University of Groningen
Improving quality of care for patients with ovarian and endometrial cancer Eggink, Florine Alexandra
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record
Publication date: 2018
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA): Eggink, F. A. (2018). Improving quality of care for patients with ovarian and endometrial cancer. Rijksuniversiteit Groningen.
Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne- amendment.
Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.
Download date: 04-10-2021 Improving quality of care for patients with ovarian and endometrial cancer
Florine A Eggink Eggink, Florine Alexandra Improving quality of care for patients with ovarian and endometrial cancer
PhD dissertation, University of Groningen, The Netherlands
ISBN electronic: 978-94-034-0283-3 ISBN printed: 978-94-034-0284-0
© Copyright 2017- Florine Eggink, Groningen, The Netherlands. All rights reserved. No part of this thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means, without prior permission of the author or, when appropriate, the publisher of the published articles.
Cover design and layout: evelienjagtman.com Printed by: Ridderprint BV
Printing of this thesis was financially supported by the University of Groningen, University Medical Center Groningen, Graduate School of Medical Sciences, Integraal Kankercentrum Nederland, Chipsoft BV, Stichting Olijf and Noord Negentig. Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen op gezag van de rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 31 januari om 14.30 uur
door
Florine Alexandra Eggink geboren op 7 september 1987 te Groningen Promotores Prof. dr. H.W. Nijman Prof. dr. R.F.P.M. Kruitwagen
Co-promotores Dr. M.A. van der Aa Dr. M. de Bruyn
Beoordelingscommissie Prof. Dr. G.A. Huls Prof. Dr. L.F.A.G. Massuger Prof. Dr. I.J.M. de Vries
Paranimfen Dr. J.I. Kamstra Drs. F.L. Komdeur TABLE OF CONTENTS
Chapter 1 General introduction 9
Part I: Improving organization of care for patients with ovarian cancer 21 Chapter 2 Improved outcomes due to changes in organization of care for patients with ovarian cancer in the Netherlands. Gynecologic Oncology. 2016 Jun;141(3):524-30
Chapter 3 The impact of centralization of services on treatment delay in ovarian 41 cancer: a study on process quality. International Journal for Quality in Health Care. 2017 Oct 1;29(6):810-816
Chapter 4 Surgery for patients with newly diagnosed advanced ovarian cancer: 57 which patient, when and extent? Current Opinion in Oncology. 2017 Sep; 29(5):351-358
Part II: Improving organization of care for patients with endometrial cancer 77 Chapter 5 Less favorable prognosis for low risk endometrial cancer patients with a discordant pre- versus post-operative risk stratification. European Journal of Cancer. 2017 Jun;78:82-90
Chapter 6 Compliance with adjuvant treatment guidelines in endometrial cancer: 97 room for improvement in high risk patients. Gynecologic Oncology. 2017 Aug;146(2):380-385
Part III: Encouraging individualization of care for patients with endometrial cancer 121 Chapter 7 POLE proofreading mutations elicit an anti-tumor immune response in endometrial cancer. Clinical Cancer Research. 2015 Jul 15;21(14):3347-55
Chapter 8 Immunological profiling of molecularly classified high-risk endometrial 151 cancers identifies POLE-mutant and microsatellite unstable carcinomas as candidates for checkpoint inhibition Oncoimmunology. 2016 Dec 9;6(2):e1264565 Chapter 9 Summary 183
Chapter 10 Nederlandse samenvatting voor de leek 189
Chapter 11 Discussion and perspectives 195
Appendices: List of contributing authors and affiliations 209 Dankwoord 219
CHAPTER 1
General introduction
General introduction
GENERAL INTRODUCTION 1
The studies presented in this thesis were aimed at improving quality of care for patients with epithelial ovarian cancer and endometrial cancer. The first and second part of this thesis comprise studies that were designed to improve quality of care for patients by assessing different aspects of the orga- nization of care. The studies presented in the third part of this thesis focus on improving quality of care by encouraging individualization of care. In the general introduction below a brief outline of this thesis is discussed, including a short overview of the presented studies.
Part I: improving organization of care for patients with ovarian cancer Approximately 1200 women are diagnosed with epithelial ovarian cancer in the Netherlands every year(1). Women may present with symptoms such as bloating, pelvic or abdominal pain, early satiety and urinary problems. Due to the aspecific nature of these symptoms, most women are diagnosed with an advanced stage of disease. Though relatively uncommon, epithelial ovarian cancer is the leading cause of death in gynecologic cancers, with a five-year survival of no more than 20-50% for patients with advanced stage disease(2). Factors that influence the prognosis of patients with ovarian cancer include histology, stage of disease, age, performance status, tumor type and tumor grade.
Epithelial carcinomas comprise around 90% of malignant tumors in the ovaries. Other tumors of the ovaries include tumors arising from ovarian stromal or germ cells and metastases from other primary tumors. The studies presented within this thesis concern epithelial ovarian carcinomas, a heteroge- neous group of cancers including high-grade serous carcinoma (70-80%), endometrioid carcinoma (10%), clear cell carcinoma (10%), mucinous carcinoma (<5%) and low-grade carcinoma (<5%).
Standard primary therapy for advanced epithelial ovarian cancer comprises a combination of surgery and chemotherapy. Traditionally, primary cytoreductive surgery (PCS) is followed by six cycles of adju- vant chemotherapy (ACT). However, increasing awareness of the importance of cytoreduction to no macroscopically visible residual tumor (termed ‘complete cytoreduction’) led to the implementation of a therapeutic regime in which three cycles of neo-adjuvant chemotherapy (NACT) are followed by interval cytoreductive surgery (ICS) and three cycles of ACT(3,4). Advocates of this regime suggest that the administration of NACT may increase the chance of achieving a complete cytoreduction at the time of ICS by reducing tumor load. Furthermore, as there is a smaller tumor mass present during surgery, there is a smaller chance of inducing surgical complications(5–7). On the other hand, the administration of NACT may induce resistance to subsequent chemotherapeutic therapy(8–10). Currently, clinical guidelines based on international consensus recommend primary cytoreductive surgery (PCS) and adjuvant chemotherapy (ACT) in patients in whom complete cytoreduction seems feasible with acceptable morbidity. Neoadjuvant chemotherapy (NACT) followed by interval cytore- ductive surgery (ICS) is recommended when complete cytoreduction is considered unlikely, or if unacceptable morbidity is expected during PCS(11,12).
11 Chapter 1
Another effort to improve cytoreductive outcomes has been undertaken by centralizing surgery for advanced epithelial ovarian cancer in specialized high volume hospitals(13–15). Within the Netherlands, the first centralization initiatives were undertaken around the year 2005 and official implementation of standards aimed at centralization of care to hospitals with annual case volumes of ≥20 cytoreductive surgeries occurred in 2013(16). Other important aspects of these multidisciplinary standards included the requirement for all patients to be discussed in a pre-operative multidisci- plinary panel and all cytoreductive surgeries to be performed by specialized gynecologic oncologists. A new version, including an English translation, of these standards was published in 2017(17).
In Chapter 2 we evaluated the impact of the centralized care system and the implementation of NACT on the surgical outcome and survival of 7987 patients that were diagnosed with advanced stage epithelial ovarian cancer in the Netherlands between 2004 and 2013.
One of the drawbacks of such a centralized care system is the possibility of inducing treatment delay. Therefore, acceptable health care intervals were defined for patients suspected of epithelial ovarian cancer in multidisciplinary standards(16). In Chapter 3 we performed a pattern of care study to measure health system intervals of patients that were suspected of ovarian cancer within our Managed Clinical Network.
Finally, in Chapter 4 we reviewed our findings in light of the most recent insights on optimal patient selection, timing and extent of surgery for patients with advanced ovarian cancer.
Part II: improving organization of care for patients with endometrial cancers Endometrial cancer is the most common gynecologic malignancy in economically developed coun- tries, affecting around 1900 women annually in the Netherlands(1,2). The incidence of endometrial cancer is increasing due to factors such as increasing life expectancies and the rapidly expanding obesity epidemic. Endometrial cancer is usually diagnosed in postmenopausal women, and as it is frequently symptomatic at an early stage most patients are diagnosed with stage I disease. The overall prognosis of patients with stage I endometrial cancer is relatively favorable at approximately 75-80%. On the other hand, 5-year survival of patients with advanced stage disease ranges from 20-60(18)%. Standard diagnostic procedures for women suspected of early stage endometrial cancer include ultrasonography and endometrial sampling. To guide therapeutic decision-making, pre-operatively collected endometrial sampling material and post-operative surgical specimens are used to stratify patients into groups according to their risk of progression and recurrence. These risk-stratifications are based on prognostic factors such as grade, histology, FIGO stage, age and lymph vascular space invasion(19–22).
12 General introduction
In patients that are pre-operatively classified as low risk, a hysterectomy and bilateral salpingo-oopho- 1 rectomy is indicated, while guidelines recommend complete surgical staging and adjuvant therapy in patients that are pre-operatively classified as high risk. The post-operative risk-stratification is used to guide the choice of adjuvant therapy. National and international guidelines do not recommend administration of adjuvant therapy in low and low-intermediate risk patients. Adjuvant therapy, con- sisting of radiotherapy and/or chemotherapy, is recommended in high-intermediate and high risk patients(23,24).
In Chapter 5 we investigated the concordance between pre-operative risk-stratifications based on endometrial sampling and post-operative risk-stratifications based on histological examination of tissue removed during surgery. The study included 3784 patients diagnosed with endometrial cancer in the Netherlands between 2005 and 2014.
In Chapter 6 we evaluated compliance of physicians with adjuvant therapy guidelines in 13,568 patients that were diagnosed with endometrial cancer in the Netherlands between 2005 and 2014.
Part III: Individualization of care for patients with endometrial cancer There is an unmet need for effective treatment in endometrial cancer patients at high risk of progres- sion and recurrence. It is widely accepted that the current ‘one size fits all’ approach is insufficient for high risk endometrial cancer patients. As such, new therapeutic modalities aimed at high risk endometrial cancers should incorporate selection of endometrial cancer patients based on the bio- markers and molecular characteristics that are specific to their tumors. In this regard, expansion of our current understanding of endometrial cancer biology is essential.
Endometrial cancers have traditionally been classified using a dualistic model(25). ‘Type I’ tumors, characterized as low grade endometrioid, hormone receptor positive tumors with a favorable prog- nosis, are the most common subtype. Non-endometrioid, high grade, hormone receptor negative tumors, traditionally classified as ‘type II’, have an unfavorable survival and are less common. More recently, the application of next-generation sequencing has led to rapid advances in our understand- ing of the molecular etiology of endometrial cancer. In 2013, the Cancer Genome Atlas published a landmark paper in which a new classification, comprising four genomically distinct subtypes, was proposed(26). These four subtypes include ultramutated endometrial cancers with somatic mutations in the exonuclease domain of POLE, microsatellite unstable hypermutated endometrial cancers, a microsatellite stable group with frequent TP53 mutations and a group with no specific molecular profile. Surprisingly, POLE-mutant endometrial cancers, comprising approximately 10% of endome- trial cancers, have an excellent prognosis despite being associated with high risk features(27–29).
13 Chapter 1
As in other malignancies, control of endometrial cancer appears to be mediated in part by the immune system. Especially the CD8+ T-lymphocytes (T-cells) play an important role in the direct elimination of cancer cells(30). The CD8+ T-cells recognize cancer cells by their malignant transformation. For example, DNA mutations in cancer cells can lead to the formation of new proteins (termed ‘neo-anti- gens’) that can be recognized by the CD8+ T-cells. These neo-antigens can elicit a strong anti-tumor immune response, as previously shown in melanoma and non-small cell lung cancer(31,32). Tumors can deregulate the anti-tumor immune response and enable disease progression by expressing proteins (termed ‘immune checkpoints’) that induce immune resistance. The clinical development of immunotherapy that blocks such immune checkpoints, thereby restoring the anti-tumor immune response, has made an unprecedented impact on the field of oncology(33).
Taking into account emerging data linking mutational burden, immune response and clinical out- comes, we hypothesized that the excellent prognosis of POLE-mutant endometrial cancers may be attributed in part to an increased immune response against the tumor. In Chapter 7 we investigated the immunogenic status of 150 endometrial cancers comprising approximately equal numbers of POLE-mutant, microsatellite unstable and microsatellite stable subtypes of low and high grade. In Chapter 8 we validated and extended our findings in a cohort of 116 high-risk endometrial cancer patients.
Finally, English and Dutch summaries of the studies presented in this thesis can be found in chapter 9 and Chapter 10, and the implications and future directions of the research presented in this thesis are discussed in Chapter 11.
14 General introduction
REFERENCES 1
1. Integraal Kankercentrum Nederland. Nederlandse Kankerregistratie [Internet]. [cited 2017 Jul 7]. Available from: http://cijfersoverkanker.nl/ 2. Siegel RL, Miller KD, Jemal A. Cancer Statistics , 2017. CA Cancer J Clin. 2017;67(7):7–30. 3. Bristow BRE, Tomacruz RS, Armstrong DK, Trimble EL, Montz FJ. Survival Effect of Maximal Cytoreductive Surgery for Advanced Ovarian Carcinoma During the Platinum Era : a Meta-analysis. J Clin Oncol. 2002;20(5):1248–59. 4. Chang SJ, Hodeib M, Chang J, Bristow RE. Survival impact of complete cytoreduction to no gross residual disease for advanced-stage ovarian cancer: a meta-analysis. Gynecol Oncol. 2013 Sep;130(3):493–8. 5. Vergote I, Tropé CG, Amant F, Kristensen GB, Ehlen T, Johnson N, et al. Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer. N Engl J Med. 2010;363(10):943–53. 6. Kehoe S et al. Chemotherapy or upfront surgery for newly diagnosed advanced ovarian cancer: results from the MRC CHORUS trial. J Clin Oncol. 2013;31(Suppl):a5500. 7. Zeng L-J, Xiang C-L, Gong Y-Z, Kuang Y, Lu F-F, Yi S-Y, et al. Neoadjuvant chemotherapy for Patients with advanced epithelial ovarian cancer: A Meta-Analysis. Sci Rep [Internet]. 2016;6:35914. Available from: http:// www.ncbi.nlm.nih.gov/pubmed/27804983 8. Luo Y, Lee M, Kim HS, Chung HH, Song YS. Effect of neoadjuvant chemotherapy on platinum resistance in stage IIIC and IV epithelial ovarian cancer. Med (United States). 2016;95(36):1–5. 9. da Costa AABA, Valadares C V., Baiocchi G, Mantoan H, Saito A, Sanches S, et al. Neoadjuvant Chemotherapy Followed by Interval Debulking Surgery and the Risk of Platinum Resistance in Epithelial Ovarian Cancer. Ann Surg Oncol [Internet]. 2015;22(S3):971–8. Available from: http://link.springer.com/10.1245/s10434-015-4623-z 10. Rauh-Hain JA, Nitschmann CC, Worley MJ, Bradford LS, Berkowitz RS, Schorge JO, et al. Platinum resistance after neoadjuvant chemotherapy compared to primary surgery in patients with advanced epithelial ovar- ian carcinoma. Gynecol Oncol [Internet]. 2013;129(1):63–8. Available from: http://dx.doi.org/10.1016/j. ygyno.2013.01.009 11. Querleu D, Planchamp F, Chiva L, Fotopoulou C, Barton D, Cibula D, et al. European Society of Gynaecologic Oncology Quality Indicators for Advanced Ovarian Cancer Surgery. Int J Gynecol Cancer. 2016;26(7):1354–63. 12. Wright AA, Bohlke K, Armstrong DK, Bookman MA, Cliby WA, Coleman RL, et al. Neoadjuvant chemotherapy for newly diagnosed, advanced ovarian cancer: Society of Gynecologic Oncology and American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol. 2016;34(28):3460–73. 13. Woo YL, Kyrgiou M, Bryant A, Everett T, Dickinson HO. Centralisation of services for gynaecological cancer. Cochrane database Syst Rev. 2012;3:CD007945. 14. Dahm-Kähler P, Palmqvist C, Staf C, Holmberg E, Johannesson L. Centralized primary care of advanced ovar- ian cancer improves complete cytoreduction and survival - A population-based cohort study. Gynecol Oncol [Internet]. 2016;142(2):211–6. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0090825816307545 15. Cowan RA, O’Cearbhaill RE, Gardner GJ, Levine DA, Roche KL, Sonoda Y, et al. Is It Time to Centralize Ovarian Cancer Care in the United States? Ann Surg Oncol [Internet]. 2016;(23):989–93. Available from: http://link. springer.com/10.1245/s10434-015-4938-9 16. Stichting Oncologische Samenwerking. Multidisciplinaire normering oncologische zorg in Nederland, SONCOS normeringsrapport 2 [Internet]. 2014 [cited 2017 Jul 7]. Available from: http://www.iknl.nl/docs/default-source/ Palliatieve-zorg-in-de-ziekenhuizen/soncos.pdf?sfvrsn=0 17. Stichting Oncologische Samenwerking. Standardization of multidisciplinary cancer care in the Netherlands, SONCOS standardization report 5 [Internet]. 2017. Available from: https://www.soncos.org 18. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JWW, Comber H, et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012. Eur J Cancer [Internet]. 2013;49(6):1374–403.
15 Chapter 1
Available from: http://dx.doi.org/10.1016/j.ejca.2012.12.027 19. Creasman WT, Morrow CP, Bundy BN, Homesley HD, Graham JE, Heller PB. Surgical pathologic spread patterns of endometrial cancer. A Gynecologic Oncology Group Study. Cancer. 1987;60(8 Suppl):2035–41. 20. Lee K-B, Ki K-D, Lee J-M, Lee J-K, Kim JW, Cho C-H, et al. The Risk of Lymph Node Metastasis Based on Myometrial Invasion and Tumor Grade in Endometrioid Uterine Cancers: A Multicenter, Retrospective Korean Study. Ann Surg Oncol [Internet]. 2009;16:2882–7. Available from: http://www.springerlink.com/index/10.1245/s10434- 009-0535-0 21. Convery P a, Cantrell L a, Di Santo N, Broadwater G, Modesitt SC, Secord AA, et al. Retrospective review of an intraoperative algorithm to predict lymph node metastasis in low-grade endometrial adenocarcinoma. Gynecol Oncol [Internet]. 2011;123(1):65–70. Available from: http://dx.doi.org/10.1016/j.ygyno.2011.06.025%5Cn- http://www.ncbi.nlm.nih.gov/pubmed/21742369 22. Koskas M, Bassot K, Graesslin O, Aristizabal P, Barranger E, Clavel-Chapelon F, et al. Impact of lymphovascular space invasion on a nomogram for predicting lymph node metastasis in endometrial cancer. Gynecol Oncol [Internet]. 2013;129(2):292–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23480871 23. Werkgroep Oncologische Gynaecologie. Landelijke richtlijn endometriumcarcinoom [Internet]. 2011 [cited 2017 Jul 6]. Available from: http://www.oncoline.nl/endometriumcarcinoom 24. Colombo N, Preti E, Landoni F, Carinelli S, Colombo A, Marini C, et al. Endometrial cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2011 Sep;22 Suppl 6:vi35-9. 25. Bokhman J V. Two pathogenetic types of endometrial carcinoma. Gynecol Oncol [Internet]. 1983 Feb [cited 2017 Jul 7];15(1):10–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/6822361 26. Cancer Genome Atlas Research Network, Kandoth C, Schultz N CA, Akbani R, Liu Y, Shen H, Robertson AG, Pashtan I, Shen R, Benz CC, Yau C L, PW, Ding L, Zhang W, Mills GB, Kucherlapati R, Mardis ER L DA. Integrated genomic characterization of endometrial carcinoma. Nature [Internet]. 2013;497(53):67–73. Available from: http://pubget.com/paper/23636398?institution=%5Cnpapers2://publication/doi/10.1038/nature12113 27. Church DN, Stelloo E, Nout R a., Valtcheva N, Depreeuw J, ter Haar N, et al. Prognostic Significance of POLE Proofreading Mutations in Endometrial Cancer. JNCI J Natl Cancer Inst [Internet]. 2014;107:dju402-dju402. Available from: http://jnci.oxfordjournals.org/cgi/doi/10.1093/jnci/dju402 28. Billingsley CC, Cohn DE, Mutch DG, Stephens J a., Suarez A a., Goodfellow PJ. Polymerase ε (POLE) mutations in endometrial cancer: Clinical outcomes and implications for Lynch syndrome testing. Cancer [Internet]. 2015;121:386–94. Available from: http://doi.wiley.com/10.1002/cncr.29046 29. Meng B, Hoang LN, McIntyre JB, Duggan MA, Nelson GS, Lee C-H, et al. POLE exonuclease domain mutation predicts long progression-free survival in grade 3 endometrioid carcinoma of the endometrium. Gynecol Oncol [Internet]. 2014 Jul [cited 2016 Mar 25];134(1):15–9. Available from: http://www.ncbi.nlm.nih.gov/ pubmed/24844595 30. de Jong RA, Leffers N, Boezen HM, ten Hoor KA, van der Zee AGJ, Hollema H, et al. Presence of tumor-infiltrating lymphocytes is an independent prognostic factor in type I and II endometrial cancer. Gynecol Oncol [Internet]. 2009 Jul [cited 2017 Jul 7];114(1):105–10. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19411095 31. Roh W, Chen P-L, Reuben A, Spencer CN, Prieto PA, Miller JP, et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci Transl Med [Internet]. 2017;9(379). Available from: http://www.ncbi.nlm.nih.gov/pubmed/28251903 32. Yarchoan M, Johnson BA, Lutz ER, Laheru DA, Jaffee EM. Targeting neoantigens to augment antitumour immu- nity. Nat Rev Cancer [Internet]. 2017;17(4):209–22. Available from: http://www.nature.com/doifinder/10.1038/ nrc.2016.154 33. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer [Internet]. 2012 [cited 2016 Jul 6];12(4):252–64. Available from: http://dx.doi.org/10.1038/nrc3239
16
PART I
Improving organization of care for patients with ovarian cancer
CHAPTER 2
Improved outcomes due to changes in organization of care for patients with ovarian cancer in the Netherlands
Eggink F.A., Mom C.H., Kruitwagen R.F., Reyners A.K., Van Driel W.J., Massuger L.F., Niemeijer G.C., Van der Zee A.G., Van der Aa M.A., Nijman H.W.
Gynecologic Oncology. 2016 Jun;141(3):524-30 Chapter 2
ABSTRACT
Objectives Objectives of this study were to evaluate the effect of changes in patterns of care, for example centralization and treatment sequence, on surgical outcome and survival in patients with epithelial ovarian cancer (EOC).
Methods Patients diagnosed with FIGO stage IIB-IV EOC (2004-2013) were selected from the Netherlands Cancer Registry. Primary outcomes were surgical outcome (extent of macroscopic residual tumor after surgery) and overall survival. Changes in treatment sequence (primary cytoreductive surgery and adjuvant chemotherapy (PCS+ACT) or neo-adjuvant chemotherapy and interval cytoreductive surgery (NACT+ICS)), hospital type and annual hospital volume were also evaluated.
Results Patient and tumor characteristics of 7987 patients were retrieved. Most patients were diagnosed with stage III-IV EOC. The average annual case-load per hospital increased from 8 to 28. More patients received an optimal cytoreduction (tumor residue ≤ 1cm) in 2013 (87%) compared to 2004 (55%, p<0.001). Complete cytoreduction (no macroscopic residual tumor), registered since 2010, increased from 42% to 52% (2010 and 2013, respectively, p<0.001). Optimal/complete cytoreduction was achieved in 85% in high volume (≥20 cytoreductive surgeries annually), 80% in medium (10-19 surger- ies) and 71% in small hospitals (<10 surgeries, p<0.001). Within a selection of patients with advanced stage disease that underwent surgery the proportion of patients undergoing NACT+ICS increased from 28% (2004) to 71% (2013). Between 2004 and 2013 a 3% annual reduction in risk of death was observed (HR 0.97, p<0.001).
Conclusion Changes in pattern of care for patients with EOC in the Netherlands have led to improvement in surgical outcome and survival.
22 Changes in organization of ovarian cancer care
INTRODUCTION
Epithelial ovarian cancer is the leading cause of death in gynecological malignancies (1), and the 2 seventh most common cancer in women worldwide (2). In 2013 there were over 1200 new cases and around 1000 deaths as a result of ovarian cancer in the Netherlands (3). Due to a lack of specific symptoms, the majority of patients presents with advanced stage disease, resulting in a poor progno- sis. Current treatment of advanced stage ovarian cancer consists of a combination of platinum-based chemotherapy and cytoreductive surgery.
In the past decade, changes in the organization of care for patients with ovarian cancer have been implemented in the Netherlands. Traditionally, patients were staged and treated in the hospital of diagnosis. Consequently, less than 20% of ovarian cancer patients were treated in specialized hospi- tals between 1996 and 2003 (4). Over the past decade increasing evidence has shown that complete cytoreduction is strongly correlated with improved disease free and overall survival, and that the likelihood of achieving this is higher when cytoreductive surgery is performed by a specialized gyne- cologic oncologist in a high-volume hospital (5–14). These insights emphasized the need for improved regional collaboration and a larger ovarian cancer case load for a smaller number of hospitals and practitioners (9–11). Centralization initiatives undertaken by the Dutch Society of Obstetrics and Gynecology resulted in a nationwide consensus in 2011. Additionally, national standards for general and specialized cancer care were compiled. An important criterion in these national standards is that surgical cytoreduction for ovarian cancer should only be performed by specialized gynecologic oncologists in institutions in which a minimum of 20 cytoreductive surgeries take place annually.
Increasing awareness of the importance of achieving complete cytoreduction has led to alterations in the therapy regimen for patients with advanced ovarian cancer (5–8,13). Administration of neoadjuvant chemotherapy to reduce tumor load and increase the chance of achieving complete cytoreduction was introduced after the publication of the EORTC-NCIC trial in 2010 (15). Comparison of standard therapy (primary cytoreductive surgery followed by adjuvant chemotherapy (PCS+ACT)) with the alter- native regimen (neoadjuvant chemotherapy followed by interval cytoreductive surgery (NACT+ICS)) demonstrated equal progression free and overall survival chances (15–22). Additionally, reduced per- and postoperative complications following NACT+ICS were demonstrated (16–22). In several other publications, not being randomized controlled trials, less favorable outcomes such as inferior overall survival and increased toxicity due to chemotherapy, were depicted (23–25). Despite these variations in outcome however, the proportion of ovarian cancer patients treated with NACT+ICS has increased in recent years(16,18,20,26).
The aim of the current study was to evaluate whether the changes in pattern of care for ovarian cancer patients, which have taken place in the Netherlands in the past decade, have led to improved surgical outcome and survival.
23 Chapter 2
MATERIALS AND METHODS
Data collection Population-based data were retrieved from the Netherlands Cancer Registry (NCR), which is main- tained by the Netherlands Comprehensive Cancer Organization. The NCR contains data of all cancer patients in the Netherlands, and relies on notifications of newly diagnosed malignancies from the automated nationwide pathology archive. Trained medical registrars use standardized forms to collect patient information from medical records and the national registry of hospital discharge diagnoses. Information regarding vital status and date of death is obtained through Statistics Netherlands, an agency responsible for the official Dutch statistics. Regular consistency checks are performed to ensure the quality of data in the NCR.
Patients Data from all consecutive patients diagnosed with FIGO stage IIB – IV ovarian cancer between January 1st 2004 and January 1st 2014 in the Netherlands were retrieved. In total, 452 patients were excluded from analysis. These patients underwent unilateral or bilateral salpingo-oophorectomy (BSO) or hys- terectomy with BSO only, and were excluded from analysis as these could not be classified as having had an attempt to achieve maximal cytoreduction, and patients that underwent staging only. Patient-, tumor- and treatment characteristics of 7987 patients were retrieved. Surgery performed within 9 months of the date of diagnosis was considered related to ovarian cancer.
To avoid understaging of patients undergoing neoadjuvant chemotherapy, determination of the stage of disease was dependent on the sequence of received treatments. Stage of disease was deter- mined using the pathological TNM stage for patients who underwent PCS+ACT. For patients receiving NACT+ICS, stage of disease was determined before initiation of primary therapy and was based on the clinical TNM stage. After careful consideration and consultation by an experienced pathologist it was decided to view serous and adenocarcinoma not otherwise specified (NOS) subtypes as one entity.
Hospitals Hospitals were categorized into three groups: academic hospitals, specialized hospitals, and general hospitals. Academic hospitals are tertiary referral hospitals that deliver highly specialized care, and are related to a university. Specialized hospitals are teaching hospitals that are not related to a univer- sity. General hospitals are non-teaching hospitals and are usually smaller than specialized hospitals. Hospital volume was defined as the average annual number of cytoreductive surgeries performed for ovarian cancer between 2004 and 2013. Annual volumes of 1-3 cytoreductive surgeries were considered to be incidents, and were not included in the volume-analysis.
24 Changes in organization of ovarian cancer care
Outcomes Primary outcomes were surgical outcome and overall survival. During the study period several alter- ations in definitions of cytoreductive outcome occurred within the NCR registration (table 1). The 2 term complete cytoreduction was introduced in the NCR in 2009 and fully implemented by 2010. Comparison of complete cytoreductive outcomes is therefore only possible between 2010 and 2013. To allow comparison of outcomes within the whole study period (2004-2013), optimal and complete results of cytoreductive surgery were compiled into one variable. Treatment sequence (PCS+ACT or NACT+ICS), type of treatment hospital and annual number of cytoreductive surgeries per hospital were also evaluated.
Table 1. Definitions utilized by NCR for result of cytoreductive surgery Term Definition in 2004-2006 Definition in 2007-2009 Definition in 2010 onwards Incomplete Residual tumor >2cm Residual tumor >1cm Residual tumor >1cm Optimal Residual tumor ≤2cm Residual tumor ≤1cm Residual tumor ≤1cm Complete - - No macroscopic residual tumor
Within the selection of patients fulfilling all in- and exclusion criteria, patients in whom ovarian cancer was detected by coincidence without an attempt to remove macroscopic tumor tissue, and patients who underwent surgery that was not further specified, were all categorized as having received incom- plete cytoreduction.
Data analysis Data analysis was performed using STATA data analysis and statistical software (StataCorp, College Station, TX). Comparison between unpaired groups was done using the Chi2 test. Overall survival was used as primary survival outcome measure, and estimated using Kaplan Meier analyses. To correct for possible confounders such as age, stage, type of tumor, grade and treatment sequence, multi- variable survival analyses were performed using Cox regression. Year of diagnosis was entered into these analyses as a continuous variable. To avoid immortal time bias when comparing survival rates between the patients that received PCS and the patients that received NACT-ICS, conditional survival analysis was used. It was assumed that all patients underwent cytoreductive surgery and the first 3 chemotherapy cycles within 6 months after diagnosis. Thus, survival analyses were performed with a landmark at 6 months. Differences were considered statistically significant at p<0.05.
25 Chapter 2
RESULTS
Data from 7987 patients were retrieved for this study. Clinicopathological characteristics are shown in table 2. Within the study-population, most patients were diagnosed with stage IIIC (60%) and serous type (86%) ovarian cancer. Primary cytoreductive surgery was the most frequently chosen therapeutic regimen. Overall, 73% of patients underwent surgery and 27% did not. The proportion of patients that did not undergo surgery increased from 21% in 2004 to 32% in 2013. Of the patients who did not receive surgery, 52% underwent chemotherapy, 5% had hormonal therapy or other palliative treatment, and in 43% further treatment was unknown or not indicated. In general, patients that did not receive surgery were older than those who did receive surgical treatment (75 years and 63 years respectively, P<0.001, data not shown). Between 2004 and 2013, the average age of patients increased from 65 years (95%CI 64-65) to 68 years (95%CI 67-69).
The number of hospitals performing cytoreductive surgery for ovarian cancer patients decreased from 90 hospitals in 2004 to 34 hospitals in 2013. As a consequence, the average annual caseload per hospital increased from 8 cases in 2004 to 28 cases in 2013. In total, 15 out of the 34 hospitals (44%) involved in cytoreductive surgery for ovarian cancer in 2013 met the minimal requirement of 20 cytoreductive surgeries per hospital per year (data not shown).
Surgical outcomes Within the study period an increase in the amount of complete/optimal cytoreductions was achieved: 55% in 2004 compared to 87% in 2013 (p<0.001 with test for trend, figure 1). Between 2010 and 2013 an increase in the rate of complete cytoreduction was seen from 42% to 52% (p<0.001, data not shown). Patients in whom a complete cytoreduction was achieved had a favorable survival compared to those who underwent an optimal or incomplete cytoreduction (data not shown).
Figure 1. Surgical outcome between 2004 and 2013
100
80 ) Complete/optimal (% 60 Incomplete ts
en Unknown
ti 40 Pa 20
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year of diagnosis
26 Changes in organization of ovarian cancer care
A correlation was found between hospital volume and the number of patients who underwent a com- plete cytoreduction. Hospitals with an annual volume of ≥20 cytoreductive surgeries achieved better cytoreductive outcomes than hospitals with annual volumes of 10-19 and <10 surgeries. Complete/ 2 optimal cytoreduction was achieved in 85%, 80% and 71% respectively (P<0.001, figure 2). Between 2010 and 2013, complete cytoreduction was achieved in 57%, 45% and 44% in hospitals that per- formed >20, 10-19 and <10 cytoreductive surgeries annually (p<0.001). Hospitals that performed ≥30 cytoreductive surgeries annually attained more complete cytoreductions compared to those that performed 20-29 cytoreductive surgeries (59% and 50% respectively between 2010 and 2013 (p=0.003, data not shown).
Figure 2. Surgical outcome for annual hospital volumes between 2004 and 2013
100 Incomplete Complete/optimal 80 )
(% 60 ts en
ti 40 Pa 20
0 0 9 + <1 20 10-1
Annual hospital volume
In hospitals that performed ≥20 cytoreductive surgeries annually, an optimal/complete cytoreduction was achieved in 69% of patients in 2004, compared to 89% in 2013. Likewise, in hospitals with an annual volume of <10 cytoreductive surgeries, optimal/complete cytoreduction was achieved in 43% of patients in 2004, compared to 83% in 2013. An unfavorable survival was found in patients that were treated in hospitals with an annual volume of < 10 cytoreductions, compared to hospitals with an annual volume of 10-19 or ≥20 cytoreductive surgeries (data not shown).
Academic hospitals achieved better surgical outcomes than specialized and general hospitals. Com- plete/optimal cytoreduction was achieved in 84%, 79% and 71% respectively (p<0.001 with chi2 test, figure 3). Between 2010 and 2013, complete cytoreduction was achieved in 53%, 48% and 42% respectively (p=0.002, data not shown). There were no differences in the number of patients with FIGO stage IIIC or IV between the three hospital types (p=0.227, data not shown).
27 Chapter 2
Figure 3. Surgical outcome for hospital types between 2004 and 2013
100 Incomplete Complete/optimal 80 )
(% 60 ts en
ti 40 Pa 20
0 l d
Genera Academic Specialize Hospital type
In patients with advanced stage disease who received NACT+ICS a complete/optimal cytoreduc- tion was reached in 81%, compared to 69% in patients who received PCS+ACT (p<0.001, figure 4). Between 2010 and 2013, 47% of patients with advanced stage disease that underwent NACT+ICS received a complete cytoreduction versus 43% of patients that underwent PCS+ACT (p=0.028). In patients receiving ICS after initial PCS+ACT surgical outcome was poor (data not shown). PCS+ACT followed by ICS is not part of standard therapy for patients with ovarian cancer in the Netherlands, as illustrated by the small number of patients involved (table 2). Patients who underwent PCS+ACT+ICS were generally younger and more frequently diagnosed with high stage serous ovarian cancer than those who underwent standard PCS+ACT therapy (data not shown).
Figure 4. Surgical outcome per treatment sequence between 2004 and 2013
100 Incomplete Optimal 80 ) Complete
(% 60 ts en
ti 40 Pa 20
0 T
PCS+AC NACT+ICS
Treatment sequence PCS: primary cytoreductive surgery, ACT: adjuvant chemotherapy, NACT: neoadjuvant chemotherapy, ICS: interval cytoreductive surgery.
28 Changes in organization of ovarian cancer care
Table 2. Clinicopathological characteristics of total patient population
Total (n=7987) n % 2 FIGO Stage
IIB 306 4
IIC 332 4
IIIA 210 2
IIIB 534 7
IIIC 4769 60
IV 1836 23
Type of tumor
Serous 6856 86
Mucinous 281 4
Endometrioid 432 5
Clear cell 222 3
Other 196 2
Grade
I 304 4
II 889 11
III 3004 38
Anaplastic 38 0
Undefined 3752 47
Treatment
Surgery 5870 73 PCS +ACT 3004 51
NACT+ICS 2635 45
PCS+ACT+ICS 231 4
No surgery 2117 27
Age at diagnosis (years)
Mean (range) 66 (20-97) FIGO: International Federation of Gynecology and Obstetrics; PCS: primary cytoreductive surgery; ACT: adjuvant chemotherapy; NACT: neoadjuvant chemotherapy; ICS: interval cytoreductive surgery.
29 Chapter 2
Survival Within the group of patients diagnosed with stage IIB-IV ovarian cancer, a small improvement in five year overall survival was demonstrated between patients diagnosed in 2004-2006 (24%) and 2010- 2013 (25%, HR 0.91, 95% CI 0.84-0.99, p=0.031). One year overall survival increased from 82% (95% CI 0.78-0.85) in 2004 to 90% (95% CI 87-92, p<0.001) in 2013 (p<0.001). Additionally, a 2% annual reduction in risk of death was demonstrated between 2004 and 2013 (HR 0.983, 95% CI 0. 970- 0.995, p=0.007, univariable analysis). Multivariable analysis, correcting for age, stage, type of tumor and grade, demonstrated a 3% annual reduction in risk of death (HR 0.974, 95% CI 0.962 – 0.987, p<0.001, Table 3).
Table 3. Multivariate analysis of overall survival between 2004 and 2013 HR 95% CI p-value
Year of diagnosis 0.974 0.962 0.987 <0.001
Age 1.020 1.017 1.023 <0.001
Stage
IIB ref ref ref ref
IIC 1.275 0.959 1.700 0.094
IIIA 2.456 1.832 3.292 <0.001
IIIB 2.325 1.819 2.971 <0.001
IIIC 3.348 2.702 4.149 <0.001
IV 4.503 3.611 5.616 <0.001
Grade
I ref ref ref ref
II 1.480 1.220 1.795 <0.001
III 1.412 1.177 1.695 <0.001
Anaplastic 1.883 1.096 3.233 0.022
Unknown 1.414 1.175 1.701 <0.001
Type of tumor
Serous ref ref ref ref
Mucinous 2.083 1.770 2.451 <0.001
Endometrioid 0.882 0.766 1.015 0.081
Clearcell 1.392 1.162 1.667 <0.001
Undefined 1.246 0.946 1.641 0.118
HR: hazard risk; CI: confidence interval.
30 Changes in organization of ovarian cancer care
Treatment regimens A total of 5870 patients underwent cytoreductive surgery for ovarian cancer in the Netherlands between 2004 and 2013. Within this timeframe, the proportion of patients receiving PCS+ACT 2 decreased considerably, whereas the proportion of patients receiving NACT+ICS showed a steady increase (figure 5). Patients receiving NACT+ICS were more frequently diagnosed with advanced stage and serous disease compared to patients receiving PCS+ACT (p<0.001 in both cases, data not shown). Within the selection of patients with advanced stage disease (FIGO IIIC-IV) who underwent complete/ optimal cytoreduction, patients receiving PCS+ACT had a 5-year overall survival of 39% (95% CI 36-42) compared to 26% (95% CI 23-28) in those receiving NACT+ICS.
Figure 5. Choice of treatment between 2004 and 2013
100
80 ) PCS+ACT
(% 60 NACT+ICS ts PCS+ACT+ICS en
ti 40 Pa 20
0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year of Diagnosis PCS: primary cytoreductive surgery, ACT: adjuvant chemotherapy, NACT: neoadjuvant chemotherapy, ICS: interval cytoreductive surgery.
The treatment sequence of NACT followed by interval cytoreductive surgery was introduced earlier in academic hospitals compared to specialized and general hospitals. In 2004, 38% of patients treated in academic hospitals received NACT+ICS compared to 20% in specialized and 21% in general hospitals. By 2013 all hospital types routinely performed NACT+ICS (64%, 59% and 63% of patients treated in academic, specialized and general hospitals, respectively).
31 Chapter 2
DISCUSSION
To our knowledge, this is the largest population-based study analyzing the changes in pattern of care for ovarian cancer patients within the past decade. A unique feature of the current study is that within the study period all patients were treated with the standard first line platinum based chemotherapy. We observed an increase in the rate of complete/optimal cytoreduction and simultaneously a small decrease in annual risk of death.
Improvement in surgical outcomes may be related to the organizational changes that have been implemented during recent years. Implementation of national standards has enforced regional collab- oration and the presence of specialized gynecologic oncologists during cytoreductive surgeries. Both of these factors are associated with favorable surgical outcomes and survival (9–11,14). Furthermore, rapid implementation of new guidelines and the presence of specialized personnel and state of the art facilities in high volume and specialized hospitals contribute strongly to the high standards of care within these institutions.
Our results demonstrate an association between the type and volume of treatment hospital and the outcome of cytoreductive surgery. Although surgical outcomes improved in both low and high volume hospitals between 2004 and 2013, hospitals that met the volume requirements (≥20 surgeries annually) attained better surgical outcomes than hospitals with lower annual volumes.
Furthermore, academic hospitals and specialized hospitals reported better cytoreductive outcomes than general hospitals. In 2013, 44% of the hospitals that performed cytoreductive surgeries for ovarian cancer in the Netherlands met the annual volume requirements of ≥20 cytoreductive surgeries. Consid- ering the association between high annual surgical volumes, centralization of care and improvement of surgical outcomes, stricter implementation of national standards is deemed essential.
An unfavorable survival was found in patients that were treated in hospitals with an annual volume of <10 cytoreductive surgeries, compared to hospitals with an annual volume of 10-19 or ≥20 cytore- ductive surgeries. Analysis of volume effects on survival may require a longer follow up time than is currently available. Although centralization initiatives started in 2005, the official implementation of centralization of care in the Netherlands took place in 2013.
A previous Dutch study demonstrated that patients undergoing surgery by high volume surgeons (the definition of high volume ranging between performing >10 and >12 cytoreductive surgeries for ovarian cancer annually) have lower operative mortality rates. Furthermore, an association was found between surgery performed by high volume surgeons and higher rates of adherence to treatment guidelines (27–30). Nevertheless, no volume requirements currently exist for individual gynecologic oncologists in the Netherlands (10,31).
32 Changes in organization of ovarian cancer care
Besides the changes in the organization of care, alterations in the therapeutic regimens themselves have contributed to the improvement seen in surgical outcomes. While the effect of cytoreductive outcome on survival is undisputed, the timing of cytoreductive surgery has been, and still is, subject of debate. 2 Our nationwide study depicts the increased implementation of NACT+ICS in the Netherlands during recent years, a trend that was previously described by Van Altena et al (26). Though there are no official guidelines, patients deemed unsuitable candidates for primary surgery are selected for NACT+ICS to downstage the tumor, facilitate subsequent cytoreduction and reduce damage to surrounding tissue. Women receiving NACT+ICS are often older, have more comorbidity and generally have tumors of higher grade and stage(32). A complete/optimal cytoreduction was achieved in 81% of women with advanced stage disease who received NACT+ICS, compared to 69% of those who underwent PCS+ACT.
Favorable surgical outcomes following NACT+ICS were previously reported in a randomized clinical trial by Vergote et al, in which residual tumor of ≤1cm was achieved in 81% with ICS and 42% with PCS (15). Similarly, a Cochrane review reported favorable cytoreductive outcome in the NACT+ICS group compared to the PCS+ACT group (RR 2.56; 95% CI 2.00-3.28)(21).
In the current study 27% of patients did not undergo any cytoreductive surgery. This is consistent with similar data from the Dutch population between 1996 and 2004 (26). Furthermore, a recent analysis of 9491 stage III/IV ovarian cancer patients in the population-based Surveillance, Epidemi- ology and End Results (SEER)-database from the United States, also showed that 27% of patients did not undergo surgery. (33). It may be expected that the proportion of patients that is unable to undergo cytoreductive surgery will rise as the age of patients with ovarian cancer slowly increases.
Within the current study, a minority (4%) of patients received ICS after initial PCS+ACT. It has pre- viously been demonstrated that the addition of secondary cytoreductive surgery to postoperative platinum-based chemotherapy does not improve survival in patients with a residual tumor exceeding 1 cm after having a maximal effort during primary cytoreductive surgery, supporting the fact that this regimen is not standard practice in the Netherlands (34).
Within our retrospectively selected study population, patients who underwent PCS+ACT had favor- able survival outcomes compared to patients who underwent NACT+ICS. Patients were selected for NACT+ICS based on initial unfavorable prognostic characteristics such as advanced stage disease and severe comorbidity. The unfavorable long term survival seen in patients who underwent NACT+ICS is therefore most likely a reflection of the selection bias within this retrospective study. This bias may readily explain the fact that our findings greatly differ from the results of two large randomized con- trolled trials (EORTC-NCIC and CHORUS) in which non inferiority of survival following NACT+ICS was demonstrated (15,22). Importantly, the EORTC-NCIC trial has been criticized for the short median survival rates (29-30 months) and the disappointing surgical outcomes (optimal cytoreduction was achieved in only 42% of patients randomized to PCS+ACT) that were reported.
33 Chapter 2
Of note, patients were categorized in the NACT+ICS group under the condition that ICS was per- formed, implying that they must have survived the NACT. For patients categorized as PCS+ACT, no such conditions were in place. As this may have led to an immortal time bias, a landmark analysis was used as described above.
A small but significant improvement in five-year overall survival was demonstrated between the peri- ods 2004-2006 and 2010-2013, and one year overall survival increased from 82% to 90% between 2004 and 2013. Furthermore, the decrease in annual risk of death, as demonstrated in the current study, remained significant when correcting for, stage, type of tumor and grade, suggesting that other factors than these played a role in this association. The increasing proportion of complete cytoreductions attained within recent years is thought to be an important factor for the improved survival, but centralization may have played an important role as well(6–8,13).
The fact that we observed an improvement in five-year survival is encouraging, as previous survival analyses failed to show improvement. A study by Van Altena et al on the influence of regional collab- oration in treatment for ovarian cancer in the Netherlands did not show a significant improvement in five-year overall survival rates between 1996 (36%) and 2010 (39%) (9). Similarly, the CONCORD-2 study estimated stable five-year net survival rates in the Netherlands between 1995-1999 (39%), 2000-2004 (37%) and 2005-2009 (38%)(35). The EUROCARE-5 study recently published survival data from ovarian cancer patients in individual European countries between 1999 and 2007. Within that period, five-year net survival of Dutch patients with ovarian cancer was 39.9 (95%CI 38.7-41.1)(36). Survival in the CONCORD-2-study and EUROCARE-5 study was adjusted for background mortality by age, sex, and calendar year. Importantly, survival rates in the current study are lower than those reported by Van Altena et al and in the CONCORD-2 and EUROCARE-5 studies due to inclusion of patients with early stage ovarian cancer in those studies.
The importance of reaching an optimal or complete cytoreduction has become increasingly evi- dent. Chang and colleagues demonstrated a 2.3-month increase in cohort median survival time for each 10% increase in patients that received a complete cytoreduction as compared to a 1.8-month improvement for each 10% increase in patients that received an optimal cytoreduction(8). Addition- ally, a recent analysis of GOG 182 showed a significant improvement in survival between optimal cytoreduction and complete cytoreduction (progression free survival of 15 versus 29 months, and overall survival of 41 and 77 months, respectively, P=0.01 for both)(13). It is important to note that as a relatively short follow-up was available for patients diagnosed within the most recent years of our study, the effects of the changes implemented in recent years may not have been fully appreciated.
The retrospective nature of this study has some inevitable limitations. First of all, this study was based on data from the NCR, and thus relies on the data that are available in this registration. In this database no information was registered about the specific chemotherapy regimens that were
34 Changes in organization of ovarian cancer care
used. As national guidelines regarding chemotherapy for EOC patients did not change within the study period, we assume all patients were treated with the same standard first line platinum based chemotherapy. However, we cannot exclude that there may have been some minor variations in 2 chemotherapy regimens.
Our study also relies on the accuracy of the NCR. Patient registration in the NCR is performed by trained and dedicated registrars and the data are regularly checked to ensure optimal quality of the database. It is important to note that registrars depend on the integrity of information available in local medical records. Nonetheless, the NCR is currently the most reliable nationwide source of information on cancer patients in the Netherlands.
Furthermore, the comparison of cytoreductive results is complicated by variation in definitions used worldwide. During the study period changes also occurred within the definitions used for registra- tion in the NCR (table 1). Complete cytoreduction was registered from 2010 onward. Compilation of complete and optimal cytoreductive outcomes enabled comparison throughout the study period, though changes within optimal cytoreduction that occurred within this timeframe were not taken into account.
Finally, the nature of the study precludes the determination of the individual contribution of factors such as centralization and changes in therapy regimes on the improvement in survival.
In conclusion, in this study, regarding 7987 patients diagnosed with ovarian cancer FIGO stage IIB and higher, changes in pattern of care for ovarian cancer patients between 2004 and 2013 were evaluated. A combination of centralization initiatives and changes in therapy regimens has led to improvements in surgical outcome and survival. Continuing centralization of oncological care and implementation of stricter guidelines may lead to further improvement of survival for patients with ovarian cancer in the Netherlands.
FUNDING AND ACKNOWLEDGEMENTS
This work was funded by the Dutch Cancer Society (grant RUG 2013-6505) to HWN. The authors thank the Netherlands Cancer Registry for providing the data and the registry clerks for their dedicated data registration.
35 Chapter 2
REFERENCES
1. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61(2):69– 90. 2. Torre L a., Bray F, Siegel RL, Ferlay J, Lortet-tieulent J, Jemal A. Global Cancer Statistics, 2012. CA a cancer J Clin [Internet]. 2015;65(2):87–108. Available from: http://onlinelibrary.wiley.com/doi/10.3322/caac.21262/abstract 3. Integraal Kankercentrum Nederland. Cijfers over Kanker [Internet]. 2014 [cited 2014 May 1]. Available from: http://www.cijfersoverkanker.nl/ 4. Vernooij F, Heintz AP, Witteveen PO, van der Heiden-van der Loo M, Coebergh JW, van der Graaf Y. Specialized care and survival of ovarian cancer patients in The Netherlands: nationwide cohort study. J Natl Cancer Inst. 2008;100(6):399–406. 5. Griffiths CT. Surgical resection of tumor bulk in the primary treatment of ovarian carcinoma. Natl Cancer Inst Monogr. 1975;42:101–4. 6. Bristow RE, Tomacruz RS, Armstrong DK, Trimble EL, Montz FJ. Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis. J Clin Oncol. 2002;20(5):1248–59. 7. du Bois A, Reuss A, Pujade-Lauraine E, Harter P, Ray-Coquard I, Pfisterer J. Role of surgical outcome as prognostic factor in advanced epithelial ovarian cancer: a combined exploratory analysis of 3 prospectively randomized phase 3 multicenter trials: by the Arbeitsgemeinschaft Gynaekologische Onkologie Studiengruppe Ovarialkarzin. Cancer. 2009;115(6):1234–44. 8. Chang SJ, Hodeib M, Chang J, Bristow RE. Survival impact of complete cytoreduction to no gross residual disease for advanced-stage ovarian cancer: a meta-analysis. Gynecol Oncol. 2013 Sep;130(3):493–8. 9. van Altena AM, van den Akker PA, de Hullu JA, Ottevanger PB, Aalders AL, Gerritse R, et al. Efficacy of a regional network for ovarian cancer care. Obstet Gynecol. 2013 Sep;122(3):668–75. 10. Vernooij F, Heintz AP, Coebergh JW, Massuger LF, Witteveen PO, van der Graaf Y. Specialized and high-vol- ume care leads to better outcomes of ovarian cancer treatment in the Netherlands. Gynecol Oncol. 2009;112(3):455–61. 11. Engelen MJ, Kos HE, Willemse PH, Aalders JG, de Vries EG, Schaapveld M, et al. Surgery by consultant gyneco- logic oncologists improves survival in patients with ovarian carcinoma. Cancer. 2006 Feb 1;106(3):589–98. 12. Woo YL, Kyrgiou M, Bryant A, Everett T, Dickinson HO. Centralisation of services for gynaecological cancer. Cochrane database Syst Rev. 2012;3:CD007945. 13. Horowitz NS, Miller a., Rungruang B, Richard SD, Rodriguez N, Bookman M a., et al. Does Aggressive Surgery Improve Outcomes? Interaction Between Preoperative Disease Burden and Complex Surgery in Patients With Advanced-Stage Ovarian Cancer: An Analysis of GOG 182. J Clin Oncol [Internet]. 2015;33(8). Available from: http://jco.ascopubs.org/cgi/doi/10.1200/JCO.2014.56.3106 14. Harter P, Muallem ZM, Buhrmann C, Lorenz D, Kaub C, Hils R, et al. Impact of a structured quality management program on surgical outcome in primary advanced ovarian cancer. Gynecol Oncol [Internet]. 2011;121(3):615– 9. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0090825811001260 15. Vergote I, Trope CG, Amant F, Kristensen GB, Ehlen T, Johnson N, et al. Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer. N Engl J Med. 2010 Sep 2;363(10):943–53. 16. Fago-Olsen CL, Ottesen B, Kehlet H, Antonsen SL, Christensen IJ, Markauskas A, et al. Does neoadjuvant che- motherapy impair long-term survival for ovarian cancer patients? A nationwide Danish study. Gynecol Oncol. 2014 Feb;132(2):292–8. 17. Milam MR, Tao X, Coleman RL, Harrell R, Bassett R, Dos Reis R, et al. Neoadjuvant chemotherapy is associated with prolonged primary treatment intervals in patients with advanced epithelial ovarian cancer. Int J Gynecol Cancer. 2011 Jan;21(1):66–71.
