation in skin cancer. In addition, Wen et R18 al37 demonstrated inverse correlations of SLPI expression with multiple tumor invasion parameters, R19 which suggests a protective role of SLPI against OSCC invasion. They also suggested SLPI as a R20 potential biomarker in evaluating prognosis and treatment of the clinically lymph node-negative R21 neck, although they did not correlate SLPI expression with lymph node metastases or survival. R22 R23 In conclusion, this is, to our knowledge, the first study that links SLPI expression with both lymph R24 node metastases and survival in a large cohort of patients with OSCC. Although SLPI expression R25 is correlated with lymph node metastases in the whole cohort, it has no additional value in R26 determining lymph node metastases in early cancers that are clinically lymph node-negative. On R27 the other hand, SLPI seems to be an independent predictor for both OS and DSS. Therefore, SLPI R28 IHC might be relevant as a prognostic biomarker for patients with OSCC. However, its molecular R29 role in progression and metastasis of different head and neck cancer subsites needs further R30 investigation. R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 REFERENCES R2 1. Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011; 61: 69-90. R3 2. Incidence of invasive cancer by gender and location per year [Internet]. The Netherlands Cancer R4 Registry 2012. Available at: http://www.cijfersoverkanker.nl. Accessed April 30, 2013. R5 3. Karim-Kos HE, de Vries E, Soerjomataram I, et al. Recent trends of cancer in Europe: a combined approach of incidence, survival and mortality for 17 cancer sites since the 1990s. Eur J Cancer 2008; R6 44: 1345-1389. R7 4. Mehanna H, West CM, Nutting C, et al. Head and neck cancer–Part 2: treatment and prognostic R8 factors. BMJ 2010; 341: c4690. R9 5. Leusink FK, van Es RJ, de Bree R, et al. Novel diagnostic modalities for assessment of the clinically node- negative neck in oral squamous-cell carcinoma. Lancet Oncol 2012; 13: e554-e561. R10 6. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases R11 from primary head and neck squamous cell carcinomas. Nat Genet 2005; 37: 182-186. R12 7. van Hooff SR, Leusink FK, Roepman P, et al. Validation of a gene expression signature for assessment R13 of lymph node metastasis in oral squamous cell carcinoma. J Clin Oncol 2012; 30: 4104-4110. 8. Lin CW, Tseng SW, Yang SF, et al. Role of lipocalin 2 and its complex with matrix metalloproteinase-9 R14 in oral cancer. Oral Dis 2012; 18: 734-740. R15 9. Chong HC, Tan CK, Huang RL, et al. Matricellular proteins: a sticky affair with cancers. J Oncol 2012; R16 2012: 351089. R17 10. Bai W, Wang L, Ji W, et al. Expression profiling of supraglottic carcinoma: PTEN and thrombospondin 2 are associated with inhibition of lymphatic metastasis. Acta Otolaryngol 2009; 129: 569-574. R18 11. Trerotola M, Cantanelli P, Guerra E, et al. Upregulation of Trop-2 quantitatively stimulates human R19 cancer growth. Oncogene 2013; 32: 222-233. R20 12. Fong D, Spizzo G, Gostner JM, et al. TROP2: a novel prognostic marker in squamous cell carcinoma of the oral cavity. Mod Pathol 2008; 21: 186-191. R21 13. Abe T, Kobayashi N, Yoshimura K, et al. Expression of the secretory leukoprotease inhibitor gene in R22 epithelial cells. J Clin Invest 1991; 87: 2207-2215. R23 14. Boudier C, Cadène M, Bieth JG. Inhibition of neutrophil cathepsin G by oxidized mucus proteinase R24 inhibitor. Effect of heparin. Biochemistry 1999; 38: 8451-8457. 15. Del Rosso M, Fibbi G, Pucci M, et al. Multiple pathways of cell invasion are regulated by multiple R25 families of serine proteases. Clin Exp Metastasis 2002; 19: 193-207. R26 16. Sun Z, Yang P. Role of imbalance between neutrophil elastase and alpha 1-antitrypsin in cancer R27 development and progression. Lancet Oncol 2004; 5: 182-190. R28 17. Cheng WL, Wang CS, Huang YH, et al. Overexpression of a secretory leukocyte protease inhibitor in human gastric cancer. Int J Cancer 2008; 123: 1787-1796. R29 18. Choi BD, Jeong SJ, Wang G, et al. Secretory leukocyte protease inhibitor is associated with MMP-2 and R30 MMP-9 to promote migration and invasion in SNU638 gastric cancer cells. Int J Mol Med 2011; 28: 527-534. R31 19. Trojan L, Schaaf A, Steidler A, et al. Identification of metastasis-associated genes in prostate cancer by R32 genetic profiling of human prostate cancer cell lines. Anticancer Res 2005; 25: 183-191. R33 20. Nakamura K, Takamoto N, Hongo A, et al. Secretory leukoprotease inhibitor inhibits cell growth R34 through apoptotic pathway on ovarian cancer. Oncol Rep 2008; 19: 1085-1091. R35 21. Dasgupta S, Tripathi PK, Qin H, et al. Identification of molecular targets for immunotherapy of patients with head and neck squamous cell carcinoma. Oral Oncol 2006; 42: 306-316. R36 22. Cordes C, Häsler R, Werner C, et al. The level of secretory leukocyte protease inhibitor is decreased in R37 metastatic head and neck squamous cell carcinoma. Int J Oncol 2011; 39: 185-191. R38 R39
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23. Westin U, Nyström M, Ljungcrantz I, Eriksson B, Ohlsson K. The presence of elafin, SLPI, IL1-RA and STNFalpha RI in head and neck squamous cell carcinomas and their relation to the degree of tumour R1 differentiation. Mediators Inflamm 2002; 11: 7-12. R2 24. Klein Nulent TJ, Van Diest PJ, van der Groep P, et al. Cannabinoid receptor-2 immunoreactivity is R3 associated with survival in squamous cell carcinoma of the head and neck. Br J Oral Maxillofac Surg 2013; 51: 604-609. R4 25. Smeets SJ, Hesselink AT, Speel EJ, et al. A novel algorithm for reliable detection of human papillomavirus R5 in paraffin embedded head and neck cancer specimen. Int J Cancer 2007; 121: 2465-2472. R6 26. Kishi M, Nakamura M, Nishimine M, et al. Loss of heterozygosity on chromosome 6q correlates with decreased thrombospondin-2 expression in human salivary gland carcinomas. Cancer Sci 2003; 94: R7 530-535. R8 27. Wang HJ, He XJ, Ma YY, et al. Expressions of neutrophil gelatinase associated lipocalin in gastric cancer: R9 a potential biomarker for prognosis and an ancillary diagnostic test. Anat Rec (Hoboken) 2010; 293: 1855-1863. R10 28. Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network R11 integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010;38(Web Server R12 issue): W214–W220. R13 29. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 2012; 13: 227-232. R14 30. Dickson BC, Mulligan AM, Zhang H, et al. High-level JAG1 mRNA and protein predict poor outcome in R15 breast cancer. Mod Pathol 2007; 20: 685-693. R16 31. Hao D, Lau HY, Eliasziw M, et al. Comparing ERCC1 protein expression, mRNA levels, and genotype in squamous cell carcinomas of the head and neck treated with concurrent chemoradiation stratified by R17 HPV status. Head Neck 2012; 34: 785-791. R18 32. Lichtinghagen R, Musholt PB, Lein M, et al. Different mRNA and protein expression of matrix R19 metalloproteinases 2 and 9 and tissue inhibitor of metalloproteinases 1 in benign and malignant prostate tissue. Eur Urol 2002; 42: 398-406. R20 33. Greenbaum D, Colangelo C, Williams K, et al. Comparing protein abundance and mRNA expression R21 levels on a genomic scale. Genome Biol 2003; 4: 117. R22 34. Won HS, Jung CK, Chun SH, et al. Difference in expression of EGFR, pAkt, and PTEN between R23 oropharyngeal and oral cavity squamous cell carcinoma. Oral Oncol 2012; 48: 985-990. 35. Hoffmann M, Quabius ES, Tribius S, et al. Human papillomavirus infection in head and neck cancer: the R24 role of the secretory leukocyte protease inhibitor. Oncol Rep 2013; 29: 1962-1968. R25 36. Alkemade HA, van Vlijmen–Willems IM, van Haelst UJ, et al. Demonstration of skin-derived R26 antileukoproteinase (SKALP) and its target enzyme human leukocyte elastase in squamous cell carcinoma. J Pathol 1994; 174: 121-129. R27 37. Wen J, Nikitakis NG, Chaisuparat R, et al. Secretory leukocyte protease inhibitor (SLPI) expression and R28 tumor invasion in oral squamous cell carcinoma. Am J Pathol 2011; 178: 2866-2878. R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 SUPPLEMENTARY INFORMATION R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 Supplementary Figure 1 | Scatter plot for interrator concordance The level of interrater concordance was high, with a Spearman’s rank correlation of 0.975 (p<.001) for R17 continuous data and a Kappa of 0.874 (95% confidence interval, 0.806–0.942; p<.001) for categorical data. R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 Supplementary Figure 2 | Cutoff point determination by ROC curve analysis R38 Optimal cutoff points revealed that gene expression is a significant predictor of lymph node metastasis for all 4 genes, see also Table 3. R39
116 Nodal metastasis and survival in oral cancer: Association with protein expression of SLPI, not with LCN2, TACSTD2, or THBS2
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 Supplementary Figure 3 | Cutoff point determination by ROC curve to correlate protein expression and LNM in cT1-2N0 OSCC R20 Optimal cut-off points could not be determined by ROC curve analysis. Protein expression of LCN2, TACSTD2, R21 THBS2 and SLPI revealed no significant correlation with lymph node metastases in a subgroup of cT1-T2N0 tumors (see also Table 4). R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 Supplementary Figure 4 | Cutoff point determination by ROC curve to correlate SLPI protein R35 expression and survival Determined cutoff points used in Kaplan–Meier analysis revealed a significant difference between SLPI R36 expression for both OS and DSS, see also Figure 3 and Supplementary Table 3. R37 R38 R39
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Supplementary Table S1 | ROC-curves for gene expression and LNM. R1 ROC curve No. of patients AUC (95% CI) p-value R2 SLPI gene expression 83 0.705 (0.590-0.820) 0.001 R3 LCN2 gene expression 83 0.713 (0.597-0.829) 0.001 R4 TACSTD2 gene expression 83 0.696 (0.580-0.811) 0.002 R5 THBS2 gene expression 83 0.704 (0.590-0.818) 0.001 R6 Abbreviations: LNM, lymph node metastasis; AUC, area under curve; CI, confidence interval; SLPI, secretory leukocyte protease inhibitor; THBS2, thrombospondin-2; LCN2, lipocalin-2; TACSTD2, tumor-associated R7 calcium signal transducer 2 R8 R9 Supplementary Table S2 | ROC-curves for protein expression and LNM. R10 ROC curve No. of patients AUC (95% CI) p-value R11 SLPI protein expression 212 0.580 (0.503-0.657) 0.046 LCN2 protein expression 212 0.577 (0.499-0.654) 0.054 R12 TACSTD2 protein expression 212 0.562 (0.483-0.640) 0.122 R13 THBS2 protein expression 212 0.504 (0.426-0.582) 0.919 R14 Abbreviations: LNM, lymph node metastasis; AUC, area under curve; CI, confidence interval; SLPI, secretory R15 leukocyte protease inhibitor; THBS2, thrombospondin-2; LCN2, lipocalin-2; TACSTD2, tumor-associated calcium signal transducer 2 R16 R17 Supplementary Table S3 | ROC-curves for SLPI expression and survival. R18 ROC curve No. of patients AUC (95% CI) p-value R19 SLPI and OS 212 0.613 (0.537-0.690) 0.005 R20 SLPI and DSS 212 0.641 (0.566-0.717) 0.001 R21 Abbreviations: AUC, area under curve; CI, confidence interval; SLPI, secretory leukocyte protease inhibitor; R22 OS, overall survival; DSS, disease specific survival R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
118 Nodal metastasis and survival in oral cancer: Association with protein expression of SLPI, not with LCN2, TACSTD2, or THBS2
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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CHAPTER 7
The co-expression of kallikrein 5 and kallikrein 7
associates with poor survival in non- HPV
oral squamous-cell carcinoma
F.K.J. Leusink P.J. van Diest* M.H. Frank* R. Broekhuizen W. Braunius S.R. van Hooff S.M. Willems* R. Koole*
* Equal contribution
Pathobiology 2015;82:58-67 Chapter 7
R1 ABSTRACT R2 R3 OBJECTIVE R4 Oral squamous cell carcinoma (OSCC) has still a poor prognosis. Lymph node metastasis is R5 a major determinant of treatment decisions and prognosis. Serine protease inhibitor Kazal- R6 type 5 (SPINK5) is the inhibitor of kallikrein 5 (KLK5) and kallikrein 7 (KLK5). SPINK5, KLK5, R7 and KLK7 are three of the genes of a recently validated gene expression profile that predicts R8 lymph node metastasis in OSCC. This study evaluates the clinicopathological role and their R9 value as biomarkers in OSCC. R10 R11 METHODS R12 83 patients with primary OSCC, treated surgically between 1996 and 2000, were included. R13 Gene expression data were acquired in a previous reported study. HPV status was determined R14 by an algorithm for human papillomavirus type 16. Protein expression for KLK5, KLK7 and R15 SPINK5 was semi-quantitatively determined in all 83 tumors by immunohistochemistry (IHC). R16 All expression data were correlated with clinicopathological parameters. R17 R18 RESULTS R19 Concurrent loss of KLK5 and KLK7 correlates with a worse disease specific and overall R20 survival. Multivariate analysis proofed that co-expression is an independent prognostic factor R21 for disease specific (p=0.029) and overall survival (p=0.001). R22 R23 CONCLUSION R24 This report demonstrates that concurrent loss of KLK5 and KLK7 associates with a poor R25 clinical outcome in OSCC and could therefore serve as prognostic marker in OSCC. R26 R27 KEYWORDS R28 Oral squamous-cell carcinoma, KLK5, KLK7, SPINK5, Survival R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
122 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
INTRODUCTION R1 R2 Oral squamous cell carcinoma (OSCC) is the most common head and neck tumor type worldwide1 R3 with tobacco smoking, betel nut chewing, alcohol consumption, and human papillomavirus R4 (HPV) infection as major risk factors.2,3 Despite advances in surgery, radiation, and chemotherapy, R5 prognosis of OSCC patients remains poor with a 5-year survival rate of approximately 50%. R6 Lymph node metastasis of OSCC is major determinant of prognosis and treatment but can be R7 difficult to detect with current diagnostic modalities.4 Approximately 30-40% of lymph node R8 metastases are left undetected in this population and will develop into overt neck disease during R9 follow-up when left untreated. Therefore, treatment of the neck is recommended even when the R10 tumor has been classified as clinically node negative. This results in unnecessary treatment and R11 morbidity in 60-70% of patients with OSCC. Additional biological markers for occult metastasis R12 may improve staging.4 Human tissue kallikreins, a family of 15 secreted serine proteases encoded R13 by a multigene cluster (KLK genes) on chromosome 19q13.47, may be such biomarkers.5 These R14 genes play different functional roles like semen liquefaction by digestion of Seminigelin by the R15 most commonly known kallikrein (KLK3), better known as prostate-specific antigen (PSA)6,7 and R16 skin desquamation by cleaving corneodesmosomes by kallikrein 5 (KLK5) and kallikrein 7 (KLK7).8 R17 Kallikreins have been implicated in different cancer types5,6,8-10 and often coexpression of KLK5 R18 and KLK7 has been reported.9,11,12 Additionally, in OSCC, KLK5, KLK7, KLK8 and KLK10 were R19 reported to be over-expressed,13 however correlations with clinical and pathological parameters R20 (a.o. nodal disease) were not analyzed. R21 R22 Lymphoepithelial Kazal-type-related inhibitor (LEKTI), the product of the gene encoding for serine R23 protease inhibitor kazal type 5 (SPINK5) inhibits KLK5 and KLK7.14-16 In head and neck squamous R24 cell carcinomas (HNSCC), SPINK5 expression is downregulated both at the mRNA and protein R25 level.17,18 Moreover, SPINK5, KLK5 and KLK7 are all part of the signature genes for predicting R26 lymph node metastases in HNSCC19,20 including OSCC.21 R27 R28 Therefore, we further characterized the value of KLK5, KLK7 and SPINK5 in primary OSCCs as R29 biomarkers of lymph node metastases and prognosis. R30 R31 R32 MATERIALS AND METHODS R33 R34 Patients and tissue samples R35 The source population consisted of patients with a histologically confirmed HNSCC, primary R36 treated by surgery and radiotherapy (on indication) between 1996 and 2000 in the University R37 Medical Center Utrecht, as shown in table 1a and as previously described in an earlier reported R38 R39
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R1 gene expression profiling study.20 Tissues were used in line with the code “Proper Secondary Use R2 of Human Tissue” as installed by the Dutch Federation of Biomedical Scientific Societies.22 R3 R4 Table 1a | Clinical characteristics of the included OSCC patients. R5 No. (%) All cases 83 (100) R6 Gender R7 Female 36 (43) R8 Male 47 (57) R9 Age at diagnosis R10 0-60 53 (44) ≥ 61 30 (56) R11 Median (range) 62 (37-87) R12 Smoking history R13 Current smoker or ceased < 1 year 58 (70) Ex smoker, ceased > 1 year 9 (11) R14 Never smoker 15 (18) R15 Alcohol consumption R16 ≥ 5 U/day 19 (23) R17 1-4 U/day 28 (34) Occasionally 17 (20) R18 Never 19 (23) R19 Clinical T-stage R20 cT1 13 (16) R21 cT2 31 (37) cT3 8 (10) R22 cT4 31 (37) R23 Clinical N-Stage R24 cN0 53 (64) cN1-3 30 (36) R25 Sub-site R26 tongue 30 (36) R27 floor of mouth 35 (42) R28 buccal cavity 10 (12) gum 8 (10) R29 Mean follow-up (months) 45 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
124 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
Table 1b | Pathological characteristics of the included OSCC patients. R1 No. (%) All cases 83 (100) R2 Pathological T-Stage R3 pT1 17 (20) R4 pT2 27 (33) R5 pT3 10 (12) pT4 29 (35) R6 Pathological N-Stage R7 pN0 38 (46) R8 pN1-3 45 (54) R9 Stage grouping I 14 (17) R10 II 9 (11) R11 III 22 (26) R12 IVA-IVB 38 (46) Infiltration depth R13 ≥ 4.0mm 72 (87) R14 < 4.0mm 11 (13) R15 Differentiation grade R16 Good / Moderate 67 (81) Poor / Undifferentiated 16 (19) R17 Keratinization R18 Present 60 (72) R19 Absent 20 (24) Missing 3 (4) R20 Vaso-invasion R21 Present 18 (22) R22 Absent 62 (75) R23 Missing 3 (3) R24 Bone-invasion R25 Present 25 (30) Absent 58 (70) R26 Perineural growth R27 Present 34 (41) R28 Absent 39 (47) Missing 10 (12) R29 Spidery growth R30 Present 65 (78) R31 Absent 18 (22) R32 High risk HPV status R33 positive 0 (0) negative 83 (100) R34 R35 R36 R37 R38 R39
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R1 Study inclusion was based on: 1) oral cavity as primary tumor site, 2) HPV-16-status, excluding R2 positive tumors, 3) follow-up time of more than five years and 4) no patients with a synchronous R3 primary tumor or a previous malignancy in the head and neck region. Tumors were selected on R4 the availability of representative formaldehyde-fixed, paraffin-embedded (FFPE) tissue blocks and R5 frozen tissue samples of the primary tumor. Twenty-one patients were excluded due to their R6 primary tumor originating from the orofarynx. HPV-16 positivity was determined in the remaining R7 OSCC patients which resulted in no further exclusions. In total, eighty-three tissue samples met R8 the inclusion criteria. All involved investigators, apart from the study statistician, were blinded R9 to patient outcome throughout all analyses. The workflow of this study is illustrated in figure 1. R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 Figure 1 | Schematic representation of the work flow of this study. Previous studies resulted in the discovery and validation of a multi-gene signature.19-21 In this study, gene R32 expression data20 were used to correlate the selected genes KLK5, KLK7 and their cognate inhibitor SPINK5 R33 with clinical and histopathological parameters. From the same cohort of tumour samples, a TMA was constructed for immunohistochemical analysis of the selected genes to correlate their protein expression with R34 clinical and histopathological parameters, and outcomes were compared. R35 R36 Representative hematoxylin and eosin (H&E) slides of 83 OSCC were selected and reviewed by R37 two experienced head and neck pathologists (PJvD and SMW), as presented in table 1b. A tissue R38 microarray (TMA) was then made of the FFPE tissue blocks. Of each block, two central and two R39
126 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
peripheral tissue cylinders with a diameter of 0.6 millimetres were punched out, avoiding areas R1 of necrosis, and arrayed in a recipient paraffin block. Normal epithelium from the floor of mouth, R2 gingiva and tonsil tissue was incorporated in each block to ensure similarity of staining between R3 the different blocks, and to compare tumor staining with staining of normal epithelium. R4 R5 KLK5, KLK7 and SPINK5 gene expression analysis R6 Genome-wide gene expression was measured using dual-channel microarrays with a pool R7 of tumor samples as a reference as described earlier.20 KLK5, KLK7 and SPINK5 were each R8 represented by a single, unique feature on this array. The expression data was normalized for dye R9 and print-tip biases using loess per print-tip normalization and subsequently variance stabilization R10 was applied.20 The data and protocols used are publicy available (ArrayExpress E-UMCU-11). R11 R12 Detection of Human papillomavirus type 16 R13 Human papillomavirus type 16 (HPV-16) active tumors were determined by a validated algorithm R14 for detection of HPV-16 in paraffin embedded head and neck cancer specimen:23,24 all tumors R15 were stained for p16 by IHC followed by GP 5+/6+ PCR when p16 staining was positive. R16 R17 Immunohistochemistry R18 TMAs were stained for KLK5, KLK7 and SPINK5 according to the manufacturer’s protocol and R19 as previously described.25 In brief, 4 micrometer thick paraffin sections were deparaffinised. R20 After blocking of endogenous peroxidase and antigen retrieval by pepsin the TMA slides were R21 incubated for 60 minutes with different dilutions of the primary antibodies: KLK5 (AF1108, R&D R22 Systems, Oxon, England; 1:100), KLK7 (AF2624, R&D Systems, Oxon, England; 1:100), and SPINK5 R23 (sc32330, clone nr 1C11G6, Santa Cruz Biotechnology, Santa Cruz, USA; 1:100). Finally slides R24 were counterstained with haematoxylin for 5 minutes, followed by dehydration and mounting. R25 R26 Evaluation of expression was performed independently by 2 observers (PJvD and FKJL), who were R27 both blinded to patient characteristics. In case of disagreement, the observers reanalyzed the R28 staining results until they reached consensus. Cores were considered lost if less than 10% of the R29 core contained tumor (‘sampling error’) or when less than 10% of tissue was present (‘absent R30 core’). Patients were excluded if more than 2 cores per case were lost. When two or more cores R31 were available from one patient, the mean score was calculated for that patient. R32 R33 To determine the score for each TMA-core, appropriate controls of normal squamous epithelium R34 were used. Protein expression was scored for both its intensity in tumor cells relative to normal R35 epithelium (normal = 2, weaker = 1, total loss = 0) and the percentage of tumor cells in the tissue R36 section with such a specific intensity. The product of these two scoring variables resulted in a R37 scoring range of 0-200, in which a score of 0 represents a complete loss of protein expression in R38 R39
