THE MOLECULAR CHARACTERIZATION OF HEAD AND NECK CANCER IN YOUNG PATIENTS

by

Jerry Machado

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Laboratory Medicine and Pathobiology University of Toronto

© Copyright by Jerry Machado 2010

THE MOLECULAR CHARACTERIZATION OF HEAD AND NECK CANCER IN YOUNG PATIENTS

Jerry Machado

Doctor of Philosopy

Laboratory Medicine and Pathobiology University of Toronto

2010

Abstract

Head and neck squamous cell carcinomas (HNSCCs) most commonly develop in older patients (≥60 years of age) with a history of tobacco and alcohol use. However, young individuals (≤45 years of age) can also develop HNSCC, often without common risk factors. Increasing evidence shows that Human Papillomavirus (HPV) infection is associated with particular HNSCC sites (e.g. oropharynx). We assessed the Roche

Linear Array HPV Genotyping Test in several lesions and then examined the prevalence of HPV in HNSCCs from young and older patients. HPV infection was most prevalent in oropharyngeal cancers (16/22, 73%), rarely found in oral cavity cancers (2/53, 4%), and other head and neck sites (1/17, 6%). HPV positive tumors were associated with patients that were >40 and <60 years old (p=0.02).

The absence or shortened time of carcinogen exposure from common risk factors and the development of oral squamous cell carcinoma (OSCC) at an early age suggest aberrant genetic events that are different than those in OSSCs from older patients. We used Affymetrix SNP 6.0 arrays to genomically profile oral tumors from young and older patients. Tumors from young patients showed different regions/genes of copy number alterations than those from older patient tumors. An increase of regions of loss of

ii heterozygosity (LOH) in tumors from older patients was observed, and there was a high prevalence of copy number neutral LOH on chromosome 9 in tumors from young and older patients. These data suggest different genetic mechanisms in these patient groups.

We have previously shown that HNSCCs from younger patients exhibited a high incidence of microsatellite instability (MSI), a marker of defective mismatch repair

(MMR). Deregulated mRNA levels of hPMS1, hPMS2 and hMLH1 were observed and absent/low expression of hPMS1, hPMS2 and hMLH1 protein levels were observed in

>50% of OSCCs. No mutations were observed in hPMS1 and hPMS2 and no significant differences of MSI or LOH were observed across genomic loci between tumors of young and older patients. The role of these genetic mechanisms in oral cancer appears complex; studies such as ours should further improve our knowledge of the molecular mechanisms leading to early-onset oral carcinomas.

iii Acknowledgements

An old proverb states that it takes a community to raise a child, and I think this proverb can similarly be applied to the many people who have given me support over the years in helping me complete my doctoral studies. I have many people to thank during my time at the Princess Margaret Hospital through the University of Toronto in the department of Laboratory Medicine and Pathobiology. I would like to thank all the students and colleagues I have been able to interact with over the years and share in their experiences.

We somehow managed to keep on striving to better ourselves.

I would like to acknowledge the people in the laboratory over the years, which have become like family to me. Specifically, I would like to thank Patricia Reis, who has been a mentor to me over the last 5 years; Nilva Cervigne, we had some good times and laughs;

Mahadeo Sukhai, a good guy to talk about science, for his revisions, and his constructive criticism; Natalie Naranjo Galloni, for being a good friend and keeping me on track; Rashmi

Goswami, for helping me understand pathology and going with me to crazy concerts; and all the other people in our lab that I’m grateful in meeting, including Mariam Thomas, Yali

Xuan, Miranda Tomenson, Rikki Bharadwaj, Paula Bohrer, Grace Bradley and the many summer students over the years. I would like to thank my Ph.D. committee members Drs.

Jeremy Squire, Jonathan Irish, and Robert Bristow; they always had words of encouragement and helpful suggestions. Lastly I would like to give a big thanks to my Ph.D. supervisor, Dr. Suzanne Kamel‐Reid, for giving me the opportunity to work in her research laboratory, scientific support, and giving me an opportunity to grow as a scientist and as a person.

iv The last group of people that I would like to thank is my family, who has given me all the support over the years. I am in debt to my in‐laws, Connie and Philip Buchanan, for all of their support, and for taking me in as a son of their own. I am also extremely lucky to have supporting parents, Maria Deontina “Tina” and Luis Machado, who have always wanted me to grow and strive, and sacrificed a lot for me to get where I am. Also, my brother, Nathan Machado, we have some good times even though we are years apart. To all of my friends, extended family members, including my aunts, uncles, cousins, and those who have passed on I say a big thanks.

To the girls of my life: my wife and daughter, Jennifer and Angelina. I am so blessed to have you both, for your love, and for keeping me grounded. We have been through a lot over the years, and our love for each other keeps growing stronger. I look forward to the future together, as we grow as a family. Good things come to those who wait.

Lastly I would like to acknowledge God, for all the things he has provided me: family, food, shelter, support, good health and an education. Thanks for giving me the strength to endure the hardships of the Ph.D. I look forward to what is in store for the future.

Jerry Machado

v Table of Contents Abstract...... ii Acknowledgements ...... iv Table of Contents...... vi List of Tables ...... ix List of Figures ...... xi List of Appendices ...... xiii Abbreviations ...... xv CHAPTER 1: HEAD AND NECK CANCER ...... 1 1.1 INTRODUCTION ...... 1 1.1.1 Head and Neck Cancer and Risk Factors...... 1 1.1.2 Worldwide Prevalence ...... 3 1.1.3 Clinical Presentation and Management ...... 4 1.1.4 Head and Neck Tumor Staging...... 5 1.1.5 Patient Survival and Tumor Recurrence...... 8 1.1.6 Cancer Stem Cells and Head and Neck Cancer...... 9 1.1.7 Conventional Therapies...... 12 1.1.8 Molecular Characterization ...... 14 1.1.9 Molecular Targeted Therapy in HNSCC ...... 17 1.1.10 Familial Involvement and Genetic Susceptibility...... 19 1.1.11 Young Patients...... 22 1.2 PhD THESIS OBJECTIVE...... 26 1.2.1 Specific Objectives and Hypotheses...... 26 CHAPTER 2: HUMAN PAPILLOMAVIRUS AND HEAD AND NECK CANCER...... 29 I. Comparison of Gel-Based PCR and the Digene HPV HC2 DNA Test to the Roche Linear Array HPV Genotyping Test in Cancers and Dysplasias from Different Anatomic Sites...... 29 2.1 INTRODUCTION ...... 29 2.2 MATERIALS AND METHODS...... 31 2.2.1 Patient Consent ...... 31 2.2.2 Tumor Samples...... 31 2.2.3 DNA Isolation from Tumor Samples ...... 31 2.2.4 HPV Detection by Gel-Based PCR...... 33 2.2.5 HPV Detection by the Digene HPV HC2 DNA Test...... 34 2.2.6 HPV Detection by the Linear Array...... 35 2.3 RESULTS ...... 36 2.4 DISCUSSION ...... 46 II. Low Prevalence of Human Papillomavirus in Head and Neck Cancer...... 50 2.6 INTRODUCTION ...... 50 2.7 MATERIALS AND METHODS...... 52 2.7.1 Patients ...... 52 2.7.2 Tumor Samples and DNA isolation...... 52 2.7.3 HPV Detection ...... 53 vi 2.7.4 Statistical Methods...... 54 2.8 RESULTS ...... 55 2.9 DISCUSSION ...... 63 CHAPTER 3: GLOBAL COPY NUMBER ALTERATIONS AND LOH IN ORAL CANCERS FROM YOUNG AND OLDER PATIENTS USING THE AFFYMETRIX SNP 6.0 ARRAY ...... 65 3.1 INTRODUCTION ...... 65 3.1.1 Single Nucleotide Polymorphisms ...... 66 3.1.2 Copy Number Alterations and LOH ...... 67 3.1.3 SNP Arrays and Global Genomic Analysis...... 69 3.1.4 SNP Array Platforms...... 72 3.1.5 Copy Number and LOH in Head and Neck Cancer ...... 73 3.2 MATERIALS AND METHODS...... 77 3.2.1 Patient Samples...... 77 3.2.2 Genomic DNA Isolation...... 77 3.2.3 Affymetrix SNP 6.0 Array Protocol...... 80 3.2.4 SNP Array Copy Number and LOH Data Analysis ...... 81 3.3 RESULTS ...... 87 3.3.1 Patient Clinicopathological Information...... 87 3.3.2 Common Copy Number Alterations in Head and Neck Cancer Compared to Our Dataset...... 87 3.3.3 Copy Number Alterations in All Oral Tumors ...... 90 3.3.4 Copy Number Alterations in Oral Tumors from Young and Older Patients.... 90 3.3.5 Common Copy Number Alterations and Significantly Associated Genes in Oral Tumors from Young and Older Patients...... 100 3.3.6 Copy Number Alterations in Tumors from Smokers and Non-Smokers ...... 109 3.3.7 Loss of Heterozygosity in Oral Tumors...... 112 3.3.8 Copy Number Neutral Loss of Heterozygosity (cnLOH) in Oral Tumors ..... 123 3.4 DISCUSSION ...... 130 CHAPTER 4: MISMATCH REPAIR AND HEAD AND NECK CANCER ...... 140 4.1 INTRODUCTION ...... 140 4.1.1 Mismatch Repair ...... 141 4.1.2 Microsatellite Instability ...... 145 4.1.3 In vivo Mouse Models of MMR...... 147 4.1.4 MMR Deficiencies and Human Phenotypes ...... 148 4.1.5 Mismatch Repair and MSI in Head and Neck Cancer ...... 149 4.1.6 MMR and OSCCs from Young and Older Patients...... 151 4.2 MATERIALS AND METHODS...... 153 4.2.1 Patients ...... 153 4.2.2 Tumor Samples...... 153 4.2.3 RNA Isolation and Quantitative RT-PCR ...... 154 4.2.4 Primer Design and Quantitative RT-PCR Amplification...... 154 4.2.5 Analysis of Quantitative RT-PCR Results...... 155 4.2.6 Immunohistochemistry ...... 155 4.2.7 Positive Criteria for Immunohistochemical Staining...... 156 4.2.8 cDNA Sequence Analysis of hPMS1 and hPMS2 ...... 157

vii 4.2.9 Tissue Needle Macrodissection and Genomic DNA Isolation...... 158 4.2.10 Microsatellite Instability and LOH Analysis ...... 158 4.2.11 Method of Statistical Analysis of QRT-PCR, IHC, MSI and LOH Data ...... 159 4.3 RESULTS ...... 161 4.3.1 Quantitative Real-Time PCR...... 161 4.3.2 Immunohistochemistry of Mismatch Repair Proteins...... 164 4.3.3 cDNA Sequencing of hPMS1 and hPMS2...... 167 4.3.4 MSI and LOH Across the Genome and at MMR Loci ...... 167 4.4 DISCUSSION ...... 172 CHAPTER 5: SUMMARY, CONCLUSIONS AND FUTURE DIRECTIONS...... 181 5.1 HPV Prevalence in Head and Neck Cancer...... 182 5.2 Global Genomic Analysis of Copy Number Alterations and Loss of Heterozygosity ...... 185 5.3 Mismatch Repair in Oral Cancers from Young and Older Patients ...... 190 5.4 Final Conclusion ...... 193 REFERENCES...... 194 APPENDICES...... 219 CHAPTER 3 APPENDIX - FIGURES ...... 220 CHAPTER 3 APPENDIX - TABLES ...... 229 CHAPTER 4 APPENDIX - TABLES ...... 253

viii List of Tables Page Chapter 1: Table 1: TNM clinical classification. 7 Table 2: Head and neck cancer staging. 7 Chapter 2: Table 1: Comparison of HPV detection and genotyping results as determined by gel-based PCR and the Roche Linear Array (LA) analyses. 37

Table 2: Number of high and low-risk HPV types from 59 lesions. 43

Table 3: Comparison of HPV subtypes using the linear array (LA) and digene HPV test. 45

Table 4: Clinical information of head and neck cancer patients and HPV tumor status. 56

Table 5: Statistical association of clinical factors and HPV status. 60

Chapter 3:

Table 1: Clinical characteristics of oral tumors and paired oral normal mucosa tissue from young and older patients used on the Affymetrix SNP 6.0 arrays. 78

Table 2: Quality control metrics including SNP genotyping call rate and median absolute pairwise difference (MAPD) on oral tumors and paired adjacent normals. 82

Table 3: Copy number using the Affymetrix SNP 6.0 array detected by Partek. 84

Table 4: Copy number in genes commonly altered in head and neck cancer. 88

Table 5: Top 20 significantly altered gene-containing regions in tumors from young and older patients. 101

Table 6: Significantly altered chromosomal regions based on age groups using stringent analyses. 108

Table 7: Top 20 significantly altered gene-containing regions in tumors based on smoking status. 113

Table 8: Regions of LOH in oral tumors from young and older patients. 120

Table 9: Regions of cnLOH in oral tumors from young patients. 125 ix

Table 10: Regions of cnLOH in oral tumors from older patients. 127

Chapter 4:

Table 1: The mismatch repair protein complexes and their function in DNA repair. 143

Table 2: Quantitative RT-PCR for mismatch gene expression in oral cancers and adjacent normal tissue from young and older patients. 163

Table 3: Immunohistochemistry for mismatch protein levels in oral cancers from young and older patients. 166

Table 4: Summary of analyses of MSI and LOH across genomic loci. 169 Table 5: Summary of analyses of MSI and LOH at MMR loci. 171

x List of Figures Page Chapter 1: Figure 1: Anatomy of the head and neck. 2 Chapter 2: Figure 1: Comparison between HPV detection by PCR (Panels A-C) and the LA (Panel D). 40

Figure 2: LA detection of HPV 16 and HPV 18 positivity using 10-5 and 10-4 DNA dilutions. 44

Chapter 3: Figure 1: Histogram of copy number genomic profile of oral tumors. 91

Figure 2: Copy number profile of at least 10 oral tumors. 92

Figure 3: Copy number profile of at least 15 oral tumors. 93

Figure 4: Copy number profile of at least 20 oral tumors. 94

Figure 5: Histogram of copy number genomic profile of oral tumors from young patients. 96

Figure 6: Histogram of copy number genomic profile of oral tumors from older patients. 97

Figure 7: Copy number profile of at least 7 oral tumors from young patients. 98

Figure 8: Copy number profile of at least 7 oral tumors from older patients. 99

Figure 9: Histogram of copy number genomic profile of oral tumors from smokers. 110

Figure 10: Histogram of copy number genomic profile of oral tumors from non-smokers. 111

Figure 11: Loss of heterozygosity in at least 7 oral tumor samples. 119

Figure 12: Loss of heterozygosity in at least 5 oral tumors from young patients. 121

Figure 13: Loss of heterozygosity in at least 5 oral tumors from older patients. 122

Figure 14: Copy number neutral LOH in at least 5 oral tumor samples. 124

xi Chapter 4: Figure 1: Mismatch repair pathway. 142 Figure 2: Microsatellite Instability. 146 Figure 3: Quantitative RT-PCR of hPMS1, hPMS2, and hMLH1 in oral cancer. 162 Figure 4: Immunohistochemistry of hPMS1, hPMS2, and hMLH1 in oral cancer. 165

xii List of Appendices Page Appendix – Chapter 3 Tables: Appendix Table 1: Significant copy number alterations in tumors from young and older patients. 229

Appendix Table 2: Significantly altered gene copy number in oral tumors from smokers and non-smokers. 239

Appendix Table 3: Regions of LOH in oral tumors. 244

Appendix Table 4: Regions of cnLOH in at least 5 oral tumors. 249

Appendix – Chapter 3 Figures: Appendix Figure 1: Gene ontology analysis of significantly altered GO biological processes between tumors of young and older patients. 221

Appendix Figure 2: Gene ontology analysis of significantly altered GO molecular functions between tumors of young and older patients. 222

Appendix Figure 3: Gene ontology analysis of significantly altered GO biological processes between tumors of smokers and non-smokers. 223

Appendix Figure 4: Gene ontology analysis of significantly altered GO molecular functions between tumors of smokers and non-smokers. 224

Appendix Figure 5: Representative figure of LOH on chromosome 9 in tumors from young patients. 225

Appendix Figure 6: Representative figure of LOH on chromosome 9 in tumors from older patients. 226

Appendix Figure 7: Copy number neutral LOH in at least 3 tumors from young patients. 227

Appendix Figure 8: Copy number neutral LOH in at least 4 tumors from older patients. 228 Appendix – Chapter 4:

Appendix Table 1: Clinical information of young and older patients with oral cancer and experiments used. 253

Appendix Table 2: Primer sequences for quantitative RT-PCR of MMR genes. 262

xiii Appendix Table 3: Primer sequences for cDNA amplification of mismatch repair genes. 263 Appendix Table 4: Primers for sequencing of hPMS1 and hPMS2 cDNA products. 264 Appendix Table 5: Clinical characteristics of patients for quantitative RT-PCR analysis. 265

Appendix Table 6: Quantitative RT-PCR of hPMS1, hPMS2, and hMLH1 in oral cancers from young and older patients. 266

Appendix Table 7: Clinical characteristics of patients for tumor immunohistochemistry analysis. 267

Appendix Table 8: Immunohistochemistry results of MMR proteins in oral cancers from young and older patients. 268

Appendix Table 9: Clinical characteristics of patients for microsatellite instability (MSI) and loss of heterozygosity (LOH) analyses. 272 Appendix Table 10: MSI and LOH analyses of genomic loci in oral cancer from young and oral patients. 275

Appendix Table 11: MSI and LOH analyses of MMR loci in oral cancer from young and older patients. 278

xiv Abbreviations aCGH – Array comparative genomic hybridization AIDS – Acquired immunodeficiency syndrome ALL – Acute lymphocytic leukemia ANED – Alive with no evidence of disease AsCN – Allele specific copy number ATP – Adenosine triphosphate AWD – Alive with disease BER – Base excision repair CGH – Comparative genomic hybridization CNA – Copy number alteration cnLOH – Copy number neutral loss of heterozygosity CNV – Copy number variation CSC – Cancer stem cell CT – Computerized tomography DOC – Dead of other causes DMD – Duchenne muscular dystrophy DSB – Double-strand break EBV – Epstein Barr virus FA – Fanconi anemia FFPE – Formalin fixed paraffin embedded FISH – Fluorescence in situ hybridization FOM – Floor of mouth GO – Gene ontology GI – Gastrointestinal GST – Glutathione-S-transferase GTC – Genotyping console

xv hMLH1 – Human MutL homolog 1 HNPCC – Hereditary nonpolyposis colorectal cancer HNSCC – Head and neck squamous cell carcinoma hPMS1 – Human postmeiotic segregation increased 1 hPMS2 - Human postmeiotic segregation increased 2 HPV – Human papillomavirus HR – Homologous recombination IDL – Insertion/deletion loop IHC - Immunohistochemistry IMRT – Intensity-modulated radiotherapy ISH – In situ hybridization LA – Linear array LFU – Lost to follow up LOH – Loss of heterozygosity MAPD – Median absolute pairwise difference mCRC – Metastatic colorectal cancer MMR – Mismatch repair MRI – Magnetic resonance imaging MSI – Microsatellite instability MSI-H – Microsatellite instability high MSI-L – Microsatellite instability low MSS – Microsatellite stable MT – Mitochondrial DNA NED – No evidence of disease NER – Nucleotide excision repair NHEJ – Non-homologous end joining NOD-SCID – Non-obese diabetic severe combined immunodeficient NSCLC – Non-small-cell lung cancer xvi OPC – Oropharyngeal carcinoma OPML – Oral potential malignant lesion OSCC – Oral squamous cell carcinoma PAH – Polycyclic aromatic hydrocarbon PCR – Polymerase chain reaction PET – Positron emission tomography PGS – Partek genomics suite RLGS – Restriction landmark genomic scanning RLU – Relative light unit RT – Radiotherapy SCC - Squamous cell carcinoma SEER – Surveillance, epidemiology and end results SKY – Spectral karyotyping SNP – Single nucleotide polymorphism TKI – Tyrosine kinase inhibitor TNM – Tumor, node, metastases staging status UPD – Uniparental disomy XPE – Xenobiotic-metabolizing enzyme

xvii CHAPTER 1: HEAD AND NECK CANCER

1.1 INTRODUCTION

1.1.1 Head and Neck Cancer and Risk Factors

Head and neck cancers are malignancies of the oral and nasal cavity, post-nasal spaces (sinuses), lips, salivary glands, pharynx (hypopharynx, oropharynx and nasopharynx), and larynx (Figure 1). Approximately 90% of head and neck cancers are squamous cell carcinomas (SCCs), and the remainder include adenocarcinomas, melanomas, and sarcomas.1 The most common risk factors are tobacco and alcohol consumption, which are associated with approximately 75% of patients with head and neck squamous cell carcinomas (HNSCCs).2

Tobacco related products include cigarettes, cigars, and smokeless tobacco.

These products contain carcinogenic nitrosamines, polycyclic aromatic hydrocarbons

(PAHs) and other toxins that have been shown to promote carcinogenesis.2, 3 Marijuana has also been shown to increase the risk of developing HNSCC.4 Alcohol on its own is not carcinogenic but its conversion to acetaldehyde is carcinogenic, and consumption of at least 3 alcoholic drinks per day has been shown to increase the risk of developing head and neck cancers.2 It is thought that alcohol acts as a solvent for carcinogens from associated risk factors, and the acetaldehyde also causes cellular DNA damage.2

Alcoholic beverages lead to an increased risk of head and neck cancer, regardless of the type of alcoholic beverage consumed (e.g. beer, wine and liquor).5 The duration and intensity of smoking, and intensity of alcohol consumption increases a person’s risk of developing head and neck cancers. When combined these risk factors are

1

Figure 1: Anatomy of the head and neck. Figure modified from http://imaging.cmpmedica.com/cancernetwork/cmhb11/11_4_Figure1.gif.

2 multiplicative, leading to a 10-20 fold increased risk of developing head and neck cancer.2, 5

Other risk factors that have been reported include, Human papillomavirus (HPV), which most often causes benign lesions,6 such as warts, however certain types of HPV can lead to specific types of cancer and have been associated with HNSCCs.7 HPV typically has a high prevalence in oropharyngeal cancers (OPCs).8 The role of HPV in head and neck cancer will be further discussed in Chapter 2. The Epstein Barr virus

(EBV) has been associated with nasopharyngeal carcinomas (NPCs), especially in individuals from Southern Asia,9 and high viral titers post-treatment have been associated with poor prognosis.10

The immune system can also play a role in the development of HNSCCs, as patients with immunological disorders, such as Acquired Immunodeficiency Syndrome

(AIDS) have a higher risk of developing HNSCCs.11 Other less common but reported risk factors include: certain occupational hazards and environmental exposures to paint fumes, plastic byproducts, wood dust, asbestos, and gasoline fumes.12

1.1.2 Worldwide Prevalence

Head and neck cancer is the sixth most common cancer, with a worldwide incidence of 650,000 cases and 350,000 deaths annually.13 The estimated incidence in

2008 within Canada was 4,600 cases and 1,680 deaths (Canadian Cancer Society); and corresponding statistics for the United States of America incidence was 47,560 cases and 11,260 deaths.14 The incidence of head and neck cancers has been generally declining in the Western world, owing to reduced levels of smoking.15

However, the incidence of OPCs has been increasing in the United States within the

3 past few decades. This is thought to be attributed to the increased level of sexual transmission of HPV by increased numbers of sexual partners.16 Oral cancers represent a significant proportion of cancer malignancies, in Melanesia, South-Central Asia,

Western and Southern Europe, and Southern Africa. Laryngeal cancers are more prevalent in Southern and Eastern Europe, South America and Western Asia.13

Consumption of smokeless tobacco products, such as betel quid and areca nut, both of which are carcinogenic, are becoming increasingly popular at a young age and are used by large populations in parts of Africa and Asia.3 Approximately 50% of oral cancers in Asia and parts of Africa can be attributed to smokeless tobacco use, compared to 4% of oral cancers in the U.S.3 However, in recent years there has been a rise in the number of young people using smokeless tobacco products in the United

States, due to increased marketing and peer pressure.3 The rate of smokeless tobacco usage in the United States is nowhere near that of Asia. For example, chewing tobacco has been found in 28% of Indian men and 12% of women, compared to 4.4% of

American men and 0.3% of American women.3 Interestingly, there has also been an increase in oral cancer incidence in lower socio-economic status populations, even when accounting for common behavioral risk factors.17

1.1.3 Clinical Presentation and Management

Patients with head and neck cancers may present symptoms to their physician including: pain, hoarseness, sore throat, mouth ulcers, poorly fitted dentures, swallowing difficulties and painfulness, earaches, oral bleeding, and abnormal breathing sounds.

The physician may find several abnormalities including: a red raised elevation, mass or ulceration, vocal cord paralysis, and swallowing dysfunction.18 In some cases, patients

4 may present with a white lesion, known as leukoplakia, considered an oral potential malignant lesion (OPML) that may progress in 16-62% of cases.19, 20 Patients may also present with red patch lesions known as erythroplakia, which are OPMLs shown to have a higher transformation rate than leukoplakia.21

Head and neck cancer clinical diagnosis is performed using a nasopharyngeal examination, laryngoscopy or oesophagoscopy depending on the clinical symptoms.18

The physician can order imaging studies including a computerized tomography (CT) scan, magnetic resonance imaging (MRI), and a positron emission tomography (PET) scan. More commonly, the patient may have a combination of imaging techniques (e.g.

PET-CT), which is more sensitive than either method alone.10, 18 A biopsy of the suspected lesion is also standard of diagnostic practice for histopathological assessment and classification of the lesion. These analyses will be used to assess patient prognosis and treatment.

1.1.4 Head and Neck Tumor Staging

Tumors are staged according to the size of the primary tumor (T), regional lymph node status (N), and the presence or absence of metastases (M); these together are known as the TNM staging system (American Joint Committee on Cancer (AJCC)).

TNM staging allows the physician to assess prognosis and decide appropriate treatment. The premise of this system is that patients with smaller tumors, and without nodal disease and distant metastases have a better prognosis than patients with larger tumors and/or presence of lymph node positivity and metastases.

A physician determines a TNM clinical classification, and once a pathological assessment has been performed on the excised tumor, a pathological TNM is

5 determined. The TNM classification as per AJCC guidelines is provided in Table 1.

Also, a staging system for head and neck cancers is utilized for each patient’s tumor.

The staging classification as per AJCC guidelines is provided in Table 2. Patient prognosis is usually based on the stage at which the cancer is presented. Patients with stage I tumors usually have a 90% survival rate, whereas patients with stage II tumors have approximately a 70% survival rate.18 Unfortunately patients with head and neck cancers often present to their physician with late stage tumors (e.g. III/IV), and therefore have a worse prognosis than early stage (e.g. I/II) patients 22 Also, in two-thirds of

HNSCC cases, patients have local lymph node tumor positivity leading to poor survival.22

Tumors are also given a grade based on the tissue architecture (differentiation) of the tumor. The severity of grade leading to a worse prognosis is as follows: well>moderately>poorly differentiated tumors. The spectrum of grading describes the tissue architecture of the cancer. Well-differentiated tumors have tissue morphology that is closest to that of normal tissues, whereas poorly differentiated tumors are those, which have lost most of the normal tissue architecture. The most commonly reported head and neck tumor differentiation status are moderately-differentiated tumors.

However, the TNM does not consider many variables, for example, it does not take in

6 Table 1: TNM clinical classification.

Category ID Clinical Characteristics T - Primary Tis Preinvasive cancer (carcinoma in situ) Tumor T0 No evidence of primary tumor T1 Tumor - ≤2 cm T2 Tumor - >2 cm and <4 cm T3 Tumor - ≥4 cm T4 Tumor with extension to bone, muscle, skin, or neck Tx Minimum requirements to assess primary tumor cannot be met

N - Regional N0 No evidence of regional lymph node involvement Lymph Nodes Evidence of involvement of ipsilateral regional lymph node - ≤3 N1 cm Evidence of involvement of ipsilateral regional lymph node - >3 N2a and ≤6 cm Evidence of involvement of multiple ipsilateral regional lymph N2b nodes - ≤6 cm Evidence of involvement of contralateral or bilateral regional N2c lymph nodes - ≤6 cm N3 Any lymph node - >6 cm NX Regional nodes cannot be assessed

M - Distant M0 No evidence of distant metastases Metastases M1 Evidence of distant metastases Mx Presence of distant metastases cannot be assessed

Table 2: Head and neck cancer staging.

N0 N1 N2a N2b N2c N3 T1 Stage I Stage III Stage IV Stage IV Stage IV Stage IV T2 Stage II Stage III Stage IV Stage IV Stage IV Stage IV T3 Stage III Stage III Stage IV Stage IV Stage IV Stage IV T4 Stage IV Stage IV Stage IV Stage IV Stage IV Stage IV

7 account tumor biology, non-tumor patient behaviors (e.g. smoking), and dynamic changes in the tumor during therapy.23

1.1.5 Patient Survival and Tumor Recurrence

The 5-year survival rate is approximately 50% for patients with HNSCC, and has remained relatively stable for several decades, despite improvements in surgical techniques, chemotherapy and radiotherapy.24 Improved survival is often not achieved due to high rates of local and regional failure.25 Interestingly, meta-analyses of reports indicates that stress-related psychosocial factors have a negative impact on the survival of head and neck cancer patients.26

Decreased survival rates are mainly due to disease relapse, which leads to treatment failure and consequently patient death. After tumor resection there may be undetectable tumor cells adjacent to the lesion during histopathological assessment because of minimal residual disease.27 This suggests that cancer cells that were not removed are still present post-treatment, and can lead to disease recurrence.

The development of a HNSCC recurrence is generally defined as a tumor that is

<2 cm away from a primary tumor site, and develops within 3 years of the primary tumor resection.28 Tumors that develop further than 2 cm away and after 3 years are considered second primary tumors. After tumor resection, patients who present with tumor positive surgical margins have a 66% risk of local recurrence.29 However, even when margins are histologically tumor negative, 10-30% of patients still show disease recurrence, with some studies showing local recurrence in up to 50% of patients.30, 31

In addition, genetic changes have been shown to precede any visual tumor phenotype, and these tissues can appear to be histologically normal under a

8 microscope.32 HNSCC risk factors (e.g. tobacco and alcohol) cover a broad surface area within head and neck sites; their carcinogenic effects may lie outside of the targeted lesion and thus individual cells may harbor genetic changes surrounding the lesion. This implies that many genetic changes surrounding the lesion may lead to premalignant lesions, a higher than expected rate of second primary tumors, and the presence of synchronous distant metastases, known as field cancerization.33, 34

Patients who have late stage disease often present with a local recurrence or distant relapse, which is detected in >50% patients within 2-years of treatment.18 In advanced HNSCC cases the tumor metastasizes to the ipsilateral submandibular and jugulodigastric nodes causing a mass in the neck.18 Head and neck cancer patient survival decreases by 50% if lymph nodes are tumor positive.35 It is generally accepted that esophageal tumors that develop in head and neck cancer patients are the result of a secondary primary tumor, whereas lung cancer is often a result of metastasis.34 The most common anatomical sites of distant metastasis are the lungs, followed by mediastinal lymph nodes, liver and bones, and in up to 5% of HNSCC cases the location of the primary lesion remains unknown.18 Due to the high morbidity and mortality rates of HNSCC, better methods of cancer detection, better prognostic markers and improved patient management are required.

1.1.6 Cancer Stem Cells and Head and Neck Cancer

The origin of cancer has been hypothesized using two main models: (1) the stochastic model36 and (2) the stem cell model.37 The first model holds that transformation occurs from random mutations and clonal selection.38 This model proposes that any cell within the tumor has the ability to propagate into a new tumor.37,

9 39 The stochastic events leading to tumor-initiating capacity can be intrinsic, such as deregulated transcription levels, or extrinsic, such as microenvironment and immune responses.39 The second model proposes that stem cells within the tumor are the only cells that have the ability to initiate a new tumor.37-39 The second model has been extensively researched and has garnered much attention in the cancer field.

Stem cells are an important subset of cells that possess the ability to self-renew and differentiate into any cell within the human body.40 They often remain in a quiescent state and group together in a niche, which are specific microenvironments that allow stem cells to maintain their function. The niche is important for stem cell function, particularly in maintenance and repair of tissues.37

Cancer stem cells (CSCs) were initially identified and examined in leukemia,41 where the hierarchical architecture and stem cell involvement has been extensively studied.42, 43 Population of cells containing CSCs have been identified in colon, breast, prostate, pancreas, brain and recently head and neck cancer.44-49 The involvement of stem cells in solid tumors has only been recently studied.