36 Changes in organization of ovarian cancer care
18. Glasgow MA, Yu H, Rutherford TJ, Azodi M, Silasi DA, Santin AD, et al. Neoadjuvant chemotherapy (NACT) is an effective way of managing elderly women with advanced stage ovarian cancer (FIGO Stage IIIC and IV). J Surg Oncol. 2013 Feb;107(2):195–200. 19. Hou JY, Kelly MG, Yu H, McAlpine JN, Azodi M, Rutherford TJ, et al. Neoadjuvant chemotherapy lessens surgical 2 morbidity in advanced ovarian cancer and leads to improved survival in stage IV disease. Gynecol Oncol. 2007 Apr;105(1):211–7. 20. Worley Jr MJ, Guseh SH, Rauh-Hain JA, Williams KA, Muto MG, Feltmate CM, et al. Does neoadjuvant che- motherapy decrease the risk of hospital readmission following debulking surgery? Gynecol Oncol. 2013 Apr;129(1):69–73. 21. Morrison J, Haldar K, Kehoe S, Lawrie TA. Chemotherapy versus surgery for initial treatment in advanced ovarian epithelial cancer. Cochrane database Syst Rev [Internet]. 2012;8(8):CD005343. Available from: http:// www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4050358&tool=pmcentrez&rendertype=abstract 22. Kehoe S, Hook J, Nankivell M, Jayson GC, Kitchener H, Lopes T, et al. Primary chemotherapy versus primary surgery for newly diagnosed advanced ovarian cancer (CHORUS): an open-label, randomised, controlled, non-inferiority trial. Lancet [Internet]. 2015;6736(14):1–9. Available from: http://linkinghub.elsevier.com/ retrieve/pii/S0140673614622236 23. Bristow RE, Chi DS. Platinum-based neoadjuvant chemotherapy and interval surgical cytoreduction for advanced ovarian cancer: a meta-analysis. Gynecol Oncol. 2006 Dec;103(3):1070–6. 24. Sehouli J, Savvatis K, Braicu EI, Schmidt SC, Lichtenegger W, Fotopoulou C. Primary versus interval cytoreductive surgery in advanced ovarian cancer: results from a systematic single-center analysis. Int J Gynecol Cancer. 2010 Nov;20(8):1331–40. 25. Rosen B, Laframboise S, Ferguson S, Dodge J, Bernardini M, Murphy J, et al. The impacts of neoadjuvant che- motherapy and of debulking surgery on survival from advanced ovarian cancer. Gynecol Oncol. 2014 Jul 12; 26. van Altena AM, Karim-Kos HE, de Vries E, Kruitwagen RF, Massuger LF, Kiemeney LA. Trends in therapy and survival of advanced stage epithelial ovarian cancer patients in the Netherlands. Gynecol Oncol. 2012 Jun;125(3):649–54. 27. Bristow RE, Zahurak ML, Diaz-Montes TP, Giuntoli RL, Armstrong DK. Impact of surgeon and hospital ovarian cancer surgical case volume on in-hospital mortality and related short-term outcomes. Gynecol Oncol. 2009 Dec;115(3):334–8. 28. Bristow RE, Chang J, Ziogas A, Anton-Culver H. Adherence to treatment guidelines for ovarian cancer as a measure of quality care. Obstet Gynecol. 2013 Jun;121(6):1226–34. 29. Goff BA, Matthews BJ, Wynn M, Muntz HG, Lishner DM, Baldwin LM. Ovarian cancer: patterns of surgical care across the United States. Gynecol Oncol. 2006 Nov;103(2):383–90. 30. Goff BA, Matthews BJ, Larson EH, Andrilla CH, Wynn M, Lishner DM, et al. Predictors of comprehensive surgical treatment in patients with ovarian cancer. Cancer. 2007;109(10):2031–42. 31. Birkmeyer JD, Stukel TA, Siewers AE, Goodney PP, Wennberg DE, Lucas FL. Surgeon volume and operative mortality in the United States. N Engl J Med. 2003 Nov 27;349(22):2117–27. 32. Leiserowitz GS, Lin JF, Tergas AI, Cliby BA, Bristow RE. Ovarian cancer patients selected for neoadjuvant chemotherapy versus primary cytoreductive surgery are not similar: A National Cancer Data Base study. Gynecol Oncol [Internet]. 2015 Apr [cited 2015 Aug 11];137:40–1. Available from: http://www.sciencedirect. com/science/article/pii/S0090825815000980 33. Urban RR, He H, Alfonso R, Hardesty MM, Gray HJ, Goff B a. Ovarian cancer outcomes: Predictors of early death. Gynecol Oncol [Internet]. 2015;140(3):474–80. Available from: http://linkinghub.elsevier.com/retrieve/ pii/S0090825815302249 34. Rose PG, Nerenstone S, Bracy MF, Clarke-pearson D, Olt G, Rubin SC, et al. Secondary Surgical Cytoreduction for Advanced Ovarian Carcinoma. N Engl J Med. 2004;351(24):2489–97.
37 Chapter 2
35. Allemani C, Weir HK, Carreira H, Harewood R, Spika D, Wang XS, et al. Global surveillance of cancer survival 1995-2009: analysis of individual data for 25 676 887 patients from 279 population-based registries in 67 countries (CONCORD-2). Lancet. 2014 Nov 26; 36. Sant M, Chirlaque Lopez MD, Agresti R, Sánchez Pérez MJ, Holleczek B, Bielska-Lasota M, et al. Survival of women with cancers of breast and genital organs in Europe 1999–2007: Results of the EUROCARE-5 study. Eur J Cancer [Internet]. 2015;51:2191–205. Available from: http://linkinghub.elsevier.com/retrieve/pii/ S0959804915007029
38
CHAPTER 3
The impact of centralization of services on treatment delay in ovarian cancer: a study on process quality
Eggink F.A., Vermue M.C., Van der Spek C., Arts H.J., Apperloo M.J., Nijman H.W., Niemeijer G.C.
International Journal for Quality in Health Care. 2017 Oct 1;29(6):810-816 Chapter 3
ABSTRACT
Objectives Emphasis on improving health care quality has led to centralization of services for patients suspected of ovarian cancer. As centralization of services may induce treatment delays, we aimed to assess health system interval guidelines in patients suspected of ovarian cancer within our managed clinical network.
Methods Compliance with national guidelines regarding health system intervals of patients treated for ovarian cancer in the University Medical Center Groningen in 2013 and 2014 was evaluated. Health system intervals were compared between 2013 and 2014, and between patients that were referred to the gynecology department in the UMCG directly and indirectly.
Results Between 2013 and 2014 a clinically relevant improvement in compliance with guidelines was demon- strated. Within this period, median treatment intervals decreased from 34 days to 29 days, and the percentage of patients in whom treatment interval guidelines were met increased from 63.5% to 72.2%. New regulations and increased awareness of health system intervals inspired changes in local practice leading to improved compliance with guidelines. Compliance was highest in patients that were directly referred to our academic hospital.
Conclusion Evaluation of health system intervals in patients suspected of ovarian cancer was feasible and may be applicable to other managed clinical networks. Though compliance with guidelines improved within the study period, there is potential for improvement. Establishing uniformity of electronic patient files in a managed clinical network is deemed essential to facilitate real-time evaluation of compliance with national guidelines in the future.
42 Treatment delay in ovarian cancer
INTRODUCTION
Within the past decade health care expenses have escalated. The increase may be attributed to factors such as an ageing population and development of expensive new treatment strategies. As expenditures place an increasing strain on health care budgets, a transition from volume-based pay- 3 ment models to value-based payment models has been suggested(1). Though many of the specific quality indicators to be used in value-based payment models still need to be defined, it is evident that standardization of services is essential.
To direct standardization of services for oncological patients, the development of guidelines based on specific, accurate and measurable quality indicators is important. It has previously been suggested that such guidelines should cover three elements: structure, process and outcome(2). In the case of ovarian cancer care, quality indicators have been identified in all three areas. For example, it has been demonstrated that complete cytoreduction is strongly associated with improved survival(3–6) and that patients treated in high volume hospitals by specialized gynecologic oncologists have better surgical outcomes(7–12). Implementation of national guidelines has led to centralization of services for patients with ovarian cancer in the Netherlands(13). In Europe, similar efforts have led to the development of Quality Indicators by the European Society of Gynecologic Oncology(14).
Within the Netherlands general practitioners act as gatekeepers and refer patients to hospital when, and if, needed. Traditionally, patients suspected of ovarian cancer were staged and treated in the hospital of their choice. However, as of January 2013, guidelines were implemented requiring central- ization of cytoreductive surgery to high-volume hospitals with specialized gynecological oncologists on staff. Other important aspects of these guidelines include mandatory discussion of all patients within a multidisciplinary setting, regional cooperation and the presence of an intensive care unit with sufficient experience in patients that have undergone large gynecologic surgeries.
To monitor quality of care for patients suspected of gynecological cancer within the north-eastern region of the Netherlands, a Managed Clinical Network (MCN) was created. The network raises aware- ness for quality indicators in oncological health care and aims to improve the quality and uniformity of care for patients with ovarian cancer within the north-eastern region of the Netherlands. The University Medical Center Groningen (UMCG) is part of this MCN and receives patients from a total of 10 regional hospitals.
One of the drawbacks of a centralized care system is the possibility of inducing delay(15). While the effect of longer waiting times on survival in ovarian cancer patients is debatable(16–18), delays in therapy have been linked to anxiety, reduced patient satisfaction and quality of life(17,19). Further- more, delay may be a reflection of inefficiently organized care. It has recently been demonstrated that early initiation of therapy in women suspected of epithelial ovarian cancer leads to additional
43 Chapter 3
quality-adjusted life years and is cost-effective in the Netherlands (20).
To prevent occurrence of delay, national guidelines were implemented regarding time intervals between first medical consultation, diagnosis and the start of primary therapy(13). The time intervals in the national guidelines are defined as follows: - referral interval, defined as the time between first medical consultation with general practitioner and first consultation with a gynecologist, - treatment interval, defined as time from first consultation with a gynecologist to start of primary therapy, - diagnostic interval, defined as time from first consultation with gynecologist to date of diagnosis or definite referral to treatment.
Additionally, our MCN locally implements the guideline waiting time for therapy (primary surgery or chemotherapy) after diagnosis or definite referral to treatment. Due to centralization of services for patients suspected of ovarian cancer, primary surgery is currently performed in specialized high volume hospitals, whereas neo-adjuvant chemotherapy may also be administered in local low volume hospitals. A graphic representation of these four time intervals, including the maximal number of days that were set for each interval according to the guidelines, is shown in figure 1.
Figure 1. Overview of time intervals used in the current study
Indirectly referred patients First consultation with Diagnosis / referral to speciali- gynecologist in referral hospital zed hospital for treatment
General practitioner
First consultation with Diagnosis or definite referral to Initiation of primary therapy oncologic gynecologist in treatment Directly referred patients specialized hospital
Referral interval (7d) Diagnostic interval (21d) Waiting time for therapy (21d)
Treatment interval (42d)
To assess compliance with these guidelines, regular measurement of specific health care intervals is essential. Within our region, these intervals have not been investigated systematically in patients sus- pected of ovarian cancer. Therefore, a pattern of care study was conducted to measure health care intervals for patients referred to the UMCG with a suspicion of ovarian cancer in the years 2013 and 2014. Within the current study, compliance with national health system interval guidelines was defined as primary outcome. Assessment of statistical process control within our managed clinical network (MCN) and identification and elimination of special causes of delay were defined as secondary outcomes.
44 Treatment delay in ovarian cancer
METHODS
Study design A retrospective cohort study was performed concerning women referred to the UMCG with a suspi- cion of ovarian cancer. We measured health system intervals within the first two years following the 3 implementation of centralized services for ovarian cancer. All patients billed with the specific hospital consultation code for ovarian cancer in the UMCG between January 1st 2013 and December 31st 2014 were included in the study. Patients with missing health system interval data were excluded, as were patients that were referred for a second opinion from outside the region. Electronic patient files were used to measure time intervals as defined in the national guidelines.
Outcome measures Compliance with national guidelines was defined as primary outcome. Assessment of statistical pro- cess control within our managed clinical network (MCN) and identification and elimination of special causes of delay were defined as secondary outcomes. Definitions of the time intervals used in the current study are the same as those used in the national guidelines and those used in previously published studies. All time intervals were measured in days.
Data analysis Health system intervals of directly referred patients (referred from general practitioner to the UMCG) and indirectly referred patients (referred from general practitioner to a regional hospital before referral to the UMCG) were analyzed separately to enable comparison of these two groups. Further- more, a comparison was made between health system intervals of patients referred to our hospital in 2013 and 2014.
To improve quality of care, identification of exceptional reasons for treatment delay is essential. This is termed ‘special cause variation’. Statistical process control, a key approach to quality improvement, was used to discriminate between expected (‘common cause’) variation and exceptional (‘special cause’) variation of the measured health system intervals, (21–23). Control charts were used to visualize and assess statistical process control within the MCN. Health system intervals from individual patients were plotted in chronological time order. Within the control charts, the dotted horizontal line represents the maximal time interval according to national guidelines. Treatment intervals marked with a red square and a 1 were located >3 standard deviations above the mean and were defined as outliers. These treatment intervals were investigated further to assess any reasons for special cause variation. Median health system intervals were compared to the national guidelines to assess compliance.
The Kruskall Wallis test for independent groups was used to investigate differences in intervals among patients diagnosed in 2013 and 2014. Differences were considered statistically significant at p<0.05. Sta- tistical analyses were carried out using Minitab 17, Minitab Inc, Pennsylvania, United States of America.
45 Chapter 3
RESULTS
Patients Medical records from 370 patients that were referred to the UMCG with a suspicion of ovarian cancer between January 1st 2013 and December 31st 2014 were retrieved. In total, 306 of the indirectly referred patients and 31 patients of the directly referred patients were included in the analyses (figure 2). An overview of the waiting times for indirectly referred patients is shown in table 1.
Figure 2. Flowchart of patients included in analyses
All patients billed with the hospital consultation code for ovarian cancer in 2013 and 2014 in the UMCG N=370
Directly referred patients Indirectly referred patients N=40 N=330
Included patients Excluded patients Included patients Excluded patients N=31 N=9 N=306 N=24
Surgery N=24 Surgery N=217 Neo-adjuvant chemotherapy N=4 Neo-adjuvant chemotherapy N=64 No treatment N=3 No treatment N=25
Treatment interval The most important interval defined in the national guidelines is the treatment interval. The maximal time for this interval is 42 days. In total, 281 indirectly referred patients that were suspected of ovarian cancer underwent treatment in the MCN in 2013 and 2014. The median treatment interval was 34.0 days (interquartile range (IQR) 22.0-51.0 days) in 2013, and 29.0 days (IQR 22.0-43.5days) in 2014.
Though not statistically significant, the median treatment intervals improved between 2013 (median of 34.0, IQR of 22.0-51.0 days) and 2014 (median of 29.0, IQR of 22.0-43.5 days), (p=0.118). The proportion of patients that were compliant with the national guideline increased over time (63.5% in 2013 compared to 72.2% in 2014) (figure 3).
46 Treatment delay in ovarian cancer
Table 1. Overview of waiting times of the indirectly referred patients N Standard Waiting time Waiting time Difference in % of patients (days) 2013 (days) 2014 (days) waiting time compliant Median, IQR Median, IQR 2013 vs 2014 with national guideline Referral interval 2013: 116 7 7.0, 5.0-9.0 5.0, 3.0-6.3 P = 0.299 2013: 80.7% 2014: 102 2014: 81.4% 3 Diagnostic interval 2013: 158 21 19.0, 13.0-29.0 18.0, 12.0-26.0 P = 0.044 2013: 60.5% 2014: 145 2014: 67.6% Waiting time 2013: 107 21 17.0, 9.0-24.0 13.0, 6.0-17.0 P = 0.000 2013: 63.1% surgery 2014: 110 2014: 83.9% Waiting time 2013: 41 21 6.0, 2.5-20.5 14.0, 7.0-17.0 * 2013: 78.0% chemotherapy 2014: 23 2014: 82.6% Treatment interval 2013: 148 42 34.0, 22.0-51.0 29.0, 22.0-43.5 P = 0.118 2013: 63.5% 2014: 133 2014: 72.2% All statistical analyses were performed using Kruskal-Wallis. *no statistical analysis was performed for waiting time to chemotherapy due to small sample size. IQR: interquartile range
Figure 3. Control chart demonstrating treatment intervals of all patients.
The red dotted horizontal line represents the maximal interval as set by the national guidelines, the green horizontal line represents the average treatment interval of patients in 2013 (left) and 2014 (right). The upper red line represents the upper control limit, and the lower red line represents the lower control limit. Treatment intervals marked with a red square and a 1 were identified as outliers.
47 Chapter 3
Further investigation of the outliers depicted in figure 3 uncovered a number of reasons for the exceptionally long treatment intervals of these patients. First of all, in some patients treatment for suspected ovarian cancer was only initiated after a second opinion. Furthermore, in a few patients treatment was postponed (due to patient’s own wishes or medical reasons), and other patients had long referral intervals due to difficulties in obtaining additional diagnostic information. A number of factors were identified which may have contributed to the reduction in treatment intervals between 2013 and 2014. For example, in 2013 there were technical problems concerning retrieval of CT-scans from external sources, and there was a relatively long waiting list for surgery. In 2014 these problems were solved, which may have contributed to the reduction in treatment intervals.
Referral interval The maximal referral interval is 7 days. The data that was needed to calculate the referral interval was available for 218 indirectly referred patients. No change was determined in median referral intervals between 2013 and 2014; they measured 7.0 days (IQR 5.0-9.0 days) in 2013 and 5.0 days (IQR 3.0-6.3 days) in 2014. Although the median referral intervals described were compliant with the national guideline, the maximal referral time was exceeded in 19.3% of patients in 2013 and 18.6% of patients in 2014. The relatively large range in referral intervals can partially be explained by a number of outliers. Within this group of outliers, a majority of patients were suspected of ovarian cancer after a coincidental finding on ultrasound imaging during the first consultation with a gynecologist.
Diagnostic interval The maximal diagnostic interval is 21 days. Diagnostic intervals were calculated for 303 indirectly referred patients. In 2013 the guideline was met in 60.5% of patients, and this increased to 67.6% of patients in 2014 (p=0.044). The median diagnostic interval was 19.0days (IQR 13.0-29.0 days) in 2013 and18.0 days (12.0-26.0 days) in 2014.
Waiting time for therapy The maximal waiting time for therapy is 21 days. Waiting time for therapy for patients undergoing surgery was evaluated for 217 indirectly referred patients. An improvement was seen in the surgical treatment intervals between 2013 and 2014 (p<0.001). In 2013 the median surgical treatment interval was 17.0 days (IQR 9.0-24.0 days) and the guideline was met in 63.1% of patients. In comparison, in the following year, the median surgical treatment interval was 13.0 days (IQR 6.0-17.0 days) and the guideline was met in 83.9% of the patients.
Waiting time for patients undergoing neo-adjuvant chemotherapy was evaluated for 64 patients. An increase in the median chemotherapy treatment interval was seen between 2013 and 2014, however the number of patients involved was too small to calculate whether this was statistically significant. In 2013, the median chemotherapy treatment interval was 6.0 days (IQR 2.5-20.5 days) and the guideline was met in 78% of patients. In 2014, the median chemotherapy treatment interval was 14.0 days (IQR
48 Treatment delay in ovarian cancer
7.0-17.0 days) and the guideline was met in 82.6% of patients.
Directly referred vs indirectly referred patients In total, 31 patients suspected of ovarian cancer were directly referred from general practitioner to the UMCG. Twenty-eight of them received treatment (figure 2). We compared treatment intervals in 3 27 directly referred patients with 281 indirectly referred patients that received treatment (figure 4). The median treatment interval for directly referred patients in 2013 was 34.0 days (IQR 22.0-51.0 days) with 88.9% of all patients meeting the national guideline, in 2014 the median treatment inter- val was 29.0 days (IQR 22.0-43.5 days) with 100% of all patients meeting the national guideline. In 2014, patients referred to the UMCG directly had a shorter median treatment interval compared to indirectly referred patients; the median treatment interval for indirectly referred patients was 28.0 days (IQR 9.0-35.5 days).
Figure 4. Control chart demonstrating treatment intervals of patients that were indirectly and directly referred to our academic hospital.
The red dotted horizontal line represents the maximal interval as set by the national guidelines, the green horizontal line represents the average treatment interval of patients that were indirectly (left) and directly (right) referred. The upper red line represents the upper control limit, and the lower red line represents the lower control limit. Treatment intervals marked with a red square and a 1 were identified as outliers.
The median diagnostic interval for directly referred patients was 6.0 days (IQR 4.5-12.5 days) in 2013, with 83.4% of all patients meeting the national guideline. In 2014 the median diagnostic interval was 6.5 days (IQR 0.0-19.0 days) with 90.0% of all patients meeting the national guideline.
49 Chapter 3
Table 2. Health system intervals of indirectly referred patients and directly referred patients in 2013 and 2014 Indirectly referred patients Directly referred patients N Waiting time in days % of patients compliant N Waiting time in days % of patients compliant with (median, IQR) with national guideline (median, IQR) national guideline Referral 2013: 116 2013: 7.0, 5.0-9.0 80.7 2013: 20 2013: 12.0, 2.3-22.8 40.0 interval 2014: 102 2014: 5.0, 3.0-6.3 81.4 2014: 10 2014: 4.5, 0.0-8.5 80.0 Diagnostic 2013: 158 2013: 19.0, 13.0-29.0 60.5 2013: 17 2013: 6.0, 4.5-12.5 83.4 interval 2014: 145 2014: 18.0, 12.0-26.0 67.6 2014: 10 2014: 6.5, 0.0-19.0 90.0 Treatment 2013: 148 2013: 34.0, 22.0-51.0 63.5 2013: 18 2013: 21.5, 12.5-30.3 88.9 interval 2014: 133 2014: 29.0, 22.0-43.5 72.2 2014: 10 2014: 28.0, 9.0-35.5 100.0
IQR: interquartile range
The median referral interval of directly referred patients in 2013 was 12.0 days (IQR 2.3-22.8 days) and 40% of all patients were compliant with the national guideline. In 2014 the referral interval was 4.5 days (IQR 0.0-8.5 days) and 80.0% of all patients were compliant with the national guideline.
A comparison of the health system intervals of patients that were directly and indirectly referred can be found in table 2.
DISCUSSION
Within the current study, health care intervals in patients suspected of ovarian cancer were measured to evaluate compliance with national guidelines. Identification of patients using hospital consultation codes for ovarian cancer and calculation of health system intervals from electronic patient files was feasible. However, the lack of shared electronic patient files and comprehensive privacy regulations impeded efficient data collection. Special causes of delay were identified and eliminated, and improve- ments in compliance with national guidelines were demonstrated between 2013 and 2014.
Within the study period, new national regulations and improved awareness of health system inter- vals led to changes in local practice, resulting in improved compliance with guidelines. For example, a standardized one-weekly consultation between regional general gynecologists and gynecologic oncologists was initiated within our referral region and access to external CT-scans was arranged. Continuous implementation of changes in local practice may explain the absence of statistically significant improvements between 2013 and 2014. Importantly, the improvements in health system intervals that were demonstrated within this period are viewed to be clinically relevant.
50 Treatment delay in ovarian cancer
Table 2. Health system intervals of indirectly referred patients and directly referred patients in 2013 and 2014 Indirectly referred patients Directly referred patients N Waiting time in days % of patients compliant N Waiting time in days % of patients compliant with (median, IQR) with national guideline (median, IQR) national guideline Referral 2013: 116 2013: 7.0, 5.0-9.0 80.7 2013: 20 2013: 12.0, 2.3-22.8 40.0 3 interval 2014: 102 2014: 5.0, 3.0-6.3 81.4 2014: 10 2014: 4.5, 0.0-8.5 80.0 Diagnostic 2013: 158 2013: 19.0, 13.0-29.0 60.5 2013: 17 2013: 6.0, 4.5-12.5 83.4 interval 2014: 145 2014: 18.0, 12.0-26.0 67.6 2014: 10 2014: 6.5, 0.0-19.0 90.0 Treatment 2013: 148 2013: 34.0, 22.0-51.0 63.5 2013: 18 2013: 21.5, 12.5-30.3 88.9 interval 2014: 133 2014: 29.0, 22.0-43.5 72.2 2014: 10 2014: 28.0, 9.0-35.5 100.0
IQR: interquartile range
Though the improvements that were achieved in compliance with health system interval guidelines were present in directly and indirectly referred patients, compliance with diagnostic and treatment interval guidelines was strikingly lower in indirectly referred patients. The reasons for this difference are currently unclear. We therefore suggest that further investigation is required, especially as the majority of patients are referred to the UMCG in an indirect manner. Another noteworthy finding was the unexpectedly low compliance with referral interval guidelines for directly referred patients in 2013 (40%). Reasons for this low compliance rate, and the increase in compliance to 80% in 2014 are also unclear.
While compliance with guidelines improved within the study period, the compliance rates themselves demonstrate the potential for improvement. Within the United Kingdom, the National Institute for Health and Care Excellence (NICE) referral guidelines for suspected cancer were implemented in 2005. In a study by Neal and colleagues, 15 cancer types were selected and diagnostic intervals were evaluated before and after implementation of these guidelines. The authors conclude that implemen- tation of the NICE guidelines contributed to a reduction of diagnostic intervals in the United Kingdom between 2001-2002 and 2007-2008(24). Further implementation of national guidelines regarding health system intervals in the Netherlands is therefore deemed essential.
Whether a further reduction in health system intervals will improve survival of patients with ovarian cancer is subject of debate(14,16,17). Indeed, the time between decision regarding treatment and start of treatment was not included in the newly published European Society of Gynecologic Oncol- ogy Quality Indicators due to a lack of evidence. Nevertheless, it has been suggested that shorter waiting times may improve patient satisfaction and quality of life (16,17,19). Furthermore, Hoyer and colleagues used a discrete event simulation model to demonstrate that a reduction in treatment
51 Chapter 3
intervals leads to additional QALY’s (20). The model estimated an incremental cost effectiveness ratio (ICER) of €2592 per QALY, which is far below the informal ceiling ratio that has been set at €80,000 in the Netherlands. Therefore, a reduction of health system intervals is deemed cost effective. As health expenditures are quickly accumulating, implementation of cost effective measures is of the utmost importance.
One of the challenging aspects of the current study was the involvement of 10 referral hospitals, and consequently a number of different electronic information systems. No readily accessible database containing medical information of all patients within the MCN exists, preventing real-time monitor- ing of health system intervals. The presence of digital borders between health care institutes was previously noted by Porter and colleagues, who stated that the current organization of healthcare and information systems impede the measurement of health care quality(19). Development of an integrated information system covering all regional hospitals (and perhaps even all national hospitals) may aid the identification of factors that prolong health system intervals and facilitate rapid interven- tion where needed. Importantly, patient privacy must be ensured at all times according to European privacy and data protection laws, making this a difficult (but seemingly not impossible) challenge.
One of the main strengths of this study is the application of statistical process control in the context of quality improvement. As control charts are relatively easy to interpret for (clinical) staff without prior experience in statistical process control, they can provide important information needed to reduce health system intervals(22,25). Within the current study, the use of control charts indeed facilitated distinction between common cause variation and special cause variation, allowing us to focus on special causes of delay within the MCN. Furthermore, these analyses demonstrated a dif- ference in health system intervals between directly and indirectly referred patients. While median health system intervals for both directly and indirectly referred patients were within the set guidelines in 2014, differences between these two pathways should be monitored closely as uniform quality of clinical care and service for patients within the region is one of the aims of the MCN. Moreover, regular evaluation of health system intervals using control charts will ensure rapid identification of potential new special causes of delay.
Another perceived strength of this study was the use of internationally accepted time intervals, allowing comparison with other studies. However, we are aware of only two other studies describing health system intervals specific for patients suspected of ovarian cancer(26,27). The large variations in health system intervals described in these three studies suggest differences in organization of care between the three hospitals/regions. These differences hinder any valuable comparison between the three studies. A comparison with other hospitals within the Netherlands may have provided useful information, however these data were not available.
52 Treatment delay in ovarian cancer
There are a number of limitations to this study. First of all, no data was gathered from the period before the implementation of the national guidelines. Moreover, the study design did not include markers of patient outcome such as survival, patient satisfaction or quality of life. Though not part of our primary objective, the absence of these data limit analyses on the impact of reducing health system intervals within this group of patients. It would be of interest to include markers of patient 3 outcome in future studies regarding health system intervals for patients with ovarian cancer. Impor- tantly, all quality measures require frequent re-evaluation and patient perspectives of care should be incorporated into evaluation of hospital quality, as previously emphasized by Dy and colleagues(28).
In conclusion, we have described a method to measure health system intervals in patients suspected of ovarian cancer using hospital consultation codes and electronic patient files. This method may be applicable to other departments wishing to raise awareness of health system intervals and improve quality of care. Within the study period awareness of health system intervals inspired changes in local practice leading to improved compliance with guidelines, though there is clearly still room for improvement. Regular monitoring and evaluation of statistical process control are essential to enable further improvement of process quality within the MCN. However, establishing uniformity of electronic patient files in the MCN is deemed essential to aid real-time measurements of health system intervals.
FUNDING AND ACKNOWLEDGEMENTS
This work was supported by Dutch Cancer Society grant RUG 2013-6505 to HWN. The authors would like to thank the hospitals involved in the MCN for their collaboration and specifically for facilitating the data collection process.
53 Chapter 3
REFERENCES
1. Liang MI, ElNaggar AC, Nekkanti S, O’Malley DM, Hade EM, Copeland LJ, et al. Setting the bar: compliance with ovarian cancer quality indicators at a National Cancer Institute-designated Comprehensive Cancer Center. Gynecol Oncol [Internet]. Elsevier Inc.; 2015;138(3):689–93. Available from: http://linkinghub.elsevier.com/ retrieve/pii/S0090825815300810 2. Donabedian A. Quality assurance. Structure, process and outcome. Nurs Stand [Internet]. Jan [cited 2016 May 4];7(11 Suppl QA):4–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/1489693 3. Griffiths CT. Surgical resection of tumor bulk in the primary treatment of ovarian carcinoma. Natl Cancer Inst Monogr. UNITED STATES; 1975;42:101–4. 4. Bristow RE, Tomacruz RS, Armstrong DK, Trimble EL, Montz FJ. Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis. J Clin Oncol. Kelly Gynecologic Oncology Service, Department of Gynecology and Obstetrics, Johns Hopkins Medical Institutions, Baltimore, MD 21287-1248, USA. [email protected]; 2002;20(5):1248–59. 5. Chang SJ, Hodeib M, Chang J, Bristow RE. Survival impact of complete cytoreduction to no gross residual disease for advanced-stage ovarian cancer: a meta-analysis. Gynecol Oncol. Department of Obstetrics and Gynecology, Ajou University School of Medicine, Suwon, Republic of Korea.: Elsevier Inc; 2013 Sep;130(3):493–8. 6. du Bois A, Reuss A, Pujade-Lauraine E, Harter P, Ray-Coquard I, Pfisterer J. Role of surgical outcome as prognostic factor in advanced epithelial ovarian cancer: a combined exploratory analysis of 3 prospectively randomized phase 3 multicenter trials: by the Arbeitsgemeinschaft Gynaekologische Onkologie Studiengruppe Ovarialkarzin. Cancer. Department of Gynecology & Gynecologic Oncology, Dr. Horst Schmidt Klinik, HSK Wies- baden, Germany (AGO-OVAR). [email protected]: American Cancer Society; 2009;115(6):1234–44. 7. Vernooij F, Heintz AP, Coebergh JW, Massuger LF, Witteveen PO, van der Graaf Y. Specialized and high-volume care leads to better outcomes of ovarian cancer treatment in the Netherlands. Gynecol Oncol. Department of Gynecological Surgery and Oncology, University Medical Center Utrecht, PO Box 85500, 3508 GA Utrecht, The Netherlands. [email protected]; 2009;112(3):455–61. 8. Harter P, Muallem ZM, Buhrmann C, Lorenz D, Kaub C, Hils R, et al. Impact of a structured quality management program on surgical outcome in primary advanced ovarian cancer. Gynecol Oncol [Internet]. Elsevier Inc.; 2011;121(3):615–9. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0090825811001260 9. Horowitz NS, Miller a., Rungruang B, Richard SD, Rodriguez N, Bookman M a., et al. Does Aggressive Surgery Improve Outcomes? Interaction Between Preoperative Disease Burden and Complex Surgery in Patients With Advanced-Stage Ovarian Cancer: An Analysis of GOG 182. J Clin Oncol [Internet]. 2015;33(8). Available from: http://jco.ascopubs.org/cgi/doi/10.1200/JCO.2014.56.3106 10. van Altena AM, van den Akker PA, de Hullu JA, Ottevanger PB, Aalders AL, Gerritse R, et al. Efficacy of a regional network for ovarian cancer care. Obstet Gynecol. Department of Obstetrics and Gynecology, Radboud Uni- versity Medical Centre, Nijmegen, the Netherlands. [email protected]; 2013 Sep;122(3):668–75. 11. Birkmeyer JD, Stukel TA, Siewers AE, Goodney PP, Wennberg DE, Lucas FL. Surgeon volume and oper- ative mortality in the United States. N Engl J Med. Department of Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA. [email protected]: Massachusetts Medical Society; 2003 Nov 27;349(22):2117–27. 12. Bristow RE, Zahurak ML, Diaz-Montes TP, Giuntoli RL, Armstrong DK. Impact of surgeon and hospital ovarian cancer surgical case volume on in-hospital mortality and related short-term outcomes. Gynecol Oncol. The Kelly Gynecologic Oncology Service, Departments of Gynecology and Obstetrics and Oncology, The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins Medical Institutions, 600 North Wolfe Street, Phipps #281, Baltimore, MD 21287, USA. (TRUNCATED; 2009 Dec;115(3):334–8. 13. Stichting Oncologische Samenwerking. Multidisciplinaire normering oncologische zorg in Nederland [Inter-
54 Treatment delay in ovarian cancer
net]. 2014. Available from: http://www.iknl.nl/docs/default-source/Palliatieve-zorg-in-de-ziekenhuizen/soncos. pdf?sfvrsn=0 14. Querleu D, Planchamp F, Chiva L, Fotopoulou C, Barton D, Cibula D, et al. European Society of Gynaecologic Oncology Quality Indicators for Advanced Ovarian Cancer Surgery. Int J Gynecol Cancer. 2016;26(7):1354–63. 15. Yun YH, Kim Y a., Min YH, Park S, Won YJ, Kim DY, et al. The influence of hospital volume and surgical treatment delay on long-term survival after cancer surgery. Ann Oncol. 2012;23(May):2731–7. 16. Neal RD, Tharmanathan P, France B, Din NU, Cotton S, Fallon-Ferguson J, et al. Is increased time to diagnosis 3 and treatment in symptomatic cancer associated with poorer outcomes? Systematic review. Br J Cancer [Internet]. 2015;112 Suppl:S92-107. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?ar- tid=4385982&tool=pmcentrez&rendertype=abstract 17. Robinson KM, Christensen KB, Ottesen B, Krasnik A. Diagnostic delay, quality of life and patient satisfaction among women diagnosed with endometrial or ovarian cancer: a nationwide Danish study. Qual Life Res [Internet]. 2012;21:1519–25. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22138966 18. Feng Z, Wen H, Bi R, Yang W, Wu X. Prognostic impact of the time interval from primary surgery to intravenous chemotherapy in high grade serous ovarian cancer. Gynecol Oncol [Internet]. Elsevier Inc.; 2016;141(3):466– 70. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0090825816301482 19. Porter M. What is the value in health care. N Engl J Med [Internet]. 2010;363:2477–81. Available from: http:// scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:New+engla+nd+journal#0 20. Hoyer T, Bekkers R, Gooszen H, Massuger L, Rovers M, Grutters JPC. Cost-Effectiveness of Early-Initi- ated Treatment for Advanced-Stage Epithelial Ovarian Cancer Patients. Int J Gynecol Cancer [Internet]. 2014;24(1):75–84. Available from: http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage &an=00009577-201401000-00013 21. Thor J, Lundberg J, Ask J, Olsson J, Carli C, Härenstam KP, et al. Application of statistical process control in healthcare improvement: systematic review. Qual Saf Health Care [Internet]. 2007 Oct [cited 2016 Mar 1];16(5):387–99. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2464970&tool=p- mcentrez&rendertype=abstract 22. Fretheim A, Tomic O. Statistical process control and interrupted time series: a golden opportunity for impact evaluation in quality improvement. BMJ Qual Saf [Internet]. 2015 Dec [cited 2016 May 6];24(12):748–52. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4680165&tool=pmcentrez&ren- dertype=abstract 23. Perla RJ, Provost LP, Murray SK. The run chart: a simple analytical tool for learning from variation in healthcare processes. BMJ Qual Saf [Internet]. 2011 Jan [cited 2016 May 6];20(1):46–51. Available from: http://www.ncbi. nlm.nih.gov/pubmed/21228075 24. Neal RD, Din NU, Hamilton W, Ukoumunne OC, Carter B, Stapley S, et al. Comparison of cancer diagnostic intervals before and after implementation of NICE guidelines: analysis of data from the UK General Practice Research Database. Br J Cancer [Internet]. Nature Publishing Group; 2014;110(3):584–92. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3915139&tool=pmcentrez&rendertype=abstract 25. Mohammed MA, Cheng KK, Rouse A, Marshall T. Bristol, shipman, and clinical governance: Shewhart’s forgotten lessons. Lancet. 2001;357(9254):463–7. 26. Vandborg MP, Christensen RD, Kragstrup J, Edwards K, Vedsted P, Hansen DG, et al. Reasons for diagnostic delay in gynecological malignancies. Int J Gynecol Cancer. 2011;21(6):967–74. 27. Hansen RP, Vedsted P, Sokolowski I, Søndergaard J, Olesen F. Time intervals from first symptom to treatment of cancer: a cohort study of 2,212 newly diagnosed cancer patients. BMC Health Serv Res [Internet]. 2011;11:284. Available from: http://www.biomedcentral.com/1472-6963/11/284 28. Dy SM, Chan KS, Chang HY, Zhang A, Zhu J, Mylod D. Patient perspectives of care and process and outcome quality measures for heart failure admissions in US hospitals: How are they related in the era of public report- ing? Int J Qual Heal Care. 2016;28(4):522–8.
55
CHAPTER 4
Surgery for patients with newly diagnosed advanced ovarian cancer: which patient, when and extent?
Eggink F.A., Koopmans C.M., Nijman H.W.
Current opinion in Oncology, 2017 Sep; 29(5):351-358 Chapter 4
ABSTRACT
Purpose of review Cytoreduction to no residual disease is the mainstay of primary treatment for advanced epithelial ovarian cancer (AdvEOC). This review addresses recent insights on optimal patient selection, timing and extent of surgery, intended to optimize cytoreduction in patients with AdvEOC.
Recent findings Clinical guidelines recommend primary cytoreductive surgery for AdvEOC patients with a high likeli- hood of achieving complete cytoreduction with acceptable morbidity. In line with this, preoperative prediction markers such as CA-125, histologic and genomic factors, innovative imaging modalities and the performance of a diagnostic laparoscopy have been suggested to improve clinical decision-making with regard to optimal timing of cytoreductive surgery. To determine whether these strategies should be incorporated into clinical practice validation in randomized clinical trials is essential.
Summary The past decade has seen a paradigm shift in the number of AvdEOC patients that are being treated with upfront neo-adjuvant chemotherapy instead of primary cytoreductive surgery. However, while neo-adjuvant chemotherapy may reduce morbidity at the time of interval cytoreductive surgery, no favorable impact on survival has been demonstrated and it may induce resistance to chemotherapy. Therefore, optimizing patient selection for PCS is crucial. Furthermore, surgical innovations in patients diagnosed with AvdEOC should focus on improving survival outcomes.
58 Surgery for advanced stage ovarian cancer patients
INTRODUCTION
Advanced Epithelial ovarian cancer (AdvEOC) is the most lethal malignancy in women(1). Ovarian car- cinoma comprise a heterogeneous group of cancers including high-grade serous carcinoma (70-80%), endometrioid carcinoma (10%), clear cell carcinoma (10%), mucinous carcinoma (<5%) and low-grade carcinoma (<5%). A lack of specific symptoms, often resulting in advanced disease at diagnosis, and frequent development of resistance to chemotherapy, play an important role in the unfavorable 4 prognosis of patients diagnosed with this aggressive disease.
Despite efforts aimed at improving survival outcomes, minimal impact on survival has been achieved thus far. Surprisingly, no significant changes have been made in the core elements of therapy for AdvEOC in the past decades. Standard therapy comprised, and still comprises, a combination of cytoreductive surgery and platinum based chemotherapy. Currently, in most countries, patients undergo primary cytoreductive surgery (PCS) and adjuvant chemotherapy (ACT) if complete cytoreduc- tion seems feasible with acceptable morbidity. Patients are treated with neoadjuvant chemotherapy (NACT) followed by interval cytoreductive surgery (ICS) when complete cytoreduction is considered unlikely, or if unacceptable morbidity is expected during PCS(2). This review will focus on organization of care, sequence of primary therapy, advances in selection of patients for primary cytoreductive surgery and perspectives in primary therapy for AdvEOC.
59 Chapter 4
ORGANIZATION OF CARE
In the past decade the importance of cytoreduction to no macroscopically visible disease (termed ‘complete cytoreduction’) has become universally accepted(3). In 2002, a landmark meta-analysis quantified the correlation between surgical outcome and survival advantage and concluded that each 10% increase in maximal cytoreduction is associated with a 5.5% increase in median survival outcomes(4). As such, all patients with AdvEOC should receive one maximal effort at complete cytore- duction.
Various efforts aimed at improving the rate of complete cytoreduction have been made. One of the aspects that have been investigated extensively in this regard is the organization of oncologic care for patients with AdvEOC. Several studies have demonstrated that the likelihood of achieving complete cytoreduction is higher when cytoreductive surgery is performed by specialized surgical teams in high volume hospitals(5–8). These insights instigated a paradigm shift in the organization of care for AdvEOC patients. Important criteria within these guidelines are a minimal required case load and the presence of specialized (surgical) personnel within the treatment hospital. According to the recently published ESGO quality indicators , surgical cytoreduction for AdvEOC patients should be centralized to hospitals that perform a minimum of 20 cytoreductive surgeries annually. Intermediate and optimal annual targets have been set at 50 and 100 surgeries, respectively(9).
60 Surgery for advanced stage ovarian cancer patients
SEQUENCE OF PRIMARY THERAPY
Increasing emphasis on the importance of achieving complete cytoreduction while keeping patient morbidity at acceptable levels, has led to the implementation of a therapeutic regime in which NACT is followed by ICS. Advocates of the NACT+ICS-regime suggest that chemotherapy may reduce tumor load and increase the chances of achieving complete cytoreduction with less surgical morbidity. Importantly, a meta-analysis published in 2006 concluded that NACT was associated with inferior 4 overall survival(10). Nevertheless, in this meta-analysis, and other analyses comprising retrospective studies, favorable survival in the PCS group may be attributable to favorable prognostic factors such as better performance status and lower tumor load.
Two landmark phase III clinical trials (EORTC 55971 and CHORUS trial) have been conducted assessing survival impact of NACT+ICS instead of PCS+ACT in AdvEOC(11,12). Although these trials demon- strated higher complete cytoreduction rates and lower surgical morbidity in patients treated with the NACT+ICS regime, the overall and progression free survival outcomes were similar between both groups. Notably, an exploratory analysis of the EORTC 55971 trial demonstrated favorable survival in patients with stage IIIC and less extensive tumor load that were treated with PCS+ACT, and favorable survival in patients with stage IV disease and high tumor load that were treated with NACT+ICS(13). Recently, a meta-analysis was conducted comprising four phase III clinical trials that have published mature survival data of patients treated with either PCS+ACT or NACT+ICS (the EORTC 55971 and CHORUS trials and two older trials)(14). This meta-analysis confirmed non-inferiority of NACT+ICS compared to PCS+ACT with regard to overall survival (HR 0.94, 95% CI 0.81-1.08, p=0.38) and progres- sion free survival (HR 0.89, 95% CI 0.77-1.03, p=0.12), and established that administration of NACT was associated with higher chances of achieving complete cytoreduction during ICS when compared to PCS (RR 2.37, 95% CI 1.94-2.91, p<0.001).
One of the potential explanations of the lack of survival benefit seen with NACT+ICS is the risk of inducing chemotherapy resistance by exposing large tumor volumes to chemotherapy (15–17). Administration of NACT may selectively eliminate the chemotherapy sensitive cells, which may drive platinum resistance. It has recently been shown that recurrences in patients that were treated with NACT+ICS were less sensitive to subsequent chemotherapy compared to patients that were treated with PCS+ACT, suggest- ing that the administration of NACT may undermine therapeutic options for recurrent disease(16,17).
In contrast to the EORTC 55971 and CHORUS trials, the recently conducted randomized phase III SCORPION trial failed to demonstrate a difference in complete cytoreduction rates between the two regimes(18). Survival data of the SCORPION trial are thus eagerly awaited. Despite the lack of improvement in cytoreduction, several other advantages of NACT+ICS regime were demonstrated in the SCORPION trial including lower morbidity (less early grade III and IV adverse events) and higher quality of life. The CHORUS trial also showed less grade III or IV adverse events in the NACT+ICS group,
61 Chapter 4
although no difference in quality of life was demonstrated(12). An overview of the three most recently conducted phase III randomized trials (EORTC 55971, CHORUS and SCORPION) is depicted in table 1.
Notably, the EORTC 55971 and CHORUS trials have important limitations such as a selection bias towards patients with poor performance status, old age and high tumor load, as well as subopti- mal cytoreductive surgery outcomes (mainly at primary surgery), low mean operative times and low median overall survival. To address limitations of the EORTC 55971 and CHORUS trials, specifically the suboptimal cytoreductive surgery outcomes, the Arbeitsgemeinschaft Gynaekologische Onkologie study group, North Eastern German Society of Gynaecologic Oncology and international collabora- tors have initiated a new randomized clinical trial: The Trial on Radical Upfront Surgery in Advanced Ovarian Cancer (TRUST)(19). Within this trial 686 AdvEOC patients will be randomized to PCS+ACT or NACT+ICS. Stringent quality assessment is in place to ensure that participating centers meet the recently published ESGO criteria for cytoreductive surgery in AdvEOC patients(9). Final analysis of overall survival in the TRUST trial is expected in 2023.
The Society of Gynecologic Oncology (SGO) and the American Society of Clinical Oncology (ASCO) have also published a clinical guideline regarding the use of NACT in patients with AdvEOC, an overview is shown in Table 2(9,20).
Table 1. Recent phase III randomized clinical trials on PCS+ACT vs NACT+ICS in AdvEOC Trial Inclusion criteria Primary Number of Complete Median OS (months) Median PFS Any grade III-IV Postoperative outcome patients per arm cytoreduction (months) Adverse events death <28 days EORTC 55971 Biopsy-proven stage IIIC or IV invasive OS PCS+ACT: 336 PCS+ACT, 19% PCS+ACT, 29 PCS+ACT, 12 No overall data PCS+ACT, 3% Vergote et al. epithelial ovarian carcinoma, primary available New England peritoneal carcinoma, or fallopian- NACT+ICS: 334 NACT+ICS, 51% NACT+ICS, 30 NACT+ICS, 12 NACT+ICS, 1% Journal of Medicine tube carcinoma. 2010 ITT: HR 0.98, 90% CI 0.84-1.13. Predefined non- ITT: HR 1.01, 90% inferiority boundary was 1.25. CI 0.89-1.15 CHORUS Kehoe Clinical or imaging evidence of stage OS PCS+ACT: 276 PCS+ACT, 17% PCS+ACT, 23 PCS+ACT, 12 PCS+ACT, 24% PCS+ACT, 6% et al. III or IV ovarian, fallopian tube or Lancet 2015 primary peritoneal cancer. NACT+ICS: 274 NACT+ICS, 43% NACT+ICS, 24 NACT+ICS, 11 NACT+ICS, 14% NACT+ICS, <1%
ITT: HR 0.87, upper bound of one-sided 90% CI ITT: HR 0.91, 95% 0.72-1.05. Predefined non-inferiority boundary CI 0.76-1.09 was 1.18. SCORPION Fagotti Histological evidence (frozen section) Surgical PCS+ACT: 55 PCS+ACT, 46% Awaiting maturation of data Awaiting PCS+ACT, 53% PCS+ACT, 4% et al. of stage IIIC or IV ovarian, fallopian adverse maturation of European Journal tube or primary peritoneal cancer and events NACT+ICS: 55 NACT+ICS, 58% data NACT+ICS, 6% NACT+ICS, 0% of Cancer 2016 high tumor load without mesenteric retraction. PCS+ACT: primary cytoreductive surgery and adjuvant chemotherapy, NACT+ICS: neoadjuvant chemotherapy and interval cytoreductive surgery, OS: overall survival, PFS: progression free survival, ITT: intention to treat analysis, HR: hazard ratio.
62 Surgery for advanced stage ovarian cancer patients
SELECTION OF PATIENTS FOR PCS
One of the future directions discussed in the SGO/ASCO guideline is the optimization of preoperative patient selection for PCS(20). More specifically, exclusion criteria for patients with high tumor load and at high risk of morbidity and/or mortality from PCS, and selection criteria for patients with low tumor load and high likelihood of complete cytoreduction with PCS should be developed. 4 Clinical and laboratory markers One of the markers which has been suggested to be of use in patient selection for PCS is Cancer Antigen 125 (CA-125). An analysis based on data that was prospectively collected for a multicenter nonrandomized trial identified CA-125 ≥600 as a marker for the presence of residual disease after PCS(21). Furthermore, a retrospective study by Mahdi et al determined that a reduction in preop- erative CA-125 of 90% was associated with complete ICS(22). Human Epididymis protein 4 (HE4) has also been studied with respect to patient selection for PCS. Though it has been identified as a strong predictor for unfavorable prognosis in AdvEOC, CA-125 currently remains the most important biomarker in AdvEOC (excluding mucinous subtypes) (23,24). Furthermore, markers of performance and nutritional status, such as age, race, smoking status, creatinine and albumin levels have also been studied with regard to selection of patients for PDS(25,26).