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R1 all tumor cells and a score of 200 represents normal expression throughout the tumor, see figure R2 2. Co-expression of both KLKs was defined as absent (0) if the sample showed complete loss of R3 expression for both KLK5 and KLK7, otherwise as non-absent (1). R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 Figure 2 | KLK5, KLK7 and SPINK5 expression in OSCC and normal mucosa. R25 Representative stainings of the TMA, consisting of 83 OSCC cases, are presented. Staining scores were R26 calculated by the product of intensity (normal = 1, strong = 2) and the proportion of stained tumour cells (%). SPINK5 staining: normal mucosa ( a ), no staining ( b ), OSCC score: normal intensity (1) × proportion (100%) R27 = 100 ( c ), OSCC score: 2 × 75% = 150 ( d ). KLK5 staining: normal mucosa ( e ), no staining ( f ), OSCC score: 1 × 75% = 75 ( g ), OSCC score: 2 × 50% = 100 ( h ). KLK7 staining: normal mucosa ( i ), no staining R28 ( j ), OSCC score: 1 × 50% = 50 ( k ), OSCC score: 2 × 40% = 80 ( l ). R29 R30 Statistical analysis R31 The Mann-Whitney test was used to determine differences in expression between several R32 clinicopathological parameters. OS was defined as the length of the time interval from surgery to R33 death from any cause. DSS was defined as the time interval from surgery to death due to disease. R34 For univariate survival analysis we dichotomized KLK5, KLK7 and SPINK5 expression in absent R35 (0) and non-absent (1-200). Kaplan-Meier curves were plotted to visualize difference in survival R36 between these two groups. Log rank test was used to obtain statistical significance. Prognostic R37 value was examined by univariate and multivariate analyses using the Cox proportional hazards R38 regression model. All p-values were based on two-tailed statistical analysis and p < 0.05 was R39
128 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
considered to be significant. Statistical analysis was performed using the SPSS 20.0 statistical R1 package (SPSS Inc, Chicago, IL). R2 R3 R4 RESULTS R5 R6 Baseline characteristics R7 Clinical data and survival was retrieved from the medical records, with follow-up times in the R8 range of 0 to 15 years, median 45 months, see table 1a and suppl. figure 1a. Kaplan-Meier plots R9 on survival in patient groups stratified by gender, nodal status (pN) and tumor stage are shown R10 in suppl. figures 1b-d. As expected, tumor stage and nodal status represented the clinical factors R11 that exhibited the strongest association with overall survival. R12 R13 Detection of Human papillomavirus type 16 R14 Human papillomavirus type 16 (HPV-16) active tumors were determined likewise a recently R15 validated algorithm.23,24 Of all 83 included OSCC in the study only 3 tumors showed strong R16 expression for p16. However, in none of these 3 OSCCs HPV-16 positivity was detected by GP R17 5+/6+ PCR. R18 R19 Gene expression and clinicopathological parameters R20 Analysis of 83 OSCC shows a statistically significant differential gene expression between lymph R21 node positive and lymph node negative patients for KLK7 (p=0.020), not for KLK5 and SPINK5. R22 OSCC lymph node positive patients have significantly less KLK7 mRNA, see suppl. figure 2. R23 R24 Analysis of the other parameters in tables 1a and b showed significant less KLK5, KLK7 and R25 SPINK5 mRNA in alcohol consuming patients, and less KLK5 and KLK7 mRNA in tobacco using R26 patients and in tumors showing absence of keratinization, see table 2. R27 R28 Gene expression and survival R29 To illustrate the prognostic impact of the gene expression of KLK5, KLK7 and SPINK5 we R30 performed a Kaplan-Meier analysis with samples dichotomized into two groups with expression R31 levels < median and levels > median for each respective gene. Supplementary figure 3a shows the R32 results for the 3 genes in the complete OSCC cohort. Furthermore, we performed univariate Cox R33 regression analysis of the three genes, see suppl. table 1. None of the genes had independent R34 prognostic association with survival. R35 R36 R37 R38 R39
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Table 2 | Correlations between gene (mRNA) and protein (IHC) expression of KLK5, KLK7 and SPINK5 R1 and clinical and pathological parameters of the included OSCC cohort (n=83). R2 Cases were stratified according to clinical and pathological characteristics. Smoking history was dichotomized to current smoker or ceased < 1 year versus ex-smoker (ceased > 1 year) and never smoker. Alcohol R3 consumption was dichotomized to 1-4 or > 5 U/day versus occasionally or never. Clinical and pathological nodal status (cN and pN) were dichotomized to cN0 versus cN+ and to pN0 versus pN+. Differentiation was R4 dichotomized to well and moderate versus poor and undifferentiated. Keratinization was dichotomized to R5 present versus absent. P-values represent the Mann-Whitney U test of these comparisons. R6 p-value R7 KLK5 KLK7 SPINK5 Clinical characteristic mRNA IHC mRNA IHC mRNA IHC R8 smoking history 0.002 0.009 0.004 0.011 0.052* 0.067* R9 alcohol consumption 0.013 0.001 0.003 0.012 0.045 NS R10 cN status 0.003 NS 0.007 NS NS NS R11 Pathological characteristic pN status NS NS 0.020 NS NS NS R12 differentiation NS 0.021 NS 0.013 NS 0.003 R13 keratinization 0.040 0.004 0.020 0.001 0.087* 0.028 R14 Abbreviations: NS is non significant, mRNA is messenger RNA, IHC is immunohistochemistry. P-values < 0.05 R15 were considered statistically significant. * P-values >0.05 and <0.10 were considered a clinically relevant trend. R16 R17 Immunohistochemistry: descriptive analysis R18 A total of 226 (68%) tumor cores stained with KLK5 antibody, 224 tumor cores (67%) stained R19 with KLK7and 221 tumor cores (67%) stained with SPINK5, were available for analysis. Due to R20 our inclusion criteria (> than 2 tumor cores available per case) 14 cases were missing for KLK5, 15 R21 for KLK7 and 17 for SPINK5. The majority of the OSCCs in this cohort showed (almost) total loss R22 of expression for KLK5 (71%), for KLK7 (59%) and for SPINK5 (59%), see suppl. table 2. R23 R24 Immunohistochemistry and clinicopathological parameters R25 Lower protein expression levels of KLK5 and KLK7 in the TMA cohort were significantly associated R26 with a positive smoking history and alcohol consumption. Furthermore, there was a comparable R27 trend for SPINK5 protein expression and a positive smoking history. Current smokers or patients R28 that ceased smoking < 1 year showed significantly less expression of KLK5, KLK7 versus patients R29 that ceased > 1 year or never smokers (p=0.009;p=0.011). Likewise, patients that consumed R30 1-4 or > 5 alcohol U/day showed significantly more loss of KLK5 and KLK7 protein expression R31 compared to patients that consumed alcohol occasionally or never (p=0.001; p=0.012). R32 R33 Correlating protein expression with the pathological characteristics (table 1b), statistical analysis R34 showed significant more loss of KLK5, KLK7 and SPINK5 expression in patients with OSCC that R35 were poorly or undifferentiated (p=0.021; p=0.013; p=0.003) and in patients with OSCC that R36 were classified as keratinization absent (p=0.004; p=0.001; p=0.028), see table 2. R37 R38 R39
130 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
Immunohistochemistry and survival R1 A Cox regression model was applied to identify any prognostic relevance for protein expression R2 of KLK5, KLK7 and SPINK5. Furthermore we added co-expression of KLK5 and KLK7 to this R3 model. All protein expressions were dichotomized into no expression (score 0) versus expression R4 (score 1). Co-expression of KLK5 and KLK7 scored only 0 if both kallikreins scored 0. Univariate R5 analysis indicated a significant prognostic impact in overall survival in OSCC of KLK5 protein R6 expression (HR=0.41, CI 0.20-0.85, p=0.017) and of KLK7 protein expression (HR=0.43, CI 0.22- R7 0.86, p=0.016), see table 3a. SPINK5 protein expression did not show any prognostic impact on R8 overall survival. Kaplan-Meier survival plots are shown in suppl. figure 3b. R9 R10 Table 3a | Univariate and multivariate OS Cox regression model for protein expression of KLK5, R11 KLK7, co-expression of KLK5 and KLK7. Co-expression was dichotomized into no expression (score 0) versus expression (score 1). R12 univariate R13 HR 95% CI p-value R14 Age* 1.75 0.94-3.27 0.078 R15 Stage* 2.37 1.10-5.11 0.028 pN status* 2.19 1.17-4.09 0.014 R16 KLK5 expression 0.41 0.20-0.85 0.017 R17 KLK7 expression 0.43 0.22-0.86 0.016 R18 co-expression KLK5 KLK7 0.36 0.18-0.74 0.005 R19 multivariate co-expression KLK5 KLK7 0.32 0.16-0.66 0.002 R20 corrected for age R21 OS = overall survival, HR = hazard ratio, 95% CI = 95% confidence interval, p-value of the Cox regression R22 model R23 * age: <60 vs >60 years, tumor stage I, II vs III, IV, pN status pN0 vs pN+ R24
Table 3b | Univariate and multivariate DSS Cox regression model R25 Protein expression of KLK5, KLK7, co-expression of KLK5 and KLK7 was dichotomized into no expression R26 (score 0) versus expression (score 1). R27 univariate HR 95% CI p-value R28 Age* 1.01 0.48-2.12 0.978 R29 Stage* 4.01 1.21-13.29 0.023 R30 pN status* 4.10 1.66-10.15 0.002 R31 KLK5 expression 0.48 0.20-1.13 0.094 R32 KLK7 expression 0.42 0.18-0.96 0.040 co-expression KLK5 KLK7 0.34 0.15-0.78 0.011 R33 multivariate R34 co-expression KLK5 KLK7 0.36 0.15-0.87 0.022 R35 corrected for pN status R36 DSS = disease specific survival, HR = hazard ratio, 95% CI = 95% confidence interval, p-value of the Cox regression model R37 * age: <60 vs >60 years, tumor stage I, II vs III, IV, pN status pN0 vs pN+ R38 R39
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R1 The prognostic impact on overall survival of co-expression of KLK5 and KLK7 was even stronger R2 than the impact of KLK5 or KLK7 alone (HR=0.36, CI 0.18-0.74, p=0.005), reason to apply a R3 multivariate Cox regression model with co-expression of KLK5 and KLK7 together with known R4 prognostic markers like age, stage and pN status. Independent of stage and pN-status but R5 corrected for age co-expression showed significant impact on overall survival (HR=0.32, CI 0.16- R6 0.66, p=0.002), see table 3a, figure3b. In this TMA cohort co-expression of KLK5 and KLK7 R7 showed 2- and 5- year survival rates of 47% and 24% for patients with complete loss of co- R8 expression (score 0) and 80% and 58% for patients with co-expression (score 1), respectively R9 figure3a. R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 Figure 3 | Survival plots of all OSCC patients (n = 83). Patients were stratified according to the co-expression level of KLK5 and KLK7: 0 or no protein expression R37 of both KLKs (red) versus 1–400, the sum of both KLK protein expression (blue). OS plotted by Kaplan-Meier R38 analysis ( a ) and Cox regression analysis ( b ). DSS plotted by Kaplan-Meier analysis ( c ) and Cox regression analysis ( d ). a, c The log-rank test of this group comparison is represented, and the p values therefore differ R39 from the significance levels of the Cox regression analysis (b, d).
132 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
With regard to disease specific survival, patients with complete loss of co-expression had a R1 shorter disease specific survival than patients with co-expression (table 3b, figure 3c-d); 2-year R2 survival rate: 53% vs. 82%, 5-year survival rate: 36% vs. 74%, HR=0.34, CI 0.15-0.78, p=0.011). R3 Multivariate analysis showed independent significant impact of co-expression on disease specific R4 survival corrected for pN status (table 3b; HR=0.36, CI 0.15-0.87, p=0.022). Follow up time of R5 patients without an event was 80.3 months (median 71.4). R6 R7 R8 DISCUSSION R9 R10 The aim of the present study was to evaluate the role of the proteases KLK5 and KLK7 as biomarkers R11 of lymph node metastases and prognosis in OSCC. We show that only underexpression of KLK7 R12 mRNA correlates with lymph node metastasis in OSCC patients which is in line with the gene R13 expression signature predicting lymph node metastasis in OSCC21 containing a.o. KLK5, KLK7 and R14 SPINK5. However, KLK5 and SPINK5 do not correlate with LNM in this cohort. R15 R16 The majority of the OSCCs had total to almost total loss of expression of KLK5 (71%), KLK7 R17 (59%) and SPINK5 (59%). Our observation concerning SPINK5 expression is consistent with 2 R18 previous studies that demonstrated down-regulation of SPINK5 mRNA expression in HNSCC. In R19 the first study a very small panel of 3 oropharynx tumors was investigated,17 whereas the second R20 study obtained genome-wide transcriptomic profiles of 53 primary oral tongue SCC.18 The second R21 study however, focused on identifying specific associated genes with oral tongue SCC and did R22 not use and provide complete clinical and pathological data of the patients and samples tested. R23 R24 Next, our observation concerning (almost) total loss of KLK5 and 7 expression seems, at first sight, R25 to be in controversy with a previous study that reported abundant protein expression of KLK5, R26 7, 8 and 10 in 50 OSCCs.13 However, analysis of sub-site and differentiation grade reveals that R27 58% of the included samples by Pettus originate from the oral tongue (vs. 39% in our cohort, R28 table 1a) and that 61% is well, 30% moderately and 9% poorly differentiated (vs. 4%, 77% and R29 17%, table 1b). Floor of mouth tumors showed lower expression than tongue tumors (data not R30 shown). Furthermore, poorly and undifferentiated tumors had lower expression of both KLK5 R31 and KLK7. These clinical and pathological differences of included samples of both studies point R32 out the influence of the composition of the studied population on the result. From this point R33 of view the conclusion as stated by others13 that mostly well differentiated OSCCs originating R34 from the tongue have abundant expression of KLK5 and KLK7 is actually in agreement with our R35 observation. R36 R37 R38 R39
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R1 In an attempt to investigate the potential functional role of KLK5, KLK7 and SPINK5 in OSCC, we R2 analyzed the expression of all three together in OSCC. KLK5 and 7 play important roles in skin R3 desquamation where shedding of corneocytes happens due to proteolytic processing of junctional R4 structures like corneodesmosin.5,26 SPINK5 regulates this desquamation by inhibiting KLK5 and 7 R5 and loss of SPINK5, in Netherton syndrome for instance, results in unregulated proteolysis in the R6 outer layers of the epidermis, causing defective barrier function.26 Normal oral squamous cells R7 have high expression of KLK5, KLK7 and SPINK5 (figure 2d, 2h and 2l) suggesting a similar role R8 for these proteases and their inhibitor in the normal oral mucosa. Our data in table 2 show that R9 OSCCs that are able to produce keratin or keratin whirls (i.e. figure 2f; high KLK7 expression) R10 have a significant higher expression of KLK5, KLK7 and SPINK5. It is interesting to speculate that R11 the role of KLK5 and 7 and the balance with their inhibitor SPINK5 might be related and limited R12 just to the process of keratinization. At least, we did not observe any uncontrolled KLK activity in R13 OSCCs with low SPINK5 expression, nor could we correlate this to tumor progression phenomena R14 like lymph node metastasis. R15 R16 Concurrent loss of KLK5 and KLK7 predicted overall survival in univariate and multivariate age R17 and lymph node status corrected survival analysis. Correction of the OS model by age resulted in R18 a decrease of the HR to 0.29 which means that older patients with a positive co-expression have R19 a better survival than younger patients with complete loss of expression of both KLK5 and KLK7. R20 Correction of the DSS model by pN status resulted in an increase of the HR to 0.39 which means R21 that patients with a positive co-expression survive better than patients without, however, this R22 difference in survival decreased when the patient has a positive pathologic nodal status. R23 R24 To date, no single molecular marker has been able to predict occult lymph node metastasis of R25 OSCC patients reliably. Recently, a gene expression signature predicting lymph node metastasis R26 has been validated21 and combined with a sentinel lymph node biopsy procedure4 it could even R27 be more helpful in decision making with regard to the clinically node negative neck. Whether R28 loss of SPINK5 tips the balance to more protease activity by KLK5 and 7 and whether such an R29 imbalance may identify patients with aggressive tumor behavior cannot be confirmed by our data R30 and remains to be elucidated. However, loss of co-expression of KLK5 and 7 does identify OSCC R31 patients with poor clinical outcome and may therefore act as a prognostic marker. R32 R33 R34 ACKNOWLEDGMENTS R35 R36 SMW is funded by the Dutch Cancer Society (clinical fellowship: 2011-4964). R37 R38 R39
134 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
REFERENCES R1 R2 1. Jemal A, Bray F, Center MM, Ferlay J, et al. Global cancer statistics. CA Cancer J Clin 2011; 61: 69-90. 2. Ang KK, Harris J, Wheeler R, et al.Human papillomavirus and survival of patients with oropharyngeal R3 cancer. N Engl J Med 2010; 363: 24-35. R4 3. Leemans CR, Braakhuis BJ, Brakenhoff RH. The molecular biology of head and neck cancer. Nat Rev R5 Cancer 2011; 11: 9-22. 4. Leusink FK, van Es RJ, de Bree R, et al. Novel diagnostic modalities for assessment of the clinically node- R6 negative neck in oral squamous-cell carcinoma. Lancet Oncol 2012; 13: e554-561. R7 5. Borgono CA, Diamandis EP. The emerging roles of human tissue kallikreins in cancer. Nat Rev Cancer 2004; 4: 876-890. R8 6. Emami N, Diamandis EP. Utility of kallikrein-related peptidases (klks) as cancer biomarkers. Clin Chem R9 2008; 54: 1600-1607. R10 7. Lilja H, Ulmert D, Vickers AJ. Prostate-specific antigen and prostate cancer: Prediction, detection and monitoring. Nat Rev Cancer 2008; 8: 268-278. R11 8. Sotiropoulou G, Pampalakis G, Diamandis EP. Functional roles of human kallikrein-related peptidases. J R12 Biol Chem 2009; 284: 32989-32994. R13 9. Dong Y, Kaushal A, Brattsand M, et al. Differential splicing of klk5 and klk7 in epithelial ovarian cancer produces novel variants with potential as cancer biomarkers. Clin Cancer Res: 2003; 9: 1710-1720. R14 10. Yousef GM, Polymeris ME, Yacoub GM, et al. Parallel overexpression of seven kallikrein genes in ovarian R15 cancer. Cancer Res 2003; 63: 2223-2227. R16 11. Li X, Liu J, Wang Y, et al. Parallel underexpression of kallikrein 5 and kallikrein 7 mrna in breast malignancies. Cancer Sci 2009; 100: 601-607. R17 12. Talieri M, Devetzi M, Scorilas A, et al. Evaluation of kallikrein-related peptidase 5 expression and its R18 significance for breast cancer patients: Association with kallikrein-related peptidase 7 expression. Anticancer Res 2011; 31: 3093-3100. R19 13. Pettus JR, Johnson JJ, Shi Z, et al. Multiple kallikrein (klk 5, 7, 8, and 10) expression in squamous cell R20 carcinoma of the oral cavity. Histol Histopathol 2009; 24: 197-207. R21 14. Deraison C, Bonnart C, Lopez F, et al. Lekti fragments specifically inhibit klk5, klk7, and klk14 and control desquamation through a ph-dependent interaction. Mol Biol Cell 2007; 18: 3607-3619. R22 15. Egelrud T, Brattsand M, Kreutzmann P, et al. Hk5 and hk7, two serine proteinases abundant in human R23 skin, are inhibited by lekti domain 6. Br J Dermatol 2005; 153: 1200-1203. 16. Schechter NM, Choi EJ, Wang ZM, et al. Inhibition of human kallikreins 5 and 7 by the serine protease R24 inhibitor lympho-epithelial kazal-type inhibitor (lekti). Biol Chem 2005; 386: 1173-1184. R25 17. Gonzalez HE, Gujrati M, Frederick M, et al. Identification of 9 genes differentially expressed in head and R26 neck squamous cell carcinoma. Arch Otolaryngol Head Neck Surg 2003; 129: 754-759. 18. Ye H, Yu T, Temam S, et al. Transcriptomic dissection of tongue squamous cell carcinoma. BMC R27 genomics 2008; 9: 69. R28 19. Roepman P, Kemmeren P, Wessels LF, et al. Multiple robust signatures for detecting lymph node R29 metastasis in head and neck cancer. Cancer Res 2006; 66: 2361-2366. 20. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases R30 from primary head and neck squamous cell carcinomas. Nat Gen 2005; 37: 182-186. R31 21. van Hooff SR, Leusink FK, Roepman P, et al. Validation of a gene expression signature for assessment of lymph node metastasis in oral squamous cell carcinoma. J Clin Oncol 2012; 30: 4104-4110. R32 22. van Diest PJ. No consent should be needed for using leftover body material for scientific purposes. BMJ R33 2002; 325: 648-651. R34 23. Rietbergen MM, Leemans CR, Bloemena E, et al. Increasing prevalence rates of hpv attributable oropharyngeal squamous cell carcinomas in the netherlands as assessed by a validated test algorithm. R35 Int J Cancer 2013; 132: 1565-1571. R36 24. Smeets SJ, Hesselink AT, Speel EJ, et al. A novel algorithm for reliable detection of human papillomavirus in paraffin embedded head and neck cancer specimen. Int J Cancer 2007; 121: 2465-2472. R37 R38 R39
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25. Noorlag R, van der Groep P, Leusink FK, et al. Nodal metastasis and survival in oral cancer: Association R1 with protein expression of slpi, not with lcn2, tacstd2, or thbs2. Head Neck 2015; 37: 1130-1136. R2 26. Descargues P, Deraison C, Bonnart C, et al. Spink5-deficient mice mimic netherton syndrome through R3 degradation of desmoglein 1 by epidermal protease hyperactivity. Nat Gen 2005; 37: 56-65. R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
136 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