The hierarchical cellular structure of cancer tissue is similar to the normal tissue architecture from which they are derived.49 This has led to the hypothesis that CSCs originate from normal stem cells, as they share many similar features. For example, they are present in low numbers, they can be separated from other cells via cell surface markers, and they have self-renewal capabilities.50 However they differ from normal stem cells in that they lack the ability to regulate cellular division, and they have the ability to invade and spread.50, 51

Cell surface molecular markers have been important for sorting out stem cell populations from the rest of the bulk tumor. Stem cell populations with specific markers

10 are the only cell populations that are able to give rise to tumors within in vivo mouse models that recapitulate their original phenotype.37

Since the reported identification of a population of stem-like cells in head and neck cancer in 2007,49 there have been few studies examining the role of stem cells in head and neck cancer. In the original identification of a population of HNSCC CSCs,

CD44+ was the cellular surface marker identified. However, large amount of cells were required for injection into non-obese diabetic severe combined immunodeficient (NOD-

SCID) mice, suggesting that further marker stratification is required for isolating pure populations of head and neck cancer stem cells.52 Interestingly, established cell lines have been shown to harbor cancer-stem like CD44+ cells, and may be important for in vitro stem cell analysis.53, 54

The theory that cancer stems cells are a rare population of cells has been recently challenged via implantation of single primary and metastatic melanoma cells from human patients into NOD/SCID interleukin-2 receptor gamma chain null mice.55

This study showed that approximately 25% of implanted cells could give rise to tumors, suggesting that selection of an appropriate mouse model can dramatically increase the detection of cancer cells with tumorigenic potential. Furthermore, suggesting that non-

CSCs from various types of cancers may have different susceptibilties in becoming a

CSC.56

Possible roles of stem cells in cancer include tumor growth, relapse, metastasis and treatment. Furthermore, stem cells have been found to be resistant to cancer treatments, such as chemotherapy in leukemia,57 and radiation resistance in glioblastomas;58, 59 these characteristics may similarly be observed in head and neck

11 cancers. This suggests that cancer stem cell targeting during treatment may have to be addressed.

1.1.7 Conventional Therapies

The most common treatments for HNSCC are surgery and radiation, however, no worldwide standard mode of therapy exists.18 Surgery’s intention is to not only resect the lesion and promote improved prognosis, but also to preserve organ function and cosmetic appearance. Surgery is especially useful for small resectable lesions in the oral cavity, pharynx and larynx. Prophylactic neck dissection is often carried out to remove any residual disease that may have metastasized to cervical lymph nodes and this is especially true if there is suspected lymph node cancer positivity.18 Other treatments for HNSCC depend on the location of the tumor and presence of metastasis and include radiation, chemotherapy, chemoradiotherapy, and more recently, targeted therapies.

Radiotherapy (RT) may be used to reduce the size of the tumor before surgical resection; and is often used to eradicate any remaining cancer cells after surgical resection. RT of HNSCCs is usually given in daily fractions of 2.0 Gy over 5 weeks, and up to 7 weeks of 70 Gy.18 It is especially useful for base of tongue, tonsillar and early staged glottic cancer. RT is often used in conjunction with imaging technologies to allow for improved delivery to the tumor, especially when imaging allows the 3D visualization of the tumor. This allows for an increased specificity to the target, lower doses, and a reduction in toxicity to surrounding adjacent tissue. RT is especially useful in treatment of laryngeal cancer so that preservation of the larynx can be achieved.

Also it is used in early staged oropharyngeal and hypopharyngeal cancer, as its cure rates are as successful as surgery, and has lower morbidity.18

12 Fractionization, whereby radiation is delivered in multiple smaller doses in a given day, has also been given to tumors in head and neck cancer patients.18 In addition, intensity-modulated RT (IMRT), whereby RT is focused on targeted tissue thereby increasing the intensity and reducing scattering to surrounding normal tissues has been utilized.60

Chemotherapy was often used as a palliative treatment for HNSCC patients, but has also shown some promise as primary treatment or as an adjuvant to standard therapy against head and neck cancers.61 Platinum compounds (e.g. cisplatin, carboplatin), antimetabolites and taxanes have been used in advanced HNSCCs that are non-resectable, show high levels of recurrence, and in metastatic HNSCCs.18

Chemotherapy has also been shown to increase patient survival and is important in organ preservation, especially in hypopharyngeal and laryngeal tumors.61

Induction neoadjuvant chemotherapy has recently been used and has been proposed to reduce the rate of distant metastasis. However, some clinical trials have found no differences in locoregional control and survival rates using this type of therapy.60 This is possibly due to the choice of chemotherapeutics utilized.18

The introduction of chemoradiotherapy has been useful in unresectable late stage (III/IV) HNSCCs, appears to be better than radiation or chemotherapy alone, and is useful for locoregional control.18 However its use in distant metastasis may not be as useful, due to the complications of treatment such as mucositis, dermatitis, and myelosuppression.18 The use of radiotherapy and chemotherapy, or both, may have long-term side effects that affect quality of life for the patient; thus, treatment may not only determine outcome but also quality of life. A better understanding of HNSCC is thus required for future treatments.

13 1.1.8 Molecular Characterization

Similar to other cancers, a genetic62 and transcriptional63 progression model has been proposed for HNSCCs. In the genetic progression model, a cell acquires a genetic alteration that is transmitted to its daughter cells. The next critical step is the expansion of cells due to additional genetic alterations, which transform cells from a pre- neoplastic to a malignant state. The transcriptional progression model, similar to the genetic progression model proposes that the majority of alterations occur prior to malignancy, and transcriptional deregulation occurs during progression from a normal to premalignant to malignant state.

Many molecular markers (DNA, RNA and protein) have been investigated during early and late stage cancers. However, no single molecular marker has been conclusively found for all individuals affected with different types of cancers, owing to the heterogeneous nature of this disease and biological variation between patients.

Many studies have attempted to address key features often found in cancer. One of these is loss of heterozygosity (LOH), a hallmark of cancer. LOH is defined as the loss normal function of one allele at a locus, often by deletion, in which the other allele was already inactivated. Often, a germline mutation is passed on by one of the parents, and the inactivation by LOH is often characteristic of the loss of tumor suppressor genes, such as TP53 and RB1. TP53 is involved in many cellular processes (e.g. cellular growth, DNA repair, and apoptosis) and it is often mutated in cancer,64 including head and neck cancers.

Most of the early analyses in cancer genetics have been to examine gross chromosomal changes. Deletions are often hallmarks of loss of tumor suppressor genes, whereas gains often show regions of oncogenes. One of the most common

14 chromosomal regions that is lost in HNSCCs, and occurs early in cancer progression includes the region 9p21-22;65 genes mapped within this region, include p16 and p14ARF at the CDKN2A locus. The loss of this region is thought to lead to a growth advantage of tumor cells, as it leads to inactivation of TP53 and RB through inactivation of

CDKN2A genes. An additional mechanism of p16/p14ARF loss includes promoter hypermethylation, whereby methylation of their shared promoter at CpG islands causes downregulation of gene expression ultimately leading to protein loss.66

Loss of chromosome 3p is another region associated with early changes during head and neck cancer progression. Many genes are mapped within this region, however no genes have been solely associated with 3p and HNSCC, as three distinct regions of loss have been observed.66 Genes within these regions that may have a role in HNSCC include the putative tumor suppressors, FHIT and RASSF1A.66 Loss of chromosomes 9p21-22 and 3p may represent early events in head and neck carcinogenesis as they have also been found in early lesions.67 Loss of chromosome region 17p13 has also been found, and subsequent investigation has implicated the tumor suppressor gene TP53 in head and neck tumorigenesis.66 TP53 mutations are more common in tumors from smokers and drinkers and depending on the site of mutation can lead to a worse outcome, especially if the mutation is within the TP53 DNA binding domain.68

Features of cancer also include amplification and overexpression of oncogenes, which are involved in deregulated cellular division and differentiation. Once mutated these genes have oncogenic activity and increase cellular division leading to cancer.

Classic examples of oncogenes include C-MYC, WNT, and RAS.

15 HNSCC genetic alterations also include amplification of the chromosome region

11q13, which is present in one-third of HNSCC cases.66 Genes within this region include BCL-1, INT-2, HST-1, EMS-1 and CCND1, but CCND1 (Cyclin D1) is the most common oncogene associated with head and neck carcinogenesis. The role of Cyclin

D1 is to phosphorylate RB1, similar to p16, thereby allowing cell cycle progression from

G1 to the S phase. Amplification of Cyclin D1 leads to constitutive activation and it is thought to be involved in the development of head and neck carcinomas. Amplification of the chromosomal region 3q26.3 is often found in HNSCC and has been associated with high levels of tobacco exposure. Two genes within this region include PIK3CA and

SCCRO (Squamous Cell Carcinoma Related Oncogene).69 Another gene within this region includes Claudin 1 (CLDN1), a tight junction protein involved in cellular adhesion.

Our laboratory has shown overexpression of CLDN1 in oral carcinomas and has shown it to be associated with invasion and aggressive features, such as perinerual and angiolymphatic invasion.70 Other common genetic events found in 30% of head and neck cancer cases include regions of LOH at chromosomes 11q and 13q.71

The epidermal growth factor receptor (EGFR) has been found to be overexpressed in >90% of HNSCCs.72 EGFR is a cell membrane tyrosine kinase that is part of the ErbB family of proteins. In HNSCCs, EGFR is often constitutively activated via different mechanisms. This can occur by overexpression of EGFR ligands, mutation, amplification, or by activation of other receptors or nonreceptor tyrosine kinases.67 Upon activation, EGFR phosphorylates a downstream cascade involving signaling pathway proteins such as MAPK, AKT, ERK and JAK/STAT. It also has the ability to translocate to the nucleus and act as a transcriptional activator.73 EGFR has a role in proliferation, apoptosis, differentiation, migration and adhesion.66 Another gene

16 eIF4E, which is involved in translation initiation, is frequently overexpressed in

HNSCCs, and elevated protein levels are associated with increased risk of recurrence.74

Additional molecular characterization of head and neck cancers will be discussed in

Chapter 3.

1.1.9 Molecular Targeted Therapy in HNSCC

Molecular targets have been studied as possible candidates to improve head and neck cancer patient outcome. The premise of this approach is to target only cancerous cells while sparing healthy normal cells from toxic therapeutic effects. An example of a molecular target includes the epidermal growth factor receptor (EGFR), and high levels of EGFR in HNSCC have been associated with poor patient survival.75 Targeted therapy of EGFR has recently been tested using monoclonal antibodies against the EGFR receptor (Cetuximab) and EGFR tyrosine kinase inhibitors (TKIs) (Gefitinib and

Erlotinib).60

Cetuximab has been assessed in head and neck cancer clinical trials. The monoclonal antibody Cetuximab works by having a higher affinity for the EGFR receptor than natural EGF ligands, such as transforming growth factor (TGF-α). The therapy prevents the dimerization of EGFR, and thereby inhibits downstream signaling cascades, which are involved in cellular proliferation, ultimately leading to decreased transformation. Also it has been shown that Cetuximab promotes EGFR internalization and degradation, and has antibody-dependent cell-mediated cytotoxicity effects.60

These new therapies are used in conjunction with gold-standard therapies (e.g. RT). In conjunction with radiotherapy, Cetuximab increased survival of head and neck cancer patients to 49 months compared to 29.3 months for radiotherapy alone.76

17 EGFR TKIs work by binding the adenosine triphosphate (ATP) pocket of the

EGFR to inhibit its catalytic activity.60 However, Gefitinib, an EGFR inhibitor, showed modest single agent survival benefit (1-11%) in recurrent or metastatic HNSCCs.77 The differences in efficacy may result from the mechanisms that take place during EGFR over-expression (amplification vs. mutation)67 and should be examined, as this has been previously shown be a predictor of efficacy of EGFR molecularly targeted therapy.77

Mutations in EGFR are present in 1-7% of HNSCC,78, 79 and 10% of non-small- cell lung cancer (NSCLC).80, 81 Mutations in EGFR have been shown to improve responsiveness and survival in NSCLC patients with Gefitinib,82 whereas higher copy number of EGFR has been shown to improve survival when treated with EGFR-TKIs in

NSCLC.77 Erlotinib treatment has also been shown to be beneficial in patients with recurrent and metastatic HNSCC; and EGFR copy number, phosphorylation status and downstream markers can be used as markers for response to treatment.83

Vascular endothelial growth factor (VEGF) mRNA and protein levels are increased in late-stage HNSCC and molecular therapies targeting VEGF have also been tested in HNSCC patients 84 This therapy is important as tumors use angiogenesis to promote a vascular architecture and to support their survival. Tumors are often hypoxic, and hypoxia can upregulate angiogenesis through the hypoxia-inducible factor alpha (HIF1α)-VEGF pathway.85 Interestingly, resistance to EGFR inhibitors has been has been associated with increased levels of VEGF.86

A monoclonal VEGF antibody, Bevacizumab, inhibits the development of tumor vasculature and has shown therapeutic promise in antitumor activity for metastatic colorectal cancer (mCRC), breast cancer, and non-small cell lung cancer (NSCLC).60

18 Phase I/II trials have shown tolerability to Bevacizumab in head and neck cancers in combination with Erlotinib, and have shown a beneficial response in a subset of 46 patients with elevated phosphorylated levels of VEGFR and EGFR.87 However, multivariate analyses and the inclusion of a larger patient cohort are necessary to assess the efficacy of the combination of these molecular targeted therapies in

HNSCC.85

Other novel targeted therapies that are currently being investigated include targeting members of the PI3K/AKT/mTOR pathway, downstream targets of EGFR signaling and interacting EGFR receptors such as G-protein-coupled receptors,

PDGFR, and insulin-like growth factor 1 receptor (IGF-1R).88 These molecular targeted therapies are currently being assessed for the treatment of HNSCC patients, which may lead to tailored therapies depending on the biology of the tumor.

1.1.10 Familial Involvement and Genetic Susceptibility

The association of a family history of cancer and predisposition to HNSCC is not as prevalent as it is in some other cancers (e.g. Hereditary nonpolyposis colorectal cancer [HNPCC]).89 However, some studies have suggested a familial predisposition to

HNSCC. In one study the authors reported that a person can have a 2-14 fold increase in developing HNSCC if a first degree relative (e.g. sibling, parent) has cancer and is a smoker; and the likelihood increases in a dose-dependent manner with smoking.90

Recently, 8,967 HNSCC cases and 13,627 controls were examined in a meta-analysis of 12 case-control studies.91 The authors reported an increase in HNSCC between first- degree relatives, and a higher increase in affected siblings compared to parents with head and neck cancer. Also the risk increased significantly if the individual had an

19 affected first-degree relative and was a tobacco and alcohol user. In their analysis the authors did not find increased rates of HNSCC with non-tobacco related cancers between family members.91

The involvement of specific genes in the predisposition to head and neck cancer has not been conclusively found. However, one study described a family with a germline point mutation in p16INK4α that had a higher rate of melanomas and

HNSCCs.92 Wild type p16 is involved in cell cycle inhibition by causing G1 cell arrest.

The mutation detected within this family causes a non-functional p16 leading to cell cycle progression compared to the wild type p16 allele.

An increased incidence of HNSCC is observed in individuals affected by HNPCC,

Li-Fraumeni syndrome, Fanconi anemia, and Ataxia Telangiectasia.93 In addition, head and neck cancers have been shown to be more prevalent in patients with Bloom

Syndrome and Xeroderma Pigmentosum.12 These data suggest that there may be an underlying genetic involvement in the development of HNSCC, however, familial pedigrees are often lacking in many epidemiological studies, and the role of a familial predisposition in head and neck cancers is not clear.

Several observations suggest that there may be a genetic predisposition to developing HNSCC. For instance, not all alcohol and tobacco users develop HNSCC;12 there has been shown to be an increased prevalence of HNSCC in families with first- degree relatives with head and neck cancer;94 and some patients are known to develop head and neck cancers with no-known risk factors.95

Most of the genetic susceptibility studies in head and neck cancer have focused on genes related to carcinogen-metabolizing enzymes and DNA repair, mainly because of the carcinogenic effects of smoking and drinking alcohol. Many of these carcinogens

20 are metabolized and detoxified in order to prevent cellular damage, such as alkylation,

DNA adducts, oxidative stress, and DNA strand breaks.12

Xenobiotic-metabolizing enzymes (XPEs) are involved in activation or breakdown of carcinogens/procarcinogens and include alcohol dehydrogenase, cytochrome p450, glutathione-S-transferases (GSTs) and N-acetyl transferases.96 XPEs are commonly found within the liver, and in the upper mucosa of the upper-aerodigestive tract.

The cytochrome p450 groups, which are metabolic enzymes, consist of a family of proteins CYP1, CYP2, and CYP3, which often activate tobacco carcinogens. Several single nucleotide polymorphisms (SNPs) can render cytochrome p450 more active, which may lead to higher rates of HNSCC due to increased activated carcinogenic metabolites.90 Also chronic exposure to alcohol in combination with increased metabolism of alcohol due to polymorphisms of the alcohol dehydrogenase (ADH1C) gene can lead to increased ethanol conversion to acetaldehyde97; and the presence of certain SNPs in acetaldehyde dehydrogenase (ALHD2) can cause decreased removal of acetate, and has been shown to increase the risk of developing HNSCC.98

The GST enzymes are involved in the detoxification of nitrosamines and PAHs from tobacco smoke by conjugating electrophilic toxic substances with the tripeptide glutathione, thereby making the compound more water soluble and less toxic for DNA damage.90 The family consists of GSTα, GSTµ, GSTπ, and GSTθ, GSTω, GSTθ, and

GSTζ, each with different metabolic targets. The association of particular SNPs and

HNSCC predisposition has been found for GSTµ and GSTπ; however studies have not produced consistent results,96 possibly due to the small sample sizes when performing such analyses.

21 DNA repair polymorphisms have been intensively studied in relation to head and neck cancer susceptibility. Two of the most extensively studied DNA repair pathways and susceptibility in HNSCC are the nucleotide excision repair (NER) and base excision repair (BER) pathways. The NER pathway is responsible for removal of bulky DNA adducts resulting from tobacco carcinogens, and both BER and NER are involved in the removal of DNA oxidative damage.

In one study, HNSCC patient lymphocytes showed reduced DNA repair capacity with exposure to certain mutagens (e.g. bleomycin).99 Specific SNPs in DNA repair genes have been associated with a higher likelihood of developing HNSCC, including

XRCC1,100 while other SNPs have been associated with improved survival.101 However, many of these studies have relied on relatively small sample sizes and multiple comparisons for several SNPs, resulting in different conclusions for specific SNPs and their associative power.102 The role of DNA repair and its involvement in head and neck cancer will be further discussed in Chapter 4.

1.1.11 Young Patients

The risk of developing cancer generally increases as a person ages. This is due to the accumulation of genetic mutations during a person’s lifetime leading to cancer.

Head and neck cancer generally occurs in the sixth or seventh decade of life.103

However, there are individuals who develop HNSCCs at a much earlier age for unknown reasons. This group of young patients can account for 0.4-6% of the total number of HNSCC patients worldwide, depending on the cutoff age utilized.103 In the literature most studies on young patients with HNSCC include patients under the age of

40-45 years.

22 There are three different groups of young patients described in the literature:104

(1) a group with a male predominance, extensive tobacco and alcohol use for several years; (2) a group with a slight male predominance, half of whom smoke. These patients tend to present with early-stage disease and tend to have better treatment responses compared to older patients; and (3) a group of young female patients generally under the age of 35 who have few risk factors, and who present with aggressive oral cancer. The second group appears to be the majority of observed young patient cases.104 Whereas HNSCC is generally a disease that has a male predominance over females (2:1 ratio),13 in the young patient cohort there generally is a 1:1 male to female ratio, however, different frequencies have been reported.103 The most prevalent head and neck site is the floor of mouth in older patients compared to the tongue in young patients. In older patients the incidence of oral cancer is generally declining, in young patients it has been increasing.105, 106

There is a wide range in the use of common risk factors (e.g. tobacco and alcohol) in the young patient population. Rates have generally been reported to range from 38-100%, this difference has been attributed to patient recall, small sample size and definition of substance abuse.107 However it is generally found that the frequency of young patient smokers is approximately 50%, but there have been numerous reports of the development of HNSCC in young patients with no known risk factors.107, 108

Although young patients that are exposed to common HNSCC risk factors, the time to tumorigenesis due to the carcinogenic effects of tobacco and alcohol should take at least several of decades, similar to older patients, and should not experience a head and neck tumor so early in their life.109 This suggests that there may be genetic susceptibility or involvement of certain genes in early-onset carcinoma. However, the

23 causes of head and neck cancers in young patients have not been fully explored, as there are few studies on genetic differences between tumors from young and older patients.

The overall survival of young patients with head and neck cancer is generally thought to be similar or better than that of older patients (e.g. >50% over 5-years).

However, there have been conflicting reports on the survival rates of young patients.

Some studies have reported a shorter overall survival time compared to older patients,110, 111 perhaps suggesting that young patients should be treated more aggressively.111, 112 However, several other studies have found no difference or better survival rates for young patients.103, 113-116 One study using a matched-pair analysis of

72 patients found that young patients appear to have a more aggressive cancer and often show a higher locoregional recurrence, however this did not always translate into a lower survival rate; furthermore, tumors from both groups had a similar depth of invasion and margin status, which are often characteristics of locoregional failure.114 An analysis of the Surveillance, Epidemiology and End Results (SEER) registry of tongue cancers that were diagnosed from 1988-1993 in 749 patients showed worse survival due to increasing age; a 10 year increase of age resulted in an 18% increased death rate.113

The wide and conflicting clinical reports on young patients with head and neck cancer may be hindered by the lack of statistical power. Most of the studies that include young patients include relatively small patient sample sizes due to the rareness of the disease in comparison to the classical head and neck cancer group (e.g. older patients), and comparisons have been performed using historical controls.114 These may

24 confound any conclusions that have been reported, which may lead to contradictory data for young patients with HNSCC.

Relatively few genetic studies have been performed on HNSCC in young patients due to the relatively low incidence of this patient group. A study performed by Regezi et al. has examined certain cell cycle proteins using immunohistochemistry for p53, Rb and p21.117 The authors were not able to find any significant differences in the levels of proteins between young versus older patients. Another study has examined loss of heterozygosity in tumors from a group of young and older patients using an older array

CGH platform and found that tumors from older patients had more genetic aberrations than those from young patients.118 However, another study looking at LOH on chromosomes 3, 9 and 17p showed similar levels of LOH between young and older patients. It should be noted that the resolution of these previously utilized methods is far less superior to presently available platforms.

The involvement of faulty DNA repair in HNSCCs of young patients has also been examined. One study found decreased DNA repair capacity in lymphocytes from young patients with head and neck cancer using a host cell reactivation assay, which measures the cells ability to repair itself after ultraviolet radiation treatment.119 Also, our group previously showed that 88% of OSCCs from younger patients exhibited microsatellite instability (MSI), whereas older patients exhibited MSI in only 36% of tumors.120 Studies of other cancers have shown that these types of changes are associated with errors in mismatch repair (MMR) (Chapter 4).

Interestingly, Fanconi Anemia (FA) patients with mutations in FA genes acquire

OSCCs at an increased level over the general population.121 FA is characterized by faulty homologous recombination (HR) via a double stranded break (DSB)

25 mechanism.122 A previous study performed in our laboratory has shown lower mRNA expression of FANCA and lower protein expression of FANCG in tumor samples from young compared to older patients.123 GSTP1 interacts with proteins of the FA complex by binding to FANCC.124 In our study we also observed decreased levels of GSTP1 in young compared to older patient tumors.123 These data suggest that DNA repair and detoxification may be altered in young patients compared to older patient OSCCs.

An understanding of the molecular mechanisms that lead to early-onset carcinoma in young patients with head and neck cancer is lacking due to the minimal amount of research within this patient group. HNSCCs that occur in young patients represent a small proportion of worldwide cancers; however there have been reports on an increasing incidence of young patients with HNSCC.105, 106 We believe that deregulated molecular mechanisms can lead to early-onset HNSCCs in the young patient population with or without common risk factors, and that these are different from mechanisms leading to HNSCCs in older patients.

1.2 PhD THESIS OBJECTIVE

To determine if oral tumors from young and older patients are genetically different.

1.2.1 Specific Objectives and Hypotheses

1. To examine HPV status in HNSCCs from young and older patients.

2. To examine genome-wide copy number alterations and loss of heterozygosity in

OSCCs from young and older patients.

3. To examine mismatch repair (MMR) in OSCCs from young and older patients.

26 Hypothesis 1: HPV prevalence is low within oral carcinoma; and similar HPV infection rates are found within HNSCCs from young and older patients.

Objective 1: The incidence of HPV infection rates in head and neck cancer is widely reported in the scientific literature, and our analysis will determine if an HPV etiology is responsible for early-onset oral carcinoma. Specifically, we will determine the prevalence of HPV in head and neck cancers from young and older patients.

Hypothesis 2: Oral carcinomas from young and older patients harbor different genomic alterations.

Objective 2: Global genomic profiling of OSCCs between young and older patients will allow us to compare copy number alterations (CNAs), loss of heterozygosity

(LOH), and copy number neutral LOH (cnLOH) between tumors of young and older patients. We will also examine common risk factor (e.g. smoking) contribution to genomic instability in OSCC.

Hypothesis 3: Oral carcinomas from young patients contain deregulated MMR mRNA and protein levels and MMR mutations compared to older patient tumors.

Objective 3: The molecular mechanisms of MMR in OSSCs from young and older patients will be assessed by examining the role of hPMS1, hPMS2, and hMLH1 genes at the DNA, mRNA and protein levels. In addition, global genomic analysis will be performed through the use of microsatellite markers for MSI and LOH. The

27 mutational status of these genes within oral cancers from young and older patients will also be assessed.

28 CHAPTER 2: HUMAN PAPILLOMAVIRUS AND HEAD AND NECK CANCER

I. Comparison of Gel-Based PCR and the Digene HPV HC2 DNA Test to the Roche Linear Array HPV Genotyping Test in Cancers and Dysplasias from Different Anatomic Sites.

2.1 INTRODUCTION

Human papilloma virus (HPV) typically infects the basal layer of squamous epithelial cells. Some HPV types can cause benign lesions (low-risk), while other HPV types have the ability to cause precancerous lesions (high-risk).125 Although most HPV infections are transient, some are able to integrate into the host cell’s genomic DNA upon entry into cells. Once inside the cell, the virus expresses early and late genes that are involved in replication, transcription and transformation.6 HPV is typically associated with cervical cancer, as over 99% of all cervical cancers harbor HPV.126 However, other types of cancers, including cervicovaginal/anogenital,127 certain head and neck (e.g. oropharynx),8 and gastrointestinal128, 129 (e.g. esophagus and colon) malignancies, have also been found to harbor HPV.

HPV is generally detected by gel-based PCR methods, in situ hybridization (ISH), immunoserum positivity, and the digene HPV HC2 DNA Test. PCR is one of the most sensitive detection methods of HPV detection available,130 but no standardized PCR method exists, and using gel-based PCR to detect multiple HPV types can be laborious.

ISH, although a good method for detecting HPV, requires multiple probes for detection of specific HPV types, and results may be difficult to interpret.131 The strength of immunoserum positivity analysis is that it may detect past exposure to HPV; however patients identified as HPV positive using another technique do not always show antibodies against HPV,132 and the site of HPV infection cannot be determined using

29 this method. The digene HPV HC2 Test is an FDA approved test for HPV detection in cervical samples. However, it only detects the presence of either low or high-risk virus, and not specific HPV types, it may suffer from cross reactivity with non-carcinogenic

HPV types,133 and it is not as sensitive as PCR-based methods.134

A relatively recent commercially available HPV detection assay, the Roche Linear

Array HPV Genotyping Test (linear array) can assess the presence of 37 different low and high-risk HPV types. It can also been used on formalin fixed paraffin embedded

(FFPE) samples.135, 136 This highly sensitive and specific assay is approved for diagnostic use in Europe for HPV detection in cervical cancers and its use for HPV detection is under consideration by the US Food and Drug Administration.137 We assessed the usefulness of this test in detecting HPV from different anatomic sites and compared it to PCR detection of HPV in tumors from cervicovaginal, gastrointestinal, and head and neck sites, and anogenital dysplasias. This assay will then be assessed in a larger head and neck cancer cohort. We also compared the linear array to the digene HPV HC2 DNA Test in cervicovaginal cancers and anogenital dysplasias.

We report here that the linear array test may be useful as a standardized approach for HPV detection in multiple cancers from paraffin embedded materials, and minimal amounts of starting DNA can be used. Importantly, it may be more suitable than other available techniques in a diagnostic setting because of its ability to sensitively detect a wide range of specific HPV types to closely monitor patients who show consistent HPV type infection, as persistent infection of the same HPV high-risk genotype has been shown to be important in the development of cancer.134, 138

30 2.2 MATERIALS AND METHODS

2.2.1 Patient Consent

The University Health Network (UHN) Research Ethics Board approved this study; informed consent was obtained from all patients prior to sample collection.

2.2.2 Tumor Samples

Samples were obtained from the Toronto General Hospital. A subset of tissues

(N = 14) was collected at the time of surgery, snap frozen and stored in liquid nitrogen until further use. Otherwise samples were processed from paraffin embedded formalin fixation tissues/liquid cytology specimens (N = 45), according to UHN protocols.

Samples included 25 anogenital dysplasias/cervicovaginal carcinomas, 5 gastrointestinal carcinomas, and 29 head and neck carcinomas. H&E stained tissues sections were examined by histopathological analysis to confirm the presence of tumor/dysplastic cells.

2.2.3 DNA Isolation from Tumor Samples

Genomic DNA was isolated from FFPE samples using the DNeasy Blood and

Tissue Kit (Qiagen, Valencia, CA). Briefly, FFPE samples were placed in a centrifuge tube containing 1200 µL xylene and vortexed. The tube was then centrifuged at 14,000 rpm for 5 minutes at room temperature. The supernatant was removed and to the pellet

1200 µL 100% ethanol was added and mixed by vortexing. The mixture was centrifuged as previously performed. The ethanol was removed and once again 1200

µL 100% ethanol was added, vortexed and centrifuged. The supernatant was removed and the pellet was allowed to dry (5-10 minutes). The pellet was resuspended in 180 µL 31 buffer ATL and 20 µL of proteinase K and incubated at 56oC overnight with occasional vortexing. Additional aliquots of 10 µL proteinase K was added, further incubation and vortexing were performed to fully digest samples if required. After digestion, 4 µL

RNase A (100 mg/mL) was added to the mixture and incubated at room temperature for

2 minutes, to obtain RNA-free genomic DNA. The sample was vortexed for 15 seconds, and 200 µL buffer AL was added. The mixture was vortexed for 15 seconds and 200 µL

100% ethanol was added and vortexed. The mixture was added to a DNeasy Mini spin column that was placed in a 2 mL collection tube. The sample was centrifuged at 8,000 rpm for 1 minute and the flow-through was discarded. To the column, 500 µL AW1 buffer was added and centrifuged at 8,000 rpm for 1 minute and the flow-through was discarded. After which, 500 µL AW2 buffer was added and centrifuged at 14,000 rpm for 3 minutes and the flow-through was discarded. Elution of genomic DNA was performed by adding 50 µL ddH20 to the filter and centrifuged at 8,000 rpm for 1 minute.

The elution was repeated with an additional 50 µL ddH20 to the filter and centrifugation.

Fresh frozen tissue samples were isolated by homogenization using liquid nitrogen and a cold steel mortar and pestle. Homogenized tissue was then lysed in

SNET buffer (1% SDS, 400 mM NaCl, 5 mM EDTA, 20 mM Tris [pH 8.0]), containing

400 µg/mL proteinase K overnight at 55oC. After digestion, 25 mg/mL RNase was added to degrade any RNA in the sample. After RNA digestion, DNA was extracted by standard techniques using a phenol/chloroform and ethanol precipitation method.

Genomic DNA quantity and quality was assessed by spectrophotometry (Nanodrop,

Thermo Scientific, Waltham, MA) and electrophoresis on a 0.8% agarose gel.