Table 1. Recent phase III randomized clinical trials on PCS+ACT vs NACT+ICS in AdvEOC Trial Inclusion criteria Primary Number of Complete Median OS (months) Median PFS Any grade III-IV Postoperative outcome patients per arm cytoreduction (months) Adverse events death <28 days EORTC 55971 Biopsy-proven stage IIIC or IV invasive OS PCS+ACT: 336 PCS+ACT, 19% PCS+ACT, 29 PCS+ACT, 12 No overall data PCS+ACT, 3% Vergote et al. epithelial ovarian carcinoma, primary available New England peritoneal carcinoma, or fallopian- NACT+ICS: 334 NACT+ICS, 51% NACT+ICS, 30 NACT+ICS, 12 NACT+ICS, 1% Journal of Medicine tube carcinoma. 2010 ITT: HR 0.98, 90% CI 0.84-1.13. Predefined non- ITT: HR 1.01, 90% inferiority boundary was 1.25. CI 0.89-1.15 CHORUS Kehoe Clinical or imaging evidence of stage OS PCS+ACT: 276 PCS+ACT, 17% PCS+ACT, 23 PCS+ACT, 12 PCS+ACT, 24% PCS+ACT, 6% et al. III or IV ovarian, fallopian tube or Lancet 2015 primary peritoneal cancer. NACT+ICS: 274 NACT+ICS, 43% NACT+ICS, 24 NACT+ICS, 11 NACT+ICS, 14% NACT+ICS, <1%
ITT: HR 0.87, upper bound of one-sided 90% CI ITT: HR 0.91, 95% 0.72-1.05. Predefined non-inferiority boundary CI 0.76-1.09 was 1.18. SCORPION Fagotti Histological evidence (frozen section) Surgical PCS+ACT: 55 PCS+ACT, 46% Awaiting maturation of data Awaiting PCS+ACT, 53% PCS+ACT, 4% et al. of stage IIIC or IV ovarian, fallopian adverse maturation of European Journal tube or primary peritoneal cancer and events NACT+ICS: 55 NACT+ICS, 58% data NACT+ICS, 6% NACT+ICS, 0% of Cancer 2016 high tumor load without mesenteric retraction. PCS+ACT: primary cytoreductive surgery and adjuvant chemotherapy, NACT+ICS: neoadjuvant chemotherapy and interval cytoreductive surgery, OS: overall survival, PFS: progression free survival, ITT: intention to treat analysis, HR: hazard ratio.
63 Chapter 4
Table 2. SGO-ASCO clinical guidelines en ESGO clinical guidelines SGO-ASCO guidelines (20) ESGO guidelines (9) Specialized All women with suspected stage IIIC or IV Surgery in low-volume and low-quality centers is decision- invasive epithelial ovarian cancer should be discouraged. The existence of an intermediate making evaluated by a gynecologic oncologist prior to care facility and access to an intensive care initiation of therapy to determine whether they unit management are required. Participation in are candidates for PCS. clinical trials is a quality indicator. Decisions that women are not eligible for All patients should be reviewed postoperatively medical or surgical cancer treatment should be at a gynecologic oncology multidisciplinary made after a consultation with a gynecologic meeting oncologist and/or a medical oncologist with gynecologic expertise. Preoperative A primary clinical evaluation should include a Clinical examination, including abdominal, workup CT of the abdomen and pelvis with oral and vaginal and rectal examinations; assessment intravenous contrast and chest imaging (CT of the breast, groins, axilla and supraclavicular preferred) to evaluate the extent of disease areas; and auscultation of the lungs should be and the feasibility of surgical resection. The use performed. of other tools to refine this assessment may A tumor marker assessment should be include laparoscopic evaluation or additional performed for at least CA125 levels. HE4 has radiographic imaging (e.g. FDG-PET scan or also been proposed. Additional markers, diffusion-weighted MRI). including AFP, hCG, CEA, CA19-9, inhibin B or AMH, estradiol, testosterone, would be useful in specific circumstances such as young age, or imaging suggesting a mucinous, or non- epithelial, or tumor of extra-adnexal origin. Selection of For women with a high likelihood of achieving Primary surgery is recommended in patients patients for a cytoreduction to <1cm (ideally to no visible who can be debulked upfront to no residual PCS+ACT disease) with acceptable morbidity, PCS is tumor with a reasonable complication rate. recommended over NACT. Risk-benefit is in favor of PCS when: - There is no unresectable tumor present; - Complete debulking to no residual tumor seems feasible when reasonable morbidity, taking into account the patients’ status; - Patient accepts potential supportive measures as blood transfusions or stoma. Selection of Women who have a high perioperative Criteria against PCS are: patients for risk profile or a low likelihood of achieving - Diffuse deep infiltration of the root of small NACT+ICS cytoreduction to <1cm (ideally to no visible bowel mesentery; disease) should receive NACT. - Diffuse carcinomatosis of the small bowel For women who are fit for PCS but are deemed involving such large parts that resection would unlikely to have cytoreduction to <1cm (ideally to lead to short bowel syndrome (remaining no visible disease) by a gynecologic oncologist, bowel < 1.5m) NACT is recommended over PCS. - Diffuse involvement/deep infiltration of stomach/duodenum (limited excision is possible) and head or middle part of pancreas (tail of pancreas can be resected)’ - Involvement of truncus coeliacus, hepatic arteries, left gastric artery (coeliac nodes can be resected). Timing of ICS ICS should be performed after ≤4 cycles of NACT ICS should be proposed to patients fit for for women with a response to chemotherapy surgery with a response or stable disease or stable disease. Alternate timing of ICS has compatible with complete resection. not been prospectively evaluated but may be If a patient did not have the opportunity of considered on patient-centered factors. surgery after 3 cycles, then a delayed debulking after more than 3 cycles of NACT may be considered on an individual basis. SGO: Society of Gynecologic Oncology, ASCO: American Society of Clinical Oncology, ESGO: European Society of Gynecologic Oncology, PCS+ACT: primary cytoreductive surgery and adjuvant chemotherapy, NACT+ICS: neoadjuvant chemotherapy and interval cytoreductive surgery.
64 Surgery for advanced stage ovarian cancer patients
Collectively, these studies suggest that preoperatively available markers such as CA-125, performance status and nutritional status could facilitate selection of patients for PCS. However, reaching con- sensus on the cut off values for each of these markers is essential, and it remains to be elucidated whether the prospective use of these markers contributes to favorable survival outcomes of AdvEOC patients.
Histologic and genomic factors 4 Taking into account the heterogeneity of ovarian carcinoma, tumor biology may also provide import- ant information for the selection of patients for PCS+ACT or NACT+ICS. With up to 75% of patients responding to primary chemotherapy, high grade serous ovarian cancer is considered chemotherapy sensitive. Mucinous, clear cell and low grade serous ovarian cancer are far less sensitive. Despite low response rates in some subtypes, the administration of chemotherapy is still standard of care in all AdvEOC patients. However, consensus reviews of rare EOC subtypes by the Gynecologic Cancer InterGroup have emphasized that the administration of NACT should be discouraged in these che- motherapy resistant subtypes(27–29). Further clinical trials are warranted to investigate the role of alternative (targeted) therapies as first line treatment for patients with advanced mucinous, clear cell or low grade serous ovarian cancer. Due to the low incidence of these subtypes international collaboration will be essential.
Genomic markers may also play a role in differentiating between patients that are sensitive to che- motherapy and those that are not. A recent genomic characterization of chemotherapy resistant high grade serous ovarian cancer identified several potential predictors of chemotherapy resistance including, among others, CCNE1 amplifications and loss of BRCA1 or BRCA2 mutations(30).
Radiographic and nuclear Imaging Preoperative imaging such as CT-scans can provide essential information regarding the extent of tumor dissemination and may aid prediction of surgical outcomes. However, a systematic review aimed at evaluating CT-based multivariable prediction models in AdvEOC concluded that externally validated studies with high predictive value are currently lacking(31).
PET/CT-scans have also been suggested as a valuable tool for prediction of cytoreductive outcomes. For instance, a prospective study on 343 AdvEOC patients that underwent preoperative PET/CT imaging identified several PET/CT features that were independently associated with incomplete cytoreduction (e.g. presence of disease in the diaphragm and small bowel mesentery implants)(32). A study comparing the predictive value of preoperative PET/CT and high-dose contrast CT showed superiority of PET/CT in detection of extra-abdominal disease(33).
The presence of malignant pleural effusion or metastatic disease above the diaphragm may result in suboptimal cytoreduction despite complete removal of all other tumor locations. However, studies
65 Chapter 4
on the impact of disease above the diaphragm on clinical decision making in AdvEOC are currently lacking. Novel surgical techniques for diaphragmatic surgery (e.g. diaphragmatic peritoneal stripping and diaphragmatic full-thickness resection) have been developed, however the impact of these techniques on overall survival is still unclear(34). A review by Escayola et al recently concluded that it is currently unclear whether pleural involvement can reliably be assessed by CT-scan and/or chest radiograph alone, and proposed that video-assisted thoracoscopy (VATS) could be a valuable tool in describing the extent of pleural disease(35). One of the key findings in a review by Di Guilmi was that among patients with negative pleural cytology, 23.5% have pleural disease determined with VATS. Herein, VATS led to a change in stage of disease in 41% of patients(36). Both Escayola et al and Di Guilmi conclude that VATS may facilitate the selection of patients for PCS. Importantly, VATS should not be performed in patients with low likelihood of complete cytoreduction of tumor in abdomen and pelvis as these patients are candidates for NACT.
Another imaging modality that may aid the selection of patients with high likelihood of complete cytoreduction for PCS is diffusion weighted MRI (DW-MRI). A study by Espada et al (N=34), showed that DW-MRI accurately predicts cytoreductive outcome in 91% of cases(37). Furthermore, within the recurrent setting, DW-MRI accurately predicted complete cytoreduction in 94% of patients that were eligible for salvage surgery, whereas CT accurately predicted complete cytoreduction in only 49% of these patients(38). The authors attributed the superiority of DW-MRI over CT to better contrast res- olution resulting in improved detection of sites that are critical for surgery such as serosal intestinal metastases, metastases around the central mesenteric vessels and unresectable distant metastases. The survival impact of using DW-MRI to select patients for PCS requires further investigation.
Diagnostic laparoscopy A number of non-randomized studies have investigated the value of assessing operability of patients with AdvEOC by diagnostic laparoscopy (39,40). More recently, the LAPOVCA trial randomized 201 patients that were expected to be eligible for PCS to preoperative diagnostic laparoscopy versus PCS(41). Within the PCS group 39% underwent unsuccessful cytoreduction compared to 10% in the diagnostic laparoscopy group. Critics of this trial include a selection bias (13% of included patients had benign/borderline disease or a malignancy of other origin) and low quality of surgery (42% of patients in the PCS group underwent an incomplete cytoreduction).
66 Surgery for advanced stage ovarian cancer patients
PERSPECTIVES IN PRIMARY THERAPY FOR ADVEOC
Lymphadenectomy While sampling of pelvic and para-aortic lymph nodes is an undisputed part of staging for early stage disease, the value of performing a full lymphadenectomy in AdvEOC is subject of debate. As retroperi- toneal lymph node involvement is expected in a majority of patients with advanced stage disease, it has been proposed that systematic pelvic and para-aortic lymphadenectomy could facilitate cytoreduction 4 and improve survival outcomes. A recent meta-analysis by Zhou et al demonstrated favorable OS and PFS, and a lower rate of recurrence, in AdvEOC patients that underwent lymphadenectomy compared to those that did not (42). The therapeutic value of lymphadenectomy in primary therapy for patients with AdvEOC is currently being investigated by the Arbeitsgemeinschaft Gynaekologische Onkologie study group in the prospective Lymphadenectomy In Ovarian Neoplasms (LION) trial(43). Within this trial, 640 patients with FIGO stage IIB-IV and without visible residual tumor have been randomized to lymphadenectomy or no lymphadenectomy. Maturation of survival data is eagerly awaited.
Intraperitoneal chemotherapy (IP) and hyperthermic intraperitoneal chemotherapy (HIPEC) As patients with AdvEOC frequently develop peritoneal recurrences, alternative methods of chemo- therapy delivery, such as the administration of (heated) chemotherapy directly into the abdominal cavity, are currently being investigated. A Cochrane systematic review by Jaaback et al confirmed the favorable survival outcomes of AdvEOC patients treated with chemotherapy that was (partially) administered intraperitoneally (HR 0.81, 95% CI 0.72-0.90), though more serious adverse events (gastrointestinal, pain, fever, infection) were registered compared to standard intravenous adminis- tration(44). A meta-analysis by Huo et al indeed confirmed the favorable survival outcomes (OR 3.46 95% CI 2.19-5.48), but showed comparable morbidity and mortality between treatment regimens consisting of cytoreduction + intravenous chemotherapy + HIPEC and cytoreduction + intravenous chemotherapy(45). Clinical trials are currently ongoing to establish optimal timing, dosing and patient selection for this treatment modality.
Intraoperative optical imaging Another innovative strategy that is currently under investigation in various solid malignancies is intraoperative fluorescent imaging. The use of tumor-specific fluorescent markers may facilitate intraoperative identification of tumor deposits and improve cytoreductive outcomes. In 2011, the first-in-human trial using intraoperative fluorescent imaging in ovarian cancer was performed. Within this trial, high sensitivity and specificity of the folate receptor α targeted agent (folate-FITC) was demonstrated in ovarian cancer patients(46). Recently, the clinical application of folate receptor α targeting agents EC17 and OTL38 rendered promising outcomes in a small number of patients under- going cytoreductive surgery for ovarian cancer(47,48). Further optimization of fluorescent agents is warranted to reduce the occurrence of auto fluorescent false-positive lesions. Moreover, the impact of intraoperative imaging on survival outcomes requires validation in a clinical trial.
67 Chapter 4
CONCLUSION
In conclusion, the past decade has seen a paradigm shift in the number of AvdEOC patients treated with upfront neo-adjuvant chemotherapy instead of primary cytoreductive surgery. Clinical guide- lines from SGO-ASCO and ESGO currently recommend primary cytoreductive surgery for AdvEOC patients with a high likelihood of achieving complete debulking with acceptable morbidity. Neo-ad- juvant chemotherapy may reduce morbidity at the time of interval cytoreductive surgery, but it does not improve survival outcomes and may undermine therapeutic options for recurrent disease by inducing chemotherapy resistance. Optimal selection of patients is crucial in an attempt to improve prognosis. Furthermore, it is imperative that surgical innovations in patients diagnosed with AvdEOC are directed at improving survival outcomes.
FUNDING AND ACKNOWLEDGEMENTS
This work was funded by the Dutch Cancer Society (grant RUG2013-6505) to HWN.
68 Surgery for advanced stage ovarian cancer patients
REFERENCES
1. Siegel RL, Miller KD, Jemal A. Cancer Statistics , 2017. CA Cancer J Clin. 2017;67(7):7–30. 2. Leiserowitz GS, Lin JF, Tergas AI, Cliby WA, Bristow RE. Factors Predicting Use of Neoadjuvant Chemotherapy Compared With Primary Debulking Surgery in Advanced Stage Ovarian CancerVA National Cancer Database Study. Int J Gynecol Cancer. 2017;27(4):675–83. 3. Chang SJ, Hodeib M, Chang J, Bristow RE. Survival impact of complete cytoreduction to no gross residual disease for advanced-stage ovarian cancer: a meta-analysis. Gynecol Oncol. 2013 Sep;130(3):493–8. 4 4. Bristow BRE, Tomacruz RS, Armstrong DK, Trimble EL, Montz FJ. Survival Effect of Maximal Cytoreductive Surgery for Advanced Ovarian Carcinoma During the Platinum Era : a Meta-analysis. J Clin Oncol. 2002;20(5):1248–59. 5. Cowan RA, O’Cearbhaill RE, Gardner GJ, Levine DA, Roche KL, Sonoda Y, et al. Is It Time to Centralize Ovarian Cancer Care in the United States? Ann Surg Oncol [Internet]. 2016;(23):989–93. Available from: http://link. springer.com/10.1245/s10434-015-4938-9 6. Dahm-Kähler P, Palmqvist C, Staf C, Holmberg E, Johannesson L. Centralized primary care of advanced ovar- ian cancer improves complete cytoreduction and survival - A population-based cohort study. Gynecol Oncol [Internet]. 2016;142(2):211–6. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0090825816307545 7. Woo YL, Kyrgiou M, Bryant A, Everett T, Dickinson HO. Centralisation of services for gynaecological cancers - A Cochrane systematic review. Gynecol Oncol [Internet]. 2012;126(2):286–90. Available from: http://dx.doi. org/10.1016/j.ygyno.2012.04.012 8. Eggink FA, Mom CH, Kruitwagen RF, Reyners AK, Van Driel WJ, Massuger LF, et al. Improved outcomes due to changes in organization of care for patients with ovarian cancer in the Netherlands. Gynecol Oncol [Internet]. 2016;141:524–30. Available from: http://dx.doi.org/10.1016/j.ygyno.2016.04.012 9. Querleu D, Planchamp F, Chiva L, Fotopoulou C, Barton D, Cibula D, et al. European Society of Gynaecologic Oncology Quality Indicators for Advanced Ovarian Cancer Surgery. Int J Gynecol Cancer. 2016;26(7):1354–63. 10. Bristow RE, Chi DS. Platinum-based neoadjuvant chemotherapy and interval surgical cytoreduction for advanced ovarian cancer: a meta-analysis. Gynecol Oncol. 2006 Dec;103(3):1070–6. 11. Vergote I, Tropé CG, Amant F, Kristensen GB, Ehlen T, Johnson N, et al. Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer. N Engl J Med. 2010;363(10):943–53. 12. Kehoe S, Hook J, Nankivell M, Jayson GC, Kitchener H, Lopes T, et al. Primary chemotherapy versus primary surgery for newly diagnosed advanced ovarian cancer (CHORUS): an open-label, randomised, controlled, non-inferiority trial. Lancet [Internet]. 2015;6736(14):1–9. Available from: http://linkinghub.elsevier.com/ retrieve/pii/S0140673614622236 13. van Meurs HS, Tajik P, Hof MH, Vergote I, Kenter GG, Mol BW, et al. Which patients benefit most from pri- mary surgery or neoadjuvant chemotherapy in stage IIIC or IV ovarian cancer? An exploratory analysis of the European Organisation for Research and Treatment of Cancer 55971 randomised trial. Eur J Cancer. 2013;49(15):3191–201. 14. Zeng L-J, Xiang C-L, Gong Y-Z, Kuang Y, Lu F-F, Yi S-Y, et al. Neoadjuvant chemotherapy for Patients with advanced epithelial ovarian cancer: A Meta-Analysis. Sci Rep [Internet]. 2016;6:35914. Available from: http:// www.ncbi.nlm.nih.gov/pubmed/27804983 15. Luo Y, Lee M, Kim HS, Chung HH, Song YS. Effect of neoadjuvant chemotherapy on platinum resistance in stage IIIC and IV epithelial ovarian cancer. Med (United States). 2016;95(36):1–5. 16. da Costa AABA, Valadares C V., Baiocchi G, Mantoan H, Saito A, Sanches S, et al. Neoadjuvant Chemotherapy Followed by Interval Debulking Surgery and the Risk of Platinum Resistance in Epithelial Ovarian Cancer. Ann Surg Oncol [Internet]. 2015;22(S3):971–8. Available from: http://link.springer.com/10.1245/s10434-015-4623-z 17. Rauh-Hain JA, Nitschmann CC, Worley MJ, Bradford LS, Berkowitz RS, Schorge JO, et al. Platinum resistance
69 Chapter 4
after neoadjuvant chemotherapy compared to primary surgery in patients with advanced epithelial ovar- ian carcinoma. Gynecol Oncol [Internet]. 2013;129(1):63–8. Available from: http://dx.doi.org/10.1016/j. ygyno.2013.01.009 18. Fagotti A, Ferrandina G, Vizzielli G, Fanfani F, Gallotta V, Chiantera V, et al. Phase III randomised clinical trial comparing primary surgery versus neoadjuvant chemotherapy in advanced epithelial ovarian cancer with high tumour load (SCORPION trial): Final analysis of peri-operative outcome. Eur J Cancer [Internet]. 2016;59:22–33. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26998845 19. Fotopoulou C, London IC, Kingdom U. Value of Neoadjuvant Chemotherapy for Newly Diagnosed Advanced Ovarian Cancer : A European Perspective. J Clin O. 2017;35(6):587–90. 20. Wright AA, Bohlke K, Armstrong DK, Bookman MA, Cliby WA, Coleman RL, et al. Neoadjuvant chemotherapy for newly diagnosed, advanced ovarian cancer: Society of Gynecologic Oncology and American Society of Clinical Oncology Clinical Practice Guideline. J Clin Oncol. 2016;34(28):3460–73. 21. Suidan RS, Ramirez PT, Sarasohn D, Teitcher J, Mironov S, Iyer R, et al. A multicenter assessment of the ability of preoperative computed tomography scan and serum CA-125 to predict gross residual disease at pri- mary debulking surgery for advanced ovarian, fallopian tube and peritoneal cancer. Gynecol Oncol [Internet]. 2017;145:27–31. Available from: http://linkinghub.elsevier.com/retrieve/pii/S009082581500027X 22. Mahdi H, Maurer K, Nutter B, Rose PG. The impact of percent reduction in CA-125 levels on prediction of the extent of interval cytoreduction and outcome in patients with advanced stage cancer of müllerian origin treated with neoadjuvant chemotherapy. Gynecol Oncol [Internet]. 2015;137(2015):44–5. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0090825815001080 23. Sölétormos G, Duffy MJ, Othman Abu Hassan S, Verheijen RHM, Tholander B, Bast RC, et al. Clinical Use of Cancer Biomarkers in Epithelial Ovarian Cancer: Updated Guidelines From the European Group on Tumor Markers. Int J Gynecol Cancer. 2016;26(1):43–51. 24. Steffensen KD, Waldstrøm M, Brandslund I, Jakobsen A. Prognostic Impact of Prechemotherapy Serum Levels of HER2, CA125, and HE4 in Ovarian Cancer Patients. Int J Gynecol cancer Off J Int Gynecol Cancer Soc. 2011;21(6):1040–7. 25. Aletti GD, Eisenhauer EL, Santillan A, Axtell A, Aletti G, Holschneider C, et al. Identification of patient groups at highest risk from traditional approach to ovarian cancer treatment. Gynecol Oncol [Internet]. 2011;120(1):23– 8. Available from: http://dx.doi.org/10.1016/j.ygyno.2010.09.010 26. Barber EL, Rutstein S, Miller WC, Gehrig PA. A preoperative personalized risk assessment calculator for elderly ovarian cancer patients undergoing primary cytoreductive surgery. Gynecol Oncol. 2015;139(3):401–6. 27. Ledermann JA, Luvero D, Shafer A, O’Connor D, Mangili G, Friedlander M. Gynecologic Cancer InterGroup (GCIG) Consensus Review for Uterine and Ovarian Carcinosarcoma. Int J Gynecol Cancer [Internet]. 2014;24(November):S55–60. Available from: http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:land- ingpage&an=00009577-201411003-00012 28. Okamoto A, Glasspool R, Mabuchi S, Matsumura N, Nomura H. Gynecologic Cancer InterGroup (GCIG) Con- sensus Review for Carcinoid Tumors of the Ovary. Int J Gynecol Cancer [Internet]. 2014;24(9 Suppl 3):S35-41. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25341578 29. Gourley C, Farley J, Provencher DM, Pignata S, Mileshkin L, Harter P, et al. Gynecologic Cancer InterGroup (GCIG) consensus review for ovarian and primary peritoneal low-grade serous carcinomas. Int J Gyne- col Cancer [Internet]. 2014;24(9 Suppl 3):S9-13. Available from: http://content.wkhealth.com/linkback/ openurl?sid=WKPTLP:landingpage&an=00009577-201411003-00004 30. Patch AM, Christie EL, Etemadmoghadam D, Garsed DW, George J, Fereday S, et al. Whole–genome charac- terization of chemoresistant ovarian cancer. Nature [Internet]. 2015;521(7553):489–94. Available from: http:// www.nature.com/doifinder/10.1038/nature14410 31. Rutten MJ, van de Vrie R, Bruining A, Spijkerboer AM, Mol BW, Kenter GG, et al. Predicting surgical outcome
70 Surgery for advanced stage ovarian cancer patients
in patients with International Federation of Gynecology and Obstetrics stage III or IV ovarian cancer using computed tomography: a systematic review of prediction models. Int J Gynecol Cancer. 2015;25(3):407–15. 32. Shim SH, Lee SJ, Kim SO, Kim SN, Kim DY, Lee JJ, et al. Nomogram for predicting incomplete cytoreduction in advanced ovarian cancer patients. Gynecol Oncol [Internet]. 2015;136(1):30–6. Available from: http://dx.doi. org/10.1016/j.ygyno.2014.11.004 33. Hynninen J, Kemppainen J, Lavonius M, Virtanen J, Matomäki J, Oksa S, et al. A prospective comparison of integrated FDG-PET/contrast-enhanced CT and contrast-enhanced CT for pretreatment imaging of advanced epithelial ovarian cancer. Gynecol Oncol [Internet]. 2013;131(2):389–94. Available from: http://dx.doi. org/10.1016/j.ygyno.2013.08.023 4 34. Bogani G, Ditto A, Martinelli F, Lorusso D, Chiappa V, Donfrancesco C, et al. Surgical Techniques for Diaphrag- matic Resection During Cytoreduction in Advanced or Recurrent Ovarian Carcinoma: A Systematic Review and Meta-analysis. Int J Gynecol Cancer [Internet]. 2016;26(2):371–80. Available from: http://www.ncbi.nlm. nih.gov/pubmed/26588238 35. Escayola C, Ferron G, Romeo M, Torrent JJ, Querleu D. The impact of pleural disease on the management of advanced ovarian cancer. Gynecol Oncol [Internet]. 2015;138(1):216–20. Available from: http://dx.doi. org/10.1016/j.ygyno.2015.05.003 36. Di Guilmi J, Salvo G, Mehran R, Sood AK, Coleman RL, Lu KH, et al. Role of Video-Assisted Thoracoscopy in Advanced Ovarian Cancer. Int J Gynecol Cancer [Internet]. 2016;26(4):801–6. Available from: http://content. wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00009577-201605000-00030 37. Espada M, Garcia-Flores JR, Jimenez M, Alvarez-Moreno E, De Haro M, Gonzalez-Cortijo L, et al. Diffu- sion-weighted magnetic resonance imaging evaluation of intra-abdominal sites of implants to predict likelihood of suboptimal cytoreductive surgery in patients with ovarian carcinoma. Eur Radiol. 2013;23(9):2636–42. 38. Michielsen KLM, Vergote I, Dresen R, Op de Beeck K, Vanslembrouck R, Amant F, et al. Whole-body diffu- sion-weighted magnetic resonance imaging in the diagnosis of recurrent ovarian cancer: a clinical feasibility study. Br J Radiol [Internet]. 2016;89(1067):20160468. Available from: http://www.ncbi.nlm.nih.gov/pub- med/27585490%5Cnhttp://www.birpublications.org/doi/10.1259/bjr.20160468 39. Fagotti A, Ferrandina G, Fanfani F, Ercoli A, Lorusso D, Rossi M, et al. A laparoscopy-based score to predict sur- gical outcome in patients with advanced ovarian carcinoma: a pilot study. Ann Surg Oncol. 2006;13(8):1156–61. 40. Fagotti A, Vizzielli G, De Iaco P, Surico D, Buda A, Mandato VD, et al. A multicentric trial (Olympia-MITO 13) on the accuracy of laparoscopy to assess peritoneal spread in ovarian cancer. Am J Obstet Gynecol [Internet]. 2013;209(5):462.e1-462.e11. Available from: http://dx.doi.org/10.1016/j.ajog.2013.07.016 41. Rutten MJ, van Meurs HS, van de Vrie R, Gaarenstroom KN, Naaktgeboren CA, van Gorp T, et al. Laparoscopy to Predict the Result of Primary Cytoreductive Surgery in Patients With Advanced Ovarian Cancer: A Randomized Controlled Trial. J Clin Oncol [Internet]. 2016;JCO2016692962. Available from: http://www.ncbi.nlm.nih.gov/ pubmed/28029317 42. Zhou J, Shan G, Chen Y. The effect of lymphadenectomy on survival and recurrence in patients with ovarian cancer: A systematic review and meta-analysis. Jpn J Clin Oncol. 2016;46(8):718–26. 43. Camara O, Sehouli J. Controversies in the management of ovarian cancer--pros and cons for lymph node dis- section in ovarian cancer. Anticancer Res [Internet]. 2009;29(7):2837–43. Available from: http://eutils.ncbi.nlm. nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=19596971&retmode=ref&cmd=prlinks%5Cnpapers2:// publication/uuid/52E8BA7F-E387-4FE8-9E60-A5EFD9F41369 44. Jaaback K, Johnson N, Lawrie T. Intraperitoneal chemotherapy for the initial management of primary epithelial ovarian cancer. Cochrane database Syst Rev [Internet]. 2014;11(3):CD005340. Available from: http://www. pubmedcentral.nih.gov/articlerender.fcgi?artid=4164826&tool=pmcentrez&rendertype=abstract 45. Huo YR, Richards A, Liauw W, Morris DL. Hyperthermic intraperitoneal chemotherapy (HIPEC) and cytore- ductive surgery (CRS) in ovarian cancer: A systematic review and meta-analysis. Eur J Surg Oncol [Internet].
71 Chapter 4
2015;41(12):1578–89. Available from: http://www.sciencedirect.com/science/article/pii/S0748798315007714 46. van Dam GM, Themelis G, Crane LM, Harlaar NJ, Pleijhuis RG, Kelder W, et al. Intraoperative tumor-specific fluorescence imaging in ovarian cancer by folate receptor-alpha targeting: first in-human results. Nat Med [Internet]. 2011;17(10):1315–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21926976 47. Tummers QRJG, Hoogstins CES, Gaarenstroom KN, de Kroon CD, van Poelgeest MIE, Vuyk J, et al. Intraopera- tive imaging of folate receptor alpha positive ovarian and breast cancer using the tumor specific agent EC17. Oncotarget [Internet]. 2016;7(22):32144–55. Available from: http://www.oncotarget.com/abstract/8282%5Cn- http://www.ncbi.nlm.nih.gov/pubmed/27014973%5Cnhttp://www.pubmedcentral.nih.gov/articlerender. fcgi?artid=PMC5078003 48. Hoogstins CES, Tummers QRJG, Gaarenstroom KN, De Kroon CD, Trimbos JBMZ, Bosse T, et al. A novel tumor-specific agent for intraoperative near-infrared fluorescence imaging: A translational study in healthy volunteers and patients with ovarian cancer. Clin Cancer Res. 2016;22(12):2929–38.
72
PART II
Improving organization of care for patients with endometrial cancer
CHAPTER 5
Less favorable prognosis for low risk endometrial cancer patients with a discordant pre- versus post-opera- tive risk stratification
Eggink F.A., Mom C.H., Bouwman K., Boll D., Becker J.H., Creutzberg C.L., Niemeijer G.C., van Driel W.J., Reyners A.K., van der Zee A.G., Bremer G.L., Ezendam N.P., Kruitwagen R.F., Pijnenborg J.M., Hollema H., Nijman H.W., van der Aa M.A.
European Journal of Cancer. 2017 Jun;78:82-90 Chapter 5
ABSTRACT
Background Pre-operative risk stratification based on endometrial sampling determines the extent of surgery for endometrial cancer (EC). We investigated the concordance of pre- and post-operative risk stratifica- tions and the impact of discordance on survival.
Methods Patients diagnosed with EC within the first 6 months of the years 2005-2014 were selected from the Netherlands Cancer Registry (N=7875). Pre- and post-operative risk stratifications were determined based on grade and/or histological subtype for 3784 eligible patients.
Results A discordant risk stratification was found in 10% of patients: 4% (N=155) had high pre- and low post-operative risk and 6% (N=215) had low pre- and high post-operative risk. Overall survival of patients with high pre- and low post-operative risk was less favorable compared to those with a concordant low risk (80% versus 89%, p=0.002). This difference remained significant when correcting for age, stage, surgical staging and adjuvant therapy (HR 1.80, 95%CI 1.28-2.53, p=0.001). Survival of patients with low pre- and high post-operative risk did not differ from those with a concordant high risk (64% versus 62%, p=0.295).
Conclusion Patients with high pre- and low post-operative risk have a less favorable prognosis compared to patients with a concordant low risk. Pre-operative risk stratifications contain independent prognostic information and should be incorporated in clinical decision-making.
78 Risk stratifications in endometrial cancer
INTRODUCTION
Endometrial cancer is the most common gynecologic malignancy in developed countries, affecting approximately 1 in 37 women (1). Standard treatment for patients with low risk endometrial cancer typically consists of hysterectomy and bilateral salpingo-oophorectomy. In patients with high risk disease, lymphadenectomy or complete surgical staging and adjuvant therapy is recommended. Adjuvant therapy usually involves vaginal brachytherapy or external beam radiotherapy, sometimes with chemotherapy. Factors that are used to stratify patients into risk groups are histological subtype, grade, stage and lymph vascular space invasion (LVSI) (2–7). 5
To guide the choice of surgical treatment and extent of surgical staging, accurate pre-operative stratification of patients into low- and high risk groups is essential. In case of clinical early stage dis- ease, pre-operative risk stratification is based on pre-operative endometrial samples, obtained by micro-curettage or curettage. The post-operative risk stratification is used to guide adjuvant therapy, and is based on the histological examination of tissue removed during surgery. Importantly, the post-operative risk stratification is currently viewed as the gold standard.
Discordance between pre-operative and post-operative risk stratification may result in over- or under treatment and may ultimately effect survival. In a high risk endometrial cancer cohort studied by Di Cello et al, failure to recognize high risk disease pre-operatively resulted in less favorable survival out- comes compared to patients that were adequately stratified (8). On the other hand, the prospective MoMaTEC trial demonstrated that patients with discordant risk stratification had an intermediate prognosis compared to patients with concordant low or high risk stratifications (9). Studies based on larger patient cohorts are needed to clarify the effect of discordant risk stratification on survival of endometrial cancer patients.
We aimed to investigate the concordance of pre-operative and post-operative risk stratifications in a large, unselected, population-based cohort, and to evaluate whether discordant risk stratification influences prognosis.
79 Chapter 5
METHODS
Data collection Data from all consecutive patients diagnosed with endometrial cancer between January 1st and July 1st of every year within the period 2005-2014 were retrospectively retrieved from the Netherlands Cancer Registry (NCR). The NCR contains data from all patients diagnosed with cancer in the Nether- lands from 1989 onwards. Newly diagnosed patients are entered into the NCR following automated notifications from the Dutch Pathology Network (PALGA). PALGA contains the pathology assessments from all of the national pathology departments. It was established in 1971, and plays an important role in facilitating epidemiological research in the Netherlands. Within the NCR, information on vital status is obtained by annual linkage to the Municipal Personal Records Database and was available up to February 1st 2016.
Patients that were selected from the NCR were matched with pathology assessments in PALGA. All pathology assessments that were available within 6 months before and 6 months after surgery were retrieved. Pathology assessments from tissue specimens taken outside of that period were consid- ered irrelevant for this study.
Risk stratification Pre-operative risk stratification was determined from the available pathology assessments retrieved from PALGA. Post-operative risk stratification was determined from the available data in the NCR, which are based on the final pathology assessments of hysterectomy specimens. Patients were considered low risk if histology showed grade 1 or 2 endometrioid adenocarcinoma, grade 1 or 2 adenocarcinoma not otherwise specified (NOS) or grade 1 or 2 mucinous adenocarcinoma. Patients were considered high risk if histology showed grade 3 adenocarcinoma (endometrioid, NOS, muci- nous) or clear cell, serous or carcinosarcoma histology. Tumour types registered as ‘other’ included squamous-cell carcinomas, adenosquamous carcinomas and pseudosarcomatous carcinomas that were not further specified. As survival of this group resembled that of endometrioid tumors, they were considered low risk when grade 1 or 2, and high risk when grade 3 (data not shown).
Outcomes Concordance of pre- and post-operative risk stratification and overall survival were defined as primary outcomes.
Data analysis Differences between pre-operative and post-operative concordance groups were determined by Chi2 test or Fisher’s Exact test. In case of continuous variables a Kruskal-Wallis followed by Mann-Whitney U test was used. Overall survival was used as primary survival outcome measure, and estimated using Kaplan Meier analyses. Overall survival was calculated from date of histological diagnosis to date of
80 Risk stratifications in endometrial cancer
last follow up or date of death. To correct for possible confounders (age, stage, surgical staging and adjuvant therapy), multivariable survival analysis was performed using Cox regression. As no informa- tion was available regarding the exact surgical procedures that were performed, surgical staging was defined as removal of at least one lymph node during surgery. Within the Cox regression analyses no correction was applied for tumour type and grade due to co-linearity between these variables and risk stratification. Differences were considered statistically significant at p<0.05. Data analysis was performed using SPSS data analysis and statistical software version 22.0 (SPSS Inc. Chicago, IL, USA).
5
81 Chapter 5
RESULTS
Patient selection and characteristics Data from 7875 patients diagnosed with endometrial cancer between January 1st and July 1st 2005- 2014 were retrieved from the NCR. In total, 4191 patients were excluded from analysis (Figure 1). Six-hundred and twenty patients were excluded from analysis as they did not undergo surgery and the post-operative risk stratification could therefore not be determined. A further 1724 patients were excluded because no pathology assessment of the pre-operative endometrial sample was available in PALGA, and 77 patients were excluded because the available pre-operative sample was obtained more than 6 months prior to surgery. Of the remaining 5454 patients, 1770 were excluded from analysis due to missing data which were needed to determine risk stratification in pre- and/ or post-operative samples or because the pre-operative sample was registered as non-malignant.
Figure 1. Flowchart demonstrating which patients were eligible for analysis Patients diagnosed with endometrial cancer January-June 2005-2014 N= 7875
No surgery N=620 Surgery N=7255
No preoperative endometrial sampling available N=1724
Preoperative sample available N=5531 Preoperative sample >6 months before surgery N=77 Preoperative sample within 6 months before surgery N=5454 No riskclassification due to missing data N=1770
Patients eligible for analysis 1483x missing preoperative riskclassification N=3684 70x no malignancy described preoperatively 73x missing postoperative riskclassification 144x missing pre- and postoperative riskclassification
In total, 3684 patients were included in the current study. These patients were most frequently diagnosed as FIGO stage I (80%), endometrioid type (79%) and grade 1 (41%) disease (table 1). The majority of patients (83%) did not undergo surgical staging. No adjuvant therapy was administered in 61% of patients, whereas 13% received vaginal brachytherapy (VBT), 21% received external beam radiotherapy (EBRT), 2% received radio-chemotherapy and 3% received chemotherapy alone.
82 Risk stratifications in endometrial cancer
Table 1. Clinicopathologic characteristics of selected patients N = 3684 N % Age at diagnosis (years) Mean (range) 67 (27-94) FIGO Stage I 2949 80 II 303 8 III 313 9 IV 112 3 5 Unknown 7 0 Postoperative tumor type Endometrioid 2923 80 Serous 219 6 Clearcell 76 2 Adenocarcinoma NOS 260 7 Mucinous 16 0 Carcinosarcoma 173 5 Other 17 0 Postoperative grade 1 1493 41 2 1154 31 3 1037 28 Surgical staging No 3049 83 Yes 635 17 Adjuvant therapy None 2263 61 VBT 474 13 EBRT 764 21 Radio + chemotherapy 62 2 Chemotherapy 120 3 Hormone therapy 1 0 NOS: not otherwise specified; VBT: vaginal brachytherapy; EBRT: external beam radiotherapy.
83 Chapter 5
Concordance of pre- and post-operative risk stratification We assessed the concordance between pre-operative and post-operative risk stratification of the 3684 patients that were eligible for analysis (Figure 2). Concordant risk stratification was found in 3314 patients (90%). Of these concordant tumour samples, 2491 (75%) were considered to be low risk and 823 (25%) were considered to be high risk. In total 370 (10%) patients were identified with discordant risk stratification. One-hundred and fifty-five (4%) were stratified as high risk pre-opera- tively and low risk post-operatively, and 215 (6%) were stratified as low risk pre-operatively and high risk post-operatively.
Figure 2. Concordance of pre-operative and post-operative risk stratifications
100 Discordant: preoperative low, postoperative high risk (5.8%) Discordant: preoperative high, postoperative low risk (4.2%) 80 Concordant: high risk (22.3%)
) Concordant: low risk (67.6%) % 60 (
s t n e i 40 t a P 20
0
Survival of patients with discordant and concordant risk stratifications Kaplan-Meier survival analyses were performed to evaluate whether discordant risk stratification influenced survival outcomes (Figure 3). Median follow up of all patients was 48 months. Patients with a low pre-operative risk stratification had favorable survival outcomes compared to patients with a high pre-operative risk stratification, (p<0.001, Figure 3A). Likewise, patients with a low post-operative risk stratification had favorable survival outcomes compared to patients with a high post-operative risk stratification (p<0.001, Figure 3B).
When combining pre- and post-operative risk stratifications, the post-operative results better strat- ified into good and poorer survival (Figure 3C). Less favorable overall survival was seen in patients with a pre-operative high and post-operative low risk compared to patients with a concordant low risk (p=0.002). No difference in survival was found between patients with a low pre-operative and high post-operative risk, and patients with a concordant high risk (p=0.295). Five-year overall survival was 89% in patients with a concordant low risk, 80% in patients with preoperative high and postoperative low risk, 64% in patients with preoperative low and postoperative high risk and 62% in patients with a concordant high risk.
Multivariable cox survival analysis demonstrated an independent prognostic value of risk stratifica- tion when correcting for age, FIGO stage, surgical staging (defined as removal of one lymph node during surgery) and adjuvant therapy (table 2). Compared to patients with a concordant low risk,
84 Risk stratifications in endometrial cancer
less favorable survival was found in patients with a high pre-operative and low post-operative risk (HR 1.80, 95% CI 1.28-2.52, p= 0.001), patients with a low pre-operative and high post-operative risk (HR 2.40, 95% CI 1.87-3.08, p<0.001) and patients with a concordant high risk (HR 2.91, 95% CI 2.46- 3.45, p<0.001). Multivariable analysis in which surgical staging was defined as removal of at least 10 lymph nodes during surgery confirmed the presence of unfavorable survival in the pre-operative high post-operative low risk group compared to the concordant low risk group (HR 1.766, 95% CI 1.256-2.481, p<0.001, data not shown).
Figure 3. Kaplan Meier survival curves according to risk stratification. 5 A B C 100 100 100
80 80 80
P=0.002 60 60 60
P=0.295 Survival (%) 40 Survival (%)
40 Survival (%) 40
20 20 20 Concordant low risk (N=2491) Preoperative high, postoperative low risk (N=155) Preoperative low risk (N=2706) Postoperative low risk (N=2647) Preoperative low, postoperative high risk (N=215) 0 Preoperative high risk (N=978) p<0.001 0 Postoperative high risk (N=1037) p<0.001 0 Concordant high risk (N=823) 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Followup (months) Followup (months) Followup (months) A B C 100 100 100
80 80 80
P=0.002 60 60 60
P=0.295 Survival (%) 40 Survival (%)
40 Survival (%) 40
20 20 20 Concordant low risk (N=2491) Preoperative high, postoperative low risk (N=155) Preoperative low risk (N=2706) Postoperative low risk (N=2647) Preoperative low, postoperative high risk (N=215) 0 Preoperative high risk (N=978) p<0.001 0 Postoperative high risk (N=1037) p<0.001 0 Concordant high risk (N=823) 0 20 40 60 80 100 120 0 20 40 60 80 100 120 0 20 40 60 80 100 120 Followup (months) Followup (months) Followup (months) A, pre-operative risk stratification. B, post-operative risk stratification. C, combined pre-operative and post-operative risk stratification.
85 Chapter 5
Table 2. Univariable and multivariable cox-regression analysis for overall survival
Univariable analyses Multivariable analyses
HR 95% CI p-value HR 95% CI p-value
Age 1.066 1.059-1.074 <0.001 1.061 1.054-1.069 <0.001
Risk classification
Concordant low ref ref ref ref ref ref
High preoperative, low postoperative 1.686 1.202-2.364 0.002 1.797 1.278-2.527 0.001
Low preoperative, high postoperative 3.324 2.618-4.219 <0.001 2.397 1.869-3.074 <0.001
Concordant high 3.742 3.233-4.331 <0.001 2.913 2.459-3.452 <0.001
Postoperative tumor type
Endometrioid ref ref ref
Serous 3.783 3.066-4.668 <0.001
Clearcell 2.595 1.796-3.750 <0.001
Adenocarcinoma NOS 1.156 0.895-1.494 0.267
Mucinous 0.305 0.043-2.166 0.235
Carcinosarcoma 4.598 3.702-5.710 <0.001
Other 1.651 0.738-3.691 0.222
Postoperative grade
1 ref ref ref
2 1.578 1.297-1.919 <0.001
3 4.416 3.713-5.254 <0.001
FIGO stage
I ref ref ref ref ref ref
II 2.209 1.792-2.723 <0.001 1.978 1.582-2.474 <0.001
III 4.577 3.838-5.460 <0.001 4.268 3.469-5.250 <0.001
IV 8.146 6.383-10.396 <0.001 5.121 3.907-6.711 <0.001
Surgical staging
No ref ref ref ref ref ref
Yes 1.461 1.241-1.721 <0.001 0.653 0.541-0.789 <0.001
Adjuvant therapy
None ref ref ref ref ref ref
VBT 1.059 0.831-1.349 0.645 0.863 0.676-1.102 0.238
EBRT 1.830 1.567-2.137 <0.001 0.841 0.706-1.001 0.051
Radio + chemotherapy 2.054 1.296-3.254 0.002 0.825 0.509-1.337 0.435
Chemotherapy 5.852 4.538-7.546 <0.001 1.371 1.016-1.850 0.039
Hormone therapy 0.001 0.000-0.000 0.919 0.000 0.000-0.000 0.923 NOS: not otherwise specified; VBT: vaginal brachytherapy; EBRT: external beam radiotherapy; HR: hazard risk;CI: confidence interval; ref: reference category.
86 Risk stratifications in endometrial cancer
Table 3. Comparison of clinicopathologic characteristics of patients included in analyses and patients excluded due to missing pre- and/or post-operative risk classifications Excluded from analysis due Included in analysis to missing risk stratification N = 3684 N = 1770 N % N % p-value Age at diagnosis (years) Mean (range) 67 (27-94) 67 (34-96) 0.933 FIGO Stage I 2949 80 1466 82.8 0.039 5 II 303 8 136 7.7 III 313 9 123 6.9 IV 112 3 39 2.2 Unknown 7 0 6 0.3 Postoperative tumor type Endometrioid 2923 79 1452 82 <0.001 Serous 219 6 37 2 Clearcell 76 2 11 1 Adenocarcinoma NOS 260 7 190 11 Mucinous 16 0 16 1 Carcinosarcoma 173 5 38 2 Other 17 1 22 1 Missing 0 0 4 0 Postoperative grade 1 1493 41 830 47 <0.001 2 1154 31 455 26 3 1037 28 260 15 Missing 0 0 225 12 Surgical staging No 3049 83 1601 90 <0.001 Yes 635 17 169 10 Adjuvant therapy None 2263 61 1147 65 0.001 VBT 474 13 204 11 EBRT 764 21 372 21 Radio + chemotherapy 62 2 18 1 Chemotherapy 120 3 29 2 Hormone therapy 1 0 0 0 NOS: not otherwise specified; VBT: vaginal brachytherapy; EBRT: external beam radiotherapy.
87 Chapter 5
Characteristics and survival of patients with missing pre- and/or post-operative risk stratification The unexpectedly large number of patients excluded from analysis due to missing pre- and/or post-operative risk stratifications prompted us to investigate the clinicopathological characteristics and survival of these patients. Compared to the 3684 patients that were included in our analyses, the 1770 patients excluded due to missing data, were more frequently diagnosed with low risk disease and were therefore less likely to receive surgical staging and adjuvant therapy (Table 3).
88 Risk stratifications in endometrial cancer
DISCUSSION
To our knowledge, this is the largest population based study focusing on concordance of pre- and post-operative risk stratifications based on the histological type and grade of endometrial cancer. A 10% discordance was determined in pre- and post-operative risk stratifications of 3684 patients diagnosed with endometrial cancer.. Less favorable overall survival was seen in patients with pre-op- erative high and post-operative low risk stratification compared to those with concordant low risk stratification. 5 In previous studies concordance of pre- and post-operative risk stratifications rates ranged from 58-84%, therefore the 90% concordance demonstrated within this study is relatively high (8–11). Werner et al demonstrated a 84% concordance between pre- and post-operative risk stratifications(9). In their study, based on prospectively collected data of 1288 patients, 30% of patients were post-oper- atively stratified as having high risk disease. This is in line with our data, in which 28% of patients were post-operatively stratified as having high risk disease. Interestingly, Werneret al reported that within the group of patients with discordant risk stratification, 73% were upgraded from low risk to high risk after assessment of the post-operative sample, whereas within the current study this group com- prised only 58% of the patients with discordant risk stratification (6 % of the total study population).
As the pre-operative risk stratification is used to guide the extent of surgical treatment, incorrect risk stratification may lead to under- or over treatment and may therefore influence survival outcomes. The consequence of not recognizing high risk disease pre-operatively is subject of debate. One study, based on 109 patients, demonstrated that failure to identify high risk endometrial cancer patients in the pre-operative setting seemed to result in unfavorable survival outcomes due to suboptimal surgical staging (8). Conversely, another study, based on 1374 patients, concluded that failure to recognize high risk disease pre-operatively was associated with a 17% increase in disease specific survival, a finding which may suggest the presence of tumour heterogeneity (9). Our analyses, based on a much larger cohort, failed to show any change in survival in high risk endometrial cancer patients with discordant pre-operative risk stratification. If our multivariable analysis is re-run without correct- ing for adjuvant therapy, no difference in survival is demonstrated in our cohort. The lack of survival impact of adjuvant therapy in this multivariable model confirms the lack of survival benefit attained by administration of adjuvant therapy in high risk endometrial cancer patients, as shown in various landmark studies regarding this topic(7,12,13). The overlapping survival outcomes of patients with pre-operative low, post-operative high risk and patients with concordant high risk within our study may be the result of two factors with opposing survival impact: improved survival outcomes due to the presence of tumour heterogeneity (as previously proposed by Werner et al(9)), and reduced survival outcomes due to surgical under treatment of patients with low pre-operative, high post-operative risk (as previously proposed by di Cello et al(8)). Indeed, within the current retrospective study patients with low pre-operative, high post-operative risk were often not surgically staged (table 4) and surgical
89 Chapter 5
staging was associated with favorable survival outcomes in multivariable analysis (HR 0.653, 95% CI 0.541-0.789, p<0.001, table 2). Prospective trials are warranted to elucidate the potential therapeutic effect of surgical staging in high risk endometrial cancer.
The effects of pre-operatively not recognizing low risk disease are less controversial. Both the study by Werner et al and our study clearly demonstrate less favorable survival outcomes for patients with low risk endometrial cancer that were pre-operatively stratified as high risk compared to patients with a concordant low risk (9). These data suggest the presence of tumour heterogeneity and/or mixed morphologic characteristics, which has previously been demonstrated in 5% or more of endome- trial tumors (14,15). The presence of minor serous or clear cell components (threshold of 5%) has been shown to adversely affect survival outcomes. Therefore, the presence of tumors with a mixed histology may provide a plausible explanation for the less favorable survival of patients with a high pre-operative and low post-operative risk stratification within the current study (16). In line with this, our data suggest that patients with a high pre-operative and low post-operative risk classification may require additional therapy to ensure local control of disease, as has been demonstrated in patients with high-intermediate and high risk disease (7,17). Collectively, our data suggest that the pre-oper- ative risk stratification comprises independent prognostic information which should be integrated into clinical decision-making.