SUPPLEMENTARY INFORMATION R1 R2 Supplementary Figure 1a | R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 Survival plot of all OSCC patients (n=83) included in this study. Each mark (+) represents a censored event. R19 The first censored event occurs after 5 years of follow-up. R20 Supplementary Figure 1b | gender R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 Kaplan-Meier survival plots of all OSCC patients (n=83) included in the expression analysis in this study. Patients were stratified in accordance to gender, nodal status (pN) and stage (Fig 1b-d). Group comparison R38 was performed using log-rank tests. For supplementary figure 1c and 1d see next page. R39
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Supplementary Figure 1c | Nodal status R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 Supplementary Figure 1d | Stage I/II vs. III/IV R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
138 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
Supplementary Figure 2 | Boxplot showing differential expression for nodal status. R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 Cases were stratified according nodal status; pN0 (blue) versus pN+ (red). P-values represent the Logistic R18 regression test of this group comparison and therefore differ from the significance levels of the Mann- R19 Whitney analysis in table 2. R20
Supplementary Figure 3a | KLK5 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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Supplementary Figure 3a | KLK7 R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 Supplementary Figure 3a | SPINK5 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 Kaplan-Meier survival plots of all OSCC patients (n=83) included in the expression analysis in this study. R34 Cases were stratified according KLK5, KLK7 and SPINK5 mRNA expression levels < median (red) versus > median (blue). P-values represent the Log-rank test of this group comparison and therefore differ from the R35 significance levels of the Cox-regression analysis, see suppl. table 1. R36 R37 R38 R39
140 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
Supplementary Figure 3b | KLK5 R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 Supplementary Figure 3b | KLK7 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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Supplementary Figure 3b | SPINK5 R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 Kaplan-Meier survival plots including all OSCC patients of the TMA cohort (n=83). Cases were stratified according to protein expression of KLK5, KLK7 and SPINK5 dichotomized to the product score (intensity x R18 proportion) 0 versus 1-200. P-values represent the Log-rank test of this group comparison and therefore differ R19 from the significance levels of the Cox-regression model, see table 3a. R20 R21 Supplementary Table S1 | Univariate Cox regression survival analysis for mRNA expression of KLK5, KLK7 and SPINK5 in the OSCC cohort (n=83). R22 univariate Cox regression analysis R23 gene HR 95% CI p-value R24 KLK5 1.41 0.27-7.36 0.687 R25 KLK7 0.55 0.27-1.14 0.106 SPINK5 1.33 0.54-3.31 0.538 R26 R27 HR = hazard ratio, p-value of the Cox regression analysis, 95% CI = 95% confidence interval R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
142 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- HPV oral squamous-cell carcinoma
Supplementary Table S2 | R1 Variable KLK5 KLK7 SPINK5 co-expression KLK5 & KLK7 R2 Number (%) of 332 cores R3 Tumor 226 (68) 224 (67) 221 (67) R4 No tumor 49 (15) 54 (16) 55 (17) R5 No core 57 (17) 54 (16) 56 (17) R6 Number of cores / case (n=83) 4 27 26 28 R7 3 30 27 23 R8 2 12 15 15 R9 1 7 9 10 R10 0 7 6 7 Score (%) / case (n=83) R11 0 (total loss) 37 (45) 19 (23) 21 (25) 17 (20) R12 1-50 (almost total loss) 22 (26) 30 (36) 28 (34) R13 51-200 (partial loss) 10 (12) 19 (23) 17 (20) R14 missing 14 (17) 15 (18) 17 (20) 16 (19) >1 50 (60) R15 R16 Immunohistochemical descriptive results of protein expression of KLK5, KLK7 and SPINK5 in the OSCC TMA cohort (n=83). R17 Percentages may not total 100 because of rounding. R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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CHAPTER 8
Cathepsin K associates with lymph node metastasis and
poor prognosis in oral squamous-cell carcinoma
F.K.J. Leusink E. Koudounarakis* M.H. Frank* R. Koole P.J. van Diest S.M. Willems
* Equal contribution
Submitted to the BMC Cancer Chapter 8
R1 ABSTRACT R2 R3 BACKGROUND R4 Lymph node metastasis is a major determinant of prognosis and treatment planning of oral R5 squamous-cell carcinoma (OSCC). Cysteine cathepsins constitute a family of proteolytic R6 enzymes with known role in the degradation of the extracellular matrix. Involvement in R7 pathological processes, such as inflammation and cancer progression, has been proved. R8 The aim of the study was to discover the clinicopathological and prognostic implications of R9 cathepsin K (CTSK) expression in oral squamous cell carcinoma. R10 R11 METHODS R12 Eighty-three patients with primary OSCC, treated surgically between 1996 and 2000, were R13 included. Gene expression data were acquired from a previously reported study. Human R14 papilloma virus (HPV) status was previously determined by an algorithm for HPV-16. R15 Protein expression for CTSK was semi-quantitatively determined by immunohistochemistry. R16 Expression data were correlated with various clinicopathological variables. R17 R18 RESULTS R19 Elevated gene and protein expression of CTSK were strongly associated to lymph node R20 metastasis and perineural invasion (p<0.01). Logistic regression analysis highlighted increased R21 CTSK protein expression as the most significant independent factor of lymphatic metastasis R22 (OR=7.65, CI:2.31-23.31, p=0.001). Survival analysis demonstrated both gene and protein R23 CTSK expression as significant indicators of poor 5-year disease specific survival (HR=2.29, R24 CI:1.01-5.21, p=0.047 for gene expression; HR=2.79, CI:1.02-7.64, p=0.045 for protein R25 expression). R26 R27 CONCLUSION R28 Upregulation of CTSK seems to be associated with high incidence of lymphatic spread and R29 poor survival in OSCC. CTSK could therefore serve as a predictive biomarker for OSCC. R30 R31 KEYWORDS R32 Oral squamous-cell carcinoma; cathepsin K; lymph node metastasis; prognosis R33 R34 R35 R36 R37 R38 R39
146 Cathepsin K associates with lymph node metastasis and poor prognosis in oral squamous-cell carcinoma
BACKGROUND R1 R2 Oral squamous-cell carcinoma (OSCC) constitutes the most common malignancy of the head and R3 neck region.1 Lymph node metastasis has been shown to be the most significant, independent R4 prognostic factor and is related to a decrease of the 5-year survival rate by 50%.2 Thus, revealing R5 the presence of occult metastasis is of the utmost importance for early and proper management R6 of the neck. Variable imaging studies have been used for this purpose, including ultrasound R7 combined with fine needle aspiration cytology, computed tomography, magnetic resonance R8 imaging and, more recently, positron emission tomography, with variable results.3 Moreover, the R9 sentinel node procedure has been currently adopted by some oncological centers and embodied R10 in the staging algorithm of early OSCC. However, its greater disadvantage is that the patient R11 undergoes an interventional procedure. In the context of molecular biology, a significant amount R12 of research has been focused during the last decades on biomarkers that may have additional R13 diagnostic value.4,5 R14 R15 Cathepsin K (also known as cathepsin O2), encoded by the CTSK gene on chromosome 1q21, R16 is one of the 11 lysosomal protein degradation enzymes called cysteine cathepsins, which R17 participate in a considerable number of physiological processes, including MHC-II-mediated R18 antigen presentation, bone remodeling, keratinocyte differentiation and prohormones activation.6 R19 It is the most potent mammalian collagenase and is highly expressed in osteoclasts and in most R20 epithelial cells. Cathepsin K is the sole matrix-degrading enzyme for which a fundamental role in R21 bone resorption has been unequivocally documented in mice and humans.6 However, increased R22 expression of this lysosomal enzyme is also observed in various pathological conditions, such R23 as neurological disorders, inflammatory diseases and cancer. Its role in cancer progression and R24 invasion, mainly through degradation of and remodeling in the tumor microenvironment, is R25 supported by several experimental studies and clinical reports in various types of tumors.7 In the R26 present study, the correlation between the expression of cathepsin K and clinicopathological R27 variables, particularly lymph node metastasis, was examined. R28 R29 R30 METHODS R31 R32 Patients and tissue samples R33 The study included 83 consecutive patients with OSCC who were diagnosed and surgically R34 treated at the University Medical Center in Utrecht, The Netherlands, between 1996 and 2000, R35 described in an earlier reported gene expression profiling study.4 The work-flow used in this study R36 is summarized in Figure 1. There were 47 men (57%) and 36 women (43%) with a median age R37 of 62 years (range 37–87 years). The primary tumor was located in the tongue in 30 patients, in R38 R39
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R1 the floor of the mouth in 35 patients, in the buccal mucosa in 10 patients and in the gingiva in 8 R2 cases. The primary tumor of 44 (53%) patients was pathologically staged as early (pT1-T2). Forty- R3 five patients (54%) had histologically confirmed lymph node metastasis at the time of diagnosis, R4 whereas the rest had no clinical or cytological/histological evidence of regional dissemination. The R5 median follow-up period was 45 months (range 0-15 years). Detailed patient characteristics are R6 shown in Table 1. Tissues were used in line with the code ‘Proper Secondary Use of Human Tissue’ R7 as installed by the Dutch Federation of Biomedical Scientific Societies. R8 R9 Table 1 | Clinical characteristics of the included OSCC patients R10 No. (%) All cases 83 (100) R11 Gender R12 Female 36 (43) R13 Male 47 (57) R14 Age at diagnosis R15 0-60 53 (44) ≥ 61 30 (56) R16 Median (range) 62 (37-87) R17 Smoking history R18 Current smoker or ceased < 1 year 58 (70) Ex-smoker, ceased > 1 year 9 (11) R19 Never smoker 15 (18) R20 Alcohol consumption R21 ≥ 5 U/day 19 (23) R22 1-4 U/day 28 (34) Occasionally 17 (20) R23 Never 19 (23) R24 Clinical T-stage R25 cT1 13 (16) cT2 31 (37) R26 cT3 8 (10) R27 cT4 31 (37) R28 Clinical N-Stage cN0 53 (64) R29 cN1-3 30 (36) R30 Sub-site R31 Tongue 30 (36) R32 Floor of mouth 35 (42) Buccal cavity 10 (12) R33 Gum 8 (10) R34 Mean follow-up (months) 45 R35 R36 R37 R38 R39
148 Cathepsin K associates with lymph node metastasis and poor prognosis in oral squamous-cell carcinoma
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 Figure 1 | Schematic presentation of the work flow of this study. Previous studies resulted in the discovery and validation of a multi-gene signature. In this study, gene R19 expression data were used to correlate the selected gene CTSK with clinical and histopathological parameters. R20 From the same cohort of tumor samples, a TMA was constructed for immunohistochemical analysis of the selected gene to correlate their protein expression with clinical and histopathological parameters, and R21 outcomes were compared. R22 R23 Representative hematoxylin and eosin (HE) slides of the 83 OSCC were selected and reviewed by R24 2 experienced head and neck pathologists (SMW, PJvD). Table 2 demonstrates the pathological R25 features of the study population. All tumors had been previously examined for HPV-16 positivity, R26 using immunohistochemistry for p16, as well as GP 5+/6+ polymerase chain reaction (PCR).8 All R27 oral carcinomas included in the current study were proved to be negative for HPV-16. A tissue R28 microarray (TMA) was constructed out of the formalin-fixed, paraffin-embedded (FFPE) tissue R29 blocks. Two central and two peripheral tissue cylinders with a diameter of 0.6 mm were punched R30 out of the blocks, avoiding areas of necrosis, and then arrayed in a recipient paraffin block, R31 using a manual tissue arrayer (MTA-I, Beecher Instruments Inc., Sun Prairie, WI, USA), which R32 was guided by the MTABooster® (Alphelys, Plaisir, France). The distribution and position of the R33 cores was determined in advance with the TMA-designer Software (Alphelys-TMA Designer®, R34 Version 1.6.8). Normal epithelium from the floor of the mouth, the gingiva and tonsil tissue was R35 incorporated in each block to ensure similarity of staining in the different blocks, and to compare R36 tumor staining with that of normal epithelium. R37 R38 R39
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Table 2 | Pathological characteristics of the OSCC patients. R1 No. (%) R2 All cases 83 (100) R3 Pathological T-Stage R4 pT1 17 (20) R5 pT2 27 (33) pT3 10 (12) R6 pT4 29 (35) R7 Pathological N-Stage R8 pN0 38 (46) pN1-3 45 (54) R9 Stage grouping R10 I 14 (17) R11 II 9 (11) R12 III 22 (26) A-IVB 38 (46) R13 Infiltration depth R14 ≥ 4.0mm 72 (87) R15 < 4.0mm 11 (13) R16 Differentiation grade Good / Moderate 67 (81) R17 Poor / Undifferentiated 16 (19) R18 Keratinization R19 Present 60 (72) Absent 20 (24) R20 Missing 3 (4) R21 Vaso-invasion R22 Present 18 (22) R23 Absent 62 (75) Missing 3 (3) R24 Bone-invasion R25 Present 25 (30) R26 Absent 58 (70) R27 Perineural growth R28 Present 34 (41) Absent 39 (47) R29 Missing 10 (12) R30 Spidery growth R31 Present 65 (78) R32 Absent 18 (22) R33 High risk HPV status positive 0 (0) R34 negative 83 (100) R35 R36 R37 R38 R39
150 Cathepsin K associates with lymph node metastasis and poor prognosis in oral squamous-cell carcinoma
CTSK gene expression analysis R1 Genome-wide gene expression was measured using dual-channel microarrays with a pool of R2 tumor samples, as described in an earlier study.4 CTSK was represented by a sole, unique feature R3 on this array. The expression data were normalized for dye and print-tip biases using print-tip R4 LOESS normalization, and variance stabilization was then applied. The data and protocol used are R5 publicly available (ArrayExpress E-UMCU-11). R6 R7 Immunohistochemistry R8 Five μm thick sections of FFPE tonsil control tissue and the TMA were cut and mounted on silane- R9 coated glass slides. After deparaffinization the endogenous peroxidase activity was blocked for R10 30 minutes in a 0.3% H2O2 phosphate-citrate buffer solution of pH5.8 with sodium azide. R11 Then, tissue sections were subjected to antigen retrieval by boiling in sodium citrate solution R12 (pH 6) for 15 minutes at 37˚C. Subsequently, the tissue slides were washed with PBS, 0.05% R13 (v/v) Tween-20 and incubated with a dilution of the primary antibody against CTSK (clone R14 CK4, Novocastra, Newcastle, UK) for 1 hour at RT. Slides were washed and incubated with the R15 following species-specific secondary antibodies: 1:250 diluted rabbit anti-mouse (RAMPO, Dako, R16 Glostrup, Denmark) followed by Powervision anti-rabbit/HRP conjugated (Klinipath, Duiven, The R17 Netherlands). All antibodies were diluted in PBS/1%BSA. After washing with PBS, the bound R18 antibodies were visualized using 3,3’-diaminobenzidine (0.6 mg/ml). Slides were counterstained R19 with hematoxylin. R20 R21 Evaluation of protein expression R22 Intensity and percentages of positive tumor cells were semi-quantitatively and independently R23 evaluated by 3 observers (SMW, PJvD and FKL) who were blinded to patient outcome. In case R24 of disagreement, the observers reanalyzed the staining results until they reached consensus. To R25 determine the score for each TMA-core, appropriate controls of normal squamous epithelium R26 were used. Protein expression was scored for both its intensity in tumor cells relative to normal R27 epithelium (strong expression = 2, normal expression = 1, no expression = 0) and the percentage R28 of tumor cells in the tissue section with such a specific intensity. Multiplying of these two R29 scoring variables resulted in a scoring range of 0 up to 200, in which a score of 0 represents a R30 complete loss or no expression of protein in all tumor cells and a score of 200 represents a high R31 expression throughout the tumor (Figure 2a-d). Cores were considered lost if less than 10% of R32 cells contained tumor (‘sampling error’) or when less than 10% of tissue was present (‘absent R33 core’). Cases were excluded if more than 2 cores were lost per case. When the scores between R34 the cores of a particular case differed, the most frequent score determined the overall score. In R35 case of 4 different scores in one case, the average score was calculated. R36 R37 R38 R39
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R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 Figure 2 | CTSK expression in OSCC and normal mucosa. R23 Representative stainings of the TMA, consisting of 83 OSCC cases, are presented. Staining scores were calculated by the product of intensity (normal = 1, strong = 2) and the proportion of stained tumor cells (%). R24 Panels A-D represent examples of CTSK staining; R25 a) normal mucosa, R26 b) OSCC with no staining, c) OSCC score = product of intensity normal (=1) x proportion (=50%) = 50, R27 d) OSCC score = 2 x 75% = 150. R28 R29 Statistical nalysis R30 The non-parametric Mann-Whitney U test was used to determine differences in CTSK expression R31 between various clinicopathologically defined groups. Logistic regression techniques were used R32 to assess correlations between CTSK expression and the incidence of neck lymph node metastasis. R33 Overall survival (OS) was defined as the length of the time from surgery to death from any cause. R34 Disease-specific survival (DSS) was defined as the time from surgery to death due to disease. R35 Receiver operating characteristic (ROC) curves were designed to determine optimal cut-off values. R36 The association between CTSK and the primary and secondary outcomes was analysed with R37 crosstabs, chi-square test (or Fisher’s Exact Test when appropriate), logistic regression, Kaplan R38 Meier/logrank survival analyses, and Cox-regression. R39
152 Cathepsin K associates with lymph node metastasis and poor prognosis in oral squamous-cell carcinoma
All p values were based on two-tailed statistical analysis and p < 0.05 was considered significant. R1 Statistical analysis was performed using the SPSS 25.0 statistical package (IBM Corp. IBM SPSS R2 Statistics for OSx, Version 25.0. Armonk, NY: IBM Corp.). R3 R4 R5 RESULTS R6 R7 Gene expression and clinicopathological variables R8 A statistically significant association of high CTSK mRNA levels to lymphatic metastasis (p<0.01) R9 was observed, as wells as to vaso- and perineural invasion (p< 0.01 in both cases; Table 3). In R10 contrast, no significant correlation was found to other pathological characteristics, such as pT R11 status, depth of invasion and tumor grade. Among the various clinical parameters, a strong R12 correlation of increased gene expression was found only to alcohol consumption (p<0.01). No R13 significant relationship was found to smoking history, age, tumor subsite and clinical T or N stage. R14 Cases were stratified according to clinical and pathological characteristics. Smoking history was R15 dichotomized to current smoker or ceased < 1 year versus ex-smoker (ceased > 1 year) and never R16 smoker. Alcohol consumption was dichotomized to 1-4 or > 5 U/day versus occasionally or never. R17 Clinical and pathological nodal status (cN and pN) were dichotomized to cN0 versus cN+ and R18 to pN0 versus pN+. Infiltration was dichotomized to <4mm versus ≥4mm. Differentiation was R19 dichotomized to well and moderate versus poor and undifferentiated. P-values represent the R20 Mann-Whitney U test of these comparisons. IHC: immunohistochemistry; NS: non-significant. R21 R22 Table 3 | Correlations between gene (mRNA) and protein (IHC) expression of CTSK and clinical and R23 pathological parameters of the included OSCC cohort (n=83). R24 CTSK Clinical characteristic mRNA IHC R25 Smoking history NS NS R26 Alcohol consumption p<0.01 NS R27 Age NS NS R28 cT status NS NS cN status NS NS R29 Subsite NS NS R30 Pathological characteristic R31 pN status p<0.01 p<0.01 R32 pT-status NS NS R33 Infiltration depth NS NS Differentiation grade NS NS R34 Vaso-invasion p<0.01 NS R35 Bone-invasion NS NS R36 Peri-neural invasion p<0.01 p<0.01 R37 Spidery growth NS NS R38 R39
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R1 CTSK gene expression and survival R2 A Cox regression analysis was performed in order to determine the prognostic significance of the R3 CTSK gene expression. Dichotomization was based on the cut-off value of -0.26, determined by R4 ROC analysis. Patients with high CTSK gene expression demonstrated a significantly worse 5-year R5 DSS (HR=2.29, CI:1.01-5.21, p=0.047; Table 4). The pathological N status was shown to have R6 the strongest impact for DSS (HR=4.10, CI:1.66–10.15, p=0.002). The prognostic significance of R7 CTSK gene expression did not hold for overall survival (OS) (p=0.2). The Kaplan-Meier survival plot R8 is shown in Figure 3a (p=0.040). R9 R10 Table 4 | Univariate and multivariate DSS Cox regression model for gene and protein CTSK expression. R11 Univariate R12 HR 95% CI p-value R13 Agea 1.01 0.48 - 2.12 0.978 R14 Tumor stageb 4.01 1.21 - 13.29 0.023 pNc 4.10 1.66 - 10.15 0.002 R15 CSTK protein expression 2.79 1.02 - 7.64 0.045 R16 CTSK gene expression 2.29 1.01 - 5.21 0.047 R17 Multivariate pN status 3.61 1.12 - 11.57 0.030 R18 corrected for CTSK R19 protein expression R20 Dichotomization was made according to the cut-off values into high and low expression. The most important R21 prognostic parameters (age, stage and pN) were added in the regression model. R22 a <60 vs. ≥60 years; b I, II vs. III, IV; c pN0 vs. pN+. R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34
R35 Figure 3 | Kaplan Meier disease specific survival (DSS) plots for all patients with OSCC (n=83). R36 Cases were stratified according to differential expression of CTSK, and were dichotomized into low and high R37 expression according to the determined cut-off point in panel A for gene expression (-0.26) and in panel B for protein expression (25). P-values in Figure 3a and 3b represent the Log-rank test of this group comparison R38 and therefore differ from the significance levels of the Cox-regression analysis in Table 4. In both analyses, R39 high CTSK expression was strongly associated with a worse 5-year DSS.