32 2.2.4 HPV Detection by Gel-Based PCR

Consensus sequence HPV detection by multiplex gel-based PCR was performed using Platinum Taq polymerase (5U/µL), 2.5mM 2X PCR Buffer (Invitrogen, Carlsbad,

CA), and 1 µg of genomic DNA. HPV consensus primer sequences were as follows: 5’-

CGTCC(A/C)A(A/G)(A/G)GGA(A/T)ACTGATC-3’ (MY09), and 5’-

GC(A/C)CAGGG(A/T)CATAA(C/T)AATGG -3’ (MY11). Duchenne Muscular Dystrophy

(DMD) control primers were 5’- AATTCACAGAGCTTGCCATGCTG-3’ (Conserved

“7632” – forward), and 5’- ACAGTCCTCTACTTCTTCCCACCA-3’ (Conserved “9918” - reverse). PCR thermal cycling conditions were 94oC for 6 minutes, followed by 35 cycles of 94oC for 1 minute, 57oC for 1 minute 30 seconds, 72oC for 1 minute, a final extension of 72oC for 7 minutes, and a 4oC hold. Expected sizes of PCR products were

452 and 423 base pairs for the HPV consensus and DMD sequences, respectively.

HPV type detection by gel-based PCR was performed using AmpliTaq Gold polymerase (5U/µL) and 10X PCR Buffer II (Applied Biosystems Inc., Foster City, CA),

25 mM MgCl2, 25 mM dNTPs, and 1 µg of genomic DNA. HPV primers for HPV-6 were

5’-TAAACAAGACATTTTAGACGTGC-3’ (HPV 6F – forward) and 5’-CTTTATGAACCG

TGCCTTGG-3’ (HPV 6R – reverse). HPV 11 primers were 5’-CGCAGAGATATATGC

ATATGC-3’ (HPV 11F – forward) and 5’- AGTTCTAAGCAACAGGCACAC-3’ (HPV 11R

– reverse). HPV- 16 primers were 5’- GGTCGGTGGACCGGTCGATG-3’ (TS16F – forward) and 5’-GCAATGTAG GTGTATCTCCA-3’ (TSR16 - reverse). HPV-18 primers were 5’-GCACGACAGGAACGACTC-3’ (HPV 18F - forward) and 5’-

ATAGAAGGTCAACCGGAATTT-3’ (HPV 18R - reverse). Primer sequences for a β- globin internal control were 5’-ACACAACTGTGTTCACTAGC-3’ (forward), and 5’-

CAACTTCATCCA CGTTCACC-3’ (reverse). PCR thermal cycling conditions were 95oC 33 for 5 minutes, followed by 40 cycles of 95oC for 30 seconds, 57oC for 30 seconds, 72oC for 45 seconds, and a final extension of 72oC for 5 minutes, and a 4oC hold. Expected sizes of PCR products were 105, 90, 96, 132, 100 base pairs for HPV 6, 11, 16, 18 and

β-globin sequences, respectively. Results were inconclusive if the internal control (β- globin) did not amplify. Tong Zhang at the UHN molecular diagnostics laboratory located at the Toronto General Hospital performed the assay.

2.2.5 HPV Detection by the Digene HPV HC2 DNA Test

The digene HPV HC2 DNA Test (Qiagen, Valencia, CA) is able to detect 13 high- risk HPVs (16/18/31/33/35/39/45/51/52/56/58/59/68) and 5 low-risk HPVs

(6/11/42/43/44) and was used according to the manufacturer’s instructions. Briefly, It is based on an in vitro nucleic acid hybridization signal, whereby HPV DNA hybridizes with an HPV RNA probe cocktail. The hybrids are then captured in antibody coated wells within a microplate that recognizes the RNA:DNA hybrids. Immobilized hybrids then react with the addition of alkaline phosphatase conjugated antibodies and a chemiluminescent substrate. Several conjugated antibodies bind to the immobilized hybrids resulting in increased signal amplification. As the alkaline phosphatase is cleaved light is emitted that is captured by a luminometer, and is measured as relative light units (RLUs). The detection and intensity of light emitted determines the presence of HPV. The UHN cytogenetics laboratory located at Toronto General Hospital performed the assay.

34 2.2.6 HPV Detection by the Linear Array

The Roche Linear Array HPV Genotyping Test (Roche Diagnostics, Branchburg,

NJ) was used for the detection of 17 high-risk (16/18/31/33/35/39/45/51/52/56/58/59/

66/68/69/73/82) and 20 low-risk (6/11/26/40/42/53/54/55/61/62/64/67/70/71/72/81/83/

84/IS39/CP6108) HPV types, according to the manufacturer’s instructions. Briefly, biotinylated PCR of the HPV L1 region is performed, and the product is reverse blot hybridized to multiple probes representing different HPV genotypes. HPV types were determined by lining up the manufacturer’s HPV reference guide with the genotyping strip. A low- and high-copy β-globin internal control is included in each run to assess the quality of DNA sample. All experiments included an HPV positive control, an HPV negative patient sample control, and a no-template control. The assay was repeated for

HPV positive tumors, separate from the positive control, to exclude risk of contamination. CaSki and HeLa cell lines, which harbor HPV-16 and HPV-18, respectively, were also used as HPV positive controls and in serial dilution assays for linear array sensitivity assessment. Tong Zhang at the UHN molecular diagnostics laboratory located at the Toronto General Hospital performed the assay.

Concordance between multiplex PCR and linear array was first assessed for the detection of HPV using the consensus sequence and then by genotyping analysis. We then compared HPV PCR genotyping of HPV types 6, 11, 16, and 18 for concordance with the linear array. The digene HC2 HPV Test was compared to the linear array for the detection of low or high-risk HPV types. Concordance analysis was performed using Cohen’s kappa statistics.139 Dr. Wei Xu, biostatistician, at the Ontario Cancer

Institute performed the statistical analyses.

35 2.3 RESULTS

Our first objective was to compare our gel-based PCR assay to the linear array for HPV detection in multiple human carcinomas or dysplasias from different sites

(cervicovaginal, anogenital, gastrointestinal, and head and neck). All samples were tested by both gel-based PCR and the linear array. By multiplex gel-based PCR, detection of the HPV consensus sequence showed 31/59 HPV positive, 23/59 HPV negative, and 7/59 HPV inconclusive samples (Table 1). Cases that were HPV positive or HPV inconclusive by multiplex gel-based PCR of HPV consensus sequences were then typed for HPV-6, -11, -16, and -18. Each sample was also genotyped using the linear array, and a comparison was made on the HPV types detectable by both methods. Comparison of these two methods showed that there was a concordance rate of 72.9% (Cohen’s Kappa score = 0.57), suggesting moderate agreement.

The linear array was able to detect low levels of HPV infection in 5 samples, which were not detected using gel-based PCR. Cases that were HPV positive by gel- based PCR for subtypes HPV-16 and 18 were also positive using the linear array; in addition, some HPV types that negative by PCR (e.g. patient 33) were detected using the linear array (Figure 1). The linear array was also able to detect additional HPV types, based on its capability to detect multiple high- and low-risk HPV genotypes.

Multiple infections were often found in anogenital dysplasias and cervicovaginal carcinomas, whereas single HPV infections were most often found in head and neck cancers, and one HPV infection was found in a colorectal cancer sample. The most common HPV type detected was HPV-16 (high-risk) in 23 cases, followed by HPV-6

36 Table 1: Comparison of HPV detection and genotyping results as determined by gel- based PCR and the Roche Linear Array (LA). (+): Positive; (-): Negative; (?): Inconclusive; FOM: Floor of Mouth.

Sample HPV HPV Type ID Sample Site Consensus HPV Type (LA) (PCR) PCR

1 Cervical + 16, 18 16, 18

2 Cervical - - -

3 Cervical + 16 16

4 Cervical + 16 16

5 Cervical + 18 18

6 Cervical + 18 18

7 Cervical + 16 16

8 Cervical ? ? -

9 Cervical + ? 6

10 Cervical + 16 16

11 Endometrial - - -

12 Vagina + ? 6 (low), 40, 42, 84, CP6108

13 Vagina - - -

14 Vagina + ? 16

15 Vagina - - -

16 Vagina ? ? -

17 Vulva - - -

18 Vulva - - -

19 Vulva 6 (low), 40, 42, 64, 84, + ? CP6108

20 Vulva ? ? 16 (low)

21 Vulva ? ? -

22 Anus + 16 16, 69, 70 (low)

37 23 Anus + 16 16, 70 (low)

24 Anus 16 (low), 31 (low), 33, 52 + 16 (low)

25 Anus + ? 6, 16 (low), 31

26 Colorectal + ? 16 (low), 18, 52?, 58

27 Small intestine - - -

28 Esophagus - - -

29 Esophagus ? - -

30 Esophagus ? - -

31 Oropharynx + 16, 18? 16

32 Oropharynx + 16 16

33 Tongue base + ? 16

34 Tongue base + 16 16

35 Tongue base + 16? 16

36 Tongue/FOM - - -

37 Tongue/FOM - - -

38 Tongue - - -

39 Tongue - - -

40 Tongue - - -

41 FOM + 16 16

42 FOM - - -

43 FOM - - -

44 Larynx + 6 6

45 Larynx - - -

46 Larynx - - -

47 Larynx - - -

48 Larynx - - -

38 49 Larynx - - -

50 Hypopharynx/Larynx - - -

51 Soft tissue: vallecula + ? 33, 52

52 Soft palate/tonsil ? ? -

53 Tonsil + - 35, 52?

54 Tonsil - - -

55 Tonsil + ? 16, 35, 52?

56 Tonsil + 16 16

57 Tonsil + - 33+, 52/33?

58 Neck + 16, 18? 16

59 Neck + ? 16

39

Figure 1: Comparison between HPV detection by PCR (Panels A-C) and the LA (Panel D). Panel A shows specific β-globin control amplification except in Neg. (no template control). Panel B is a multiplex PCR reaction showing 2 bands - DMD amplification (top) and HPV consensus (bottom). HPV-consensus positivity is shown in samples from Pt. 3 (cervical carcinoma), Pt. 33 (tongue base carcinoma) and Pt. 51 (soft tissue carcinoma) and the CaSki cell line (positive control for HPV 16). Panel C illustrates HPV genotyping for the high-risk types 16 (left) and 18 (right). HPV 16 positivity is shown for the cervical carcinoma sample (Pt. 3). HPV 18 is negative in tumor samples from the three patients; HPV 18 positivity is detected only in the HeLa cell line (positive control for HPV 18). Panel D depicts the LA results showing that HPV 16 is positive in carcinomas from Pts. 3 and 33. LA analysis showed HPV 33/52 positivity in the soft tissue carcinoma (Pt. 51), demonstrating the feasibility of this method to detect multiple HPV infections.

40 (low-risk) in 5 cases from different lesions, but other high and low-risk HPV types were also detected, although at a lower frequency (Table 2).

We also tested the sensitivity of the linear array using different dilutions of HPV

DNA from CaSki (HPV-16) and HeLa (HPV-18) cell lines. We diluted our initial starting material (1 µg) up to 106 fold (0.001 ng) and were able to detect HPV positive bands using the linear array in these dilute samples (Figure 2), showing that a low amount of starting material is sufficient for HPV detection and typing using the linear array. Using gel-based PCR, HPV consensus sequence was detected by PCR at only 104-fold dilution of DNA (0.1 ng) (data not shown).

We next compared the HPV detection rates using the linear array and the digene HPV

HC2 DNA Test in 16 cervicovaginal and anogenital lesions (Table 3). The digene assay is only able to differentiate between the presence of either high or low-risk HPV infection, and specific HPV genotypes cannot be identified using this assay. However, using the linear array we can determine whether the 37 HPV genotypes are high or low- risk and compare these data to those from the digene HPV HC2 DNA Test. We compared 16 HPV positive samples (10 cervical and 6 anal) using both assays and determined their concordance for identification of high or low-risk HPV infection. The digene HPV HC2 DNA Test was positive for high-risk HPV in 13/16 cases, whereas the linear array was positive for high-risk HPVs in 11/16 cases. In 3/16 cases the two assays did not agree on the same type of high or low-risk HPV. The concordance rate was 81.3% (Cohen’s Kappa score = 0.45), suggesting moderate agreement. In one case (Sample ID #6) the HPV-51 infection was low and not detected by the digene HPV

HC2 DNA Test, thus rendering the test’s high-risk HPV detection outcome as negative.

The linear array was able to determine the specific HPV type present in each sample,

41 and had the advantage of being able to simultaneously detect multiple infections by different HPV types.

42 Table 2: Number of high and low-risk HPV types from 59 lesions.

High-risk HPV type N of cases Low-risk HPV type N of cases

16 23 6 5

18 4 40 2

31 2 42 2

33 3 64 1

35 2 69 1

52 2 70 2

84 2

CP6108 2

43

Figure 2: LA detection of HPV 16 and HPV 18 positivity using 10-5 and 10-4 DNA dilutions.

44 Table 3: Comparison of HPV subtypes using the linear array (LA) and digene HPV test. *This analysis was done on an independent sample set using the same sample origin tested by digene and LA only. Low: low signal intensity using the LA. Cases where the risk type is discordant are bolded.

Sample Sample HPV type (LA) Risk type (LA) Risk Type (Digene) ID* Site

1 Cervical 51 High High

2 Cervical 39 High High

3 Cervical 66 Low High

4 Cervical 70 Low High

5 Cervical 51 High High

6 Cervical 51 (low) High Low

7 Cervical 16, 18, 42 (low), 59, 61, 66 High High

8 Cervical 16, 18, 53, 59 High High

9 Cervical 51 High High

10 Cervical 42, 73, CP6108 Low Low

11 Anus 6, 39, 55, 61, 69, 70, 84 High High

12 Anus 51 High High

13 Anus 26, 35, 42, 58, 61, 69, 84 High High

14 Anus 6 (low) Low Low

15 Anus 6, 18, 53, 54, 55, 56, 70, 81, High High 84

16 Anus 6, 31, 45, 53, 62, 70, 83 High High

45 2.4 DISCUSSION

In our study, we compared different HPV detection methodologies across multiple lesions that have been associated with HPV infection. We assessed HPV detection using gel-based PCR, the digene HPV HC2 DNA Test and the Roche Linear

Array HPV Genotyping Test. These three tests have been routinely used for HPV detection in both research and diagnostic settings. However, the use of different technologies can lead to apparent differences in HPV prevalence even when analyzing the same samples.140 Comparison of cervical carcinoma samples from the same patients using the linear array, Innogenetics INNO-LiPA (line probe assay [LiPA]) and two non-commercial reverse blot assays using HPV consensus primers (GP5+/GP6+ and broad spectrum primers BSGP5+/BSGP6+) was previously performed.140 The linear array and the broad spectrum reverse blot assay showed a higher rate of multiple HPV infections compared to the other assays tested. Importantly, proper detection of specific

HPV subtypes may be relevant as persistent infections of the same high-risk HPV type can lead to pre-cancerous lesions.134 Sensitive detection of HPV infection is also valuable as HNSCC patients who have HPV associated cancers have increased survival rates.141 Additionally, HPV detection in HPV related sites might be relevant given the economic burden of HPV-related cancers.142, 143

We compared HPV detection by gel-based PCR to the linear array. We found that PCR was able to routinely detect HPV, but gave inconclusive results in some samples because of the low quality of genomic DNA in some samples. However, when we used the linear array detection system, we were able to discern specific HPV types even with low DNA quality that the gel-based PCR alone did not detect. For example in figure 1, patient 33 had a HPV-16 infection that was detected by the linear array, but not

46 by gel-based PCR, thereby showing the improved HPV level of sensitivity in the linear array. Also the sensitivity of the linear array may be useful for detecting low-copy number infections of HPV as well as multiple genotypes.

We also compared the digene HPV HC2 DNA Test to the linear array to compare their ability to detect low and high-risk HPV types. We found the majority of cases were concordant between the two HPV detection techniques; however some cases were in disagreement and could possibly be due to the cross-reactivity of high or low risk detection in the digene HPV HC2 DNA Test.144 As high-risk HPVs have been associated with the progression of pre-cancerous lesions,134 it is crucial to reliably detect high-risk

HPV subtypes. The linear array was able to detect high-risk HPVs that had a relative low level of HPV infection; these may have been present at a level that was lower than the limit of sensitivity of the digene HPV HC2 DNA Test. The linear array was also useful as it was able to determine mixtures of HPV types with some samples having more than one high-risk HPV type present. This may be important as infection with multiple HPV types may be associated with increased risk for cervical cancer progression.145 However, the natural history and role of multiple infections, survival and association with different subtypes is unclear, with some studies suggesting a worse prognosis and others showing no difference.146, 147

In addition, the linear array is useful in HPV detection and typing from DNA isolated from FFPE samples. This is important considering the ample availability of archival samples, and there may be limited cytological specimen available for analysis.

In addition, the linear array can use minimal amounts of starting material, suggesting that small amounts of DNA can be used when limited material is available. The linear array is also useful for detecting HPV genotypes, which can then be compared in

47 different geographic regions,135 and may help in addressing HPV susceptibilities from different parts of the world.

A previous study compared the digene HPV HC2 DNA Test and the linear array method and found a 91.3% correlation in 218 cervical cancer samples, suggesting a high level of agreement.148 The advantage of the linear array is that it allows specific identification of the genotypes in each sample, and the ability to detect these multiple

HPV infections. Importantly, It can also be used on paraffin embedded tissues, unlike the digene HPV test. Another study found a moderate level of agreement between the two assays (Cohen’s Kappa score = 0.547) in women with abnormal Pap smear results, but found that the digene HPV HC2 DNA Test could call some low-risk HPV samples as high-risk and vice-versa.144

Other studies have utilized and compared the linear array to other relatively new

HPV detection systems. For instance, a comparison study using the Roche linear array and a sequencing-based HPV genotyping system found a high concordance rate

(91.2%) between these two methods, but the Roche linear array was superior in detecting multiple HPV types.149 Also, the linear array has also been compared to the

Amplicor HPV test, as well as the new PapilloCheck in cervical samples and has shown comparable results.139, 150

The detection of subtypes is important because the role of specific HPV types and their clinical significance can then be more easily assessed. Interestingly, we found that head and neck cancers tended to predominantly harbor single infections with HPV genotypes compared to cervicovaginal and anogenital lesions, which tended to have infection with more than one genotype. Interestingly, multiple HPV high-risk infections

48 have been associated with tumor progression in cervical neoplasia,138, 151 and a lower treatment response rate.152

Our comparison of the gel-based PCR and the digene HPV HC2 DNA Test to the linear array test showed that the linear array is a suitable technology for use in different cancer/dyplastic types requiring the detection and genotyping of HPV. It is a robust and sensitive technique that is a useful method for identifying specific HPV genotypes that may be used in a diagnostic and clinical setting. The use of a test that provides specific genotyping information may be important for individual risk stratification, therapeutic decisions, epidemiological studies and vaccine development.145

49 II. Low Prevalence of Human Papillomavirus in Head and Neck Cancer

2.6 INTRODUCTION

In 2008, head and neck squamous cell carcinoma (HNSCC) had an estimated incidence of 47,560 cases (11,260 deaths) within the U.S. (American Cancer Society), and 4,600 cases (1,680 deaths) in Canada (Canadian Cancer Society). The most common risk factors for HNSCC development are excessive tobacco and/or alcohol consumption.2 In addition, human papillomavirus (HPV) infection has been associated with some HNSCC subsites.7

Most HPV research has primarily focused on cervical cancer, as >99% of cervical cancers harbor HPV.126 Over 130 HPV types are known; these are classified as low- or high-risk based on their association with cervical carcinoma. HPV-16 and HPV-18 are the most commonly detected high-risk types.126 High-risk HPVs promote tumorigenesis through expression of the E6 and E7 oncoproteins, which inactivate the tumor suppressors TP53 and RB1, respectively.153 HPV-positive tumors often show

CDKN2A/P16 over-expression due to a feedback mechanism involving RB1 inactivation.154

Data from the literature show widely variable HPV infection rates in HNSCC.130,

155-158 This variability may be attributable to: sample storage and preparation of DNA;159 ethnicity and geography;158, 160 small number of samples analyzed;158 possible contamination;130 detection technique used, and HNSCC site analyzed ;130, 155 In a case- controlled study of HPV infection in HNSCC, HPV-16 was present in 72% of oropharyngeal carcinomas (OPCs), and associated with specific sexual behaviors,8 and marijuana use.161 Unlike the striking association with oropharyngeal cancers, the

50 incidence of HPV in other head and neck sites remains unclear. Interestingly, HPV positive HNSCCs are biologically different than HPV negative tumors, as shown by distinct gene expression profiles.162, 163 Importantly, patients with HPV positive HNSCC have better survival due to increased sensitivity to chemoradiotherapy.159

We examined the prevalence of HPV in oral squamous cell carcinomas (OSCCs) from young and older patients, and compared it to other head and neck subsites and

OPCs. For HPV detection and genotyping, a sensitive and specific PCR-based method, the Roche Linear Array HPV Genotyping test was used for these studies.

51 2.7 MATERIALS AND METHODS

2.7.1 Patients

The University Health Network (UHN) Research Ethics Board approved this study; informed consent was obtained from all patients prior to sample collection. All patients had surgery as the primary treatment. Medical records were examined to obtain detailed clinical and histopathological information, including age, sex, disease site, histopathological diagnosis, disease stage, history of tobacco and alcohol use, nodal metastasis, and outcome. Subsets of patients were classified as social or rare alcohol drinkers as written in their clinical reports. Patients having no use of tobacco or alcohol within one year prior to surgery were classified as former smokers and drinkers, respectively. Tumors were staged according to the current TNM classification, as recommended by the American Joint Committee on Cancer (UICC, 2002).

Patients over 60 years of age often present with HNSCC, whereas young patients with HNSCC are often characterized as patients under 35-45 years of age.103

The patients examined in our study were stratified as young (≤40 years of age), intermediate age (>40 and <60) or older patients (≥60 years old). The median age was

59 (range, 22-93 years of age). The male-female ratio was approximately 2:1. Median follow up time was 22 months (range, 1-140 months).

2.7.2 Tumor Samples and DNA isolation

78 HNSCC samples were obtained at the time of surgery from the Toronto

General Hospital. Tissues were snap frozen and stored in liquid nitrogen. In addition, 14

HNSCCs were included that were formalin fixed paraffin embedded samples. H&E

52 stained sections were examined by histopathology (Dr. Bayardo Perez-Ordonez) to confirm >80% tumor in the specimens being tested.

DNA was isolated from following fresh frozen tissue homogenization in liquid nitrogen using a cold steel mortar and pestle. Homogenized tissue was lysed in SNET buffer (1% SDS, 400 mM NaCl, 5 mM EDTA, 20 mM Tris, pH 8.0), containing 400

µg/mL proteinase K overnight at 55oC. After digestion, 25 mg/mL RNase was added and DNA was extracted by standard techniques using phenol/chloroform and ethanol precipitation. DNA quantity and quality was assessed by spectrophotometry (Nanodrop,

Thermo Scientific, Waltham, MA) and electrophoresis on a 0.8% agarose gel. Genomic

DNA was isolated from FFPE samples using the DNeasy Blood and Tissue Kit (Qiagen,

Valencia, CA).

2.7.3 HPV Detection

The Roche Linear Array HPV Genotyping test (Roche Diagnostics, Branchburg,

NJ) was used for the detection of 37 low- and high-risk HPV types, according to manufacturer’s instructions. Briefly, it utilizes biotinylated PCR of the HPV L1 region and reverse blotting to multiple HPV genotypes. HPV types were determined by lining up the manufacturer’s HPV reference guide with the genotyping strip. A low- and high- copy β-globin internal control is included in each run to assess the quality of DNA sample. All experiments included an HPV positive control, an HPV negative control, and a no-template control. Cases that were HPV positive were repeated, without the presence of a positive control, to verify results and exclude the risk of contamination.

53 2.7.4 Statistical Methods

Descriptive statistics were examined as median and range for continuous variables, and frequencies and proportions for categorical variables. The Fisher’s exact and Pearson’s chi-square tests were used for statistical evaluation. Overall survival was calculated using the Kaplan-Meier method. A Cox Proportional Hazard regression model was applied for continuous predictors. Results were considered significant if p≤0.05. Statistical analyses were performed using the SAS 9.1 software package (SAS

Institute, Cary, NC). Dr. Wei Xu, biostatistician, at the Ontario Cancer Institute performed the statistical analyses.

54 2.8 RESULTS

All 92 HNSCC samples had a positive β-globin internal control on the linear array and were thus suitable for HPV analysis. Of these, 53 were from sites in the oral cavity: tongue, floor of mouth (FOM), palate, buccal mucosa and gingiva; 17 were from other sites (pharynx, nasopharynx, hypopharynx and larynx); and 22 were from the oropharynx (tongue base and tonsil) (Table 4). The oropharyngeal cases were selected for comparison, as these tumors are known to have a higher incidence of HPV infection.

HPV positivity was stratified according to distinct HNSCC subsites: 2/53 (4%)

OSCCs, 16/22 (73%) OPCs, and 1/17 (6%) tumors from other HNSCC sites were HPV positive. HPV positivity was significantly associated with OPCs (p<0.0001) (Table 5).

The most prevalent HPV subtype found in our analysis was the high-risk HPV-16 (12/19 cases), which is the most common subtype observed in HNSCC 158. We detected low- risk HPV-6 in one sample (HN31), and high-risk HPV types: 18, 33, 45, and 52/58 in the remaining 18 HPV positive samples. 3/19 HPV positive cases (HN37, HN39, and

HN90), which were OPCs, had confirmed multiple infections; and 1/19 HPV positive case (HN71, Tongue SCC) had a low-level infection of HPV-18, as compared to the β- globin control.

HPV was significantly associated in node positive patients vs. node negative patients (p<0.0001). When we stratified individuals into young (≤40 years old), intermediate age (>40 and <60 years old), and older (≥60 years old) HNSCC patients,

HPV positive tumors were associated with the intermediate age patient group (p=0.02).

There were no significant associations between HPV and smoking or alcohol status.

55 Table 4: Clinical information of head and neck cancer patients and HPV tumor status. M - Male; F – Female; Y – Yes; N – No; FOM – Floor of mouth; DOC – Dead of other causes; AWD - Alive with disease; ANED - Alive with no evidence of disease; DOD - Died of disease; LFU – Lost to follow up.

Node HPV HPV Number Age Sex Smoker Alcohol Stage Differentiation Tumor Site Outcome (+/-) (+/-) Strain HN01 38 M N N - IV Moderately Alveolar ANED - HN02 40 M Y Former - II Moderately Alveolar ANED - HN03 87 F Former Socially - IV Poorly Alveolar/Buccal ANED - HN04 65 F Y Y - II Moderately Alveolus/FOM ANED - HN05 69 M Former Former - IV Moderately Buccal Mucosa ANED - HN06 82 F N N - I in situ Buccal Mucosa ANED - HN07 39 M N N + IV Moderately Buccal Mucosa ANED - HN08 80 F N N - II Moderately FOM DOC - HN09 73 F Y Socially + IV Moderately FOM ANED - HN10 72 F Y Socially - IV Moderately FOM ANED - HN11 70 M Y Y - IV Moderately FOM ANED - HN12 63 M Y Y + IV Moderately FOM ANED - HN13 58 M Y Y + IV Moderately FOM ANED - HN14 53 M Y Y + IV Moderately FOM AWD + 16 HN15 38 M Y Y - IV Moderately FOM AWD - HN16 49 M Y Former + III Moderately Hypopharynx/Larynx AWD - HN17 39 M Y Y - III Moderately Hypopharynx/Larynx AWD - HN18 72 M Y Y - III Moderately Larynx ANED - HN19 72 M Y Y - II Poorly Larynx AWD - HN20 71 M Y Y - III Moderately Larynx ANED - HN21 72 M Y Y - II Moderately Larynx ANED - HN22 59 M Y N - I Moderately Larynx ANED - HN23 59 M Y Former - III Moderately Larynx AWD - HN24 64 F Y Y + IV Moderately Larynx ANED - HN25 61 M Y Y - I Moderately Larynx ANED - HN26 48 M Y Y - I Moderately Larynx ANED - HN27 49 M Y Y - II Poorly Larynx AWD -

56 HN28 44 M Y Y - III Moderately Larynx AWD - HN29 40 F Y Y - II Moderately Larynx ANED - HN30 38 M N Y - III Moderately Larynx AWD - HN31 51 M Y Unknown Unknown Unknown Unknown Larynx ANED + 6 HN32 93 F N N - IV Moderately Maxilla ANED - HN33 74 F N N - IV Moderately Maxilla ANED - HN34 42 M Y Socially + III Poorly Nasopharynx AWD - HN35 76 F Y Y + III Poorly Oropharynx ANED + 33 HN36 65 M Y Y - I Moderately Oropharynx AWD - HN37 66 M N Socially - II Moderately Oropharynx AWD + 16, 45 HN38 59 M N Y + III Moderately Oropharynx ANED + 16 HN39 61 F Unknown Unknown Unknown Unknown Unknown Oropharynx LFU + 33, 52 Spindle Cell HN40 37 F N N - II Oropharynx DOD - Type HN41 67 M N Y + IV Moderately Oropharynx ANED + 16 HN42 59 M Unknown Unknown Unknown Unknown Moderately Oropharynx LFU + 16 HN43 82 F N Rarely - II Moderately Tongue AWD - HN44 70 M Y Y - II Moderately Tongue DOC - HN45 79 F N N + IV Poorly Tongue DOC - HN46 68 M Y Y + IV Poorly Tongue AWD - HN47 65 M Y Y + III Moderately Tongue ANED - HN48 67 M Y Y - II Moderately Tongue ANED - HN49 67 M Former Former - II Moderately Tongue ANED - HN50 71 M Former Y - IV Moderately Tongue ANED - HN51 63 F Y Y - II Moderately Tongue ANED - HN52 72 F N N - II Moderately Tongue ANED - HN53 58 M N Y + III Moderately Tongue AWD - HN54 65 F Y N - II Moderately Tongue AWD - HN55 56 M Y Y - IV Moderately Tongue ANED - HN56 44 M N Rarely - II Moderately Tongue ANED - HN57 41 M Y Y - II Moderately Tongue AWD - HN58 40 M N Y - II Moderately Tongue ANED - HN59 38 F Y Former + III Moderately Tongue ANED - 57 HN60 40 F N N + IV Moderately Tongue DOD - HN61 36 M N Socially + IV Moderately Tongue ANED - HN62 36 F N Socially - II Moderately Tongue AWD - HN63 40 F N N + III Moderately Tongue AWD - HN64 41 F Y Y - I Poorly Tongue ANED - HN65 34 M Y Y - IV Moderately Tongue ANED - HN66 37 M N Socially + IV Moderately Tongue ANED - HN67 32 M N N - II Moderately Tongue ANED - HN68 31 M Y Y - IV Moderately Tongue ANED - HN69 26 F N Rarely + IV Moderately Tongue DOD - HN70 24 M N Socially + III Moderately Tongue ANED - 18 HN71 22 M Y Rarely + IV Moderately Tongue AWD + (low copy) HN72 69 M Former Y + IV Basaloid Tongue Base AWD - HN73 65 F Y Y + III Moderately Tongue Base AWD - 52 or HN74 53 M N Socially + III Poorly Tongue Base ANED + 58 HN75 46 M N N + IV Poorly Tongue Base ANED + 16 HN76 47 F N N + IV Moderately Tongue Base ANED + 16 HN77 71 M Y Y + IV Moderately Tongue Base ANED + 16 HN78 59 M Y Y + IV Poorly Tongue Base ANED + 16 HN79 73 M Y Y + IV Poorly Tongue/FOM AWD - HN80 47 M Y Y + IV Moderately Tongue/FOM ANED - HN81 43 M N Rarely + III Poorly Tongue/FOM ANED - HN82 34 F N N + IV Poorly Tongue/FOM AWD - HN83 73 M Y Y + IV Poorly Tongue/FOM DOD - HN84 43 M N N + II Poorly Tongue/FOM ANED - HN85 47 M Y Socially + III Poorly Tonsil ANED - HN86 49 F Y Socially + III Poorly Tonsil ANED + 16 33, HN87 63 M Y Y + III Moderately Tonsil ANED + 52? 58 HN88 42 M Y Y + IV Moderately Tonsil ANED - 35, HN89 60 M Y Y + IV Moderately Tonsil ANED + 52? 16, HN90 64 M Y Y + IV Moderately Tonsil ANED + 35, 52? HN91 54 M Y Socially + IV Moderately Tonsil ANED + 16 HN92 72 M N N - IV Well Upper Palate ANED -

59 Table 5: Statistical association of clinical factors and HPV status. Clinical categories and the variables were examined for significant associations with HPV status. Statistical significance was assessed by Fisher’s exact and Pearson’s chi-square tests (p≤0.05). M - Male; F – Female; Y – Yes; N – No; FOM – Floor of mouth. Unknown values were not included in analysis.