A strategy to further improve the pre-operative risk classification, and potentially overcome the prob- lems associated with endometrial sampling of heterogenic/mixed tumors, may be to incorporate molecular alterations into clinical decision making, as recently reviewed by Bendifallah et al(18). This strategy is already in use for breast cancer and ovarian cancer (19,20). Indeed, molecular alterations in pre-operative endometrial samples have been shown to predict the alterations in post-operative samples with a concordance of 88% for immunohistochemical and 99% for DNA techniques (21). In the specific case of endometrial cancer, four molecular subtypes have been identified by the Cancer Genome Atlas Network: POLE-ultramutated, microsatellite unstable-hypermutated (MSI-H), p53-mutant and those with no specific molecular profile (NSMP) (22). Clinically applicable molecular classification strategies using formalin-fixed paraffin embedded specimens have been devised by groups in Canada and the Netherlands (23,24). The Canadian group has previously demonstrated a concordance of 89% between classification of pre-operative and post-operative samples using the Proactive Molecular Risk classification tool for Endometrial cancers (PROMISE), which is in agreement with the 90% concordance seen within the current study (25). Furthermore, a recent publication of the Canadian group demonstrated improved stratification of patients according to clinical outcomes by ProMisE compared to traditional stratification by clinicopathologic factors(26). Moreover, the Dutch group evaluated a strategy in which molecular alterations were combined with established clinicopathologic factors (lymph vascular space invasion, L1CAM expression and CTNNB1 mutation), which resulted in an improved post-operative risk assessment compared to pathology assessment, centralized pathology review or molecular classification alone (27).
90 Risk stratifications in endometrial cancer
Table 4. Patient characteristics of patients in the four pre- and post-operative combined risk groups.
Low-Low High-Low Low-High High-High
N = 2491 N = 155 N = 215 N = 823
N % N % N % N %
Age at diagnosis (years)
Mean (range) 66 (27-93) 67 (43-89) 68 (30-93) 69 (42-94)
FIGO Stage I 2183 88 125 80 131 61 510 62 5 II 171 7 15 10 29 13 88 11
III 105 4 12 8 42 20 154 19
IV 27 1 3 2 13 6 69 8
Unknown 5 0 0 0 0 0 2 0
Postoperative tumor type
Endometrioid 2307 93 135 87 150 70 331 40
Serous 0 0 0 0 26 12 193 24
Clearcell 0 0 0 0 6 3 70 9
Adenocarcinoma NOS 167 7 18 12 13 6 62 7
Mucinous 13 0 2 1 1 0 0 0
Carcinosarcoma 0 0 0 0 17 8 156 19
Other 4 0 0 0 2 1 11 1
Postoperative grade
1 1447 58 46 30 0 0 0 0
2 1044 42 109 70 0 0 0 0
3 0 0 0 0 215 100 823 100
Surgical staging
No 2338 94 114 74 163 76 434 53
Yes 153 6 41 26 52 24 389 47
Adjuvant therapy
None 1806 73 101 65 73 34 283 35
VBT 313 13 21 14 30 14 110 13
EBRT 350 14 33 21 86 40 295 36
Radio + chemotherapy 12 0 0 0 8 4 42 5
Chemotherapy 10 0 0 0 18 8 92 11
Hormone therapy 0 0 0 0 0 0 1 0
NOS: not otherwise specified; VBT: vaginal brachytherapy; EBRT: external beam radiotherapy.
91 Chapter 5
Inevitably, the retrospective design of the current study has some limitations. First of all, this study is based on data from the Netherlands Cancer Registry (NCR) and the Dutch Pathology Network (PALGA). We thus depend heavily on the quality and availability of data within these two registries. Due to the absence of information on recurrences no progression free survival or disease free survival analyses could be performed. Secondly, the retrospective nature of the current study, in combination with the anonymous nature of the data available in the NCR and PALGA, impeded the recovery of missing data . As myometrial invasion and lymph vascular invasion were not reliably available pre-operatively, we did not include these factors into the risk stratification. Furthermore, the presence of missing data unfortunately led to the exclusion of a large number of patients from our analyses. Lastly, as the NCR does not register the presence of comorbidities, it was not possible to correct for this possible confounder in the multivariable analysis. As an estimation for cause-specific survival, a relative survival analysis was performed according to the Ederer II method (data not shown). This analysis did not show any differences between 5-year relative survival outcomes and 5-year overall survival outcomes of the four risk groups. The possibility that comorbidities confounded our results is thus deemed unlikely. In conclusion, within this population based analysis a 90% concordance was demonstrated between pre-operative and post-operative risk stratifications. Less favorable survival was demonstrated in patients with a pre-operative high and post-operative clinicopathologic low risk compared to patients with concordant low risk. Our data underline the independent prognostic information provided by the pre-operative sample, which should be incorporated into clinical decision-making.
FUNDING AND ACKNOWLEDGEMENTS
The authors thank the Netherlands Cancer Registry and the Dutch Pathology Network for providing the data used in this manuscript. This work was supported by Dutch Cancer Society grant RUG 2014- 7117 to HWN. The funders had no role in collection and analysis of the data, interpretation of the results or writing of the manuscript. The views and opinions expressed in the manuscript are those of the authors and do not necessarily reflect those of the Dutch Cancer Society.
92 Risk stratifications in endometrial cancer
REFERENCES
1. Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2015. CA Cancer J Clin. 2015;65(1):5–29. 2. Creasman WT, Morrow CP, Bundy BN, Homesley HD, Graham JE, Heller PB. Surgical pathologic spread patterns of endometrial cancer. A Gynecologic Oncology Group Study. Cancer. 1987;60(8 Suppl):2035–41. 3. Lee K-B, Ki K-D, Lee J-M, Lee J-K, Kim JW, Cho C-H, et al. The Risk of Lymph Node Metastasis Based on Myometrial Invasion and Tumor Grade in Endometrioid Uterine Cancers: A Multicenter, Retrospective Korean Study. Ann Surg Oncol [Internet]. 2009;16:2882–7. Available from: http://www.springerlink.com/index/10.1245/s10434- 009-0535-0 4. Convery P a, Cantrell L a, Di Santo N, Broadwater G, Modesitt SC, Secord AA, et al. Retrospective review of an intraoperative algorithm to predict lymph node metastasis in low-grade endometrial adenocarcinoma. Gynecol 5 Oncol [Internet]. 2011;123(1):65–70. Available from: http://dx.doi.org/10.1016/j.ygyno.2011.06.025%5Cn- http://www.ncbi.nlm.nih.gov/pubmed/21742369 5. Milam MR, Tao X, Coleman RL, Harrell R, Bassett R, Dos Reis R, et al. Neoadjuvant chemotherapy is associated with prolonged primary treatment intervals in patients with advanced epithelial ovarian cancer. Int J Gynecol Cancer. 2011 Jan;21(1):66–71. 6. Koskas M, Bassot K, Graesslin O, Aristizabal P, Barranger E, Clavel-Chapelon F, et al. Impact of lymph vascular space invasion on a nomogram for predicting lymph node metastasis in endometrial cancer. Gynecol Oncol [Internet]. 2013;129(2):292–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23480871 7. Creutzberg CL, van Putten WL, Koper PC, Lybeert ML, Jobsen JJ, Wárlám-Rodenhuis CC, et al. Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial. Lancet. 2000;355:1404–11. 8. Di Cello A, Rania E, Zuccal V, Venturella R, Mocciaro R, Zullo F, et al. Failure to recognize preoperatively high-risk endometrial carcinoma is associated with a poor outcome. Eur J Obstet Gynecol Reprod Biol. 2015;194:153– 60. 9. Werner HMJ, Trovik J, Marcickiewicz J, Tingulstad S, Staff AC, Engh ME, et al. A discordant histological risk classification in preoperative and operative biopsy in endometrial cancer is reflected in metastatic risk and prognosis. Eur J Cancer [Internet]. 2013 Feb [cited 2015 Feb 26];49(3):625–32. Available from: http://www. sciencedirect.com/science/article/pii/S0959804912007204 10. Nofech-Mozes S, Ismiil N, Dubé V, Saad RS, Ghorab Z, Grin a., et al. Interobserver Agreement for Endometrial Cancer Characteristics Evaluated on Biopsy Material. Obstet Gynecol Int [Internet]. 2012;2012:1–6. Available from: http://www.hindawi.com/journals/ogi/2012/414086/ 11. Helpman L, Kupets R, Covens a., Saad RS, Khalifa M a., Ismiil N, et al. Assessment of endometrial sampling as a predictor of final surgical pathology in endometrial cancer. Br J Cancer [Internet]. 2014;110(August 2013):609–15. Available from: http://www.nature.com/bjc/journal/v110/n3/full/bjc2013766a.html 12. Blake P, Swart A, Orton J, Kitchener H. Adjuvant external beam radiotherapy in the treatment of endome- trial cancer (MRC ASTEC and NCIC CTG EN.5 randomised trials): pooled trial results, systematic review, and meta-analysis. Lancet [Internet]. 2009;373:137–46. Available from: http://dx.doi.org/10.1016/S0140- 6736(08)61767-5 13. Kong A, Johnson N, Kitchener HC, Lawrie TA. Adjuvant radiotherapy for stage i endometrial cancer: An updated cochrane systematic review and meta-analysis. J Natl Cancer Inst. 2012;104(21):1625–34. 14. Williams KE, Waters ED, Woolas RP, Hammond IG, McCartney AJ. Mixed serous-endometrioid carcinoma of the uterus: pathologic and cytopathologic analysis of a high-risk endometrial carcinoma. Int J Gynecol Cancer [Inter- net]. 1994 Jan [cited 2016 Jul 26];4(1):7–18. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11578379 15. Carcangiu ML, Chambers JT. Uterine papillary serous carcinoma: a study on 108 cases with emphasis on the
93 Chapter 5
prognostic significance of associated endometrioid carcinoma, absence of invasion, and concomitant ovarian carcinoma. Gynecol Oncol [Internet]. 1992 Dec [cited 2016 Jul 26];47(3):298–305. Available from: http://www. ncbi.nlm.nih.gov/pubmed/1473741 16. Quddus MR, Sung CJ, Zhang C, Lawrence WD. Minor serous and clear cell components adversely affect progno- sis in mixed-type’’ endometrial carcinomas: a clinicopathologic study of 36 stage-I cases. Reprod Sci [Internet]. 2010 Jul [cited 2016 Jul 26];17(7):673–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20393071 17. Nout RA, Smit VTHBM, Putter H, Jürgenliemk-Schulz IM, Jobsen JJ, Lutgens LCHW, et al. Vaginal brachytherapy versus pelvic external beam radiotherapy for patients with endometrial cancer of high-intermediate risk (PORTEC-2): an open-label, non-inferiority, randomised trial. Lancet. 2010;375(9717):816–23. 18. Bendifallah S, Darai E, Ballester M. Predictive Modeling: A New Paradigm for Managing Endometrial Cancer. Ann Surg Oncol [Internet]. 2016;23:975–88. Available from: http://www.scopus.com/inward/record. url?eid=2-s2.0-84947436603&partnerID=40&md5=92061910ba8678770a785740d8e4e5bf 19. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumors. Nature [Internet]. 2012 Oct 4 [cited 2016 Jul 26];490(7418):61–70. Available from: http://www.ncbi.nlm.nih.gov/ pubmed/23000897 20. Yang D, Khan S, Sun Y, Hess K, Shmulevich I, Sood AK, et al. Association of BRCA1 and BRCA2 mutations with survival, chemotherapy sensitivity, and gene mutator phenotype in patients with ovarian cancer. JAMA [Internet]. 2011 Oct 12 [cited 2016 Jul 26];306(14):1557–65. Available from: http://www.ncbi.nlm.nih.gov/ pubmed/21990299 21. Stelloo E, Nout RA, Naves LCLM, Ter Haar NT, Creutzberg CL, Smit VTHBM, et al. High concordance of molecular tumor alterations between pre-operative curettage and hysterectomy specimens in patients with endome- trial carcinoma. Gynecol Oncol [Internet]. 2014;133(2):197–204. Available from: http://dx.doi.org/10.1016/j. ygyno.2014.02.012 22. Cancer Genome Atlas Research Network, Kandoth C, Schultz N CA, Akbani R, Liu Y, Shen H, Robertson AG, Pashtan I, Shen R, Benz CC, Yau C L, PW, Ding L, Zhang W, Mills GB, Kucherlapati R, Mardis ER L DA. Integrated genomic characterization of endometrial carcinoma. Nature [Internet]. 2013;497(53):67–73. Available from: http://pubget.com/paper/23636398?institution=%5Cnpapers2://publication/doi/10.1038/nature12113 23. Talhouk a, McConechy MK, Leung S, Li-Chang HH, Kwon JS, Melnyk N, et al. A clinically applicable molecu- lar-based classification for endometrial cancers. Br J Cancer [Internet]. 2015;113(2):299–310. Available from: http://www.nature.com/doifinder/10.1038/bjc.2015.190 24. Stelloo E, Bosse T, Nout R a, MacKay HJ, Church DN, Nijman HW, et al. Refining prognosis and identifying targetable pathways for high-risk endometrial cancer; a TransPORTEC initiative. Mod Pathol [Internet]. 2015;28(6):836–44. Available from: http://www.nature.com/doifinder/10.1038/modpathol.2015.43 25. Talhouk A, Hoang LN, McConechy MK, Nakonechny Q, Leo J, Cheng A, et al. Molecular classification of endo- metrial carcinoma on diagnostic specimens is highly concordant with final hysterectomy: Earlier prognostic information to guide treatment. Gynecol Oncol [Internet]. 2016;143(1):46–53. Available from: http://dx.doi. org/10.1016/j.ygyno.2016.07.090 26. Talhouk A, McConechy MK, Leung S, Yang W, Lum A, Senz J, et al. Confirmation of ProMisE: A simple, genom- ics-based clinical classifier for endometrial cancer. Cancer [Internet]. 2017;1–12. Available from: http://doi. wiley.com/10.1002/cncr.30496 27. Stelloo E, Nout RA, Osse EM, Jurgenliemk-Schulz IJ, Jobsen JJ, Lutgens LC, et al. Improved risk assessment by integrating molecular and clinicopathological factors in early-stage endometrial cancer - combined analysis of PORTEC cohorts. Clin Cancer Res [Internet]. 2016;1–10. Available from: http://clincancerres.aacrjournals. org/cgi/doi/10.1158/1078-0432.CCR-15-2878
94
CHAPTER 6
Compliance with adjuvant treatment guidelines in endometrial cancer: room for improvement in high risk patients
Eggink F.A., Mom C.H., Boll D., Ezendam N.P., Kruitwagen R.F., Pijnenborg J.M., van der Aa M.A., Nijman H.W.
Gynecologic Oncology. 2017 Aug;146(2):380-385 Chapter 6
ABSTRACT
Objectives Compliance of physicians with guidelines has emerged as an important indicator for quality of care. We evaluated compliance of physicians with adjuvant therapy guidelines for endometrial cancer patients in the Netherlands in a population-based cohort over a period of 10 years.
Methods Data from all patients diagnosed with endometrial cancer between 2005 and 2014, without residual tumor after surgical treatment, were extracted from the Netherlands Cancer Registry (N=14564). FIGO stage, grade, tumor type and age were used to stratify patients into risk groups. Possible changes in compliance over time and impact of compliance on survival were assessed.
Results Patients were stratified into low/low-intermediate (52%), high-intermediate (21%) and high (20%) risk groups. Overall compliance with adjuvant therapy guidelines was 85%. Compliance was highest in patients with low/low-intermediate risk (98%, no adjuvant therapy indicated). The lowest compliance was determined in patients with high risk (61%, external beam radiotherapy with/without chemother- apy indicated). Within this group compliance decreased from 64% in 2005-2009 to 57% in 2010-2014. In high risk patients with FIGO stage III serous disease compliance was 55% (chemotherapy with/ without radiotherapy indicated) and increased from 41% in 2005-2009 to 66% in 2010-2014.
Conclusion While compliance of physicians with adjuvant therapy guidelines is excellent in patients with low and low-intermediate risk, there is room for improvement in high risk endometrial cancer patients. Eagerly awaited results of ongoing randomized clinical trials may provide more definitive guidance regarding adjuvant therapy for high risk endometrial cancer patients.
98 Compliance to adjuvant therapy guidelines in endometrial cancer
INTRODUCTION
Endometrial cancer is the most common gynecologic cancer in developed countries (1,2). Approx- imately 1900 women are newly diagnosed with endometrial cancer every year in the Netherlands. While most patients are diagnosed with low risk disease and have relatively favorable survival outcomes, there is a subgroup of patients with a higher risk of recurrence and metastasis facing unfavorable survival outcomes (3,4).
Within the Netherlands, standard primary therapy for endometrial cancer consists of hysterectomy and bilateral salpingo-oophorectomy. Generally, in patients with high risk endometrial cancer com- plementary lymphadenectomy or complete staging (including peritoneal sampling and omentectomy) 6 is performed. To guide the choice of adjuvant therapy, patients are stratified into risk groups based on European Society of Medical Oncology (ESMO) clinical practice guidelines (5). National guidelines, based on the best available evidence, recommend no adjuvant therapy in low and low-intermediate risk patients and adjuvant therapy, consisting of vaginal brachytherapy or external beam radiotherapy and/or chemotherapy, for high-intermediate and high risk patients (summarized in table 1) (6–9). Despite the large body of literature aimed at the evaluation of therapeutic strategies, management of high-intermediate and high risk endometrial cancer remains a controversial topic as available liter- ature is inconsistent. The lack of unequivocal evidence has resulted in widespread variation in surgical and adjuvant management of endometrial cancer (10–12). Variation in treatment strategies has also been demonstrated on a national level. Compliance of physicians with adjuvant therapy guidelines in early stage endometrial cancer ranged between 53% and 72% in two relatively small Dutch studies based on data from 1995-2008 and 1995-1999, respectively (13,14). This variation in treatment may be due to the lack of high quality evidence, prompting policymakers to devise national guidelines which leave room for interpretation. Importantly, compliance with guidelines may be affected by physician factors such as judgment of benefit of therapy, as well as individual patient factors such as age, physical condition and treatment preferences (15).
Compliance of physicians with evidence-based guidelines has been viewed as an important indicator for quality of care (16–19). In this study we aimed to assess compliance of physicians with national adjuvant therapy guidelines by conducting a population-based study in patients diagnosed with endometrial cancer in the Netherlands between 2005 and 2014.
99 Chapter 6
Table 1. Risk groups and corresponding adjuvant treatment guidelines in the Netherlands Risk group FIGO Grade Tumor type Age Recommended adjuvant therapy stage Low + low- IA 1-2 Endometrioid - None intermediate IB 1-2 Endometrioid <60 IA 3 Endometrioid <60 High-intermediate IB 1-2 Endometrioid ≥60 Radiotherapy, VBT preferred over EBRT IA 3 Endometrioid ≥60 High IB 3 Endometrioid - EBRT, chemotherapy may be considered II-III - Endometrioid - I-III - Clearcell - I-II - Serous - III - Serous - Chemotherapy, radiotherapy may be considered VBT: vaginal brachytherapy; EBRT: external beam radiotherapy
100 Compliance to adjuvant therapy guidelines in endometrial cancer
METHODS
Data Collection Data were retrieved from the Netherlands Cancer Registry (NCR), which contains clinicopathologic characteristics from all patients diagnosed with cancer from 1989 onwards in the Netherlands. Data from all consecutive patients diagnosed with endometrial cancer between January 1st 2005 and Jan- uary 1st 2015 were requested. Patients that did not undergo surgery and those with residual tumor after surgery were excluded from analyses.
The NCR is linked to the Municipal Personal Records Database to obtain information on vital status of patients in the registry. For our analyses the information concerning vital status was available up to 6 February 1st 2016. Tumor stage according to International Federation of Gynecology and Obstetrics (FIGO) 2009 criteria was determined from the pathological Tumor lymph Node Metastasis (TNM) classification, which was available in the NCR. Data with regard to adjuvant therapy were available in the NCR and categorized into the following groups: no adjuvant therapy, vaginal brachytherapy (VBT), external beam radiotherapy (EBRT), radiotherapy and chemotherapy, chemotherapy and hormonal therapy. Information concerning socioeconomic status (SES) was based on reference data from The Netherlands Institute for Social Research. Scores were derived from income, education and occupa- tion per four-digit postal code. Patients were assigned to three SES categories: low (1st-3rd decile), intermediate (4th-7th decile) and high (8th-10th decile).
Within the Netherlands, care for patients with gynecologic cancers is divided into 8 regions which com- prise at least one academic/specialized referral hospital. Region of treatment hospital was available in the NCR, to guarantee anonymity of hospital-specific-data regions were categorized 1 through 8.
Classification in risk groups and corresponding adjuvant therapy guidelines Patients were classified into one of four risk groups according to national guidelines (table 1). As presence of lymph vascular space invasion was not registered in the NCR, this could not be taken into account in the stratification. Because adjuvant therapy is not recommended in both low risk and low-intermediate risk patients these groups were combined. A sub-analysis was performed on high risk patients with FIGO stage III serous disease as recommended adjuvant therapy for these patients varies from that in other high risk patients. Patients with stage IV disease were analyzed separately because individualization of therapy is recommended for this group. For each risk group corresponding adjuvant therapy guidelines are depicted in table 1.
Outcomes Compliance of physicians with guidelines was defined as the primary outcome. Adjuvant therapy guidelines state that in high risk patients EBRT is recommended and chemotherapy may be consid- ered, we therefore regarded EBRT with or without chemotherapy as compliant with the guideline.
101 Chapter 6
Adjuvant therapy guidelines are slightly different for high risk patients with FIGO stage III serous dis- ease. For this subgroup of high risk patients adjuvant therapy guidelines recommend chemotherapy with or without radiotherapy, we therefore regarded chemotherapy with or without radiotherapy (EBRT or VBT) as compliant with the guideline.
Assessment of variation in compliance between the periods 2005-2009 and 2010-2014, between the eight oncologic regions within the Netherlands and the impact of compliance on overall survival were defined as secondary outcomes.
Statistical analyses Compliance of physicians was assessed for all risk groups, and comparisons were made between compliance with guidelines in patients diagnosed in the periods 2005-2009 and in 2010-2014. Simi- larly, comparisons were made between compliance in the eight oncologic regions in the Netherlands. Differences between groups were determined by Chi2 test or Fisher’s Exact test. Overall survival was measured from date of diagnosis until date of death or last follow-up. Impact of compliance on overall survival was estimated using Kaplan Meier analyses and accompanying log-rank tests. Multivariable analyses were performed in which survival was corrected for age, type of tumor, grade, FIGO stage and socioeconomic status based on postal code. In all statistical analyses differences were considered statistically significant at p<0.05. Data analysis was performed using SPSS data analysis and statistical software version 22.0 (SPSS Inc. Chicago, IL, USA).
102 Compliance to adjuvant therapy guidelines in endometrial cancer
RESULTS
Patient characteristics A total of 14,564 endometrial cancer patients were eligible for analysis (table 2). The majority of patients were diagnosed with FIGO stage I (58% stage IA and 28% stage IB) endometrioid (91%) endometrial cancer. Most patients did not receive any adjuvant therapy (64%).
Table 2. Clinicopathologic characteristics of endometrial cancer patients eligible for analysis (N=14564)
N %
Age at diagnosis (years) Mean (range) 66 (25-100) 6 FIGO stage
IA 8469 58
IB 4070 28
II 1072 7
III 792 5
IV 140 1
Unknown 21 0
Tumor type
Endometrioid 13175 91
Clearcell 220 1
Serous 618 4
Other 551 4
Grade
1 7088 49
2 4090 28
3 2228 15
Unknown 1158 8
Adjuvant therapy
None 9396 64
VBT 1986 14
EBRT 2762 19
Radio- and chemotherapy 177 1
Chemotherapy 222 2
Other 21 0 FIGO: International Federation of Gynecology and Obstetrics; VBT: vaginal brachytherapy; EBRT: external beam radiotherapy.
103 Chapter 6
A total of 7531 patients (52%) were stratified as low and low-intermediate risk, 3042 as high-interme- diate risk (21%), and 2995 as high risk (20%). One hundred and forty patients (1%) were categorized as FIGO stage IV and analyzed separately, and 856 patients (6%) could not be categorized due to missing information. Risk stratification was validated by survival analysis, which confirmed the presence of three independent prognostic groups (figure 1).
Figure 1. Survival of patients with low + low-intermediate, high-intermediate or high risk endometrial cancer
100
80
60
40 Overall Survival (%)
20 Low + low-intermediate risk High-intermediate risk 0 High risk
0 20 40 60 80 100 120 Followup (months)
Compliance according to risk groups (N=13568) Compliance of physicians with adjuvant therapy guidelines was assessed for patients that were categorized as low and low-intermediate, high-intermediate or high risk. Overall, compliance with guidelines was 85%. Within our database, patients that were not treated according to adjuvant ther- apy guidelines had unfavorable overall survival outcomes compared to patients that were treated according to the guidelines. Mean overall survival was 105 months in the compliant group versus 82 months in the non-compliant group (p<0.001, data not shown). This remained significant when correcting for age, type of tumor, grade, FIGO stage and socioeconomic status (adjusted HR 1.33, 95% CI 1.20-1.46, p<0.001, table 3).
Compliance was 98% in the low and low-intermediate group, 78% in the high-intermediate and 61% in the high risk group (p<0.001, figure 2A). There were no differences in compliance between the periods 2005-2009 (85%) and 2010-2014 (85%, p=0.847, figure 2B). Compliance with guidelines varied from 80-90% in the eight oncologic regions (p<0.001, figure 3).
104 Compliance to adjuvant therapy guidelines in endometrial cancer
Table 3 Univariable and multivariable cox-regression analysis for overall survival of low, low-inter- mediate, high-intermediate and high risk patients (N=13568).
Univariable analyses Multivariable analyses
HR 95% CI p-value HR 95% CI p-value
Treatment compliant with guideline
Yes ref ref ref ref ref ref
No 2.793 2.555-3.052 <0.001 1.325 1.202-1.461 <0.001
Age at diagnosis 1.078 1.074-1.082 <0.001 1.071 1.066-1.075 <0.001
Tumor type
Endometrioid ref ref ref ref ref ref Clearcell 2.289 1.791-2.925 <0.001 1.063 0.799-1.413 0.676 6 Serous 3.295 2.861-3.795 <0.001 1.332 1.117-1.588 0.001
Other 3.664 2.937-4.570 <0.001 1.782 1.405-2.260 <0.001
Grade
1 ref ref ref ref ref ref
2 1.684 1.525-1.860 <0.001 1.335 1.207-1.476 <0.001
3 3.604 3.256-3.989 <0.001 2.114 1.885-2.371 <0.001
Unknown 3.842 3.102-4.759 <0.001 1.738 1.320-2.289 <0.001
FIGO stage
IA ref ref ref ref ref ref
IB 1.867 1.699-2.050 <0.001 1.313 1.192-1.446 <0.001
II 2.711 2.385-3.082 <0.001 1.899 1.666-2.164 <0.001
III 4.860 4.275-5.526 <0.001 3.453 3.021-3.947 <0.001
Unknown 2.586 0.364-18.380 0.342 1.032 0.144-7.383 0.975
Socioeconomic Status
High ref ref ref ref ref ref
Medium 1.035 0.937-1.144 0.499 1.013 0.917-1.120 0.795 Low 1.206 1.087-1.338 <0.001 1.115 1.005-1.238 0.041 FIGO: International Federation of Gynecology and Obstetrics; HR: hazard risk; CI: confidence interval.
105 Chapter 6
Figure 2. Compliance with adjuvant therapy guidelines in low, low-intermediate, high-intermediate and high risk endometrial cancer patients (N=13568). A B 100 100 No
No Yes Yes 80 80
60 60
40 40
20 20 all patients) all patients) % % ( Compliance to guideline ( 0 Compliance to guideline 0 2005-2009 2010-2014 Period of diagnosis High risk ow + low- L intermediate risk
High-intermediate risk
A, percentage of patients treated according to adjuvant therapy guidelines per risk group (p<0.001). B, percentage of patients treated according to adjuvant therapy guidelines per period of diagnosis (P=0.847).
Figure 3. Percentage of low, low-intermediate, high-intermediate and high risk endometrial cancer patients treated according to adjuvant therapy guidelines per oncologic region (N=13568, p<0.001)
100 No
Yes 80
60
40
20 all patients) % Compliance to guideline ( 0 1 2 3 4 5 6 7 8 Oncologic Region
Compliance in high risk patients (N=2995) The compliance with the guidelines of only 61% in high risk patients prompted us to conduct a sub-analysis on this group. Of the 2995 high risk patients within our database 55% received EBRT, 11% received brachytherapy, 5% received radio- and chemotherapy, 4% received chemotherapy alone and 25% did not receive any adjuvant therapy (figure 4). Unfavorable survival was demonstrated in patients that were not treated according to adjuvant therapy guidelines compared to those that were treated according to the guidelines. Mean overall survival was 86 months in the compliant group versus 76 months in the non-compliant group (p<0.001, data not shown). This remained significant when correcting for age, type of tumor, grade, FIGO stage and socioeconomic status (adjusted HR 1.28, 95% CI 1.12-1.46, p<0.001, data not shown).
106 Compliance to adjuvant therapy guidelines in endometrial cancer
Figure 4. Administered adjuvant treatment regimen for high risk endometrial cancer patients (N=2995). 100 Hormone therapy ) t s Chemotherapy t n 80 n e e i m Radio and chemotherapy t t a a p e 60
r EBRT t k
s t i n r VBT
a 40 h v
g No adjuvant therapy u i j h d
A % 20 (
0 EBRT: external beam radiotherapy, VBT: vaginal brachytherapy. 6
Compliance with guidelines for high risk patients decreased from 64% in 2005-2009 to 57% in 2010- 2014 (p<0.001, figure 5A). A decrease was seen in the administration of EBRT (from 61% to 49%), while an increase in administration of VBT (from 7% to 14%) and combined radio-and chemotherapy (from 3% to 7%, data not shown) was observed. Compliance with guidelines in this group of patients showed large variation between the eight oncologic regions, ranging from 48-73% (p<0.001, figure 5B).
Compliance in high risk patients with FIGO stage III serous disease (N=124) Another sub-analysis was conducted for high risk patients with FIGO stage III serous disease, as the adjuvant therapy guidelines for this group are different from other high risk patients. An evaluation of the administered adjuvant therapy revealed that 36% received chemotherapy, and 19% received radio- and chemotherapy, both of which are in compliance with the guidelines. Furthermore, 27% received EBRT, 1% received brachytherapy and 17% did not receive any adjuvant therapy (figure 6). Patients treated according to adjuvant therapy guidelines had favorable survival outcomes compared to those that were not treated according to guidelines. Mean overall survival was 57 months in the compliant group versus 39 months in the non-compliant group (p=0.017, data not shown). This dif- ference disappeared when correcting for age and socioeconomic status (adjusted HR 1.49, 95% CI 0.90-2.45, p=0.119, data not shown).
107 Chapter 6
Figure 5. Compliance with adjuvant therapy guidelines in high risk endometrial cancer patients (N=2995). A B 100 No 100 No Yes Yes
80 80
60 60
40 40
20 20 high risk patients) high risk patients) % % ( ( Compliance to guideline 0 Compliance to guideline 0 2005-2009 2010-2014 1 2 3 4 5 6 7 8 Period of diagnosis Oncologic Region A B 100 No 100 No Yes Yes
80 80
60 60
40 40
20 20 high risk patients) high risk patients) % % ( Compliance to guideline ( 0 Compliance to guideline 0 2005-2009 2010-2014 1 2 3 4 5 6 7 8 Period of diagnosis Oncologic Region A, percentage of patients treated according to adjuvant therapy guidelines per period of diagnosis (<0.001). B, percentage of patients treated according to adjuvant therapy guidelines per oncologic region (p<0.001). The number of high risk endometrial cancer patients was 435 in region 1, 218 in region 2, 539 in region 3, 353 in region 4, 282 in region 5, 653 in region 6, 250 in region 7 and 263 in region 8.
Figure 6. Administered adjuvant treatment regimen for high risk FIGO stage III serous endometrial cancer patients (N=124). )
s 100 t Hormone therapy n e i t
t Chemotherapy n
a 80 e p
m Radio and chemotherapy s t u a o
e 60 r
r EBRT t e
t s
n I VBT I a I 40
v
O No adjuvant therapy u j G I d F A
20 f o
% ( 0 EBRT: external beam radiotherapy, VBT: vaginal brachytherapy.
108 Compliance to adjuvant therapy guidelines in endometrial cancer
Compliance with the guidelines improved from 41% in 2005-2009 to 66% in 2010-2014 for patients with high risk FIGO stage III serous disease (p=0.007, figure 7A). Between the eight oncologic regions compliance with guidelines ranged from 21-100% (p=0.014, figure 7B).
Figure 7. Compliance with adjuvant therapy guidelines in high risk FIGO stage III serous endometrial cancer patients (N=124). A B 100 No 100 No Yes Yes
80 80
60 60 40 40 6 20 20 FIGO III serous patients) FIGO III serous patients) % % Compliance to guideline ( Compliance to guideline ( 0 0 2005-2009 2010-2014 1 2 3 4 5 6 7 8 Period of diagnosis Oncologic Region A B 100 No 100 No Yes Yes
80 80
60 60
40 40
20 20 FIGO III serous patients) FIGO III serous patients) % % Compliance to guideline ( Compliance to guideline ( 0 0 2005-2009 2010-2014 1 2 3 4 5 6 7 8 Period of diagnosis Oncologic Region
A, percentage of patients treated according to adjuvant therapy guidelines per period of diagnosis (p=0.007). B, percentage of patients treated according to adjuvant therapy guidelines per oncologic region (p=0.014). The number of high risk FIGO stage III serous endometrial cancer patients was 21 in region 1, 2 in region 2, 23 in region 3, 10 in region 4, 14 in region 5, 26 in region 6, 13 in region 7 and 15 in region 8.
Analysis of adjuvant treatment in patients with FIGO stage IV disease (N=140) National guidelines recommend individualization of adjuvant treatment in patients with FIGO stage IV disease. Within the study period, the most frequently administered adjuvant treatment for patients with FIGO stage IV disease was chemotherapy (43%, figure 8). Thirty percent of patients did not receive any adjuvant therapy. When comparing the periods 2005-2009 and 2010-2014, the administration of chemotherapy increased from 30% to 49%, and the administration of EBRT decreased from 34% to 9%, respectively (data not shown). The administration of chemotherapy for patients with FIGO stage IV disease varied between 20% and 69% across the eight oncologic regions in the Netherlands (p=0.537, statistical significance not reached due to small numbers, data not shown).
109 Chapter 6
Figure 8. Administered adjuvant treatment regimen for FIGO stage IV endometrial cancer patients (N=140).
100
) Hormone therapy s e t n n i Chemotherapy l e i e 80 t d a i Radio and chemotherapy p u
g V
I 60 EBRT
o t e
g e VBT a c t
n 40 s
a No adjuvant therapy i l O p G I m
F 20
% C o ( 0 EBRT: external beam radiotherapy, VBT: vaginal brachytherapy.
110 Compliance to adjuvant therapy guidelines in endometrial cancer
DISCUSSION
Within the current study, compliance of physicians with national adjuvant therapy guidelines was assessed in 13,568 endometrial cancer patients between 2005 and 2014. Compliance was highest in low and low-intermediate risk patients (98%), and lowest in high risk patients (61%). Within the high risk patients a decrease in compliance with guidelines was demonstrated between the periods 2005-2009 and 2010-2014. Low, but increasing, compliance with guidelines was determined in a sub-analysis of high risk patients with FIGO stage III serous disease. Large variations were seen in clinical practice for high risk patients between the eight oncologic regions in the Netherlands.
Within the study period almost all patients with low and low-intermediate risk were treated according 6 to the national guidelines. The specific guideline for low and low-intermediate risk groups is based on a strong body of evidence from the ‘Post-Operative Radiation Therapy in Endometrial Cancer’ (PORTEC) 1 and 2 studies and (pooled) trial results from the ‘A Study in the Treatment of Endometrial Cancer’ (ASTEC) and ‘A Phase III Randomized Trial Comparing TAH BSO versus TAH BSO Plus Adjuvant Pelvic Irradiation in Intermediate Risk, Carcinoma of the Endometrium’ (EN.5) trials (7,8,20–23). These studies demonstrated that postoperative radiotherapy reduces locoregional recurrence in early stage endometrial cancer, but does not improve overall survival. In patients with low and low-intermediate risk (loco-regional recurrence risk of <5%) postoperative radiotherapy is not indicated. The excellent compliance with guidelines for low and low-intermediate risk patients may largely be attributable to the availability of high quality evidence supporting this guideline.
Compared to patients with low or low-intermediate risk, a lower compliance of physicians with guide- lines was observed in patients with high-intermediate and high risk endometrial cancer. The indication for adjuvant radiotherapy in high-intermediate and high risk endometrial cancer is largely based on results from the PORTEC-1 and 2 studies (7,20,22,23). Importantly, no difference in overall or disease free survival was demonstrated in these studies. The lack of survival benefit from adjuvant radiotherapy (EBRT and VBT) compared to observation was also confirmed by in other studies such as the ASTEC study and a Cochrane review (8,9). Considering the lack of survival benefit of adjuvant radiotherapy and the risk of potential toxicity (20,23,24), it is possible that some high-intermedi- ate and high-risk patients may have been deemed unfit or unwilling to undergo radiotherapy. In this regard, comorbidities and patient preferences may have affected compliance with guidelines in high-intermediate and high-risk patients, but this could not be assessed as this information was not available in the NCR.
Within the study period a strong increase in administration of chemotherapy for high risk patients, and specifically for patients with FIGO stage III serous disease, was demonstrated. An increase in administration of chemotherapy was also described in an evaluation of clinical practice regarding adjuvant therapy for endometrial cancer in Germany between 2009 and 2013 (25). This change in
111 Chapter 6
clinical practice may be attributed to studies suggesting a beneficial role for chemotherapy in high risk patients (26–28). The eagerly awaited results from the PORTEC 3 and Gynecologic Oncology Group (GOG) 0258 may provide more definitive direction regarding chemotherapy as therapeutic modality in the management of high risk endometrial cancer patients.
Although an increase in use of adjuvant chemotherapy in FIGO stage III serous disease was seen, its value in this specific group of patients is subject of debate. Available literature is inconsistent and of low quality as it is based on retrospective analyses or unplanned subgroup analyses from randomized controlled trial data. For example, a comprehensive review by the Society of Gynecologic Oncology concluded that platinum/taxane chemotherapy should be considered in the treatment of early- and advanced stage serous endometrial cancer (29). In contrast, an analysis of patients participating in GOG chemotherapy trials demonstrated that response to chemotherapy was not associated with histology (30). Furthermore, an unplanned data-driven subgroup analysis on results from the NSGO/ EORTC study did not demonstrate any survival benefit from radiotherapy in combination with che- motherapy compared to radiotherapy alone in patients with serous and clear cell tumors (overall survival was 77% and 78%, respectively) (27). It is likely that low compliance with guidelines in adjuvant treatment of patients with high risk FIGO stage III serous endometrial cancer is attributable to the lack of unequivocal evidence on this topic. Importantly, the relatively small number of patients with high risk FIGO stage III serous endometrial cancer within our cohort may have influenced the clinical variation between the 8 oncologic regions.
In an effort to decrease differences in clinical management of endometrial cancer worldwide, an ESMO-ESGO-ESTRO consensus conference was held in 2014 (31). At this conference a multidisciplinary panel comprising 40 experts in the management of endometrial cancer developed evidence-based guidelines on selected clinically relevant topics. The effect of these new evidence-based guidelines on discrepancies in management of endometrial cancer around the globe, and on compliance of physicians with guidelines within the Netherlands, remains to be elucidated.
While our database comprises a large population-based sample of patients, the retrospective design of this study has some limitations. First of all, no information was available concerning the presence of lymph vascular space invasion. This prognostic factor could therefore not be incorporated into the risk stratification. Secondly, the NCR does not register presence of comorbidity. As such, comparison of survival in patients treated according to guidelines and patients treated otherwise could not be corrected for the presence of comorbidity. To address this in part, we corrected for socioeconomic status which is known to be associated with comorbidity (32). Furthermore, progression free survival could not be assessed due to the absence of information on recurrences. Finally, it was impossible to evaluate whether noncompliance was attributable to patient factors (such as refusal of therapy or frailty) or to clinician factors (such as negative judgment of benefit of therapy) as information regard- ing reasons for noncompliance were lacking. Prospective registration of reasons for noncompliance
112 Compliance to adjuvant therapy guidelines in endometrial cancer
with adjuvant therapy guidelines in endometrial cancer is warranted to gain insight in reasons for noncompliance, especially considering current emphasis on shared decision-making.
In conclusion, we assessed compliance of physicians with national adjuvant therapy guidelines in 13,568 endometrial cancer patients between 2005 and 2014. While compliance with guidelines was excellent in low and low-intermediate risk patients, there is room for improvement in high risk endo- metrial cancer patients. Though recent efforts aimed at reducing the lack of international consensus regarding clinical management of endometrial cancer may have provided some guidance, large clinical studies are warranted to resolve the remaining controversies. In line with this, results of ongoing randomized clinical trials are eagerly awaited. 6 FUNDING AND ACKNOWLEDGEMENTS
The authors would like to thank the Netherlands Cancer Registry for providing the data used in this manuscript. This work was supported by Dutch Cancer Society grant RUG 2014-7117 to HWN.
113 Chapter 6
REFERENCES
1. Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2015. CA Cancer J Clin. 2015;65(1):5–29. 2. Torre L a., Bray F, Siegel RL, Ferlay J, Lortet-tieulent J, Jemal A. Global Cancer Statistics, 2012. CA a cancer J Clin [Internet]. 2015;65(2):87–108. Available from: http://onlinelibrary.wiley.com/doi/10.3322/caac.21262/abstract 3. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JWW, Comber H, et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012. Eur J Cancer [Internet]. 2013;49(6):1374–403. Available from: http://dx.doi.org/10.1016/j.ejca.2012.12.027 4. Creasman WT, Odicino F, Maisonneuve P, Quinn MA, Beller U, Benedet JL, et al. Carcinoma of the corpus uteri. FIGO 26th Annual Report on the Results of Treatment in Gynecological Cancer. Int J Gynaecol Obstet [Internet]. 2006;95 Suppl 1:S105-43. Available from: http://books.google.com/ books?hl=en&lr=&id=WPZiZGGD_iYC&oi=fnd&pg=PA47&dq=Carcinoma+of+the+Corpus+Uteri&ots=F6fbN- jZWw7&sig=m7mmRDMrnzkXUiQMRSZiZT8eXgY%5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/17161155 5. Colombo N, Preti E, Landoni F, Carinelli S, Colombo A, Marini C, et al. Endometrial cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol [Internet]. 2013 Oct [cited 2016 Aug 9];24 Suppl 6(suppl 6):vi33-8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24078661 6. Morice P, Leary A, Creutzberg C, Abu-Rustum N, Darai E. Endometrial cancer. Lancet [Internet]. 2016;387(10023):1094–108. Available from: http://dx.doi.org/10.1016/S0140-6736(15)00130-0 7. Creutzberg CL, van Putten WL, Koper PC, Lybeert ML, Jobsen JJ, Wárlám-Rodenhuis CC, et al. Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial. Lancet. 2000;355:1404–11. 8. Blake P, Swart A, Orton J, Kitchener H. Adjuvant external beam radiotherapy in the treatment of endome- trial cancer (MRC ASTEC and NCIC CTG EN.5 randomised trials): pooled trial results, systematic review, and meta-analysis. Lancet [Internet]. 2009;373:137–46. Available from: http://dx.doi.org/10.1016/S0140- 6736(08)61767-5 9. Kong A, Johnson N, Kitchener HC, Lawrie TA. Adjuvant radiotherapy for stage i endometrial cancer: An updated cochrane systematic review and meta-analysis. J Natl Cancer Inst. 2012;104(21):1625–34. 10. Fotopoulou C, Kraetschell R, Dowdy S, Fujiwara K, Yaegashi N, Larusso D, et al. Surgical and systemic manage- ment of endometrial cancer: an international survey. Arch Gynecol Obstet. 2015;291(4):897–905. 11. Wong AT, Rineer J, Schwartz D, Weiner J, Safdieh J, Choi K, et al. Patterns of adjuvant radiation usage and sur- vival outcomes for stage I endometrial carcinoma in a large hospital-based cohort. Gynecol Oncol [Internet]. 2017;144(1):113–8. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0090825816315219 12. Battista MJ, Steiner E, Rieks N, Steetskamp J, Seeger A, Sicking I, et al. Nationwide analysis on surgical staging procedures and systemic treatment for patients with endometrial cancer in Germany. Int J Gynecol Cancer [Internet]. 2013;23(1):105–12. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23262520 13. Boll D, Verhoeven RHA, Van Der Aa MA, Lybeert MLM, Coebergh JWW, Janssen-Heijnen MLG. Adherence to national guidelines for treatment and outcome of endometrial cancer stage i in relation to co-morbidity in southern Netherlands 1995-2008. Eur J Cancer [Internet]. 2011;47(10):1504–10. Available from: http://dx.doi. org/10.1016/j.ejca.2011.03.023 14. Lankveld MAL Van, Koot NCM, Peeters PHM, van Lankveld MA, van Leeuwen JS, Jurgenliemk-Schulz IM, et al. Compliance to surgical and radiation treatment guidelines in relation to patient outcome in early stage endometrial cancer. JEvalClinPract. 2006;12(1356–1294 (Print)):196–201. 15. Molena D, Mungo B, Stem M, Poupore AK, Chen SY, Lidor AO. Does Quality of Care Matter? A Study of Adher- ence to National Comprehensive Cancer Network Guidelines for Patients with Locally Advanced Esophageal
114 Compliance to adjuvant therapy guidelines in endometrial cancer
Cancer. J Gastrointest Surg. 2015;19(10):1739–47. 16. Werbrouck J, Bouche G, De Jonge E, Jacomen G, D’Hondt V, Denys H, et al. Evaluation of the quality of the management of cancer of the corpus uteri-Selection of relevant quality indicators and implementation in Belgium. Gynecol Oncol. 2013;131(3):512–9. 17. Grimshaw JM, Russell IT. Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet. 1993;342(8883):1317–22. 18. Hébert-Croteau N, Brisson J, Latreille J, Rivard M, Abdelaziz N, Martin G. Compliance with consensus recom- mendations for systemic therapy is associated with improved survival of women with node-negative breast cancer. J Clin Oncol. 2004;22(18):3685–93. 19. Galvan-Turner VB, Chang J, Ziogas A, Bristow RE. Observed-to-expected ratio for adherence to treatment guidelines as a quality of care indicator for ovarian cancer. Gynecol Oncol [Internet]. 2015;139(3):495–9. Available from: http://dx.doi.org/10.1016/j.ygyno.2015.09.015 20. Nout RA, Putter H, Jurgenliemk-Schulz IM, Jobsen JJ, Lutgens LCHW, van der Steen-Banasik EM, et al. Quality of 6 Life After Pelvic Radiotherapy or Vaginal Brachytherapy for Endometrial Cancer: First Results of the Random- ized PORTEC-2 Trial. J Clin Oncol [Internet]. 2009 Jul 20 [cited 2016 Nov 11];27(21):3547–56. Available from: http://jco.ascopubs.org/cgi/doi/10.1200/JCO.2008.20.2424 21. Creutzberg CL, van Stiphout RGPM, Nout RA, Lutgens LCHW, Jürgenliemk-Schulz IM, Jobsen JJ, et al. Nomo- grams for Prediction of Outcome With or Without Adjuvant Radiation Therapy for Patients With Endometrial Cancer: A Pooled Analysis of PORTEC-1 and PORTEC-2 Trials. Int J Radiat Oncol Biol Phys [Internet]. 2015 Mar 1 [cited 2015 Feb 20];91(3):530–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25680597 22. Nout RA, Smit VTHBM, Putter H, Jürgenliemk-Schulz IM, Jobsen JJ, Lutgens LCHW, et al. Vaginal brachytherapy versus pelvic external beam radiotherapy for patients with endometrial cancer of high-intermediate risk (PORTEC-2): an open-label, non-inferiority, randomised trial. Lancet. 2010;375(9717):816–23. 23. Nout RA, van de Poll-Franse L V., Lybeert MLM, Warlam-Rodenhuis CC, Jobsen JJ, Mens JWM, et al. Long-Term Outcome and Quality of Life of Patients With Endometrial Carcinoma Treated With or Without Pelvic Radio- therapy in the Post Operative Radiation Therapy in Endometrial Carcinoma 1 (PORTEC-1) Trial. J Clin Oncol [Internet]. 2011 May 1 [cited 2016 Nov 11];29(13):1692–700. Available from: http://jco.ascopubs.org/cgi/ doi/10.1200/JCO.2010.32.4590 24. Boer SM De, Powell ME, Mileshkin L, Katsaros D, Bessette P, Haie-meder C, et al. Toxicity and quality of life after adjuvant chemoradiotherapy versus radiotherapy alone for women with high-risk randomised , phase 3 trial. Lancet Oncol [Internet]. 2016;17(8):1–13. Available from: http://dx.doi.org/10.1016/S1470-2045(16)30120-6 25. Battista MJ, Schmidt M, Rieks N, Sicking I, Albrich S, Eichbaum M, et al. Adjuvant treatment decisions for patients with endometrial cancer in Germany: results for the nationwide AGO pattern of care studies from the years 2013, 2009 and 2006. J Cancer Res Clin Oncol. 2015;141:555–62. 26. Johnson N, Bryant A, Miles T, Hogberg T, Cornes P. Adjuvant Chemotherapy for Endometrial Cancer. Int J Gynecol Cancer. 2011;21(5):885–95. 27. Hogberg T, Signorelli M, de Oliveira CF, Fossati R, Lissoni AA, Sorbe B, et al. Sequential adjuvant chemother- apy and radiotherapy in endometrial cancer–Results from two randomised studies. Eur J Cancer [Internet]. 2010 Sep [cited 2016 Nov 11];46(13):2422–31. Available from: http://linkinghub.elsevier.com/retrieve/pii/ S0959804910004776 28. Galaal K, Al Moundhri M, Bryant A, Lopes AD, Lawrie TA. Adjuvant chemotherapy for advanced endometrial cancer (Review). Cochrane Database Syst Rev [Internet]. 2014;(5):CD010681. Available from: http://ovidsp.ovid. com/ovidweb.cgi?T=JS&PAGE=reference&D=coch&NEWS=N&AN=00075320-100000000-09070%5Cnwww. thecochranelibrary.com 29. Boruta DM, Gehrig PA, Fader AN, Olawaiye AB. Management of women with uterine papillary serous cancer: A Society of Gynecologic Oncology (SGO) review. Gynecol Oncol [Internet]. 2009;115(1):142–53. Available from:
115 Chapter 6
http://dx.doi.org/10.1016/j.ygyno.2009.06.011 30. McMeekin DS, Filiaci VL, Thigpen JT, Gallion HH, Fleming GF, Rodgers WH. The relationship between histology and outcome in advanced and recurrent endometrial cancer patients participating in first-line chemotherapy trials: A Gynecologic Oncology Group study. Gynecol Oncol. 2007;106(1):16–22. 31. Colombo N, Creutzberg C, Amant F, Bosse T, González-Martín A, Ledermann J, et al. ESMO-ESGO-ESTRO Con- sensus Conference on Endometrial Cancer: diagnosis, treatment and follow-up. Radiother Oncol [Internet]. 2015;117(1):559–81. Available from: http://annonc.oxfordjournals.org/lookup/doi/10.1093/annonc/mdv484 32. Louwman WJ, Aarts MJ, Houterman S, van Lenthe FJ, Coebergh JWW, Janssen-Heijnen MLG. A 50% higher prevalence of life-shortening chronic conditions among cancer patients with low socioeconomic status. Br J Cancer [Internet]. 2010;103(11):1742–8. Available from: http://dx.doi.org/10.1038/sj.bjc.6605949
116
PART III
Encouraging individualization of care for patients with endometrial cancer
CHAPTER 7
POLE proofreading mutations elicit an anti-tumor immune response in endometrial cancer
Van Gool I.C., Eggink F.A., Freeman-Mills L., Stelloo E., Marchi E., De Bruyn M., Palles C., Nout R.A., De Kroon C.D., Osse E.M., Klenerman P., Creutzberg C.L., Tomlinson I.P., Smit V.T., Nijman H.W., Bosse T., Church D.N.