154 Cathepsin K associates with lymph node metastasis and poor prognosis in oral squamous-cell carcinoma
CTSK protein expression and clinicopathological variables R1 A total of 213 (64%) tumor cores stained with the CTSK antibody were available for analysis. Due R2 to our inclusion criteria (≥2 tumor cores available per case), 19 cases were missing. The majority R3 of the OSCCs in this cohort showed an almost total loss of expression for CTSK (71%), whereas R4 only 5% demonstrated total loss (Additional file 1: Table S1). R5 A cut-off value of 25 was determined by ROC analysis, in order to divide patients into low and R6 high protein expression groups. No statistically significant correlation to clinical variables was R7 found (Table 3). In contrast, there was a significant association of increased CTSK expression with R8 histopathologically proven lymph node metastasis (p<0.01). A similar strong relationship to peri- R9 neural invasion was also demonstrated (p=0.01). No association to other pathological variables R10 was evident. R11 R12 In logistic regression analysis, factors with known impact to nodal disease were incorporated R13 into the model, including T stage, perineural and vaso-invasion, depth of infiltration and spidery R14 growth pattern, along with CTSK protein expression (Table 5). In univariate analysis, high CTSK R15 expression appeared to be an important independent predictive factor of lymph node involvement R16 (OR=7.65, CI: 2.51-23.32; p<0.001). In multivariate analysis, CTSK protein expression, corrected R17 for pathological T stage, remained a strong prognostic factor for regional disease, demonstrating R18 an odds ratio of 9 (CI: 2.83-31.65; p<0.01). R19 R20 Table 5 | Univariate and multivariate logistic regression model for CTSK protein expression on R21 lymph node metastasis. R22 Univariate OR 95% CI p-value R23 pT* 1.49 1.01 – 2.20 0.044 R24 Peri-neural invasion * 5.20 1.87 – 14.45 0.002 R25 Vaso-invasion * 9.71 2.06 – 45.89 0.004 R26 Depth of invasion 1.51 0.85 – 2.69 0.165 Spidery growth 4.44 1.41 – 14.00 0.011 R27 CTSK protein expression 7.65 2.51-23.32 <0.001 R28 Multivariate R29 CTSK protein expression 9.46 2.83-31.65 <0.01 R30 corrected for pT-status R31 Dichotomization was made into low expression (score 0-25) versus high expression (score 26-200). The most R32 important predictive parameters (pT, peri-neural invasion, vaso-invasion, depth of invasion, spidery growth) were added in the model. OR= odds ratio, HR = hazard ratio, 95% CI = 95% confidence interval, p-value of R33 the Cox regression model. R34 * age: <60 vs >60 years, tumor stage I, II vs III, IV, pN status pN0 vs pN+ R35 R36 R37 R38 R39
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R1 Next, the predictive value of CTSK as a biomarker of occult metastasis in early stage (cT1-T2N0) R2 OSCC was examined. A total of 24 patients had early T stage without clinically detectable nodal R3 disease. Out of the ten patients with yet occult metastases in the neck dissection specimen, nine R4 had a high protein CTSK expression, whereas only one patient showed a value lower than the R5 cut-off (Table 6). The sensitivity of high protein expression in detecting occult metastasis in early R6 stage OSCC was, thus, calculated at 90%, whereas the specificity was 57%. Additionally, the R7 positive predictive value was found at 60%, with a negative predictive value of 89%. R8 R9 Table 6 | Allocation of cT1-T2N0 patients based on their pathological N-status and CTSK protein expression. R10 pN status Total R11 pN0 pN+ R12 ≤25 8 1 9 CTSK R13 >25 6 9 15 R14 Total 14 10 24 R15 The value of CTSK protein expression in predicting occult metastasis (≤25 predicts pN0, >25 predicts pN+) in R16 cT1-T2N0 patients is calculated as follows: sensitivity (9/9+1) x 100% = 90%; R17 specificity (8+6) x 100% =57%; R18 positive predictive value (9/6+9) x 100%= 60%; negative predictive value (8/8+1) x 100%= 89%. R19 R20 CTSK protein expression and survival R21 In Cox regression, CTSK protein expression was dichotomized into low versus high based on the R22 previously reported cut-off value, demonstrating a significantly worse DSS in OSCC subjects with R23 increased CTSK protein expression (HR = 2.79, CI 1.02–7.64, p = 0.045; Table 4). No prognostic R24 impact on overall survival was found. The Kaplan-Meier survival plot is shown in Figure 3b R25 (p=0.035). In multivariate analysis, pN status was corrected for CTSK protein expression, and R26 pathological N status showed once more showed a strong correlation (HR=3.61, CI:1.12-11.57, R27 p=0.03), with a change though of the beta coefficient greater than 10%, confirming the role of R28 CTSK as a significant confounder for DSS. R29 R30 R31 DISCUSSION R32 R33 Cancer metastasis is a complex process that includes a number of different events, referred R34 as the invasion-metastasis cascade. The first critical step of the process is the invasion of the R35 malignant cells into the surrounding extracellular matrix (ECM) and stromal cell layers.9 The R36 biological role of cathepsins in promoting tumor invasion and migration has been proved ex R37 vivo in cell-based systems.10,11 Apart from their well-known function of ECM degradation and R38 remodeling, cathepsins are also suggested to participate in the activation cascade of pro- R39
156 Cathepsin K associates with lymph node metastasis and poor prognosis in oral squamous-cell carcinoma
urokinase-type plasminogen activator and other proteases, enhancing thus their effect in the R1 dissolution of the tumor matrix and basic membrane.12 In addition to their extracellular function, R2 there is evidence that intracellular cathepsins promote tumor progression by affecting processes R3 acting both as pro-tumorigenic and anti-tumorigenic.13 Intracellular collagen degradation is an R4 example of the potential intracellular pro-tumorigenic activity of cathepsins. Recent research has R5 also proposed that cathepsins play a role as mediators in programmed cell death (apoptosis), a R6 process of utmost importance in cancer development and progression.13 Different hypotheses R7 have been supported in mediating cell death, such as the indirect activation of caspases through R8 degradation of the anti-apoptotic Bcl-2 family members, engaging a mitochondrial apoptotic R9 pathway, and the proteolytic activation of Bid.14 R10 R11 The pathophysiological role of the cathepsins in cancer metastasis has attracted the interest of R12 studying its value as a biomarker of metastasis and prognosis in various types of malignancy. R13 Increased protein expression of cathepsins V, B and D has been associated with distant metastasis R14 and worse DSS in breast cancer.15 Similar results have been found for cathepsin A in malignant R15 melanoma and cathepsin B in non-small cell lung carcinoma.16,17 Overexpression of CTSK has R16 been observed in invasive ductal carcinoma of the breast, lung and prostate adenocarcinoma.18-20 R17 In all these studies, increased protein expression was related to high metastatic potential. R18 Interestingly, high expression of CTSK was found in the desmoplastic reactive stroma of the lung R19 adenocarcinoma, indicating that stromal production of CTSK can favor or modulate the invasion R20 of tumor cells.21 Interestingly, de Koning et al reported downregulation of SERPINB13, the cognate R21 inhibitor of CTSK, in oral and oropharyngeal SCC to be associated with lymph node metastasis R22 and poor prognosis.22 However, only one study exists in the literature regarding the prognostic R23 value of CTSK in tongue SCC.23 In that study, Bitu et al. reported that CTSK was expressed in R24 both stromal and tumor cells by immunohistochemistry. The only significant finding was that R25 CTSK expression in stromal cells exhibited a potential protective role, since a poorer prognosis R26 in early stage tumors was correlated to weak CTSK expression in the tumor microenvironment R27 front. However, the same study found decreased invasion of HSC-3 tumor cells when cathepsin R28 K silencing was applied. It was, thus, concluded that different prognosis could be exhibited, R29 depending on whether CTSK is expressed more in tumor or stromal cells. R30 R31 The present study is the first conducted to explore the predictive and prognostic value of CTSK in R32 OSCC. Combined evaluation of both gene and protein expression was used to augment the validity R33 of clinicopathological correlations. Although some discrepancies were found in the associations R34 with the clinicopathological parameters between gene and protein expression, the results are R35 likewise. Some variation may be expected since mRNA levels are not directly proportional to the R36 protein concentration due to post-translational mechanisms, that control protein turnover and R37 abundance, and different translational rates, which are determined by constants that are not R38 R39
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R1 completely known.24 Another factor could also be that gene expression data were acquired using R2 biopsies taken at the border of the primary tumor and samples were included if they consisted R3 of at least 50% tumor cells. The other part of the sample consisted of stromal cells and epithelial R4 cells adjacent to the tumor. Consequently, gene expression was measured using tumor, stromal R5 and epithelial cells, whereas protein expression was scored by immunoreactivity in tumor cells R6 only. R7 R8 There are various explanations for the disagreement with the results reported by Bitu et al. The R9 different scoring system as well as the different antibody clone used to detect cathepsin K could R10 play a role. In the previous study, there is also insufficient information about the diagnostic R11 approach to the lymph node status and incomplete pathological data, such as infiltration depth R12 and perineural invasion, of the studied cohort. Lack of these data could underestimate the R13 clinicopathological correlations. Third, the findings of the previous study were solely based on R14 immunohistochemistry and were, partly, contradictory with the observation, reported by the R15 same authors, of the diminished invasion potential of the HSC-3 tumor cells, when cathepsin K R16 was silenced or inhibited. R17 R18 The results of the current study suggest that CTSK may be used as a predictive biomarker in R19 patients with OSCC. Its high sensitivity (90%) combined to the high negative predictive value R20 (89%) makes it particularly valuable in excluding occult metastasis in early T1-2N0 OSCC, allowing R21 to perhaps rely on a “wait and see” policy for the management of the neck. Moreover, it is R22 shown that tumors with up-regulation of CTSK harbored a high potential for perineural invasion. R23 This can be interpreted by the proteolytic action on the nerves’ epineurium and perineurium, R24 facilitating tumor cell migration into the nerve fasciculus. Hence, CTSK can be a molecular R25 determinant of perineural invasion, apart from the various neurotrophins and chemokines that R26 are involved in this process.25 The strong relationship of CTSK with both lymphatic spread and R27 perineural invasion is also reflected by its significant impact on DSS. R28 The current study was based on a relatively limited cohort of 83 patients with OSCC. The results R29 should be further validated by studies including higher number of patients with emphasis on R30 predicting occult metastasis in cases of N0 stage. Furthermore, serum levels of CTSK can be also R31 evaluated at different stages of the disease and correlate them to clinicopathological variables. R32 Important prognostic implications of elevated serum levels of cathepsins have been observed in R33 other types of malignancies, such as in prostate cancer.26 Finally, the emergence of new CTSK R34 inhibitors can provide in the future a new tool for the suppression of tumor progression. R35 R36 R37 R38 R39
158 Cathepsin K associates with lymph node metastasis and poor prognosis in oral squamous-cell carcinoma
CONCLUSION R1 R2 In conclusion, our findings provide evidence that increased CTSK expression is associated with R3 lymphatic spread and poor prognosis of OSCC. Due to the high negative predictive value (89%) R4 of CTSK protein expression, this biomarker can be a simple and useful tool in the diagnostic R5 work-up of cT1-T2N0 OSCC. R6 R7 R8 ABBREVIATIONS R9 R10 CTSK, cathepsin K; OSCC, oral squamous cell carcinoma; PCR, polymerase chain reaction; HPV, R11 human papilloma virus; OR, odds ratio; CI, confidence interval; HR, hazard ratio; HE, hematoxylin R12 and eosin; TMA, tissue microarray; FFPE, formalin-fixed, paraffin-embedded; OS, overall survival; R13 DSS, disease-specific survival; ROC, receiver operating characteristic; IHC, immunohistochemistry R14 R15 R16 ACKNOWLEDGMENTS R17 R18 A special thanks to the technicians of the laboratory of pathology for their valuable help. R19 R20 R21 FUNDING R22 R23 No funding was received for this study. R24 R25 R26 AVAILABILITY OF DATASETS AND MATERIALS R27 R28 All data supporting this study’s findings are found in the manuscript text, tables, and supplemental R29 files. R30 R31 R32 AUTHORS’CONTRIBUTIONS R33 R34 FKL and SMW planned and designed the study. FKL is the principal investigator and performed R35 data analysis. MHF conducted the statistical analyses. FKL and EK wrote the manuscript. PJvD and R36 RK critically reviewed the article. All authors read and approved the final manuscript. R37 R38 R39
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R1 COMPETING INTERESTS R2 R3 The authors declare that they have no competing interests. R4 R5 R6 CONSENT FOR PUBLICATION R7 R8 This manuscript does not contain any individual person’s data; hence, no consent for publication R9 is needed. R10 R11 R12 ETHICS APPROVAL R13 R14 The study was approved by the institutional review board of the University Medical Centre R15 Utrecht, Utrecht, The Netherlands (RP 2010-33). R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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REFERENCES R1 R2 1. Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011; 61: 69-90. R3 2. RJ Sanderson, JAD Ironside. Squamous cell carcinomas of the head and neck. BMJ 2002; 325: 822–7. R4 3. Leusink FK, van Es RJ, de Bree R, et al. Novel diagnostic modalities for assessment of the clinically node- negative neck in oral squamous-cell carcinoma. Lancet Oncol 2012; 13: e554-61. R5 4. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases R6 from primary head and neck squamous cell carcinomas. Nat Genet 2005; 37: 182–6. R7 5. van Hooff SR, Leusink FK, Roepman P, et al. Validation of a gene expression signature for assessment of lymph node metastasis in oral squamous cell carcinoma. J Clin Oncol 2012; 30: 4104-10. R8 6. Turk V, Turk B, Turk D. Lysosomal cysteine proteases: facts and opportunities. EMBO J 2001; 20: 4629- R9 33. R10 7. Turk V, Stoka V, Vasiljeva O, et al. Cysteine cathepsins: from structure, function and regulation to new R11 frontiers. Biochim Biophys Acta 2012; 1824: 68-88. R12 8. Leusink FK, van Diest PJ, Frank MH, et al. The Co-Expression of Kallikrein 5 and Kallikrein 7 Associates with Poor Survival in Non-HPV Oral Squamous-Cell Carcinoma. Pathobiology 2015; 82: 58-67. R13 9. Valastyan S, Weinberg RA. Tumor metastasis: molecular insights and evolving paradigms. Cell 2011; R14 147: 275-92. R15 10. Premzl A, Zavasnik-Bergant V, Turk V, et al. Intracellular and extracellular cathepsin B facilitate invasion R16 of MCF-10A neoT cells through reconstituted extracellular matrix in vitro. Exp Cell Res 2003; 283: 206- 14. R17 11. Yang Z, Cox JL. Cathepsin L increases invasion and migration of B16 melanoma. Cancer Cell Int 2007; R18 7: 8. R19 12. Guo M, Mathieu PA, Linebaugh B, et al. Phorbol ester activation of a proteolytic cascade capable of R20 activating latent transforming growth factor-betaL a process initiated by the exocytosis of cathepsin B. J Biol Chem 2002; 277: 14829-37. R21 13. Vasiljeva O, Turk B. Dual contrasting roles of cysteine cathepsins in cancer progression: apoptosis versus R22 tumor invasion. Biochimie 2008; 90: 380-6. R23 14. Droga-Mazovec G, Bojic L, Petelin A, et al. Cysteine cathepsins trigger caspase-dependent cell death R24 through cleavage of bid and antiapoptotic Bcl-2 homologues. J Biol Chem 2008; 283: 19140-50. R25 15. Sun T, Jiang D, Zhang L, et al. Expression profile of cathepsins indicates the potential of cathepsins B and D as prognostic factors in breast cancer patients. Oncol Lett 2016; 11: 575-83. R26 16. Kozlowski L, Wojtukiewicz MZ, Ostrowska H. Cathepsin A activity in primary and metastatic human R27 melanocytic tumors. Arch Dermatol Res 2000; 292: 68-71. R28 17. Chen Q, Fei J, Wu L, et al. Detection of cathepsin B, cathepsin L, cystatin C, urokinase plasminogen activator and urokinase plasminogen activator receptor in the sera of lung cancer patients. Oncol Lett R29 2011; 2: 693-99. R30 18. Brubaker KD, Vessella RL, True LD, et al. Cathepsin K mRNA and protein expression in prostate cancer R31 progression. J Bone Miner Res 2003; 18: 222-30. R32 19. Littlewood-Evans AJ, Bilbe G, Bowler WB, et al. The osteoclast-associated protease cathepsin K is expressed in human breast carcinoma. Cancer Res 1997; 57: 5386-90. R33 20. Cordes C, Bartling B, Simm A, et al. Simultaneous expression of Cathepsins B and K in pulmonary R34 adenocarcinomas and squamous cell carcinomas predicts poor recurrence-free and overall survival. R35 Lung Cancer 2009; 64: 79-85. R36 21. Rapa I, Volante M, Cappia S, et al. Cathepsin K is selectively expressed in the stroma of lung adenocarcinoma but not in bronchioloalveolar carcinoma. A useful marker of invasive growth. Am J R37 Clin Pathol 2006; 125: 847-54. R38 R39
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22. de Koning PJ, Bovenschen N, Leusink FK, et al. Downregulation of SERPINB13 expression in head and R1 neck squamous cell carcinomas associates with poor clinical outcome. Int J Cancer 2009; 125: 1542- R2 50. R3 23. Bitu CC, Kauppila JH, Bufalino A, et al. Cathepsin K is present in invasive oral tongue squamous cell carcinoma in vivo and in vitro. PLoS One 2013; 8: e709-25. R4 24. Schwanhäusser B, Busse D, Li N, et al. Global quantification of mammalian gene expression control. R5 Nature 2011; 473: 337-42. R6 25. Marchesi F, Piemonti L, Mantovani A, Molecular mechanisms of perineural invasion, a forgotten R7 pathway of dissemination and metastasis. Cytokine Growth Factor Rev 2010; 21: 77-82. R8 26. Miyake H, Hara I, Eto H. Serum level of cathepsin B and its density in men with prostate cancer as novel markers of disease progression. Anticancer Res 2004; 24: 2573-7. R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
162 Cathepsin K associates with lymph node metastasis and poor prognosis in oral squamous-cell carcinoma
SUPPLEMENTARY INFORMATION R1 R2 R3 Supplementary table S1. | Immunohistochemical descriptive results of protein expression of CTSK in the OSCC TMA cohort (n=83) R4 Variable CTSK R5 Number (%) of 332 cores R6 Tumor 213 (64) R7 No tumor 68 (20) No core 51 (15) R8 Number of cores / case (n=83) R9 4 23 R10 3 29 R11 2 12 1 11 R12 0 8 R13 Score (%) / case (n=83) R14 0 (total loss) 4 (5) R15 1-50 (almost total loss) 35 (42) 51-200 (partial loss) 26 (31) R16 missing 18 (22) R17
Percentages may not total 100 because of rounding. R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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General discussion Chapter 9
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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GENERAL DISCUSSION R1 R2 Management of the clinically node-negative (cN0) neck has to deal with a problem of risk-benefit R3 evaluation between probability of neck metastases, the problem of complications associated R4 with any type of neck dissection, and the prognostic influence of delayed diagnosis of metastasis R5 during follow-up.1 The choice in management of the cN0 neck used to be either elective neck R6 dissection (END) with possible overtreatment of 60–70% of patients without occult metastases R7 or watchful waiting (WW) with possible undertreatment of patients with occult metastases in R8 the remaining 30–40%. WW entails careful monitoring of the neck (e.g. by ultrasound-guided R9 fine-needle aspiration cytology - USgFNAC - during follow-up) and therapeutic neck dissection R10 (TND) treatment for patients who develop manifest metastasis. END encompasses a surgical R11 procedure potentially causing disfigurement and associated morbidity but prevents disease in the R12 neck becoming more advanced once previously occult metastases become clinically apparent or R13 are detected late during follow-up. R14 In an attempt to optimize management of the cN0 neck and to make it more personalized, R15 improvement in staging of the neck preferably with methods that are non-invasive and not R16 dependent on the size of the metastasis are needed. R17 R18 Therefore, the main focus in this thesis has been on lymph node metastasis (LNM) predicting R19 biomarkers in clinically T1-T2N0 oral squamous-cell carcinoma (OSCC). The multicenter validation R20 of a gene expression LNM predicting signature has been described and specific signature genes R21 were evaluated on a protein level for their correlation with LNM and prognosis. R22 R23 Considerations in management of the neck in early stage OSCC patients R24 Although consensus— partly based on an often cited but outdated decision analysis model— R25 still suggests that elective neck treatment is indicated when the chance of occult nodal disease R26 exceeds 20%,2 there are more reasons besides the above mentioned associated morbidity of R27 END why it is important to prevent unnecessary treatment of the neck. It is the high rate of R28 second primary tumors (SPT) of 1% per year during follow-up3 and the fact that a LNM of a R29 SPT is more difficult to detect in a previously treated neck due to edema and fibrosis. Moreover, R30 neck dissection removes part of the natural barrier for spread to distant organs of a future SPT, R31 resulting in incurable disease.4 R32 R33 Furthermore, a review by Rodrigo et al.5 including clinical trials and retrospective studies did not R34 conclude that END is superior to WW, with regard to survival and control of neck disease, but R35 other studies did. Dik et al. reported in a very recent retrospective cohort study that patients R36 managed by WW who turned out to be nodal positive (WWN+) showed significantly more extra- R37 capsular spread (ECS) as compared to patients treated by END and being diagnosed as nodal R38 R39