Category Variable Patient (#) HPV Positive HPV Negative p-value Age <=40 22 1 21 >40 and <60 28 10 18 0.02 >=60 42 8 34

Sex F 28 4 24 0.32 M 64 15 49

Smoking Y 53 11 42 N 32 6 26 0.59 Former 5 0 5

Alcohol Y 46 9 37 N 19 2 17 0.47 Former 6 0 6 Other 18 5 13

Stage I 6 0 6 II 22 1 21 0.12 III 21 5 16 IV 40 10 30

Differentiation Well 1 0 1 Moderately 68 12 56 0.51 Poorly 18 5 13 Other 5 2 3

Site Tongue 29 1 28 FOM 8 1 7 FOM+Tongue 6 0 6 <0.0001 Oropharynx (Base of Tongue) 22 16 6 Larynx/Pharynx/Nasopharynx 17 1 16 Other 10 0 10

Node + 43 15 28 <0.0001 - 46 1 45

Outcome Alive 83 17 66 0.24 60

Dead 7 0 7

HPV + 19 - 73

61 We also found no association between HPV infection and tumor differentiation and stage. In other studies, these associations have been inconsistent.155

Survival was not significantly different for HPV-positive vs. HPV-negative

HNSCCs (p=0.24), as non-OPC sites were mainly HPV negative. Further examination of the 22 patients with OPC, outcomes for patients with HPV positive tumors included:

1/16 patients was alive with disease, 14/16 patients were alive with no evidence of disease, and 1/16 patients was lost to follow up. In contrast, 3/6 patients with HPV negative tumors were alive with disease, 2/6 patients were alive with no evidence of disease and 1/6 patients died of disease. Regardless of HPV status, we observed a significant gender-based difference in overall survival. Of the 7 deceased patients, 5 were females, non-smokers and non-drinkers; 3 of these were young and 2 were older patients; all of these patients were HPV negative. It has been suggested that a subgroup of young non-smoking female patients may have an aggressive form of

HNSCC.104

62 2.9 DISCUSSION

In our study, we sought to determine HPV infection rates in OSCC, in comparison to other HNSCC sites such as OPC. We observed significant differences in HPV positivity among different HNSCC sites, with OPCs having the highest infection rate

(73%) and OSCC samples having a much lower infection rate (4%). In the literature, most HPV studies do not provide stratification between oral cavity subsites, e.g. mobile tongue vs. base of tongue; the latter is associated with a higher HPV prevalence due to the involvement of the oropharynx.159 Indeed, high prevalence of HPV infection has been consistently shown in OPC compared to a lower prevalence in other HNSCC subsites.164 A recent comprehensive examination of the Surveillance, Epidemiology and

End Results (SEER) database showed a significant increase in HPV-related vs. HPV- unrelated OPCs from 1973-2004.16 In this SEER analysis, HPV-related HNSCCs mainly involved the oropharynx (base of tongue, and palatine tonsils), whereas HPV-unrelated

HNSCCs were from the mobile tongue, floor of mouth and palate. Gillison et al. used in situ hybridization for HPV detection and showed a higher frequency of HPV in tumors of the oropharynx, low frequency in larynx and oral cavity, and an absence of HPV in the hypopharynx or nasopharynx.161

In our study, we observed a significant association of HPV positive tumors in

HNSCCs from intermediate age patients. It has been suggested that, from the time of sexual transmission of HPV, a clinical lesion can appear within two decades, in contrast to conventional risk factors (tobacco smoking and alcohol consumption), which may take several decades.165

Interestingly, in our study, HPV positive HNSCCS were also associated with lymph node metastasis. In another study, HPV positive salivary samples were

63 correlated with node-positive HNSCC patients, suggesting that HPV infection may result from a cellular immunological deficiency that impedes the cancer cell’s ability to clear

HPV and thus results in cancer development, recurrence and metastasis.166 Also, an increase in HPV positive HNSCC with lymph node status has been associated with better overall survival, and better response to radiotherapy.167 We did not detect significant differences in survival for patients with HPV positive vs. negative tumors, as most OSCCs were HPV negative in our study, and possibly due to the relative small sample size of OPCs analyzed.

Some studies have shown a higher prevalence of HPV in OSCCs and other head and neck sites outside the oropharynx. For example, examination of HPV status in

OSCC from 50 Brazilian patients (excluding OPC), showed 24% HPV positivity, mostly

HPV-16/18.168 In a Mexican patient cohort, a high frequency of HPV positivity (43.5%) was found in OSCC.169 Such differences may be attributable to different HPV susceptibilities in different ethnicities/geographic regions or they may be due to different

HPV detection methods. Boy et al. detected 7/59 (12%) HPV-18 positivity in oral cancer using quantitative real-time PCR and 0% positivity when using in situ hybridization assays on the same samples.170 Our data have added information to in the literature by using a sensitive and comprehensive HPV detection and genotyping assay in HNSCC.

This assay is currently used as a standardized diagnostic test for HPV detection and genotyping in cervical carcinomas in Europe.137 In our study, by using this robust HPV detection and genotyping method, we showed that HPV is rarely involved in oral carcinomas and other non-OPC head and neck sites from young and older patients.

64 CHAPTER 3: GLOBAL COPY NUMBER ALTERATIONS AND LOH IN ORAL CANCERS FROM YOUNG AND OLDER PATIENTS USING THE AFFYMETRIX SNP 6.0 ARRAY

3.1 INTRODUCTION

Common chromosomal changes in head and neck squamous cell carcinoma

(HNSCC) have been previously detected using low-resolution technology, such as microsatellite marker analysis, chromosomal genomic hybridization (CGH), and fluorescent in situ hybridization (FISH).171-173 Recent studies have examined these chromosomal alterations using high-resolution genome-wide technology, such as array

CGH and SNP arrays.174-177 These newer studies have confirmed earlier low-resolution studies, have further refined reported genomic regions, and have been able to identify novel regions that are altered in HNSCC. Due to their high-resolution capabilities, these new technologies may elucidate the development of HNSCC, especially in early-onset head and neck cancer.

We utilized the Affymetrix SNP 6.0 array to assess global genomic alterations in oral cancer from young and older patients. This array has the ability to concurrently examine single nucleotide polymorphisms (SNPs), copy number alterations (CNAs), loss of heterozygosity (LOH), and copy number neutral LOH (cnLOH). It offers one of the highest resolution platforms for examining DNA alterations that is commercially available. In this chapter the alterations that the SNP array platform can detect will be described (SNP, CNAs, LOH and copy number neutral LOH) along with their role in cancer; and commonly described head and neck genomic alterations in head and neck cancer will also be reviewed. The role of these genetic mechanisms will then be

65 examined in tumors from young and older patients to assess the genetics of early-onset carcinoma.

3.1.1 Single Nucleotide Polymorphisms

SNPs are inherited nucleotide changes that are present in ≥1% of a given population, whereas mutations are nucleotide changes present in <1% of a population.178 There are an estimated 10 million SNPs in the genome, with each SNP occurring every 400-1000 base pairs.179 SNPs often result in synonymous nucleotide changes, resulting in retention of the original amino acid, however they may result in non-synonymous changes leading to an amino acid change.180

These polymorphisms are frequently present in non-coding regions of the genome, but may occur within coding regions. SNPs occurring within non-coding regions often have no observable phenotype, however SNPs may have an effect on gene expression if present within a promoter region, may cause altered gene transcript lengths if present within splice junctions, and can affect mRNA stability if present in a 3’ untranslated region (3’-UTR).180 Alternatively, when present within coding regions, they may cause an altered structure/function of the original gene product, prevent protein- protein interactions, and affect downstream protein function.180

SNPs tend to occur in blocks within the genome, named haplotypes, and often segregate together.181 Haplotype tagging SNPs have been useful in determining an individual’s genotype, which is more efficient and less costly as opposed to sequencing every SNP in the genome.179 SNPs may be found in genes related to cancer that are involved in DNA repair, carcinogen metabolism, apoptosis, and angiogenesis, and are associated with an increased risk of development of multiple cancer types.180, 182-184

66 Cancer is considered a multi-factorial disease, caused by genetic (e.g. SNPs) and environmental factors.180 These genetic factors, including SNPs, can be used to assess cancer patient prognosis, and in the study of pharmacogenomics because select

SNPs are associated with drug efficacy.185 SNP associations are often not replicated in different studies; proper design of experiments and utilizing newer genome-wide technologies, such as SNP arrays may be useful in producing consistent SNP genome- wide results. This should prevent the many pitfalls that exist in SNP genotyping due to small sample sizes, interactions of multiple SNPs, failure of multiple-factorial analyses, genotyping errors, poor case-control design and selective reporting.186

3.1.2 Copy Number Alterations and LOH

Copy number changes are genomic gains or losses of the normal 2 copy genetic material. For example, >2 copies of a particular segment in the human genome is considered a gain, loss of 1 copy, a heterozygous deletion, and loss of both copies of a genomic region, a homozygous deletion. They can occur de novo, such as copy number alterations (i.e. somatic) or can be present in germline DNA, such as copy number variations (CNVs) (i.e. inherited). The latter are normally present within the general population, and both are defined as copy number changes that are ≥1 kilobases in length.187, 188 They are involved in many genomic regions and can be observed within genes, and at disease loci, segmental duplications, and functional elements.189

The International HapMap data has been useful in the assessment of CNVs across populations. The HapMap data contains genomic information from 4 different populations: Yoruba families (30 parent-offspring trios) from Ibadan, Nigeria (YRI - 90 individuals), European descended families (30 trios) from Utah, US (CEU - 90

67 individuals), unrelated Japanese citizens from Tokyo, Japan (JPT – 45 individuals), and unrelated Han Chinese citizens from Beijing, China (CHB – 45 individuals).190 The term

CNV is more suitable than copy number polymorphisms (CNP), which suggests the presence of the copy number in ≥1% of the population, although the presence of CNVs have not been well defined across populations.191

SNPs were once thought to be the most predominant form of genetic variability between individuals in a population.191 However, CNVs have recently been recognized as an important genomic variance between individuals, involving more of one’s genome than SNPs.189 One study that examined CNVs using the available HapMap data reported 1447 copy number variable regions across these populations, which encompasses 12% of the human genome.189

Cancer has been shown to be the result of many genetic alterations including mutations, translocations and copy number alterations.192 CNAs are of particular interest in cancer, as copy number has been associated with gene expression deregulation in tumors from sites such as colon,193 and breast.194 In a breast cancer study, a 2-fold copy number change related to a 1.5-fold change in gene expression,194 suggesting that

CNAs are involved in the transcriptional deregulation of genes in cancer. Also, chromosomal regions in cancer that contain copy number gains are of particular interest because they may represent regions with oncogenes, whereas regions of loss may indicate presence of tumor suppressors.195

LOH is another mechanism that is important in genomic alterations and is a genetic hallmark of cancer. Regions of LOH are suggestive of the presence of tumor suppressor genes at these loci. LOH can occur by deletion, chromosome nondisjunction, mitotic nondisjunction followed by reduplication of the other

68 chromosome, mitotic recombination and gene conversion.196 The definition of LOH and cancer associated genes that are affected by this mechanism have been previously detailed in Chapter 1.

Copy number neutral LOH refers to an LOH event, but with 2 copy number regions remaining at the particular locus. This may occur when an individual has inherited two copies from the same parent (uniparental disomy – UPD).197 UPD can result from the deletion of one chromosome/genomic segment and the duplication of the remaining chromosome. Constitutive UPD results from meiotic errors leading to developmental diseases, whereas acquired UPD results from mitotic errors in somatic cells.198 It may lead to cancer if the copies from the same parent contain mutations or genetic variants that predispose to cancer,199 or through epigenetic mechanisms.200

UPD is prevalent in many cancers, 198, 199, 201, 202 and has been found more frequently in solid tumors than in leukemia, and appears to be non-random.198 SNPs, CNAs, LOH have all been examined in cancer, however, not until recently has there been a genome-wide platform that can simultaneously assess all of these genomic alterations.

3.1.3 SNP Arrays and Global Genomic Analysis

Single nucleotide polymorphism arrays (SNP arrays) are DNA microarrays that can provide information regarding genome-wide SNP associations, LOH, and CNAs using a high-resolution DNA platform.203 Due to the high density of SNPs on these arrays, it is possible to assess SNPs across the genome to make clinical associations.

These types of analyses have shown SNP associations in individuals with early-onset obesity,204 schizophrenia,205 and various cancers.206-208

69 Copy number analysis using SNP arrays has been useful in detecting genomic alterations in various diseases including, schizophrenia,209 autism,210 and multiple cancer types.211-213 SNP arrays can also be combined with other array platforms, such as gene expression arrays to correlate gene copy number results with expression data.

One study that examined genomic alterations in 24 acute lymphocytic leukemia (ALL) cases, showed no genomic alterations using standard techniques (e.g. karyotyping,

CGH).214 Interestingly, when the group used SNP arrays (Affymetrix 100K) and gene expression arrays (Affymetrix HG-U133 Plus 2.0) for molecular characterization of these leukemias, 79% (19/24) of cases were found to harbor molecular alterations using these higher resolution techniques. The cases that did not show such molecular changes may require further analysis using platforms that have an even higher resolution (e.g. genome wide sequencing).

Many genomic platforms that assess genomic gains and losses fail to detect individual contributions of particular genes (e.g. alleles) to such alterations.192 Allelic imbalance may occur through genomic loss, amplification, and somatic recombination; the latter may lead to copy number neutral LOH.214 The SNP array platform allows the identification of allele specific copy number (AsCN) due to the presence of SNPs across the genome and overall copy number variation within the genome. In addition, AsCN analysis minimizes the contamination of normal non-malignant DNA within the tumor because the probe intensity from the opposite allele may be used as an internal reference for the detection of hemizygous gain or loss.215 Also, allelic imbalance using

SNP arrays can be detected within a considerable background of normal cells.196 The authors in one study mixed normal DNA with tumor DNA, and found that allelic

70 differences were still detectable with up to 50% contamination with normal DNA, but calls were more easily discerned with purer tumor samples.196

Historically, LOH has been determined using microsatellite markers. The high density of SNPs across the genome can be utilized for the determination of LOH.

Similar to microsatellite markers, SNPs that are heterozygous are informative, whereas homozygous SNPs are not. Genome wide LOH can thus be compared between a patient’s normal DNA, ideally from lymphocytes, and compared to tumor DNA.

Additionally, LOH can be examined in cancers without paired normal DNA.216 These analyses were performed by examining long consecutive strings of homozygous SNPs that would not be expected to occur by chance alone; but the resolution is not as great as tumor and paired normal.216, 217 This is especially useful for cell lines, xenografts, leukemia with unavailable matched normal samples, and reduces experimental costs.

While the majority of reported use of SNP array platforms have been utilized on

DNA from fresh patient material (e.g. blood and tissue), SNP arrays may also be useful in DNA isolated from formalin-fixed paraffin embedded (FFPE) material.218-220 However, certain guidelines, such as exclusion of larger fragments and GC content correction have to be applied due to the high degree of DNA degradation in sample material.221, 222

Multiple reports have compared the use of different DNA platforms (e.g. array

CGH and SNP arrays) in cancer, and depending on the platform utilized, specific platforms offer advantages and disadvantages over one another (e.g. higher resolution, signal-to-noise ratio).223-226 SNP arrays offer additional features (e.g. LOH and cnLOH), which make it a suitable platform for global genomic analyses compared to other DNA arrays. In one study that examined colorectal cancer cell lines using SNP arrays and array CGH, multiple LOH was observed that could not be picked up from array CGH,

71 because the latter only screens for copy number changes.227 In addition the use of reference DNA in many array CGH platforms assumes that the reference copy number is stable for comparison of different samples; in contrast, SNP arrays use one sample per array, and comparisons are assessed by a copy number baseline from multiple samples. SNP arrays are also useful because they are able to identify allele specific copy number changes, unlike array CGH.228

Although the use of DNA arrays have many features for assessing global DNA alterations, presently they still cannot completely replace standard cytogenetic techniques, as they are not able to determine inversions and balanced translocations observed by karyotyping. This is because the linearity of the genome is not preserved due to the fragmentation of DNA for analysis using these newer technologies.220, 229

3.1.4 SNP Array Platforms

There are two major SNP array technologies that are preferentially utilized for genomic research. These are (a) the Affymetrix DNA microarrays, which utilize oligo probes that are printed on slides, and (b) the Illumina DNA microarrays, which have

DNA probes adsorbed on beads.217

The Affymetrix arrays consist of 25-mer oligonucleotides per each probe set

(www.affymetrix.com) spotted on a chip. The SNPs on the array consist of SNP probes that are perfectly matched to the sample and also contain mismatched SNP probes based on available SNP data. The position of each SNP is present in different areas within the probe and the intensity of the bound DNA determines the amount of DNA present. Genomic DNA is digested using restriction enzymes, ligated using adapter- ligands and then PCR amplified to isolate reduced representation of the genome.217 The

72 DNA is then labeled, hybridized to the array and the intensity of fluorescence for each

SNP is detected.

In contrast, Illumina SNP arrays contain adsorbed beads on a DNA microarray

(www.illumina.com). DNA is amplified using φ-29 phage based whole genome amplification, then fragmented and hybridized, similar to what is done with the

Affymetrix platform. Intensities are determined using fluorescently labeled nucleotides that are extended on each array. Both of these platforms have been instrumental in the analysis of genomic alterations in cancer.

3.1.5 Copy Number and LOH in Head and Neck Cancer

Copy number analyses have been examined in HNSCC using many different genomic platforms. The most commonly reported chromosomal losses in head and neck cancers include 2p, 3p, 4, 5q, 7q, 8p, 9p, 11, 13, 14q, 15p, 16p and 18q, whereas gains include 3q, 5p, 7q, 8q24, 9q22-34, 11q13, 14q24, 15q, 16p, 19, 20q24, and

22q.230-232 In addition additional studies have found similar and additional chromosomal changes. One study examined copy number gains using restriction landmark genomic scanning (RLGS), a technique that examines gene amplifications and low-level copy number changes.233 The authors reported similarly published amplified regions, 3q26.3 and 11q13.3, and additional new regions including, 3q29, 8q13.1, 8q22.3, 9q32,

10q24.32, 14q32.32, 17q25.1, and 20q13.33. In addition, they suggest that amplifications from 3p26.3 and 11q13.3 may be the result of multiple amplicons.

Amplified genes reported in this study included MYCL1, CCNL1, PIK3CA, EGFR,

FGFR1, EDD1, LRP12, MYCC, CCND1, EMS1, MDM2, and ERBB2.233

73 In another study, head and neck cancers were examined using a combination of techniques including CGH, spectral karyotyping (SKY) and gene expression microarrays.234 While CGH has been used for detection of gross chromosomal changes in cancers, it cannot detect inversions and translocations. The authors utilized SKY, a technique that uses fluorescent chromosome-specific paints to detect complex chromosomal rearrangements,235 such as those seen in HNSCCs.234 Using CGH in seven cell lines and four primary tumors, the most frequent gains were observed on chromosomes 3q24-27, 8q and 6q22-qter and loss on chromosome 18q22-qter.

Examination of the same seven cell lines and one of the primary tumors (three other primary tumors were not adequate for analysis) using SKY showed similar altered regions as those previously reported in HNSCCs, and in addition complex rearrangements and heterogeneity within samples.

Copy number analysis and loss of heterozygosity in HNSCCs using low- throughput techniques (e.g. FISH and PCR-based methods) and high-throughput techniques (e.g. array CGH) and their relation to recurrence and survival has been recently extensively reviewed.236 One of the most well known copy number change and clinical association is the amplification of epidermal growth factor receptor (EGFR), which has been shown to correlate with poor survival.237 The most commonly reported copy number variations associated with survival included chromosomal regions, 3q, 7p,

11q, and 22q, and LOH in 3p, 8p, 9p, and 13q.236 Genes common to these regions include: CCND1, EMS1, INT-2, BCL-1 at 11q13; CCNL1 at 3q; EGFR at 7p12; and

BTAK and E2F1 at 22q. Commonly reported chromosomal regions related to recurrence include CNA at 11q and LOH at 9p and 17p. In addition, several chromosomal abnormalities have been related to survival including gains on 3q21-29,

74 and 12q24 and losses on 5q11-15, 6q14-21, 8p21-22, 18q22, and 21q11-21.238-241 One study also found the deletion of chromosome 22q13 to be associated with a worse prognosis and a family history of cancer.242

Recently there was a large study of 117 late-stage HNSCCs using CGH, with subsequent analysis (5 patients) of commonly altered regions using array-CGH (BAC array - Humarray 3.2) and fluorescence in situ hybridization (FISH).175 The authors reported significant gains of chromosome 1q43 and 16q23-24 and chromosomal loss on

18q22 and indicated that these changes were related to poorer prognosis.175 This group also reported other chromosomal gains or losses that were dependent on the head and neck cancer site analyzed.

The role of chromosomal alterations in HNSCC from young patients has not been thoroughly investigated, however a few studies have been reported. One study examined HNSCC DNA from 36 young patients for LOH using microsatellite markers for regions that are commonly lost in head and neck cancer.243 The authors examined LOH at chromosomal regions 3p, 9p and 17p. 92% of the samples examined had at least one

LOH present. The average incidence of LOH was 38.8% at chromosome 3p, 50% at chromosome 9p, and 50% at chromosome 17p. No significant differences in prevalence rates were reported for tumors of young and older patients. The authors reported that the young patients examined had an early and relatively long history of tobacco (>10 years) and alcohol consumption, suggesting similar molecular mechanisms as those observed in tumors of older patients.

Another study used an earlier lower resolution Affymetrix HuMap SNP array in oral carcinomas from young patients.174 In this study the authors compared tumors from young and older patients for genomic differences and found variable global rates of

75 allelic differences. No significant differences were observed between the two groups of patients. In their analysis they compared DNA isolated from FFPE samples from 18 nonsmokers, ranging from 23-57 years of age (median age = 39 years) to that of DNA from 17 smokers, ranging from 47-81 years of age (median age = 64 years). In addition they found a poor low call rate (62.1%) of their SNP data. They reported lower rates of allelic imbalance using their SNP array than concurrent microsatellite analysis of the same regions. The authors explain the discordance by suggesting further refinement in

SNP array technology would be useful in the genetic analysis of HNSCCs. However, this study failed to include similar risk factors within each patient group and the DNA samples from young patients needed to be examined according to reported young patient age cutoffs (i.e. <45 years).103

In our study, we utilized the Affymetrix SNP 6.0 array,244 to simultaneously examine genome-wide loci for CNAs and LOH from tumors of young and older patients.

In addition we assessed cnLOH in head and neck cancer, which to our knowledge has not been reported in HNSCCs. As there are few molecular studies in young patients with head and neck cancer, our study allowed us to determine if oral tumors from young patients harbor different genomic alterations than those from older patients by assessing a wide range of genetic mechanisms that can be involved in cancer. This may help to explain the role of specific genes/regions involved in early-onset oral carcinoma.

76 3.2 MATERIALS AND METHODS

3.2.1 Patient Samples

The University Health Network (UHN) Research Ethics Board approved this study; informed consent was obtained from all patients prior to sample collection. All patients had surgery as the primary treatment.

78 patient samples were collected from Toronto General Hospital, including 19- paired oral tumors and adjacent normal oral tissue (38 samples total) from young patients (<45 years), and 20-paired oral tumors and adjacent normal oral tissue (40 samples total) from older patients (≥45 years). Patient clinicopathological information is provided in Table 1.

3.2.2 Genomic DNA Isolation

Genomic DNA was isolated from 13-paired young and 19-paired older patient fresh frozen oral tumors and adjacent normal tissue using the phenol-chloroform method with SNET buffer and ethanol precipitation as described in Chapter 2, Materials and Methods. To increase our sample size we also included 7-paired oral tumors and adjacent normal oral tissue (6 young and 1 older patient paired tumor and adjacent normal oral tissue – Sample ID – 3N, 3T, 4N, 4T, 8N, 8T, 10N, 10T, 15N, 15T, 16N,

16T, 32N, and 32T) isolated for a previous analysis.120

DNA quantity and quality was assessed by spectrophotometry (Nanodrop,

Thermo Scientific, Waltham, MA) and electrophoresis on a 0.8% agarose gel. Pilot studies found that genomic quality was deemed usable for further studies if high molecular weight and spectrophotometric results: 260/280 ≥1.8 and 260/230 ≥ 1.8 was

77 Table 1: Clinical characteristics of oral tumors and paired oral normal mucosa tissue from young and older patients used on the Affymetrix SNP 6.0 arrays. M = Male; F = Female; Y = Yes; N = No.

Patient ID Age Sex Smoker Alcohol Tumor Site Node Stage Differentiation 1 26 M Y N Tongue + IV Poorly 2 41 M Y Y Aveolar - II Moderately 3 42 M Y Y Tongue - II Moderately 4 38 M Y Y Floor of Mouth - IV Moderately 5 31 M Y Y Tongue - IV Moderately 6 22 M Y N Tongue + IV Moderately 7 34 M Y Y Tongue - IV Moderately 8 38 F Y N Tongue + III Moderately 9 42 F Y Y Tongue - I Poorly 10 25 M N N Tongue + III Poorly 11 39 M N N Buccal + IV Poorly 12 32 M N N Tongue - II Moderately 13 39 M N N Alveolar - IV Moderately 14 36 M N Y Tongue + IV Moderately 15 36 F N N Tongue - II Moderately 16 26 F N N FOM/tongue + IV Moderately 17 39 F N N Tongue + III Moderately 18 34 F N N Tongue + IV Poorly 19 40 F N N Tongue + IV Moderately 20 68 M Y Y Floor of Mouth + IV Moderately 21 55 M Y Y Tongue - III Moderately 22 72 M Y Y Tongue - II Moderately 23 68 M Y Y Tongue + IV Moderately 78 24 74 M Y Y Floor of Mouth - IV Moderately 25 70 F Y Y Tongue + IV Poorly 26 73 F Y Y Floor of Mouth + IV Moderately 27 79 F Y Y FOM/Tongue - IV Moderately 28 70 F Y Y Tongue - II Moderately 29 87 F Y Y Alveolus - IV Poorly 30 56 M N N Tongue Base + III Poorly 31 71 M N N Tongue - II Moderately 32 73 M N N Upper Palate - IV Well 33 64 M N Y Tongue + III Moderately 34 54 M N N Tongue - IV Moderately 35 75 F N N Maxilla - IV Moderately 36 94 F N N Maxilla - IV Moderately 37 80 F N N Tongue + IV Poorly 38 83 F N N Buccal - I In situ 39 72 F N N Tongue - II Moderately

79 obtained. Some of the DNA from the previous study did not meet these standards, but was still included to increase the young patient sample size. To increase DNA purity these samples that were purified using the 5Prime ArchivePure DNA Cell/Tissue Kit

(Intermedico, Markham, ON). After purification, these samples were deemed usable from our pilot studies on the Affymetrix SNP 6.0 array.

3.2.3 Affymetrix SNP 6.0 Array Protocol

The Affymetrix Genome-Wide Human SNP Nsp/Sty 6.0 array that was used contains more than 906,600 SNPs and 946,000 copy number probes. The genomic

DNA samples were processed at The Centre for Applied Genomics (TCAG, Toronto,

ON). The complete experimental protocol is provided in the Affymetrix Genome-Wide

Human SNP Nsp/Sty 6.0 User Guide (www.affymetrix.com). Briefly, 250 ng of genomic

DNA was digested using the Sty I restriction enzyme, and 250 ng of genomic DNA using the Nsp I restriction enzyme and stored at 4oC until further use. Adaptor linkers were then added to digested DNA ends and stored at -20oC until PCR amplification. During

PCR amplification the restriction digested DNA with adaptor linkers was amplified using a generic primer that recognizes the adaptor sequences.

The PCR product was then purified and quantified. Subsequently, the amplified product was fragmented and labeled. The labeled probes were hybridized to the

Affymetrix Genome-Wide Human SNP Nsp/Sty 6.0 arrays and then washed to remove any unbound probes, stained, and then scanned using the GeneChip Scanner 3000 7G.

80 3.2.4 SNP Array Copy Number and LOH Data Analysis

Affymetrix Genotyping Console (GTC) (v. 3.0.2) and Partek Genomics Suite

(PGS) (v. 6.09.0602) software programs were used in our experiments to detect copy number changes. Initially, CEL files from the SNP 6.0 array provided by the TCAG were inputted in Affymetrix GTC. Quality control of the SNP array genotype calls by

Affymetrix GTC was checked and all samples were “In Bounds”, meaning good quality for data analysis as determined by GTC (Table 2). The median genotype call rate was

99.6% for adjacent normal tissue and 99.1% for paired tumors. Using the Affymetrix

GTC we were able to create genotyping data (CHP files). Affymetrix GTC also performs a quality control check to assess signal-to-noise ratio using a median absolute pairwise difference (MAPD) metric (www.affymetrix.com). Briefly, the log2 ratio is taken between neighbors and a median is expressed. The higher the MAPD, the greater the noise between the test sample and reference set. Each sample was assessed for a

MAPD score using the software’s cutoff of 0.35 (Table 2). All samples passed the

MAPD metric except for 1 sample (Sample ID# 6T), although this sample was still utilized for analysis as it was near the MAPD cut off (0.38) and was from a young patient.

We next used Partek Genomics Suite software for copy number analyses. We performed copy number analyses to assess the total copy number per sample. From the 78 samples that were analyzed, 4 normal tissues had more copy number alterations than their paired oral tumor and were excluded, leaving 74 samples for all further analyses (Table 3). Copy number analysis was performed using a paired analysis for 70 samples, and an unpaired analysis for 4 samples; the latter was assessed using 74

81 Table 2: Quality control metrics including SNP genotyping call rate and median absolute pairwise difference (MAPD) on oral tumors and paired adjacent normals. Affymetrix Genotyping Console analysis of genotyping call rates for each sample used on the Affymetrix SNP 6.0 arrays. Call rates are a percentage of readable calls from genotyping software. MAPD measures the signal to noise ratio of each sample and provides a metric on the quality of sample.

Contrast Sample ID Call_Rate Bounds MAPD Bounds QC 1N 99.47 2.41 In 0.19 In 1T 99.21 2.43 In 0.20 In 2N 99.26 2.16 In 0.21 In 2T 99.15 2.33 In 0.24 In 3N 99.79 2.3 In 0.17 In 3T 99.63 2.36 In 0.19 In 4N 99.29 2.12 In 0.25 In 4T 97.73 1.6 In 0.26 In 5N 99.74 2.7 In 0.16 In 5T 99.71 2.22 In 0.19 In 6N 99.80 2.33 In 0.16 In 6T 97.22 0.88 In 0.38 Out 7N 99.71 2.48 In 0.19 In 7T 98.20 1.64 In 0.20 In 8N 99.79 2.48 In 0.15 In 8T 99.54 3.04 In 0.19 In 9N 99.73 2.93 In 0.16 In 9T 99.25 1.93 In 0.19 In 10N 99.56 2.3 In 0.19 In 10T 99.51 2.55 In 0.19 In 11N 99.44 1.93 In 0.20 In 11T 97.28 1.04 In 0.35 In 12N 99.76 2.88 In 0.20 In 12T 99.42 2.16 In 0.21 In 13N 99.80 2.97 In 0.19 In 13T 98.52 2.16 In 0.27 In 14N 99.43 2.26 In 0.20 In 14T 98.03 1.16 In 0.27 In 15N 99.49 2.59 In 0.19 In 15T 97.33 1.94 In 0.27 In 16N 99.46 2.54 In 0.18 In 16T 98.40 1.93 In 0.26 In 17N 99.43 2.22 In 0.21 In 17T 99.40 2.07 In 0.21 In 18N 99.37 1.71 In 0.19 In 18T 99.61 1.97 In 0.20 In 19N 99.66 2.44 In 0.17 In 82 19T 98.88 2.06 In 0.22 In 20N 99.69 2.39 In 0.19 In 20T 98.03 2.23 In 0.24 In 21N 97.55 1.34 In 0.30 In 21T 98.44 1.72 In 0.24 In 22N 99.78 2.76 In 0.17 In 22T 99.74 2.78 In 0.16 In 23N 99.68 2.6 In 0.18 In 23T 99.52 2.07 In 0.19 In 24N 97.71 1.84 In 0.22 In 24T 99.70 2.46 In 0.19 In 25N 99.34 2.09 In 0.20 In 25T 98.37 1.48 In 0.25 In 26N 99.65 2.58 In 0.16 In 26T 99.64 2.82 In 0.18 In 27N 99.68 2.72 In 0.20 In 27T 99.75 2.37 In 0.18 In 28N 99.54 2.29 In 0.18 In 28T 99.56 2.19 In 0.18 In 29N 99.51 2.17 In 0.18 In 29T 98.11 1.77 In 0.21 In 30N 98.87 1.72 In 0.21 In 30T 99.24 2.4 In 0.19 In 31N 99.40 2.45 In 0.22 In 31T 95.69 1.08 In 0.29 In 32N 98.14 1.73 In 0.34 In 32T 97.97 1.88 In 0.25 In 33N 99.79 2.57 In 0.16 In 33T 98.04 1.16 In 0.27 In 34N 97.87 1.79 In 0.31 In 34T 99.33 2.76 In 0.21 In 35N 98.06 1.42 In 0.31 In 35T 99.15 1.61 In 0.23 In 36N 99.67 3.06 In 0.16 In 36T 98.40 1.65 In 0.22 In 37N 99.76 2.67 In 0.17 In 37T 99.06 2.08 In 0.20 In 38N 99.69 3.12 In 0.15 In 38T 98.29 1.92 In 0.25 In 39N 99.41 2.08 In 0.20 In 39T 97.73 2.11 In 0.18 In

83 Table 3: Copy number using the Affymetrix SNP 6.0 array detected by Partek. Absolute copy number of each sample is provided for each respective tumor and matched paired normal. * Represent normal samples that were excluded for analyses.