Clinical Cancer Research. 2015 Jul 15;21(14):3347-55 Chapter 7
ABSTRACT
Aim Recent studies have shown that 7-12% of endometrial cancers (ECs) are ultramutated due to somatic mutation in the proofreading exonuclease domain of the DNA replicase POLE. Interestingly, these tumors have an excellent prognosis. In view of the emerging data linking mutation burden, immune response and clinical outcome in cancer, we investigated whether POLE-mutant ECs showed evidence of increased immunogenicity.
Methods We examined immune infiltration and activation according to tumor POLE proofreading mutation in a molecularly defined EC cohort including 47 POLE-mutant tumors. We sought to confirm our results by analysis of RNAseq data from the TCGA EC series and used the same series to examine whether differences in immune infiltration could be explained by an enrichment of immunogenic neoepitopes in POLE-mutant ECs.
Results Compared to other ECs, POLE-mutants displayed an enhanced cytotoxic T cell response, evidenced by increased numbers of CD8+ tumor infiltrating lymphocytes and CD8A expression, enrichment for a tumor-infiltrating T cell gene signature, and strong upregulation of the T cell cytotoxic differ- entiation and effector markers T-bet, Eomes, IFNG, PRF and granzyme B. This was accompanied by upregulation of T cell exhaustion markers, consistent with chronic antigen exposure. In-silico analysis confirmed thatPOLE -mutant cancers are predicted to display more antigenic neo- epitopes than other ECs, providing a potential explanation for our findings.
Conclusions Ultramutated POLE proofreading-mutant ECs are characterized by a robust intratumoral T cell response, which correlates with, and may be caused by an enrichment of antigenic neo-peptides. Our study provides a plausible mechanism for the excellent prognosis of these cancers.
122 Immunity of POLE-mutant endometrial cancer
INTRODUCTION
Endometrial cancer (EC) is the commonest gynecological malignancy in the Western world, and affects approximately 150,000 women each year in Europe and the US combined(1). ECs have tra- ditionally been classified into endometrioid (EEC) and non-endometrioid (NEEC) tumors according to clinical and histopathological criteria. However, recent work has shown that this dualistic model can be improved upon by a molecular classification into subgroups that more accurately reflect underlying tumor biology and clinical outcome(2,3).
One interesting subgroup is the 7-12% of ECs with somatic mutation in the proofreading exonucle- ase domain of the DNA replicase POLE(2,4–6). Polymerase proofreading is essential for ensuring fidelity of DNA replication(7), and inkeeping with its dysfunction, POLE proofreading-mutant cancers have a frequency of base substitution mutation among the highest in human cancer(2,8,9). POLE-mu- 7 tant ECs display other distinctive features, including a characteristic mutation signature, with a preponderance of C>A transversions and bias for particular amino acid substitutions, and strong associations with endometrioid histology, high grade, and microsatellite stability (MSS)(2,4–6,10). We and others have recently shown that, despite the association with high grade, POLE-mutant ECs have an excellent prognosis(5,6,11). However, the reasons for this were unclear.
Although the ability of the immune system to suppress malignant disease has long been recog- nized(12), the last few years have seen a remarkable increase in our understanding of the complex and dynamic interplay between cancers and the host immune response. For example, preclinical and translational studies have confirmed that tumor missense mutations can lead to presenta- tion of antigenic neo-epitopes by MHC class I molecules, resulting in activation of T cell-mediated cytotoxicity(13–16). Consequently, mutations that cause strongly antigenic epitopes are likely to undergo negative selection in developing tumors (13,14). Cancers also demonstrate multiple alter- native mechanisms of immune escape, including downregulation of HLA class I expression, and upregulation of immunosuppressive molecules including PD1/PD-L1, TIM3, LAG3 and TIGIT in a phenomenon referred to as adaptive immune resistance(17,18). Despite this, it is clear that in many cancers the immune system retains a degree of control over tumor growth – illustrated by the association between increased density of tumor infiltrating lymphocytes (TILs), particularly CD8+ cytotoxic T cells, and favorable outcome in multiple cancer types, including EC(19–22). Interestingly, a recent study has shown that CD8+ cell infiltration correlates strongly with the number of predicted antigenic mutations in tumors(23), suggesting that the immunogenicity of cancers is determined at least partly by their mutational burden(24). These data are consistent with the observations that hypermutated microsatellite unstable (MSI) endometrial and colorectal cancers typically display greater TIL density than other tumors(25,26).
123 Chapter 7
During our previous studies(4–6), we noted that POLE-mutant ECs frequently displayed strikingly high TIL density, often accompanied by a Crohn’s-like reaction. Similar observations have recently been reported following pathological review of POLE-mutant TCGA ECs(27). We hypothesized that this may represent infiltration by cytotoxic T lymphocytes, which could in turn contribute to the favorable prognosis of these tumors. We also speculated that this might relate to an increase in antigenic neo-epitopes in POLE-mutant ECs secondary to ultramutation. We tested this using a molecularly defined cohort of ECs, including 47POLE -mutant tumors, and the recently published TCGA series(2).
124 Immunity of POLE-mutant endometrial cancer
MATERIALS AND METHODS
Patients and tumors Tumors were selected from the PORTEC-1 and PORTEC-2 studies (n=57)(28,29) and EC series from the Leiden University Medical Centre (LUMC) (n=67) and the University Medical Centre Groningen (UMCG) (n=26), to provide similar numbers of low (grade 1/2) and high grade (grade 3) tumors of the common molecular subtypes: POLE wild-type, microsatellite stable (MSS); POLE wild-type, micro- satellite unstable (MSI); and POLE (proofreading) mutant (Table 1). All cases were of endometrioid histology (EEC), and all POLE-mutant tumors were MSS. Of 373 cases reported in the TCGA series, 245 had paired whole exome and RNAseq data and were informative for this analysis(2). Of these 197 were EECs, four mixed EEC/NEECs, and 44 serous (NEEC) cancers. Ethical approval for tumor molecular analysis was granted at LUMC, UMCG and by Oxfordshire Research Ethics Committee B (Approval No. 05\Q1605\66). 7
POLE mutation and microsatellite instability status DNA was extracted from FFPE blocks and sequencing of POLE hotspot exons 9 and 13 (which contain around 90% of pathogenic proofreading mutations) was performed in tumors from the PORTEC, LUMC and UMCG series as previously reported(4,5). All mutations were confirmed in at least duplicate PCR reactions. Details of whole exome sequencing performed by the TCGA have been previously reported(2). Pathogenic POLE proofreading mutations were defined as somatic variants within the exonuclease domain associated with ultramutation and a frequency of C>A transversions of ≥20%(10).
MSI status was determined in the PORTEC, LUMC and UMCG cases by five-marker panel of micro- satellites as reported previously(30), with exception of four cases in which this failed, where status was determined by MLH1, MSH2, MSH6 and PMS2 immunohistochemistry(6). Determination of MSI in the TCGA series was by seven-marker panel, with MSI-H defined as alteration at ≥3 markers(2).
Classification of POLE mutant TCGA ECs was also informed by results of recent analysis of mono- nucleotide repeats in 48 genes(10). In our analysis, only MSI-H cases were classified as MSI – we excluded one TCGA case for which MSI status could not be determined. The single tumor that dis- played both POLE proofreading mutation and MSI by these criteria was assigned to the POLE mutant group on the basis of its characteristic mutation signature, in accordance with a recent report(10).
Histological assessment Tumors were evaluated for the presence/absence of TILs and for Crohn’s like reaction by a gyne- cologic pathologist (TB) blinded to other clinicopathological data.
125 Chapter 7
Table 1. Demographic and clinicopathological characteristics of cases by EC molecular subtype
MSS MSI POLE Total PORTEC, LUMC & UMCG series
Age
Median 62.5 68.8 63.5 65.0
Range 36.84 35-89 46-82 35-89
FIGO stage (2009)
I/II 48 39 45 132
III/IV 6 10 2 18
Grade
1/2 45 39 33 117
3 9 10 14 33
Type
EC 54 49 47 150
NEEC 0 0 0 0
Total 54 49 47 150
TCGA series
Age
Median 64 63 57 63
Range 34-90 35-88 33-87 33-90
FIGO stage (2009)
I/II 116 57 14 187
III/IV 40 12 4 56
Unknown 1 1 0 2
Grade
1/2 97 45 9 151
3 60 24 9 93
Type
EC 114 69 18 201
NEEC 43 0 0 43
Total 157 69 18 244 MSS – microsatellite stable, POLE wild-type; MSI – microsatellite unstable, POLE wild-type; POLE – POLE proofreading mutant; EEC – endometrioid endometrial cancer; NEEC – non-endometrioid endometrial cancer. Note: (i) TCGA analysis excludes one NEEC for which MSI status could not be determined. (ii) all POLE-proofreading mutant ECs were MSS with exception of one TCGA tumor (TCGA-AP-A051) which was assigned to the POLE-mutant subgroup on basis of characteristic mutation signature
126 Immunity of POLE-mutant endometrial cancer
Immunohistochemistry and cell quantification Following de-paraffinization, antigen retrieval and blocking of peroxidase activity, whole slides were incubated overnight at 4°C (CD8, CD3) or room temperature (TIA-1, HLA, FoxP3) with primary antibodies against CD8 (1:50, clone C8/144B, DAKO, Agilent technologies, Glostrup, Denmark), CD3 (1:25, clone F7.2.38, DAKO), HCA2 and HC10 (both 1:800, kindly provided by Prof. Dr. J. Neefjes, the Netherlands Cancer Institute), FoxP3 (1:100, mAbcam 450, Abcam, Cambridge, UK), and TIA-1 (1:400, clone 2G9A10F5, Beckman Coulter, Miami FL, USA). Sections were subsequently incubated with either anti-mouse Envision+ reagent (K4000, DAKO) for 30 minutes (CD8 primary), RAMpo (1:100) and GARpo (1:100) secondary and tertiary antibodies, (CD3 primary), or BrightVision-Poly/HRP (Poly-HRP-GAM/R/R; DPV0110HRP; ImmunoLogic) (HCA2, HC10, FoxP3 and TIA-1) before 3,3-diam- inobenzidine (DAB) treatment and hematoxylin counterstaining. Slides were then dehydrated and mounted before digitalization (ScanScope, Aperio Technologies, USA or Ultra Fast Scanner 1.6 RA. Philips), and analysis. 7
CD8+, CD3+, FOXP3+ and TIA-1+ cell numbers were quantified in intraepithelial and intrastromal regions in the center of the tumor (CT) and the invasive margin (IM) as previously reported(19,22). For each region, the mean number of positive cells in eight high power fields (200μm x 200μm) was calculated. For analysis of HLA expression, the percentage of tumor cells with membranous HCA2 and HC10 staining was quantified as previously described(31). In each case, scoring was performed independently by two observers, blinded to other clinicopathological data.
Immunofluorescence Following de-paraffinization, antigen retrieval and blocking ofperoxidase activity, whole slides were stained overnight at 4°C with primary antibody against TIA-1 (1:50, ab2712, Abcam). Sections were subsequently incubated with anti-mouse Envision+ reagent (K4000, DAKO) for 30 minutes and HRP visualized using cyanine 5 tyramide signal amplification (TSA) according to the manufacturer’s instructions (PerkinElmer). Next, whole slides were stained overnight with primary antibody against CD8 (1:25, clone C8/144B, DAKO) and a biotinylated antibody against fibronectin (1:50, ab6584, Abcam). Slides were incubated with GaM-AF555 (1:150 Life Technologies) and streptavidin-dy- light488 (1:150, Thermo Scientific), counterstained with DAPI (Life Technologies) and mounted in prolong gold mounting medium (Life Technologies). Immunofluorescent slides were scanned using a TissueFaxs imaging system (TissueGnostics, Austria). Processed channels were merged using Adobe Photoshop CS5 (Adobe).
Leukocyte methylation scores Leukocyte methylation scores (syn1809223)(32,33) were downloaded from Synapse (https://www. synapse.org/) and annotated according to MSI and POLE status.
127 Chapter 7
TCGA RNAseq data Details of the TCGA RNAseq analysis have been previously reported(2). RSEM normalized (34) and raw RNAseq count data were downloaded from FireBrowse (http://firebrowse.org/?cohort=UCE- C&download_dialog=true) accessed 11/11/14. After removal of normal tissue controls and technical duplicates, 245 samples with RSEM normalized and 231 samples with raw count data were infor- mative for analysis.
Gene set enrichment analysis TCGA raw counts were annotated by molecular subtype prior to normalization and ranking of genes differentially expressed between POLE-mutant (n=16) and other (n=205) ECs using DESeq(35). Gene set enrichment analysis (GSEA)(36) was then performed with the PreRanking setting, using GO Biological Processes and C7 Immunologic Signatures sets from the Molecular Signatures Database (MSigDB) http://www.broadinstitute.org/gsea/msigdb/genesets.jsp?collection=BP, and a published 200-gene T cell tumor infiltration gene signature(18).
Prediction of antigenic neo-epitopes We created an algorithm to estimate the immunogenicity of individual tumors taking into account the following considerations: i) to generate a functional neo- epitope a missense mutation must be expressed; ii) most functional neo-epitopes identified to date are predicted to bind MHC class I molecules (IC50 < 500nM) by NetMHCPan(23,37,38); iii) the likelihood that a neo-epitope is antigenic is reduced if the corresponding wild-type peptide also binds the MHC with similar affinity asT cells to the epitope may be centrally deleted or tolerized (39). Our strategy was similar to others reported recently(15,23,38,40). For each tumor we calculated all possible 9mers for every missense mutation in expressed genes (defined as non-zero reads from RNAseq) and calculated the binding affinity of the mutant and corresponding wild-type peptide for HLA-A*02:01 (as a single model example HLA allele) using NetMHCPan 2.8 (37). In the event that several peptides had an IC50 <500nM, the strongest binder was used for analysis. We defined antigenic mutations as neo-epitopes predicted to bind MHC molecules (IC50 < 500nM) for which the corresponding wild-type peptide was not predicted to bind MHC (IC50 > 500nM).
Statistical analysis We used the non-parametric Mann-Whitney test for all comparisons of continuous data and Spear- man’s rho to analyze correlation between variables. Categorical variables were compared using Fisher’s exact test. All statistical tests were two-sided, with a P value of <0.05 taken to indicate significance. Except where indicated, statistical tests were unadjusted. Statistical analyses were performed using STATA (Texas), and Prism 6.0 (GraphPad, LaJolla).
128 Immunity of POLE-mutant endometrial cancer
RESULTS
POLE proofreading-mutant ECs show increased lymphocytic infiltrate Preliminary analysis of H&E-stained sections suggested that POLE proofreading-mutant ECs fre- quently displayed a prominent lymphocytic infiltrate and Crohn’s-like lymphocytic reaction (Figure 1A,B). Formal quantification of this ina set of 150 ECs comprising approximately equal numbers of POLE proofreading- mutant/microsatellite stable (POLE-mutant), POLE wild-type/microsatellite-un- stable (MSI) and POLE wild-type/microsatellite-stable (MSS) subtypes of low and high grade (Table 1), confirmed that TILs were more frequent inPOLE -mutant (22/47) than in both MSS (8/54; P=0.0009, Fisher’s exact test), and MSI (10/49; P=0.009) subtypes (Figure 1C). Crohn’s-like reaction was also sig- nificantly more common in POLE-mutant than other tumors (P<0.001 both comparisons; Figure 1D).
+ Increased density of intratumoral CD8 lymphocytes in POLE-mutant ECs 7 Mindful of the relationship between cytotoxic T cell infiltrate andfavorable cancer outcome (19-22), and the excellent prognosis of POLE-mutant ECs(5,6,11), we next examined whether POLE-mutants showed evidence of increased T cell infiltrate in our EC cohort. While as anticipated(25), CD8+ cell numbers in intraepithelial and intrastromal compartments in the tumor center (CT) and the invasive margin (IM) were higher in MSI than MSS ECs (P<0.0001, all comparisons, Mann-Whitney test), in
POLE-mutant tumors the density of CD8+ infiltrate was frequently striking (Figure 2A), and signi- ficantly exceeded that of bothMSS (P<0.0001 for all four regions) and MSI cancers in the CT (median
5.9 vs. 2.6 intraepithelial CD8+ cells per high power field [HPF], P=0.001; 26.0 vs. 13.5 intrastromal
CD8+ cells, P=0.002) (Figure 2B). Furthermore, the proportion of tumors with numbers of CD8+ cells exceeding the median in all four regions was substantially higher in POLE-mutant (60.0%) than MSI (31.3%, P=0.007, Fisher’s exact test) and MSS tumors (7.2%, P<0.0001). Staining for CD3 and the cytolytic marker TIA-1 in a subset of cases confirmed increased T cell density inPOLE -mutant tumors (Figure 3A,B) and suggested that the infiltrate contained lymphocytes capable of cytotoxic activity
(Figure 4A,B). Co-immunofluorescence confirmed co- expression of TIA-1 in the CD8+ lymphocytes comprising the POLE-mutant tumor infiltrate (Figure 5A-F), further supporting the conclusion that these cells were capable of mediating an anti-tumor effect.
Interestingly, in light of the correlation previously reported between B and T cell subsets at the IM(41), we found that dense CD20 stromal infiltrate in this region was more common in POLE-mutants
(Figure 6A), while a tendency to increased numbers of FOXP3+ cells in both MSI and POLE-mutant tumors (Figure 6B) was also notable, given that this has been associated with favorable cancer prognosis in some studies(41).
129 Chapter 7
Figure 1. Tumor infiltrating lymphocytes and Crohn’s like reaction in POLE- mutant endometrioid endometrial cancers.
A
MSS MSI POLE Grade 1 Grade 3
B C D *** 60 ** 60 *** **
40 40
20 20
0 0 % with lymphocytic inflitrate % witth Crohn’s like reaction % witth Crohn’s MSS MSI MSS MSI POLE POLE A, Representative H&E stained sections demonstrating tumor-infiltrating lymphocytes (TILs) in POLE wild-type, micro- satellite stable (MSS), POLE wildtype, microsatellite unstable (MSI) and POLE proofreading-mutant (POLE) ECs in both low and high grade tumors. Scale bar corresponds to 50µm. B, Representative H&E stained section demonstrating Crohn’s like reaction (indicated by arrows) in POLE-mutant tumor. Scale bar corresponds to 500µm. C, Frequency of lymphocytic infiltrate by EC molecular subtype. D, Frequency of Crohn’s like reaction by EC molecular subtype. Comparison between groups in C and D was made by two-sided Fisher’s exact test. ** and *** indicate P<0.01 and P<0.001 respectively.
130 Immunity of POLE-mutant endometrial cancer
Figure 2. Increased CD8+ lymphocyte infiltration in POLE-mutant endometrial cancers. A MSS MSI POLE Low power
7 Centre of tumor (CT) Invasive margin (IM)
B CT intraepithelial CT intrastromal IM intraepithelial IM intrastromal *** *** *** *** 30 ** * 60 60 60 *** *** *** *** 20 40 40 40
10 20 20 20
No. CD8+ cells / HPF 0 0 0 0
MSI MSI MSS MSS MSS MSI MSS MSI POLE POLE POLE POLE
A, Results of CD8 immunohistochemistry by EC molecular subtype shown at low magnification (upper panels) and high power views of the center of the tumor (CT) and invasive margin (IM). Arrows highlight intraepithelial CD8+ cells in POLE- mutant tumor. Scale bars correspond to 500 μm in upper panel and 100 μm in the middle and lower panels.
B, Quantification of CD8+ cell number in intraepithelial and intrastromal compartments in the CT and IM by EC molecular subtype. Boxes represent the interquartile range (IQR), with upper whisker indicating the 75th percentile plus 1.5 x IQR, and the lower whisker the 25thpercentile minus 1.5 x IQR. The median and mean values are indicated by a horizontal line and cross respectively. Statistical comparison between groups was made by unadjusted two-sided Mann- Whitney test, *, **, and *** correspond to P<0.05, P<0.01, and P<0.001 respectively.
131 Chapter 7
Figure 3. CD3+ tumor infiltrate according to EC molecular subtype. A MSS MSI POLE Low power
MSS Center of tumor (CT) Invasive margin (IM)
B CT: intraepithelial CT: intrastromal IM: intraepithelial IM: intrastromal
** ** 40 * * * 80 40 100 ** 30 60 30 80 60 20 40 20 40 10 20 10 20
No of CD3+ cells / HPF 0 0 0 0
MSI MSI MSI MSS MSS MSI MSS POLE POLE POLE MSS POLE A, Representative images of CD3+ staining in microsatellite stable and unstable POLE wild-type cancers (MSS & MSI) compared to POLE proofreading mutant (POLE) tumor at low magnification (upper panels) and high magnification in the center of the tumor (CT) and invasive margin (IM) (middle and lower panels). Scale bar in upper panel indicates 500 µm, bar in middle and lower panels indicates 100µm. B, Quantification of CD3+ cell numbers per high power (200 x 200µm) field in intraepithelial and intrastromal com- partments within the center of the tumor (CT) and invasive margin (IM) according to EC molecular subtype. Horizontal bars indicate the median. Comparison between groups was made by unadjusted Mann-Whitney test, * and ** indicate P<0.05 and P<0.01 respectively.
132 Immunity of POLE-mutant endometrial cancer
Figure 4. Number of TIA-1+ cytolytic T cells according to molecular subtype. A POLE Center of tumor (CT)
B CT: intraepithelial CT: intrastromal IM: intraepithelial IM: intrastromal ** ** 20 60 * 50 50 * 40 15 40 40 7 30 30 10 20 20 5 20 10 10 0 0 0 0 No of TIA-1+ cells / HPF No of MSI MSI MSS MSI MSS MSI MSS POLE MSS POLE POLE POLE A, Representative high magnification image of TIA-1+ staining in a POLE proofreading mutant (POLE) tumor. Scale bar indicates 50µm. B, Quantification of TIA-1+ cell numbers per high power (200 x 200µm) field in intraepithelial and intrastromal compart- ments within the center of the tumor (CT) and invasive margin (IM) for MSS, MSI and POLE-mutant tumors. Horizontal bars indicate the median. Comparison between groups was made by unadjusted Mann-Whitney test, * and ** indicate P<0.05, and P<0.01 respectively.
133 Chapter 7
Figure 5. CD8+ infiltrating lymphocytes in POLE-mutant tumors show cytolytic potential.
A B D
DAPI fibronectin CD8 TIA-1 CD8 C E
DAPI fibronectin CD8 TIA-1 DAPI TIA-1
F
DAPI DAPI DAPI CD8 fibronectin DAPI fibronectin fibronectin fibronectin CD8 TIA-1 TIA-1 CD8 TIA-1
A, Low-magnification image of POLE-mutant tumor following co- immunofluorescence (Co-IF) staining for DAPI (nuclei), fibronectin (extracellular matrix), CD8 and the cytolytic marker TIA-1. B, Co-IF images of the tumor center following staining for all markers, C, DAPI, D, CD8, and
E, TIA-1, confirming co-expression of TIA-1 by tumor-infiltrating CD8+ lymphocytes, shown at high magnification inF .
134 Immunity of POLE-mutant endometrial cancer
Figure 6. CD20+ and FOXP3+ tumor infiltrate according to EC molecular subtype. A, A CT: stromal IM: stromal * 100 * 100 * * 80 80
60 60
40 40 CD20 infiltrate 20 20
% of tumours with dense 0
MSI MSS MSI MSS POLE POLE
B CT: intraepithelial CT: intrastromal IM: intraepithelial IM: intrastromal * * 5 ** 40 15 50 ** 7 * 4 30 40 3 10 30 20 * 2 20 1 10 5 10 0 0 0 0 No of FOXP3+ cells / HPF
MSS MSI MSS MSI MSI MSS MSI POLE POLE MSS POLE POLE
A, Percentages of tumors displaying dense intrastromal CD20+ infiltrate in the center of the tumor (CT) and invasive margin (IM). B, Quantification of number of FOXP3+ cells in tumor regions by EC molecular subtype. Horizontal bars indicate the median. Statistical comparisons in A and B were made by unadjusted two-sided Mann-Whitney test; * and ** indicate P<0.05 and P<0.01 respectively.
135 Chapter 7
Figure 7. Clinical outcome and T cell response according to tumor molecular subtype in TCGA endometrial cancers. A B C A B 0.6 C 100 T cell infiltration signature 0.6 0.6 ** T cell infiltration signature 100 P=0.07 P=0.04 80 0.6 ** 0.4 P=0.07 P=0.04 80 NES0.4 1.58 60 0.4 P<0.0001 0.2 NES 1.58 0.4 40 60 MSS P<0.0001 Enrichment score 0.2 MSI 0.2 40 MSS 0.0 20 POLE Enrichment score MSI 0.2 0 20 0.0 0.0 Recurrence-free survival (%) POLE Leucocyte methylation score 0 24 48 72 96 MSS MSI POLE 0 Months 0.0 Recurrence-free survival (%) 0 5000 10,000 15,000 Leucocyte methylation score 0 24 48 72 96 MSS MSI Months POLE Gene rank 0 5000 10,000 15,000 Gene rank
D e MSS MSI POLE vs MSS vs MSI Gene Fold chang P P D vs MSS e CD8A 3.0 0.002 0.26 T cells / cytotoxic cells MSS MSI CD3POLEE 2.2 0.009 0.35 vs MSS Inhibitory vs MSI / T cell exhaustion CD3G 2.2 Gene 0.02 Fold chang0.19 P P CD2 2.1 0.005 vs0.28 MSS MHC class II CD4 1.1CD8A 0.39 3.0 0.63 0.002 0.26 T cells / cytotoxic cells TBX21 1.9CD3E 0.006 2.2 0.15 0.009 Tfh0.35 cells Inhibitory / T cell exhaustion Eomes 2.3CD3G 0.008 2.2 0.05 0.02 Treg0.19 cells LCK 2.3CD2 0.01 2.1 0.18 0.005 0.28 MHC class II B cells IFNG 3.6CD4 0.0003 1.1 0.07 0.39 0.63 PRF 2.5 0.001 0.02 TBX21 1.9 0.006 0.15Inflammation Tfh cells GZMA 1.7 0.08 0.34 Eomes 2.3 0.008 0.05 GZMB 1.6 0.003 0.1 Treg cells GZMH 2.3LCK 0.002 2.3 0.04 0.01 0.18 GZMK 2.3IFNG 0.02 3.6 0.19 0.0003 0.07 B cells PRF 2.5 0.001 0.02 GZMM 2.0 0.02 0.15 Inflammation CXCL9 4.3GZMA <0.0001 1.7 0.03 0.08 0.34 CXCL10 3.5GZMB 0.002 1.6 0.03 0.003 0.1 PDCD1 2.0GZMH 0.008 2.3 0.18 0.002 0.04 PDL1 2.3GZMK 0.02 2.3 0.09 0.02 0.19 LAG3 2.9GZMM 0.0001 2.0 0.02 0.02 0.15 TIM3 1.7CXCL90.03 4.3 0.09 <0.0001 0.03 TIGIT 3.6CXCL10<0.0001 3.5 0.15 0.002 0.03 HLA-DPA1 2.0PDCD10.24 2.0 0.34 0.008 0.18 HLA-DPB1 1.8 0.21 0.21 PDL1 2.3 0.02 0.09 HLA-DQA1 2.1 0.05 0.09 HLA-DRA 1.6LAG3 0.30 2.9 0.19 0.0001 0.02 HLA-DRB1 1.7TIM3 0.27 1.7 0.17 0.03 0.09 CXCR5 3.9TIGIT 0.0004 3.6 0.08 <0.0001 0.15 CXCL13 7.0HLA-DPA0.00011 2.0 0.29 0.24 0.34 CTLA4 1.5HLA-DPB0.0051 1.8 0.10 0.21 0.21 FOXP3 1.8HLA-DQA0.00031 2.1 0.24 0.05 0.09 CD19 2.1HLA-DR0.27A 1.6 0.16 0.30 0.19 CD20 4.7HLA-DRB0.011 1.7 0.37 0.27 0.17 BLK 2.1CXCR50.14 3.9 0.43 0.0004 0.08 IL1A 4.2CXCL130.16 7.0 1.0 0.0001 0.29 IL1B 1.3 0.2 0.95 CTLA4 1.5 0.005 0.10 IL8 1.7 0.34 0.81 FOXP3 1.8 0.0003 0.24 CD19 2.1 0.27 0.16 A, Kaplan-Meier curves demonstrating recurrence-free survival of POLE wild-type,CD20 microsatellite 4.7 0.01 stable0.37 (MSS, n=147), BLK 2.1 0.14 0.43 microsatellite unstable (MSI, n=63) and POLE proofreading mutant (POLE,IL1A n=18) 4.2ECs in 0.16the TCGA1.0 series (note that IL1B 1.3 0.2 0.95 survival data were not available for all cases). Comparison between subgroupsIL8 was 1.7 made 0.34by two- sided0.81 log-rank test. B, Leucocyte methylation scores according to EC molecular subtype. Unadjusted comparison between groups was made by two-sided Mann- Whitney test. ** indicates P<0.01. C, Gene set enrichment analysis (GSEA) of 200- gene tumor-associated T cell signature (Ref. 18) in POLE-mutant ECs compared to other tumors. Raw RNAseq counts were normalized and ranked using DESeq prior to GSEA analysis with pre-ranking. D, Heatmap showing expression of immunological genes according to EC molecular subtype. RSEM-normalized RNAseq expression data were log2 transformed, zero centered and assigned unit variance. For each gene, the mean fold change in expression in POLE mutants relative to MSS ECs was calculated and expression compared between POLE mutants, MSS and MSI ECs by unadjusted, two-sided Mann-Whitney test.
136 Immunity of POLE-mutant endometrial cancer
Cytotoxic T cell infiltration and activation in POLE mutant ECs in TCGA series We sought to confirm our results using the TCGA EC series(2), in whichthe improved clinical outcome of POLE-mutant tumors was first suggested (Figure 7A). Of 244 informative tumors in this study, 157 were MSS, 69 MSI and 18 POLE- proofreading-mutant (the single tumor with both POLE proofreading mutation and MSI was categorized as POLE-mutant according to its mutation spectrum, in keeping with a recent report(10)). 43 of the MSS tumors were NEECs, while all MSI and POLE-mutant ECs cases were EECs (Table 1).
We first examined leucocyte methylation scores, which estimate the proportion of heterogeneous tumor sample that consists of leucocytes(32,33). After confirming that scores correlated strongly with CD8A expression (ρ=0.65, P<0.0001), we noted that, following exclusion of MSI and POLE-mu- tant tumors, both leucocyte methylation scores and CD8A expression did not differ between EECs and NEECs (P=0.52 and P=0.16 respectively, Mann-Whitney test). We therefore included tumors of 7 both histologies in the MSS cohort in all subsequent analyses. Leucocyte methylation scores were similar in MSI and MSS tumors (median 15.5% vs 14.2%, P=0.2), in contrast to a significant increase in POLE mutants (median 23.3%; P=0.006 vs. MSS, P=0.07 vs MSI) (Figure 7B), concordant with our previous results. Given the biological differences between NEECs and EECs, we formally confirmed that these results were essentially unaltered following exclusion of the former from the MSS group (median 14.7%, P=0.008 vs POLE-mutant ECs).
We proceeded to explore whether infiltration ofPOLE -mutant ECs by cytotoxic T cells was manifest as immune expression signatures and/or increased expression of key immunological genes. Agnos- tic pathway analysis of TCGA RNAseq data by gene set enrichment analysis (GSEA) demonstrated significant enrichment of immune-related pathways in POLE-mutant ECs compared to other tumors, including Immune Response (normalized enrichment score [NES] 4.12 FDR q<0.0001) and Immune System Process (NES 3.77, q<0.0001). GSEA also confirmed that POLE-mutant cancers showed striking enrichment of a recently reported, highly specific 200 gene signature corresponding to tumor T cell infiltration(18)(Figure 7C).
Focused analysis of genes involved in T cell-mediated cytotoxicity confirmedthat, compared to MSS tumors, MSI ECs had higher expression of CD8A (2.1 fold, P=0.0005) and interferon γ (IFNG) (2.1 fold, P=0.0006) though expression of the cytotoxic differentiation and activation markers T-bet (TBX21), Eomes, perforin and granzymes B,H,K and M was either essentially unchanged (≤1.1fold) or not sig- nificantly increased. In contrast, and once again consistent with ourprevious results, POLE-mutants demonstrated substantial upregulation of CD8A (3.0 fold vs all MSS tumors, P=0.002; 3.2 fold vs MSS EECs only, P=0.004), accompanied by significant increases in T-bet (1.9 fold, P=0.006), Eomes (2.3 fold, P=0.008), IFNG (3.6 fold, P=0.0003), PRF (2.5 fold, P=0.001), granzymes B,H,K and M (1.6 to 2.3 fold, P=0.002 to 0.02), and the IFNG-induced cytokines CXCL9 (4.3 fold, P<0.0001) and CXCL10 (3.5 fold, P=0.002) (Figures 7D and 8).
137 Chapter 7
Figure 8. Expression of T cell markers and cytotoxic effectors by EC molecular subtype.
CD8A CD3E IFNG PRF ** ** ** ** ** ** 12 * 12 *** 12 8 10 10 10 6 8 8 8 4 6 6 6 2 4 Log 2 expression 4 2 4 0 2 0 MSS MSI MSS MSI MSS MSI MSS MSI POLE POLE POLE POLE
GZMH CXCL9 CXCL10 ** *** 15 ** 12 * 15 *** * * 10 8 10 10 6 4 5 5 Log 2 expression 2
0 0 0
MSI MSS MSI MSS MSI MSS POLE POLE POLE
Expression of genes encoding T cell subunits CD8α (CD8A), CD3ε (CD3E), cytotoxic effector molecules interferon γ (IFN), perforin (PRF) and granzyme H (GZMH), and IFNγ-induced cytokines CXCL9 and CXCL10 was compared between molec- ular subtypes using RSEM normalized TCGA RNAseq data. Box and whiskers signify IQR ± 1.5 x IQR, mean and median as previously. Statistical comparison between groups was made by unadjusted two-sided Mann-Whitney test. *, ** and *** indicate P<0.05, P<0.01 and P<0.001 respectively.
Upregulation of most of these genes in tumors has been shown to predict good prognosis (19,41). POLE-mutants also demonstrated striking upregulation of the T follicular helper genes CXCL13 (7.0- fold, P=0.0001) and CXCR5 (3.9-fold, P=0.0004), which have recently been shown to strongly predict favorable outcome in colorectal cancer(41). Notably, despite limited numbers, in several cases expression of cytotoxic markers and cytokines exemplifying effector status in POLE mutants significantly exceeded that in MSI tumors (e.g. PRF, P=0.02; GZMH P=0.04; CXCL9/10 both P=0.03) (Figures 7 and 8). Collectively, these data not only corroborated our previous finding that POLE mutant tumors had greater T lymphocyte infiltration than other ECs, but also strengthened the conclusion that these lymphocytes were capable of exerting anti- tumor activity.
Mechanisms of immune escape in POLE-mutant ECs POLE-mutant cancers have significantly better prognosis than other ECs(5,6,11), as evidenced by the absence of recurrences in the TCGA series(2). However, the presentation of all patients in this study with clinically detectable tumors, in some cases with lymphatic spread, indicates that any
138 Immunity of POLE-mutant endometrial cancer
immune response was, at best, only partially successful in suppressing POLE-mutant EC growth. We therefore explored potential mechanisms of immune escape in these tumors.
We first considered the possibility that POLE-mutant ECs may escape from immune surveillance by loss or inactivation of components required for antigen presentation. While 31.8% of POLE mutant ECs in our study set of 150 ECs showed loss of HLA class I protein expression by IHC, this was not significantly different to that observed in MSI (28.6%, P=0.82, Fisher’s exact test) or MSS
(20.0%, P=0.24) tumors and was not reflected in increased CD8+ cell numbers. Similarly, although we found a tendency to over-representation of POLE mutants among TCGA ECs with HLA class I gene expression in the lowest quartile, this was also not significantlydifferent from other molecular subtypes (P=0.07 vs MSS).
Interestingly, while mutations in MHC pathway components were common in POLE-mutant tumors 7 in the TCGA series, most were of unlikely pathogenicity, with exception of two tumors with potentially functional variants. The first, a beta2- microglobulin R117* mutation also detected in a POLE-mutant CRC(9), had a variant allele fraction of 0.59 and is likely to affect stability of the MHC complex by disruption of the interaction with the HLA heavy chain(42). The second, an HLA-B S112R substitution with variant allele fraction 0.71, lies near the F pocket essential for peptide display, and is predicted to be deleterious by both Mutation Assessor and SIFT (Table 2). However, the effect of each variant on antigen presentation is, at present, uncertain.
We proceeded to examine whether the cytotoxic T cell response in POLE- mutant ECs may be attenuated by upregulation of immunosuppressive mediators – a phenomenon termed adaptive immune resistance(17). We found that the T cell exhaustion markers LAG3, TIM-3 and TIGIT, and the T cell inhibitors PD1 and CTLA4 were strongly correlated with CD8A expression across all ECs of all molecular subtypes (ρ=0.65 to 0.87; P<0.0001, all cases), though the correlation with PD-L1 was more modest (ρ=0.34, P<0.0001) (Figure 9A). Interestingly, while expression of these markers in MSI compared to MSS ECs was either unchanged/minimally altered (TIM-3, CTLA4, PD-L1) or moderately increased (LAG3 1.9 fold, P<0.002; TIGIT 2.2 fold, P<0.0001), in POLE mutants all were significantly, and substantially upregulated (e.g. LAG3 2.9 fold, P<0.0001 vs. MSS, P<0.02 vs. MSI; TIGIT 3.6 fold, P<0.0001 vs. MSS, P<0.15 vs. MSI) (Figures 7D and 9B), consistent with prolonged antigen stimulation. However, as noted above, the overall increase in expression of cytotoxic effec- tor markers suggested that this upregulation was insufficient to fully suppress the T cell response in POLE mutants.
139 Chapter 7
Table 2. Antigen presentation pathway mutations in TCGA POLE proofreading-mutant ECs TCGA ID POLE No. Exonic CD8A Antigen Variant Predicted functional effect mutation mutations expres- presenting allele sion* pathway frequency mutations Mutation SIFT assessor TCGA-B5-A0JY P286R 8890 High HLA-B E253K 0.33 Medium Deleterious (0.00) B2M R117* 0.59 N/A N/A PDIA3 R344C 0.36 Medium Deleterious (0.00) PDIA3 E384D 0.30 Neutral Deleterious (0.00) TCGA-BS-A0TC M444K 1203 Low None TCGA-D1-A103 A456P 5965 High PDIA3 R344C 0.30 Medium Deleterious (0.00) TCGA-D1-A16X P286R 1755 High None TCGA-D1-A16Y V411L 952 Low None TCGA-A5-A0GP V411L 1235 Low None TCGA-AP-A051 L424I 6716 High PDIA3 A69T 0.31 Low Deleterious (0.04) TCGA-AP-A056 V411L 7226 High HLA-C E288K 0.16 Low Tolerated (0.06) CALR S300Y 0.34 Neutral Deleterious (0.00) TCGA-AP-A059 S297F 9187 High None TCGA-AP-A0LM V411L 10489 High B2M A6V 0.13 Low Tolerated (1.0) CANX K182N 0.20 Medium Deleterious (0.00) TCGA-AX-A05Z P286R 5677 High None TCGA-AX-A0J0 P286R 6913 Low HLA-B S112R 0.71 High Deleterious (0.00) CALR K62R 0.50 Medium Deleterious (0.00) TAPBP R383Q 0.57 Medium Tolerated (0.34) TCGA-B5-A11E V411L 8210 High PDIA3 E384* 0.44 N/A N/A TCGA-B5-A11N P286R 1557 High None TCGA-BG-A0VX L424V 163 High None TCGA-BS-A0UF P286R 7391 High CANX D37N 0.33 Medium Deleterious (0.00) TCGA-BS-A0UV P286R 7801 High None TCGA-D1-A17Q P286R 5141 Low CALR Y128C 0.13 High Deleterious (0.00) CANX Q508H 0.48 Medium Deleterious (0.00) TAPBP R383Q 0.38 Medium Tolerated (0.34) * relative to median CD8A expression in all TCGA ECs
140 Immunity of POLE-mutant endometrial cancer
Figure 9. T cell exhaustion markers according to tumor CD8A expression and EC molecular subtype. A r=0.65 r=0.87 15 r=0.75 15 15
10 10 10 log2 TIGIT log2 LAG3 5 log2 TIM3 5 5
0 0 0 2 4 8 16 2 4 8 16 2 4 8 16 log2 CD8A log2 CD8A log2 CD8A
15 10 15 r=0.68 r=0.72 r=0.34 8 POLE 10 10 6 MSI MSS 4
5 log2 PD1 5 log2 PD-L1 7 log2 CTLA4 2
0 0 0 2 4 8 16 2 4 8 16 2 4 8 16 log2 CD8A log2 CDlog8 2 CD8A log2 CD8A
B CTLA4 LAG3 TIM3 TIGIT ** * *** *** ** * 10 12 * 10 *** 12 8 10 10 8 6 8 6 8 4 6 4 4 6 Log 2 expression 2 2 2 0 4 0 0
MSS MSI MSS MSI MSS MSI MSS MSI POLE POLE POLE POLE
A, Correlation between CD8A expression and T cell exhaustion markers (P<0.001, all cases). B, T cell exhaustion markers according to EC molecular subtype. Box and whiskers signify IQR ± 1.5 x IQR, mean and median as previously. Statistical comparison between groups was made by unadjusted Mann-Whitney test, *, **, and *** correspond to P<0.05, P<0.01, and P<0.001 respectively.
141 Chapter 7
POLE proofreading-mutant ECs are likely to display increased numbers of antigenic neopeptides We hypothesized that the T cell response in POLE-mutant ECs might be due to an excess of antigenic neo-epitopes as a consequence of ultramutation. To quantify this, we analyzed the TCGA EC series using a methodology similar to several recent studies(23,40). Our algorithm was based on three assumptions – first, that amutation must be in an expressed gene to exert an effect, second, for a neopeptide to act as an antigen it must bind MHC class I molecules (IC50 <500nM), and third, that neopeptides for which the corresponding wild-type peptide also binds MHC molecules are less likely to be immunogenic due to central deletion or tolerization(39).
Applying these criteria, we found that 5.9% (7880/134,473) of the total number of missense muta- tions in TCGA ECs were predicted to be potentially antigenic. Of these, 73% (5767) occurred in POLE-mutant tumors, reflected in a significantly higher number of antigenic mutations per cancer compared to both MSI and MSS subtypes (median 365.5 vs 16 vs 2 respectively, P<0.0001 all comparisons, Mann-Whitney test) (Figure 10A), though this is likely to underestimate the number of antigenic mutations in MSI tumors as frameshift mutations were not included in our analysis. A substantial majority of antigenic mutations were in tumors with greater than median CD8A expression in both the whole series (78.4%), and the POLE- mutant subgroup (83.9%), though the strength of correlation between the two variables was modest, possibly as a result of immune escape mechanisms (Figure 10B).
142 Immunity of POLE-mutant endometrial cancer
Figure 10. Antigenic mutation burden and tumor CD8A expression according to POLE mutation. A 800 *** *** 600
400 *** 200 Predicted no. of antigenic mutations 0
MSS MSI POLE B 7000
2000
1500 7
1000 CD8A expression CD8A 500
0
200
400 HLA-B S211R mutation 600 B2M R117* mutation No. of antigenic mutations 800
A, The number of mutations predicted to generate antigenic neo-peptides in individual TCGA tumors was calculated using exome and RNAseq data (see Methods). Box and whisker plots signify IQR ± 1.5 x IQR, mean and median as pre- viously. Comparison between groups was made by unadjusted two-sided Mann- Whitney test, *** denotes P<0.0001. B, Relationship between number of antigenic missense tumor mutations and tumor CD8A expression. POLE-mutant samples are highlighted in gray, together with possible mechanisms of immune escape in two cases.
143 Chapter 7
DISCUSSION
By complementary analysis of two independent series totaling nearly four hundred patients, and including over sixty POLE-proofreading mutant tumors, we have shown that POLE-mutant ECs are characterized by a striking CD8+ lymphocytic infiltrate, a gene signature of T cell infiltration, and marked upregulation of cytotoxic T cell effector markers. Furthermore, we show that, as a conse- quence of their remarkable mutation burden, POLE proofreading-mutant cancers are predicted to display substantially more antigenic peptides than other tumors, providing a possible explanation for our findings. While our data demonstrate correlation rather than causation, the strong associa- tion between cytotoxic lymphocyte infiltration and favorable outcome in multiple cancers(19–22,41) leads us to speculate that an enhanced T cell anti-tumor response may contribute to the excellent prognosis of POLE-mutant ECs.
During the last few years, a combination of next generation sequencing technology, improved in silico peptide-MHC binding prediction(37), and the clinical application of immune checkpoint inhibi- tors(43–45) have helped facilitate remarkable insights into the mechanisms of tumor immunoediting and immune escape. The intriguing observation that clinical benefit from CTLA4, PD1 and PD-L1 inhibition is greater in melanoma and cigarette smoking-associated lung cancer than most other malignancies can now be interpreted in light of the understanding that in these highly mutated tumors, adaptive immune resistance is a key enabler of disease progression(17), and its inhibition can restore the ability of T cells to respond to antigenic peptides presented by these cancers(43). In keeping with this, in melanoma response to checkpoint inhibitors has very recently been shown to correlate both with the number of predicted antigenic tumor mutations(40), and with the degree of cytotoxic T lymphocyte tumor infiltration prior to treatment(46). In light of these data, the associ- ation between the number of predicted antigenic peptides and T cell response in POLE mutant ECs in our study is noteworthy, as is the marked increase in T cell exhaustion markers, as these have recently been shown to identify tumor neo-antigen- specific CD8+ cells in cancer patients(47,48). Despite upregulation of these immunosuppressors in POLE-mutant ECs, we also found substantial increases in cytotoxic differentiation markers and effectors suggesting that the degree of adaptive immune resistance in these cancers may be insufficient to fully suppress CD8+ T cell cytotoxicity(49). Collectively, our data suggest a complex interaction between the antigenic landscape of POLE-mu- tant ECs and the immune response. In this regard, molecular analysis of recurrences from the few patients with POLE-mutant ECs who do experience relapse may provide insights into mechanisms of immune escape. Lastly, these patients may be good candidates for immune checkpoint inhibitor therapy, as might those patients with POLE proofreading-mutant tumors of other histologies, for which outcomes are more uncertain.
Interestingly, while as anticipated we observed moderately increased T cell infiltration in MSI tumors(25), this was not associated with the marked increase in cytotoxic effector markers seen
144 Immunity of POLE-mutant endometrial cancer
in POLE mutants. While some studies have reported improved prognosis of MSI ECs, this is incon- sistent(50), in contrast to the clear association of MSI with favorable outcome in early colorectal cancer(26). Comparison of the immune response between MSI tumors of both types may provide insights into this discordance.
Our study has limitations. Due to differing sample preservation (FFPE vsfresh frozen) we were unable to validate either the IHC or RNAseq analysis between series, although the results from both analyses were highly concordant. Furthermore, the retrospective nature of our study meant that we were unable to investigate the repertoire and antigen response of T cells in patients with POLE-mutant cancers. This and other functional analyses will require prospective investigation.
In summary, we have demonstrated that ultramutated POLE proofreading- mutant ECs are char- acterized by a robust intratumoral T cell response, which correlates with, and may be caused 7 by an enrichment of antigenic neo-peptides. Our study provides a plausible mechanism for the excellent prognosis of these cancers, and further evidence of the link between somatic mutation and immunoediting in cancers.
FUNDING AND ACKNOWLEDGEMENTS
The authors are grateful to Enno Dreef, Natalja ter Haar and Reinhardt van Dijk (LUMC) for excellent technical assistance. This work was supported by the Dutch Cancer Society Grant (UL2012-5719; Cancer Research UK (C6199/A10417), the European Union Seventh Framework Programme (FP7/207- 2013) grant 258236 collaborative project SYSCOL, the Oxford NIHR Comprehensive Biomedical Research Centre and NIHR senior fellowship (PK), the Oxford Martin School and core funding to the Wellcome Trust Centre for Human Genetics from the Wellcome Trust (090532/Z/09/Z) and fellowship funding to PK (WT091663MA). The UMCG Imaging and Microscopy Center (UMIC), is sponsored by NWO-grants 40-00506-98-9021(TissueFaxs) and 175-010-2009-023 (Zeiss 2p). DNC is funded by a Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship, and has received funding from the Oxford Cancer Research Centre. The results published here include data generated by The Cancer Genome Atlas project funded by the NCI and NIH (cancergenome.nih.gov).