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R1 positive (END-N+) (56% vs. 14%, P = 0.016).6 The WW-N+ patients had a significantly worse R2 3-year disease specific survival (DSS) than END-N+ patients (56% vs. 82%, P = 0.02). Fasunla et R3 al. reported in the same year as Rodrigo a meta-analysis on the existing randomized controlled R4 clinical trials which compared (a form of) END with watchful waiting with a total of 283 patients.7 R5 They showed that END significantly reduced the risk of disease specific death (fixed-effects model R6 RR=0.57, 95% CI 0.36-0.89, p=0.014). However, the time period spanned four decades, T3 R7 tumors were included, half of END patients underwent radical neck dissection, follow-up differed R8 among studies and it is doubtful whether the studies were truly homogenous.8 The same D’Cruz R9 et al. described a prospective randomized clinical trial (RCT) including 500 patients, in which END R10 resulted in higher rates of overall survival (80% vs. 67.5%, P=0.01) and disease-free survival R11 (69.5% vs. 45.9%, P<0.001) than did WW (with TND if WWN+) among patients with early-stage R12 oral squamous-cell cancer.9 However, according to an editorial about this RCT this study also has R13 “some caveats that need careful consideration before the findings can be accepted”.10 In the WW R14 group a neck conversion was reported in 45% which is much higher than the percentages of neck R15 conversions (18%, 21% and 28%) reported by three studies in The Netherlands11-13 suggesting a R16 less accurate diagnostic work-up in the RCT which may hamper generalizability of their results. In R17 the RCT the cN0 neck was not clearly defined; the ultrasound scoring criteria were not described R18 and during follow-up only half of the patients had ultrasound without guidance by fine needle R19 aspiration cytology (USgFNAC) which is known to be able to reduce the false negative rate of US R20 alone.14 Furthermore the meticulousness of the follow-up of the RCT is questioned since 28% of R21 patients had a metastasis larger than 3 cm (18% larger than 6 cm), 93% had extranodal spread R22 and 18% had unresectable neck disease. One can therefore conclude the following: a) there R23 seems to be a substantial difference in pretreatment work-up and follow-up between head and R24 neck centers in the world, b) this RCT clearly shows a prognostic benefit of elective treatment R25 in the population in India, using the quite strict follow-up without routine USgFNAC, and c) R26 if routine very strict follow-up using USgFNAC by a well-trained ultrasonographer cannot be R27 assured, END is the safest strategy.10 R28 R29 Lymph node metastasis detection by the sentinel node biopsy procedure R30 In an attempt to more reliably select the lymph nodes that potentially contain metastases, the R31 sentinel lymph node (SLN) concept has been introduced. The SLN is likely to be the first lymph R32 node to harbor metastasis and can be used to provide information on the rest of the nodal basin. R33 After surgical removal, this node is studied meticulously by histopathological examination, using R34 step sectioning and immunohistochemistry. If the SLN contains metastatic tumor cells, treatment R35 of the neck is recommended, usually in a second procedure.15 R36 The SLN procedure is deemed more precise than imaging procedures. Two recently published R37 meta-analyses on SLN biopsy show highly accurate results and negative predictive values ranging R38 between 80 and 100%.16,17 However, in the majority of the analyzed studies END was used as R39
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the reference test for the SLN biopsy. When END is performed irrespective of the SLN biopsy R1 result, the reference is histopathological examination, often considered to be the gold standard. R2 However, if routine techniques and not step sectioning and immunohistochemistry are used, R3 histopathological examination of END specimen will miss minimal disease18 and is likely to R4 overestimate sensitivity and negative predictive values. Therefore, long-term observation of the R5 untreated neck (no neck dissection or radiotherapy) is the true gold standard for determining R6 the value of diagnostic techniques (e.g. the SLN procedure) for detection of occult lymph node R7 metastases.19 R8 R9 More recently, two multi-institutional trials on SLN biopsy were published that used observation R10 (WW) for SLN negative patients. Flach et al. reported 20 of a total of 62 patients (32%) had a R11 positive SLN and 5 of the 42 SLN negative patients developed a recurrence resulting in a sensitivity R12 of 80% and a NPV of 88%.20 Schilling et al. reported 94 of a total of 415 patients (23%) had a R13 positive SLN and a false negative result in 15 of the 109 whom were rescued by salvage TND.21 R14 Sensitivity was 86% and the NPV was 95%. The latter study, however, had a 3:1 distribution of R15 T1:T2 OSCC and after staging by SLN 23% were nodal positive. In the detection of occult lymph R16 node metastases, if the incidence of metastasis is low the NPV will be high almost regardless of R17 the performance of the diagnostic technique. An upstage by a positive SLN of 30% as reported R18 by Thompson et al.16 shows that the studied patient population reflects the daily clinical practice R19 and the results of the study can be implemented in routine clinical practice. R20 R21 Compared with END, SLN biopsy is obviously less invasive, SLN biopsy scars are smaller resulting R22 in improved esthetic outcome, and SLN biopsy is associated with significantly less postoperative R23 morbidity and better shoulder function.22-24 However, SLN biopsy remains a labor intensive invasive R24 procedure for which injection of radioactive tracers and admission is required and additional R25 second stage surgery in case of positive SLN is needed. Damage of the facial and accessory nerve R26 do occur, as well as pain, hematoma, infection, edema and especially fibrosis which spreads R27 through the neck and is likely to hamper a TND in case of a positive SLN. Furthermore, the R28 SLN procedure itself could potentially facilitate dissemination of tumor in the neck. Technical R29 difficulties that remain are a) differentiation between a true SLN and possible second echelon R30 lymph nodes on current imaging methods, and b) visualization of SLNs close to the injection site R31 (known as the ‘shine through’ phenomenon) resulting in lower accuracy rates for flour of mouth R32 tumors. R33 Although further improvements are needed, several centers have adopted the SLN biopsy as R34 an alternative to END and SLN biopsy is also already mentioned as an alternative for END in the R35 American NCCN guidelines, the UK NICE guidelines, as well as in the guidelines of the Dutch R36 Head and Neck Society.25-27 R37 R38 R39
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R1 Biomarkers for predicting lymph node metastases and prognosis R2 The limitation of imaging techniques to detect small metastatic deposits has led to a search for R3 additional characteristics or biomarkers assessable on the primary tumor to predict nodal disease. R4 Histopathological features of the primary tumor like lymphatic vessel density (chapter 2), tumor R5 thickness (TT), perineural growth (PG), vascular invasive growth (VG), and infiltrative growth (IG) R6 can predict the presence of nodal metastases in the individual patient, irrespective of the size of R7 the tumor and the metastasis.28 However, the histologic parameters (PG, VG and IG) in biopsy R8 specimens do not represent the resection specimen.29 Determination of histologic parameters R9 in routinely taken biopsy specimens of OSCC is not helpful in deciding whether to treat the R10 neck. Furthermore, TT and IG are ascertained on histopathological examination of the resection R11 specimen, implying a second-stage surgical procedure when elective neck dissection is indicated. R12 Measurement of TT before surgery, using an intraoral ultrasound probe, could be more promising R13 in this respect.30 R14 R15 Molecules involved in several metastasis pathways have been studied, but the complexity of R16 the metastatic process makes it unlikely that a single marker for metastasis can be identified R17 with an acceptable predictive value. Gene expression profiling, allowing the simultaneous study R18 of expression levels of multiple genes, could be the most promising technique. By using RNA R19 from the primary tumor specimen, a particular profile that could predict N-stage was identified.31 R20 Chapter 3 describes a multicenter validation study, undertaken at all head and neck oncological R21 centers in The Netherlands, in which the expression profile to predict lymph node metastasis R22 was transferred to a diagnostic platform to facilitate clinical implementation. Subsequently, the R23 profile was validated with an independent series of 222 samples of OSCC and oropharyngeal R24 squamous-cell carcinoma. Although the array platform was changed, the profile predicted R25 N-stage as expected. For the group of cT1–T2N0 or early-stage oral SCC (n=101), a NPV of 89% R26 was recorded. For these cases the issue of elective neck treatment is most relevant, because R27 early stage OSCC is treated by transoral surgery and, thus, there is no need to enter the neck for R28 excision of the primary tumor. By applying the microarray predictor in patients with early-stage R29 (cT1–T2N0) OSCC, overtreatment by unnecessary END can potentially be reduced from 73% to R30 41%. R31 Some remarks should be made however. Although overtreatment is reduced, 41% is still rather R32 high. R33 In 41 out of 65 patients classified as being at risk of LNM, evidence for nodal disease could not be R34 found resulting in a low specificity of the microarray based classification causing still a considerate R35 degree of overtreatment by elective neck dissection. This percentage is likely to be lower since the R36 used routine histopathological examination of END specimen misses minimal disease in 5-58% R37 of cases (mean 20%).18 Besides overtreatment there is psychological uncertainty of WW for the R38 other 36 node negative classified patients of whom 4 will develop a regional recurrence and risk R39
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delayed diagnosis and intensified treatment, however timely treatment is still possible for these R1 4% understaged patients.32 Furthermore, the validation study acquired gene expression data R2 out of samples taken from fresh frozen tumor resection specimen. It remains to be determined R3 whether gene expression prediction of LNM using standard pre-operatively taken biopsies is as R4 reliably as gene expression prediction using the samples from the tumor resection specimen. R5 Between pre-op and per-op taken samples certain differences exist: 1. timing, the pre-op sample is R6 taken 4 to 6 weeks before the per-op sample, and 2. genes itself due to intratumor heterogeneity R7 for which solid epithelial tumors (including head and neck tumors) are known.33 This could mean R8 that a single biopsy specimen might not be representative for the entire tumor. R9 R10 Morbidity remains a relevant issue with current management of early-stage (cT1–T2N0) OSCC. R11 The overtreatment (by END in case of pN+ prediction) in the Van Hooff algorithm could be reduced R12 further by replacing END for SLN biopsy as is proposed by the algorithm described in Chapter 4. R13 By applying this algorithm, oncological safety is not so much pursued by overtreatment and its R14 associated unnecessary morbidity but rather by further reduction of the rate of occult metastasis R15 and accurate follow-up.32 Decision-analysis techniques recommend WW below risk thresholds R16 that vary from 17% to 44%,2,34,35 thus, we judge the NPV of gene-expression profiling and SLN R17 biopsy acceptable because such thresholds are much higher than the 6% risk of occult metastases R18 projected by the proposed algorithm. With adoption of WW, 30–40% of patients will need neck R19 dissection, and under the proposed algorithm in chapter 4 this proportion would fall to 6%. In the R20 end, how to weigh the expected 6% of patients needing second-stage (and maybe intensified) R21 treatment against the inevitable morbidity of 60–70% of patients who undergo unnecessary R22 treatment of their neck will remain a matter of subjective judgment. Combining gene-expression R23 profiling and SLN biopsy in addition to current imaging techniques will further reduce the rate of R24 occult metastasis. In view of the accuracy of these techniques, this rate can be reduced to a level R25 acceptable to allow WW for the neck in patients with OSCC classified as T1 and T2. Furthermore, R26 restriction of SLN biopsy to individuals who are classified as node-positive on gene expression R27 profiling leads to further reduction of morbidity of the neck. R28 R29 Another example of the possibilities of gene expression microarray analysis is shown in chapter R30 5 in which the biological basis of the determinants of locoregional recurrence was established. R31 Perineural growth (OS and LRFS) and non-cohesive invasive growth were correlated with worse R32 prognosis (OS and LRFS). For the pattern of extensive non-cohesive growth, but not for perineural R33 growth, a differential set of 160 genes was established, that included genes involved in extra- R34 cellular matrix modeling. From the biological point of view it makes sense that certain genes R35 involved in the homeostasis of the extracellular matrix like matrix metalloproteinases (MMPs), were R36 overexpressed in tumors that showed a relatively high level of this non-cohesive growth. Other R37 studies also noted a correlation between MMP-expression and poor prognosis.36-38 The presence R38 R39
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R1 of perineural growth was not reflected in differences in gene expression between tumor groups R2 with and without this characteristic. Possible explanations are that a) standard histopathological R3 examination of the tumor resection specimen misses a substantial amount of perineural growth R4 falsely classifying the tumor having no perineural growth and thus creating incorrect groups or b) R5 that gene expression is only altered in the relatively small group of cells involved in this process. R6 Another important finding in the present study is that, despite treatment with radiotherapy, R7 extensive perineural and non-cohesive growth retained their association with worse OS. Thus R8 it seems that this study suggests that for OSCC with extensive perineural growth or extensive R9 non-cohesive growth radiotherapy combined with chemotherapy might be of additional value. R10 R11 Microarray prediction is costly, and although cost-effectiveness of all different diagnostic R12 modalities for LNM is beyond the scope of this thesis, it is interesting to search for more R13 affordable methods. Therefore, selected genes of the LNM predicting profile were investigated R14 by immunohistochemistry and correlated with clinical and pathological variables (chapter 6 - 8). R15 In search for more single LNM predicting biomarkers we studied the proteins kallikrein 5 (KLK5), R16 kallikrein 7 (KLK7) and their cognate inhibitor serine protease inhibitor kazal-type 5 (SPINK5) R17 together to investigate the functional role of KLK5, KLK7 and SPINK5 in OSCC, as described in R18 chapter 6. Whether a loss of SPINK5 tips the balance to more protease activity by KLK5 and KLK7, R19 and whether such an imbalance may identify patients with aggressive tumor behavior in OSCC R20 patients cannot be confirmed by our data. However, multivariate survival analyses showed that R21 concurrent complete loss of KLK5 and KLK7 identifies OSCC patients with poor clinical outcome R22 (OS HR 0.29 and DSS HR 0.39) and may therefore act as a prognostic marker. At first glance, R23 implementation of such a biomarker for worse outcome could be performed relatively easy by R24 excising tumors with larger surgical margins and increasing dose of postoperative radiotherapy, R25 resulting in a higher morbidity. On the other hand, de-escalated therapy (less intensive treatment R26 by i.e. smaller margins) for patients that do not have complete concurrent loss of KLK5 and KLK7 R27 could result in lower survival rates. However, how much and in what form this could happen is R28 not clear, although recent data showed there is no evidence for local adjuvant treatment in case R29 of resection margins ≥3 mm with ≤2 unfavorable histological features.39 This suggests resection R30 of early OSCC with smaller margins could result in similar survival rates. Perhaps, KLK5 and KLK7 R31 or other biomarkers like the invasive growth associated gene expression profile (chapter 5) can R32 be helpful in pointing out which tumors can be excised with margins ≥3 mm instead of ≥5 mm. R33 R34 Other genes with a strong differential gene expression for LNM that could potentially serve as a R35 single LNM biomarker (chapter 7) are LCN2, THBS2, TACSTD2, and SLPI. Although the precise R36 function of these genes is yet not fully understood, an explanation might be their joint role in R37 matrix remodeling.40 However, SLPI, also known as antileukoproteinase, is a protease inhibitor of R38 neutrophil elastase, cathepsin G, chymotrypsin, and trypsin,41 enzymes with extracellular matrix R39
172 General discussion
degradative properties, and associated with cancer development, invasiveness, and progression R1 and seems inversely correlated with nodal metastases. Despite a significant correlation between R2 SLPI protein expression and LNM in the entire cohort, SLPI expression had no additional diagnostic R3 value as a predictor for LNM in a subgroup of early cancers, which are clinically lymph node R4 negative in this cohort of OSCC. SLPI was identified as an independent predictor for OS and R5 DSS in OSCC. Patients with low SLPI protein expression had a worse OS and DSS compared with R6 patients with any SLPI expression. Thus, SLPI might be relevant as a prognostic biomarker for R7 patients with OSCC. R8 R9 Cathepsin K (CTSK) is a proteolytic enzyme with a known role in the degradation of the extracellular R10 matrix. Involvement in pathological processes, such as inflammation and cancer progression, has R11 been proved. SerpinB13 (SB13) is its cognate inhibitor, and loss of SB13 expression has been R12 correlated to poor outcome and LNM.42 Both SB13 and CTSK are on the LNM associated gene list R13 forming the microarray predictor. Therefore, we investigated CTSK expression values as a single R14 predictive marker for disease outcome and LNM (chapter 8). A clinically useful cut-off value was R15 determined and the negative predictive value of CTSK for LNM in the relevant group (cT1-2N0 R16 OSCC) was calculated at 89%. Although these results are promising, the relevant group was small R17 (n=24). Furthermore, the IHC analysis has been performed on a TMA built up with 6 resection R18 specimen cores per tumor, enabling assessment of intratumoral heterogeneity, which is essential R19 for potential biomarkers. It seems relevant to validate the CTSK predictive value on incisional R20 biopsies as well, to confirm its diagnostic value in daily clinical practice. Such a validation study R21 should be prospective and use WW or SLN biopsy combined with therapeutic neck dissection only R22 for SLN-positive patients as treatment of the neck in an attempt to reduce the chance of missing R23 a micrometastasis.18,19 R24 R25 In summary, this thesis shows the potential of molecular markers in solving the debate about the R26 management of cN0 neck in OSCC patients. Better understanding of genes that associate with R27 LNM and other pathological characteristics of OSCC is an important step forward in realizing R28 more personalized cancer treatment and management of the neck. This will eventually improve R29 survival and lower morbidity in OSCC patients in the future. R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 REFERENCES R2 1. Kowalski LP, Sanabria A. Elective neck dissection in oral carcinoma: a critical review of the evidence. R3 Acta Otorhinolaryngol Ital 2007; 27: 113-117. R4 2. Weiss MH, Harrison LB, Isaacs RS. Use of decision analysis in planning a management strategy for the R5 stage N0 neck. Arch Otolaryngol Head Neck Surg 1994; 120: 699-702. 3. Chuang SC, Scelo G, Tonita JM, et al. Risk of second primary cancer among patients with head and R6 neck cancers: A pooled analysis of 13 cancer registries. Int J Cancer. 2008; 123: 2390-6. R7 4. Haigentz M Jr, Hartl DM, Silver CE, et al. Distant metastases from head and neck squamous cell R8 carcinoma. Part III. Treatment. Oral Oncol 2012; 48: 787-93. R9 5. Rodrigo JP, Shah JP, Silver CE, et al. Management of the clinically negative neck in early-stage head and neck cancers after transoral resection. Head Neck 2011; 33: 1210-9. R10 6. Dik EA, Willems SM, Ipenburg NA, et al. Watchful waiting of the neck in early stage oral cancer is R11 unfavourable for patients with occult nodal disease. Int J Oral Maxillofac Surg 2016; 45: 945-50. R12 7. Fasunla AJ, Greene BH, Timmesfeld N, et al. A meta-analysis of the randomized controlled trials on elective neck dissection versus therapeutic neck dissection in oral cavity cancers with clinically node- R13 negative neck. Oral Oncol 2011; 47: 320-4. R14 8. D’Cruz AK, Dandekar MR. Elective versus therapeutic neck dissection in the clinically node negative R15 neck in early oral cavity cancers: Do we have the answer yet? Oral Oncol 2011; 47: 780-782. R16 9. D’Cruz AK, Vaish R, Kapre N, et al. Elective versus Therapeutic Neck Dissection in Node-Negative Oral Cancer. N Engl J Med 2015; 373: 521-9. R17 10. de Bree R, van den Brekel MW. Elective neck dissection versus observation in the clinically node negative R18 neck in early oral cancer: Do we have the answer yet? Oral Oncol 2015; 51: 963-5. R19 11. Melchers LJ, Schuuring E, van Dijk BA, et al. Tumour infiltration depth > 4 mm is an indication for an elective neck dissection in pT1cN0 oral squamous cell carcinoma. Oral Oncol 2012; 48: 337-42. R20 12. Nieuwenhuis EJ, Castelijns JA, Pijpers R, et al. Wait-and-see policy for the N0 neck in early-stage oral R21 and oropharyngeal squamous cell carcinoma using ultrasonography-guided cytology: is there a role for R22 identification of the sentinel node? Head Neck 2002; 24: 282-89. R23 13. Flach GB, Tenhagen M, de Bree R, et al. Outcome of patients with early stage oral cancer managed by an observation strategy towards the N0 neck using ultrasound guided fine needle aspiration cytology: R24 No survival difference as compared to elective neck dissection. Oral Oncol 2013; 49: 157-64. R25 14. Borgemeester MC, van den Brekel MW, van Tinteren H, et al. Ultrasound-guided aspiration cytology R26 for the assessment of the clinically N0 neck: factors influencing its accuracy. Head Neck 2008; 30: 1505-13. R27 15. Alkureishi LW, Burak Z, Alvarez JA, et al. Joint practice guidelines for radionuclide lymphoscintigraphy R28 for sentinel node localization in oral/oropharyngeal squamous cell carcinoma. Ann Surg Oncol 2009; R29 16: 3190-210. 16. Thompson CF, St John MA, Lawson G, et al. Diagnostic value of sentinel lymph node biopsy in head R30 and neck cancer: a meta-analysis. Eur Arch Otorhinolaryngol 2013; 270: 2115-22. R31 17. Govers TM, Hannink G, Merkx MA, et al. Sentinel node biopsy for squamous cell carcinoma of the oral R32 cavity and oropharynx: a diagnostic meta-analysis. Oral Oncol, 2013; 49: 726-732. R33 18. Ferlito A, Shaha AR, Rinaldo A. The incidence of lymph node micrometastases in patients pathologically staged N0 in cancer of oral cavity and oropharynx. Oral Oncol 2002; 38: 3-5. R34 19. de Bree R. How to analyze the diagnostic value of sentinel node biopsy in head and neck cancer. Eur R35 Arch Otorhinolaryngol 2013; 270: 789-91. R36 20. Flach GB, Bloemena E, Klop WMC, et al. Sentinel lymph node biopsy in clinically N0 T1–T2 staged oral cancer: The Dutch multicenter trial. Oral Oncol 2014; 50: 1020-1024. R37 21. Schilling C, Stoeckli SJ, Haerle SK, et al. Sentinel European Node Trial (SENT): 3-year results of sentinel R38 node biopsy in oral cancer. Eur J Cancer. 2015 Dec;51(18):2777-84. R39