Sample Copy Sample Copy Young Patients ID Number Older Patients ID Number Smokers (S) 1N 30 Smokers (S) 20N 27 1T 183 20T 157 2N 28 21N 747* 2T 68 21T 60 3N 37 22N 29 3T 48 22T 33 4N 57 23N 36 4T 749 23T 78 5N 32 24N 183* 5T 30 24T 30 6N 34 25N 122 6T 2851 25T 108 7N 38 26N 30 7T 204 26T 20 8N 25 27N 19 8T 23 27T 28 Non-smokers (NS) 9N 32 28N 37 9T 46 28T 48 10N 27 29N 27 10T 33 29T 116 11N 53 Non-smokers (NS) 30N 27 11T 2734 30T 70 12N 28 31N 52 12T 27 31T 444 13N 37 32N 2111* 13T 1320 32T 104 14N 128 33T 43 14T 82 33N 1090 15N 22 34N 50 15T 408 34T 76 16N 48 35N 782* 16T 55 35T 26 17N 57 36N 50 17T 43 36T 53 18N 35 37N 36 18T 46 37T 46 19N 29 38N 50 19T 203 38T 48 39N 32 39T 212

84 samples as the baseline copy number reference. The results of the two analyses were merged into one spreadsheet.

We used the genomic segmentation algorithm in the software program’s workflow to detect CNAs. To increase the likelihood of statistically significant genomic aberrations as suggested by Partek’s recommendations, we increased the stringency of the segmentation parameters by assessing at least 10 consecutive genomic markers with a cut off of p=0.0001 (from p=0.001), and with a signal to noise cutoff of 0.3 for amplified regions. In addition, we used an even higher stringency cut off of p=0.0001

(from p=0.001) and a signal to noise cutoff of 0.5 (from 0.3) for deleted regions; the latter was performed because deletions can show higher noise using genomic DNA platforms (www.partek.com - tutorials). Gains were considered more that 2.3 copies and deletions as less than 1.7 copies. Regions were analyzed by a Chi-square analysis for statistical differences in age (p≤0.01) and smoking (p≤0.01). Additionally to further filter our data, genes on sex chromosomes, chromosomal regions with no known genes, and any genomic regions with known normal copy number variation were excluded; the latter was performed using the Database of Genomic Variants

(http://projects.tcag.ca/variation/) through PGS software.

In addition, in a separate analysis we applied an even more stringent comprehensive CNA analysis, by merging the results of three different CNA analysis tools: 1) PGS; 2) Affymetrix GTC; and 3) Birdsuite. The latter is an analytic framework tool that is able to determine copy number and SNP genotypes, and it has been used with Affymetrix SNP 6.0 arrays.245 Briefly, Birdsuite assigns copy number along regions of CNAs and then assigns genotypes (AA, AB, and BB). Afterwards, it applies a Hidden

Markov Model to discover rare or de novo CNAs, and finally SNP allele and copy

85 number information are provided for each locus, using a confidence score cutoff of 10.

These CNAs were merged using the outside probe boundaries if they were detected in the same sample by more than one algorithm. These analyses were performed by Dr.

Christian Marshall (post-doctoral fellow) from Dr. Stephen Scherer’s laboratory located at the Medical and Related Sciences (MaRS) centre (Toronto, ON). Dr. Wei Xu, biostatistician, at the Ontario Cancer Institute performed the statistical analyses.

LOH analysis was performed using the LOH workflow in PGS by importing genotyping data from the samples and using a paired analysis. Integrating copy number data with LOH analysis assessed the role of cnLOH in the oral tumors analyzed. Filtering the data was performed similar to copy number analysis filtering: genes on sex chromosomes, gene absence and CNV exclusion.

86 3.3 RESULTS

3.3.1 Patient Clinicopathological Information

A summary of the clinicopathological features of the patients used in our study is as follows: the median young patient age was 36 years (range 22-42 years), while the median age of older patients was 72 years (range 54-94 years) (Table 1). Males made up 12/19 young patients and 10/20 older patients. There were 5 early-staged (I/II) and

14 late-staged (III/IV) young patient tumors; and 5 early-staged and 15 late-staged older patient tumors. There were 10 young patients and 7 older patients that were node positive. Smokers or alcohol drinkers constituted 50% of older patients, and 47% and

50% of young patients, respectively. In addition, most tumors from young and older patients were moderately differentiated and were from the tongue.

3.3.2 Common Copy Number Alterations in Head and Neck Cancer Compared to Our Dataset

Common gains and losses in head and neck cancer were assessed in our SNP array analyses (Table 4). We found gains of epidermal growth factor receptor (EGFR) in 32% (11/34), and cyclin D1 (CCND1) in 41% (14/34) of the cases; and loss of cyclin- dependent kinase inhibitor 2A (CDKN2A) in 24% (8/34), fragile histidine triad gene

(FHIT) in 38% (13/34), and retinoblastoma 1 (RB1) in 26% (9/34) of the cases. These results are consistent with reported copy number altered genes/regions within the head and neck cancer literature: 17-54% for EGFR/7p12;237, 246-248 17-50% for

CCND1/11q13;246, 249, 250 29% for RB1; 177 20.5% for p16(CDK2A/9p21);251 and 47% for

FHIT.252

87 Table 4: Copy number in genes commonly altered in head and neck cancer. CCND1 – Cyclin D1; EGFR – Epidermal growth factor; RB1 – Retinoblastoma 1; CDKN2A (p14/p16) – Cyclin dependent kinase 2A; FHIT – Fragile histidine triad gene; TP53 – Tumor protein 53. Red shaded areas represent paired samples that had amplifications. Green shaded areas represent paired samples that had deletions. Grey shaded areas represent paired samples that were excluded for copy number analysis. Yellow shaded areas represent paired samples with both gains and losses. Red font numbers represent normal samples that were excluded for analysis.

Sample ID CCND1 EGFR RB1 CDKN2A (p14/p16) FHIT TP53 1N 2 2 2 2 2 2 1T 2 2 2 2 2 2 2N 2 2 2 2 2 2 2T 2 3 2 2 1 2 3N 2 2 2 2 2 2 3T 2 2 2 2 1 2 4N 2 2 2 2 2 2 4T 4 2 1 1 1 2 5N 2 2 2 2 2 2 5T 2 2 2 2 2 2 6N 2 2 2 2 2 2 6T 3 3 1_4 2 1 3 7N 2 2 2 2 2 2 7T 4 2 3 1 1 2 8N 2 2 2 2 2 2 8T 2 2 2 2 2 2 9N 2 2 2 2 2 2 9T 3 2 2 2 2 2 10N 2 2 2 2 2 2 10T 2 2 2 2 2 2 11N 2 2 2 2 2 2 11T 3 3 1 1 1 3 12N 2 2 2 2 2 2 12T 2 2 2 2 2 2 13N 2 2 2 2 2 2 13T 2 3 1 2 1 2 14N 3 3 2 2 1 2 14T 2 2 2 2 1 2 15N 2 2 2 2 2 2 15T 3 3 1 1 1 2 16N 2 2 2 2 2 2 16T 2 3 2 3 2 3 17N 2 2 2 2 2 2 17T 2 2 2 2 2 2 18N 2 2 2 2 2 2

88 18T 4 2 2 2 2 2 19N 2 2 2 2 2 2 19T 2 3 1 3 1 2 20N 2 2 2 2 2 2 20T 3 4 2 1 1 2 21N 4 3_4 4 4 1 4 21T 2 2 2 2 2 2 22N 2 2 2 2 2 2 22T 2 2 2 2 2 2 23N 2 2 2 2 2 2 23T 3 3 2 2 2 2 24N 2 4 1 1 1 1 24T 2 2 2 2 2 2 25N 3 3 2 3 1 3 25T 4 2 2 2 1 2 26N 2 2 2 2 2 2 26T 2 2 2 2 2 2 27N 2 2 2 2 2 2 27T 2 2 2 2 2 2 28N 2 2 2 2 2 2 28T 2 2 2 2 2 2 29N 2 2 2 2 2 2 29T 3 2 1 1 2 2 30N 2 2 1_2 2 2 2 30T 2 2 1 2 1 2 31N 2 2 2 2 2 2 31T 3 3 1 1 1 3 32N 2 2 1 1 0_1 2 32T 4 4 2 2 2 2 33N 2 2 2 2 2 2 33T 3 3 4 3 1 3 34N 2 2 2 2 2 2 34T 2 2 2 2 2 2 35N 3 3 1 2 1 3 35T 2 2 2 2 2 2 36N 2 2 2 2 2 2 36T 2 2 2 2 2 2 37N 2 2 2 2 2 2 37T 3 4 2 2 2 2 38N 2 2 2 2 2 2 38T 2 2 2 2 2 2 39N 39T 4 2 2 1 1 2

89 3.3.3 Copy Number Alterations in All Oral Tumors

We assessed copy number status using the Partek Genomics Suite software program. Significant amplifications and deletions were detected in all tumors regardless of any specific clinical factors (Figure 1). Common chromosomal gains were detected on chromosomes: 3q, 5p, 7, 8q, 9q, 11q, 17, 19, 20, and 22q; common losses were detected on chromosomes 3p, 5q, 8p, 9p, 11q, 18q, and 21q. Analysis of global genetic alterations showed that every chromosome in the oral cancers examined had gains or losses, although chromosomal arms 13p, 14p, 15p, 21p, and 22p did not exhibit copy number alterations. We then compared the number of chromosomal alterations common to at least 10 (26% (10/39), Figure 2), 15 (38% (15/39), Figure 3) and 20 (51% (20/39),

Figure 4) patient tumors, irrespective of clinicopathological features. The number of tumors selected from each patient cohort was selected to assess a representative copy number profile in each group. As more samples were included the number of shared copy number changes decreased, as expected. At least 15 patients showed chromosomal changes that were consistently altered in oral cancer samples, including gains of chromosomes 3q, 8q, 11q and 20q, and deletion of chromosomes 3p, 5q, 8p, and 11q (Figure 3). Interestingly chromosomal loss was found in mitochondrial DNA in up to 20 tumor samples (Figure 4).

3.3.4 Copy Number Alterations in Oral Tumors from Young and Older Patients

We then assessed copy number alteration differences in tumors between young and older patients to identify any age-dependent chromosomal changes. Contributions of copy number alterations from each tumor of young and older patients were examined

90

Figure 1: Histogram of copy number alterations common in of oral tumors. Chromosome numbers are provided below each respective chromosome. Red shaded bars represent gains to the right of each chromosome and green shaded bars represent deletions to the left of each chromosome. Pink cytoband represents centromere.

91

Figure 2: Copy number alterations common in of at least 10 oral tumors. Red shaded bars represent gains and blue shaded bars represent deletions to the right of each chromosome. MT- Mitochondrial DNA. Pink cytoband represents centromere.

92

Figure 3: Copy number alterations common in at least 15 oral tumors. Red shaded bars represent gains and blue shaded bars represent deletions to the right of each chromosome. MT- Mitochondrial DNA. Pink cytoband represents centromere.

93

Figure 4: Copy number alterations common in at least 20 oral tumors. Red shaded bars represent gains and blue shaded bars represent deletions to the right of each chromosome. MT- Mitochondrial DNA. Pink cytoband represents centromere.

94 (Figures 5 and 6). In oral tumors from young patients, common chromosomal gains included: 3q, 8q, 11q, 17, 20 and 22q; common chromosomal losses included: 3p, 5q,

9p, 11q, 13q, 18q, and 21q. In oral tumors from older patients, common chromosomal gains included: 3q, 5p, 7, 8q, 11q, 13q, and 20; common chromosomal losses included:

1p, 3p, 4p, 5q, 7q, 8p, 9p, 10p, 11q, 15q, 17, 18q, 21q, and 22q.

Multiple tumors were compared from young and older patients, respectively. At least 7 oral tumors from young and at least 7 oral tumors from older patients were selected because they showed a good representation of age-dependent chromosomal changes. In addition, oral tumors from at least 7 patients in each age group exhibited similar common copy number alterations to all oral tumors (Figure 1). Tumors from young patients shared higher rates of chromosomal loss than gains compared to tumors from older patients (Figures 7 and 8). A group of young patients had tumors that harbored relatively high copy number changes (especially deletions) compared to their paired normal tissue and older patient tumors (Sample ID #’s: 1T (183), 4T (749), 6T

(2851), 11T (2734), 13T (1320), 15T (408), and 19T (203); Table 4). The latter sample

(Sample ID# 6T) had a high MAPD value (Table 3), which may explain the high copy number alteration. Common chromosomal gains shared by young patient tumors were on chromosomes 3q, 8q, 11q, 17q, 20, and 22q. Chromosome gains shared by older patients included 3q, 5p, 7, 8q, 11q, 13q and 20. Interestingly, chromosome 11q in young patients had a broader amplification region than older patients (Figures 5 and 6).

95

Figure 5: Histogram of copy number alterations common in oral tumors from young patients. Chromosome numbers are provided below each respective chromosome. Red shaded bars represent gains to the right of each chromosome and green shaded bars represent deletions to the left of each chromosome. Pink cytoband represents centromere.

96

Figure 6: Histogram of copy number alterations common in oral tumors from older patients. Chromosome numbers are provided below each respective chromosome. Red shaded bars represent gains to the right of each chromosome and green shaded bars represent deletions to the left of each chromosome. Pink cytoband represents centromere.

97

Figure 7: Copy number alterations common in at least 7 oral tumors from young patients. Red shaded bars represent gains and blue shaded bars represent deletions to the right of each chromosome. MT‐ Mitochondrial DNA. Pink cytoband represents centromere.

98

Figure 8: Copy number alterations common in of at least 7 oral tumors from older patients. Red shaded bars represent gains and blue shaded bars represent deletions to the right of each chromosome. MT- Mitochondrial DNA. Pink cytoband represents centromere.

99 3.3.5 Common Copy Number Alterations and Significantly Associated Genes in Oral Tumors from Young and Older Patients

In order to filter our dataset we removed genes on sex chromosomes, gene absent regions, and copy number variations; the latter according to the Database of

Genomic Variants. From this filtered dataset, we found that 184 genes were significantly different (p≤0.01) between tumors of young and older patients (Appendix

Table 1). The most significantly altered genes were found on chromosomes 1, 2, 3, 5,

6, 7, 8, and 12. The top 20 genes that were amplified and deleted in tumors from young and older patients are provided in Table 5.

A gene ontology (GO) analysis of the statistical different copy number containing genes was examined between tumors of young and older patients using PGS. GO examines the relationship of genes and their gene products. GO biological processes that were significantly different included developmental processes, pigmentation, biological adhesion and biological regulation (Appendix Figure 1). GO molecular functions that were significantly different included chemoattractant activity, catalytic activity, and enzyme regulator activity (Appendix Figure 2).

A more stringent analysis (Affymetrix GTC, Partek Genomics Suite, and

Birdsuite) that found common copy number variations in at least 2 of 3 software programs was used. We found 16 regions that were significantly altered in tumors of young and older patients (Table 6). Significantly altered chromosomal regions included

1q, 2p, 3p, 4q, 6q, 8q, 9, 14q, 16, 21q, and 22 q. However, only 5 genes were found to be statistically significantly altered within these regions, including activating transcription factor 6 (ATF6), nuclear receptor coactivator 7 (NCOA7), eyes absent homolog 1

(EYA1), contactin associated protein-like 4 (CNTNAP4), heat shock transcription factor

100 Table 5: Top 20 significantly altered gene-containing regions in tumors from young and older patients. Highlighted regions represent genes that were detected in another column. Genes were organized based on ascending p-value (p≤0.01).

YOUNG PATIENT TUMORS AMPLIFIED DELETED Gene ID(s) Gene Name(s) Function/Process Gene ID(s) Gene Name(s) Function/Process Mitochondrial MRPL48 THSD7A Thrombospondin, type I, ribosomal Translation Unknown (11q13.4) (7p21.3) domain containing 7A protein L48 Uncoupling UCP3 protein 3 Brain-specific angiogenesis Angiogenesis Metabolism BAI3 (6q12) (11q13) (mitochondrial, inhibitor 3 Inhibition proton carrier) Solute carrier organic anion 3-hydroxymethyl-3- SLCO2B1 HMGCLL1 transporter Ion transport methylglutaryl-Coenzyme A Metabolism (11q13) (6p12.1) family, member lyase-like 1 2B1 Chromosome 13 C13orf16 ZBTB20 Zinc finger and BTB domain Transcription open reading Unknown (13q34) (3q13.2) containing 20 regulation frame 16 Major facilitator MFSD11 superfamily PBX3 (9q33- Pre-B-cell leukemia homeobox Transcription Unknown (17q25) domain q34) 3 regulation containing 11

PCID2 PCI domain DENND1B DENN/MADD domain Protein binding Unknown (13q34) containing 2 (1q31.3) containing 1B

101 Transcription Family with sequence similarity EP300 E1A binding FAM19A2 factor; transferase 19 (chemokine (C-C motif)-like), Chemokine activity (22q13.2) protein p300 (12q14.1) activity member A2

Integrator INTS2 Protein binding; FLJ44048 complex subunit FLJ44048 protein Unknown (17q23.2) snRNA processing (2q32.1) 2

Potassium UDP-N-acetyl-alpha-D- voltage-gated GALNT13 KCNQ1 Ion channel galactosamine:polypeptide N- Ion binding; channel, KQT- (2q23.3- (11p15.5) activity acetylgalactosaminyltransferase transferase activity like subfamily, q24.1) 13 member 1

Arylsulfatase G; Solute carrier ARSG family 16, (17q24.2); Metabolism; KCNT2 Potassium channel, subfamily Ion transport; member 6 SLC16A6 Transport (1q31.3) T, member 2 metabolism (monocarboxylic (17q24.2) acid transporter 7)

ZNF721 Zinc finger Transcription RAP1GDS1 RAP1, GTP-GDP dissociation GTPase activator (4p16.3) protein 721 regulation (4q23-q25) stimulator 1 activity

102 Insulin-like IGF2 growth factor 2 (11p15.5); (somatomedin IGF2AS A); Insulin-like (11p15.5); growth factor 2 LRRIQ1 Leucine-rich repeats and IQ Metabolism Protein binding INS antisense; (12q21.31) motif containing 1 (11p15.5); Insulin; INS- INS-IGF2 IGF2 (11p15.5) readthrough transcript BRCA1 interacting Cell adhesion; BRIP1 DNA binding; DNA ALCAM Activated leukocyte cell protein C- receptor binding; (17q22-q24) repair activity (3q13.1) adhesion molecule terminal helicase signal transduction 1 Histone deacetylase Glutamate- GCLM HDAC9 activity; cysteine ligase, Metabolism Histone deacetylase 9 (1p22.1) (7p21.1) transcription modifier subunit regulation Archaelysin AMZ2 family (17q24.2); Peptidase activity; PHF14 Ion binding; protein metallopeptidase PHD finger protein 14 ARSG Metabolism (7p21.3) binding 2; Arylsulfatase (17q24.2) G

Receptor activity; FOXL1 Transcription LRP1B Low density lipoprotein-related Forkhead box L1 ion binding; protein (16q24) regulation (2q21.2) protein 1B (deleted in tumors) binding

103 Inhibitor of kappa light Transcription Peptidase activity; IKBKB polypeptide TINAG Tubulointerstitial nephritis activator activity; nucleotide and (8p11.2) gene enhancer (6p11.2-p12) antigen transferase activity protein binding in B-cells, kinase beta

ATP-binding ABCA4 V-erb-a erythroblastic leukemia Receptor activity; cassette, sub- ATPase; ERBB4 (1p22.1- viral oncogene homolog 4 transferase activity; family A (ABC1), transporter activity (2q33.3-q34) p21) (avian) signal transduction member 4

Discs, large (Drosophila) DLGAP2 Protein binding; NXPH1 homolog- neurexophilin 1 Receptor binding (8p23) cell-cell signaling (7p22) associated protein 2 CAPS2 ERICH1 Glutamate-rich 1 Unknown (12q21.1- calcyphosine 2 Ion binding (8p23.3) q21.2)

104 OLDER PATIENT TUMORS AMPLIFIED DELETED Gene ID(s) Gene Name(s) Function/Process Gene ID(s) Gene Name(s) Function/Process Zinc finger, B- PTK2B protein Signal transduction; ZBBX (3q26.1) box domain Ion binding PTK2B (8p21.1) tyrosine kinase 2 transferase activity containing beta Leucine, Coiled-coil LEKR1 (3q25.31) glutamate and Unknown CCDC25 (8p21.1) domain containing Unknown lysine rich 1 25 LIM domain Discs, large containing Ion binding; protein (Drosophila) Protein binding; cell- LPP (3q28) preferred binding; cell DLGAP2 (8p23) homolog- cell signaling translocation adhesion associated protein partner in lipoma 2 RSRC1 Arginine/serine- Protein binding; ERICH1 (8p23.3) Glutamate-rich 1 Unknown (3q25.32) rich coiled-coil 1 RNA splicing Rho guanine Regulation of Rho Calcitonin Calcitonin binding; ARHGEF10 nucleotide CALCR (7q21.3) protein signal receptor signaling (8p23) exchange factor transduction (GEF) 10 Rho guanine nucleotide ARHGEF10 Regulation of Rho Cyclin- Cell cycle control; exchange factor CDK6 (7q21- (8p23); protein signal dependent transcription (GEF) 10; Kelch q22) KBTBD11 transduction; Protein kinase 6 regulation repeat and BTB (8p23.3) binding (POZ) domain containing 11 Ceroid- FGF10 (5p13- Fibroblast Growth factor CLN8 (8p23) lipofuscinosis, Metabolism p12) growth factor 10 activity; proliferation neuronal 8

105 Catenin Kelch repeat and Cell adhesion; CTNND2 (cadherin- KBTBD11 BTB (POZ) protein binding; Protein binding (5p15.2) associated (8p23.3) domain containing signal transduction protein), delta 2 11 ATP, nucleotide, and CDH18 (5p15.2- Cadherin 18, Cell adhesion; ion Kinesin family KIF13B (8p12) protein binding; signal p15.1) type 2 and protein binding member 13B transduction Olfactory Scavenger OR5H6 (5p15.2- receptor, family Receptor activity; Ion and protein binding; SCARA5 (8p21.1) receptor class A, p15.1) 5, subfamily H, signal tranduction receptor activity member 5 member 6 Cholinergic receptor, nicotinic, Ion transport; signal CHRNA2 (8p21); CPNE4 (3q22.1) Copine IV Ion binding alpha 2; PTK2B transduction; PTK2B protein tyrosine transferase activity kinase 2 beta Limbic system- LSAMP (3q13.2- associated Cell adhesion; Exostoses Ion and protein binding; EXTL3 (8p21) q21) membrane protein binding (multiple)-like 3 transferase activity protein Protein catabolic SUMO1/sentrin FBXO16 F-box protein 16; Peptidase activity; process; Receptor SENP7 (3q12) specific (8p21.1); FZD3 Frizzled homolog protein sumoylation activity; signal peptidase 7 (8p21) 3 (Drosophila) transduction STXBP5L Syntaxin binding Exocytosis; protein Frizzled homolog Receptor activity; FZD3 (8p21) (3q13.33) protein 5-like transport 3 (Drosophila) signal transduction Matrix Leptin receptor Peptidase activity; LEPROTL1 MMP16 (8q21.3) metallopeptidase overlapping Unknown ion binding (8p21.2-p21.1) 16 transcript-like 1

106 ATPase, Ca++ ATPase activity; ion ATP2C1 transporting, PNOC (8p21); binding; signal Prepronociceptin Signal transduction (3q22.1) type 2C, ZNF395 transduction member 1 TMEM108 Transmembrane Transmembrane Unknown TMEM66 (8p12) Unknown (3q21) protein 108 protein 66 3- hydroxymethyl- HMGCLL1 Zinc finger protein Ion and DNA binding; 3-methylglutaryl- Metabolism ZNF395 (8p21.1) (6p12.1) 395 transcription regulation Coenzyme A lyase-like 1 Homeobox Transcription Zinc finger and HMBOX1 Transcription containing 1; regulation; Protein ZBTB20 (3q13.2) BTB domain (8p21.1); INTS9 regulation Integrator binding; snRNA containing 20 (8p21.1) complex subunit 9 processing Activated Cell adhesion; leukocyte cell ALCAM (3q13.1) receptor binding; MCPH1 (8p23.1) Microcephalin 1 DNA repair adhesion signal transduction molecule

107 Table 6: Significantly altered chromosomal regions based on age groups using stringent analyses. Copy number was provided that was identified in at least of the 2 of 3 software programs: Affymetrix Genotyping Console, Partek Genomics Suite, and Birdsuite. Pt. - patient. p≤0.05.

Young Pt. Tumors Older Pt. Tumors No No P- Chromosome Cytoband Gain Loss Gain Loss Gene ID Gene Name Change Change value Nuclear receptor 6 6q22.32 - - 19 - 8 12 0.002 NCOA7 coactivator 7 Activating 1 1q23.3 - 5 14 - - 20 0.014 ATF6 transcription factor 6 16 16p11.2 - - 19 1 6 13 0.017 - - 16 16p11.2 - - 19 - - 20 0.017 - - Heat shock 21 21q22.3 - 1 18 - 7 13 0.022 HSF2BP transcription factor 2 binding protein 4 4q34.1 3 - 16 10 - 10 0.023 - - 14 14q21.3 - 4 15 - - 20 0.030 - - Contactin 16 16q23.1 - 4 15 - - 20 0.030 CNTNAP4 associated protein-like 4 2 2p22.3 - 4 15 - - 20 0.030 - - 22 22q11.22 - 4 15 - - 20 0.030 - - 4 4q22.3 - 4 15 - - 20 0.030 - - Glutathione S- 22 22q11.23 - - 19 - 4 16 0.040 GSTT1 transferase theta 1 3 3p12.1 - - 19 - 4 16 0.040 - - Eyes absent 8 8q13.3 - 1 18 - 6 14 0.044 EYA1 homolog 1 9 9q34.3 - 1 18 - 6 14 0.044 - - 9 9p11.2 7 - 12 2 - 18 0.047 - -

108 2 binding protein (HSF2BP), and glutathione S-transferase theta 1 (GSTT1). Only

CNTNAP4 was found in our previous analysis (Appendix Table 1), suggesting this method may be too stringent for selecting significantly altered genes involved in copy number alterations.

3.3.6 Copy Number Alterations in Tumors from Smokers and Non-Smokers

We assessed the role of the major risk factor, smoking, and its relation to copy number changes in tumors from smokers vs. non-smokers. The majority of copy number alterations were similar between tumors from smokers and non-smokers (Figures 9 and

10), suggesting that smoking may play a minor role in copy number alteration differences in tumors from young and older patients. However, there were some regions that were different between patients that appeared dependent on smoking status. Focal amplification in tumors from smokers was observed on chromosomes 7q, 13q, 15q

(near the centromere), and deletions on chromosome 17p; focal amplifications in tumors from non-smokers were observed on chromosome 19 and Y, and deletions on chromosome 19p.

We also wanted to determine if there were genes located in regions of copy number alterations that were significantly different in tumors from smokers and non- smokers. Using the same filtering criteria as age, we found that 3 genes were significantly different (p≤0.01) between tumors from these patient groups. These genes included: ankyrin repeat and IBR domain containing 1 (ANKIB1), calcitonin receptor

(CALCR) on chromosome 7, and zinc finger protein 507 (ZNF507) on chromosome 19.

Decreasing the stringency to p≤0.05 increased the number of differential copy number

109

Figure 9: Histogram of copy number alterations common in oral tumors from smokers. Chromosome numbers are provided below each respective chromosome. Red shaded bars represent gains to the right of each chromosome and green shaded bars represent deletion to the left of each chromosome. Pink cytoband represents centromere.

110

Figure 10: Histogram of copy number alterations common in oral tumors from non-smokers. Chromosome numbers are provided below each respective chromosome. Red shaded bars represent gains to the right of each chromosome and green shaded bars represent deletions to the left of each chromosome. Pink cytoband represents centromere.

111 to 213 CNAs (Appendix Table 2). The top 20 genes that were amplified and deleted in smokers and non-smokers are provided in Table 7.

An analysis of the gene family members in tumors from smokers and non- smokers was examined using a gene ontology program. GO biological processes that were significantly different included biological adhesion, metabolic process and biological regulation (Appendix Figure 3). GO molecular functions that were significantly different included binding, electron carrier activity and molecular transducer activity

(Appendix Figure 4).

3.3.7 Loss of Heterozygosity in Oral Tumors

We also assessed the degree of LOH that was present in oral cancers. For this, we measured the degree of LOH that was shared in at least 7 tumors (Appendix Table

3). This number of tumors was selected because this allowed a representative LOH profile in oral cancers to be examined. Common regions that showed high levels of

LOH were present on chromosomes 3, 5q, 9 and 17p, but small, yet notable, regions of

LOH were also seen on chromosomes 2q, 6p 11q, 12p 13q, 15q, and 18q (Figure 11).

We also compared the level of LOH from at least 5 tumors from young and at least 5 tumors from older patients that showed common representative regions of LOH in

OSCC (Table 8). Tumors from at least 5 young patients consistently showed LOH only on chromosomes 9p and 11q (Figure 12), whereas more regions of LOH were found in older patients, including chromosomes 3p, 5q, 9, 11q, 13q, and 17p (Figure 13). An example of an individual LOH profile on chromosome 9 from tumors of young and older patients is shown in appendix figures 5 and 6. Tumors from at least 5 young

112 Table 7: Top 20 significantly altered gene-containing regions in tumors based on smoking status. Highlighted regions represent genes that were detected in another column; Genes were organized based on ascending p-value (p≤0.05).