145 Chapter 7
REFERENCES
1. Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JWW, Comber H, et al. Cancer incidence and mortality patterns in Europe: Estimates for 40 countries in 2012. Eur J Cancer [Internet]. 2013;49(6):1374–403. Available from: http://dx.doi.org/10.1016/j.ejca.2012.12.027 2. Kandoth C, Schultz N CA, Akbani R, Liu Y, Shen H, Robertson AG, Pashtan I, Shen R, Benz CC, Yau C L, PW, Ding L, Zhang W, Mills GB, Kucherlapati R, Mardis ER L DA. Integrated genomic characterization of endometrial carcinoma. Nature [Internet]. 2013;497(53):67–73. Available from: http://pubget.com/paper/23636398?insti- tution=%5Cnpapers2://publication/doi/10.1038/nature12113 3. Murali R, Soslow R a, Weigelt B. Classification of endometrial carcinoma: more than two types. Lancet Oncol [Inter- net]. 2014;15(7):e268–78. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1470204513705916 4. Church DN, Briggs SEW, Palles C, Domingo E, Kearsey SJ, Grimes JM, et al. DNA polymerase ε and δ exo- nuclease domain mutations in endometrial cancer. Hum Mol Genet [Internet]. 2013 Jul 15 [cited 2016 Mar 25];22(14):2820–8. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?ar- tid=3690967&tool=pmcentrez&rendertype=abstract 5. Church DN, Stelloo E, Nout R a., Valtcheva N, Depreeuw J, ter Haar N, et al. Prognostic Significance of POLE Proofreading Mutations in Endometrial Cancer. JNCI J Natl Cancer Inst [Internet]. 2015;107:dju402-dju402. Available from: http://jnci.oxfordjournals.org/cgi/doi/10.1093/jnci/dju402 6. Stelloo E, Bosse T, Nout R a, MacKay HJ, Church DN, Nijman HW, et al. Refining prognosis and identifying targetable pathways for high-risk endometrial cancer; a TransPORTEC initiative. Mod Pathol [Internet]. 2015;28(6):836–44. Available from: http://www.nature.com/doifinder/10.1038/modpathol.2015.43 7. Albertson TM, Ogawa M, Bugni JM, Hays LE, Chen Y, Wang Y, et al. DNA polymerase epsilon and delta proof- reading suppress discrete mutator and cancer phenotypes in mice. Proc Natl Acad Sci U S A [Internet]. 2009 Oct 6 [cited 2016 Mar 25];106(40):17101–4. Available from: http://www.pubmedcentral.nih.gov/articlerender. fcgi?artid=2761330&tool=pmcentrez&rendertype=abstract 8. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio S a JR, Behjati S, Biankin A V, et al. Signatures of mutational processes in human cancer. Nature [Internet]. 2013;500:415–21. Available from: http://www.pubmedcentral. nih.gov/articlerender.fcgi?artid=3776390&tool=pmcentrez&rendertype=abstract 9. The Cancer Genome Atlas. Comprehensive molecular characterization of human colon and rectal cancer. - PubMed - NCBI [Internet]. Nature. 2012 [cited 2016 Apr 5]. p. 330–7. Available from: http://www.ncbi.nlm.nih. gov/pubmed/22810696 10. Shinbrot E, Henninger EE, Weinhold N, Covington KR, Göksenin AY, Schultz N, et al. Exonuclease mutations in DNA polymerase epsilon reveal replication strand specific mutation patterns and human origins of replication. Genome Res [Internet]. 2014 Nov [cited 2016 Apr 5];24(11):1740–50. Available from: http://www.pubmedcen- tral.nih.gov/articlerender.fcgi?artid=4216916&tool=pmcentrez&rendertype=abstract 11. Meng B, Hoang LN, McIntyre JB, Duggan MA, Nelson GS, Lee C-H, et al. POLE exonuclease domain mutation predicts long progression-free survival in grade 3 endometrioid carcinoma of the endometrium. Gynecol Oncol [Internet]. 2014 Jul [cited 2016 Mar 25];134(1):15–9. Available from: http://www.ncbi.nlm.nih.gov/ pubmed/24844595 12. Burnet FM. The concept of immunological surveillance. Prog Exp Tumor Res. 1970;13:1–27. 13. Matsushita H, Vesely MD, Koboldt DC, Rickert CG, Uppaluri R, Magrini VJ, et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature [Internet]. 2012 Feb 8 [cited 2017 Jul 21];482(7385):400–4. Available from: http://www.nature.com/doifinder/10.1038/nature10755 14. DuPage M, Mazumdar C, Schmidt LM, Cheung AF, Jacks T. Expression of tumour-specific antigens underlies cancer immunoediting. Nature [Internet]. 2012;482(7385):405–9. Available from: http://dx.doi.org/10.1038/
146 Immunity of POLE-mutant endometrial cancer
nature10803%5Cnhttp://www.nature.com/doifinder/10.1038/nature10803%5Cnhttp://www.nature.com/ doifinder/10.1038/nature10803 15. van Rooij N, van Buuren MM, Philips D, Velds A, Toebes M, Heemskerk B, et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J Clin Oncol [Internet]. 2013 Nov 10 [cited 2016 Jul 5];31(32):e439-42. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24043743 16. Tran E, Turcotte S, Gros A, Robbins PF, Lu Y, Dudley ME, et al. Cancer Immunotherapy Based on Mutation-Spe- cific CD4+ T Cells in a Patient with Epithelial Cancer. Science (80- ). 2014;9:641–5. 17. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer [Internet]. 2012 [cited 2016 Jul 6];12(4):252–64. Available from: http://dx.doi.org/10.1038/nrc3239 18. Johnston RJ, Comps-Agrar L, Hackney J, Yu X, Huseni M, Yang Y, et al. The immunoreceptor TIGIT regulates antitumor and antiviral CD8(+) T cell effector function. Cancer Cell [Internet]. 2014 Dec 8 [cited 2017 Jul 21];26(6):923–37. Available from: http://linkinghub.elsevier.com/retrieve/pii/S153561081400422X 19. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006 Sep 29;313(5795):1960– 4. 20. Zhang L, Conejo-Garcia JR, Katsaros D, Gimotty PA, Massobrio M, Regnani G, et al. Intratumoral T cells, recur- 7 rence, and survival in epithelial ovarian cancer. N Engl J Med. 2003 Jan 16;348(3):203–13. 21. Pagès F, Berger A, Camus M, Sanchez-Cabo F, Costes A, Molidor R, et al. Effector memory T cells, early metasta- sis, and survival in colorectal cancer. N Engl J Med [Internet]. 2005 Dec 22 [cited 2017 Jul 21];353(25):2654–66. Available from: http://www.nejm.org/doi/abs/10.1056/NEJMoa051424 22. Kondratiev S, Sabo E, Yakirevich E, Lavie O, Resnick MB. Intratumoral CD8+ T Lymphocytes as a Prog- nostic Factor of Survival in Endometrial Carcinoma. Clin Cancer Res [Internet]. 2004 Jul 1 [cited 2017 Jul 21];10(13):4450–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15240536 23. Brown SD, Warren RL, Gibb EA, Martin SD, Spinelli JJ, Nelson BH, et al. Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res. 2014;24(5):743–50. 24. Heemskerk B, Kvistborg P, Schumacher TNM. The cancer antigenome. EMBO J [Internet]. 2013;32(2):194–203. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3553384&tool=pmcentrez&ren- dertype=abstract%5Cnhttp://emboj.embopress.org/cgi/doi/10.1038/emboj.2012.333 25. Shia J, Black D, Hummer AJ, Boyd J, Soslow RA. Routinely assessed morphological features correlate with microsatellite instability status in endometrial cancer. Hum Pathol [Internet]. 2008 Jan [cited 2017 Jul 21];39(1):116–25. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0046817707002997 26. Vilar E, Gruber SB. Microsatellite instability in colorectal cancer—the stable evidence. Nat Rev Clin Oncol [Internet]. 2010 Mar 9 [cited 2017 Jul 21];7(3):153–62. Available from: http://www.ncbi.nlm.nih.gov/ pubmed/20142816 27. Hussein YR, Weigelt B, Levine D a, Schoolmeester JK, Dao LN, Balzer BL, et al. Clinicopathological analysis of endometrial carcinomas harboring somatic POLE exonuclease domain mutations. Mod Pathol [Internet]. 2014;1–10. Available from: http://www.nature.com/doifinder/10.1038/modpathol.2014.143 28. Nout RA, Smit VTHBM, Putter H, Jürgenliemk-Schulz IM, Jobsen JJ, Lutgens LCHW, et al. Vaginal brachytherapy versus pelvic external beam radiotherapy for patients with endometrial cancer of high-intermediate risk (PORTEC-2): an open-label, non-inferiority, randomised trial. Lancet. 2010;375(9717):816–23. 29. Creutzberg CL, van Putten WL, Koper PC, Lybeert ML, Jobsen JJ, Wárlám-Rodenhuis CC, et al. Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial. Lancet. 2000;355:1404–11. 30. Nout R a., Bosse T, Creutzberg CL, Jürgenliemk-Schulz IM, Jobsen JJ, Lutgens LCHW, et al. Improved risk assess- ment of endometrial cancer by combined analysis of MSI, PI3K-AKT, Wnt/β-catenin and P53 pathway activation. Gynecol Oncol [Internet]. 2012;126(3):466–73. Available from: http://dx.doi.org/10.1016/j.ygyno.2012.05.012
147 Chapter 7
31. Reimers MS, Engels CC, Putter H, Morreau H, Liefers GJ, van de Velde CJH, et al. Prognostic value of HLA class I, HLA-E, HLA-G and Tregs in rectal cancer: a retrospective cohort study. BMC Cancer [Inter- net]. 2014 Jul 5 [cited 2017 Jul 21];14(1):486. Available from: http://bmccancer.biomedcentral.com/ articles/10.1186/1471-2407-14-486 32. Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun [Internet]. 2013 Oct 11 [cited 2017 Jul 21];4:2612. Available from: http://www.nature.com/doifinder/10.1038/ncomms3612 33. Carter SL, Cibulskis K, Helman E, McKenna A, Shen H, Zack T, et al. Absolute quantification of somatic DNA alterations in human cancer. Nat Biotechnol [Internet]. 2012 Apr 29 [cited 2017 Jul 21];30(5):413–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22544022 34. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics [Internet]. 2011 Aug 4 [cited 2017 Jul 21];12(1):323. Available from: http://www. ncbi.nlm.nih.gov/pubmed/21816040 35. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol [Internet]. 2010 [cited 2017 Jul 21];11(10):R106. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20979621 36. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment anal- ysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A [Internet]. 2005 Oct 25 [cited 2017 Jul 21];102(43):15545–50. Available from: http://www.pnas.org/cgi/ doi/10.1073/pnas.0506580102 37. Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, et al. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS One. 2007;2(8). 38. Khalili JS, Hanson RW, Szallasi Z. In silico prediction of tumor antigens derived from functional missense mutations of the cancer gene census. Oncoimmunology [Internet]. 2012;1(November):1281–9. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3518500&tool=pmcentrez&rendertype=abstract 39. Duan F, Duitama J, Al Seesi S, Ayres CM, Corcelli S a, Pawashe AP, et al. Genomic and bioinformatic pro- filing of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J Exp Med [Internet]. 2014;211(11):2231–48. Available from: http://www.pubmedcentral.nih.gov/articleren- der.fcgi?artid=4203949&tool=pmcentrez&rendertype=abstract%5Cnhttp://www.ncbi.nlm.nih.gov/ pubmed/25245761%5Cnhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4203949 40. Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, et al. Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma. N Engl J Med [Internet]. 2014;2189–99. Available from: http://www.ncbi.nlm. nih.gov/pubmed/25409260 41. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, et al. Spatiotemporal dynamics of intra- tumoral immune cells reveal the immune landscape in human cancer. Immunity [Internet]. 2013 Oct 17 [cited 2017 Jul 21];39(4):782–95. Available from: http://linkinghub.elsevier.com/retrieve/pii/S1074761313004378 42. Trinh CH, Smith DP, Kalverda AP, Phillips SE V, Radford SE. Crystal structure of monomeric human beta-2-mi- croglobulin reveals clues to its amyloidogenic properties. Proc Natl Acad Sci U S A [Internet]. 2002 Jul 23 [cited 2017 Jul 21];99(15):9771–6. Available from: http://www.pnas.org/cgi/doi/10.1073/pnas.152337399 43. Brahmer JR, Tykodi SS, Chow LQM, Hwu W-J, Topalian SL, Hwu P, et al. Safety and Activity of Anti–PD-L1 Antibody in Patients with Advanced Cancer. N Engl J Med [Internet]. 2012 Jun 28 [cited 2017 Jul 21];366(26):2455–65. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22658128 44. Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, Activity, and Immune Correlates of Anti–PD-1 Antibody in Cancer. N Engl J Med [Internet]. 2012 Jun 28 [cited 2017 Jul 21];366(26):2443–54. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22658127 45. Hodi FS, O’Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, et al. Improved survival with ipilimumab
148 Immunity of POLE-mutant endometrial cancer
in patients with metastatic melanoma. N Engl J Med [Internet]. 2010 Aug 19 [cited 2016 Aug 8];363(8):711–23. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20525992 46. Tumeh P, Harview C, Yearley J, Al E. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515(7528):568–71. 47. Yadav M, Jhunjhunwala S, Phung QT, Lupardus P, Tanguay J, Bumbaca S, et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature [Internet]. 2014 Nov 27 [cited 2017 Jul 21];515(7528):572–6. Available from: http://www.nature.com/doifinder/10.1038/nature14001 48. Gros A, Robbins PF, Yao X, Li YF, Turcotte S, Tran E, et al. PD-1 identifies the patient-specific CD8+ tumor-re- active repertoire infiltrating human tumors. J Clin Invest. 2014;124(5):2246–59. 49. Speiser DE, Utzschneider DT, Oberle SG, Münz C, Romero P, Zehn D. T cell differentiation in chronic infection and cancer: functional adaptation or exhaustion? Nat Publ Gr [Internet]. 2014;(September):1–7. Available from: http://dx.doi.org/10.1038/nri3740 50. Diaz-Padilla I, Romero N, Amir E, Matias-Guiu X, Vilar E, Muggia F, et al. Mismatch repair status and clinical outcome in endometrial cancer: a systematic review and meta-analysis. Crit Rev Oncol Hematol [Inter- net]. 2013 Oct [cited 2017 Jul 21];88(1):154–67. Available from: http://linkinghub.elsevier.com/retrieve/pii/ S1040842813000620 7
149
CHAPTER 8
Immunological profiling of molecularly classified high-risk endometrial cancers identifies POLE-mutant and microsatellite unstable carcinomas as candidates for checkpoint inhibition
Eggink F.A., Van Gool I.C., Leary A., Pollock P.M., Crosbie E.J., Mileshkin L., Jordanova E.S., Adam J., Freeman-Mills L., Church D.N., Creutzberg C.L., De Bruyn M., Nijman H.W., Bosse T.
Oncoimmunology. 2016 Dec 9;6(2):e1264565 Chapter 8
ABSTRACT
Objectives High-risk endometrial cancer (EC) is an aggressive disease for which new therapeutic options are needed. Aims of this study were to validate the enhanced immune response in highly mutated ECs and to explore immune profiles in other EC subgroups.
Methods We evaluated immune infiltration in 116 high-risk ECs from the TransPORTEC consortium, previ- ously classified into four molecular subtypes: (i) ultramutatedPOLE exonuclease domain-mutant ECs (POLE-mutant); (ii) hypermutated microsatellite unstable (MSI); (iii) p53-mutant; and (iv) no specific molecular profile (NSMP).
Results Within The Cancer Genome Atlas (TCGA) EC cohort, significantly higher numbers of predicted neo- antigens were demonstrated in POLE-mutant and MSI tumors compared to NSMP and p53-mutants. This was reflected by enhanced immune expression and infiltration inPOLE -mutant and MSI tumors in both the TCGA cohort (mRNA expression) and the TransPORTEC cohort (immunohistochemistry) with high infiltration of CD8+ (90% and 69%), PD-1+ (73% and 69%) and PD-L1+ immune cells (100% and 71%). Notably, a subset of p53-mutant and NSMP cancers were characterized by signs of an antitumor immune response (43% and 31% of tumors with high infiltration of CD8+ cells, respectively), despite a low number of predicted neoantigens.
Conclusion In conclusion, the presence of enhanced immune infiltration, particularly high numbers of PD-1 and PD-L1 positive cells, in highly mutated, neoantigen-rich POLE-mutant and MSI endometrial tumors suggests sensitivity to immune checkpoint inhibitors.
152 Immunity of high risk endometrial cancer
INTRODUCTION
The development of novel immunotherapeutic strategies such as checkpoint inhibitors has the poten- tial to transform the field of oncology. So far, durable responses have been established in subsets of patients, for example with metastatic melanoma, non-small cell lung cancer, and mismatch repair-de- ficient cancers including two patients with endometrial cancer (EC) 1–7. While the clinical efficacy of immune checkpoint inhibitors is evident in a subset of patients, selecting the patients who may ben- efit from this therapy remains challenging. A key mechanism for the benefit of immune checkpoint inhibition in these cancers is the induction of a strong neoantigen-driven T-cell response against the tumor. Indeed, comprehensive analysis of large genomic datasets such as The Cancer Genome Atlas (TCGA) have provided a clear link between mutational load and activation of the immune system, implicating the involvement of neoantigens in driving cytotoxic T-cell responses in cancer 8–10. Further- more, several clinical trials have shown a strong association between the presence of high numbers of predicted neoantigens, immune infiltration and response to cancer immunotherapy11–15. In particular, the presence of CD8+ cytotoxic T-cells and expression of the immune checkpoints PD-1 and PD-L1 8 have been proposed as important predictors of objective tumor regression3,16.
Characterization of the immune contexture of individual tumors may provide guidance in selecting appropriate immunotherapy for each individual patient, especially when integrated with an analysis of genomic alterations10,17,18. A molecular classification has recently been proposed by The Cancer Genome Atlas (TCGA), which identified four genomically distinct EC subgroups: an ultramutated group characterized by somatic mutations in the exonuclease domain of POLE (encoding the catalytic sub- unit of DNA polymerase epsilon), a microsatellite unstable (MSI) hypermutated group with many substitutions as well as insertions and deletions due to mismatch repair deficiency, a copy-number high (serous-like) group with frequent TP53 mutation and a copy-number low (microsatellite stable (MSS)) group with no specific molecular profile (NSMP)19.
In line with this, we, and others, have recently demonstrated high numbers of predicted immuno- genic mutations and enhanced anti-tumor immune infiltration in ultramutatedPOLE -mutant and, to a lesser extent, in hypermutated microsatellite unstable EC 20–23. These studies combined with the emerging data linking mutational load, immune activation and response to cancer immunotherapy render POLE-mutated and MSI cancers plausible candidates for immune checkpoint inhibition3,10–13,24. This is further underlined by recent case reports demonstrating the efficacy of anti-PD-1 inhibi- tors in advanced POLE-mutant or mismatch repair deficient cancers, including those of endometrial origin7,25,26.
In the current study, we aimed to validate our previous findings of an enhanced immune response in POLE-mutant and MSI endometrial cancers in a cohort of high-risk patients. High-risk EC patients are a particularly relevant subgroup, as most have no or only very modest gain from standard local or
153 Chapter 8
systemic treatment after surgery. Novel treatment options are therefore urgently needed. The use of a molecularly defined cohort of high-risk endometrial cancer also enabled us to explore the immune profiles of the poorly characterized NMSP and p53-mutant subgroups. With this approach we provide a rationale for the administration of checkpoint inhibition strategies in subsets of POLE-mutant and MSI endometrial cancer patients.
154 Immunity of high risk endometrial cancer
METHODS
Selection of patients and tissues A previously described cohort of 116 high-risk EC patients was used in the current study (Table 1)42. In brief, tumor tissues from high-risk EC patients were selected from partner institutions of the TransPORTEC consortium using inclusion criteria of the PORTEC-3 study. Patients included in the PORTEC-3 had EC with one of the following FIGO 2009 stages and grade: 1A grade 3 with myometrial- and lymph vascular space invasion; IB grade 3; II, IIIA or IIIC; IIIB if only parametrial invasion; stage IA (with invasion), 1B, II or III with serous or clear cell histology51.
Construction of Tissue Microarray Morphologically representative paraffin-embedded tissue blocks containing at least 70% tumor cells were selected by two experienced gyneco-pathologists (VS and TB). The selected tumor blocks were used to construct (and validate) a Tissue Microarray (TMA) as previously described42. One millime- ter-sized tumor (center of the tumor) and tumor/stroma (invasive margin) cores of each tumor block 8 were randomly distributed on the TMA in triplicate.
Assessment of POLE, MSI, p53 and NSMP status Classification of patients into the four molecular subgroups was performed as previously described42. In brief, tumor DNA isolation was performed fully automated using the Tissue Preparation System (Siemens Healthcare Diagnostics)52. Bi-directional Sanger sequencing was used to screen exons 9, 13 and 14 of the POLE exonuclease domain for somatic mutations. Microsatellite instability and p53 mutational status were determined as previously described 42,53.
Immunohistochemistry TMA sections were deparaffinized and rehydrated. Antigen retrieval was performed using 0.01M citrate buffer pH 6.0, and endogenous peroxidase activity was blocked. Slides were incubated overnight at room temperature (CD3, TIA-1, T-Bet and PD-1), for one hour at room tempera- ture (CD8, CD20) or overnight at 4°C (CD103) with primary antibodies against CD3 (1:100, clone PS-1, Diagnostic BioSystems), CD8 (1:50, clone C8/144B, DAKO), CD20 (1:200, clone L26, DAKO), CD103 (1:200, Integrin αEβ7, Abcam), TIA-1 (1:200, clone 2G9A10F5, Beckman Coulter), T-bet (1:400 in 10% normal goat serum, sc-21003, Santa Cruz Biotechnology), PD-1 (1:200, AF1086, R&D), and PD-L1 (1 µg/mL, clone E1L3N, Cell Signalling Technology). Slides were incubated with BrightVision Poly-HRP (poly-HRP-GAM/R/R, DPV0110HRP, Immunologic; CD3, TIA-1, T-bet), a goat HRP-polymer kit (GHP516H, Biocare Medical; PD-1), anti-mouse secondary antibody (K4007, DAKO, CD8, CD20) or anti-rabbit secondary antibody (K4011, DAKO, CD103) for 30 minutes. For CD103, a slightly different method using avidin/biotin blocking was used as described previously 54. PD-L1 staining was performed using the Ventrana Discovery Ultra Platform for automatic stain- ing, detection was performed using the Discovery Amp-HQ kit (tyramide-based amplification).
155 Chapter 8
Table 1. Clinicopathological characteristics of the high-risk endometrial cancer patient cohort. All patients POLE-mutant MSI NSMP p53-mutant N = 116 N = 15 N = 19 N = 42 N = 40 N % N % N % N % N % P-value Age at diagnosis (years) Mean (range) 66 (21-85) 61 (49-80) 65 (49-82) 64 (21-84) 71 (45-85) 0.004
Stage I 42 36.2 8 53.3 5 26.3 16 38.1 13 32.5 0.246 II 21 18.1 3 20.0 2 10.5 12 28.6 4 10.0 III 41 35.3 3 20.0 10 52.6 11 26.2 17 42.5 IV 11 9.5 1 6.7 2 10.5 3 7.1 5 12.5 Unknown 1 0.9 0 0.0 0 0.0 0 0.0 1 2.5
Tumor type Endometrioid 86 74.1 14 93.3 17 89.5 35 83.3 20 50.0 <0.001 Serous 12 10.3 0 0.0 0 0.0 0 0.0 12 30.0 Clearcell 18 15.5 1 6.7 2 10.5 7 16.7 8 20.0
Grade 1 13 11.2 0 0.0 2 10.5 8 19.0 3 7.5 0.036 2 5 4.3 1 6.7 3 15.8 1 2.4 0 0.0 3 98 84.5 14 93.3 14 73.7 33 78.6 37 92.5
Lymph vascular space invasion Yes 55 47.4 6 40 15 78.9 18 42.9 16 40 0.103 No 40 34.5 9 60.0 2 10.5 18 42.9 11 27.5 Unknown 21 18.1 0 0.0 2 10.5 6 14.3 13 32.5
Depth of myometrial invasion <50% 23 19.8 4 26.7 2 10.5 6 14.3 11 27.5 0.261 >50% 87 75.0 11 73.3 17 89.5 33 78.6 26 65.0 Unknown 6 5.2 0 0.0 0 0.0 3 7.1 3 7.5
Adjuvant therapy Yes 82 70.7 14 93.3 15 78.9 33 78.6 20 50.0 0.134 No 10 8.6 1 6.7 1 5.3 2 4.8 6 15.0 Unknown 24 20.7 0 0.0 3 15.8 7 16.7 14 35.0 Characteristics are shown for the whole group, as well as for each of the molecular subgroups analyzed. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile.
156 Immunity of high risk endometrial cancer
Table 1. Clinicopathological characteristics of the high-risk endometrial cancer patient cohort. All patients POLE-mutant MSI NSMP p53-mutant N = 116 N = 15 N = 19 N = 42 N = 40 N % N % N % N % N % P-value Age at diagnosis (years) Mean (range) 66 (21-85) 61 (49-80) 65 (49-82) 64 (21-84) 71 (45-85) 0.004
Stage I 42 36.2 8 53.3 5 26.3 16 38.1 13 32.5 0.246 II 21 18.1 3 20.0 2 10.5 12 28.6 4 10.0 III 41 35.3 3 20.0 10 52.6 11 26.2 17 42.5 IV 11 9.5 1 6.7 2 10.5 3 7.1 5 12.5 Unknown 1 0.9 0 0.0 0 0.0 0 0.0 1 2.5
Tumor type Endometrioid 86 74.1 14 93.3 17 89.5 35 83.3 20 50.0 <0.001 8 Serous 12 10.3 0 0.0 0 0.0 0 0.0 12 30.0 Clearcell 18 15.5 1 6.7 2 10.5 7 16.7 8 20.0
Grade 1 13 11.2 0 0.0 2 10.5 8 19.0 3 7.5 0.036 2 5 4.3 1 6.7 3 15.8 1 2.4 0 0.0 3 98 84.5 14 93.3 14 73.7 33 78.6 37 92.5
Lymph vascular space invasion Yes 55 47.4 6 40 15 78.9 18 42.9 16 40 0.103 No 40 34.5 9 60.0 2 10.5 18 42.9 11 27.5 Unknown 21 18.1 0 0.0 2 10.5 6 14.3 13 32.5
Depth of myometrial invasion <50% 23 19.8 4 26.7 2 10.5 6 14.3 11 27.5 0.261 >50% 87 75.0 11 73.3 17 89.5 33 78.6 26 65.0 Unknown 6 5.2 0 0.0 0 0.0 3 7.1 3 7.5
Adjuvant therapy Yes 82 70.7 14 93.3 15 78.9 33 78.6 20 50.0 0.134 No 10 8.6 1 6.7 1 5.3 2 4.8 6 15.0 Unknown 24 20.7 0 0.0 3 15.8 7 16.7 14 35.0 Characteristics are shown for the whole group, as well as for each of the molecular subgroups analyzed. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile.
157 Chapter 8
Antibody binding was visualized with 3,3’-diamino-benzidine-tetrahydrochloride (DAB) and haematox- ylin counterstaining. Slides were dehydrated and mounted before digitalization (Ultra Fast Scanner 1.6 RA. Philips or ScanScope, Aperio technologies) and analysis.
Quantification of IHC Total numbers of CD3+, CD8+, CD103+, CD27+, TIA-1+, T-Bet+, CD20+, CD45RO+ and PD-1+ cell numbers were quantified per core. The percentage of tumor and stroma surface area within each core were estimated, and used to extrapolate cell counts to 100% surface area. Cores taken from the tumor center were included in the analysis if at least 2 out of the 3 cores contained >20% tumor. Cores from the infiltrative margin were included in the analysis if at least 2 out of the 3 cores contained >20% stroma and if there was tumor tissue present. Average cell counts per 100% surface area were recorded for the tumor center and infiltrative margin. Slides were counted manually by two individuals (FE and IG) that were blinded for other clinicopathological data. Inter-observer variation was evaluated by Spearman rank correlation (median R2 0.935, range 0.682-0.988).
Quantification of PD-L1 was evaluated on tumor-infiltrating immune cells and tumor cells as previously described1. In brief, the proportion of PD-L1 expressing tumor cells (tumor score) was noted as a percentage of the total number of tumor cells within that core. Due to very low expression of PD-L1 it was decided to consider any expression of PD-L1 on tumor cells as positive. Furthermore, the per- centage of tumor-infiltrating immune cells (immune score) with moderate to strong PD-L1 expression was registered. Immune cells were defined positive when cells displayed clearly visible cytoplasmic and/or membranous staining. Patients were included in the analysis if at least 2 out of 3 cores were evaluated; the final score was based on the core with the highest PD-L1 expression. For the analyses of the immune score, PD-L1 positivity was defined as >1% (based on the median score in the cohort).
Immunofluorescence Three combinations of multi-color immunofluorescent stainings were performed as described previ- ously55. The first combination consisted of anti-CD163 (polyclonal rabbit, ab87099, Abcam), anti-CD68 (monoclonal mouse IgG2a, clone 514H12, ABDserotec) and anti-keratin (monoclonal mouse IgG1, clone AE1/AE3, MAB3412, Milipore). The second combination consisted of anti-PD-L1 (polyclonal rabbit, clone SP142, Roche),and anti-PD1 (monoclonal mouse IgG1, clone NAT105, Abcam), and the third of anti-CD8 (mouse monoclonal IgG2b, clone 4B11, Novo Castra) and anti-PD-1 (polyclonal goat, R&D systems).
In short, after slides were deparaffinized and rehydrated, antigen retrieval was achieved by microwave oven treatment in a Tris-EDTA buffer at pH 9.0. Slides were incubated with the listed primary antibod- ies overnight. The following secondary Alexa Fluor labeled antibodies were used for the CD163 – CD68 – keratin and PD-L1 – PD-1 combinations: 647 goat anti-rabbit, 546 goat anti-mouse IgG2a, and 488 goat anti-mouse IgG1 (all from Invitrogen, Life Technologies, Carlsbad, USA). Donkey anti-goat 488
158 Immunity of high risk endometrial cancer
and donkey anti-mouse IgG 647 were used for PD-1/CD8 detection. The slides were counterstained with DAPI and cover-slipped. Immunofluorescent images were acquired with an LSM700 confocal laser scanning microscope equipped with an LCI Plan-Neofluar 25x/0.8 Imm Korr DIC M27 objective (Zeiss, Göttingen, Germany). Double or triple positivity of cells in the center of the tumor as well as at the invasive margin was determined using LSM Image Browser (version 4.2.0.121, Zeiss). Images from the two triple immunofluorescent stainings were merged using Adobe Photoshop CS6.
TCGA RNA sequencing TCGA RNAseq analysis was performed as previously reported19,20. Data were downloaded from Fire- Browse on November 11, 2014 (http://firebrowse.org/?cohort=UCEC&download_dialog=true). In total, 245 samples with RSEM normalized data were available for analysis.
Prediction of antigenic neoepitopes Prediction of antigenic neoepitopes was performed as previously reported 20. In brief, an algorithm was developed to estimate the immunogenicity of individual tumors in which the following con- 8 siderations were taken into account: i) to generate a functional neoepitope a missense mutation must be expressed; ii) most functional neoepitopes are predicted to bind Major Histocompatibility Complex (MHC) class I molecules (IC50 < 500nM) by NetMHCPan8,56,57; iii) the likelihood that a neoepi- tope is antigenic is reduced if the corresponding wild-type peptide also binds the MHC with similar affinity as T-cells to the epitope may be centrally deleted or tolerized 58. Our strategy was similar to that reported by others 8,13,57,59. For each tumor all possible 9mers for every missense mutation in expressed genes (defined as non-zero reads from RNAseq) and the binding affinity of the mutant and corresponding wild-type peptide for HLA-A*02:01 were calculated using NetMHCPan 2.8 56. If several peptides had an IC50 <500nM, the strongest binder was used for analysis. We defined antigenic mutations as neoepitopes predicted to bind MHC molecules (IC50 < 500nM) for which the corresponding wild-type peptide was not predicted to bind MHC (IC50 > 500nM).
Statistical methods Comparison between clinicopathological characteristics of the four molecular subgroups was made using Kruskal-Wallis followed by Man-Whitney U (for age) and Chi2 tests (for all other variables). Cor- relations between immunohistochemical stainings and the four molecular subgroups were evaluated using Kruskal-Wallis followed by Mann-Whitney U tests. The same method was used to evaluate correlations between RNA expression from the TCGA cohort of immune-related genes and the four molecular subgroups. Additionally, analyses were performed combining POLE-mutant and MSI sam- ples versus NSMP and p53-mutant samples. All tests were performed two-sided. Significance was defined as a P-value of <0.05. Statistical analyses were performed using IBM SPSS version 22 (SPSS Inc., Chicago, USA) and GraphPad Prism (GraphPad Software Inc., CA, USA).
159 Chapter 8
RESULTS
Enhanced infiltration of intratumoral CD3+, CD8+ and CD103+ lymphocytes in POLE-mutant and MSI tumors We first sought to characterize the lymphocytic infiltrate in the four EC molecular subtypes by immu- nohistochemical analysis of CD3+, CD8+, CD103+ and CD20+ (Fig 1A-B). Compared to NSMP and p53-mutant tumors, both POLE-mutant and MSI tumors demonstrated increased density of CD3+ T-lymphocytes within the tumor center (POLE vs NSMP p=0.002, MSI vs NSMP p=0.001, MSI vs p53 p=0.018). Staining for cytotoxic T-lymphocyte marker CD8+ and the intraepithelial T-lymphocyte marker CD103+ revealed similarly increased infiltrate in the tumor center (comparison of CD8+ cells: POLE vs NSMP p<0.001; POLE vs p53 p=0.021; MSI vs NSMP p=0.016, comparison of CD103+ cells: POLE vs MSI p=0.023; MSI vs NSMP p=0.035; MSI vs p53 p=0.030). Based on a median of 80.5 CD8+ cells/core in the whole cohort, 90% of POLE-mutant, 69% of MSI, 31% of NSMP and 43% of p53-mutant tumors were categorized as highly infiltrated with CD8+ cells. There was no difference in numbers of CD20+ B-lymphocytes within the tumor center. A combined analysis in which the two molecular subgroups with a high expected neoantigen load (POLE-mutant and MSI) were compared with the two molecular subgroups with lower expected neoantigen load (NSMP and p53-mutant), supported the apparent differences in immune infiltrate between EC subtypes (Fig 2A).
Within the infiltrative margin, CD3+, CD8+, CD103+ or CD20+ infiltration did not significantly differ between the four molecular subgroups (Fig 1C). Combined analysis showed a higher infiltration of CD8+ and CD103+ in POLE-mutant and MSI (CD8 p=0.010, CD103 p=0.016, Fig 2B).
160 Immunity of high risk endometrial cancer
Figure 1. Infiltration of CD3+, CD8+, CD103+ and CD27+ cells in POLE-mutant, MSI, NSMP and p53-mutant endometrial cancers.
A CD3 CD8 CD103 CD20
B Tumor center 1145 172 * * 2000 1000 * 600 ** *** * ** * 60
e *
r 800 1500 o
c 400 / 40
s 600 l l 1000 400 200 20 500 200
Stained c e 0 0 0 0 13 13 21 22 10 16 29 30 10 15 24 28 10 13 25 27 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53 8 C Infiltrative margin
1000 500 e r 2000 o 800 400 200 c / s
l 1500 l 600 300 e
1000 400 200 100
500 200 100 Stained c 0 0 0 0 3 7 9 6 4 8 28 19 3 4 23 19 2 2 21 21 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53
A, Representative immunohistochemical stainings of CD3+, CD8+, CD103+ and CD20+ cells. B, Average number of positively stained intratumoral cells for each of the markers in the above panel, counted per core, corrected for the number of cells present. C, Average number of positively stained cells for each of the markers in the above panel, counted per core within the infiltrative margin, corrected for the number of cells present. The numbers of cases analyzed for each molecular subgroup are listed below the x-axis. Boxes represent the interquartile range (IQR), with the upper whisker indicating the 75th percentile and the lower whisker the 25th percentile. The median and mean values are indicated by a horizontal line and cross, respectively. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile; p53, p53-mutant. * = p<0.05, **= p<0.01, ***= p<0.001.
161 Chapter 8
Figure 2. Infiltration of CD3+, CD8+, CD103+ and CD20+ cells in POLE-mutant/MSI compared to NSMP/p53-mutant endometrial cancers.
CD3 CD8 CD103 CD20
A Tumor center 1145 172
800 ** 600 * *** 60 1500 e 600 r
o 400 c / 1000 40 s
l 400 l 200 20 500 200
0 0 0 0 Stained c e 26 43 26 59 25 52 23 52 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53
B Infiltrative margin * 2500 1000 * 500
e 400 r 2000 800 200 o c / 300
s 1500 600 l l e
c 1000 400 200 100
500 200 100
Stained 0 0 0 0 10 15 12 47 7 42 4 42 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53
A, Average number of positively stained cells for each of the markers in the above panel, counted per core within the tumor center, corrected for the number of cells present. B, Average number of positively stained cells for each of the markers in the above panel, counted per core within the infiltrative margin, corrected for the number of cells present. The numbers of cases analyzed for each molecular subgroup are listed below the x-axis. Boxes represent the interquartile range (IQR), with the upper whisker indicating the 75th percentile and the lower whisker the 25th percentile. The median and mean values are indicated by a horizontal line and cross, respectively. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile; p53, p53-mutant.* = p<0.05, **= p<0.01, ***= p<0.001.
Increased infiltration of CD45RO+ and TIA-1+ lymphocytes in MSI tumors To analyze the function of the tumors lymphocytic infiltrate, we performed immunohistochemis- try for CD45RO, CD27, T-Bet and TIA-1 (Fig 3A-B). Within the tumor center, MSI tumors contained more CD45RO+ memory T-lymphocytes compared to NSMP and p53-mutant tumors (MSI vs NSMP p=0.029, MSI vs p53 p=0.008). MSI tumors also harbored more TIA-1+ cytolytic lymphocytes within the tumor center (MSI vs NSMP p=0.019, MSI vs p53 p=0.043). There were no differences in the num- bers of CD27+ naïve T-cells and T-Bet+ differentiated cells between the four molecular subgroups. Combined analysis of molecular groups revealed the presence of more CD45RO+ and TIA-1+ cells in POLE-mutant/MSI tumors compared to NSMP/p53-mutant tumors (Fig 4A). Moreover, this also demonstrated higher numbers of T-Bet+ differentiated cells within POLE-mutant/MSI tumors com- pared to NSMP/p53-mutant tumors (p=0.021).
162 Immunity of high risk endometrial cancer
Concordant with our findings in the tumor center, the infiltrative margin of MSI tumors contained more CD45RO+ lymphocytes (MSI vs NSMP p=0.002, MSI vs p53 p=0.003) and more TIA-1+ cytolytic T-lymphocytes (MSI vs NSMP p=0.002, Fig 3C). NSMP tumors demonstrated more TIA-1+ lymphocytes compared to p53-mutant tumors (NSMP vs p53 p=0.023). The numbers of CD27+ and T-Bet+ cells did not significantly differ between the four molecular subgroups. Data from the combined analyses sup- ported the increased density of CD45RO+ and TIA-1+ cells within POLE-mutant/MSI tumors (Fig 4B).
Figure 3. Infiltration of TIA-1+, T-Bet+, CD20+ and CD45RO+ cells inPOLE -mutant, MSI, NSMP and p53-mutant endometrial cancers.
A CD45RO CD27 T-Bet TIA-1
8
B Tumor center
1370 500 ** * 1000 * 400
e 600 *
r 400
o 800 c
/ 300
s 300 l 400 600 l 200 200 400 200 100 100 200
Stainedl c e 0 0 0 0 9 16 27 28 10 14 25 28 11 16 32 29 12 16 30 33 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53
C Infiltrative Margin
500 400 ** 1000 ** 600 **
e 400 * r 300 800 o c / 300 s 400 600 l l 200 e
c 200 400 200 100 100 200
Stained 0 0 0 0 3 7 26 19 6 3 20 15 6 8 23 20 4 7 19 15 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53
A, Representative immunohistochemical stainings of CD45RO+, CD27+, T-Bet+ and TIA-1+ cells. B, Average number of positively stained intratumoral cells for each of the markers in the above panel, counted per core within the tumor center, corrected for the number of cells present. C, Average number of positively stained cells for each of the markers in the above panel, counted per core within the infiltrative margin, corrected for the number of cells present. The numbers of cases analyzed for each molecular subgroup are listed below the x-axis. Boxes represent the interquartile range (IQR), with the upper whisker indicating the 75th percentile and the lower whisker the 25th percentile. The median and mean values are indicated by a horizontal line and cross, respectively. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile; p53, p53-mutant. * = p<0.05, **= p<0.01, ***= p<0.001.
163 Chapter 8
Figure 4. Infiltration of CD45RO+, CD27+, T-Bet+ and TIA-1+ cells in POLE-mutant/MSI compared to NSMP/p53-mutant endometrial cancers.
CD45RO CD27 T-Bet TIA-1 A Tumor center
1370 500 ** * 1000 ** 400 600 e r 400 800 o
c 300 / 300 s
l 400 600 l 200 200 400 200 100 100 200
Stained c e 0 0 0 0 25 55 24 53 27 61 28 63 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53
B Infiltrative margin
400 *** 400 1000 * 600 e r 300 800 o 300 c /
s 400
l 600 l 200
e 200 c 400 200 100 100 200
Stained 0 0 0 0 10 45 9 35 14 43 11 34 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53
A, Average number of positively stained cells for each of the markers in the above panel, counted per core within the tumor center, corrected for the number of cells present. B, Average number of positively stained cells for each of the markers in the above panel, counted per core within the infiltrative margin, corrected for the number of cells present. The numbers of cases analyzed for each molecular subgroup are listed below the x-axis. Boxes represent the interquartile range (IQR), with the upper whisker indicating the 75th percentile and the lower whisker the 25th percentile. The median and mean values are indicated by a horizontal line and cross, respectively. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile; p53, p53-mutant.* = p<0.05, **= p<0.01, ***= p<0.001.
164 Immunity of high risk endometrial cancer
Increase in infiltration of PD-1+ and PD-L1+ lymphocytes in POLE-mutant and MSI tumors The increased lymphocytic infiltrate of POLE-mutant and MSI tumors, in combination with their expected ultramutated (POLE-mutant tumors) or hypermutated (MSI tumors) status, prompted us to investigate the presence of PD-1+ and PD-L1+ cells within this cohort (Fig 5A).
The tumor center of POLE-mutant and MSI tumors harbored high numbers of PD-1+ immune cells (POLE vs NSMP p<0.001, POLE vs p53 p=0.050, and MSI vs NSMP p=0.003, Fig 5B). This was supported by the combined analysis (Fig 6A). Based on a median of 14.0 PD-1+ cells/core in all patients, 73% of POLE-mutant, 69% of MSI, 31% of NSMP and 48% of p53-mutant tumors were categorized as highly infiltrated with PD-1+ cells.
POLE-mutant and MSI tumors showed markedly increased infiltration of PD-L1+ immune cells within the tumor center compared to NSMP and p53-mutant tumors (POLE vs NSMP p<0.001, POLE vs p53 p<0.001, MSI vs NSMP p<0.001, MSI vs p53 p=0.002, Fig 5C). The combined analysis showed similar POLE results (Fig 6B). In total, 100% of -mutant, 71% of MSI, 18% of NSMP and 29% of p53-mutant 8 tumors were categorized as PD-L1-positive (based on the immune score). Strikingly, only one tumor sample, a p53-mutant EC, contained PD-L1 expressing tumor cells (noted as a positive tumor score, data not shown).
Within the infiltrative margin, only thePOLE- mutant subgroup showed high densities of PD-1+ immune cells (POLE vs NSMP p=0.008, POLE vs p53 p=0.007, Fig 5D). Combined analysis supported the pres- ence of high numbers of PD-1+ cells within the POLE-mutant/MSI group compared to the NSMP/ p53-mutant group (Fig 6C).
165 Chapter 8
Figure 5. Infiltration of PD-1+ and PD-L1+ cells inPOLE -mutant, MSI, NSMP and p53-mutant endo- metrial cancers. PD-1 PD-L1 A
B Tumor center C e r
742 o c *** 300 *** * 60 **
e *** r ** *** o
c 200 / 40 s l l
100 20 % of tumor infiltrating PD-L1+ immune cells / Stained c e 0 0 11 16 26 31 12 17 34 31 POLE MSI NSMP p53 POLE MSI NSMP p53
D Infiltrative margin ** ** 300 e r o c /
s 200 l l e c 100
Stained 0 4 10 27 22 POLE MSI NSMP p53 A, Representative immunohistochemical stainings of PD-1+ and PD-L1+ cells. B, Average number of PD1+ cells counted per core within the tumor center, corrected for the number of cells present. C, Percentage of PD-L1-positive tumor-infiltrating immune cells within the tumor core and infiltrative margin core. D, Average number of PD1+ stained cells counted per core within the infiltrative margin. The numbers of cases analyzed for each molecular subgroup are listed below the x-axis. Boxes represent the interquartile range (IQR), with the upper whisker indicating the 75th percentile and the lower whisker the 25th percentile. The median and mean values are indicated by a horizontal line and cross, respectively. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile; p53, p53-mutant. * = p<0.05, **= p<0.01, ***= p<0.001.
PD-L1 is preferentially expressed on myeloid cells Recently, several studies have shown PD-L1 expression on tumor-associated myeloid cells1,27–30. Therefore, to determine whether this was also the case for our cohort, we performed two multi- color immunofluorescence stainings on consecutive whole slides of a highly infiltratedPOLE- mutant tumor sample using the following combinations of monoclonal antibodies: CD68 - CD163 – epithelial cell marker cytokeratin, and PD-L1 - PD-1 respectively (Fig 7). CD68+ and/or CD163+ myeloid cells
166 Immunity of high risk endometrial cancer
(including macrophages and myeloid dendritic cells) were found in the stromal regions within the center of the tumor, demarcated by the cytokeratin+ tumor cells (Fig 7A). PD-1+ and PD-L1+ cells were seen in close proximity, also predominantly located in the intratumoral stromal areas (Fig 7B). A co-immunofluorescent staining of PD-1 and CD8 shows frequent co-localization, indicating that PD-1 can be expressed by (cytotoxic) T cells (Fig 8). PD-L1 expression co-localized with CD68 and CD163, supporting the idea that in our cohort PD-L1 is not mainly expressed by tumor cells but by myeloid cells (Fig 7C and D).
Figure 6. Infiltration of PD-1+ and PD-L1+ cells inPOLE -mutant/MSI compared to NSMP/p53-mu- tant endometrial cancers.
PD-1 PD-L1
A Tumor center B e r
742 o c 60 150 ** *** e r
o 40
c 100 / s
l 8 l
50 20 % of tumor infiltrating PD-L1+ immune cells / Stained c e 0 0 27 57 29 65 POLE/MSI NSMP/p53 POLE/MSI NSMP/p53
C Infiltrative margin
*
e 300 r o c / s
l 200 l e c
100
Stained 0 14 49 POLE/MSI NSMP/p53
A, Average number of PD1+ stained cells counted per core within the tumor center, corrected for the number of cells present. B, Percentage of tumor-infiltrating immune cells with moderate to strong PD-L1 expression per core within cores taken from the tumor and infiltrative margin, corrected for the number of cells present. C, Average number of PD1+ stained cells counted per core within the infiltrative margin, corrected for the number of cells present. The number of cases analyzed for each molecular subgroup are listed below the x-axis. Boxes represent the interquartile range (IQR), with the upper whisker indicating the 75th percentile and the lower whisker the 25th percentile. The median and mean values are indicated by a horizontal line and cross, respectively. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile; p53, p53-mutant.* = p<0.05, **= p<0.01, ***= p<0.001.
167 Chapter 8
Figure 7. Immunofluorescent stainings of PD-1, PD-L1 and myeloid markers. A CD68/CD163/cytokeratin CD68 cytokeratin CD163
B PD-L1 PD-1 PD-1/PD-L1
C D CD68/CD163/cytokeratin/PD-L1 CD163 PD-L1
CD68 cytokeratin
Representative image of a POLE-mutant endometrial cancer stained with in A, keratin (green) - CD163 (blue) - CD68 (red) in B, keratin (green) - CD163 (blue) - PD-1 (green) - PD-L1 (blue). The two triple immunofluorescent stainings from A and B, performed on sequentially cut slides, are layered in C, with single channel markers for the inset in D, with keratin (green), PD-L1 (blue), CD68 (red) and CD163 (yellow), demonstrating the co-localization of PD-L1 with myeloid markers.
168 Immunity of high risk endometrial cancer
Figure 8. Immunofluorescent staining of CD8 and PD-1. CD8 PD-1 CD8/PD-1
Representative image of a POLE-mutant endometrial cancer stained with CD8 and PD-1, demonstrating co-localization of CD8 and PD-1 on immune cells. TCGA RNA sequencing data demonstrates higher expression of CD8A, CD3E, ITGAE (CD103), MS4A1 (CD20), PTPRC (CD45RO), CD27,TBX21 (T-Bet) and PDCD1 (PD-1) in POLE-mutant and MSI tumors
Next, we compared our data with the expression of above-described immune markers in The Cancer Genome Atlas (TCGA) cancer cohort, which was originally used to devise the molecular classification 8 of EC (Fig 9)19. Previously we have shown higher expression of, among others, CD3E, CD8A, TBX21 (T-Bet) and PDCD1 (PD-1) in POLE-mutant compared to MSI and MSS tumors20. We now extend this analysis to specifically compare the four proposed prognostic subgroups19. Of the 244 informative samples, the TCGA cohort included 18 POLE-mutant, 69 MSI, 96 NSMP and 62 TP53-mutant. Analysis of the RNA sequencing data of this cohort demonstrated higher expression of CD8A, CD3E, ITGAE (CD103), MS4A1 (CD20), PTPRC (CD45RO), CD27,TBX21 (T-Bet) and PDCD1 (PD-1) in POLE-mutant and MSI tumors compared to NSMP and TP53-mutant. TIA-1 expression did not differ between the four molecular subgroups. POLE-mutant ECs showed a trend towards increased expression of CD274 (PD-L1) (p=0.057).
169 Chapter 8
Figure 9. Expression of immune markers in according to tumor molecular subtype in TCGA series. CD3E CD8A ITGAE (CD103) MS4A1 (CD20) ** 15 ** 15 ** 12 *** * ** ** * ** ** *** ** ** 10 * 10 10 10
5 8 5 5
Log2 expression 6 0 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53
PTPRC (CD45RO) CD27 TBX21 (T-Bet) TIA-1
* 15 * 10 ** 14 * ** * * ** 10 ** 12 10 5 10 5 8 5
Log2 expression 0 0 6 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53 POLE MSI NSMP p53
PDCD1 (PD-1) CD274 (PD-L1)
* 8 * * 10 6
4 5 2
Log2 expression 0 0 POLE MSI NSMP p53 POLE MSI NSMP p53
RSEM normalized RNAseq data were log2 transformed and analyzed according to tumor molecular subtype. Boxes represent the interquartile range (IQR), with the upper whisker indicating the 75th percentile and the lower whisker the 25th percentile. The median and mean values are indicated by a horizontal line and cross, respectively. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile; p53, p53-mutant. * = p<0.05, **= p<0.01, ***= p<0.001.
Patients with POLE-mutated and MSI tumors have higher numbers of predicted neoantigens, regardless of their immune infiltration status The presence of a subset of POLE-mutant and MSI tumors with a relatively low immune infiltration and NSMP and TP53-mutant tumors with a relatively high immune infiltration led us to evaluate the rela- tionship between immune infiltrate and numbers of predicted neoantigens within the TCGA cohort (Fig 10). First of all, we demonstrated the presence of higher numbers of expected neoantigens in POLE-mutant and MSI tumors compared to NSMP and TP53-mutant tumors (Fig 10A). The molecular subgroups were dichotomized according to CD8A expression from RNAseq data, with high infiltration defined as expression above the median of the respective molecular subgroup. Subsequently, we
170 Immunity of high risk endometrial cancer
quantified predicted neoantigens for high and low infiltrated tumors within the molecular subgroups (Fig 10B). No differences were found in the numbers of predicted neoantigens between samples with high or low CD8A expression within the molecular subgroups.
Figure 10. Predicted number of HLA-A2-binding neoantigens across the four molecular subgroups in The Cancer Genome Atlas endometrial cancer cohort. A *** *** 10000 2 *** A
- *** A s
L ***
n 1000 H e
g d i t e t n
c 100 a i o d e e r n
p
10 g f n o i
r d e n
i 1 b b m u
N 0 8 18 67 108 47 POLE MSI NSMP TP53
B 2
A 10000 - A s L n H e 1000
g d i t e t n c a
i 100 o d e e r n
p
10 g f n i o
d r n e
i 1 b b m u 0 N 13 5 39 28 52 56 16 31 h w h w h w g h w g o ig lo i lo ig lo i l h h I h h 3 E I S P 3 E L S M P M L O M M S TP5 O P S N TP5 P N
A, Comparison between the number of predicted HLA-A2 binding neoantigens in POLE-mutant, MSI, NSMP and TP53-mu- tant subgroups based on RNAseq. B, Comparison between patients with high and low infiltration (based on CD8A expression from RNAseq, relative to median within the group) of lymphocytes within POLE-mutant, MSI, NSMP and TP53-mutant subgroups. The numbers of cases analyzed for each molecular subgroup are listed below the x-axis. Boxes represent the interquartile range (IQR), with upper whisker indicating the 75th percentile and the lower whisker the 25th percentile. The median and mean values are indicated by a horizontal line and cross, respectively. Abbreviations: POLE, POLE-mutant; MSI, microsatellite unstable; NSMP, no specific molecular profile; p53, p53-mutant. * = p<0.05, **= p<0.01, ***= p<0.001.
171 Chapter 8
DISCUSSION
In the current study we demonstrate the presence of high numbers of tumor-infiltrating T-cells in POLE-mutant and MSI tumors, both predicted to be neoantigen-rich, from a clinically relevant cohort of high-risk EC patients. Moreover, these two molecular subtypes harbor high densities of PD-1- and PD-L1-expressing immune cells, rendering them attractive candidates for immune checkpoint inhi- bition strategies.