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22. Murer K, Huber GF, Haile SR, et al. Comparison of morbidity between sentinel node biopsy and elective neck dissection for treatment of the n0 neck in patients with oral squamous cell carcinoma. Head Neck R1 2011; 33: 1260–64. R2 23. Schiefke F, Akdemir M, Weber A, et al. Function, postoperative morbidity, and quality of life after R3 cervical sentinel node biopsy and after selective neck dissection. Head Neck 2009; 31: 503–512. R4 24. Hernando J, Villarreal P, Alvarez-Marcos F, et al. Comparison of related complications: sentinel node biopsy versus elective neck dissection. Int J Oral Maxillofac Surg 2014; 43: 1307-12. R5 25. NCCN Guidelines Version 1.2016. http://oralcancerfoundation.org/wp-content/uploads/2016/09/ R6 head-and-neck.pdf Accesses on November 9, 2016 R7 26. NICE Guidelines. [NG36]1.3.5 Published date February 2016 https://www.nice.org.uk/guidance/NG36/ chapter/recommendations. Accessed on November 9, 2016 R8 27. Dutch Guideline Head and Neck Tumors. https://www.nvmka.nl/sites/www.nvmka.nl/files/Richtlijn%20 R9 Hoofd-halstumoren%202015.pdf Accesses on November 9, 2016 R10 28. Takes RP, Rinaldo A, Rodrigo JP, et al. Can biomarkers play a role in the decision about treatment of the R11 clinically negative neck in patients with head and neck cancer? Head Neck 2008; 30: 525-38. 29. Dik EA, Ipenburg NA, Adriaansens SO, et al. Poor Correlation of Histologic Parameters Between Biopsy R12 and Resection Specimen in Early Stage Oral Squamous Cell Carcinoma. Am J Clin Pathol 2015; 144: R13 659-66. R14 30. Lodder WL, Teertstra HJ, Tan IB, et al. Tumour thickness in oral cancer using an intra-oral ultrasound probe. Eur Radiol 2011; 21: 98-106. R15 31. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases R16 from primary head and neck squamous cell carcinomas. Nat Genet 2005; 37: 182-86. R17 32. Yuen AP, Ho CM, Chow TL, et al. Prospective randomized study of selective neck dissection versus R18 observation for N0 neck of early tongue carcinoma. Head Neck 2009; 31: 765-72. 33. Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev R19 Cancer 2012; 12: 323-34. R20 34. Song T, Bi N, Gui L, et al. Elective neck dissection or “watchful waiting”: optimal management strategy R21 for early stage N0 tongue carcinoma using decision analysis techniques. Chin Med J (Engl) 2008; 121: 1646-50. R22 35. Okura M, Aikawa T, Sawai NY, et al. Decision analysis and treatment threshold in a management for R23 the N0 neck of the oral cavity carcinoma. Oral Oncol 2009; 45: 908-11. R24 36. Al-Azri AR, Gibson RJ, Keefe DMK, et al. Matrix metalloproteinases: do they play a role in mucosal pathology of the oral cavity? Oral Diseases 2013; 19: 347-359. R25 37. Jordan RC, Macabeo-Ong M, Shiboski CH, et al. Overexpression of matrix metalloproteinase-1 and -9 R26 mRNA is associated with progression of oral dysplasia to cancer. Clin Cancer Res 2004; 10: 6460-6465. R27 38. Katayama A, Bandoh N, Kishibe K, et al. Expressions of matrix metalloproteinases in early-stage oral R28 squamous cell carcinoma as predictive indicators for tumor metastases and prognosis. Clin Cancer Res 2004; 10: 634-640. R29 39. Dik EA, Willems SM, Ipenburg NA, et al. Resection of early oral squamous cell carcinoma with positive R30 or close margins: relevance of adjuvant treatment in relation to local recurrence: margins of 3 mm as R31 safe as 5 mm. Oral Oncol 2014; 50: 611-5. 40. Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network R32 integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010; 38(Web R33 Server issue): W214–W220. R34 41. Boudier C, Cadène M, Bieth JG. Inhibition of neutrophil cathepsin G by oxidized mucus proteinase inhibitor. Effect of heparin. Biochemistry 1999;38:8451–8457. R35 42. de Koning PJ, Bovenschen N, Leusink FK, et al. Downregulation of SERPINB13 expression in head and R36 neck squamous cell carcinomas associates with poor clinical outcome. Int J Cancer 2009; 125:1542-50. R37 R38 R39
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CHAPTER 10
Future perspectives Chapter 10
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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FUTURE PERSPECTIVES R1 R2 Current state: watchful waiting, sentinel lymph node biopsy and elective neck dissection R3 The 2005 lymph node metastasis (LNM) gene expression profile (GEP)1 was validated in the Dutch R4 multicenter validation study (Chapter 3) with a negative predictive value (NPV) of 89% in the R5 clinically relevant group (cT1-T2N0 OSCC, n=101). This impressive NPV was achieved despite a R6 change of platform, lab and patient cohort. R7 R8 Often, such a high NPV implies a low positive predictive value (PPV) and indeed, the validation R9 study gave a PPV of only 37%. However, by using the LNM GEP in conjunction with current R10 diagnostic methods the risk of occult metastases can be reduced from 30-40% to 11%. This R11 NPV is approaching that of sentinel lymph node biopsy (SLNB) and exceeds the commonly used R12 standard of a 20%.2 The question is whether this is low enough for surgeons to refrain from an R13 elective neck dissection (END) and instead counsel the patient towards a watchful waiting (WW) R14 approach. R15 R16 One can argue that SLNB is the current diagnostic gold standard for the N0 neck and features R17 in the guidelines of the Dutch Head & Neck society, the UK NICE guidelines and the American R18 NCCN guidelines as an alternative for END. Indeed the NPV of SLN approaches 95% in some R19 series. SLNB is however not without its disadvantages. Firstly, SLNB is an invasive procedure with R20 its own morbidity. Second, it requires highly specialized equipment which involves considerable R21 costs. Thirdly, SLNB-positive patients are currently treated with a completion modified radical R22 neck dissection (MRND). This procedure needs to be performed without significant delay leading R23 to difficulties with hospital logistics. The MRND itself is also complicated by the prior SLNB R24 causing a non-virgin operative field. This leads us to speculate a completion MRND is neither R25 as oncologically sound nor as functionally good as an END performed on a virgin neck. These R26 disadvantages are the reason that several head and neck centers still consider the SLNB procedure 10 R27 as inferior to an END performed on a virgin neck. Ideally, a diagnostic procedure should be non- R28 invasive and compromise therapeutic procedures as little as possible. R29 R30 Watchful waiting (WW) in the appropriate patient population is also a valid approach. It certainly R31 minimizes any over treatment of patients who are truly pN0 but may compromise the survival R32 outcomes of the minority of patients with occult metastasis (although contradicting results R33 are reported).3-5 Moreover, many patients tend to conceive this management approach as too R34 hazardous when explained the statistical risk of an occult metastasis. Finally, the costs associated R35 and time investment involved with repeated hospital visits, sonography and fine needle aspiration R36 cytology, also have to be considered. R37 R38 R39
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R1 The perfect work-up R2 Ideally there would exist a safe, non-invasive, readily available and cheap diagnostic work-up for R3 the cN0 neck to better predict the risk of occult neck metastases, guiding patients and surgeons R4 to decide between WW, SLNB and END in each individual case. This diagnostic LNM nomogram R5 will no doubt combine several diagnostic modalities like imaging, cytology, histopathology R6 and molecular analysis. Pathologists already routinely report on tumor size, depth of invasion, R7 differentiation, infiltrative spread, vascular and perineural invasion, which all potentially have R8 therapeutic consequences and the LNM nomogram can be seen as an additional parameter R9 within this work-up. R10 It must however be emphasized that a LNM-nomogram is a diagnostic method still in development. R11 The exact combination of molecular markers (i.e. the LNM GEP) needs to be optimized to give the R12 best compromise between costs, availability and oncological accuracy. R13 R14 In terms of costs and availability, immunohistochemistry (IHC) is widely available and reasonably R15 cheap whereas more advanced techniques such as DNA and RNA sequencing, which allow R16 to examine all genes in one go, are not yet widespread. A future, generally applicable LNM- R17 nomogram would therefore ideally use only a few biomarkers, to be tested with IHC. On the R18 other hand, DNA and RNA sequencing could very well be used to discover additional markers R19 for the future LNM-nomogram. Until now, DNA and RNA sequencing studies have used only R20 primary tumor tissues or cancer cell lines. This leaves a void which coming projects can fill; by also R21 sequencing tissue from lymph node metastases to look for possible genetic alterations that could R22 expose the ability of metastasizing.6 R23 R24 Practical issues also need to be resolved. For example, should testing be performed on a biopsy R25 or after excision and analysis of the whole primary tumor? The first approach would allow R26 simultaneous management of the primary tumor and the neck with either SLNB or END but with R27 the uncertainty of whether the biopsy is truly representative of the whole tumor.7 Does intra- R28 tumor genetic heterogeneity influence the accuracy of the LNM GEP?8 The latter approach would R29 presumably give more reliable results for a future LNM-nomogram (the worst tissue with the R30 worse prognostic indicator could be identified), but would arguably negate the possibility of SLNB R31 and imply a delayed “second stage” END if the risk for occult metastasis was deemed too high. R32 R33 The presence of intra-tumor genetic heterogeneity in oral cancer was hinted/suggested from data R34 in a yet unpublished study, conducted on tumor specimen of the multicenter study in Chapter 3. R35 This study was conducted to determine whether the standard pre-operative biopsy specimen R36 could be analyzed as reliably as biopsies from the resection specimen. Both biopsy samples R37 were collected in 20 cT1-T2N0 patients. All samples were analyzed using the same platform R38 and laboratory as in the multicenter study (chapter 3). Seven patients were excluded because R39
180 Future perspectives
one of the two biopsy samples could not be used. In 9 of the included 13 bridging samples the R1 N-stage was predicted the same, in the other 4 (31%) the prediction differed between biopsy R2 and resection specimen. R3 Possible explanations for the difference in N-stage prediction are intra-tumor genetic heterogeneity R4 or genetic dedifferentiation due to tumor progression in time, because of the delay of ca. 4 weeks R5 between acquiring both samples in each patient. Both phenomena deserve further research. R6 R7 In our opinion, the addition of any molecular test to the standard work-up should not cause R8 undue delays. Many small oral cavity tumors are amenable to excision under local anesthesia R9 in an outpatient setting. Prompt excision of a small primary tumor instead of excision after the R10 usual 4-5 weeks waiting for complete diagnostic work-up of the neck may improve oncological R11 outcomes.9 This means that molecular analysis of small primaries can occur concomitantly to R12 staging of the neck. An eventual staged neck dissection would also benefit from reduced risk of R13 a fistula to the oral cavity. R14 R15 What is the reference standard? R16 Current publications on management of the cN0 neck often suffer from unclear pathological R17 standards. To properly evaluate the value of any new test or nomogram we need to first be clear R18 what these pathological standards are. Historically, ENDs probably under-staged the neck. This is R19 because pathologists did not have the time nor the techniques to thoroughly examine specimens R20 sent to them by the surgeon. Indeed, using more modern pathological techniques that are better R21 able to detect micrometastases, isolated tumor cells and other minimal changes may lead to R22 upstaging the neck in up to 20% of cases.10 R23 R24 An example of this effect of changing the reference standard, is shown in the study by de Koning11: R25 step sectioning of END specimen of all pN0 staged neck specimen by standard histopathology, R26 resulted in upstaging of the neck in 5 (10%) patients out of 48 patients. Upstaging changed the 10 R27 association of SERPINB13 with LNM from non- significant to significant. R28 R29 The current step-serial sectioning and immunohistochemistry used in SLNB is extremely thorough R30 as compared to routine pathological examination used in END.12 Although this may represent the R31 “new” gold standard of pathological examination, it may still not be the best reference standard R32 for staging the N0 neck. The European Sentinel node trial (SENT- EORTC 20421) used long term R33 observation of the untreated neck with the assumption that any neck metastases within a short R34 time period indicated a false negative for SLN.13 We would argue that “long term follow-up R35 of the untreated neck” is a more reliable gold standard for staging the neck than pathological R36 examination of either SLN biopsies or END as it negates any inadvertent mistake by the surgeon R37 (e.g. wrong node sampled, non-thorough END) or pathologist (isolated tumor cells missed on R38 pathological examination). R39
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R1 Unfortunately, if we wish to use long term follow up as the gold standard for staging the neck R2 rather than pathological examination, it means that much of the published data on biomarkers R3 cannot be used. Our future LNM-nomogram would thus have to be trialed in a prospective R4 manner or failing that, be used retrospectively on a patient group being observed such as the R5 SENT cohort13 or other cohorts based on a WW approach.4 Indeed, validation of a nomogram R6 could be performed on such studies before finding its way into a prospective trial. R7 R8 Patient selection R9 A diagnostic test is only useful if it gives the clinician new information which he didn’t already R10 have. For example, it is not useful to perform SLNB on a patient with palpable nodal disease. For R11 this, the better diagnostic approach is fine needle aspiration cytology. Similarly, in a patient with a R12 small primary exhibiting few aggressive markers the SLN will more than likely be negative. R13 R14 Such inherent bias due to patient selection can be seen when comparing the results of the SENT R15 trial13 with the Dutch multicenter trial described in chapter 3. In the former trial the NPV was 95% R16 but the ratio of T1:T2 was 3:1. In our multicenter trial the NPV was 89% but the ratio of T1:T2 R17 was 3:7 i.e. seven times as many T2 patients with a de facto higher risk for occult metastases. R18 R19 It is pivotal that clinicians recognize that the individual patient in their consultation room may not R20 be reflected by published data sets and that the fairly arbitrary cut off of T1 or T2 is too blunt a R21 tool to choose the best approach for management of the neck. R22 R23 In closing R24 This thesis started with the premise that current methods for managing the cN0 neck can be R25 improved upon. We believe that molecular (bio)markers, used as part of an appropriate nomogram R26 in the appropriate patient group, have an important role to play and that this technique will R27 complement the existing options of WW, SLN and END. One possible path forward for research R28 is to design such a nomogram retrospectively by using an existing SLNB dataset being the current R29 pathological gold standard, before prospectively trialing a patient cohort. R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
182 Future perspectives
REFERENCES R1 R2 1. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas. Nat Genet 2005; 37: 182-86. R3 2. Weiss MH, Harrison LB, Isaacs RS. Use of decision analysis in planning a management strategy for the R4 stage N0 neck. Arch Otolaryngol Head Neck Surg 1994; 120: 699-702. R5 3. Yuen AP, Ho CM, Chow TL, et al. Prospective randomized study of selective neck dissection versus R6 observation for N0 neck of early tongue carcinoma. Head Neck 2009; 31: 765-72. R7 4. Dik EA, Willems SM, Ipenburg NA, et al. Watchful waiting of the neck in early stage oral cancer is unfavourable for patients with occult nodal disease. Int J Oral Maxillofac Surg 2016; 45: 945-50. R8 5. D’Cruz AK, Vaish R, Kapre N, et al. Elective versus Therapeutic Neck Dissection in Node-Negative Oral R9 Cancer. N Engl J Med 2015; 373: 521-9. R10 6. Tabatabaeifar S, Kruse TA, Thomassen M, et al. Use of next generation sequencing in head and neck R11 squamous cell carcinomas: A review. Oral Oncol 2014; 50: 1035-40. R12 7. Dik EA, Ipenburg NA, Adriaansens SO, et al. Poor Correlation of Histologic Parameters Between Biopsy and Resection Specimen in Early Stage Oral Squamous Cell Carcinoma. Am J Clin Pathol 2015; 144: R13 659-66. R14 8. Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by R15 multiregion sequencing. N Engl J Med 2012; 366: 883-92. 9. van Harten MC, Hoebers FJ,Kross KW, et al. Determinants of treatment waiting times for head and neck R16 cancer in the Netherlands and their relation to survival. Oral Onc 2015; 51: 272-278. R17 10. Ferlito A, Rinaldo A, Devaney KO, et al. Detection of lymph node micrometastases in patients with R18 squamous carcinoma of the head and neck. Eur Arch Otorhinolaryngol 2008; 265: 1147-53. R19 11. de Koning PJ, Bovenschen N, Leusink FK, et al. Downregulation of SERPINB13 expression in head and neck squamous cell carcinomas associates with poor clinical outcome. Int J Cancer 2009; 125: 1542-50. R20 12. de Bree R. How to analyze the diagnostic value of sentinel node biopsy in head and neck cancer. Eur R21 Arch Otorhinolaryngol 2013; 270: 789-91. R22 13. Schilling C, Stoeckli SJ, Haerle SK, et al. Sentinel European Node Trial (SENT): 3-year results of sentinel R23 node biopsy in oral cancer. Eur J Cancer 2015; 51: 2777-84. R24 R25 R26 10 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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Summary Chapter 11
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
186 Summary
SUMMARY R1 R2 Oral cancer is the 15th most common cancer worldwide, with more than 300,000 new cases R3 diagnosed (2% of the total) and with more than 145,000 deaths in 2012 (also 2% of the total). R4 Of all oral cancers, around 90% are squamous cell carcinomas, originating in the tissues that line R5 the mouth and lips. Oral squamous-cell carcinomas (OSCC) metastasize frequently to regional R6 lymph nodes in the neck and this occurs in 50% of patients. The presence of nodal metastases R7 is a determinant of prognosis and clinical management. The neck is staged by palpation and R8 imaging, but accuracy of these techniques to detect small metastases is low. Especially in the R9 diagnosis of T1-T2 oral cavity cancers, in which the risk of occult metastases is about 30%, the R10 inaccuracy of lymph node diagnostics is important. In these patients, the primary tumor can often R11 be resected transorally without opening the neck, and in that situation the dilemma exists how R12 to treat the neck: either by elective neck dissection (END) with unnecessary treatment in 70% of R13 patients or watchful waiting (WW) followed by therapeutic neck dissection (TND) in patients who R14 develop manifest metastases with the risk of delayed treatment and worse prognosis in case of R15 metastatic disease.1,2 These unsatisfying therapeutic options have been the subject of debate for R16 decades and discovery of a better predictor of the nodal status in OSCC will likely be hailed as the R17 Holy Grail in head and neck surgery. R18 R19 The goal of this thesis was to find and develop new methods to diagnose or predict occult R20 metastases in the neck for improved regional staging and prognosis of OSCC patients to allow R21 more personalized management of the neck. To achieve this goal an attempt was made to R22 validate an earlier published lymph node metastasis (LNM) predicting gene expression signature, R23 and to discover other biomarkers associated with LNM and prognosis in OSCC patients. R24 R25 The area of the head and neck features a rich lymphatic network, including approximately R26 three hundred out of the total eight hundred lymph nodes of the human body.3 Tumor R27 lymphangiogenesis, the formation of new lymphatics related to malignant disease, has become R28 a new field of studying the lymphatic dissemination of various neoplasms.4 The initial concept of 11 R29 lymphatic metastasis was that tumor cells spread solely through preexisting, peritumoral lymphatic R30 vessels that serve as passive channels.5 However, the emergence of a number of lymphatic vessel R31 markers has provided new insights into the active process of developing a tumor-associated R32 lymphatic vasculature, analogous to the process of neovascularization. Moreover, research has R33 revealed a number of growth factors and chemokines that are involved in the process of tumor R34 lymphangiogenesis and provide additional information to understand the active role of malignant R35 cells in the actual development of a pathological lymphatic vasculature, which serves as a conduit R36 for regional spread.6 Studies oriented to the prognostic role of tumor lymphangiogenesis use R37 immunohistochemistry (IHC) to quantify the lymphatic vessels density (LVD) inside the tumor R38 R39
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R1 mass, as well as in the peritumoral area in close proximity to the tumor margin. Chapter 2 R2 summarizes the current knowledge regarding the robustness of the correlation of intratumoral R3 and peritumoral LVD to LNM in an attempt to discover an additional tool to estimate the risk R4 of occult metastasis in HNSCC. Oral cancer was the main primary malignancy studied, but also R5 patients with laryngeal, hypopharyngeal and oropharyngeal carcinoma were included. The R6 majority of the articles reported a significant correlation of increased LVD to the presence of LNM. R7 However, previous studies suffered from several limitations and therefore do not allow drawing R8 definitive conclusions other than that LVD could potentially be used as an additional tool for the R9 prediction of occult metastasis. R10 R11 Another method to predict occult lymph node metastases is gene expression profiling, which has R12 been shown to be useful for diagnosis and prognosis of several cancers.7,8 With regard to HNSCC, R13 a previous study identified a gene expression signature for distinguishing metastasizing (N+) from R14 N0 OSCC and N0 oropharynx SCC (OPSCC).9 Although promising, the independent validation R15 cohort in that study was small (n=22), with all samples (n=104) derived from a single clinical R16 center. In Chapter 3, this predictive gene set was first re-evaluated using a different microarray R17 platform on 94 samples, transferred to a dedicated diagnostic microarray, and subsequently R18 tested in a large, multicenter patient cohort (n=222). The negative predictive value (NPV) of the R19 diagnostic signature on the entire validation cohort (n=222) was 72%. The signature performed R20 well on the most relevant subset of early-stage (cT1-T2N0) OSCC (n=101), with an NPV of 89%. R21 Combining current clinical assessment with the expression signature would decrease the rate R22 of undetected nodal metastases from 28% to 11% in early-stage OSCC. Testing the proposed R23 clinical decision model that incorporates the expression signature in a prospective study is needed R24 to assess whether the microarray results are able to guide management of the clinically node- R25 negative neck in OSCC patients. R26 R27 Presentation of the study described in chapter 3 during the 3rd World Congress of the International R28 Academy of Oral Oncology in Singapore 2011 prompted an invitation to write a review about R29 novel diagnostic modalities for assessment of the clinically node-negative neck (cN0) in OSCC R30 which is described in Chapter 4. Again the limitations of imaging techniques to detect small R31 metastatic deposits that have led to a search for additional characteristics or biomarkers R32 assessable on the primary tumor to predict nodal disease, are outlined. In this Personal View, we R33 restrict ourselves to discussion of the two most promising techniques, which are arguably ready R34 for clinical implementation and have a very different but complementary nature: gene expression R35 profiling and sentinel lymph node (SLN) biopsy. A new staging algorithm incorporating both R36 methods is suggested, to optimize management of the cN0 neck in patients with early-stage R37 OSCC. By applying our proposed algorithm, oncological safety is not so much pursued by over R38 treatment and its associated unnecessary morbidity but rather by further reduction of the rate of R39