TUMORS FROM SMOKERS AMPLIFIED DELETED Gene ID(s) Gene Name(s) Function/Process Gene ID(s) Gene Name(s) Function/Process

Family with Epithelial cell ECT2 Rho GTPase sequence similarity transforming FAM19A4 (3q26.1- binding; signal 19 (chemokine (C-C Unknown sequence 2 (3p14.1) q26.2) transduction motif)-like), member oncogene A4

SPATA16 Spermatogenesis PCDH11Y Protocadherin 11 Y- Ion and protein binding; cell Spermatogenesis (3q26.31) associated 16 (Yp11.2) linked adhesion

Ankyrin repeat and Family with ANKIB1 Ion and protein FAM3C IBR domain sequence similarity Cytokine activity (7q21.2) binding (7q31) containing 1 3, member C

Cell cycle control; CDK6 (7q21- Cyclin-dependent CADPS2 Ca++-dependent Ion and lipid binding; transcription q22) kinase 6 (7q31.3) secretion activator 2 protein transport regulation Transducin-like SLC2A2 Transporter Solute carrier family TLE4 enhancer of split 4 Signal transduction; (3q26.1- activity; 2 (3q26.1-q26.2) (9q21.31) (E(sp1) homolog, transcription regulation q26.2) metabolism Drosophila) CALCR Calcitonin binding; TMC1 Transmembrane Calcitonin receptor Unknown (7q21.3) signaling (9q21.12) channel-like 1 A kinase (PRKA) Receptor binding; Erythrocyte AKAP9 EPB41L3 anchor protein transport; signal membrane protein Actin binding (7q21-q22) (18p11.32) (yotiao) 9 transduction band 4.1-like 3

113 CLDN12 FBXO5 Ion and protein binding; cell Claudin 12 Cell-cell adhesion F-box protein 5 (7q21) (6q25.2) proliferation Mitochondrial DNA binding; MTERF L3MBTL4 l(3)mbt-like 4 Transcription regulation; transcription transcription (7q21-q22) (18p11.31) (Drosophila) ion binding termination factor regulation Kinase activity; ZFP161 Zinc finger protein Transcription regulation; PFTK1 PFTAIRE protein ATP, nucleotide (18pter- 161 homolog DNA, protein and ion (7q21-q22) kinase 1 and protein p11.2) (mouse) binding binding Proprotein PCSK2 convertase Peptidase activity; TMEM106B Transmembrane Unknown (20p11.2) subtilisin/kexin type protein binding (7p21.3) protein 106B 2

FBXO5 F-box protein 5; (6q25.2); Mitochondrial Ion and protein binding; cell Guanine nucleotide GNG11 GTPase activity; MTRF1L translational release proliferation; Translational binding protein (G (7q21) signal transduction (6q25-q26); factor 1-like; termination; Signal protein), gamma 11 RGS17 Regulator of G- transduction (6q25.3) protein signaling 17

Protein Actin and protein Mitogen-activated Signal transduction; ATP, PPP1R9A phosphatase 1, MAP2K4 binding; cell protein kinase nucleotide and protein (7q21.3) regulatory (inhibitor) (17p11.2) differentiation kinase 4 binding subunit 9A Microtubule DYNC1I1 Dynein, cytoplasmic Protein tyrosine Cellular proliferation and binding and PTPN12 (7q21.3- 1, intermediate phosphatase, non- differentiation; protein activity; protein (7q11.23) q22.1) chain 1 receptor type 12 binding binding

114 Fucose-1- GTP binding and FPGT phosphate nucleotide binding; Frizzled homolog 1 Receptor activity; (1p31.1); FZD1 (7q21) guanylyltransferase; metabolism; ATP, (Drosophila) signal transduction TNNI3K TNNI3 interacting nucleotide and ion binding; (1p31.1) kinase phosphorylation; Arylesterase and Chromosome 9 PON3 Paraoxonase 3 hydrolase activity; C9orf41 open reading frame Unknown (7q21.3) metabolism 41 SAMD9 Sterile alpha motif Calcification; CD163L1 CD163 molecule- Scavenger receptor activity (7q21.2) domain containing 9 signal transduction (12p13.3) like 1 Metabolism; Solute carrier family SLC2A2 SLC25A13 transporter Solute carrier family Transporter activity; 25, member 13 (3q26.1- (7q21.3) activity; ion 2 (3q26.1-q26.2) metabolism (citrin) q26.2) binding Cytochrome P450, Ion binding; CYP3A7 ATRNL1 family 3, subfamily oxidation Attractin-like 1 Sugar binding (7q21-q22.1) (10q26) A, polypeptide 7 reduction LHFPL3 Lipoma HMGIC TNNI3K TNNI3 interacting ATP, nucleotide and ion (7q22.2- Unknown fusion partner-like 3 (1p31.1) kinase binding; phosphorylation; q22.3)

115 TUMORS FROM NON-SMOKERS AMPLIFIED DELETED Gene ID(s) Gene Name(s) Function/Process Gene ID(s) Gene Name(s) Function/Process

Family with Epithelial cell ECT2 Rho GTPase sequence similarity transforming FAM19A4 (3q26.1- binding; signal 19 (chemokine (C-C Unknown sequence 2 (3p14.1) q26.2) transduction motif)-like), member oncogene A4

SPATA16 Spermatogenesis PCDH11Y Protocadherin 11 Y- Ion and protein binding; cell Spermatogenesis (3q26.31) associated 16 (Yp11.2) linked adhesion

Ankyrin repeat and Family with ANKIB1 Ion and protein FAM3C IBR domain sequence similarity Cytokine activity (7q21.2) binding (7q31) containing 1 3, member C

Cell cycle control; CDK6 (7q21- Cyclin-dependent CADPS2 Ca++-dependent Ion and lipid binding; transcription q22) kinase 6 (7q31.3) secretion activator 2 protein transport regulation Transducin-like SLC2A2 Transporter Solute carrier family TLE4 enhancer of split 4 Signal transduction; (3q26.1- activity; 2 (3q26.1-q26.2) (9q21.31) (E(sp1) homolog, transcription regulation q26.2) metabolism Drosophila) CALCR Calcitonin binding; TMC1 Transmembrane Calcitonin receptor Unknown (7q21.3) signaling (9q21.12) channel-like 1 A kinase (PRKA) Receptor binding; Erythrocyte AKAP9 EPB41L3 anchor protein transport; signal membrane protein Actin binding (7q21-q22) (18p11.32) (yotiao) 9 transduction band 4.1-like 3 CLDN12 FBXO5 Ion and protein binding; cell Claudin 12 Cell-cell adhesion F-box protein 5 (7q21) (6q25.2) proliferation

116 Mitochondrial DNA binding; MTERF L3MBTL4 l(3)mbt-like 4 Transcription regulation; transcription transcription (7q21-q22) (18p11.31) (Drosophila) ion binding termination factor regulation Kinase activity; ZFP161 Zinc finger protein Transcription regulation; PFTK1 PFTAIRE protein ATP, nucleotide (18pter- 161 homolog DNA, protein and ion (7q21-q22) kinase 1 and protein p11.2) (mouse) binding binding Proprotein PCSK2 convertase Peptidase activity; TMEM106B Transmembrane Unknown (20p11.2) subtilisin/kexin type protein binding (7p21.3) protein 106B 2

FBXO5 F-box protein 5; (6q25.2); Mitochondrial Ion and protein binding; cell Guanine nucleotide GNG11 GTPase activity; MTRF1L translational release proliferation; Translational binding protein (G (7q21) signal transduction (6q25-q26); factor 1-like; termination; Signal protein), gamma 11 RGS17 Regulator of G- transduction (6q25.3) protein signaling 17

Protein Actin and protein Mitogen-activated Signal transduction; ATP, PPP1R9A phosphatase 1, MAP2K4 binding; cell protein kinase nucleotide and protein (7q21.3) regulatory (inhibitor) (17p11.2) differentiation kinase 4 binding subunit 9A Microtubule DYNC1I1 Dynein, cytoplasmic Protein tyrosine Cellular proliferation and binding and PTPN12 (7q21.3- 1, intermediate phosphatase, non- differentiation; protein activity; protein (7q11.23) q22.1) chain 1 receptor type 12 binding binding Fucose-1- GTP binding and FPGT phosphate nucleotide binding; Frizzled homolog 1 Receptor activity; (1p31.1); FZD1 (7q21) guanylyltransferase; metabolism; ATP, (Drosophila) signal transduction TNNI3K TNNI3 interacting nucleotide and ion binding; (1p31.1) kinase phosphorylation;

117 Arylesterase and Chromosome 9 PON3 Paraoxonase 3 hydrolase activity; C9orf41 open reading frame Unknown (7q21.3) metabolism 41 SAMD9 Sterile alpha motif Calcification; CD163L1 CD163 molecule- Scavenger receptor activity (7q21.2) domain containing 9 signal transduction (12p13.3) like 1 Metabolism; Solute carrier family SLC2A2 SLC25A13 transporter Solute carrier family Transporter activity; 25, member 13 (3q26.1- (7q21.3) activity; ion 2 (3q26.1-q26.2) metabolism (citrin) q26.2) binding Cytochrome P450, Ion binding; CYP3A7 ATRNL1 family 3, subfamily oxidation Attractin-like 1 Sugar binding (7q21-q22.1) (10q26) A, polypeptide 7 reduction LHFPL3 Lipoma HMGIC TNNI3K TNNI3 interacting ATP, nucleotide and ion (7q22.2- Unknown fusion partner-like 3 (1p31.1) kinase binding; phosphorylation; q22.3)

118

Figure 11: Loss of heterozygosity in at least 7 oral tumor samples. Red shaded bars to the right of each chromosome represent regions of LOH. Pink cytoband represents centromere.

119 Table 8: Regions of LOH in oral tumors from young and older patients. Regions of LOH detected in at least 5 oral tumors from young patients and 5 oral tumors from older patients.

Age Gene Chromosome Cytoband Gene Name(s) Category ID(s) Young Pts. 9 9p22.3 FREM1 FRAS1 related extracellular matrix 1 Anoctamin 1, calcium activated chloride 11 11q13.3 TMEM16A channel Mediator complex subunit 12-like; MED12L; Older Pts. 3 3q25.1 purinergic receptor P2Y, G-protein P2RY12 coupled, 12 3 3q25.33 IQCJ IQ motif containing J 3 3q26.33 ATP11B ATPase, class VI, type 11B 3 3q28 FGF12 Fibroblast growth factor 12 5 5q35.3 COL23A1 Collagen, type XXIII, alpha 1 9 9p24.2 RFX3 Regulatory factor X, 3 Glutamate receptor, ionotropic, N- 9 9q31.1 GRIN3A methyl-D-aspartate 3A FKTN; Fukutin; T-cell acute lymphocytic 9 9q31.2 TAL2; leukemia 2; transmembrane protein 38B TMEM38B Anoctamin 1, calcium activated chloride 11 11q13.3 TMEM16A channel Smg-6 homolog, nonsense mediated 17 17p13.3 SMG6 mRNA decay factor (C. elegans) 17 17p13.2 NLRP1 NLR family, pyrin domain containing 1 Major facilitator superfamily domain FLJ35773; 17 17p13.1 containing 6-like; phosphoinositide-3- PIK3R6 kinase, regulatory subunit 6

120

Figure 12: Loss of heterozygosity in at least 5 oral tumors from young patients. Red shaded bars to the right of each chromosome represent regions of LOH. Pink cytoband represents centromere.

121

Figure 13: Loss of heterozygosity in at least 5 oral tumors from older patients. Red shaded bars to the right of each chromosome represent regions of LOH. Pink cytoband represents centromere.

122 patients showed 2 genes involved in LOH, FRAS1 related extracellular matrix 1

(FREM1), and transmembrane protein 16A (TMEM16A) or otherwise known as anoctamin 1, calcium activated chloride channel (ANO1) (Table 8). Chromosomal regions that contained 16 genes that consistently showed LOH in at least 5 tumors from older patients are shown in Table 8.

3.3.8 Copy Number Neutral Loss of Heterozygosity (cnLOH) in Oral Tumors

Copy number neutral LOH, which can be assessed using SNP arrays, was examined in at least 5 oral tumors. cnLOH was mainly found on chromosome 9 with a small region on chromosome 13q (Figure 14). These two regions included 124 gene- containing regions of cnLOH that are listed in appendix table 4. We also compared the cnLOH pattern between tumors of young and older patients, and found that young patient tumors contained 36 regions of cnLOH on chromosome 9, whereas tumors from older patients harbored slightly more cnLOH on several chromosomes, but also had a genomic prevalence of cnLOH on chromosome 9 (Tables 9 and 10). Only a few patient tumors shared similar regions of cnLOH. cnLOH was found on chromosome 9 in at least

3 of 5 young patient tumors (Appendix Figure 7) and in focal regions of chromosomes

3q, 5q, 6p, 9, 12q and 13q in at least 4 of 5 older patient tumors (Appendix Figure 8).

123

Figure 14: Copy number neutral LOH in at least 5 oral tumor samples. Red shaded bars to the right of each chromosome represent regions of cnLOH. Pink cytoband represents centromere.

124 Table 9: Regions of cnLOH in oral tumors from young patients. Regions of copy number neutral LOH detected in at least 3 oral tumors from young patients.

Chromo- Cytoband Gene ID(s) Gene Name(s) some 9 9q34.1 ABL1 C-abl oncogene 1, receptor tyrosine kinase 9 9q31-q33 AKAP2; PALM2-AKAP2 A kinase (PRKA) anchor protein 2 9 9q33.1 ASTN2 Astrotactin 2 9 9q22.31 AUH AU RNA binding protein/enoyl-Coenzyme A hydratase Chromosome 9 open reading frame 6; Catenin (cadherin-associated C9orf6; CTNNAL1; 9 9q31.3; 9q31.2; 9q31 protein), alpha-like 1; Inhibitor of kappa light polypeptide gene enhancer IKBKAP in B-cells, kinase complex-associated protein 9 9q31.3 C9orf84 Chromosome 9 open reading frame 84 9 9q22.3 CORO2A Coronin, actin binding protein, 2A 9 9q22.3; 9q22.33 CORO2A; TRIM14 Coronin, actin binding protein, 2A; Tripartite motif-containing 14 9 9q31.2 CTNNAL1 Catenin (cadherin-associated protein), alpha-like 1 9 9q33.1-q33.3 DAB2IP DAB2 interacting protein DEAD (Asp-Glu-Ala-Asp) box polypeptide 31; General transcription 9 9q34.13; 9q34.13 DDX31; GTF3C4 factor IIIC, polypeptide 4 9 9q33.3 DENND1A DENN/MADD domain containing 1A 9 9q31-q32 EPB41L4B Erythrocyte membrane protein band 4.1 like 4B 9 9q33.3 FAM125B Family with sequence similarity 125, member B 9 FGD3 FYVE, RhoGEF and PH domain containing 3 9 9q34 FNBP1 formin binding protein 1 9 9q34 FNBP1; USP20 Formin binding protein 1; ubiquitin specific peptidase 20 9 9q33.3 GARNL3 GTPase activating Rap/RanGAP domain-like 3 Guanine nucleotide binding protein (G protein), gamma 10; DNAJC25- 9 9q31.3; 9q31.3 GNG10; LOC552891 GNG10 readthrough transcript 9 9q34.13 GTF3C4 General transcription factor IIIC, polypeptide 4 Inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase 9 9q31 IKBKAP complex-associated protein

125 9 9p24.1 JMJD2C JmjC domain-containing histone demethylation protein 3C 9 9q31.3-q32 MUSK Muscle, skeletal, receptor tyrosine kinase 9 9p24.1-p23; 9q22.33 NANS; TRIM14 N-acetylneuraminic acid synthase; tripartite motif-containing 14 9 9q34.11 NUP188 Nucleoporin 188kDa 9 9q34.1 NUP214 Nucleoporin 214kDa 9 9q33.2 PAPPA Pregnancy-associated plasma protein A, pappalysin 1 9 9q34.3 PTGES Prostaglandin E synthase 9 9q31 PTPN3 Protein tyrosine phosphatase, non-receptor type 3 9 9q31.2 RAD23B RAD23 homolog B (S. cerevisiae) 9 9q34.3 RAPGEF1 Rap guanine nucleotide exchange factor (GEF) 1 9 9p24.2 RFX3 Regulatory factor X, 3 (influences HLA class II expression 9 9q22 ROR2 Receptor tyrosine kinase-like orphan receptor 2 9 9q22 SYK Spleen tyrosine kinase 9 9q31.2 TMEM38B Transmembrane protein 38B 9 9q32 ZNF618 Zinc finger protein 618

126 Table 10: Regions of cnLOH in oral tumors from older patients. Regions of copy number neutral LOH detected in at least 3 oral tumors from older patients.

Chromosome Cytoband Gene ID(s) Gene Name(s) 12 12q21.3-q22 ALX1 ALX homeobox 1 9 9q22.3 BAAT Bile acid Coenzyme A: amino acid N-acyltransferase 9 9q31.3 C9orf84 Chromosome 9 open reading frame 84 9 9q21.13 C9orf85 Chromosome 9 open reading frame 85 C9orf85; Chromosome 9 open reading frame 85; Family with 9 9q21.13; 9q21.13 FAM108B1 sequence similarity 108, member B1 9 9q21.31 CHCHD9 Coiled-coil-helix-coiled-coil-helix domain containing 9 11 11q21-q22.2 CNTN5 Contactin 5 5 5q35.3 COL23A1 Collagen, type XXIII, alpha 1 18 18q21.3 DCC Deleted in colorectal carcinoma 9 9q33.3 DENND1A DENN/MADD domain containing 1A 5 5q35.1 DOCK2 Dedicator of cytokinesis 2 3 3q24-q28 EVI1 MDS1 and EVI1 complex locus 9 9q21.13 FAM108B1 Family with sequence similarity 108, member B1 5 5q21 FER Fer (fps/fes related) tyrosine kinase 3 3q28 FGF12 Fibroblast growth factor 12 9 9q31-q33 FKTN Fukutin Major facilitator superfamily domain containing 6-like; 17 17p13.1; 17p13.1 FLJ35773; PIK3R6 Phosphoinositide-3-kinase, regulatory subunit 6 13 13q13.3 FREM2 FRAS1 related extracellular matrix protein 2 9 9p24.2 GLIS3 GLIS family zinc finger 3 9 9q31.1 GRIN3A Glutamate receptor, ionotropic, N-methyl-D-aspartate 3A Glutamate receptor, ionotropic, N-methyl-D-aspartate 3A; 9 9q31.1; 9q31.1 GRIN3A; PPP3R2 Protein phosphatase 3 (formerly 2B), regulatory subunit B, beta isoform 5 5q35.1 KCNIP1 Kv channel interacting protein 1

127 Kv channel interacting protein 1; Potassium large 5 5q35.1; 5q34 KCNIP1; KCNMB1 conductance calcium-activated channel, subfamily M, beta member 1 9 9p24.1 KIAA1432 Connexin 43-interacting protein 150 5 5q33.1-qter LCP2 Lymphocyte cytosolic protein 2 5q33.1-qter; Lymphocyte cytosolic protein 2; Hypothetical protein 5 LCP2; LOC133874 5q35.1 LOC133874 9 9p21.2-p21.1 LINGO2 Leucine rich repeat and Ig domain containing 2 6 6p22.1 LOC651503 Seven transmembrane helix receptor 9 9q21.12 MAMDC2 MAM domain containing 2 15 15q26 MEF2A Myocyte enhancer factor 2A 13 13q12.11 N6AMT2 N-6 adenine-specific DNA methyltransferase 2 13 13q12.11; 13q11 N6AMT2; XPO4 N-6 adenine-specific DNA methyltransferase 2; exportin 4 13 13q13 NBEA Neurobeachin 5 5q34 ODZ2 Odz, odd Oz/ten-m homolog 2 (Drosophila) 6 6p22.1 OR14J1 Olfactory receptor, family 14, subfamily J, member 1 Olfactory receptor, family 3, subfamily A, member 1; 17 17p13.3; 17p13.3 OR3A1; OR3A2 Olfactory receptor, family 3, subfamily A, member 2 6 6p22.1 OR5V1 Olfactory receptor, family 5, subfamily V, member 1 11 11q24.2 OR8G1 Olfactory receptor, family 8, subfamily G, member 1 Olfactory receptor, family 8, subfamily G, member 1; 11 11q24.2; 11q24.2 OR8G1; OR8G5 Olfactory receptor, family 8, subfamily G, member 5 9 9q21.3 PCSK5 Proprotein convertase subtilisin/kexin type 5 4 4q25-q27 PDE5A Phosphodiesterase 5A, cGMP-specific PDS5, regulator of cohesion maintenance, homolog B (S. 13 13q12.3 PDS5B cerevisiae) 17 17p13.1 PIK3R5 Phosphoinositide-3-kinase, regulatory subunit 5 17 17p13.1 PIK3R6 Phosphoinositide-3-kinase, regulatory subunit 6 9 9p24.2 RFX3 Regulatory factor X, 3 (influences HLA class II expression) 5 5q35.3 RNF130 Ring finger protein 130 15 15q24 SCAPER S-phase cyclin A-associated protein in the ER 128 9 9p22 SH3GL2 SH3-domain GRB2-like 2 9 9q31.1 SMC2 Structural maintenance of chromosomes 2 9 9q32 TAL2 T-cell acute lymphocytic leukemia 2 9 9q13-q21 TMEM2 Transmembrane protein 2 9 9q31.2 TMEM38B Transmembrane protein 38B Transient receptor potential cation channel, subfamily C, 13 13q13.1-q13.2 TRPC4 member 4 5 5q35.2 UBXD8 UBX domain containing 8 9 9q21 VPS13A Vacuolar protein sorting 13 homolog A (S. cerevisiae) 13 13q12.11 ZDHHC20 Zinc finger, DHHC-type containing 20

129 3.4 DISCUSSION

In this study we examined chromosomal alterations in oral tumors from young and older patients. We utilized one of the highest genomic resolution platforms commercially available, the Affymetrix SNP 6.0 arrays. Historically, the spacing of genomic markers using older technologies has been sparse; these methods could not pick up focal regions of chromosomal imbalance compared to newer DNA arrays with greater resolution.206 The SNP array used in this study allowed us to assess distinct chromosomal aberrations in tumors from young and older patients because of its high resolution and ability to determine copy number, LOH and cnLOH. These genetic instability mechanisms play an important role in the development of cancer and may offer explanations in the development of oral cancer, especially early-onset carcinoma.

In our analyses, we first compared the presence of gains and deletions in oral tumors, regardless of clinical factors. The most commonly observed amplifications were on chromosomes 3q, 5p, 7, 8q, 9q, 11q, 17, 19, 20, and 22q and deletions were found on chromosomes 3p, 5q, 8p, 9p, 11q, 18q, and 21q. We observed that the copy number alterations we found were similar to previously reported studies,230-232 as well as additional regions that are thus far unreported. The Affymetrix SNP 6.0 array is useful since higher resolution arrays are able to increase the resolution of amplifications and deletions from their surrounding regions.253 In addition, DNA arrays, such as SNP arrays, allow the analysis of genome-wide SNP associations, LOH and cnLOH all in one platform.

An earlier small study used the Affymetrix SNP 10k and 100k arrays to assess amplified DNA from five tongue tumor squamous cell carcinoma tissue biopsies.253 The authors noted loss on chromosomes 3p, 8p, and 9p and gains on chromosomes 8q, 11q

130 and 20. These regions of gains and losses were also observed in our SNP array analyses, and we were able to detect additional regions involved in copy number alterations.

The analysis of LOH is useful in implicating regions containing tumor suppressors.254 Furthermore, the examination of LOH on SNP arrays using SNPs has the unique advantage over microsatellites in that SNPs are not susceptible to repeat expansion, thus ensuring reliability in the analysis of LOH.255 However, SNPs have a lower rate of heterozygosity (e.g. 0.33) compared to higher rates observed in microsatellites,256 which may lead us to underestimate the level of LOH. However, SNP analysis uses many more genomic markers for the identification of LOH detection, thereby making it a genome-wide assessment.

The role of LOH was examined in the oral tumor samples from our study, and we observed that common regions of LOH that were shared in at least 5 tumors were located on chromosomes 3, 5q, 9, and 17p. These are regions that have been commonly found to be altered in head and neck cancer.96 Genetic regions that are common for LOH in head and neck cancer include 3p14, 3p21.3, and 3p24, suggesting the presence of as yet unknown tumor suppressors; 9p21 which includes p16; and

17p13, where TP53 resides.96 We also found other regions of LOH that may be of interest including focal regions of LOH on chromosomes 2q, 6p, 11q, 12p, 13q, 15q, and 18q.

The role of potential genetic differences in cancers from young and older patients is not new, as it has been addressed in other early-onset cancers. For example, gastric cancers, which mainly affect individuals between 50 to 70 years of age, can also occur in young patients. One study compared tumors from young patients, less than 50 years

131 old, and tumors of older patients using array-CGH (BAC array).257 This study reported genomic alterations in tumors from 17 young and 29 older patients. From these data the authors suggested a different disease mechanism exists between the two groups of patients.

Molecular profiling in head and neck cancer from young patients has only been examined in a few studies,117, 118, 243 and this has been using low-resolution analysis.

Most head and neck cancer studies that have assessed chromosomal anomalies have examined tumors from older patient populations and have used analyses, such as SKY,

CGH or array CGH.172, 234, 258

An earlier study examined genomic differences between oral tumors from young and older patients using the GenoSensor array 300 system (array CGH from Vysis).118

This DNA array has a resolution of 100kb, at best, with uneven spacing across the genome, and an approximate coverage of 40 Mb per chromosome.118 The study included microdissected oral carcinomas from 10 young patients (≤40 years) and 10 older patients (>40 years) and similar numbers of smokers and non-smokers in each group; smokers were included for young patient (mean pack years = 16.5) and older patient (mean pack years = 35) analysis. The authors noted more gains than losses; in addition they observed that tumors from older patients had more alterations than those from younger patients, especially from non-smoking young patients. However, tumors from older patient smokers and non-smokers generally had similar genomic alterations, similar to our findings. In their recently published data set,118 gains that were most frequently observed in older patients included those on chromosomes 1p, 5p, 5q33, 7p,

11p, 11q (FGF4), 13, and 22q13 (PDGFB), and losses at 3p, 3q24 (THRB) and 9p21

(P16INK4). In young patients the most frequent copy number gains were found on

132 chromosomes 1p, 3q (MF12), 11p (INS), 11q13 (FGF4), 11q13-q14 (PAK1), 14q32.1

(TCL1A), and 22q11.2 (TBX1) and loss on 3p (subtelomeric). Some of these chromosomal regions appeared in our dataset, for example we observed chromosomal gains on 5p, 7, 11q, and chromosome loss on 3p, and 9p in tumors from older patients; and chromosomal gains on 3q, 11q, and 22q, and chromosome loss on 3p in tumors from young patients. However the only gene similarly found in our significantly altered genes from our dataset was gain of INS in young patient tumors. Additionally, the authors from this study,118 observed that the most significantly altered genes that were found in tumors from young patients were also found in those from older patients, but in fewer samples, and they also found an absence of deletion in p16, in tumors from the young patient population. In contrast, we were able to detect p16 loss in tumors from our young patient population, with similar number of p16 copy number alterations in tumors from older patients. The authors note their small sample size in their analysis and did not validate any of their findings. Our DNA array platform has a higher resolution than the array CGH platform discussed above, suggesting that additional regions of chromosomal changes may be detected due to the close spacing of genomic markers. In addition, we could also assess LOH status of the oral tumors analyzed.

In our analysis, copy number alterations common in tumors from young and older patients appeared similar for regions that are commonly altered in head and neck cancer. However, there were regions that were altered exclusively in tumors of young patients, such as gains on chromosome 17q and 22q. In addition there appeared to be more shared deletions in tumors from young patients as compared to those from older patients. No significant clinical differences were observed (e.g. smoking) in these groups of young patients compared to the other young and older patient tumors. In our

133 data set all quality control tests examined showed DNA from young patient tumors produced good SNP genotyping calls and signal to noise ratios (with the exception of

Sample ID# 6T). These data suggest that there may exist a group of young patients who harbor very genetically unstable tumors (e.g. high chromosomal ploidy status).

This could have also resulted because many chromosomal studies assume a diploid reference, and ploidy status is often ignored when comparing tumors against a normal reference set.228 This is because a fixed amount of DNA is applied to a DNA array without assessing the number of cells from which the DNA has been derived. Also, the normalization process tends to smooth the data, which further negates ploidy status.228

The role of ploidy status may add an additional layer of complexity to copy number analysis, which may have caused earlier studies to underestimate copy number alterations in tumors.

In cancer genetics, many chromosomal regions can be altered, giving rise to

“driver” and “passenger” mutations.259 Driver mutations are functionally important changes, which allow tumor initiation or progression, whereas passenger mutations are random somatic events that have no clonal selective advantage. Genes commonly altered in cancer may have a direct role in the carcinogenesis if they have been reported in multiple cancers. We examined genes that were commonly altered in our cohort and assessed their role in the cancer literature. We applied a stringent analysis to our dataset because of the large number of genetic changes and heterogeneity that occurs in head and neck cancer; however being too stringent can result in very little information; flexibility in data analysis should therefore be encouraged to allow meaningful data sets for validation.

134 After filtering genetic regions that were important in oral cancer, we detected numerous genes that were amplified and deleted in tumors from young and older patients. Genes that were commonly deleted in oral tumors from older patients included: Discs, large (Drosophila) homolog-associated protein 2 (DLGAP2), a membrane associated guanylate kinase that is involved in cell signaling, which is a putative tumor suppressor that is commonly deleted in bladder cancer;260 Glutamate- rich 1 (ERICH1), a putative tumor suppressor of which little physiological function is known, although it is deleted in pancreatic ductal adenocarcinoma;261 Rho guanine nucleotide exchange factor (GEF) 10 (ARHGEF10), a Rho GTPase that is involved in cell signaling, apoptosis through G coupled receptors and is a candidate tumor suppressor, and is deleted in breast cancer.262 These genes are located on chromosome 8, and all implicate this chromosome’s importance in oral cancer of older patients. In young patients with oral tumors, genes commonly deleted included: Brain- specific angiogenesis inhibitor (BAI3), a TP53 target gene involved in angiogenesis, and has decreased mRNA expression in brain tumors;263, 264 Histone deacetylase 9

(HDAC9), which has a role in transcriptional regulation, has low mRNA and protein expression in high grade glioblastomas;265 and PHD finger protein 14 (PHF14), a transcriptional regulation protein, which is deleted in the colon cancer cell line

HCT116.266

Tumors from older patients showed amplification of genes including: Cyclin- dependent kinase 6 (CDK6), a protein involved in cell cycle progression of the G1-S phase through phosphorylation of RB1, which is amplified in gastroesophageal junction adenocarcinomas;267 Fibroblast growth factor 10 (FGF10), a growth factor involved in cell growth and tissue repair, which has been shown be overexpressed at the mRNA

135 level in breast cancer;268 and Catenin (cadherin-associated protein), delta 2 (CTNND2), a tight-junction associated protein of the β-catenin superfamily, which has mRNA overexpression in prostate cancer.269 Tumors from young patients showed amplification of genes including: Solute carrier organic anion transporter family, member 2B1

(SLCO2B1/OATP-B), which is an Na+-independent membrane protein responsible for the uptake of anionic compounds,270 and shown to have a high level mRNA expression in metastatic breast cancer;271 E1A binding protein p300 (EP300), a histone acetyltransferase that functions in transcriptional regulation and is important in cellular differentiation and proliferation, and is highly expressed in prostate cancer;272 and

Insulin-like growth factor 2 (IGF2), a member of the insulin family involved in development and growth, and was overexpressed in oral cancer cell lines.273 These altered/deregulated genes found in young patient tumors may be potential candidates in the development of early-onset carcinoma, and different from genes that are commonly altered/deregulated in tumors of older patients.

When we examined the role of LOH in young vs. older patient tumors, we found that LOH was more common in tumors from older patients. In young patients tumors, 2 genes (FREM1 and ANO1) were commonly found in regions of LOH. FREM1, is an extracellular matrix protein that is important in epithelial adhesion during embryonic development.274 The other gene is ANO1, a calcium activated chloride channel protein, which are known to be overexpressed in cancer.275 This gene falls in the 11q13 amplification cluster in head and neck cancer,276 however there are no published reports of loss or underexpression of this gene in cancer. This gene was also found in a region of LOH in tumors from older patients, suggesting its general involvement in OSCC.

136 Tumors from older patients exhibited more LOH containing gene regions than those from younger patients. These included genes that were located in the classical

HNSCC LOH chromosomes 3, 9 and 17. However, in our analysis it was chromosome

3q that showed common regions of LOH, whereas the literature has suggested chromosome’s 3p involvement in LOH.96 These genes found on chromosome 3q have no published reports of their relation to cancer. These may offer additional genes that may be of interest as putative tumor suppressors. In addition, another gene on chromosome 9q, FKTN, present in a region of LOH in older patient tumors has been shown to have a possible role in the suppression of cellular proliferation through c- jun;277 LOH of this gene may have a role in the development of OSSC in older patients.

It is important to analyze copy number and LOH simultaneously as this can yield additional genomic information.254 For instance a genomic loss of one allele can be a hemizygous deletion. If LOH has occurred with no change in copy number, this may be a result of copy-neutral LOH. However, LOH can occur by the preferential amplification of one of the alleles. And finally, LOH can arise by deletion of one of the alleles followed by amplification of the remaining allele.