The presence of a prominent immune infiltrate inPOLE- mutant and MSI high-risk EC is in concordance with our previous findings in a pre-selected cohort including 47POLE- mutant, 49 microsatellite unsta- ble and 54 microsatellite stable tumors, in which we demonstrated that POLE-mutant tumors, and to a lesser extent MSI tumors, are characterized by a robust intratumoral T-cell response 20. These initial findings have recently been extended to other unselected EC cohorts, in which high densities of peritumoral and tumor-infiltrating T-lymphocytes have been described inPOLE- mutant tumors 21,22,31. High expression of PD-1 and PD-L1 on intraepithelial immune cells in POLE-mutant and MSI ECs has previously been suggested by Howitt et al, albeit in a cohort which included only three POLE-mutant cases21. An interesting difference between the data presented by Howittet al and the current study, is the expression of PD-L1 on tumor cells. Howitt et al describe that 20% of ECs (POLE-mutant, MSI and MSS) show PD-L1 positive tumor cells, whereas within our high-risk cohort only 1 out of 116 tumors showed any expression of PD-L1 on the tumor cells (using the same PD-L1 antibody). Our use of tissue micro-arrays may have led to an underestimation of PD-L1 expressing tumor cells, as PD-L1 expression is known to be heterogeneously distributed32. Moreover, consecutive full slides of one POLE-mutant case were stained using multi-color immunofluorescence: PD-L1 expression was predominantly found in the intratumoral stromal regions in close proximity with PD-1+ cells. Fur- thermore, PD-L1 expression co-localized with CD68 and CD163, suggesting that in this case PD-L1 is primarily expressed by myeloid cells rather than tumor cells. PD-L1+ immune cells have previously been described by (among others) Heeren et al and Herbst et al; the latter also showed that PD-L1 positivity on immune cells, but not on tumor cells, was associated with response to immune check- point inhibition1,28.
Comparisons of outcomes from our immunohistochemical analyses in the TransPORTEC high-risk cohort and analyses of the RNA sequencing data from The Cancer Genome Atlas (TCGA) showed similar results for five out of ten markers, namely CD3, CD8, CD103, CD45RO and PD1. The immuno- histochemical analyses of the TransPORTEC cohort did not reveal significant differences in numbers of CD20+ and CD27+ cells between the four molecular subgroups, while analysis of the TCGA cohort demonstrated increased expression of CD20+ and CD27+ cells within the POLE-mutant and MSI sub- groups. This inconsistency may be attributed to the use of a TMA for immunohistochemical analyses of CD20+ and CD27+ cells. These immune cells frequently reside in tertiary lymphoid structures in the myometrium, which are frequently seen in POLE-mutant tumors 20,33–35. The areas containing these
172 Immunity of high risk endometrial cancer
structures may not have been present in the TMA. Secondly, outcomes regarding TIA-1- ,T-Bet- and PD-L1- positivity were discordant. These differences may be due to the known discrepancy between mRNA and protein expression36. Another possible explanation for these discrepancies may be the relatively high proportion of clear cell EC (15.5%) within the TransPORTEC high-risk cohort, while only endometrioid, serous and mixed histologies were included in the TCGA study.
The presence of high numbers of CD8+ and PD-1+ cells in POLE-mutant and MSI tumors, may sug- gest the presence of high numbers of tumor-specific T-cells targeting neoantigens within these subgroups of patients. Similarly, our analysis of the TCGA EC cohort demonstrates that POLE-mutant and MSI tumors are characterized by a significantly higher number of mutations predicted to result in major histocompatibility complex-binding neoantigens, and a correspondingly higher number of tumor-infiltrating CD8+ T-cells, as assessed by CD8A mRNA levels. This link between neoantigen accumulation and infiltration by immune cells is supported by a recent genomic characterization of colorectal cancers, in which an association between high neoantigen load, overall lymphocytic
10 infiltration, tumor-infiltrating lymphocytes and survival was demonstrated . 8
Surprisingly, the number of predicted immunogenic mutations did not directly reflect the levels of CD8A mRNA expression within each molecular subgroup (Fig 10). Similarly, in our immunohisto- chemical analysis, we found MSI tumors, expected to be neoantigen-rich, with almost no signs of CD8+ T-cell infiltration, and p53-mutant tumors, expected to have low numbers of neoantigens, with an enhanced intratumoral immune response. One explanation for this apparent discrepancy between immune infiltration and the number of predicted neoantigens could be that the nature (i.e. clonal versus subclonal) of the neoepitopes, instead of the crude number of predicted neoantigens, determine the degree of immune response13. Another explanation may be that within our analyses only predicted binding to HLA-A*02:01 was taken into account rather than to individual HLA alleles. Furthermore, immune responses may be impeded by impairment of MHC class I expression due to mutations in HLA, Beta-2 microglobulin and JAK-1 in highly mutated ECs20,37. Therefore, a logical next step in understanding the interaction between neoepitopes and immune response within the four molecular subgroups would be the direct identification and characterization of tumor-specific T-cells targeting these neoantigens, as has recently been performed by Gros et al in melanoma24.
With regard to the p53-mutant tumors with an enhanced antitumor immune response despite low expected neoantigen load, we hypothesize that this response may be aimed at self-antigens or cancer/testis antigens instead of neoepitopes. Taking into account their unfavorable survival out- comes, further investigation of the highly infiltrated p53-mutant subset will be of great interest as this may provide new insight in the selection of candidates for immune checkpoint therapies.
The data on mutational load, neoantigens and immune infiltration reported by us and others suggest that checkpoint inhibition may be a strategy of particular interest for treating advanced stage patients
173 Chapter 8
with POLE-mutant and MSI tumors. Recent case reports provide proof of principle by demonstrating the efficacy of anti-PD-1 inhibitors in a limited number of advanced stagePOLE- mutant or mismatch repair deficient cancers7,25,26. Moreover, a Phase II trial evaluating immune related objective responses to Pembrolizumab in patients with or without mismatch repair (MMR) deficiency, demonstrated objec- tive responses in 40% of patients with MMR deficient colorectal cancer and 71% of patients with MMR deficient non-colorectal cancers (including 2 EC). Contrastingly, no objective responses were observed in the MMR proficient colorectal cancers. Moreover, data from this study adds to the growing body of evidence suggesting that high numbers of somatic mutations (in this case due to MMR deficiency) and high numbers of predicted neoantigens play an important role in the sensitivity to checkpoint inhibition11–13,15. Furthermore, an in-depth analysis of patients treated with anti-PD-1 therapy pri- oritized PD-L1 expression as being the most closely associated with objective tumor regression38. Further analyses of non-responders may uncover other mutations affecting epitope presentation, T-cell infiltration and response to checkpoint inhibition.
From a clinical point of view, as checkpoint inhibitors are associated with significant costs and potential toxicities, it is essential to select individual patients that will benefit from these therapies. Patients with low/intermediate-risk disease carrying POLE mutations have an excellent prognosis under standard treatment, and therefore checkpoint inhibition is unlikely to be appropriate for this group19,39–41. However, (although infrequently occurring) POLE-mutant and MSI patients with recurring or meta- static disease are possible candidates 7,25,26. Clinical trials, in which high-risk EC patients are grouped according to molecular subtype, will be required to determine clinical benefit of immunotherapy. Importantly, the data thus far regarding POLE-mutant EC may be applicable to other tumor types harboring POLE mutations. While POLE mutations are found in 7-12% of EC, they are also found in other malignancies including colorectal cancers, cancers of the brain, breast, pancreas and stomach, albeit at lower frequencies19,39,42–48. Although a prognostic advantage of this mutation has now been established in glioblastoma and stage II/III colorectal cancer, patients with recurrent or metastatic hypermutated disease may also benefit from immunotherapeutic strategies such as checkpoint inhibitors as proposed for EC7,47,49. Basket trials stratifying patients according to tumor molecular alterations such as POLE mutations should be initiated to investigate whether these patients may also benefit from checkpoint inhibition.
In summary, taking into account the strong immune infiltration, high numbers of PD-1+ and PD-L1+ lymphocytes, large numbers of somatic mutations and neoantigens, and the recently demonstrated clinical efficacy in these cohorts of patients, POLE-mutant and MSI tumors are expected to benefit from checkpoint inhibition 21,25,50.
174 Immunity of high risk endometrial cancer
FUNDING AND ACKNOWLEDGEMENTS
The authors would like to thank the members of the PORTEC study group, the patients involved in the PORTEC studies, Mark Glaire for his assistance with quantification of the immunohistochemical stainings, and Annechien Plat and Enno Dreef for their technical assistance. This work was supported by Dutch Cancer Society/Alpe d’Huzes grant UMCG 2014−6719 to MB; a Dutch Cancer Society Grant (UL2012-5719) to ICG, CLC, TB; and a Jan Kornelis de Cock Stichting grant to FAE. DNC is supported by a Clinician Scientist Award from the Academy of Medical Sciences / Health Foundation, and has received funding from the Oxford Cancer Centre, University of Oxford.
8
175 Chapter 8
REFERENCES
1. Herbst RS, Soria J-C, Kowanetz M, Fine GD, Hamid O, Gordon MS, Sosman J a., McDermott DF, Powderly JD, Gettinger SN, et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature [Internet] 2014; 515:563–7. Available from: http://www.nature.com/doifinder/10.1038/nature14011\ nhttp://www.ncbi.nlm.nih.gov/pubmed/25428504\nhttp://dx.doi.org/10.1038/nature14011 2. Hodi FS, O’Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, Gonzalez R, Robert C, Schadendorf D, Hassel JC, et al. Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med [Inter- net] 2010 [cited 2016 Aug 8]; 363:711–23. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20525992 3. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D, et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N Engl J Med [Internet] 2015 [cited 2015 Jun 2]; 372:2509–20. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26028255 4. Powles T, Eder JP, Fine GD, Braiteh FS, Loriot Y, Cruz C, Bellmunt J, Burris HA, Petrylak DP, Teng S, et al. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature [Internet] 2014 [cited 2016 Aug 8]; 515:558–62. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25428503 5. Hamid O, Robert C, Daud A, Hodi FS, Hwu W-J, Kefford R, Wolchok JD, Hersey P, Joseph RW, Weber JS, et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N Engl J Med [Internet] 2013 [cited 2016 Aug 8]; 369:134–44. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23724846 6. Borghaei H, Paz-Ares L, Horn L, Spigel DR, Steins M, Ready NE, Chow LQ, Vokes EE, Felip E, Holgado E, et al. Nivolumab versus Docetaxel in Advanced Nonsquamous Non-Small-Cell Lung Cancer. N Engl J Med [Internet] 2015 [cited 2016 Aug 8]; 373:1627–39. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26412456 7. Bouffet E, Larouche V, Campbell BB, Merico D, de Borja R, Aronson M, Durno C, Krueger J, Cabric V, Ramaswamy V, et al. Immune Checkpoint Inhibition for Hypermutant Glioblastoma Multiforme Resulting From Germline Biallelic Mismatch Repair Deficiency. J Clin Oncol [Internet] 2016 [cited 2016 Aug 9]; 34:2206–11. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27001570 8. Brown SD, Warren RL, Gibb EA, Martin SD, Spinelli JJ, Nelson BH, Holt RA. Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res 2014; 24:743–50. 9. Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and Genetic Properties of Tumors Associated with Local Immune Cytolytic Activity. Cell [Internet] 2015 [cited 2015 Jan 15]; 160:48–61. Available from: http:// www.ncbi.nlm.nih.gov/pubmed/25594174 10. Giannakis M, Mu XJ, Shukla SA, Qian ZR, Cohen O, Nishihara R, Bahl S, Cao Y, Amin-Mansour A, Yamauchi M, et al. Genomic Correlates of Immune-Cell Infiltrates in Colorectal Carcinoma. Cell Rep [Internet] 2016; 15:857–65. Available from: http://linkinghub.elsevier.com/retrieve/pii/S2211124716303643 11. Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer [Internet] 2012 [cited 2016 Jul 6]; 12:252–64. Available from: http://dx.doi.org/10.1038/nrc3239 12. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, Lee W, Yuan J, Wong P, Ho TS, et al. Muta- tional landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science (80- ) [Internet] 2015 [cited 2015 Mar 15]; 348:124–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25765070 13. Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A, Walsh L a, Postow M a, Wong P, Ho TS, et al. Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma. N Engl J Med [Internet] 2014; :2189–99. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25409260 14. Chan TA, Wolchok JD, Snyder A. Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma. N Engl J Med [Internet] 2015 [cited 2016 Oct 6]; 373:1984–1984. Available from: http://www.nejm.org/doi/10.1056/ NEJMc1508163 15. Zou W, Wolchok JD, Chen L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms,
176 Immunity of high risk endometrial cancer
response biomarkers, and combinations. Sci Transl Med 2016; 8:328rv4. 16. Tumeh P, Harview C, Yearley J, Al E. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014; 515:568–71. 17. Mlecnik B, Bindea G, Angell HK, Maby P, Angelova M, Tougeron D, Church SE, Lafontaine L, Fischer M, Fredrik- sen T, et al. Integrative Analyses of Colorectal Cancer Show Immunoscore Is a Stronger Predictor of Patient Survival Than Microsatellite Instability. Immunity 2016; 44:698–711. 18. Becht E, Giraldo NA, Germain C, de Reyniès A, Laurent-Puig P, Zucman-Rossi J, Dieu-Nosjean M-C, Sau- tès-Fridman C, Fridman WH. Immune Contexture, Immunoscore, and Malignant Cell Molecular Subgroups for Prognostic and Theranostic Classifications of Cancers. 2016; 130:95–190. Available from: http://linkinghub. elsevier.com/retrieve/pii/S006527761530002X 19. Cancer Genome Atlas Research Network, Kandoth C, Schultz N CA, Akbani R, Liu Y, Shen H, Robertson AG, Pashtan I, Shen R, Benz CC, Yau C L, PW, Ding L, Zhang W, Mills GB, Kucherlapati R, Mardis ER L DA. Integrated genomic characterization of endometrial carcinoma. Nature [Internet] 2013; 497:67–73. Available from: http:// pubget.com/paper/23636398?institution=\npapers2://publication/doi/10.1038/nature12113 20. van Gool IC, Eggink F a., Freeman-Mills L, Stelloo E, Marchi E, de Bruyn M, Palles C, Nout R a., de Kroon CD, Osse EM, et al. POLE proofreading mutations elicit an anti-tumor immune response in endometrial cancer. Clin Cancer Res [Internet] 2015; :3347–56. Available from: http://clincancerres.aacrjournals.org/cgi/ doi/10.1158/1078-0432.CCR-15-0057 21. Howitt BE, Shukla S a., Sholl LM, Ritterhouse LL, Watkins JC, Rodig S, Stover E, Strickland KC, D’Andrea AD, Wu CJ, 8 et al. Association of Polymerase e–Mutated and Microsatellite-Instable Endometrial Cancers With Neoantigen Load, Number of Tumor-Infiltrating Lymphocytes, and Expression of PD-1 and PD-L1. JAMA Oncol [Internet] 2015; :1–5. Available from: http://oncology.jamanetwork.com/article.aspx?doi=10.1001/jamaoncol.2015.2151 22. Bellone S, Centritto F, Black J, Schwab C, English D, Cocco E, Lopez S, Bonazzoli E, Predolini F, Ferrari F, et al. Polymerase ε (POLE) ultra-mutated tumors induce robust tumor-specific CD4+ T cell responses in endometrial cancer patients. Gynecol Oncol [Internet] 2015; Available from: http://linkinghub.elsevier.com/retrieve/pii/ S0090825815008562 23. van Gool IC, Bosse T, Church DN. POLE proofreading mutation, immune response and prognosis in endome- trial cancer. Oncoimmunology [Internet] 2016 [cited 2016 Oct 6]; 5:e1072675. Available from: http://www. ncbi.nlm.nih.gov/pubmed/27141333 24. Gros A, Parkhurst MR, Tran E, Pasetto A, Robbins PF, Ilyas S, Prickett TD, Gartner JJ, Crystal JS, Roberts IM, et al. Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients. Nat Med [Internet] 2016; Available from: http://www.nature.com/doifinder/10.1038/nm.4051 25. Mehnert JM, Panda A, Zhong H, Hirshfield K, Damare S, Lane K, Sokol L, Stein MN, Rodriguez-rodriquez L, Kaufman HL, et al. Immune activation and response to pembrolizumab in POLE -mutant endometrial cancer. JCI 2016; 126:1–7. 26. Santin AD, Bellone S, Buza N, Choi J, Schwartz PE, Schlessinger J, Lifton RP. Regression of chemotherapy-resis- tant Polymerase epsilon (POLE) ultra-mutated and MSH6 hyper-mutated endometrial tumors with nivolumab. Clin Cancer Res [Internet] 2016 [cited 2016 Aug 26]; 1:5–6. Available from: http://www.ncbi.nlm.nih.gov/ pubmed/27486176 27. Dong H, Strome SE, Salomao DR, Tamura H, Hirano F, Flies DB, Roche PC, Lu J, Zhu G, Tamada K, et al. Tumor-as- sociated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat Med [Internet] 2002 [cited 2016 Sep 12]; 8:793–800. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12091876 28. Heeren AM, Koster BD, Samuels S, Ferns DM, Chondronasiou D, Kenter GG, Jordanova ES, de Gruijl TD. High and interrelated rates of PD-L1+CD14+ antigen-presenting cells and regulatory T cells mark the microenvi- ronment of metastatic lymph nodes from patients with cervical cancer. Cancer Immunol Res [Internet] 2015 [cited 2016 Sep 12]; 3:48–58. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25361854
177 Chapter 8
29. Schultheis AM, Scheel AH, Ozretić L, George J, Thomas RK, Hagemann T, Zander T, Wolf J, Buettner R. PD-L1 expression in small cell neuroendocrine carcinomas. Eur J Cancer [Internet] 2015 [cited 2016 Sep 12]; 51:421– 6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25582496 30. Curiel TJ, Wei S, Dong H, Alvarez X, Cheng P, Mottram P, Krzysiek R, Knutson KL, Daniel B, Zimmermann MC, et al. Blockade of B7-H1 improves myeloid dendritic cell–mediated antitumor immunity. Nat Med [Internet] 2003 [cited 2016 Nov 10]; 9:562–7. Available from: http://www.nature.com/doifinder/10.1038/nm863 31. Bakhsh S, Kinloch M, Hoang LN, Soslow R a., Köbel M, Lee CH, Mcalpine JN, Mcconechy MK, Gilks CB. Histo- pathological features of endometrial carcinomas associated with POLE mutations: Implications for decisions about adjuvant therapy. Histopathology 2015; 32. McLaughlin J, Han G, Schalper KA, Carvajal-Hausdorf D, Pelekanou V, Rehman J, Velcheti V, Herbst R, LoRusso P, Rimm DL, et al. Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non–Small-Cell Lung Cancer. JAMA Oncol [Internet] 2016 [cited 2016 Sep 13]; 2:46. Available from: http://oncology.jamanetwork. com/article.aspx?doi=10.1001/jamaoncol.2015.3638 33. Dieu-Nosjean MC, Giraldo NA, Kaplon H, Germain C, Fridman WH, Saut??s-Fridman C. Tertiary lymphoid struc- tures, drivers of the anti-tumor responses in human cancers. Immunol Rev 2016; 271:260–75. 34. Germain C, Gnjatic S, Dieu-Nosjean MC. Tertiary lymphoid structure-associated B cells are key players in anti-tumor immunity. Front Immunol 2015; 6:1–14. 35. Kroeger DR, Milne K, Nelson BH. Tumor-Infiltrating Plasma Cells Are Associated with Tertiary Lymphoid Struc- tures, Cytolytic T-Cell Responses, and Superior Prognosis in Ovarian Cancer. Clin Cancer Res [Internet] 2016; :3005–15. Available from: http://clincancerres.aacrjournals.org/cgi/doi/10.1158/1078-0432.CCR-15-2762 36. Zhang H, Liu T, Zhang Z, Chan DW, Rodland KD, Zhang H, Liu T, Zhang Z, Payne SH, Zhang B, et al. Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer Resource Integrated Proteog- enomic Characterization of Human High-Grade Serous Ovarian Cancer. 2016; :1–11. 37. Stelloo E, Versluis MA, Nijman HW, de Bruyn M, Plat A, Osse EM, van Dijk RH, Nout RA, Creutzberg CL, de Bock GH, et al. Microsatellite instability derived JAK1 frameshift mutations are associated with tumor immune evasion in endometrioid endometrial cancer. Oncotarget [Internet] 2016; 7. Available from: http://www.onco- target.com/abstract/9414 38. Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, Chen L, Pardoll DM, Topalian SL, Anders RA. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res 2014; 20:5064–74. 39. Billingsley CC, Cohn DE, Mutch DG, Hade EM, Goodfellow PJ. Prognostic Significance of POLE Exonuclease Domain Mutations in High-Grade Endometrioid Endometrial Cancer on Survival and Recurrence. Int J Gynecol Cancer [Internet] 2016; 0:1. Available from: http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:land- ingpage&an=00009577-900000000-98739 40. Church DN, Stelloo E, Nout R a., Valtcheva N, Depreeuw J, ter Haar N, Noske a., Amant F, Tomlinson IPM, Wild PJ, et al. Prognostic Significance of POLE Proofreading Mutations in Endometrial Cancer. JNCI J Natl Cancer Inst [Internet] 2014; 107:dju402-dju402. Available from: http://jnci.oxfordjournals.org/cgi/doi/10.1093/jnci/ dju402 41. McConechy MK, Talhouk A, Leung S, Chiu D, Yang W, Senz J, Reha-Krantz LJ, Lee C-H, Huntsman DG, Gilks CB, et al. Endometrial Carcinomas with POLE Exonuclease Domain Mutations Have a Favorable Prognosis. Clin Cancer Res [Internet] 2016; :1–10. Available from: http://clincancerres.aacrjournals.org/cgi/doi/10.1158/1078- 0432.CCR-15-2233 42. Stelloo E, Bosse T, Nout R a, MacKay HJ, Church DN, Nijman HW, Leary A, Edmondson RJ, Powell ME, Crosbie EJ, et al. Refining prognosis and identifying targetable pathways for high-risk endometrial cancer; a TransPORTEC initiative. Mod Pathol [Internet] 2015; 28:836–44. Available from: http://www.nature.com/doifinder/10.1038/ modpathol.2015.43
178 Immunity of high risk endometrial cancer
43. Church DN, Briggs SEW, Palles C, Domingo E, Kearsey SJ, Grimes JM, Gorman M, Martin L, Howarth KM, Hodgson S V, et al. DNA polymerase ε and δ exonuclease domain mutations in endometrial cancer. Hum Mol Genet [Internet] 2013 [cited 2016 Mar 25]; 22:2820–8. Available from: http://www.pubmedcentral.nih.gov/ articlerender.fcgi?artid=3690967&tool=pmcentrez&rendertype=abstract 44. The Cancer Genome Atlas. Comprehensive molecular characterization of human colon and rectal cancer. - PubMed - NCBI [Internet]. Nature2012 [cited 2016 Apr 5]; :330–7. Available from: http://www.ncbi.nlm.nih. gov/pubmed/22810696 45. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio S a JR, Behjati S, Biankin A V, Bignell GR, Bolli N, Borg A, Børre- sen-Dale A-L, et al. Signatures of mutational processes in human cancer. Nature [Internet] 2013; 500:415–21. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3776390&tool=pmcentrez&ren- dertype=abstract 46. Shinbrot E, Henninger EE, Weinhold N, Covington KR, Göksenin AY, Schultz N, Chao H, Doddapaneni H, Muzny DM, Gibbs RA, et al. Exonuclease mutations in DNA polymerase epsilon reveal replication strand specific muta- tion patterns and human origins of replication. Genome Res [Internet] 2014 [cited 2016 Apr 5]; 24:1740–50. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4216916&tool=pmcentrez&ren- dertype=abstract 47. Erson-Omay EZ, Çağlayan AO, Schultz N, Weinhold N, Omay SB, Özduman K, Köksal Y, Li J, Serin Harmancı A, Clark V, et al. Somatic POLE mutations cause an ultramutated giant cell high-grade glioma subtype with better prognosis. Neuro Oncol [Internet] 2015 [cited 2016 Apr 5]; 17:1356–64. Available from: http://www.ncbi.nlm. 8 nih.gov/pubmed/25740784 48. Rayner E, van Gool IC, Palles C, Kearsey SE, Bosse T, Tomlinson I, Church DN. A panoply of errors: polymerase proofreading domain mutations in cancer. Nat Rev Cancer [Internet] 2016; 16:71–81. Available from: http:// www.nature.com/doifinder/10.1038/nrc.2015.12 49. Domingo E, Freeman-Mills L, Rayner E, Glaire M, Briggs S, Vermeulen L, Fessler E, Medema JP, Boot A, Morreau H, et al. Somatic POLE proofreading domain mutation, immune response, and prognosis in colorectal cancer: a retrospective, pooled biomarker study. Lancet Gastroenterol Hepatol [Internet] 2016 [cited 2016 Aug 26]; Available from: http://linkinghub.elsevier.com/retrieve/pii/S2468125316300140 50. Gargiulo P, Della Pepa C, Berardi S, Califano D, Scala S, Buonaguro L, Ciliberto G, Brauchli P, Pignata S. Tumor genotype and immune microenvironment in POLE-ultramutated and MSI-hypermutated Endometrial Cancers: New candidates for checkpoint blockade immunotherapy? Cancer Treat Rev [Internet] 2016; 48:61–8. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0305737216300470 51. Boer SM De, Powell ME, Mileshkin L, Katsaros D, Bessette P, Haie-meder C, Ottevanger PB, Ledermann JA, Khaw P, Colombo A, et al. Articles Toxicity and quality of life after adjuvant chemoradiotherapy versus radiotherapy alone for women with high-risk randomised , phase 3 trial. Lancet Oncol [Internet] 2016; 17:1–13. Available from: http://dx.doi.org/10.1016/S1470-2045(16)30120-6 52. van Eijk R, Stevens L, Morreau H, van Wezel T. Assessment of a fully automated high-throughput DNA extraction method from formalin-fixed, paraffin-embedded tissue for KRAS, and BRAF somatic mutation analysis. Exp Mol Pathol [Internet] 2013; 94:121–5. Available from: http://dx.doi.org/10.1016/j.yexmp.2012.06.004 53. Van Gool IC, Stelloo E, Nout RA, Nijman HW, Edmondson RJ, Church DN, MacKay HJ, Leary A, Powell ME, Milesh- kin L, et al. Prognostic significance of L1CAM expression and its association with mutant p53 expression in high-risk endometrial cancer. Mod Pathol [Internet] 2016; 29:174–81. Available from: http://www.nature.com/ doifinder/10.1038/modpathol.2015.147\nhttp://www.ncbi.nlm.nih.gov/pubmed/26743472 54. Workel HH, Komdeur FL, Wouters MCA, Plat A, Klip HG, Eggink FA, Wisman GBA, Arts HJG, Oonk MHM, Mourits MJE, et al. CD103 defines intraepithelial CD8+ PD1+ tumour-infiltrating lymphocytes of prognostic significance in endometrial adenocarcinoma. Eur J Cancer [Internet] 2016; 60:1–11. Available from: http://www.ncbi.nlm. nih.gov/pubmed/27038842
179 Chapter 8
55. Jordanova ES, Gorter A, Ayachi O, Prins F, Durrant LG, Kenter GG, van der Burg SH, Fleuren GJ. Human leukocyte antigen class I, MHC class I chain-related molecule A, and CD8+/regulatory T-cell ratio: which variable deter- mines survival of cervical cancer patients? Clin Cancer Res [Internet] 2008 [cited 2016 Aug 10]; 14:2028–35. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18381941 56. Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, R??der G, Peters B, Sette A, Lund O, et al. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS One 2007; 2. 57. Khalili JS, Hanson RW, Szallasi Z. In silico prediction of tumor antigens derived from functional missense mutations of the cancer gene census. Oncoimmunology [Internet] 2012; 1:1281–9. Available from: http:// www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3518500&tool=pmcentrez&rendertype=abstract 58. Duan F, Duitama J, Al Seesi S, Ayres CM, Corcelli S a, Pawashe AP, Blanchard T, McMahon D, Sidney J, Sette A, et al. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J Exp Med [Internet] 2014; 211:2231–48. Available from: http://www.pubmedcentral.nih. gov/articlerender.fcgi?artid=4203949&tool=pmcentrez&rendertype=abstract\nhttp://www.ncbi.nlm.nih.gov/ pubmed/25245761\nhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=PMC4203949 59. van Rooij N, van Buuren MM, Philips D, Velds A, Toebes M, Heemskerk B, van Dijk LJA, Behjati S, Hilkmann H, El Atmioui D, et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J Clin Oncol [Internet] 2013 [cited 2016 Jul 5]; 31:e439-42. Available from: http://www.ncbi.nlm. nih.gov/pubmed/24043743
180
CHAPTER 9
Summary
Summary
SUMMARY
The studies presented in this thesis were aimed at improving quality of care for patients with ovarian cancer and endometrial cancer. In part I and II we focused on the organization of care for patients with ovarian and endometrial cancer. In part III we focused on individualization of care for patients with endometrial cancer.
Part I: Improving organization of care for patients with ovarian cancer Ovarian cancer is the leading cause of death in gynecologic cancers. Standard primary treatment consists of a combination of cytoreductive surgery and platinum based chemotherapy. Traditionally, patients were treated with primary cytoreductive surgery (PCS) followed by 6 cycles of adjuvant che- motherapy (ACT) in the hospital of diagnosis. Within the past decade the importance of removing all macroscopically visible tumor deposits, termed ‘complete cytoreduction’, has been established. Moreover, favorable cytoreductive outcomes have consistently been demonstrated in the presence of specialized gynecologic oncologists in high volume hospitals. Therefore, treatment for patients with advanced stage ovarian cancer has been centralized to hospitals with annual case volumes of ≥20 surgeries. Within the same period, cytoreductive neoadjuvant chemotherapy (NACT) was introduced 9 to reduce tumor load and increase the chance of achieving a complete cytoreduction during interval cytoreductive surgery (ICS).
In chapter 2, we evaluated the impact of these changes in patterns of care on surgical outcomes and survival of patients diagnosed with advanced stage epithelial ovarian cancer in the Netherlands between 2004 and 2013. The rise in the annual case load of hospitals treating patients with advanced stage ovarian cancer due to centralization of care, in combination with the expanding proportion of patients undergoing NACT, coincided with improved cytoreductive outcomes and a small improve- ment in overall survival.
In chapter 3 we performed a pattern of care study to measure health system intervals of patients that were referred to the University Medical Center Groningen with a suspicion of ovarian cancer. During the course of the study increased awareness of health system intervals inspired changes in local practice. This resulted in improved compliance to national health system interval guidelines.
In chapter 4 we conducted a review of the literature concerning optimal patient selection, timing and extent of surgery for patients with advanced stage ovarian cancer. Despite recent changes in orga- nization of care and the paradigm shift in timing of cytoreductive surgery, no convincing impact on survival has been achieved thus far. We concluded that studies aimed at improving survival, especially those focused on optimizing patient selection for PCS, should be prioritized.
185 Chapter 9
Part II: Improving organization of care for patients with endometrial cancer Endometrial cancer is the most common gynecologic malignancy in economically developed countries. Standard diagnostic procedures for women suspected of endometrial cancer include ultrasonogra- phy and endometrial sampling. The pre-operatively collected endometrial sampling material and post-operative surgical specimens are used to stratify patients into groups according to their risk of progression and recurrence. This information is used to guide therapeutic decisions. Currently, the pre-operatively obtained tissue is used to guide clinical decisions regarding the extent of surgery and the tissue obtained during surgery is used to guide clinical decisions regarding adjuvant therapy. In chapter 5 we evaluated the concordance between the pre- and postoperative risk stratifications of patients that were diagnosed with endometrial cancer in the Netherlands between 2005 and 2014. Within this period, the concordance between pre-operative and post-operative risk-stratifications was 90%. Notably, unfavorable survival outcomes were determined in patients with a high pre- and low post-operative risk compared to patients with a concordant low risk. We therefore concluded that the pre-operative risk stratification contains independent prognostic information.
The postoperative risk stratification is used to guide clinical decisions regarding adjuvant therapy. In chapter 6 we evaluated compliance of physicians with adjuvant therapy guidelines in patients that were diagnosed with endometrial cancer in the Netherlands between 2005 and 2014. Excellent compliance to guidelines was determined in low and low-intermediate risk patients. In contrast, com- pliance was much lower in high-intermediate and high risk patients. Furthermore, large variations in compliance to guidelines were demonstrated between the eight oncologic regions in the Netherlands.
Part III: Individualization of care for patients with endometrial cancers While the prognosis of patients with early stage, low risk disease is relatively favorable, there is an unmet need for effective treatment options in advanced stage, high risk endometrial cancer. The cur- rent ‘one size fits all’ approach is clearly insufficient for high risk endometrial cancers. It has therefore been suggested that new treatment modalities for high risk endometrial cancer should be aimed at specific tumor features such as biomarkers and molecular characteristics.
Traditionally, endometrial cancer was classified according to a dualistic model based on grade, his- tology and hormone receptor expression. In 2013, the Cancer Genome Atlas Network introduced a new classification with four genomically distinct subtypes based on molecular markers. Surprisingly, one of these subgroups, comprising ultramutated tumors with somatic mutations in the exonuclease domain of POLE, had excellent survival outcomes despite being associated with high risk features. Within the past decade, it has been suggested that these highly mutated tumors can generate high levels of neoantigens which may elicit strong immune responses. We therefore hypothesized that the excellent prognosis of patients with POLE-mutated endometrial cancers may be due to the presence of a strong anti-tumor immune response. In chapter 7 we investigated whether POLE-mutant endome- trial tumors show evidence of increased immunogenicity. Our data demonstrated that POLE-mutant
186 Summary
endometrial cancers and hypermutated microsatellite unstable endometrial cancers are character- ized by a robust intra-tumoral immune response and an enrichment of antigenic neo-peptides. In Chapter 8 we validated our findings in a cohort of high-risk patients. Moreover, we demonstrated the presence of high densities of PD-1- and PD-L1-expressing immune cells within the POLE-mutant and microsatellite unstable subgroups. These characteristics make these two subgroups of endometrial cancer patients attractive candidates for immune checkpoint inhibition therapy.
9
187
CHAPTER 10
Nederlandse samenvatting voor de leek Chapter 10
NEDERLANDSE SAMENVATTING VOOR DE LEEK
De studies die in dit proefschrift zijn beschreven hebben als doel de kwaliteit van zorg voor patiënten met eierstokkanker en baarmoederkanker te verbeteren. In deel I en II van het proefschrift worden de onderzoeken die wij hebben verricht naar de organisatie van zorg beschreven. Deel III bevat de onderzoeken die wij hebben verricht in het kader van individualiseren van zorg voor patiënten met baarmoederkanker.
Deel I: Verbeteren organisatie van zorg voor patiënten met eierstokkanker In Nederland wordt jaarlijks bij ongeveer 1300 vrouwen eierstokkanker vastgesteld. Helaas wordt eierstokkanker door het ontbreken van symptomen vaak pas in een gevorderd stadium ontdekt, hierdoor is de prognose van deze vrouwen slecht. Vroeger werd een patiënt met eierstokkanker eerst geopereerd waarna 6 kuren chemotherapie werden toegediend, maar tegenwoordig wordt bij een belangrijk deel van de patiënten eerst chemotherapie gegeven. Het verrichten van een operatie voordat er chemotherapie is toegediend wordt ‘primaire debulking’ genoemd. Het verrichten van een operatie nadat er al chemotherapie is toegediend wordt ‘interval debulking’ genoemd.
Wetenschappelijk onderzoek heeft aangetoond dat het voor de overleving van de patiënt essentieel is om tijdens de operatie alle zichtbare tumor (in de buik) te verwijderen. Een operatie waarbij alle zichtbare tumor is verwijderd wordt een ‘complete debulking’ genoemd. Een complete debulking wordt vaker bereikt in ziekenhuizen met gespecialiseerde gynaecoloog-oncologen die deze debul- kingsoperatie vaak uitvoeren dan in ziekenhuizen waar deze operatie minder vaak wordt uitgevoerd. Om deze reden is de zorg voor patiënten met eierstokkanker in Nederland in de afgelopen jaren gecentraliseerd naar ziekenhuizen waar jaarlijks minstens 20 keer een debulkingsoperatie wordt uitgevoerd. Bij patiënten waarbij men verwacht geen complete debulking te kunnen bereiken wordt tegenwoordig begonnen met het geven van 3 kuren chemotherapie om de diverse tumorlokalisaties in de buik te verkleinen en daarmee de kans op een complete debulking te vergroten.
In hoofdstuk 2 hebben wij onderzocht of centralisatie van de zorg en introductie van de inter- val debulkingsoperatie in Nederland heeft geleidt tot meer complete debulkingen en een betere overleving. De resultaten van deze studie laten ons zien dat de toename in het aantal uitgevoerde debulkingsoperaties per ziekenhuis, en de stijging in het aantal patiënten dat een interval debul- kingsoperatie onderging, samenviel met een verbetering in chirurgische uitkomsten en een kleine verbetering in overleving tussen 2004 en 2013.
Als gevolg van de centralisatie worden er nu meer patiënten met (een verdenking op) eierstok- kanker naar het Universitair Medisch Centrum Groningen verwezen dan vroeger. In hoofdstuk 3 beschrijven wij onze studie naar de wachttijden van deze patiënten. Tijdens het uitvoeren van deze studie ontdekten wij enkele factoren die bijdroegen aan lange wachttijden. Zo waren er bijvoorbeeld
190 Nederlandse samenvatting
problemen met het inscannen van CT-scans die gemaakt waren in andere ziekenhuizen. Door de identificatie van deze factoren konden er specifieke maatregelen worden genomen waardoor de wachttijden korter werden.
Hoofdstuk 4 bevat een overzicht van de recente literatuur op het gebied van debulkingsoperaties bij patiënten met eierstokkanker. Ondanks de veranderingen in de organisatie van zorg voor patiën- ten met eierstokkanker en de invoering van de interval debulkingsoperatie is de prognose van deze patiënten niet overtuigend verbeterd. In dit hoofdstuk suggereren wij dat toekomstige studies zich zouden moeten richten op de vraag welke patiënten een primaire of een interval debulkingsoperatie zouden moeten krijgen.
Deel II: Verbeteren organisatie van zorg voor patiënten met baarmoederkanker Jaarlijks wordt bij ongeveer 1900 vrouwen in Nederland baarmoederkanker vastgesteld. In eco- nomisch ontwikkelde landen is het zelfs de meest voorkomende gynaecologische kanker. Om de diagnose baarmoederkanker vast te stellen wordt een echo verricht en een klein stukje weefsel van het baarmoederslijmvlies afgenomen. Indien er baarmoederkanker wordt vastgesteld wordt het stukje baarmoederslijmvlies gebruikt om een inschatting te maken van het risico op uitzaaiing van de ziekte. Deze pre-operatieve risico-inschatting wordt gebruikt om de uitgebreidheid van de operatie die de patiënt zal ondergaan te bepalen. Na de operatie wordt het verwijderde weefsel gebruikt om 10 opnieuw een risico-inschatting op uitzaaiing van de ziekte te maken, de zogenaamde ‘post-operatieve risico-inschatting’. Deze wordt gebruikt in de besluitvorming rondom het wel of niet geven van een aanvullende behandeling in de vorm van chemotherapie en/of bestraling.
Hoofdstuk 5 beschrijft ons onderzoek naar de overeenkomst tussen de pre-operatieve en post-ope- ratieve risico-inschatting bij patiënten die tussen 2005 en 2014 werden gediagnosticeerd met baarmoederkanker in Nederland. In deze periode was de overeenkomst 90%. Dat is relatief hoog in vergelijking met andere studies waarin overeenkomsten van 58-84% werden gemeld In onze studie toonden we aan dat de overleving van patiënten met een hoog risico in het pre-operatieve weefsel en een laag risico in het post-operatieve weefsel een slechtere overleving hadden dan patiënten die op beide momenten een laag risico hadden. We concludeerden daarom dat de pre-operatieve risico-inschatting belangrijke en onafhankelijke prognostische informatie bevat.
De beslissing over het geven van een nabehandeling in de vorm van chemotherapie of bestraling wordt genomen aan de hand van de richtlijn endometriumcarcinoom. Volgens deze richtlijn worden patiënten ingedeeld op basis van het risico op uitzaaiing van de ziekte. In hoofdstuk 6 beschrijven we de naleving van deze richtlijn bij patiënten die tussen 2005 en 2014 werden gediagnosticeerd met baarmoederkanker in Nederland. Binnen deze periode was er een uitstekende naleving bij patiënten met een laag of laag-intermediair risico op uitzaaiing van de ziekte. Opvallend was dat de naleving bij patiënten met een hoog-intermediair of hoog risico veel lager was. Binnen de groep patiënten
191 Chapter 10
met hoog risico werden ook grote praktijk variaties gezien tussen de 8 oncologische regio’s binnen Nederland. Onze resultaten laten zien dat er ruimte voor verbetering is in de naleving van de richtlijn bij patiënten met een hoog risico op uitzaaiingen.
Deel III: Individualiseren van zorg voor patiënten met baarmoederkanker Het merendeel van de patiënten met baarmoederkanker wordt in een vroeg stadium gediagnosti- ceerd en heeft een laag risico op uitzaaiingen. Deze patiënten hebben een relatief goede prognose. Er zijn helaas ook patiënten met een hoog risico op uitzaaiingen en een slechte prognose. Gezien de slechte prognose is het noodzakelijk om effectievere behandelingen te ontwikkelen voor patiënten met baarmoederkanker met een hoog risico op uitzaaiingen. In de literatuur wordt veelvuldig gesug- gereerd dat het bij de ontwikkeling van nieuwe behandelingen belangrijk is om rekening te houden met specifieke kenmerken van de tumoren van deze patiënten. Deze ‘therapie op maat’ wordt ook wel ‘individualiseren van zorg’ genoemd.
Traditioneel worden baarmoedertumoren geclassificeerd als type I of type II tumoren. In 2013 is door de Cancer Genome Atlas Network, een toonaangevend Amerikaans consortium, een nieuwe classifi- catie geïntroduceerd met vier categorieën. Patiënten in één van deze vier categorieën, met tumoren met een ‘POLE-mutatie’ en veel DNA fouten, hadden een verrassend goede prognose ondanks de aanwezigheid van tumorkenmerken die we normaal zouden associëren met een hoog risico op uitzaaiingen en een slechte prognose. In de literatuur is in de afgelopen jaren gesuggereerd dat de aanwezigheid van veel DNA fouten in een tumor kan leiden tot activatie van het immuunsysteem waardoor de tumor effectiever kan worden opgeruimd. Om die reden presenteren wij inhoofdstuk 7 een studie waarin wij hebben onderzocht of specifiek bij baarmoedertumoren met POLE-mutatie een versterkte immuunrespons aanwezig was. We vonden inderdaad bij tumoren met een POLE-mutatie, maar ook bij tumoren van een andere categorie, namelijk met zogenaamde ‘microsatelliet instabiliteit’, een versterkte immuunrespons. In hoofdstuk 8 hebben we deze bevindingen nog eens bevestigd bij een geselecteerde groep patiënten met baarmoedertumoren met klassieke kenmerken voor een hoog risico op uitzaaiing van de ziekte. Ook in deze hoog risico patiëntenpopulatie werd een versterkte immuunrespons aangetoond in tumoren met een POLE-mutatie en tumoren met microsa- telliet instabiliteit. Deze versterkte immuunrespons, in combinatie met andere specifieke kenmerken van deze twee groepen baarmoedertumoren, suggereren dat deze patiënten baat zouden kunnen hebben bij checkpoint inhibitors, een bepaalde vorm van immunotherapie.
192
CHAPTER 11
Discussion and future directions
Discussion
DISCUSSION AND FUTURE DIRECTIONS
In the field of oncology, increasing emphasis is being placed on improving quality of care to improve outcomes and optimize the use of available resources. The studies presented in this thesis focused on improving quality of care in patients with ovarian and endometrial cancer by improving organization of care and encouraging individualization of care.
Efforts aimed at improving organization of care for patients with ovarian cancer Currently, standard therapy for patients with advanced ovarian cancer comprises a combination of cytoreductive surgery and platinum-based chemotherapy. Within the past decade the importance of cytoreduction to no macroscopic residual disease has become widely accepted. This has triggered initiatives aimed at improving cytoreductive outcomes such as centralization of care to high volume hospitals with highly specialized staff and the implementation of neoadjuvant chemotherapy (NACT) (as reviewed in chapter 4). Within the Netherlands, centralization of care and the implementation of NACT coincided with improvements in surgical outcome and survival (as demonstrated in chapter 2).
The impact of centralization of care to high volume centers has not been studied in randomized clinical trials. Nonetheless, the value of centralization of services is widely acknowledged as favorable surgical outcomes have consistently been demonstrated in high volume hospitals with specialized sur- gical teams[1–3]. As the number of gynecologic oncologists involved in cytoreductive surgeries differs per hospital, it is surprising that the discussion regarding volume requirements has mainly focused 11 on case-volumes of hospitals instead of individual physicians. The recently published ESGO guidelines have taken this into account, requiring ≥95% of surgeries to be performed or supervised by surgeons performing at least 10 cytoreductive surgeries for ovarian cancer patients annually. Furthermore, within these guidelines minimal as well as optimal targets for quality indicators have been defined. The minimal annual volume targets of ≥20 cytoreductive surgeries, which are based on available literature, have been extended with optimal annual volume targets of ≥100 cytoreductive surgeries based on expert consensus. Within our ovarian cancer cohort, favorable surgical outcomes were demonstrated in hospitals with annual case volumes of ≥30 surgeries (unpublished data). Though solid evidence is still lacking, it is likely that the required annual case volumes in the Netherlands will be increased within the near future, generating new logistical challenges for the small number of high volume hospitals that will remain[4]. Importantly, further intensification of centralization of care will have profound consequences for ovarian cancer patients as they will be required to travel longer distances for their surgical treatment.
One of the drawbacks of centralization of care to hospitals with annual case volumes of ≥20 surgeries is the possibility of inducing delays. While the prognostic impact of delay in ovarian cancer patients is unclear, there is evidence that it can lead to anxiety and reduce patient satisfaction and quality of life[5–7]. We therefore measured health system intervals in patients suspected of ovarian cancer
197 Chapter 11
within our Managed Clinical Network in 2013 and 2014 (chapter 3). During the study period increased awareness of health system intervals led to changes in clinical practice and improved compliance with national health system interval guidelines. As further intensification of centralization is likely, regular monitoring and evaluation of health system intervals is deemed essential. In line with this, the development and implementation of uniform electronic patient files will facilitate real-time monitoring of health system intervals and may provide crucial feedback with regard to the prevention of delays.
Notably, the impact of NACT on survival is subject of heated debate. Two landmark randomized clinical trials with mature survival data have shown similar overall survival for patients undergoing primary cytoreductive surgery (PCS) and adjuvant chemotherapy (ACT) compared to patients undergoing NACT and interval cytoreductive surgery (ICS), despite higher complete cytoreduction rates and lower surgical morbidity in the NACT group[8,9]. An exploratory analysis of one of these randomized trials demonstrated favorable survival in patients with stage IIIC disease with a low tumor load that under- went PCS and patients with stage IV disease and extensive tumor load that underwent NACT[10]. Data from the National Cancer Data Base, comprising women with stage IIIC and IV EOC diagnosed in the period 2003-2011, aged ≤60 with a Charlson comorbidity index of 0, confirmed the favorable sur- vival of PCS within this subgroup[11]. Importantly, the two randomized clinical trials have important limitations such as a selection bias toward patients with unfavorable prognostic features as well as suboptimal cytoreductive outcomes. To that end, results of the randomized Trial on Radical Upfront Surgery in Advanced Ovarian Cancer (TRUST), with stringent cytoreductive surgery criteria such as annual surgical case volumes of ≥36 and a ≥50% complete resection rate, are eagerly awaited. Final data collection of the TRUST trial is expected in 2023.
Based on currently available data, new clinical guidelines were developed for ovarian cancer care by the European Society of Gynaecologic Oncology (ESGO), American Society of Gynecologic Oncology (SGO) and American Society of Clinical Oncology (ASCO) in 2016[4,12]. These guidelines recommend PCS and ACT for patients with a high likelihood of achieving complete cytoreduction with acceptable morbidity, and NACT and ICS for patients in whom complete cytoreduction is deemed unlikely or with unacceptable morbidity. However, selection of patients for PCS requires further optimization. As discussed extensively in chapter 4, pre-operatively available markers such as age, performance status, Cancer Antigen 125 and pre-operative imaging may provide valuable information about tumor load and the risk of developing complications. Moreover, histologic and genomic tumor features have been suggested as potential markers for assessing the chance of chemotherapy resistance. There is considerable tumor heterogeneity within ovarian cancer tumors, and this may in itself have predictive value for survival after chemotherapy treatment as sub-clonal tumor populations may expand during chemotherapy resulting in clinical relapse[13–17]. As the cell types that are responsible for chemo- therapy resistance are rare it is important to note that gene expression data from bulk tumor samples may not be an adequate marker for chemotherapy resistance. Single cell analyses comprising samples from before and after the development of chemotherapy resistance may prove useful in helping to
198 Discussion
define chemotherapy resistant single cell signatures. Another approach for preoperative patient selection may be pre-operative assessment of operability through diagnostic laparoscopy. Though criticized for having sub-optimal cytoreductive outcomes, the results from the LapOvCA trial suggest that diagnostic laparoscopy can reduce the number of futile laparotomies and is cost-effective[18,19]. It is likely that optimal selection of patients for PCS will require a combination of approaches.
Efforts aimed at improving organization of care for patients with endometrial cancer Surgery forms the cornerstone of therapy for endometrial cancer patients. Patients at high risk of metastatic spread and recurrence also receive adjuvant therapy in the form of chemotherapy and/or radiotherapy. As of 2013, treatment with curative intent for patients with stage II, III or IV endometrial cancer has been centralized to specialized hospitals in the Netherlands. Furthermore, prospective discussion of all high risk patients in a multidisciplinary setting is required[20].
To guide therapeutic decisions, endometrial cancer patients are categorized into risk groups based on age, FIGO stage, myometrial invasion depth, tumor grade, tumor type and lymph vascular space invasion, according to ESMO clinical practice guidelines[21,22]. Histological evaluation of the tissue obtained pre-operatively is used to guide decisions regarding the extent of surgery, and histologi- cal evaluation of the tissue obtained during surgery is used to guide decisions regarding adjuvant therapy. In Chapter 5, we demonstrated an overall concordance rate of 90% between the pre- and post-operative risk stratifications. This implies that in 90% of cases clinical decisions regarding the extent of surgery were based on a correct risk stratification, minimizing the risk of over- or under 11 treatment. Compared to the concordance rates stated in previous studies, ranging from 58-84%, this is relatively high. Notably, our analyses demonstrated less favorable survival outcomes of patients with high pre- and low post-operative risks (4% of our study population), suggestive of the presence of intra-tumor heterogeneity and/or mixed morphologic characteristics. As such, despite their post-op- erative low risk, these patients may require adjuvant therapy for local control of their disease[23–25].
In general, intra-tumor heterogeneity is seen as a diagnostic and therapeutic challenge. High intra-tu- mor genetic heterogeneity has been demonstrated in endometrial cancer, though it has been suggested that genetic analysis of endometrial cancer samples captures this heterogeneity[26,27]. Intra-tumor heterogeneity is also present at a morphologic level, leading to considerable inter-ob- server variation in pathologic assessment of endometrial samples[27]. Therefore, to overcome stratification inaccuracies due to intra-tumor genetic and morphologic heterogeneity, incorporation of molecular alterations into clinical risk stratification models has been suggested. Two classification models have been developed incorporating molecular markers. When compared with traditional stratification models, improved risk assessment has been demonstrated in both molecular models [28,29]. Both tools also showed a high concordance between pre-operative and post-operative classifications[30,31]. Recently, low intra-tumor heterogeneity was demonstrated among the well-es- tablished prognostic molecular markers POLE, CTNNB1 and p53, which are also part of one of the
199 Chapter 11
molecular risk stratification models[32]. The intra-tumor heterogeneity impacted the risk stratifica- tion in 10% of cases, thus in agreement with the 10% discordance seen in chapter 5, however the authors suggest that the level of intratumor heterogeneity may have been overestimated due to the selection of large tumors with at least 3 tumor fractions. Risk stratification using clinicopathologic and molecular markers has been shown to result in a better risk assessment compared to molecular markers alone[29]. In line with this, selection of patients for adjuvant therapy using a combination of clinicopathologic and molecular markers is currently being investigated in the Post-Operative Radiation Therapy in Endometrial Carcinoma (PORTEC)-4 trial.