188 Summary
occult metastasis and accurate follow-up. Timely treatment is still possible for patients who are R1 under-staged. Combining gene expression profiling and SLN biopsy in addition to current imaging R2 techniques will further reduce the rate of occult metastases to an estimated 6%. Furthermore, R3 restriction of SLN biopsy to individuals who are classified as node-positive on gene expression R4 profiling eliminates overtreatment. Prospective clinical trials implementing this staging algorithm R5 are needed to investigate whether gene expression profiling and SLN biopsy can be combined R6 and whether oncological and functional outcomes in patients with oral SCC will indeed improve. R7 R8 In Chapter 5 another example is described of how gene expression analysis can be used in R9 an attempt to stratify patients who are prone to have locoregional recurrent disease and who R10 are not. This is important since HNSCC has still a fairly bad prognosis with more than 50% R11 of patients dying within five years after diagnosis and a considerable part of these deaths is R12 caused by locoregionally recurrent cancer.10 This study aimed to identify tumor characteristics that R13 are associated with the development of locoregional recurrences and a poor prognosis and to R14 reveal their biological basis. Perineural growth and non-cohesive invasive growth were correlated R15 with worse prognosis. The negative effect of these characteristics on survival was maintained R16 in the group that received post-operative radiotherapy. No association was found for degree of R17 differentiation and bone invasion and presence of dysplasia or tumor at the margins. For the R18 pattern of extensive non-cohesive growth, but not for perineural growth, a differential set of R19 160 genes was established, that included genes involved in extra-cellular matrix modeling. The R20 present study could not reveal a distinct association between the non-cohesive gene set and R21 worse prognosis. However, for patients from whom the tumor is not surgically removed and R22 only small biopsies are taken when chemoradiation is planned as primary therapy, it is hard to R23 assess histopathological characteristics, like pattern of invasion. Expression measurement in such R24 biopsies for instance of MMPs could be useful in this respect. In summary we can state that with R25 gene expression array analysis the biological basis of the determinants of locoregional recurrence R26 was established. R27 R28 In chapters 6 to 8 we zoomed in on a few specific genes of the validated LNM predicting profile. 11 R29 We evaluated their correlations with clinical and histopathological variables on a gene and R30 protein level including only OSCC. HPV status was determined by an algorithm for HPV-16 to R31 exclude HPV positive tumors.11 A tissue microarray (TMA) was made of the paraffin-embedded R32 tissue. For each tumor block, 2-3 central tissue cylinders and 2-3 tissue cylinders at the tumor R33 front with a diameter of 0.6 mm were punched out, avoiding areas of necrosis, and arrayed in a R34 recipient paraffin block. Normal epithelium from the floor of the mouth, gingiva, and tonsil was R35 incorporated in each block to ensure similarity of staining between the different blocks. R36 R37 R38 R39
189 Chapter 11
R1 In Chapter 6 we describe four of the most predictive genes: secretory leukocyte protease R2 inhibitor (SLPI), lipocalin-2 (LCN2), thrombospondin-2 (THBS2), and tumor-associated calcium R3 signal transducer 2 (TACSTD2). Gene and protein expression was correlated with LNM, overall R4 survival (OS), and disease-specific survival (DSS). SLPI protein expression correlated with LNM in R5 the whole cohort, not in a subgroup of cT1 to 2N0. SLPI expression correlated with OS (hazard R6 ratio[HR]=0.61) and DSS (HR=0.47) in multivariate analysis. LCN2, THBS2, and TACSTD2 did not R7 show any correlation with LNM, OS, or DSS. Although SLPI expression correlates with LNM, it had R8 no additional value in determining LNM in early OSCC. However, it is an independent predictor R9 for both OS and DSS and therefore a relevant prognostic biomarker in OSCC R10 R11 In Chapter 7 a similar analysis was performed as in chapter 6 only now the proteases kallikrein R12 5 (KLK5) and kallikrein 7 (KLK7) and their cognate inhibitor “serine protease inhibitor kazal type R13 5” (SPINK5) were evaluated as predictive or prognostic markers in non-HPV OSCC. Concurrent R14 loss of KLK5 and KLK7 correlated with worse disease-specific and overall survival (DSS and OS). R15 Multivariate analysis proved that co-expression is an independent prognostic factor for DSS R16 (p=0.029) and OS (p=0.001). This report demonstrates that concurrent loss of KLK5 and KLK7 R17 associates with a poor clinical outcome in OSCC and could therefore serve as prognostic marker R18 in this disease. R19 R20 Finally, in Chapter 8 the role of cathepsin K (CTSK) as a biomarker for LNM and prognosis R21 was studied in non-HPV OSCC. Gene expression data were acquired from a previous study9 R22 and protein expression was semi-quantitatively determined by immunohistochemistry on TMA- R23 slides. All expression data were correlated with clinicopathological variables. Elevated gene and R24 protein expression of CTSK were strongly associated to LNM and perineural invasion (p<0.01). R25 Logistic regression analysis highlighted increased CTSK protein expression as the most significant R26 independent factor of lymphatic metastasis (OR=7.65, CI:2.31-23.31, p=0.001). Survival analysis R27 demonstrated both gene and protein CTSK expression as significant indicators of poor 5-year R28 disease specific survival (HR=2.29, CI:1.01 5.21, p=0.047 for gene expression; HR=2.79, CI:1.02- R29 7.64, p=0.045 for protein expression). A cut-off value of 25 was determined by ROC analysis, R30 in order to divide patients into low and high protein expression groups. There was a significant R31 association of increased CTSK expression with histopathologically proven LNM (p<0.01). Next, R32 the predictive value of CTSK as a biomarker of occult metastasis in early stage (cT1-T2N0) OSCC R33 was examined. A total of 24 patients had early T stage without clinically detectable nodal disease. R34 Out of the ten patients with yet occult metastases in the neck dissection specimen, nine had a R35 high protein CTSK expression, whereas only one patient showed a value lower than the cut-off. R36 The sensitivity of high protein expression in detecting occult metastases in early stage OSCC was, R37 thus, calculated at 90%, whereas the specificity was 57%. Additionally, the positive predictive R38 value was found at 60%, with a negative predictive value of 89%. The current study was based R39
190 Summary
on a relatively limited cohort of 83 patients with OSCC. The results should therefore be further R1 validated by prospective studies including higher number of patients with emphasis on predicting R2 occult metastases in cases of N0 stage. R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 11 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 REFERENCES R2 1. D’Cruz AK, Vaish R, Kapre N, et al. Elective versus Therapeutic Neck Dissection in Node-Negative Oral R3 Cancer. N Engl J Med 2015; 373: 521-9. R4 2. Dik EA, Willems SM, Ipenburg NA, et al. Watchful waiting of the neck in early stage oral cancer is R5 unfavourable for patients with occult nodal disease. Int J Oral Maxillofac Surg 2016; 45: 945-50. R6 3. Rouviere H. Lymphatic system of the head and neck. Tobias MJ, Translator. Ann Arbor, MI: Edwards Brothers; 1938. R7 4. Hirakawa S. From tumor lymphangiogenesis to lymphvascular niche. Cancer Sci 2009; 100: 983-89. R8 5. Zeidman I, Copeland BE, Warren S. Experimental studies on the spread of cancer in the lymphatic R9 system. II. Absence of a lymphatic supply in carcinoma. Cancer 1955; 8: 123-27. R10 6. Karatzanis AD, Koudounarakis E, Papadakis I, et al. Molecular pathways of lymphangiogenesis and R11 lymph node metastasis in head and neck cancer. Eur Arch Otorhinolaryngol 2012; 269: 731-37. R12 7. van’t Veer LJ, Bernards R: Enabling personalized cancer medicine through analysis of gene expression patterns. Nature 2008; 452: 564-570. R13 8. Cardoso F, van’t Veer LJ, Bogaerts J, et al. 70-Gene Signature as an Aid to Treatment Decisions in Early- R14 Stage Breast Cancer. N Engl J Med 2016; 375: 717-29. R15 9. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases from primary head and neck squamous cell carcinomas. Nat Genet 2005; 37: 182-186. R16 10. Graveland AP, Braakhuis BJM, Eerenstein SEJ, et al. Molecular diagnosis of minimal residual disease in R17 head and neck cancer patients. Cell Oncol 2012; 35: 367-375. R18 11. Smeets SJ, Hesselink AT, Speel EJ, et al. A novel algorithm for reliable detection of human papillomavirus R19 in paraffin embedded head and neck cancer specimen. Int J Cancer 2007; 121: 2465-2472. R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
192 Summary
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 11 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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CHAPTER 12
Nederlandse samenvatting Chapter 12
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
196 Nederlandse samenvatting
SAMENVATTING R1 R2 Mondkanker is de 15e meest voorkomende kankersoort wereldwijd. In 2012 bedroeg de globale R3 incidentie meer dan 300.000 (2% van het totaal) en was het aantal sterfgevallen meer dan R4 145.000 (ook 2% van het totaal). Ongeveer 90% van alle maligne tumoren van de mondholte R5 behoort tot de plaveiselcarcinomen, welke hun oorsprong vinden in de slijmvliezen van de lippen R6 en de mond. Het orale plaveiselcelcarcinoom (OPCC) metastaseert frequent (ca. 50%) naar de R7 lymfeklieren in de hals. De aanwezigheid van metastasen in de lymfeklieren is bepalend voor R8 de prognose en de behandeling. Diagnostiek van de hals bestaat uit palpatie en beeldvorming, R9 echter de nauwkeurigheid van deze technieken om kleine (<3mm) metastasen te detecteren is R10 laag. R11 R12 Vooral bij patiënten met een vroeg stadium OPCC, gestageerd als cT1-T2N0 (tumoren tot 4 cm R13 zonder aantoonbare metastasen in de cervicale lymfeklieren), waarbij het risico op occulte (niet R14 detecteerbare) metastasen ongeveer 30% bedraagt, is de onnauwkeurigheid van de diagnostiek R15 van de lymfeklieren belangrijk. Indien de hals van deze patiënten niet behandeld wordt dan zullen R16 deze occulte metastasen zich ontwikkelen tot klinisch detecteerbare lymfekliermetastasen met R17 een verdere verslechtering van de prognose als mogelijk gevolg. Behandeling van de primaire R18 tumor kan bij deze patiënten vaak middels transorale resectie zonder de hals te moeten openen. R19 Tot voor kort bestond de keuze voor behandeling van de hals uit electieve halsklierdissectie (EHD) R20 of zorgvuldige observatie (ZO) gevolgd door therapeutische halsklierdissectie (THD) bij patiënten R21 bij wie zich alsnog lymfekliermetastasen manifesteren. Kiest men voor behandeling middels R22 EHD dan krijgt 70% van deze cT1-T2N0 patiënten een onnodige chirurgische behandeling R23 van de hals met als mogelijke bijwerkingen deformiteit van de hals en morbiditeit bestaande R24 uit pijn, schouderdysfunctie en uitval van de ramus marginalis mandibulae, een tak van de R25 aangezichtszenuw, de nervus facialis. Het alternatief, de ZO houdt in dat de hals 3 tot 4 x per R26 jaar echografisch beoordeeld wordt, zonodig aangevuld met dunne naald aspiratie cytologie en R27 dat alleen die patiënten, bij wie zich een manifeste lymfekliermetastase ontwikkelt, behandeld R28 worden met een (therapeutische) gemodificeerde radicale halsklierdissectie. Recente studies1,2 R29 tonen een slechtere overleving aan bij ZO. R30 R31 Het dilemma bestaat uit enerzijds de keuze voor oncologische veiligheid van 30% van de 1 R32 cN0 patiënten met een occulte metastase, die beter af zijn met een EHD en anderzijds een R33 vermindering van morbiditeit van 70% bij patiënten die geen occulte metastasen hebben en dus R34 gebaat zijn bij ZO. R35 De ontdekking van een optimale voorspeller van occulte metastasen van het OPCC kan worden R36 beschouwd als het vinden van de Heilige Graal in de hoofd-hals chirurgie. R37 R38 R39
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R1 Het doel van dit proefschrift bestond uit het vinden van alsook het verder ontwikkelen van nieuwe R2 methoden om occulte metastasen in de hals op te sporen, ten einde een meer gepersonaliseerde R3 behandeling van de hals mogelijk te maken. Om dit doel te bereiken werd een eerder gepubliceerd R4 genexpressieprofiel gevalideerd, dat lymfekliermetastasering bij OPCC voorspelt. Daarnaast R5 werd er getracht andere biomarkers gerelateerd aan lymfekliermetastasering en prognose van R6 patiënten met OPCC te ontdekken. R7 R8 Het hoofd-halsgebied is voorzien van een uitgebreid lymfatisch netwerk en bevat ongeveer R9 driehonderd van de in totaal achthonderd lymfeklieren van het menselijk lichaam.3 Tumor R10 lymfangiogenese, de vorming van nieuwe lymfevaten rondom maligne tumoren, is een relatief R11 nieuw gebied ter bestudering van lymfogene metastasering van verschillende neoplasmata.4 Het R12 initiële concept van lymfogene metastasering hield in dat tumorcellen zich alleen verspreiden R13 door reeds bestaande peritumorale lymfevaten, welke dienen als passieve kanalen.5 Het gebruik R14 van markers waarmee lymfevaten zichtbaar(der) gemaakt kunnen worden leidde tot nieuwe R15 inzichten m.b.t. de actieve ontwikkeling van een tumor-geassocieerde lymfatische vasculatuur, R16 geheel analoog aan het proces van neovascularisatie. Verder onderzoek heeft daarnaast een aantal R17 groeifactoren en chemokinen, betrokken bij het proces van tumor lymfangiogenese, ontdekt en R18 leverde informatie over de actieve rol van tumorcellen bij de ontwikkeling van een pathologische R19 lymfangiogenese, welke dient als voorwaarde voor regionale metastasering.6 Onderzoek naar R20 de prognostische betekenis van tumorlymfevaten maakt gebruik van immunohistochemie (IHC) R21 om zo de dichtheid van lymfevaten (lymfevatdichtheid, LVD) in en rondom de tumormassa te R22 kwantificeren. R23 R24 Hoofdstuk 2 geeft een overzicht van de huidige kennis over de relatie tussen intratumorale R25 en peritumorale LVD en lymfekliermetastasering met als doel het ontwikkelen van een R26 aanvullend instrument ter voorspelling van het risico op occulte metastasen in patiënten met R27 plaveiselcelcarcinoom van het hoofd-hals gebied (HHPCC). Het OPCC was de belangrijkste R28 primaire maligniteit die onderzocht werd, aangevuld met tumoren afkomstig uit de keelholte en R29 strottenhoofd. De meeste artikelen tonen een significante correlatie tussen verhoogde LVD en R30 de aanwezigheid van lymfekliermetastasering. Een groot deel van dergelijke studies kampte met R31 diverse beperkingen in zowel opzet als uitvoering van de studie. Voorlopige conclusie is daarom R32 dat LVD potentieel zou kunnen dienen als marker ter detectie van occulte lymfekliermetastasen R33 maar dat aanvullend onderzoek nodig is om dit nader uit te werken. R34 R35 Een andere methode ter bepaling van het risico op occulte lymfekliermetastasen is het meten van R36 de expressie van een genprofiel in de tumor. Middels diverse genexpressieprofielen is klinische R37 toepasbaarheid aangetoond voor de diagnose en prognose van verschillende kankers.7,8 Ook R38 voor plaveiselcarcinomen van het hoofd-halsgebied werd een diagnostisch genexpressieprofiel R39
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geïdentificeerd, waarmee onderscheid tussen metastaserende en niet-metastaserende tumoren R1 afkomstig uit de mond- en keelholte gemaakt kon worden.9 Hoewel de onafhankelijke set tumoren R2 gebruikt ter validatie klein was (n = 22), scoorde het genexpressieprofiel een veelbelovende R3 negatief voorspellende waarde (NVW) van 100%. R4 R5 In hoofdstuk 3, werd dit genexpressieprofiel9 opnieuw geëvalueerd in een groter patiënten R6 cohort afkomstig uit 8 ziekenhuizen (n = 222). De NVW van het genexpressieprofiel over het R7 gehele validatie cohort (n = 222) bleek 72%. In een subset met alleen de klinisch meest relevante R8 tumoren (cT1-T2N0) OPCC (n = 101), werd een NVW van 89% vastgesteld. Door toevoeging R9 van het genexpressieprofiel aan de huidige klinische en beeldvormende diagnostiek is men in R10 staat het aantal occulte lymfekliermetastasen van 28% naar 11% te reduceren. Prospectieve R11 studies zijn nodig om te beoordelen of toevoeging van het genexpressieprofiel aan de huidige R12 diagnostiek zal leiden tot meer gepersonaliseerde behandeling van de hals van patiënten met een R13 klinisch vroeg stadium OPCC. R14 R15 Presentatie van de in hoofdstuk 3 beschreven studie tijdens het 3e Wereld Congres van de R16 International Academy of Oral Oncology in Singapore 2011 resulteerde in een uitnodiging tot R17 het schrijven van een review over nieuwe diagnostische methoden voor de beoordeling van R18 de klinisch klier-negatieve hals (cN0) in patiënten met OPSCC (hoofdstuk 4). Beperkingen van R19 beeldvormende technieken om kleine metastasen op te sporen, hebben geleid tot een zoektocht R20 naar additionele kenmerken van, of biomarkers in de primaire tumor, ter voorspelling van R21 (occulte) lymfekliermetastasering. In dit review, beperkten wij ons tot de bespreking van de twee R22 meest belovende technieken: het genexpressieprofiel (GEP) en de schildwachtklier biopsie (SKB). R23 Het SKB is een methode die de lymfeklieren met het hoogste risico op metastasen opspoort R24 en middels chirurgische ingreep verwijderd, waarna de klieren zorgvuldig histopathologisch R25 onderzocht worden. Een nieuw algoritme ter stagering van de cN0 hals waarin beide methoden R26 zijn geïncorporeerd, wordt beschreven met als doel verdere optimalisatie van de behandeling R27 van de hals bij patiënten met een vroeg stadium OPCC. Door toepassing van dit algoritme R28 wordt oncologische veiligheid niet zozeer nagestreefd middels overbehandeling met (EHD) en R29 de bijbehorende onnodige morbiditeit, maar meer door verdere reductie van het percentage R30 occulte metastasen en nauwkeurige follow-up. De combinatie van het GEP en SKB als aanvulling R31 op de huidige beeldvormende technieken, zal het aantal occulte metastasen theoretisch verder 1 R32 reduceren naar ongeveer 6%. Daarnaast zal het gebruik van de SKB alleen bij hoog risico op R33 occulte lymfekliermetastasen bepaald volgens het GEP verdere invasieve overbehandeling R34 reduceren. Prospectieve klinische studies waarin een dergelijk diagnostisch algoritme wordt R35 geïmplementeerd is nodig om te onderzoeken of het GEP en de SKB kunnen worden gecombineerd R36 en of oncologische en functionele uitkomsten bij patiënten met OPCC daadwerkelijk verbeteren. R37 R38 R39
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R1 Hoofdstuk 5 beschrijft hoe analyse van genexpressie ook kan worden gebruikt om het risico R2 op het ontwikkelen van lokaal dan wel regionaal recidief te bepalen. Dit is belangrijk omdat de R3 prognose van patiënten met HHPCC nog steeds matig is en meer dan 50% van deze patiënten R4 binnen vijf jaar na diagnose overlijdt. Een aanzienlijk deel van deze sterfgevallen wordt veroorzaakt R5 door lokaal dan wel regionaal recidief.10 Dit onderzoek was gericht op het identificeren van de R6 biologische basis van tumorkenmerken die zijn geassocieerd met de ontwikkeling van locoregionale R7 recidieven en een slechte prognose. Perineurale- en sprieterige groei waren significant R8 gecorreleerd met een slechtere prognose. Voor het patroon van uitgebreide sprieterige groei, R9 maar niet voor perineurale groei, werd een genexpressieprofiel van 160 genen geïdentificeerd. Er R10 bleek echter geen relatie tussen dit genexpressieprofiel en overleving aantoonbaar. Bij patiënten R11 van wie de tumor middels chemoradiatie wordt behandeld en er slechts kleine diagnostische R12 biopten beschikbaar zijn, kan bepaling van histopathologische karakteristieken als sprieterige R13 groei lastig zijn. Het meten van genexpressie in dergelijke biopsieën kan nuttig zijn in dit opzicht. R14 Samenvattend bleek dat middels genexpressie analyse de biologische basis van de determinanten R15 van locoregionaal recidief werd ontrafeld. R16 R17 In de hoofdstukken 6-8 werd ingezoomd op een aantal specifieke genen van de het R18 gevalideerde genexpressieprofiel ter voorspelling van lymfekliermetastasering. Correlaties met R19 klinische en histopathologische variabelen zowel op gen- als op eiwitniveau werden geëvalueerd R20 in patiënten met OPCC. HPV status werd bepaald middels een algoritme voor HPV-16 om HPV- R21 positieve tumoren te excluderen.11 Een “tissue microarray (TMA)” werd vervaardigd en gebruikt R22 om expressie van de verschillende eiwitten middels immunohistochemie te meten. R23 R24 In hoofdstuk 6 werden de eiwitten van 4 genen uit de top 10 van meest voorspellende genen R25 uit het GEP nader geanaliseerd: secretoire leukocyt proteaseremmer (SLPI), lipocaline-2 (LCN2), R26 thrombospondine-2 (THBS2) en tumor geassocieerde calcium signaalomvormer 2 (TACSTD2). R27 Gen- en eiwitexpressie werd gecorreleerd met lymfekliermetastasering, overleving (algehele R28 overleving, AO) en ziektespecifieke overleving (ZSO). SLPI proteïne expressie correleerde wel R29 met lymfekliermetastasering in het gehele cohort, maar niet in de relevante subgroep van R30 de vroeg stadium (cT1-T2N0) OPCCs. SLPI expressie correleerde met AO (hazard ratio [HR] R31 = 0,61) en ZSO (HR = 0,47) in een multivariate analyse. LCN2, THBS2 en TACSTD2 hadden R32 geen correlatie met lymfekliermetastasering, AO of ZSO. Hoewel SLPI expressie correleerde R33 met lymfekliermetastasering, had het geen toegevoegde waarde bij de detectie van occulte R34 lymfekliermetastasen in patiënten met vroeg stadium OPCC. SLPI is wel een onafhankelijke R35 voorspeller van zowel AO en ZSO en derhalve een relevante prognostische biomarker in OPCC. R36 R37 R38 R39