In our analysis of cnLOH, a feature that has not been described in the head and neck cancer literature, we found that many oral cancers examined harbored regions with cnLOH. However, when examining common regions of cnLOH in at least 5/39 tumors there was a predominance of regions of cnLOH located on chromosome 9q, suggesting this chromosome’s susceptibility to cnLOH. Interestingly, regions of cnLOH have been observed on chromosome 9 in mantle cell lymphoma.278 Mutations in JAK2 and UPD, which is located on chromosome 9p24, has been observed in myeloproliferative disorders.279 Regions of UPD on chromosome 9p21 have also been

137 detected in pediatric acute lymphoblastic leukemia (ALL), involving homozygous deletions of CDK2NA.280 These specific genes were not detected in our dataset, but these same chromosomal regions were found. Other regions detected included cnLOH chromosome 5q, which has also been found in rectal adenomas and carcinomas.198 A review of uniparental disomy in cancer, reported that genes located in cnLOH regions that are mutated, deleted, hypermethylated are specific for cancer type.198

In our study we used a high-resolution DNA platform to assess chromosomal abnormalities in tumors from young and older patients via copy number and LOH analyses. These types of chromosomal changes are important mechanisms in the development of cancer. In addition this is the first study looking at cnLOH, which is a relatively recent and overlooked mechanism of cancer initiation/progression. We found tumors from young and older patients share many similar regions of chromosomal changes that are common in head and neck cancer. However there were specific regions that were prevalent in either patent cohort. Due to the high resolution of the platform used in this study, many genes that we observed in regions of CNAs and LOH have not been described before in oral cancer. In addition, there were regions/genes that were more prevalent in tumors from young patients. These regions/genes may have a role in the development of early-onset carcinoma. These genes will have to be validated in a larger cohort of tumors from young and older patients using more gene specific methodology, such as quantitative PCR.

The future of genomic resolution is constantly changing, one study compared the

Affymetrix SNP 6.0 arrays with massively parallel sequencing for copy number analysis.281 The sequencing analysis of 14 million aligned sequence reads had comparable detection resolution and had a two-fold increase in detecting breakpoints

138 over current DNA array technology. The advent of newer technologies should further enable researchers to further define chromosomal regions that are important in the development of early onset-carcinoma.

139 CHAPTER 4: MISMATCH REPAIR AND HEAD AND NECK CANCER

4.1 INTRODUCTION

Cellular DNA is regularly exposed to damage by endogenous and exogenous agents, such as chemical agents (e.g. carcinogens from tobacco smoke), physical agents (e.g. ionizing radiation), biological agents (e.g. microorganisms), and spontaneously events, such as oxidative damage.96 As DNA can be damaged in many different ways, multiple DNA repair mechanisms have evolved.282 DNA repair pathways have been categorized into base excision repair (BER), nucleotide excision repair

(NER), direct damage reversal, DNA double-strand break (DSB) repair, and mismatch repair (MMR).283

The repair mechanisms that occur during cellular damage depend on the insults that incur damage, and a cell may utilize multiple DNA repair pathways to repair the resulting DNA damage.284 Generally, BER resolves abasic sites caused by x-rays, oxygen radicals, alkylating agents and spontaneous reactions; NER resolves pyrimidine dimers caused by UV light and polycyclic aromatic hydrocarbons; DSB repair including

Homologous recombination (HR) and Non-homologous End Joining (NHEJ), resolves interstrand cross-links and double strand breaks caused by X-rays and anti-tumor agents (e.g. cisplatin and MMC); and MMR resolves nucleotide mismatches, and insertions and deletions caused by replication errors.284

Defects in a number of DNA repair genes may result in genetic disease, and are often involved in hereditary cancer syndromes. Mutations in the NER genes, XPA-XPG, are involved in skin cancer (Xeroderma Pigmentosum), double-strand break repair genes NBS1 and MRE11 in Lymphomas, BRCA1 and BRCA2 genes in breast/ovarian

140 cancers, FANC genes in Fanconi Anemia, and mismatch repair genes (e.g. hMLH1 and hMSH2) in colon cancer.285 The importance of DNA repair mechanisms is also illustrated by the hereditary cancer predisposition syndromes, Ataxia Telangectasia, and

Bloom’s syndrome.286 The key features that define these pathologies have been related to defects in DNA repair genes, cell cycle checkpoint activation, and control of apoptosis, which are responsible for cancer predisposition phenotypes.282, 286, 287 The involvement of different DNA repair mechanisms have been examined in HNSCC,288-290 but we are specifically interested in MMR because our laboratory and others have previously shown that defective MMR is involved in HNSCC.120, 291, 292

4.1.1 Mismatch Repair

The MMR system was first examined in E. coli mutator strains mutS, mutL, mutH, and uvrD from reconstituted MMR genes.293 However, the mammalian MMR is more complex, involving more genes, which maintain genomic integrity by repairing nucleotide mismatches in DNA, and insertion/deletion loops (IDLs) due to DNA damage, and is also involved in normal cellular processes (e.g. replication, recombination) and the diversification of antibodies.294 The human mismatch repair machinery includes the

MutS proteins (hMSH2, hMSH3, hMSH4, hMSH5, and hMSH6) and the MutL proteins

(hMLH1, hMLH3, hPMS1, and hPMS2).294 Members of the MutS family are involved in the recognition of mismatches or IDLs, and form a complex with members of the MutL family (Figure 1).295 The nature of the mutation (mismatch vs. IDLs) determines the associated MMR proteins involved (Table 1), which are described below.

141

Figure 1: Mismatch repair pathway. Upon recognition of a base-pair mismatch (or insertion deletion loop) the MSH6-MSH2 (MutSα) heterodimer binds to the mismatch alongside PCNA and introduces a nick (arrow) near the lesion. The MutS complex recruits the MLH1-PMS2 (MutLα) heterodimer, which further recruits the exonuclease, EXO1, to excise the DNA error and surrounding DNA. DNA polymerase is involved in proper DNA replication and the remaining nick is resolved by ligation. Figure modified from Martin et al. 2002.15

142 Table 1: The mismatch repair protein complexes and their function in DNA repair. The mammalian mismatch repair proteins form heterodimers depending on the repair of specific base pair mismatches or insertion deletions loops (IDLs), and during mitosis or meiosis.

Complex MMR Proteins Functions MSH2 and MutSα MSH6 Recognizes mismatches and small IDLs MSH2 and MutSβ MSH3 Recognizes IDLs MLH1 and Associates with MutSα and functions in meiotic MutLα PMS2 recombination MLH1 and MutLβ PMS1 Unknown MLH1 and Involved in meiotic recombination primarily, but can MutLγ MLH3 repair mismatches and small IDLs

143 The hMSH2-hMSH6 heterodimer (MutSα) is the most important MutS binding partner, and is involved in the repair of base pair mismatches and one or two extrahelical nucleotides, whereas the hMSH2-hMSH3 heterodimer (MutSβ) is involved in repair of larger IDLs. 296, 297 The MutS family members are ATPases that move along the DNA, as a sliding clamp searching for DNA mismatches.297 The MutS family is able to recruit members of the MutL, which are also ATPases, for repair of DNA mismatches.

The most important MutL heterodimer is hMLH1-hPMS2 (MutLα), which is involved in repair of DNA mismatches and is involved in meiotic recombination. Other MutL heterodimers include hMLH1-hPMS1 (MutLβ), which has an unknown function, and hMLH1-hMLH3, MutLγ, whose primary function is in meiotic recombination,298 but can also repair DNA mismatches and small IDLs.296, 297

The MMR proteins act in concert with other proteins involved in DNA replication during DNA repair. One of these is the homotrimeric proliferating cell nuclear antigen

(PCNA).296 The PCNA sliding clamp associates with the Okazaki fragment of the 3’ prime end or the 3’ terminus of the leading strand by replication factor C (RFC). It then associates with DNA polymerase and functions as a processivity factor. PCNA has also been shown to associate with the MutS family, but dissociates when a mismatch is detected. Another enzyme that is involved in MMR includes exonuclease-1, EXO1; it is responsible for removing the mismatched DNA and surrounding nucleotides. Finally,

DNA polymerase δ is responsible for filling in the appropriate nucleotides based on the template strand, and DNA ligase 1 is involved in closing the remaining nicks.

144 4.1.2 Microsatellite Instability

The MMR system repairs replication errors in DNA, and MMR deficiencies lead to several cancer abnormalities.299 Inactivation of this DNA repair mechanism increases the mutation rate and can lead to the development of human cancers with a microsatellite instability (MSI) phenotype (Figure 2).300 Microsatellites are repetitive DNA sequences, usually 1-5 nucleotides long that are repeated 5-100 times, such as [A]n and

301 [CA]n. They are found across the genome and are primarily located near the centromere, telomeres, and in intergenic regions. However, they are known to occur within the coding regions of 32 genes within the human genome that have 7 or more mononucleotide repeats, including MMR genes.302

During DNA replication, the DNA polymerase may undergo slippage resulting in microsatellite expansion or contraction. Although the data are not clear, MSI generation has also been reported to be caused by oxygen radicals, lipid adducts, smoking, and diet.301 MSI is often found in cancers that have mutations in hMSH2 or hMLH1.303 Gene mutations in hMLH1, hMSH2 and hMSH6 occur in over 95% hereditary non-polyposis colorectal cancer (HNPCC) patients, also known as Lynch syndrome.89 Heterozygous mutations in hPMS2 may lead to early-onset sporadic colorectal cancer,304 whereas homozygous mutations are related to Turcot syndrome.305 However, MSI is found only in 15-20% of sporadic colorectal cancers.306 Interestingly, studies have reported improved prognosis for tumors that show MSI,307 and were cancer stage independent.308

Due to the differences of MSI found between multiple studies of colon cancer, the definition of the level of MSI was earlier established during the Bethesda Consensus

145

Figure 2: Microsatellite instability. During DNA replication, defective mismatch repair fails to correct DNA slippage caused by the DNA polymerase at microsatellite regions. This results in insertions (expansion) or deletions (contraction) of microsatellite regions. Figure modified from Eshleman et al. 1995.20

146 Conference and National Cancer Institute of US.303 A panel of five microsatellite markers was selected for the determination of MMR deficiencies by MSI. If two or more of the markers show MSI then the tumor is deemed to have a high frequency of MSI

(MSI-H), whereas if only one of these five markers show MSI, the tumor is deemed have a low frequency of MSI (MSI-L).303 If no MSI is found using these markers the tumor is defined as microsatellite stable (MSS). The panel includes three dinucleotide repeat microsatellite markers and two mononucleotide repeat microsatellite markers. If researchers decide to use more than 5 microsatellite markers in their studies then microsatellite markers that are MSI positive in >30-40% cases are defined as MSI-H; lower MSI levels (<30%) are classified as MSI-L; and if no MSI is detected than the tumor is classified as MSS.

Microsatellite markers have also been useful in determining loss of heterozygosity (LOH), due to their polymorphic nature. This can be determined using heterozygous (informative) microsatellite loci, whereby loss of one of the alleles leads to

LOH. Each microsatellite marker has a heterozygosity index, a measure of heterozygous loci as determined by population genomic analyses. The higher the index of heterozygosity for the particular locus the more informative it is for LOH status.

Homozygous microsatellite markers are non-informative for LOH status.

4.1.3 In vivo Mouse Models of MMR

The importance of each MMR protein and their respective binding partner has been explored in MMR knockout mouse models.309 Msh2-/- mice, completely lack MMR activity, and have the most severe tumor phenotype, including lymphomas, and cancers of the gastrointestinal (GI) tract, skin and other tumors, suggesting the importance of

147 this gene in mismatch repair. In comparison, Msh6-/- mice have a less severe phenotype because MutSβ can repair most IDLs. Msh3-/- mice have been reported to only develop GI tumors because MSHα can repair many DNA mismatches and IDLs.309

In the MutL family, Mlh1-/- mice have the most severe phenotype resulting in tumors similar to Msh2-/- animals.309 Pms2-/- mice have a reduced phenotype (e.g. reduced number of tumors, increased survival), and unlike Mlh1-/- mice, do not develop gastrointestinal tumors. These findings suggest that back up mechanisms/redundancies exist, which can be found in other Mlh1 binding partners, such as Pms1 and Mlh3. However, cells from Pms1-/- mice lack MMR activity in vitro,310 although these mice do not develop tumors they do show mononucleotide tracts of

MSI.311 Mlh3-/- mice develop tumors and show mononucleotide MSI,312, 313 and show defects in repairing mismatched base pairs and single nucleotide IDLs.314 Interestingly,

Mlh3;Pms2 double deficient mice have increased tumor severity, shortened lifespan and faulty DNA repair, and have very similar tumor phenotypes to Mlh1-/- mice, suggesting a redundancy between Mlh3 and Pms2.312, 315

4.1.4 MMR Deficiencies and Human Phenotypes

Functional loss of MMR genes can occur by biallelic loss due to MMR mutations,316 loss of heterozygosity,317 or hypermethylation.318 Individuals who inherit homozygous mutations of any of the MMR genes and do not produce a functional protein usually develop hematological and brain cancers within the first decade of life.299

Patients who inherit only one of these MMR mutations (heterozygous) usually develop hematological, brain and gastrointestinal cancers during the second to fourth decades of

148 life. Heterozygous mutations in any of the MMR genes do not show MSI within cellular

DNA, suggesting haplosufficiency is able to permit normal DNA repair.

Interestingly, MMR-deficient cells are more resistant to death by alkylating and chemotherapeutic agents than MMR-proficient cells.319 These cells can become more sensitive by restoring MMR function, by using demethylating agents or restoration of the absent MMR gene (e.g. hMLH1).320

4.1.5 Mismatch Repair and MSI in Head and Neck Cancer

The role of MMR has been examined in numerous sporadic cancers, including prostate,321-323 esophageal,324, 325 and glioblastoma,326, 327 with variable results.

Similarly, the role of MMR in head and neck cancer is unclear as there are relatively few studies and conflicting conclusions have been reported. To date, most of the published studies in HNSCC have specifically assessed the level of MSI as a measure of defective

MMR. The reported rate of MSI in head and neck cancer tumors ranges from 2 to

88%.301 This large variability is largely due to differences in the number of loci and types of microsatellite markers assessed (mononucleotides, dinucleotides, etc.), as well as patient sample size, patient age, and detection methods utilized.301

One study has suggested that MSI is important during the progression of oral cancer, as HNSCC patients had lower levels of MSI in 15% of patients with preinvasive lesions, compared to 30% of patients with invasive carcinomas.328 This was also observed by Ha et al., who reported an increase of MSI in 111 lesions starting from hyperplasia (2/34 = 5.9%) to mild dysplasias (1/12 = 8.3%) to moderate dysplasias (2/21

= 9.5%) to high-grade dysplasias/carcinoma in situ (7/26 = 26.9%) to invasive

149 carcinomas (6/18 = 33%) of the head and neck, using 6 microsatellite markers that previously showed high rates of MSI and allelic imbalance.329

However other studies have observed relatively lower rates of MSI in HNSCCs.

In one study of 67 patients, the authors reported low MSI levels, and no differences in

MSI were found between patients with single (9%) vs. multiple (7%) primary tumors.330

In another study, in 91 patients with OSCC, only 6 patients (7%) showed MSI using 19 microsatellite loci.331 In a study of 153 HNSCC patients using 22 microsatellite markers, a low frequency of MSI was reported in only 13% of tumors analyzed (3% MSI-H and

10% MSI-L).332

One of the mechanisms that have been assessed in HNSCC is MMR gene promoter hypermethylation. In a recent study of 28 OSCCs, one group found promoter hypermethylation in 10/28 (36%) and 5/28 (18%) OSCC samples at hMSH2 and hMLH1 loci, respectively; and 9/9 (100%) of tumors from patients with multiple oral neoplasms.291 The authors also found both hMLH1 and hMSH2 protein loss in 10/28 cases, however they did not find a correlation between methylation status and protein loss. The authors acknowledge the heterogeneity of MMR methylation status in head and neck cancer studies, and suggest that increased methylation may be related to advanced tumor grade.

In another study detecting MMR promoter methylation status, 116 patients with matched normal and HNSCC were examined for MSI, and hMLH1 and hMSH2 methylation patterns using methylation-specific restriction enzymes.292 The authors reported MSI in 41% of tumors, and promoter methylation was found in 47% (55/116) and 30% (35/116) of hMLH1 and hMSH2 promoters, respectively. Promoter

150 hypermethylation thus appears to be one of the mechanisms of defective MMR in

HNSCCs.

A potential role for MSI assessment in head and neck cancer treatment has been considered, as MMR deficient cells show resistance to chemotherapy, including cisplatin, and methylation agents.333 One study compared tissues from patients who did and did not respond to chemotherapy (cisplatin and 5-fluorouracil). Low levels of MSI were reported and no differences were found in MSI prior to or after treatment; the latter suggesting the inability of the tumors to select for MMR deficient clones.333 In this study, out of 56 tumors only 1 tumor had an MSI-H genotype and 6 tumors had an MSI-Low genotype. However, high rates of LOH were observed in 75% of HNSCCs, indicating that this mechanism plays an important role in HNSCC.

4.1.6 MMR and OSCCs from Young and Older Patients

In a previous study, our group observed that 88% of HNSCCs from young patients exhibited microsatellite instability (MSI) at 2 or more loci, whereas older patients exhibited MSI at 2 or more loci in only 36% of HNSCCs.120 This has been similarly found in colorectal cancer whereby young patients can have higher rates of

MSI (50.9%) compared to older patients (12-21%).334

In our previous study we were unable to find any differences in the classical mismatch repair genes (e.g. hMLH1 and hMSH2),120 often mutated in colon cancer.89

We thus further investigated hMLH1 and other members of the MutL family of mismatch repair genes. We hypothesized that other defective MutL genes may be involved in deregulated mismatch repair in oral cancer from young patients. In our current study we examined oral cancer, which makes up the majority of head and neck cancers in young

151 and older patients; while excluding other head and neck cancer sites, as was assessed in our previous study.120

In the current study, we undertook a molecular analysis of hPMS1 and hPMS2 genes, which have not been assessed in HNSCC, and further investigated their binding partner hMLH1 in OSCCs, from young and older patients. We specifically examined

MMR gene and protein expression, mutational status, genomic MSI and LOH status, and looked for genomic aberrations involving these genes, including MSI and LOH at

MMR loci. For MSI and LOH status analyses, we utilized fluorescently labeled microsatellite primers and capillary electrophoresis, a more sensitive test compared to our previously utilized Southern Blot gel electrophoresis method.335, 336 We did not examine hMLH3, due to the lack of a commercial antibody, and the lack of a heterozygosity index for known microsatellite markers within the region. The involvement of MMR genes in OSCCs is not well understood and little research has been undertaken to clarify their role in the development of OSCC. Research into MMR and its involvement in the development of OSCC is important to gain an understanding of deregulated molecular pathways in early-onset oral carcinomas.

152 4.2 MATERIALS AND METHODS

4.2.1 Patients

The University Health Network (UHN) Research Ethics Board approved this work and informed consent was obtained from patients prior to sample collection. All patients had surgery as the primary treatment and only patients with primary OSCCs from the oral cavity were studied. Medical records were examined to obtain detailed clinicopathological information for each patient, including age, sex, site, histopathological diagnosis, disease stage, history of smoking and consumption of alcohol, and outcome. Outcome data comprises the period between the dates of surgery and last follow up dates. Patients were stratified by age as follows: < 45 years of age (young patient group) and patients ≥ 45 years of age (older patient group). For statistical analysis we also stratified patients as: ≤40 years of age and ≥ 60 years of age, thereby excluding an intermediate age group that may confound young vs. older patient analysis. Patient clinical information and use in each experiment is provided in

Appendix Table 1. Tumors were staged according to the current TNM classification

(International Union Against Cancer, 2002).

4.2.2 Tumor Samples

OSCC samples and adjacent histologically normal oral mucosa were obtained at the time of surgery from the Toronto General Hospital. Tissues were snap frozen in liquid nitrogen and stored at -80°C until further use. H&E stained tissues sections were examined and histopathological analysis was performed to determine the presence of tumor cells in at least 80% of the specimen. In addition, formalin fixed paraffin

153 embedded (FFPE) tissues collected over the past 16 years from patients was available from the UHN and the University of Toronto school of Dentistry (Toronto, ON).

4.2.3 RNA Isolation and Quantitative RT-PCR

Total RNA was isolated from fresh frozen tumor and adjacent normal oral tissues using the Trizol reagent (Invitrogen, Burlington, ON), and purified on Qiagen RNeasy mini-columns (Qiagen, Valencia, CA). RNA quality was evaluated by spectrophotometry and electrophoresis. RNA (1µg) was used to synthesize cDNA by reverse transcription using the MoMLV polymerase and an oligo (dT) primer

(Stratagene, Vancouver, BC). cDNA was used from 10-12 older and 9-14 young patient tumors and paired adjacent normal tissue for each gene (hMLH1, hPMS1, and hPMS2) depending on the RNA amount initially available. The ABI Prism 7700 Sequence

Detection System (Applied Biosystems Inc.) was used for relative quantification of gene expression. Data was quantified and analyzed using the Sequence Detection System software (v. 1.7) (Applied Biosystems Inc.).

4.2.4 Primer Design and Quantitative RT-PCR Amplification

Gene sequences were retrieved from the NCBI database

(http://www.ncbi.nlm.nih.gov/). Primers were designed for the hMLH1, hPMS1, and hPMS2 genes and an internal control, GAPDH, using Primer Express software (version

1.5) (Applied Biosystems Inc., Foster City, CA). Primer sequences are provided in

Appendix Table 2. cDNA was added to 2X SYBR Green PCR Master mix (Applied

Biosystems Inc., Foster City, CA) containing a final primer concentration of 400 ng.

Amplification conditions were: 50°C for 2 minutes; 95°C for 10 minutes; 35 cycles at

154 95°C for 15 seconds followed by 60°C for 1 minute. Each experiment also included a non-template control. Experiments were performed in duplicate for each sample. Test and GAPDH levels were consistently reproducible.

4.2.5 Analysis of Quantitative RT-PCR Results

Relative quantification of transcript for each sample was analyzed by measuring the threshold cycle (Ct) at which the amount of PCR product reaches a threshold and is directly related to the point at which amplification is detected. The Ct values were determined for each test and internal control gene (GAPDH). Calculations to determine relative expression were measured using the Delta-Delta Ct method,337 Briefly, the Ct values were averaged and the GAPDH Ct was subtracted to obtain ΔCt [ΔCt = Ct (target gene) – Ct (GAPDH gene)]. Ct values were calculated for each test (tumor and normal) and reference sample, a commercially available normal tongue RNA (Stratagene,

Vancouver, BC). Relative expression level was determined as 2-ΔΔCt, whereby ΔΔCt =

ΔCt (target sample) – ΔCt (reference sample). For the reference sample, ΔΔCt equals 0 and 20 equals 1, so the fold change in the reference sample equals 1 by definition. For the test samples, 2-ΔΔCt, indicates the fold change in gene expression relative to the reference sample.

4.2.6 Immunohistochemistry

Paraffin embedded OSCCs (tissue blocks) were used to cut 5 µm thick sections, and immunohistochemistry (IHC) for MMR proteins was performed. Samples were available from 17-32 young and 23-38 older patients depending on the protein examined. Slides were deparaffinized in xylene for 20 minutes, hydrated with serial 155 ethanol-ddH20 dilutions, and finally rinsed in running H20 for 30 minutes. The slides were then incubated in 0.5% (v/v) H2O2 in methanol for 30 minutes, to block endogenous peroxidase activity. Slides were then washed with Tris-buffered saline

(TBS, pH 7.4) and the antigen was retrieved by heating the slides for 15 minutes at

100°C in 10 mM sodium citrate buffer (pH 6.0) for hMLH1 and hPMS1 or 1 mM EDTA

(pH 8.0) for hPMS2. After they were rinsed in TBS, sections were incubated with hPMS1 primary antibody (Santa Cruz Biotechnology, Inc, Santa Cruz, CA) at 1:200; hMLH1 (BD Biosciences, Mississauga, ON) at 1:150; and hPMS2 (Santa Cruz

Biotechnology Inc, Santa Cruz, CA) at 1:200 in Dako antibody diluent (Dako Co.,

Denmark) for 16 h at 4°C. The primary antibody was detected by the avidin-biotin complex using the Dako LSAB plus HRP system (Dako Co., Denmark). The color was developed using diaminobenzidine (DAB) and chromogen. Slides were washed with

TBS (3 times for 5 minutes) after every step. Lastly, the slides were counter-stained with Haematoxylin (BioRad, Hercules, CA), rinsed with ddH20 and dipped in 0.5% (v/v)

Ammonium Hydroxide (Caledon Laboratories Ltd., Georgetown, ON) in 1X PBS (pH

7.4) for 1 minute. The slides were rinsed with ddH20, air dried, mounted with D.P.X. mountant (Sigma-Aldrich, Oakville, ON), coverslipped, and stored in the dark slide box at room temperature until further use. For negative controls, the appropriate antibody was omitted.

4.2.7 Positive Criteria for Immunohistochemical Staining

Immunostained slides were scored independently and in a blinded fashion by two pathologists (Drs. Rashmi Goswami and Christina MacMillan). Adjacent normal oral epithelium was used as positive MMR protein staining and designated as moderate intensity. Tissue sections were graded as immunopositive if epithelial cells showed

156 nuclear staining. MMR protein levels were evaluated using a semi-quantitative scoring system as previously reported:338 0 for absence of immunostaining or detectable immunostaining in <10% cells, +1 for 10-30% positive cells, +2 for 31-60% positive and

+3 for >60% positive epithelial cells. Intensity of MMR protein immunostaining in tumor cells was evaluated as follows: 0 for absent, 1 for weak, 2 for moderate, 3 for strong intensity compared to normal adjacent epithelium in the same section. The sum of these scores (percentage of cells with detectable immunostaining and intensity) was used to determine a final score for MMR protein expression in OSCCs from young and older patients.

4.2.8 cDNA Sequence Analysis of hPMS1 and hPMS2

We sequenced hPMS1 and hPMS2 using cDNA preparation of OSCCs from 10 young and 20 older patients with similar clinical characteristics (excluding age, Table 1).

The PCR products were amplified using hPMS1 primers and hPMS2 primers (Appendix

Table 3); primers for the latter were previously designed by others to exclude any hPMS2 pseudogene amplification.339 PCR for hPMS1 was performed using two amplification products (Fragment A and B). Amplification products and molecular weight was assessed by electrophoresis on a 1% agarose gel. The PCR product was purified using the QIAquick PCR purification kit (Qiagen, Valencia, CA). Capillary-based fluorescent sequencing was performed using an ABI 3730XL instrument at The Centre for Applied Genomics (TCAG, Toronto, ON). Sequencing for hPMS1 was performed utilizing amplification primers and an additional primer; sequencing primers for hPMS2 were supplied by Dr. Katharina Wimmer via email (Appendix Table 4).

157 4.2.9 Tissue Needle Macrodissection and Genomic DNA Isolation

Paraffin embedded OSCCs, and normal tissues (margins) were retrieved from 25 young and 23 older patients. Tissue blocks were used to cut ten 5 µm thick sections.

Slides were deparrafinized in xylene for 10 minutes, and serially hydrated in ethanol- ddH20 dilutions and finally into ddH20. Each slide was then incubated in diluted (1:5)

Haemotoxylin (BioRad, Hercules, CA):ddH20 for 15 seconds and then kept in ddH20.

Each section was needle macrodissected using a Leica MZ6 stereomicroscope (Leica

Microsystems, Richmond Hill, ON) for normal (margins), adjacent normal and cancerous oral tissue within the same slide, based on demarcated selected regions from H&E slides by a pathologist (Dr. Bayardo Perez-Ordonez). Genomic DNA extraction was performed using the DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA) and stored at

4°C until further use.

4.2.10 Microsatellite Instability and LOH Analysis

In addition to the OSSC and normal tissues isolated from 25 young and 23 older patients, available DNA from our previous analysis was utilized to increase our sample size, which included an additional 11 young and 12 older patients, bringing the total to

36 young and 35 older patients. The samples were analyzed using 13 polymorphic dinucleotide microsatellite markers that were used in our previous study,120 across the genome for analysis of MSI and LOH status. A subset of these samples (15-26 young and 13-23 older patients) depending on DNA availability, were also analyzed using polymorphic dinucleotide microsatellites within or adjacent to MMR gene loci;

Microsatellite markers used included D2S2027, D2S300 and D2S118 for hPMS1;

158 D7S481, D7S472 and D7S2514 for hPMS2; and D3S1260, D3S1561 and D3S1611 for hMLH1.

The 5’ fluorescently labeled primers were obtained from Applied Biosciences

(Applied Biosciences, Foster City, CA) and 3’ unlabeled primers from ACTG Corporation

(Toronto, ON). Amplification conditions were 94°C for 15 minutes, 35 cycles of 94°C for

30 seconds, 50–55°C for 30 seconds and 72°C for 30 seconds in a 50 µL reaction volume containing 25 ng of genomic DNA and Qiagen Multiplex Master Mix (Qiagen,

Valencia, CA). DNA fragment sizes were separated using capillary electrophoresis on the ABI PRISM 310 Genetic Analyzer (Applied Biosystems Inc., Foster City, CA) and analyzed using GeneScan v. 3.7 and Genotyper v. 3.7 software. MSI and LOH analysis using fluorescently labeled microsatellite primers for PCR has been previously reported.335, 336 We assessed MSI status based on the frequency per sample: MSI-H

(high): ≥4 MSI (+), MSI-L (low): ≤3 MSI, and MSS (stable): 0 MSI. We also compared our MSI positivity based on criteria used in our previous study of at least one MSI positive marker or at least two MSI positive markers per each sample. For MMR loci, any occurrence of LOH or MSI was considered a positive, as only 3 markers were chosen for each locus. Two independent observers recorded all analyzed data and any discrepancies were resolved by an initial agreement or a decision after re-running the

DNA fragment using capillary electrophoresis as described above.

4.2.11 Method of Statistical Analysis of QRT-PCR, IHC, MSI and LOH Data

Descriptive statistics were examined as median and range for continuous variables, and frequencies and proportions for categorical variables. The Fisher’s exact and Pearson’s chi-square tests were used for statistical evaluation. Results were 159 considered significant if p≤0.05. Statistical analyses were performed using the SAS 9.1 software package (SAS Institute, Cary, NC). Dr. Wei Xu, biostatistician, at the Ontario

Cancer Institute performed the statistical analyses.

160 4.3 RESULTS

4.3.1 Quantitative Real-Time PCR

The relative mRNA expression of hPMS1, hPMS2 and hMLH1 from OSSCs of young and older patients was measured by quantitative RT-PCR from matched tumors and adjacent normal samples (Figure 3). No significant clinical differences existed between both patient groups (Appendix Table 5). No significant differences in MMR gene expression were found when we segregated patients based on <45 and ≥45 years old criteria only; we thus also examined expression differences in patients that were specifically ≤40 and ≥60 years old to exclude potentially confounding data from intermediate age patients (Table 2).

hPMS1 levels had a trend for overexpression in young patient adjacent normal tissues compared to their respective tumors (Figure 3A, Table 2, p=0.08). There appeared to be a biphasic distribution with some tumors being high and some being lower expressers of hPMS1. Also, hPMS1 had higher mRNA expression in young patient adjacent normal tissue compared to older patient adjacent normal tissue

(p=0.05). hPMS2 levels were found to be significantly increased in tumor samples from young patients compared to older patients (Figure 3B, Table 2, p=0.05). hMLH1 was found to have higher mRNA expression in young patient tumors compared to their respective normal tissue (Figure 4C, Table 2, p=0.03). QPCR values are listed in

Appendix Table 6.

It is known that MMR gene expression in normal tissues have similar stoichiometric levels to their respective binding partners (e.g. hMLH1-hPMS1, hMLH-

161

FIGURE 3

Figure 3: Quantitative RT-PCR of (A) hPMS1, (B) hPMS2, and (C) hMLH1 in oral cancer. Relative mRNA expression of hPMS1, hPMS2, and hMLH1 was observed in tumors and adjacent normal tissue from young and older patients. Values were compared to a commercially available normal tongue. Bars represent standard error of the mean.

162 Table 2: Quantitative RT-PCR for mismatch gene expression in oral cancers and adjacent normal tissue from young and older patients. Relative mRNA expression of the mismatch genes hPMS1, hPMS2, and hMLH1 was measured in oral cancers and adjacent normal tissue from young (<45 years) and older (≥45 years) patients. Median expression values are given and range of mRNA expressions are shown. Significant values are reported for young (≤40 years) and older (≥60 years) patients; p≤0.05 and are denoted (*).