In clinical guidelines, the post-operative risk assessment, considered to be the gold standard, is used to guide the choice of adjuvant therapy for patients with endometrial cancer[33]. As compliance to guidelines is viewed as an important quality indicator we proceeded to evaluate compliance of phy- sicians with these guidelines in a population-based study (Chapter 5). While an excellent compliance with guidelines was determined in low and low-intermediate risk patients, the compliance dropped significantly in patients with high-intermediate and high risk. Compliance with guidelines was lowest in a subgroup of high risk patients with FIGO stage III serous disease. We attribute the low compli- ance in high-intermediate and high risk groups to the lack of survival benefit of adjuvant therapy and inconsistencies present in current literature and guidelines. As data regarding the reasons for noncompliance were lacking, it was not possible to take this into account.
In June 2017, the first results of the PORTEC-3 were presented at the conference of the American Society of Clinical Oncology (ASCO), suggesting prolonged 5-year failure free survival of patients with high risk stage III endometrial cancer treated with chemotherapy and radiation compared to those treated with radiotherapy alone. Importantly, significantly more adverse events were reported in the first 12 months in the combined chemotherapy and radiotherapy group. The first results of the GOG 0258 were also presented at the ASCO 2017 meeting. Within this trial chemo-radiation was compared to chemotherapy alone. No difference was determined in recurrence free survival, how- ever chemo-radiation did improve loco-regional control. Maturation of overall survival data is eagerly awaited for both trials. Current clinical guidelines will require an update once the outcomes of these two clinical trials have officially been published.
Another effort which is currently being undertaken to improve selection of patients for adjuvant ther- apy is the randomized controlled trial entitled: Selective Targeting of Adjuvant Therapy for Endometrial Carcinoma (STATEC). In brief, the STATEC trial aims to determine whether lymphadenectomy, used to restrict adjuvant treatment to node positive women, results in non-inferior survival compared to adju- vant therapy given to all high risk apparent stage I endometrial cancer patients. Important secondary outcomes include quality of life, and performance of sentinel lymph node assessment. Of note, the therapeutic value of performing a pelvic and para-aortic lymphadenectomy in high risk endometrial cancer patients will be studied in the Endometrial Cancer Lymphadenectomy trial (ECLAT).
200 Discussion
Efforts aimed at individualization of care for ovarian and endometrial cancer Despite efforts aimed at optimization of traditional treatment strategies such as surgery, chemother- apy and radiotherapy, there is an unmet need for effective treatment in advanced ovarian cancer and high risk endometrial cancer. Recent insights in genomic and epigenomic inter-tumor heterogeneity have emphasized the importance of individualization of care based on tumor-specific features of ovarian and endometrial cancer[34,35]. Examples of such individualized approaches include targeted therapies and immunotherapies, which have already been studied extensively in melanoma and non- small cell lung cancer, among other cancers. Although the clinical efficacy of these strategies is evident in a subset of patients, selecting the patients who may benefit remains a challenge.
In chapter 6 and chapter 7, we demonstrated the presence of an enhanced immune response, with high numbers of neoantigens as well as high numbers of lymphocytes expressing the immu- nomodulatory molecules PD-1 and PD-L1, in patients with POLE-mutant and microsatellite unstable endometrial cancers. These data have been validated by others[36–38]. Based on emerging data linking mutational burden and immune response, it is expected that these patients, with so called ‘hot’ tumors, will benefit from checkpoint inhibition strategies that block the PD-1 and PD-L1 immuno- modulatory molecules present within their tumors[39]. Recently, immunogenic differences were also demonstrated between endometrial cancers with hereditary microsatellite instability and sporadic microsatellite instability, suggesting that future clinical trials should separately evaluate the efficacy of immune checkpoint inhibition in patients with these two forms microsatellite instability[38]. So far, data on anti-tumor activity of checkpoint inhibitors in microsatellite endometrial cancer is prom- 11 ising[40,41]. In line with these findings, the FDA approved the administration of pembrolizumab, a checkpoint inhibitor, for microsatellite unstable solid cancers as of May 2017. Clinical data on the anti-tumor effect of checkpoint inhibition in POLE-mutant endometrial cancers is currently based on case reports[42,43]. From a clinical perspective, checkpoint inhibition may not be suitable for patients with low-intermediate risk POLE-mutant endometrial cancer as they have an excellent prognosis when treated with standard therapy. In fact, considering this excellent prognosis, one may wonder whether these patients require adjuvant therapy at all. As such, optimal adjuvant therapy for patients with POLE-mutant endometrial cancer is currently being investigated in the PORTEC-4a clinical trial. In contrast, in patients with low levels of tumor infiltrating lymphocytes, termed ‘cold’ tumors, thera- peutic strategies should be aimed at activation of the immune response. In these patients enhancing the number of specific anti-tumor immune cells within the tumor should be considered. These anti-tu- mor immune cells may be targeted toward antigens which are expressed by endometrial cancers such as human epidermal growth factor 2 (HER2), folate receptor alpha (FRα), mesothelin and p53[44]. Approaches for targeted therapies against these antigens include monoclonal antibodies, bi-specific antibodies, vaccines and adoptive T-cell therapies using chimeric antigen receptors.
Whether immunotherapeutic strategies have a place in the neoadjuvant or adjuvant setting, or whether they should be combined with standard therapy, remains to be elucidated. With this in
201 Chapter 11
mind, we are currently investigating the effects of standard carboplatin/taxol chemotherapy on sys- temic immunity in a cohort of high-grade serous ovarian cancer patients. So far, we have observed a depletion of circulating myeloid suppressor cells without major systemic changes in T-cell subsets during carboplatin/taxol chemotherapy, a finding that was previously also reported in cervical cancer patients[45]. Although further validation in humanized patient-derived xenografts and clinical trials is required, our preliminary data suggest that combining immunotherapeutic (checkpoint inhibi- tion) strategies with standard chemotherapy seems feasible during first line treatment of high-grade serous ovarian cancer.
As novel treatment modalities are costly and have the potential to induce severe toxicity, adequate patient selection and considerations concerning cost-effectiveness are essential. To aid clinical deci- sion making, and enable affordable cancer care, the European Society of Medical Oncology (ESMO) has developed and validated the Magnitude of Clinical Benefit Scale (MCBS) to prioritize novel anti-cancer therapies in solid tumors[46]. This tool provides a rational, structured and consistent approach for the stratification of the magnitude of clinical benefit that can be anticipated from novel anti-cancer treat- ment modalities. The scale was recently applied to a cohort of contemporary randomized controlled trials, suggesting that a majority of published trials with statistically significant results fail to identify therapies that have a meaningful clinical benefit[47]. As such, clinicians and investigators should keep the ESMO MCBS in mind when designing future clinical trials with novel agents. Furthermore, the ESMO MSBC may also provide valuable information for policy makers involved in clinical approval of novel anti-cancer therapeutics.
In conclusion, the field of oncology is slowly moving away from traditional ‘one size fits all’ treatment strategies, which were provided in almost all regional hospitals. First of all, emphasis on quality indi- cators and their corresponding targets has encouraged centralization of specialized oncologic care, surgical or in the form of novel treatment strategies, to dedicated high volume hospitals. Secondly, advances in our understanding of cancer biology have provided a rationale for individualization of care. Optimal tailoring of (novel) oncologic therapies to patients with specific prognostic tumor features is likely to improve outcomes while minimizing unnecessary toxicity and reducing futile healthcare expenditure. Lastly, future clinical trials should be aimed at the demonstration of mean- ingful clinical benefit of novel cancer therapies, and this should be a requirement for clinical approval of such therapies.
202 Discussion
REFERENCES
1 Y.L. Woo, M. Kyrgiou, A. Bryant, T. Everett, H.O. Dickinson, Centralisation of services for gynaecological cancer, Cochrane Database Syst. Rev. 3 (2012) CD007945. doi:10.1002/14651858.CD007945.pub2 [doi]. 2 P. Dahm-Kähler, C. Palmqvist, C. Staf, E. Holmberg, L. Johannesson, Centralized primary care of advanced ovarian cancer improves complete cytoreduction and survival - A population-based cohort study, Gynecol. Oncol. 142 (2016) 211–216. doi:10.1016/j.ygyno.2016.05.025. 3 R.A. Cowan, R.E. O’Cearbhaill, G.J. Gardner, D.A. Levine, K.L. Roche, Y. Sonoda, O. Zivanovic, W.P. Tew, E. Sala, Y. Lakhman, H.A. Vargas Alvarez, D.M. Sarasohn, S. Mironov, N.R. Abu-Rustum, D.S. Chi, Is It Time to Centralize Ovarian Cancer Care in the United States?, Ann. Surg. Oncol. (2016) 989–993. doi:10.1245/s10434-015-4938-9. 4 D. Querleu, F. Planchamp, L. Chiva, C. Fotopoulou, D. Barton, D. Cibula, G. Aletti, S. Carinelli, C. Creutzberg, B. Davidson, P. Harter, L. Lundvall, C. Marth, P. Morice, A. Rafii, I. Ray-Coquard, A. Rockall, C. Sessa, A. van der Zee, I. Vergote, A. du Bois, European Society of Gynaecologic Oncology Quality Indicators for Advanced Ovarian Cancer Surgery., Int. J. Gynecol. Cancer. 26 (2016) 1354–1363. doi:10.1097/IGC.0000000000000767. 5 R.D. Neal, P. Tharmanathan, B. France, N.U. Din, S. Cotton, J. Fallon-Ferguson, W. Hamilton, a Hendry, M. Hendry, R. Lewis, U. Macleod, E.D. Mitchell, M. Pickett, T. Rai, K. Shaw, N. Stuart, M.L. Tørring, C. Wilkinson, B. Williams, N. Williams, J. Emery, Is increased time to diagnosis and treatment in symptomatic cancer associated with poorer outcomes? Systematic review., Br. J. Cancer. 112 Suppl (2015) S92-107. doi:10.1038/bjc.2015.48. 6 K.M. Robinson, K.B. Christensen, B. Ottesen, A. Krasnik, Diagnostic delay, quality of life and patient satisfaction among women diagnosed with endometrial or ovarian cancer: a nationwide Danish study., Qual. Life Res. 21 (2012) 1519–25. doi:10.1007/s11136-011-0077-3. 7 Z. Feng, H. Wen, R. Bi, W. Yang, X. Wu, Prognostic impact of the time interval from primary surgery to intrave- nous chemotherapy in high grade serous ovarian cancer, Gynecol. Oncol. 141 (2016) 466–470. doi:10.1016/j. ygyno.2016.04.016. 8 I. Vergote, C.G. Tropé, F. Amant, G.B. Kristensen, T. Ehlen, N. Johnson, R.H.M. Verheijen, M.E.L. van der Burg, 11 A.J. Lacave, P.B. Panici, G.G. Kenter, A. Casado, C. Mendiola, C. Coens, L. Verleye, G.C.E. Stuart, S. Pecorelli, N.S. Reed, Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer., N. Engl. J. Med. 363 (2010) 943–953. doi:10.1056/NEJMoa0908806. 9 S. et al. Kehoe, Chemotherapy or upfront surgery for newly diagnosed advanced ovarian cancer: results from the MRC CHORUS trial, J. Clin. Oncol. 31 (2013) a5500. 10 H.S. van Meurs, P. Tajik, M.H. Hof, I. Vergote, G.G. Kenter, B.W. Mol, M.R. Buist, P.M. Bossuyt, Which patients benefit most from primary surgery or neoadjuvant chemotherapy in stage IIIC or IV ovarian cancer? An explor- atory analysis of the European Organisation for Research and Treatment of Cancer 55971 randomised trial, Eur. J. Cancer. 49 (2013) 3191–3201. doi:10.1016/j.ejca.2013.06.013 [doi]. 11 J.A. Rauh-Hain, A. Melamed, A. Wright, A. Gockley, J.T. Clemmer, J.O. Schorge, M.G. Del Carmen, N.L. Keating, V. I, K. S, M. Rj, W. Mj, B. Re, E. El, H. Wj, A. Gd, C. Ds, S. Jo, C. Ds, V.M. Hs, R. Mv, K. Cn, C. Me, W. Dp, M. K, R. Pr, B. Re, B. Re, R. Pr, L. Dy, H. Ns, T. S, C. A, B. Re, L. Jj, W. Jd, D. Sb, C. S, Overall Survival Following Neoadjuvant Chemotherapy vs Primary Cytoreductive Surgery in Women With Epithelial Ovarian Cancer: Analysis of the National Cancer Database, JAMA Oncol. 3 (2017) 76–82. doi:10.1001/JAMAONCOL.2016.4411. 12 A.A. Wright, K. Bohlke, D.K. Armstrong, M.A. Bookman, W.A. Cliby, R.L. Coleman, D.S. Dizon, J.J. Kash, L.A. Meyer, K.N. Moore, A.B. Olawaiye, J. Oldham, R. Salani, D. Sparacio, W.P. Tew, I. Vergote, M.I. Edelson, Neoadjuvant chemotherapy for newly diagnosed, advanced ovarian cancer: Society of Gynecologic Oncology and Ameri- can Society of Clinical Oncology Clinical Practice Guideline, J. Clin. Oncol. 34 (2016) 3460–3473. doi:10.1200/ JCO.2016.68.6907. 13 B.J. Winterhoff, M. Maile, A.K. Mitra, A. Sebe, M. Bazzaro, M.A. Geller, J.E. Abrahante, M. Klein, R. Hellweg,
203 Chapter 11
S.A. Mullany, K. Beckman, J. Daniel, T.K. Starr, Single cell sequencing reveals heterogeneity within ovarian cancer epithelium and cancer associated stromal cells, Gynecol. Oncol. 144 (2017) 598–606. doi:10.1016/j. ygyno.2017.01.015. 14 R.F. Schwarz, C.K.Y. Ng, S.L. Cooke, S. Newman, J. Temple, A.M. Piskorz, D. Gale, K. Sayal, M. Murtaza, P.J. Bald- win, N. Rosenfeld, H.M. Earl, E. Sala, M. Jimenez-Linan, C.A. Parkinson, F. Markowetz, J.D. Brenton, Spatial and Temporal Heterogeneity in High-Grade Serous Ovarian Cancer: A Phylogenetic Analysis, PLoS Med. 12 (2015) 1–20. doi:10.1371/journal.pmed.1001789. 15 A.A.B.A. da Costa, C. V. Valadares, G. Baiocchi, H. Mantoan, A. Saito, S. Sanches, A.P. Guimarães, M.I.W. Achatz, Neoadjuvant Chemotherapy Followed by Interval Debulking Surgery and the Risk of Platinum Resistance in Epithelial Ovarian Cancer, Ann. Surg. Oncol. 22 (2015) 971–978. doi:10.1245/s10434-015-4623-z. 16 J.A. Rauh-Hain, N. Rodriguez, W.B. Growdon, a. K. Goodman, D.M. Boruta, N.S. Horowitz, M.G. Carmen, J.O. Schorge, Primary Debulking Surgery Versus Neoadjuvant Chemotherapy in Stage IV Ovarian Cancer, Ann. Surg. Oncol. 19 (2012) 959–965. doi:10.1245/s10434-011-2100-x. 17 L. Khalique, A. Ayhan, M. Weale, I. Jacobs, S. Ramus, S. Gayther, Genetic intra-tumour heterogeneity in epi- thelial ovarian cancer and its implications formolecular diagnosis of tumours, J. Pathol. 220 (2010) 114–125. doi:10.1002/path. 18 M.J. Rutten, H.S. van Meurs, R. van de Vrie, K.N. Gaarenstroom, C.A. Naaktgeboren, T. van Gorp, H.G. Ter Brugge, W. Hofhuis, H.W.R. Schreuder, H.J.G. Arts, P.L.M. Zusterzeel, J.M.A. Pijnenborg, M. van Haaften, G. Fons, M.J.A. Engelen, E.A. Boss, M.C. Vos, K.G. Gerestein, E.M.J. Schutter, B.C. Opmeer, A.M. Spijkerboer, P.M.M. Bossuyt, B.W. Mol, G.G. Kenter, M.R. Buist, Laparoscopy to Predict the Result of Primary Cytoreductive Surgery in Patients With Advanced Ovarian Cancer: A Randomized Controlled Trial., J. Clin. Oncol. (2016) JCO2016692962. doi:10.1200/JCO.2016.69.2962. 19 R. van de Vrie, H.S. van Meurs, M.J. Rutten, C.A. Naaktgeboren, B.C. Opmeer, K.N. Gaarenstroom, T. van Gorp, H.G. Ter Brugge, W. Hofhuis, H.W.R. Schreuder, H.J.G. Arts, P.L.M. Zusterzeel, J.M.A. Pijnenborg, M. van Haaften, M.J.A. Engelen, E.A. Boss, M.C. Vos, K.G. Gerestein, E.M.J. Schutter, G.G. Kenter, P.M.M. Bossuyt, B.W. Mol, M.R. Buist, Cost-effectiveness of laparoscopy as diagnostic tool before primary cytoreductive surgery in ovarian cancer, Gynecol. Oncol. (2017). doi:10.1016/j.ygyno.2017.06.019. 20 Stichting Oncologische Samenwerking, Standardization of multidisciplinary cancer care in the Netherlands, SONCOS standardization report 5, 2017. https://www.soncos.org. 21 W.T. Creasman, C.P. Morrow, B.N. Bundy, H.D. Homesley, J.E. Graham, P.B. Heller, Surgical pathologic spread patterns of endometrial cancer. A Gynecologic Oncology Group Study., Cancer. 60 (1987) 2035–41. http:// www.ncbi.nlm.nih.gov/pubmed/3652025 (accessed November 11, 2016). 22 N. Colombo, C. Creutzberg, F. Amant, T. Bosse, A. González-Martín, J. Ledermann, C. Marth, R. Nout, D. Querleu, M.R. Mirza, C. Sessa, ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer: diagnosis, treatment and follow-up, Radiother. Oncol. 117 (2015) 559–581. doi:10.1093/annonc/mdv484. 23 C.L. Creutzberg, W.L. van Putten, P.C. Koper, M.L. Lybeert, J.J. Jobsen, C.C. Wárlám-Rodenhuis, K.A. De Winter, L.C. Lutgens, A.C. van den Bergh, E. van de Steen-Banasik, H. Beerman, M. van Lent, Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial, Lancet. 355 (2000) 1404–1411. doi:10.1016/S0140-6736(00)02139-5. 24 R.A. Nout, V.T.H.B.M. Smit, H. Putter, I.M. Jürgenliemk-Schulz, J.J. Jobsen, L.C.H.W. Lutgens, E.M. van der Steen-Banasik, J.W.M. Mens, A. Slot, M.S. Kroese, B.N.F.M. van Bunningen, A.C. Ansink, W.L.J. van Putten, C.L. Creutzberg, Vaginal brachytherapy versus pelvic external beam radiotherapy for patients with endometrial cancer of high-intermediate risk (PORTEC-2): an open-label, non-inferiority, randomised trial, Lancet. 375 (2010) 816–823. doi:10.1016/S0140-6736(09)62163-2. 25 H.M. Keys, J.A. Roberts, V.L. Brunetto, R.J. Zaino, N.M. Spirtos, J.D. Bloss, A. Pearlman, M.A. Maiman, J.G. Bell, A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk
204 Discussion
endometrial adenocarcinoma: A Gynecologic Oncology Group study, Gynecol. Oncol. 92 (2004) 744–751. doi:10.1016/j.ygyno.2003.11.048. 26 A. Mota, E. Colás, P. García-Sanz, I. Campoy, A. Rojo-Sebastián, S. Gatius, Á. García, L. Chiva, S. Alonso, A. Gil-Moreno, X. González-Tallada, B. Díaz-Feijoo, A. Vidal, P. Ziober-Malinowska, M. Bobiński, R. López-López, M. Abal, J. Reventós, X. Matias-Guiu, G. Moreno-Bueno, Genetic analysis of uterine aspirates improves the diagnostic value and captures the intra-tumor heterogeneity of endometrial cancers., Mod. Pathol. (2016) 1–12. doi:10.1038/modpathol.2016.143. 27 S. Gatius, D. Cuevas, C. Fernández, B. Roman-Canal, V. Adamoli, J.M. Piulats, N. Eritja, M. Martin-Satue, G. Moreno-Bueno, X. Matias-Guiu, Tumor Heterogeneity in Endometrial Carcinoma: Practical Consequences, Pathobiology. (2017). doi:10.1159/000475529. 28 A. Talhouk, M.K. McConechy, S. Leung, W. Yang, A. Lum, J. Senz, N. Boyd, J. Pike, M. Anglesio, J.S. Kwon, A.N. Karnezis, D.G. Huntsman, C.B. Gilks, J.N. McAlpine, Confirmation of ProMisE: A simple, genomics-based clinical classifier for endometrial cancer, Cancer. (2017) 1–12. doi:10.1002/cncr.30496. 29 E. Stelloo, R.A. Nout, E.M. Osse, I.J. Ju rgenliemk-Schulz, J.J. Jobsen, L.C. Lutgens, E.M. van der Steen-Banasik, H.W. Nijman, H. Putter, T. Bosse, C.L. Creutzberg, V.T.H.B.M. Smit, Improved risk assessment by integrating molecular and clinicopathological factors in early-stage endometrial cancer - combined analysis of PORTEC cohorts, Clin. Cancer Res. (2016) 1–10. doi:10.1158/1078-0432.CCR-15-2878. 30 A. Talhouk, L.N. Hoang, M.K. McConechy, Q. Nakonechny, J. Leo, A. Cheng, S. Leung, W. Yang, A. Lum, M. Köbel, C.-H. Lee, R.A. Soslow, D.G. Huntsman, C.B. Gilks, J.N. McAlpine, Molecular classification of endometrial carci- noma on diagnostic specimens is highly concordant with final hysterectomy: Earlier prognostic information to guide treatment, Gynecol. Oncol. (2016). doi:http://dx.doi.org/10.1016/j.ygyno.2016.07.090. 31 E. Stelloo, R.A. Nout, L.C.L.M. Naves, N.T. Ter Haar, C.L. Creutzberg, V.T.H.B.M. Smit, T. Bosse, High concordance of molecular tumor alterations between pre-operative curettage and hysterectomy specimens in patients with endometrial carcinoma, Gynecol. Oncol. 133 (2014) 197–204. doi:10.1016/j.ygyno.2014.02.012. 32 M. van Esterik, I.C. van Gool, C.D. de Kroon, R.A. Nout, C.L. Creutzberg, V.T.H.B.M. Smit, T. Bosse, E. Stelloo, 11 Limited impact of intratumour heterogeneity on molecular risk assignment in endometrial cancer, Oncotarget. 8 (2017) 25542–25551. 33 Werkgroep Oncologische Gynaecologie, Landelijke richtlijn endometriumcarcinoom, (2011). http://www.onco- line.nl/endometriumcarcinoom (accessed July 6, 2017). 34 D. Bell, a. Berchuck, M. Birrer, J. Chien, D.W. Cramer, F. Dao, R. Dhir, P. DiSaia, H. Gabra, P. Glenn, a. K. Godwin, J. Gross, L. Hartmann, M. Huang, D.G. Huntsman, M. Iacocca, M. Imielinski, S. Kalloger, B.Y. Karlan, D. a. Levine, G.B. Mills, C. Morrison, D. Mutch, N. Olvera, S. Orsulic, K. Park, N. Petrelli, B. Rabeno, J.S. Rader, B.I. Sikic, K. Smith-McCune, a. K. Sood, D. Bowtell, R. Penny, J.R. Testa, K. Chang, H.H. Dinh, J. a. Drummond, G. Fowler, P. Gunaratne, a. C. Hawes, C.L. Kovar, L.R. Lewis, M.B. Morgan, I.F. Newsham, J. Santibanez, J.G. Reid, L.R. Trevino, Y.-Q. Wu, M. Wang, D.M. Muzny, D. a. Wheeler, R. a. Gibbs, G. Getz, M.S. Lawrence, K. Cibulskis, a. Y. Sivachenko, C. Sougnez, D. Voet, J. Wilkinson, T. Bloom, K. Ardlie, T. Fennell, J. Baldwin, S. Gabriel, E.S. Lander, L. Ding, R.S. Fulton, D.C. Koboldt, M.D. McLellan, T. Wylie, J. Walker, M. O’Laughlin, D.J. Dooling, L. Fulton, R. Abbott, N.D. Dees, Q. Zhang, C. Kandoth, M. Wendl, W. Schierding, D. Shen, C.C. Harris, H. Schmidt, J. Kalicki, K.D. Delehaunty, C.C. Fronick, R. Demeter, L. Cook, J.W. Wallis, L. Lin, V.J. Magrini, J.S. Hodges, J.M. Eldred, S.M. Smith, C.S. Pohl, F. Vandin, B.J. Raphael, G.M. Weinstock, E.R. Mardis, R.K. Wilson, M. Meyerson, W. Winckler, G. Getz, R.G.W. Verhaak, S.L. Carter, C.H. Mermel, G. Saksena, H. Nguyen, R.C. Onofrio, M.S. Lawrence, D. Hubbard, S. Gupta, a. Crenshaw, a. H. Ramos, K. Ardlie, L. Chin, a. Protopopov, J. Zhang, T.M. Kim, I. Perna, Y. Xiao, H. Zhang, G. Ren, N. Sathiamoorthy, R.W. Park, E. Lee, P.J. Park, R. Kucherlapati, D.M. Absher, L. Waite, G. Sherlock, J.D. Brooks, J.Z. Li, J. Xu, R.M. Myers, P.W. Laird, L. Cope, J.G. Herman, H. Shen, D.J. Weisenberger, H. Noushmehr, F. Pan, T. Triche Jr, B.P. Berman, D.J. Van Den Berg, J. Buckley, S.B. Baylin, P.T. Spellman, E. Purdom, P. Neuvial, H. Bengtsson, L.R. Jakkula, S. Durinck, J. Han, S. Dorton, H. Marr, Y.G. Choi, V. Wang, N.J.
205 Chapter 11
Wang, J. Ngai, J.G. Conboy, B. Parvin, H.S. Feiler, T.P. Speed, J.W. Gray, D. a. Levine, N.D. Socci, Y. Liang, B.S. Taylor, N. Schultz, L. Borsu, a. E. Lash, C. Brennan, a. Viale, C. Sander, M. Ladanyi, K. a. Hoadley, S. Meng, Y. Du, Y. Shi, L. Li, Y.J. Turman, D. Zang, E.B. Helms, S. Balu, X. Zhou, J. Wu, M.D. Topal, D.N. Hayes, C.M. Perou, G. Getz, D. Voet, G. Saksena, J. Zhang, H. Zhang, C.J. Wu, S. Shukla, K. Cibulskis, M.S. Lawrence, a. Sivachenko, R. Jing, R.W. Park, Y. Liu, P.J. Park, M. Noble, L. Chin, H. Carter, D. Kim, R. Karchin, P.T. Spellman, E. Purdom, P. Neuvial, H. Bengtsson, S. Durinck, J. Han, J.E. Korkola, L.M. Heiser, R.J. Cho, Z. Hu, B. Parvin, T.P. Speed, J.W. Gray, N. Schultz, E. Cerami, B.S. Taylor, a. Olshen, B. Reva, Y. Antipin, R. Shen, P. Mankoo, R. Sheridan, G. Ciriello, W.K. Chang, J. a. Bernanke, L. Borsu, D. a. Levine, M. Ladanyi, C. Sander, D. Haussler, C.C. Benz, J.M. Stuart, S.C. Benz, J.Z. Sanborn, C.J. Vaske, J. Zhu, C. Szeto, G.K. Scott, C. Yau, K. a. Hoadley, Y. Du, S. Balu, D.N. Hayes, C.M. Perou, M.D. Wilkerson, N. Zhang, R. Akbani, K. a. Baggerly, W.K. Yung, G.B. Mills, J.N. Weinstein, R. Penny, T. Shelton, D. Grimm, M. Hatfield, S. Morris, P. Yena, P. Rhodes, M. Sherman, J. Paulauskis, S. Millis, a. Kahn, J.M. Greene, R. Sfeir, M. a. Jensen, J. Chen, J. Whitmore, S. Alonso, J. Jordan, a. Chu, J. Zhang, a. Barker, C. Compton, G. Eley, M. Ferguson, P. Fielding, D.S. Gerhard, R. Myles, C. Schaefer, K.R. Mills Shaw, J. Vaught, J.B. Vockley, P.J. Good, M.S. Guyer, B. Ozenberger, J. Peterson, E. Thomson, Integrated genomic analyses of ovarian carcinoma, Nature. 474 (2011) 609–615. doi:10.1038/nature10166. 35 C. Kandoth, N. Schultz, A. Cherniack, R. Akbani, Y. Liu, H. Shen, A. Robertson, I. Pashtan, R. Shen, C. Benz, C. Yau, P. Laird, L. Ding, W. Zhang, G. Mills, R. Kucherlapati, E. Mardis, D. Levine, Integrated genomic characterization of endometrial carcinoma., Nature. 497 (2013) 67–73. doi:10.1038/nature12113. 36 B.E. Howitt, S. a. Shukla, L.M. Sholl, L.L. Ritterhouse, J.C. Watkins, S. Rodig, E. Stover, K.C. Strickland, A.D. D’Andrea, C.J. Wu, U. a. Matulonis, P. a. Konstantinopoulos, Association of Polymerase e–Mutated and Micro- satellite-Instable Endometrial Cancers With Neoantigen Load, Number of Tumor-Infiltrating Lymphocytes, and Expression of PD-1 and PD-L1, JAMA Oncol. (2015) 1–5. doi:10.1001/jamaoncol.2015.2151. 37 S. Bellone, F. Centritto, J. Black, C. Schwab, D. English, E. Cocco, S. Lopez, E. Bonazzoli, F. Predolini, F. Fer- rari, D.-A. Silasi, E. Ratner, M. Azodi, P.E. Schwartz, A.D. Santin, Polymerase ε (POLE) ultra-mutated tumors induce robust tumor-specific CD4+ T cell responses in endometrial cancer patients, Gynecol. Oncol. (2015). doi:10.1016/j.ygyno.2015.04.027. 38 J.B. Pakish, Q. Zhang, Z. Chen, H. Liang, G. Chisholm, Y. Yuan, S.C. Mok, R.J. Broaddus, K. Lu, M.S. Yates, Immune microenvironment in microsatellite instable endometrial cancers: Hereditary or sporadic origin matters, Clin. Cancer Res. (2017) clincanres.2655.2016. doi:10.1158/1078-0432.CCR-16-2655. 39 J.M. Piulats, E. Guerra, M. Gil-Martín, B. Roman-Canal, S. Gatius, R. Sanz-Pamplona, A. Velasco, A. Vidal, X. Matias- Guiu, Molecular approaches for classifying endometrial carcinoma, Gynecol. Oncol. 145 (2016) 200–207. doi:10.1016/j.ygyno.2016.12.015. 40 D.T. Le, J.N. Uram, H. Wang, B.R. Bartlett, H. Kemberling, A.D. Eyring, A.D. Skora, B.S. Luber, N.S. Azad, D. Laheru, B. Biedrzycki, R.C. Donehower, A. Zaheer, G.A. Fisher, T.S. Crocenzi, J.J. Lee, S.M. Duffy, R.M. Goldberg, A. de la Chapelle, M. Koshiji, F. Bhaijee, T. Huebner, R.H. Hruban, L.D. Wood, N. Cuka, D.M. Pardoll, N. Papa- dopoulos, K.W. Kinzler, S. Zhou, T.C. Cornish, J.M. Taube, R.A. Anders, J.R. Eshleman, B. Vogelstein, L.A. Diaz, PD-1 Blockade in Tumors with Mismatch-Repair Deficiency., N. Engl. J. Med. 372 (2015) 2509–20. doi:10.1056/ NEJMoa1500596. 41 P.A. Ott, Y. Bang, D. Berton-Rigaud, E. Elez, M.J. Pishvaian, H.S. Rugo, I. Puzanov, J.M. Mehnert, K.L. Aung, J. Lopez, M. Carrigan, S. Saraf, M. Chen, J.-C. Soria, Safety and Antitumor Activity of Pembrolizumab in Advanced Programmed Death Ligand 1-Positive Endometrial Cancer: Results From the KEYNOTE-028 Study., J. Clin. Oncol. (2017) JCO2017725952. doi:10.1200/JCO.2017.72.5952. 42 J.M. Mehnert, A. Panda, H. Zhong, K. Hirshfield, S. Damare, K. Lane, L. Sokol, M.N. Stein, L. Rodriguez-rodriquez, H.L. Kaufman, S. Ali, J.S. Ross, D.C. Pavlick, G. Bhanot, E.P. White, R.S. Dipaola, A. Lovell, J. Cheng, S. Ganesan, Immune activation and response to pembrolizumab in POLE -mutant endometrial cancer, JCI. 126 (2016) 1–7. doi:10.1172/JCI84940.expression.
206 Discussion
43 A.D. Santin, S. Bellone, N. Buza, J. Choi, P.E. Schwartz, J. Schlessinger, R.P. Lifton, Regression of chemothera- py-resistant Polymerase epsilon (POLE) ultra-mutated and MSH6 hyper-mutated endometrial tumors with nivolumab., Clin. Cancer Res. 1 (2016) 5–6. doi:10.1158/1078-0432.CCR-16-1031. 44 A. Rodriguez-Garcia, N.G. Minutolo, J.M. Robinson, D.J. Powell, T-cell target antigens across major gynecologic cancers, Gynecol. Oncol. 145 (2017) 426–435. doi:10.1016/j.ygyno.2017.03.510. 45 M.J. Welters, Vaccination during myeloid cell depletion by cancer chemotherapy fosters robust T-cell responses, Sci. Transl Med. 8 (2016). doi:10.1126/scitranslmed.aad8307. 46 N.I. Cherny, R. Sullivan, U. Dafni, J.M. Kerst, A. Sobrero, C. Zielinski, E.G.E. de Vries, M.J. Piccart, A standardised, generic, validated approach to stratify the magnitude of clinical benefit that can be anticipated from anti-cancer therapies: The European Society for Medical Oncology Magnitude of Clinical Benefit Scale (ESMO-MCBS), Ann. Oncol. 26 (2015) 1547–1573. doi:10.1093/annonc/mdv249. 47 J.C. Del Paggio, B. Azariah, R. Sullivan, W.M. Hopman, F. V. James, S. Roshni, I.F. Tannock, C.M. Booth, Do con- temporary randomized controlled trials meet ESMO thresholds for meaningful clinical benefit?, Ann. Oncol. 28 (2017) 157–162. doi:10.1093/annonc/mdw538.
11
207
APPENDIX
List of contributing authors and affiliations
List of contributing authors and affiliations
CONTRIBUTING AUTHORS AND AFFILIATIONS
M.A. van der Aa Department of research Netherlands Comprehensive Cancer Organization Utrecht, the Netherlands
M.J. Apperloo Department of Obstetrics and Gynecologie Medical Center Leeuwarden Leeuwarden, the Netherlands
H.J. Arts University of Groningen University Medical Center Groningen Department of Obstetrics and Gynecology Groningen, the Netherlands
J.H. Becker St. Antonius hospital Department of Obstetrics and Gynecology Nieuwegein, the Netherlands
D. Boll A Catharina Hospital Department of Obstetrics and Gynecology Eindhoven, the Netherlands
T. Bosse Department of Pathology Leiden University Medical Center Leiden, the Netherlands
K. Bouwman University of Groningen University Medical Center Groningen Department of Obstetrics and Gynecology Groningen, the Netherlands
211 Appendix
G.L. Bremer Zuyderland Medical Center Department of Obstetrics and Gynecology Heerlen/Sittard, the Netherlands
M. de Bruyn University of Groningen University Medical Center Groningen Department of Obstetrics and Gynecology Groningen, the Netherlands
D.N. Church Molecular and Population Genetics Laboratory The Wellcome Trust Centre for Human Genetics University of Oxford and Oxford Cancer Centre Oxford, United Kingdom
C.L. Creutzberg Leiden University Medical Center Department of Radiation Oncology Leiden, the Netherlands
E.J. Crosbie Institute of Cancer Sciences University of Manchester St Marys Hospital Manchester, United Kingdom
W.J. van Driel Antoni van Leeuwenhoek Hospital Department of Gynecologic Oncology Center for Gynecologic Oncology Amsterdam Amsterdam, the Netherlands
N.P.M. Ezendam Department of research Netherlands Comprehensive Cancer Organization Utrecht, the Netherlands
212 List of contributing authors and affiliations
L. Freeman-Mills Molecular and Population Genetics Laboratory The Wellcome Trust Centre for Human Genetics University of Oxford Oxford, United Kingdom
I.C. van Gool Department of Pathology Leiden University Medical Center Leiden, the Netherlands
H. Hollema University of Groningen University Medical Center Groningen Department of Pathology Groningen, the Netherlands
P. Klenerman Immunity Theme Oxford Comprehensive Biomedical Research Centre University of Oxford Oxford, United Kingdom
C.M. Koopmans A University of Groningen University Medical Center Groningen Department of Obstetrics and Gynecology Groningen, the Netherlands
C.D. de Kroon Department of Gynecology Leiden University Medical Center Leiden, the Netherlands
R.F.P.M. Kruitwagen Maastricht University Medical Center Department of Obstetrics and Gynecology and Grow, School for Oncology and Developmental Biology Maastricht, the Netherlands
213 Appendix
A. Leary Department of Medical Oncology Gustave Roussy Cancer Center Villejuif, France
E. Marchi Immunity Theme Oxford Comprehensive Biomedical Research Centre University of Oxford Oxford, United Kingdom
L.F. Massuger Radboud University Medical Center Department of Obstetrics and Gynecology Nijmegen, the Netherlands
L. Mileshkin Division of Cancer Medicine Peter MacCallum Cancer Centre East Melbourne, Australia
C.H. Mom VU University Medical Center Center for Gynecologic Oncology Amsterdam Amsterdam, the Netherlands
G.C. Niemeijer University Medical Center Groningen Department of UMC staff Groningen, the Netherlands
H.W. Nijman University of Groningen University Medical Center Groningen Department of Obstetrics and Gynecology Groningen, the Netherlands
214 List of contributing authors and affiliations
R.A. Nout Leiden University Medical Center Department of Radiation Oncology Leiden, the Netherlands
E.M. Osse Department of Pathology Leiden University Medical Center Leiden, the Netherlands
C. Palles Molecular and Population Genetics Laboratory The Wellcome Trust Centre for Human Genetics University of Oxford Oxford, United Kingdom
J.M. Pijnenborg Radboud University Medical Center Nijmegen Department of obstetrics and gynecology Nijmegen, the Netherlands
P.M. Pollock Queensland University of Technology Translational Research Institute Brisbane, Australia A
A.K. Reyners University of Groningen University Medical Center Groningen Department of Medical Oncology Groningen, the Netherlands
V.T.H.B.M. Smit Department of Pathology Leiden University Medical Center Leiden, the Netherlands
215 Appendix
C. van der Spek University Medical Center Groningen Department of UMC staff Groningen, the Netherlands
E. Stelloo Department of Pathology Leiden University Medical Center Leiden, the Netherlands
I.P.M. Tomlinson Molecular and Population Genetics Laboratory and Genomic Medicine Theme Oxford Comprehensive Biomedical Research Centre The Wellcome Trust Centre for Human Genetics University of Oxford Oxford, United Kingdom
M.C. Vermue University of Groningen University Medical Center Groningen Department of Obstetrics and Gynecology Groningen, the Netherlands
A.G.J. van der Zee University of Groningen University Medical Center Groningen Department of Obstetrics and Gynecology Groningen, the Netherlands
216
APPENDIX
Dankwoord
Dankwoord
DANKWOORD
Promoveren doe je natuurlijk niet alleen. Zonder hulp, advies en de nodige afleiding waren de afgelo- pen jaren minder leerzaam en een stuk minder leuk geweest. Graag wil ik iedereen die een bijdrage heeft geleverd aan de totstandkoming van dit proefschrift bedanken.
Mijn 1e promotor, Prof. Dr. H.W. Nijman. Beste Hans, het voelt als de dag van gisteren dat we de mogelijkheden van een wetenschappelijke stage bij de gynaecologische oncologie bespraken. Je respectvolle manier van samenwerken, gevoel voor humor en vermogen om overal de positieve kant van te belichten waardeer ik enorm. De wijze waarop je klinische taken weet te combineren met management en het begeleiden van onderzoekers is bewonderenswaardig. Bedankt voor je vertrouwen, het is een eer dat je me bij allerlei mooie projecten hebt betrokken. Bij het opzetten van de Nederlandse tak van de STATEC-trial hebben we samen al enkele tegenvallers verwerkt (zelf noem je die tegenvallers overigens liever ‘uitdagingen’, een tactiek die ik van je over hoop te nemen). Ik hoop van harte dat we deze mooie studie in de komende jaren samen tot een goed einde kunnen brengen!
Mijn 2e promotor, Prof. Dr. R.F.P.M. Kruitwagen. Beste Roy, hoewel we niet vaak samen om de tafel hebben gezeten was je vanaf de andere kant van het land een betrouwbare en waardevolle kracht binnen onze KWF-studies. Jouw aanwezigheid tijdens onze regelmatige telefonische besprekingen leidde vaak tot nieuwe inzichten en aanvullende analyses. Niet zelden zag jij een fout waar alle andere auteurs, inclusief ikzelf, overheen hadden gelezen. Bedankt voor de aanmoedigingen en de kritische evaluatie van mijn manuscripten.
Mijn co-promotor, Dr. M. de Bruyn. Beste Marco, aan jou de taak de harem eigenwijze onderzoeksters A van de gynaecologische oncologie op het juiste pad te houden. Dat is niet altijd makkelijk. Desondanks wist je ons elke dag weer op te vrolijken met het zingen van (kerst)liedjes en huppelde je fluitend door de gangen. Je hebt mij kennis laten maken met de fascinerende wereld van de tumor immunologie. Bovendien heb je me geleerd dat je niet alles kan plannen, een wijze les waar ik in de kliniek vast veel aan zal hebben. Het was inspirerend om samen te filosoferen over de betekenis van resultaten en plannen te maken voor vervolgonderzoek. Dank je wel voor het delen van je kennis, creativiteit en optimisme. Ik blijf aan je denken als er eend op de menukaart staat!
Mijn co-promotor, Dr. M.A. van der Aa. Lieve Maaike, zonder jouw expertise in de Nederlandse Kanker Registratie en statistische ondersteuning waren onze KWF-studies absoluut in de soep gelopen. Bedankt dat je keer op keer bereid was om analyses te herhalen als we weer een nieuw plannetje hadden bedacht. Ik ben heel blij dat ik mijn eerste stappen in de wetenschap heb kunnen zetten met jou in mijn team. Dat je de supervisie opvoerde tot ongeveer 24/7 als we ons op internationaal grondgebied bevonden zie ik als een teken van echte toewijding. Ik hoop dat ik vanaf de zijlijn betrok- ken kan blijven bij de mooie studies die jullie doen bij het IKNL.
221 Appendix
Graag zou ik de leden van de leescommissie, Prof. Dr. L.F.A.G. Massuger, Prof. Dr. G.A. Huls en Prof. Dr. I.J.M. de Vries willen bedanken voor het beoordelen van mijn proefschrift.
Alle co-auteurs zou ik graag willen bedanken voor de productieve samenwerking. In het bijzonder zou ik graag Stijn Mom, Harry Hollema en Inge van Gool willen bedanken. Lieve Stijn, bedankt voor je aanstekelijke enthousiasme en het zorgvuldig nalezen van mijn manuscripten. Het was erg fijn om klinische onderzoeksvragen met jou te kunnen bespreken. Je bent een voorbeeld, ik hoop dat we contact blijven houden! Beste Harry, bedankt dat ik altijd bij je kon binnenlopen met allerhande pathologische vraagstukken. Dat je meestal eerst ruim de tijd nam om het leven in zijn algemeenheid te bespreken vond ik ontzettend leuk. Ik heb veel van je geleerd. Beste Inge, samen hebben we ons verdiept in de POLE-mutaties bij het endometriumcarcinoom. We hebben cellen geteld tot we er bij neervielen en daarna hebben we nog meer cellen geteld. Het was prettig te weten dat ik tijdens deze immense klus een medestander had in Leiden. Bedankt voor de fijne samenwerking!
Zonder het harde werk van de (registratie)medewerkers van IKNL zou de Nederlandse Kanker Regi- stratie niet bestaan en was een groot deel van ons onderzoek niet mogelijk geweest. Bedankt voor het onderhouden van deze waardevolle bron van informatie.
Graag zou ik ook het secretariaat van de afdeling Obstetrie en Gynaecologie bedanken voor hun hulp met praktische (en onpraktische) vragen. Speciale dank gaat uit naar Diana Woltinge, dank je wel voor de logistieke ondersteuning en het feit dat ik altijd bij je binnen kon lopen.
Collega-onderzoekers van de gynaecologische oncologie. Lieve Fenne, Hagma, Kim, Maartje, Sterre, Joyce, Arjan, Nienke en Ninke, het was fantastisch om deel uit te mogen maken van zo’n fijne onder- zoeksgroep. Bedankt voor de koffiepauzes met ‘echte’ koffie, de luisterende oortjes, adviezen, extra handjes en de gezelligheid. Nienke, bedankt dat je mijn congres-maatje wilde zijn! Heb je nog een per- sonal assistant nodig tijdens de IGCS in Kyoto? Dank ook aan alle andere onderzoekers residerend in Y4.240 en Y4.234, met name Mariette, Anne, Dorieke en Simone. Het was fijn om lief en leed met jullie te kunnen delen. Heel veel succes met het afronden van jullie eigen promotieonderzoeken.
De allerliefsten. Lieve Amber en Jacomijn, bedankt dat jullie er altijd voor me zijn. Hoewel het niet zo mak- kelijk blijkt om elkaar regelmatig te zien blijft het vertrouwd. Jullie vriendschap is me ontzettend dierbaar!
Huisgenootjes van huize Haai en de Heymanslaan. Lieve Haaien, bedankt voor het zijn van mijn Groningse familie en voor het relativeren van mijn studie-frustraties toen het allemaal even niet zo lekker liep. Zonder jullie aanmoedigingen was ik misschien wel geen arts geworden en dan was dit proefschrift er ook niet geweest. Lieve Jolanda, Frederieke, Sophie en Nienke, wat was het fijn om aan de Heymanslaan samen langzaam af te kicken van het studentenleven en te proeven aan het werkende leven.
222 Dankwoord
Clubgenootjes. Lieve Coco’s, bedankt dat we al meer dan 10 jaar vriendinnen zijn! Laten we onze traditionele kerstdiners en clubweekenden nog vele jaren volhouden.
Lieve Elian en Anne-Marie, het was een feest om samen onze sociale stage te lopen in Paramaribo! Ik droom nog steeds over een reünie op locatie, inclusief trip naar de jungle en een middagdutje in een hangmat. Zullen we binnenkort een concreet plan maken?
LOCA bestuursgenootjes. Lieve Rianne, Kim, Lisanne en Margriet, wie had ooit gedacht dat ons LOCA bestuursjaar zo’n waardevolle vriendschap zou opleveren? Het is bijzonder om te zien hoe we alle- maal onze eigen weg in de medische wereld weten te vinden zonder elkaar uit het oog te verliezen.
Groningse vrienden. De angst om door de grote uittocht naar de Randstad alleen achter te blijven in het hoge Noorden blijkt volledig onterecht. Bedankt voor de vele gezellige etentjes, (vrijdagmiddag) borrels en goede gesprekken. Door mijn verhuizing krijgen wij nu onverwacht te maken met een LAT-vriendschap, dus we verplaatsen onze dates naar de weekenden. Fijn dat jullie Louren door de week een beetje willen vermaken.
Collega's van de afdeling Gynaecologie uit het Medisch Spectrum Twente. Bedankt voor het warme welkom in Enschede! De transitie van onderzoek naar kliniek is flink wennen, maar dankzij jullie hulp, geduld en goede adviezen kan ik groeien in mijn nieuwe rol als ANIOS.
Paranimfen. Ik ben zo blij dat jullie aan mijn zijde willen staan tijdens mijn verdediging! Lieve Fenne, toen we in 2013 ongeveer tegelijk begonnen aan een wetenschappelijke stage op de gynaecologische oncologie hadden we nog niet door dat we aan de wieg stonden van het legendarische F-team. Ik ben A zo blij dat we dit hele traject samen hebben doorlopen! Zonder jouw relativeringsvermogen, grapjes en gezelligheid was het stuk minder leuk geweest. Het zal flink wennen zijn dat ik straks niet meer op de hoogte gehouden word van de laatste nieuwtjes over Justin Bieber en over wanneer de sale bij de van Arendonk losbarst. Binnenkort ruil je de gynaecologische oncologie in Groningen voor een opleidingsplek bij de klinische genetica in Amsterdam. Ik ben trots op je en ik ga je missen! Succes met de laatste onderzoeks-loodjes, het gaat je zeker lukken! Lieve Jolanda, sinds 2005 hebben we samen allerlei prachtige avonturen beleefd. We hebben mooie reisjes in warme oorden gemaakt, samen aan de Heymanslaan gewoond en ook samen een jaar het ISCOMS congres georganiseerd. Nieuwe avonturen beleven we nu op de racefiets, nogmaals bedankt dat ik mijn eerste meters op die van jou mocht fietsen. Je lijkt een eindeloze hoeveelheid energie te hebben en het is ongelofelijk hoe je je opleiding hebt weten te combineren met promotieonderzoek en het opvoeden van de twee leukste dochtertjes op deze aardbol. Je hebt afgelopen jaar met verve jouw promotie onderzoek verdedigd en het was een eer om destijds jouw paranimf te mogen zijn. Ik hoop dat we nog jaren samen in het hoge Noorden kunnen blijven!
223 Appendix
Familie. Lieve familie en schoonfamilie, bedankt dat jullie in de afgelopen jaren zo hebben mee geleefd met alle hoogte en dieptepunten van mijn promotietraject! Extra veel dank gaat uit naar Gouke, Jolanda en Tirsa, bedankt dat jullie mij zo liefdevol hebben opgenomen in jullie gezin. Toen ik mijn blin- dedarm had ingeleverd in het UMCG en er direct een bed in de woonkamer werd opgebouwd zodat ik in Harderwijk kon bijkomen van de operatie wist ik dat ik thuis was. Dank voor de goede zorgen!
Zusjes, lieve Hester en Maura, door het veelvuldige verhuizen waren wij altijd op elkaar aangewe- zen tot er nieuwe vriendschappen waren gevormd. In de afgelopen jaren is onze band nog sterker geworden en ik besef steeds meer hoe bijzonder het is om twee zusjes te hebben die altijd voor je klaar staan. Dank jullie wel voor de lieve kaartjes, berichtjes, telefoontjes en gezellige zusjes-dates. Ik ben ontzettend blij met jullie!
Lieve pap en mam, bedankt voor jullie onvoorwaardelijke steun. Hoewel jullie soms ver weg woonden stonden jullie altijd voor me klaar, het is fijn om zo’n stabiele basis te hebben. Ik weet dat jullie ontzet- tend trots zijn en dat maakt het extra bijzonder om mijn proefschrift binnenkort te mogen verdedigen.
Lieve Louren, dank je wel voor je steun, humor en liefde. Wat zijn we een goed team, wie had dat gedacht toen we elkaar tijdens de coschappen leerden kennen? Met mijn (r)entree in de kliniek gaan we een spannende periode tegemoet. Gelukkig hebben we de Polou en de Volflo om de afstand tussen Groningen en Enschede te overbruggen. Ik heb er alle vertrouwen in dat we ook dit avontuur samen aan kunnen. Ik hou van je!
224