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In hoofdstuk 7 werd een vergelijkbare analyse uitgevoerd zoals in hoofdstuk 6 alleen werden R1 nu de proteasen kallikreïne 5 (KLK5) en kallikreïne 7 (KLK7) en hun aanverwante remmer «serine R2 proteaseinhibitor Kazal type 5» (SPINK5) geëvalueerd als predictieve of prognostische markers R3 in OPCC. Compleet verlies van expressie van zowel KLK5 als KLK7 (co-expressie) correleerde R4 met een slechtere ZSO en AO. Multivariate analyse toonde dat co-expressie een onafhankelijke R5 prognostische factor is voor ZSO(p = 0,029) en AO (p = 0,001) en kan dienen als prognostische R6 marker bij deze ziekte. R7 R8 Tenslotte werd in hoofdstuk 8 de rol van cathepsine K (CTSK) als biomarker voor R9 lymfekliermetastasering en prognose onderzocht in patiënten met OPCC. Genexpressie data R10 werd verkregen uit eerder onderzoek9 en eiwitexpressie werd semi-kwantitatief bepaald R11 middels immunohistochemie op de vervaardigde TMA. Alle expressie data werd gecorreleerd R12 met klinische en pathologische variabelen. Expressie van CTSK bleek sterk geassocieerd met R13 lymfekliermetastasering en perineurale groei (p <0,01). Logistische regressie-analyse duidde R14 CTSK eiwitexpressie als de belangrijkste onafhankelijke factor van lymfekliermetastasering (odds R15 ratio = 7,65, CI: 2,31-23,31, p = 0,001). Daarnaast had CTSK expressie ook significante invloed R16 op de 5-jaars ZSO (HR = 2,29, CI: 1,01 5,21, p = 0,047 voor genexpressie; HR = 2,79, CI: 1,02- R17 7,64, p = 0,045 voor eiwitexpressie). Middels ROC-analyse werd een afkappunt van 25 bepaald, R18 om patiënten in te delen in groepen met lage en hoge CTSK eiwitexpressie. Hoge CTSK expressie R19 correleerde significant met lymfekliermetastasering (p <0,01). In de klinisch relevante subgroep, R20 bestaande uit patiënten met cT1-T2N0 OPCC (n=24), bleek de voorspellende waarde voor occulte R21 lymfekliermetastasen van CTSK groot. Van de tien patiënten met occulte lymfekliermetastasen in R22 de hals, hadden negen een hoge CTSK eiwitexpressie, terwijl slechts één patiënt een CTSK-waarde R23 lager dan de cut-off bleek te hebben. De sensitiviteit van CTSK eiwitexpressie ter detectie van R24 occulte metastasen in cT1-T2N0 OPCC was 90%, de specificiteit 57%, de positieve voorspellende R25 waarde 60% en de negatief voorspellende waarde 89%. De huidige studie was gebaseerd op R26 een relatief beperkt cohort van 83 patiënten met OSCC. De resultaten dienen verder gevalideerd R27 te worden in bij voorkeur prospectieve studies met een groter aantal patiënten. Eerste stappen R28 zijn reeds gezet t.b.v. een dergelijke studie en zodra bekend zullen de resultaten gepubliceerd R29 worden. R30 R31 1 R32 R33 R34 R35 R36 R37 R38 R39
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R1 REFERENTIES R2 R3 1. D’Cruz AK, Vaish R, Kapre N, et al. Elective versus Therapeutic Neck Dissection in Node-Negative Oral Cancer. N Engl J Med 2015; 373: 521-9. R4 2. Dik EA, Willems SM, Ipenburg NA, et al. Watchful waiting of the neck in early stage oral cancer is R5 unfavourable for patients with occult nodal disease. Int J Oral Maxillofac Surg 2016; 45: 945-50. R6 3. Rouviere H. Lymphatic system of the head and neck. Tobias MJ, Translator. Ann Arbor, MI: Edwards R7 Brothers; 1938. R8 4. Hirakawa S. From tumor lymphangiogenesis to lymphvascular niche. Cancer Sci 2009; 100: 983-89. 5. Zeidman I., Copeland B. E., Warren S. Experimental studies on the spread of cancer in the lymphatic R9 system. II. Absence of a lymphatic supply in carcinoma. Cancer 1955; 8: 123-27. R10 6. Karatzanis AD, Koudounarakis E, Papadakis I, et al. Molecular pathways of lymphangiogenesis and R11 lymph node metastasis in head and neck cancer. Eur Arch Otorhinolaryngol 2012; 269: 731-37. R12 7. van’t Veer LJ, Bernards R: Enabling personalized cancer medicine through analysis of gene expression patterns. Nature 2008; 452: 564-570. R13 8. Cardoso F, van’t Veer LJ, Bogaerts J, et al. 70-Gene Signature as an Aid to Treatment Decisions in Early- R14 Stage Breast Cancer. N Engl J Med 2016; 375: 717-29. R15 9. Roepman P, Wessels LF, Kettelarij N, et al. An expression profile for diagnosis of lymph node metastases R16 from primary head and neck squamous cell carcinomas. Nat Genet 2005; 37: 182-186. R17 10. Graveland AP, Braakhuis BJM, Eerenstein SEJ, et al. Molecular diagnosis of minimal residual disease in head and neck cancer patients. Cell Oncol 2012; 35: 367-375. R18 11. Smeets SJ, Hesselink AT, Speel EJ, et al. A novel algorithm for reliable detection of human papillomavirus R19 in paraffin embedded head and neck cancer specimen. Int J Cancer 2007; 121: 2465-2472. R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
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R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 1 R32 R33 R34 R35 R36 R37 R38 R39
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List of publications Chapter 13
R1 LIST OF PUBLICATIONS R2 R3 Lymphatic vessel density as a marker of lymph node metastasis in head and neck R4 squamous-cell carcinoma. A systematic review of the literature. R5 Koudounarakis E, Leusink FKJ, Willems SM, Brekel van den MW, Zuur CL. R6 Accepted in European Journal of Surgical Oncology, 2016. R7 R8 Pain as primary symptom of a malignant parotid tumor. R9 Pijn als primair symptoom van een maligne parotis tumor. R10 Kamp van der M, Leusink FKJ, Al Mamgani A, Lohuis PJ, Brekel van den MW. R11 Accepted in Nederlands Tijdschrift voor Tandheelkunde, 2016, A9815, Dutch. R12 R13 Cathepsin K associates with lymph node metastasis and poor prognosis in oral R14 squamous-cell carcinoma. R15 Leusink FKJ, Koudounarakis E, Frank MH, Broekhuizen R, Braunius W, Hooff van SR, Diest van R16 PJ, Koole R, Willems SM. R17 Submitted to BMC Cancer. R18 R19 Tumor biological determinants of locoregional recurrence of non-HPV head and neck R20 squamous-cell carcinoma. R21 Leusink FKJ, Braakhuis BJM, Wieringen van WN, Bloemena E, Rustenburg F, Ylstra B, Kummer R22 JA, Diest van PJ, Voorham Q, Smeets S, Roepman P, Koole R, Leemans CR, Brakenhoff RH. R23 In preparation. R24 R25 The co-expression of kallikrein 5 and kallikrein 7 associates with poor survival in non- R26 HPV oral squamous-cell carcinoma. R27 Leusink FKJ, Diest van PJ, Frank MH, Broekhuizen R, Braunius W, Hooff van SR, Willems SM, R28 Koole R. Pathobiology, 2015; 82: 58-67. R29 R30 Nodal metastasis and survival in oral cancer: Association with protein expression of R31 SLPI, not with LCN2, TACSTD2, or THBS2. R32 Noorlag R, van der Groep P, Leusink FKJ, Hooff van SR, Frank MH, Willems SM, van Es RJ. R33 Head Neck, 2015; 37: 1130-6. R34 R35 Novel diagnostic methods for detection of occult lymph node metastases in oral cancer. R36 Nieuwe onderzoeksmethoden om occulte halskliermetastasen van mondkanker op te sporen. R37 Leusink FKJ, Takes RP, Brakenhoff RH, Bree de R, Es van RJ. R38 Het Tandheelkundig Jaar 2014, Bohn Stafleu van Loghum, 197 - 207. ISBN 978-90-368-0454-7. R39
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Cannabinoid receptor-2 immunoreactivity is associated with survival in squamous-cell R1 carcinoma of the head and neck. Klein Nulent TJ, Van Diest PJ, van der Groep P, Leusink FKJ, R2 Kruitwagen CL, Koole R, Van Cann EM. R3 Br J Oral Maxillofac Surg, 2013; 51: 604-9. R4 R5 Validation of a gene expression signature for assessment of lymph node metastasis in R6 oral squamous-cell carcinoma. R7 Leusink FKJ, Hooff van SR, Roepman P, Baatenburg de Jong RJ, Speel EM, Brekel van den MW, R8 Velthuysen van MF, Diest van PJ, Es van RJ, Merkx MA, Kummer JA, Leemans CR, Schuuring E, R9 Langendijk JA, Lacko M, De Herdt MJ, Jansen JC, Brakenhoff RH, Slootweg PJ, Takes RP, Holstege R10 FC. Journal of Clinical Oncology, 2012; 30: 4104-10. R11 R12 Novel diagnostic modalities for the clinically node negative neck in oral squamous-cell R13 carcinoma. R14 Leusink FKJ, Es van RJ, Bree de R, Baatenburg de Jong RJ, Hooff van SR, Holstege FC, Slootweg R15 PJ, Brakenhoff RH, Takes RP. R16 Lancet Oncology, 2012; 13: e554-61. R17 R18 Downregulation of Serpin B13 expression in head and neck squamous-cell carcinomas R19 associates with poor clinical outcome. R20 Koning de PJ, Bovenschen NA, Leusink FKJ, Broekhuizen R, Quadir R, van Gemert JT, Hordijk GJ, R21 Chang WS, van der Tweel I, Tilanus MG, Kummer JA. R22 International Journal of Cancer, 2009; 125: 1542-50. R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 13 R35 R36 R37 R38 R39
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Dankwoord Chapter 14
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11 R12 R13 R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39
210 Dankwoord
DANKWOORD R1 R2 Nu de afronding een feit is heb ik aan den lijve ondervonden dat het schrijven van een proefschrift R3 vooral bestaat uit hard werken (perspiratie), een klein beetje inspiratie en veel eenzame uren R4 achter je laptop. Dankzij dit onderzoek heb ik verschillende mensen ontmoet en zijn er diverse R5 werelden voor me open gegaan. Als belangrijkste les noteer ik: zonder samenwerking geen R6 proefschrift. R7 R8 Promotor, prof. dr. R. Koole, beste Ron, aan u heb ik het meest te danken. U heeft me de R9 ruimte gegeven me te ontwikkelen tot de mens en professional die ik nu ben. Ik heb zowel deze R10 promotie als de opleiding tot MKA-chirurg onder uw begeleiding afgerond. Hoewel een enkel R11 artikel (KLK) wat langer op zich liet wachten en de voortgang van het onderzoek tijdens de R12 opleiding soms wat vertraging opliep voelde ik toch altijd voldoende vertrouwen bij u hetgeen R13 me motiveerde om weer verder te gaan. Veel dank daarvoor, aimabel mens. R14 R15 Promotor, prof. dr. P.J. van Diest, beste Paul, ongelofelijk. Is het eerste woord dat me te binnen R16 schiet als ik aan jou denk. De hoeveelheid onderzoekers die jij weet te bereiken en laat groeien. R17 Dat is iets heel moois. Niet alleen ben je een geweldige motivator en inspirator, je hebt ook R18 een goed getraind luisterend oor. Veel dank voor al je hulp bij het scoren, het aanhoren van de R19 vorderingen, en natuurlijk het lezen en herschrijven van alle manuscripten. R20 R21 Co-promotor, Dr. S.M. Willems, beste Stefan, hoewel je pas enkele jaren na het vertrek van de R22 toenmalige hoofd-hals patholoog op de afdeling pathologie binnenkwam, speelde je al snel R23 een grote rol in het onderzoek van velen en zo ook dat van mij. Dank voor je hulp bij het scoren R24 en herschrijven van de artikelen. Het lijkt me mooi om in toekomst op je deur te mogen blijven R25 kloppen met vragen over alle verschillende omics en de implementatie ervan in de klinische R26 praktijk. Bij voorkeur in een gezamenlijk project over de cN0 hals. R27 R28 Co-promotor, Dr. R.J.J. van Es, beste Robert, jouw uithoudingsvermogen kent geen grenzen. Je R29 hart voor de patiënten is groot en ik bewonder je operatiekunsten. Vond het heel bijzonder en R30 een grote eer om tijdens mijn fellowship hoofd-hals oncologie in het NKI-AvL opnieuw enkele R31 fijne kneepjes van jou in het UMCU te mogen leren tijdens de reconstructieve chirurgie en de R32 bijbehorende microchirurgie. Dank! R33 R34 Geachte leden van de leescommissie, prof. dr. R. de Bree, beste Remco, prof. dr. M.W.M. van R35 den Brekel, beste Michiel, prof. dr. J.L.N. Roodenburg, beste Jan, prof. dr. C.H.J. Terhaard, beste R36 Chris, prof. dr. G.J.A. Offerhaus, beste Johan, hartelijk dank voor jullie deelname aan de lees- en R37 promotiecommissie. 14 R38 R39
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R1 Prof. dr. E. Bloemena, beste Elisabeth en dr. J.A. Kummer, beste Alain, veel dank voor alle R2 pathologielessen tijdens het reviseren van alle geïncludeerde tumoren in de studies. In het begin R3 duizelde het me wel eens zo snel als jullie soms die coupes onder die microscoop doortrokken. R4 Toch namen jullie ook vaak de moeite er een leuke les van te maken. Het zijn lessen waar ik tot R5 op de dag van vandaag wat aan heb, zeker als snijdend specialist. Dank. R6 R7 Dr. A.J.W.P. Rosenberg, beste Toine, dank voor je interesse in het onderzoek gedurende de R8 opleiding. R9 Dr. E.M. van Cann, beste Ellen, jouw chirurgische kwaliteiten staan buiten elke discussie. Dank R10 voor je interesse en ik kijk uit naar verdere samenwerking in de toekomst. R11 Dr. M.S.M. Muradin, beste Marvick, het was elke keer een groot genoegen om met jou te R12 implanteren. Erg gezellig ook. Dank voor je interesse in het onderzoek. R13 R14 Beste (oud) assistenten MKA-chirurgie, dank voor de steun en flexibiliteit gedurende de gehele R15 opleiding. Een aantal wil ik persoonlijk noemen: Dorien, tijdens de opleiding verschilden we R16 wel eens van mening, toch vond ik het leuk dat we elkaar weer tegen kwamen tijdens ons R17 beider fellowship hoofd-hals oncologie, jij voor de ablaties en ik voor de reconstructies. Ik kijk R18 uit naar verdere samenwerking in de hoofd-hals werkgroep VUmc-MCA. Esther, we hebben R19 een goede tijd samen in Breda en Utrecht gehad. Denk met veel plezier terug aan de AO-cursus R20 in Davos. Stroyke, dank voor je hulp bij het vullen van de gaten in het rooster als ik weer eens R21 een researchoverleg had in Amsterdam of elders. KIO-cursussen met jou erbij waren niet alleen R22 leerzaam maar ook memorabel. Erg mooi dat we elkaar na de opleiding in het UMC Utrecht nu R23 als staflid in het VUmc weer tegen zullen komen. R24 R25 Alle medewerkers van het Pathology Reseach Lab. Bij jullie in het lab is het allemaal begonnen. R26 De ontspannen manier van werken en de grote bereidwilligheid maakten het werken bij jullie R27 tot een leuke ontdekkingsreis. Speciale dank gaat uit naar Grada voor het bouwen van de TMA R28 en naar Roel voor hulp bij het monnikenwerk bestaande uit het serieel opsnijden en kleuren van R29 alle halsklier preparaten. Toen was dat bijzonder, nu gaat iedereen het doen maar dan m.b.v. de R30 schildwachtklier. R31 R32 Alle collega’s van het hoofd-hals consortium wil ik hier bedanken voor hun hulp bij het tot stand R33 komen van de studie die als steunpilaar dient van dit proefschrift (hoofdstuk 3). Specifiek wil ik R34 danken: Frank Holstege voor de scherpe analyses en sturing van dit enorme project, Robert Takes R35 voor het bewaren van de rust tijdens het aanhoren van alle meningen, Paul Roepman voor de R36 uitleg over genexpressie analyse en natuurlijk ook Sander van Hooff voor de samenwerking van R37 niet alleen dit project maar ook van enkele andere studies. R38 R39
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Drs. E.S. Nijdam, beste Eelco, dank voor je interesse in mijn onderzoek en advies over allerlei R1 zaken. Dento-alveolair en op het gebied van de implantologie (eerste SBE) heb ik veel van je R2 geleerd. R3 R4 Beste stafleden van de Maatschap MKA-chirurgie Breda, beste Peter, Bert, Eelco, Jan, Erik en R5 Gertjan, veel dank voor de meters die ik bij jullie heb mogen maken, zowel in de ok als achter R6 mijn pc t.b.v. dit proefschrift. R7 R8 Beste Marc Heijke, tijdens mijn B-opleiding in Breda ook veel van jou geleerd op het gebied van R9 de implantologie. Dank voor je interesse in mijn onderzoek en in mij als persoon. R10 R11 Alle zusters van de polikliniek MKA-chirurgie in het Maasstad ziekenhuis en natuurlijk ook de R12 heren zelf, te weten Hans, Kees, Robert en Willem. Veel dank voor jullie interesse tijdens alle R13 waarnemingen in de afgelopen 5 jaar. Met veel plezier heb ik met regelmaat jullie polikliniek R14 mogen bezoeken en ik dank jullie voor het respect en de waardering die ik altijd heb gekregen. R15 Ook leuk om mijn NVOI-registratie te kunnen behouden met die ene dag per week. R16 R17 Prof. dr. R.H. Brakenhoff en dr. B.M.J. Braakhuis, beste Boudewijn en Ruud, de sectie KNO- R18 tumor biologie van het VUmc is er een van naam. Heb de vele bezoeken als zeer constructief en R19 inspirerend ervaren. Dank voor jullie interesse en delen van kennis en kunde. Tot slot, Boudewijn R20 veel plezier toegewenst in LDF en Ruud, de uitwerking van de future perspectives zou ik graag in R21 samenwerking willen uitvoeren. R22 R23 Alle Scherfjes waaronder Schelte, Ernst, Dirk-Jan, Michiel, Thijs en Otto en alle Rammers, R24 Robbie, Thorwald, Wichers, Gijs, Bram, Pieter, Huub, Hans, Postma, Boreel en Henkie, en andere R25 liefhebbende vrienden zoals Dustin en Anne, Douwe en Harry, Vincent en Birgitte, dank dat jullie R26 er zijn. Ik kijk er naar uit om jullie nu het boekje af is, weer meer te meeten. R27 R28 Hoofd-halschirurgen in het NKI AvL, dank voor het mogen werken op jullie afdeling. De manier R29 waarop jullie wetenschap en zorg combineren inspireerden me om de draad weer op te pakken R30 en mijn proefschrift af te maken. Dat dat nu tijdens het fellowship hoofd-hals oncologie is R31 gelukt heb ik mede te danken aan de ruimte die ik daarvoor van jullie kreeg om het schrijven te R32 combineren met opereren en de poliklinische zorg. R33 R34 Fellows hoofd-hals chirurgie, differentianten van de KNO, arts-assistenten MKA (AMC), AGNIO’s R35 en arts-onderzoekers in het NKI AvL, dank jullie allen voor gezellige tijd samen op de afdeling en R36 op de ok’s. In het bijzonder wil ik noemen: Richard, wij vormden samen een sterk duo waarbij R37 we elkaar completeerden. We bespraken de talloze mogelijkheden om de zorg voor de hoofd- 14 R38 R39
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R1 hals patiënt te verbeteren. Ik wens je er veel succes mee na terugkomst uit Australië in het AvL? R2 Xander, je wordt opgeleid voor het MST en gaat daar ongetwijfeld veel voor de Twentse hoofd- R3 hals patiënten betekenen. Een goede zaak. Heel veel succes daar. Dank voor je gezelligheid R4 tijdens de cursussen en congressen die we samen hebben afgelopen. Dear Thomas, although R5 our collaboration was short, it was fruitful. Thanks for brainstorming about everything in life that R6 really matters and of course your help in rephrasing a few lines of the future perspectives. Would R7 be cool to build up that nomogram with you. Best wishes. R8 R9 Elephtherios Koudounarakis, beste Leffe, jou ben ik erg dankbaar voor je directe hulp bij het R10 schrijven van het CTKS paper en ook het LVD review. Deze samenwerking is voor herhaling R11 vatbaar. Je hebt veel talent en verdient een mooie toekomst. Samen met jou opereren is een R12 groot genoegen en ik hoop dan ook dat we samen op Kreta ooit een commando gaan doen. R13 Lijkt me fantastisch. R14 R15 Michaël Frank, dank voor je statistische hulp bij het beruchte KLK paper. Het succes smaakte naar R16 meer en er volgde een mooi verhaal over CTSK. Gelukkig kunnen we veel meer samen dan alleen R17 kletsen over de significantie van een of ander eiwit. Je vormt je eigen mening, met een open blik, R18 en laat je daarbij niet beïnvloeden door anderen. Het zijn deze eigenschappen waarom ik je graag R19 als paranimf straks naast me heb staan en ik weet dat we in de toekomst nog veel lol gaan maken R20 op de golfbaan, achter de laptop of op de ok? Ik kijk er naar uit. R21 R22 Marijn Rutgers, gast! Vanaf 1998 zijn we al collega’s in allerlei soorten en maten. Huffen, juffen, R23 bluffen, co-schappen lopen op Curaçao, huwelijken organiseren, you name it, we shared it. En R24 nu dus ook elkaars paranimf. Met jou naast mijn zijde heb ik er alle vertrouwen in dat we er R25 een memorabele plechtigheid van gaan maken. Ik waardeer je om wie je bent en heb zin in alle R26 events die nog zullen volgen, amigo. R27 R28 Lieve familie Van Hussen, Hans en Renée, jullie wil ik danken voor de getoonde belangstelling R29 voor mijn werk en studie. R30 R31 Lieve Oma Ria, ik vind het prachtig dat ik door het schrijven van dit boek in uw voetsporen mag R32 treden en kijk er naar uit u een exemplaar te overhandigen. Liefs van uw kleinzoon. R33 R34 Lieve papa en mama, wat hebben jullie me veel gegeven. En nog steeds. Altijd kan ik een beroep R35 op jullie doen en altijd zullen jullie voor me klaar staan. Ook was er elke keer tijd om de pieken R36 en de dalen behorende bij dit proefschrift te bespreken. Ik kan me geen betere ouders wensen. R37 Apart dan nog aan jou papa, ik vind het heel erg tof dat een Karel de cover van mijn proefschrift R38 opsiert. Het doek zal een speciale plek in mijn studeerkamer krijgen. R39
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Lieve Naomi, mijn grote zus. Ook al hebben we elkaar de laatste jaren minder gezien dan we R1 misschien zouden willen, toch weet ik van binnen dat het wel goed zit. Je bent altijd welkom. R2 R3 Lieve Jurjan, wat is het toch chill om jou als broer te hebben. Dank voor de interesse maar R4 met name de steun in vele opzichten die je me de laatste jaren en dagen bij het schrijven van R5 het proefschrift hebt gegeven. Je bent een goede vent, één waar je op kan bouwen. Net een R6 implantaat, robuust en toch ook zeer verfijnd, weinig onderhoud nodig en toch zeer solide, en er R7 past altijd wel iets moois op. Ik hoop dat we er samen nog velen zullen plaatsen en ook andere R8 mooie dingen zullen bouwen. Dat komt goed. R9 R10 Lieve Job, de afgelopen jaren woonden we in dezelfde steden, altijd binnen 1 km van elkaar. Daar R11 hebben we veel gebruik van gemaakt en heb ik erg van genoten. Vind het super dat je ook voor R12 mijn kids zo’n grote rol wil spelen samen met Selma. Geweldig hoe ik jullie tot twee keer toe tot R13 diep in de nacht om hulp mocht vragen en het kreeg. Nu is het af. Mijn dank is groot. R14 R15 Tim en Emma, als ik jullie zie dan maken jullie me blij. Jullie geven me de mogelijkheid om vader R16 te zijn, hetgeen ik een prachtige uitdaging vind en waar ik door jullie ontzettend van geniet. R17 R18 Lieve Liesbeth, als laatste richt ik me tot jou. Wat geef je me veel, warmte en liefde, twee R19 prachtige kinderen. Bij jou voel ik me thuis. Dank voor je vertrouwen in mij, je prikkel aan mij om R20 dit proefschrift af te maken, je vrolijke karakter en je organisatie thuis waarmee je alles draaiende R21 weet te houden. Dat is niet makkelijk met een man zoals ik, die steeds nieuwe uitdagingen ziet R22 en zoekt en waarvan het soms lijkt alsof hij het liefst in zeven sloten tegelijk loopt. De manier R23 waarop wij samen tegenslagen hebben overwonnen geeft me alle vertrouwen in de toekomst, R24 ook al zijn we het niet altijd eens. Echter, een beetje wrijving geeft naast warmte ook glans. Nu R25 dit boek is afgerond kunnen we samen gaan bouwen aan ons huis, waarvandaan we nog een R26 heleboel dingen samen gaan ontdekken en beleven. Zin in! X R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 14 R38 R39
215 R1 Publication and distribution of this thesis was kindly supported by: R2 R3 Prof. dr. P. Egyedi Stichting R4 MKA-chirurgie UMC Utrecht www.umcutrecht.nl R5 Nederlandse Vereniging voor Mondziekten, www.nvmka.nl R6 Kaak- en Aangezichtschirurgie (NVMKA) R7 Mondzorg Zeist B.V. www.tandartsleusink.nl R8 Pro-Cam Implants B.V. | Camlog NL www.pro-cam.nl R9 Supra Solutions B.V. www.supra-solutions.com R10 Kees van Steenis R11 Van Prooijen B.V. R12 K.J. Leusink kareljleusink.blogspot.nl R13 Zimmer Biomet www.zimmerbiomet.nl R14 KLS Martin Group www.martinnederland.nl R15 V.O.F. Tand Techniek Zeist www.tandtechniekzeist.nl R16 DENVA Accountants & Adviseurs www.denva.nl R17 Henry Schein Dental www.henryschein.nl R18 Atos Medical B.V. www.atosmedical.nl R19 Autohan - de youngtimer specialist - www.autohan.nl R20 Dam Medical www.dammedical.nl R21 Tessier Foundation www.tessierfoundation.nl R22 Chipsoft B.V. www.chipsoft.nl R23 Dentaid BeNeLux B.V. www.dentaid.nl R24 Groenstate Vermogensbeheer B.V. www.groenstate.nl R25 A-dam Underwear B.V. www.adamunderwear.com R26 R27 R28 R29 R30 R31 R32 R33 R34 R35 R36 R37 R38 R39