Gene Tissue Young (<45) Older (≥45) p value Young (≤40) Older (≥60) p value 5.82 (0.31- 0.78 (0.08- 8.76 (0.31- 0.78 (0.08- hPMS1 Normal 73.03) 79.23) 0.24 73.03) 2.42) 0.05* Tumor 1.05 (0.24-4.39) 0.93 (0.12-6.4) 0.78 1.2 (0.24-4.39) 1.18 (0.15-6.4) 0.96

0.1 (0.001- hPMS2 Normal 0.28 (0.01-0.83) 0.1 (0.001-0.92) 0.2 0.3 (0.01-0.83) 0.92) 0.38 Tumor 0.75 (0.02-2.94) 0.25 (0.01-0.7) 0.08 0.99 (0.07-2.94) 0.38 (0.08-0.7) 0.05*

0.53 (0.02- hMLH1 Normal 0.97 (0.14-2.61) 0.38 (0.02-1.86) 0.18 1.02 (0.14-2.61) 1.86) 0.54 1.04 (0.34- Tumor 0.97 (0.06-4.33) 0.98 (0.06-3.82) 0.96 1 (0.34-4.33) 3.82) 0.27

163 hPMS2), and deregulated levels of MMR binding partners can lead to aberrant MMR.340

We thus compared gene expression levels across the different tissues between young and older patients. We were able to see differences in stoichiometric levels of hMLH1 with hPMS1 (p=0.003) and hPMS2 (p=0.002) in young patient normals. There was also a trend for differences in hMLH1 and hPMS2 levels (p=0.08) in older patient tumors.

MMR gene expression profiles were also assessed for clinical association. hPMS1 gene expression levels were significantly associated with early stage tumors (I/II)

(p=0.03). No other clinical parameters were associated with MMR gene expression.

4.3.2 Immunohistochemistry of Mismatch Repair Proteins

Protein levels of the MMR proteins hPMS1 and hPMS2 and their binding partner hMLH1 were examined within oral tumors of young and older patients by immunohistochemistry. No significant differences existed for patients except for tumor grade, where there was a higher prevalence of well-differentiated tumors from older patients (Appendix Table 7). Protein scores are listed in Appendix Table 8. Normal

MMR protein nuclear staining was observed in the basal layer of adjacent normal oral squamous epithelium. MMR protein expression often had normal levels of expression around regions of keratin pearls in OSCCs.

MMR protein intensity was generally decreased in dysplastic tissue, with further reduction or absence in OSCCs (Figure 4). hPMS1 was found to have weak expression in 59% cases, hPMS2 in 80% cases, and hMLH1 in 52% cases (Table 3). No significant differences were found when we compared young and older patient tumors, regardless of the young age cutoff. We also compared the final scores for each MutL

164

FIGURE 4

Figure 4: Immunohistochemistry of hPMS1, hPMS2, and hMLH1 in oral cancer. Adjacent normal tissue was used as positive MMR staining (moderate) and compared to oral cancer tissue. Adjacent normal and oral cancer tissue are depicted at 10X and 40X magnification, respectively. A representative picture of each protein’s intensity is shown. Arrowheads indicate nuclear expression of MMR proteins.

165 Table 3: Immunohistochemistry for mismatch protein levels in oral cancers of young and older patients. Protein levels were examined for hPMS1, hPMS2, and hMLH1 in young and older patient tumors. Values represent final scores from the sum of intensity (weak, moderate, strong) and percentage of tissue positive for MMR protein expression. Ranges of final scores are given. Significant values are p≤0.05.

Young Older Young Older Protein (<45) (≥45) p value (≤40) (≥60) p value hPMS1 4 (2-7) 4 (0-7) 0.39 4 (2-7) 4 (0-7) 0.25

hPMS2 4.5 (2-6) 3.5 (2-6) 0.4 4 (0-7) 5 (4-7) 0.39

hMLH1 5 (2-6.5) 4 (3-6.5) 0.07 5 (2-6.5) 4 (3-6.5) 0.07

166 binding partner, since they should have similar stoichiometric levels for proper DNA repair. We found there was a trend for differences in hPMS1 and hMLH1 scores in young patient tumors (p=0.06). Also, there was a significant difference in hPMS2 and hMLH1 scores in older patient tumors (p=0.01). Assessment of clinical and pathological features and MMR protein levels showed a statistical correlation between smoking and hPMS1 expression, with smokers having higher hPMS1 protein levels (p=0.02).

4.3.3 cDNA Sequencing of hPMS1 and hPMS2

Mutations in MMR are common in HNPCC patients and can lead to MMR protein loss.89 We assessed the mutational status of hPMS1 and hPMS2 from tumors of 10 young and 20 older patients with OSCC. We did not examine the mutational status of hMLH1 as no mutations in this gene have been previously found in OSCCs.120, 317, 332,

333, 341 We were not able to detect any mutations in hPMS1 and hPMS2 in OSCCs from our 30 patient tumors. However, we were able to detect one amino acid change in hPMS2 in an older patient sample (Sample ID #1). The change was a Proline (P) to

Serine (S) polymorphism at codon 470 (Pro470Ser) in exon 11. This single nucleotide polymorphism is a non-synonymous SNP (rs1805321), which may have a functional role in MMR.

4.3.4 MSI and LOH Across the Genome and at MMR Loci

In order to assess defective mismatch repair, we examined microsatellite instability and loss of heterozygosity across the genome and also at MMR loci. No clinical differences were observed between young and older patients (Appendix Table

9). We repeated our previously published analysis using a different set of patient

167 samples with some samples from our previous analysis and looked specifically at

OSCCs only (Appendix Table 10). We assessed the frequency of MSI levels across the genome using the definition of MSI-H (≥30% MSI), MSI-L (<30% MSI), and MSS (0%

MSI). MSI appeared have a varied range (1-11 MSI+) across all ages depending on the microsatellite markers used. We found 13/71 (18.3%) samples were MSI-H, 40/71

(56.3%) samples were MSI-L, and 18/71 (25.4%) samples were MSS. We were not able to find any significant differences in MSI between OSCCs from young and older patients (p=0.24).

In addition, when we used the same definition of MSI (e.g. MSI is present if there is instability at ≥ 2 loci) from our previously published results,120 we found that our data showed that the incidence of MSI was similar between young and older patient OSCCs

(p=0.17) (Table 4). When we looked at LOH, the overall LOH for at least one marker was found in 50% of samples, and although LOH was higher in older patients when at least 1 marker was positive, no significant differences were found between young and older patient OSCCs when at least 2 or more markers showed LOH (p=0.23).

We were also interested in determining whether MMR deficiencies were due to

LOH at MMR genes and surrounding loci. Analysis of microsatellite loci around the hPMS1 region at 2q31.1, the hPMS2 region at 7p22.2 and the hMLH1 region at 3p21.3 was carried out using 3 polymorphic dinucleotide markers for each gene (Appendix

Table 11). Microsatellite regions were selected based on close proximity of markers

(e.g. intragenic or intergenic) and a high heterozygosity index (e.g. >65%). At the hPMS1 locus, 13% and 38% of patients exhibited LOH and MSI, respectively; at the

168 Table 4: Summary of analysis of MSI and LOH across genomic loci.

Young Patients Older Patients MSI ≥ 1 78% (28/36) 71% (25/35) MSI ≥ 2 50% (18/36) 60% (21/35) LOH ≥ 1 39% (14/36) 60% (21/35) LOH ≥ 2 28% (10/36) 34% (12/35)

169 hPMS2 locus, 11% and 7% of patients exhibited LOH and MSI, respectively; and at the hMLH1 locus, 17% and 21% of patients exhibited LOH and MSI respectively (Table 5).

Interestingly, at the hPMS1 locus, D2S2027, there was 19% LOH in young patient tumors compared to 5% LOH in older patient tumors. The D2S2027 marker is an intragenic hPMS1 microsatellite marker. There was also a higher incidence of MSI in older patient tumors at the hPMS1 locus (50% in older patient tumors vs. 27% in young patient tumors), whereas a higher incidence of MSI in young patient tumors was found at the hMLH1 locus (29% in young patient tumors vs. 13% in older patient tumors). No other significant differences in LOH and MSI were found in samples from young and older patients.

170 Table 5: Summary of analyses of MSI and LOH at MMR loci.

Young Patients hPMS1 hPMS2 hMLH1 MSI 27% (7/26) 13% 29% (2/15) (7/24) LOH 19% (5/26) 13% 17% (2/15) (4/24) Older Patients hPMS1 hPMS2 hMLH1 MSI 50% 15% 13% (11/22) (2/13) (3/23) LOH 5% (1/22) 8% (1/13) 17% (4/23)

171 4.4 DISCUSSION

Molecular studies have revealed an important role of MMR protein complexes in promoting genome stability and in the prevention of tumorigenesis.297 Defective MMR is associated with cancers such as HNPCC and can often lead to malignancies such as endometrial cancer.89 The role of MMR in head and neck cancer is not clear because of conflicting data, perhaps because studies have used different methodologies for assessing deficient MMR.332

The involvement of MMR in tumors from young and older patients with head and neck cancer was previously assessed in our laboratory.120 Our previous analysis found a higher incidence of MSI in young patient tumors compared to those from older patients, suggesting an association of defective MMR within early-onset HNSCCs. We further explored whether deregulation and/or mutations of members of the MutL family, namely hPMS1, hPMS2 and hMLH1 were involved in the pathogenesis of OSCCs, and if any differences exist between tumors from young and older patients. We specifically assessed only OSCCs, which make up the majority of young and older patient head and neck cancers,13, 103 because of the known molecular heterogeneity between head and neck sites.342, 343

In this study, gene and protein expression levels of a number of MMR genes were examined in oral carcinomas. We were not able to observe any significant differences in gene expression when we compared tumors from <45 and ≥45 year old patients, and thought that tumors from intermediate aged patients (>40 and <60 years) may have confounded our results. We therefore compared OSCCs from young and older patients only ≤40 years of age and ≥60 years of age. We found high expression of hPMS1 and hMLH1 in a subset of young patient adjacent normals and tumors, 172 respectively, compared to older patient tissues; and hPMS2 levels were deregulated in tumors of older patients compared to young patient tumors. Interestingly, in yeast, higher Mlh1 levels leads to defective MMR, and overexpression of Pms1 (yeast Pms2 homologue) leads to a mutator phenotype and can be corrected by overexpression of

Mlh1.344 Stoichiometric differences between hMLH1 with hPMS1 and hPMS2 were found in adjacent normal tissue from young patients and a trend was also observed between hMLH1 and hPMS2 in older patient tumors. The results of our MMR gene expression analysis in tumors from young and older patients are not clear, but it is well known that the deregulation of MMR gene and protein expression can lead to altered

DNA repair.340 One study measuring the mRNA expression levels of hMSH2, hMSH6, hMLH1, hPMS1, and hPMS2 found decreased expression of hMLH1 and hMSH6 in lymphocytes from 78 head and neck patients compared to 87 healthy controls.345 All of which, suggests that mRNA deregulation of MMR genes may be involved in faulty DNA leading to cancer, but the role of MMR mRNA in tumors from young and older patients is not clear.

Protein levels of the MMR proteins were decreased in young and older patient

OSCCs compared to adjacent normal epithelia. hMLH1 is responsible for stabilization of hPMS1, hPMS2, and hMLH3, as hMLH1 protein loss leads to binding partner loss, probably due to rapid turnover.308 hMLH1 is known to interact with hPMS1, hPMS2 and hMLH3 through its N-terminal domain, and is thought that competition between the different binding partners for hMLH1 are required for a quantitative balance, and may be important for their functions.346, 347 Unfortunately, we were not able to find any significant differences between MMR protein loss in young and older patient samples, although deregulation was observed when comparing protein scores, but the relevance of the

173 latter not clear for the differences between tumors of young and older patients. These data suggest that hMLH1, hPMS1 and hPMS2 protein loss is more likely involved in oral cancer in general, and may not be age independent. In our previous study we did not find hMLH1 or hMSH2 protein loss in 12 head and neck tumors from young and older patients with or without MSI.120 However, a few other studies have examined MMR protein expression in HNSCC, and some have reported loss of MMR protein expression in head and neck cancer, as described below.

One study examined 78 HNSCCs for hMLH1 protein expression and reported

24/78 (31%) of HNSCC cases with reduced hMLH1 protein levels.348 The authors then selected 8 cases that had reduced hMLH1 expression and 8 cases that had normal hMLH1 expression, and performed hMLH1 hypermethylation analysis. The authors observed 7/8 cases that had reduced hMLH1 protein levels had promoter hypermethylation, whereas no methylation was found for hMLH1 positive cases. In another study that examined allelic imbalance in head and neck cancer, 23/24 (96%) of

HNSCC samples had reduced expression in either hMLH1 or hMSH2, and 50% of tumors had a reduction of both of these proteins.317 However, no association was found between protein loss and allelic loss at tested MMR loci, and they were not able to associate allelic imbalance with microsatellite instability.

Since decreased function and protein expression may result from MMR mutations, we assessed the mutational status of hPMS1 and hPMS2 in OSCCs. We did not find any exonic mutations in hPMS1 and hPMS2 in all OSCCs examined. While mutations in hMLH1 and hMSH2 are common in HNPCC,316 mutations in MMR repair genes are rare in sporadic cancers. In addition, there are no reported mutations of hPMS1 and very few hPMS2 mutations in HNPCC families.349 However, hPMS2

174 mutations have been hampered by the presence of multiple hPMS2 pseudogenes, and may have been greatly underestimated in colorectal cancers.304

Mutations in hPMS1 and hPMS2 do not appear to be involved in oral cancers, although we did find one SNP in one older patient OSCC. This SNP has been reported in sporadic endometrial cancer,350 HNPCC351, 352, and colorectal cancer.304, 353 Often

SNPs can lead to the functional loss of MMR, as a SNP may alter the structure of the

MMR protein and lead to decreased DNA repair.354 However, due to our sample size, the likelihood of finding any SNP association is small, unless we can increase the size of our patient cohort. A recent study that examined SNPs in BER, NER, and MMR genes in 151 lung cancer cases, 251 head and neck cancer cases, and 172 hospital controls, reported that polymorphisms in 62 DNA repair genes were associated with lung and head and neck cancer.355 Interestingly, hPMS1 SNPs were associated with head and neck cancer patients when compared to hospital controls.

We examined young and older patient tumors for MSI and LOH using the same dinucleotide microsatellite markers from our previous study and looked for comparable results strictly in oral cancers. In our current analysis, we did not find significant differences in LOH between young and older patient OSCCs. A similar finding using microsatellite markers has been reported in young and older patients tumors by another group.356 In addition, we did not find any significant differences in MSI between tumors from young and older patients. The MSI status detected in OSCCs in this study compared to our previous publication is likely due to the current study focusing on oral cancer, whereas the previous study examined head and neck cancer from several sites.

In the previous study there was a significant difference in MSI in HNSCCs (tongue and laryngeal cancers) of young and older patients (p=0.0382). Older patients had 10

175 laryngeal tumors and young patients had 3 laryngeal tumors; whereas older patients had 9 tongue tumors, compared to young patients with 15 tongue tumors. MSI was associated with tongue cancers and not laryngeal tumors (p=0.0075). In addition, MSI was significantly associated with non-smokers (p=0.0002), and the young patient cohort had more non-smokers than older patients (p=0.001). We did not find any significant differences in smoking status and MSI in our data set, although this is not surprising since the earlier study only found an association with non-smokers, who were mainly young patients and this only correlated with oral tumors. We only included oral tumors in our study thereby no association was observed with smoking and MSI status.

In the previous published analysis, these factors may have contributed to the reported higher rates of MSI in young patient tumors compared to older patient tumors.

In addition, in our current study of MSI in oral cancers we have utilized a fluorescently

PCR based method, which is more sensitive and precise compared to the gel-based electrophoretic methods301 used in our previous analysis. Our data therefore may be a truer representation of MSI status in OSCCs from young and older patients; similar results have also been reported and are described below.

A few recent studies have investigated MSI status in young patient OSCCs.

Using a panel of 5 microsatellite markers from the Bethesda Consensus and National

Cancer Institute of USA used for HNPCC,357 no significant differences in MSI were observed in 10 young (≤45 years) and 106 older (>45 years) patients (5/10 = 50% vs.

43/106 = 41%; p=0.398).292 10% of patients were MSI-H, 32% were MSI-L and 59% were MSS. The authors noted that 74% of their samples were from laryngeal, although they did not provide individual patient clinical characteristics, but did note that laryngeal tumors had similar numbers of MSI (+) and MSI (-) tumors. However, when they

176 segregated individual MSI markers and tested for age differences, BAT25 was the only marker that showed more MSI in young patients (p=0.006).

In another study, using 91 HNSCCs, only a low level of MSI (MSI-L) was found in

7.7% of all tumors and LOH was observed in 32.5% of markers analyzed; in addition, no significant associations with MSI or LOH or clinical parameters, including age, were found (15 young (≤45 years old) patients were included).341 The authors used 6 mononucleotide markers, 8 dinucleotide markers, and 1 tetranucleotide repeat marker, and fluorescent PCR-based capillary electrophoresis. The use of mononucleotide vs. dinucleotides in HNSCC has been examined in HNSCC using 112 mononucleotide and

1120 dinucleotide markers in the same samles.358 MSI was only observed at 17 dinucleotide microsatellite markers, translating into 1.5% MSI positivity, showing low

MSI positivity regardless if either mono- vs. dinucleotide microsatellite marker were used.

MSI was also found to be low in 29 tumors (13%) in another HNSCC patient group.356 However, the authors note that the number of microsatellite markers between studies is variable and may make direct comparisons difficult to interpret. LOH was found to be present in 30% of the population tested using 17 microsatellite markers on chromosomes 3p, 4q, 7q, 9p, 17p and 18q. In this study, LOH was more prevalent in the hypopharynx/larynx compared to oral carcinomas, suggesting that different head and neck anatomical sites are molecularly heterogeneous. The authors also failed to find any significant association between LOH and clinical parameters, including age, and no MSI was found in tumors from young patients (≤45 years old) although only 6 young patients were included in their cohort of 30 patients.

177 A recent study examining 123 paired HNSCC and normals and 27 pre-malignant leukoplakia samples analyzed the methylation status of hMLH1 and hMSH2 promoters and MSI status.318 The authors utilized restriction enzyme methylation techniques and observed 28/123 (23%) and 16/123 (13%) HNSCCs were hypermethylated at the hMLH1 and hMSH2 promoters, respectively. In addition, 4/27 (15%) and 5/27 (18%) of leukoplakias showed hypermethylation at the hMLH1 and hMSH2 promoters, respectively. Interestingly the authors found that adjacent normal tissue was also hypermethylated at the hMLH1 (54%) and hMSH2 (19%) loci. The authors noted 57% of older patients (≥ 50 years) had a higher frequency of hypermethlation compared to

37% of younger patients (<50 years) (p=0.018), but failed to mention or provide details on any MSI differences between the two age cohorts. They noted that MSI was present in 60% of cases when only one of the 8 microsatellite markers was used, and 32% when two or more markers were positive for MSI. The authors found that hypermethylation was associated with smoking. However, they did not find any association between MSI and methylation status. A multi-variable analysis was not performed examining different clinical parameters.

Another study examined allelic imbalance at MMR loci (LOH) in 35 HNSCC patients (different head and neck sites).317 In this study, the authors found that 28/35

(80%) of patient tumors had at least one allelic imbalance of the MMR genes investigated. Allelic imbalances were 54.5%, 29%, 18%, 25% and 42%, for hMLH1, hMSH3, hMSH/hMSH6, hPMS2, and hPMS1, in at least one out of six LOH markers, respectively. However the authors have previously investigated MSI status of these tumors and observed that 4/35 (11%) showed MSI-H or MSI-L and the remainder were

MSS tumors.359 14/35 (20%) samples showed reduced hMLH1 protein expression. We

178 extended their study to examine whether there were any differences in allelic imbalance between young and older patient tumors. We examined the incidence of LOH and MSI in the MMR genes hPMS1, hPMS2 and hMLH1 in OSCC from young and older patients.

We found a relatively low presence of LOH across the three MMR loci. However we did find an increased level of LOH in hPMS1 in OSCC from young patients, but did not observe a corresponding protein loss in hPMS1 in tumors from young patients. This may have resulted in different patient cohorts used between the two different analyses.

In addition higher levels of MSI were found in hPMS1 and hMLH1 in older and younger patient tumors, respectively, but the role of increased MSI within these loci remain unclear.

In our study we examined the role of hPMS1, hPMS2 and hMLH1 in OSCCs from young and older patients. We were able to observe deregulated mRNA and protein levels in young and older patients with oral cancer. There were decreased MMR protein levels for hPMS1, hPMS2 and hMLH1, although this appears to be age independent.

The stoichiometric levels of MMR mRNA and protein levels were deregulated in some tumors and adjacent normals from young and older patients, but these results remain unclear. No significant MSI differences were found in tumors from young and older patients, however a higher frequency of LOH was noted at within the hPMS1 locus in young patient OSCCs. Unfortunately, the role of hPMS1 is not well understood as hMLH1-hPMS1 does not have MMR activity in vitro,310 and hPms1 deficient mice lack tumors and do not develop severe MSI instability compared to other MMR knockout mice.311 This leaves the significance of our observations still unknown.

Interestingly, yeast mlh2 (hPMS1 homologue) mutants fail to show any defects in

MMR mutation rates;360, 361 although these mutants have been shown to be more

179 resistant to chemotherapeutic agents (cisplatin, carboplatin, and doxorubicin).362 Also, a recent study has found that DNA damage causes the accumulation of hPMS1, hPMS2, and hMLH1 through ATM protein stabilization, and hMLH1 and hPMS1 were found to be important in p53 phosphorylation.363 These findings suggest that MMR protein loss may have other roles in oral cancer in addition to DNA repair. This was recently supported by a study using high throughput immunoprecipitation that showed that hMLH1, hPMS1 and hPMS2 proteins can bind to other important proteins involved in DNA metabolism/repair intracellular transport, cell signaling, cell morphology, recombination, and ubiquitylation.364 These binding partners may be of interest for potential interaction and involvement in oral carcinomas.

The role of MMR in head and neck cancers has been hampered by conflicting reports in the literature regarding their significance. Our study indicates that defective

MMR may have a role in a subset of oral cancers, but it is not age dependent. The interaction between MMR, MSI and LOH in OSCC appears complex, and further studies are required to clarify their role, if any, in these tumors.

180 CHAPTER 5: SUMMARY, CONCLUSIONS AND FUTURE DIRECTIONS

Although annual incidence of head and neck cancer cases has been decreasing over the last few decades, the incidence of oral squamous cell carcinomas (OSCCs) in young patients has been increasing, for unknown reasons.105, 106 There are published reports, albeit sometimes conflicting, on the comparison of clinicopathological features of head and neck cancers (e.g. prognosis)103 in young and older patients, while few molecular studies have been reported to date in head and neck tumors from young patients. The relatively low attention paid to these tumors from young patients may be due to the low incidence (≤6.7%) of total head and neck cancer cases worldwide in this patient group;103 and thus obtaining sufficient cases for molecular analysis may be difficult compared to tumors from older patients. However, there is a need to examine these tumors from young patients because of the diagnostic, prognostic and potential therapeutics that could arise from understanding the biology underlying these tumors.

Our laboratory has access to one of the largest banks of head and neck tumor specimens from patients in Canada, including young patient tumors. These include both fresh frozen tissue and formalin fixed paraffin embedded (FFPE) tissues. We were thus able to perform molecular analyses on oral tumors from young and older patients for this study on early-onset carcinoma.

Our objective was to examine genetic differences between oral tumors from young and older patients. In this study, we specifically examined: (1) presence of human papillomavirus (HPV); (2) global genomic analysis for loss of heterozygosity

(LOH), copy number alteration (CNA) and copy-neutral LOH (CN-LOH); and (3) mismatch repair (MMR).

181 5.1 HPV Prevalence in Head and Neck Cancer

Our first objective was to examine the prevalence of HPV in oral cancers from young and older patients. This was important, as there have been numerous publications recently about HPV 16 and its association with head and neck cancers.365

As some young patients with oral carcinoma have no major risk factors prior to developing cancer (e.g. abuse of smoking and/or alcohol), this suggests that their disease may arise due to some other mechanism, such as infection with HPV.107, 108

HPV has also been suggested as a possible mechanism of oral cancer development in young patients, due to the correlation of increasing rates of young patients with OSCC and increasing HPV infection rates over the past several decades.106

In the head and neck cancer literature, there is a wide range of reported values

(e.g. 0-100%) of HPV infection in HNSCCs, which has led to the confusion surrounding

HPV association, especially in oral cancers. This wide range or reported values has been attributed to sample preparation, sample site of origin, detection methodologies, and geographical differences (Chapter 2). We thus used a sensitive and robust HPV detection platform, the Roche Linear Array HPV Genotyping Test (“linear array”) in order to detect HPV in oral tumors from young and older patients. This assay has an advantage over other HPV detection assays because it is able to examine 37 low- and high-risk HPV types, and examine HPV from DNA isolated from FFPE samples.

We first tested the use of the linear array using multiple tissues known to harbor

HPV, including anogenital dysplasias, and cervicovaginal, anogenital, gastrointestinal, and head and neck cancers from multiple sources (e.g. FFPE and fresh frozen). We were able to determine that the linear array can be used with a low concentration of

DNA, can be successfully used with FFPE material, and can detect HPV from multiple

182 lesion types. We also compared the linear array to a gel-based PCR HPV detection method. Data using the linear array had a good concordance with data from gel-based

PCR, but had the advantage of being able to determine more HPV genotypes, and having a higher sensitivity, thereby making it more useful than our gel-based PCR method.

The linear array assay was also compared to the digene HPV HC2 DNA test. The digene HPV test can detect 17 high or low-risk HPV types, but does not specify the HPV genotype. Although there was good agreement between results from the two assays, a few of the high and low-risk HPV categories were not concordant. This could be because the digene HPV test is known to cross hybridize between some high and low- risk HPVs.144 Specific genotyping can be important, as detection of persistent infection of the same high-risk HPV types can be associated with increased cancer risk.134, 138

Using the linear array methodology we sought to analyze HPV prevalence in head and neck tumors, specifically addressing the prevalence of HPV in oral cavity cancers, and whether its incidence differs in oral tumors from young and older patients.

In our analysis of HPV detection in head and neck cancers we found a low prevalence of HPV infection in oral cavity (2/53 cases, 4%), and other non- oropharyngeal cancers (1/17 cases, 6%). In comparison, as expected, oropharyngeal cancers (OPCs) had a high prevalence of HPV infection (16/22, 73%), consistent with previous reports.8 In our study, HPV positive tumors were associated with node positive patients. The association of HPV infection, nodal status and cancer has been previously reported, suggesting HPV’s role in immune surveillance and cancer.166 In our study,

HPV positive tumors were associated with intermediate age patients (>40 and <60 years old). In an analysis of HPV associated OPCs by another group, patients with

183 HPV associated OSSCs (including oropharynx) were diagnosed earlier than non-HPV associated tumors (61 vs. 63.8 years old; p<0.001).16 These data suggest that the course leading to HNSCC may be dependent on the risk factors associated with head and neck cancer development.

The association of HPV with oropharyngeal cancer has been strongly supported by recent studies linking HPV infection with certain sexual behaviors and oropharyngeal cancer,8 especially in the past three decades.16 Most HPV-associated head and neck cancers are infected with HPV-16, but there were cases observed in our study with additional HPV infections. A broad analysis of only oropharyngeal tumors from young and older patients would be interesting to assess the role of HPV in these specific groups of patients. An analysis of this kind could utilize the linear array as it is a sensitive, robust, and HPV typing detection method. A thorough analysis of the specific

HPV types and their role in head and neck cancer prognosis has not been previously examined. Specific HPV types may have a role in patient prognosis. The linear array could therefore be used to determine the role, if any, of HPV types in patient outcome.

In addition, an analysis of genetic susceptibility and HPV infection can be examined in patients with recurring squamous papillomas of the head and neck region.

These patients could be assessed for infection of particular HPV types using the Roche linear array. In addition, an analysis of germline single nucleotide polymorphisms

(SNPs) or copy number variants (CNVs) using SNP arrays may give an indication of putative genetic targets that are involved in HPV susceptibility in these patients.

184 5.2 Global Genomic Analysis of Copy Number Alterations and Loss of

Heterozygosity

Genetic platforms that assess global genetic changes have been useful in head and neck cancer for their diagnostic and prognostic utilities. We used the Affymetrix

SNP 6.0 array for copy number and LOH analysis in oral tumors from young and older patients. These data are novel, as genetic analyses of oral tumors from young patients have only been examined in very few studies. Furthermore, many of these studies traditionally used low-resolution techniques to examine genetic alterations and lack the resolution available from newer technologies, such as the Affymetrix SNP 6.0 array.

Also, the concurrent analysis of copy number and LOH information can be instrumental in determining putative diagnostic, prognostic and therapeutic targets.

We assessed the level of CNAs and LOH in oral tumors from young and older patients using the Affymetrix SNP 6.0 arrays, and the following software: Affymetrix

Genotyping Console (GTC) and Partek Genomics Suite (PGS). We identified known copy number changes in oral cancer (Chapter 3), and in addition identified new chromosomal regions that have not been reported in OSCC. Specifically, we observed significant gains on chromosomes 3q, 5p, 7, 8q, 9q, 11q, 17, 19, 20, and 22q, and significant loss on chromosomes 3p, 5q, 8p, 9p, 11q, 18q, and 21q in OSCCs.

Tumors from young and older patients showed classical copy number alterations observed in the head and neck cancer literature (see Chapter 3). We also observed distinct copy number alterations patterns between tumors of young and older patients.

Specifically we found significant chromosomal gains on 3q, 8q, 11q, 17, 20 and 22q; and significant chromosomal loss of 3p, 5q, 9p, 11q, 13q, 18q, and 21q in tumors from young patients. We found significant chromosomal gains on 3q, 5p, 7, 8q, 11q, 13q, and

185 20 and significant chromosomal loss 1p, 3p, 4p, 5q, 7q, 8p, 9p, 10p, 11q, 15q, 17, 18q,

21q, and 22q in tumors from older patients. Many genes that were observed within these regions of CNAs have not been reported previously, possibly due to the low- resolution platforms used previously. This suggests the importance of CNAs in oral cancer development in both young and older patients.

Interestingly, we observed more shared regions of deletions in young patient oral tumors when compared to older patient oral tumors. In addition, there was a group of young patients who harbored high copy number alterations within their tumors. These data suggest that there may exist a group of young patients who harbor very genetically unstable tumors. Segregating tumors based on the clinicopathological status of patients

(e.g. smoking), we were able to determine that smoking was not a significant contributor to the genetic differences observed between tumors of young and older patients.

Regions of LOH observed in our oral cancer samples included chromosomes 3,

5q, 9 and 17p, which have been reported in previous studies (Chapter 3). However, we found additional significant regions of LOH including chromosomes 2q, 6p 11q, 12p

13q, 15q, and 18q. We observed an increase of LOH in tumors from older patients compared to young patients, with common chromosomal regions in tumors from older patients showing LOH on chromosomes 3p, 5q, 9, 11q, 13q, and 17p and tumors from young patients showing LOH mainly on chromosomes 9p and 11q. These data suggest that LOH may have a more prominent role in oral tumors from older patients compared to oral tumors of young patients. It would be important to further refine these regions of

LOH for examination of tumor suppressor genes by looking at minimal deleted regions

(MDRs). An analysis of this type could utilize fluorescent-labeled high-density microsatellite markers for allelotyping, as has been previously performed by others in

186 nasopharyngeal carcinoma.366 An assessment of MDR using quantitative PCR could also be useful in determining patient outcome, as has been shown in chronic myeloid leukemia.367

We also assessed the mechanism of CN-LOH in our patient samples. We are the first group to report this genetic mechanism in HNSCCs. There was a high prevalence of CN-LOH on chromosome 9, suggesting a role for this chromosome in CN-LOH. In addition, genes located within these regions may have been overlooked in previous studies because they would not show copy number alterations. Although CN-LOH was observed in many tumors only a small subset shared similar CN-LOH profiles with tumors from older patients sharing more common regions of CN-LOH compared to tumors from young patients. Validation of these regions can be performed using other techniques, such as microsatellite analysis for loss of heterozygosity and QPCR for copy number. The role of particular genes within regions of CN-LOH can be examined for their role in oral cancer; similarly this has been done for JAK2 and CDKN2A in other cancers (Chapter 3). Identifying known/putative oncogenes and tumor suppressors within these regions can then be examined for deletions, mutations or promoter hypermethylation, which may lead to tumorigenesis.

The wealth of information obtained from SNP array data can be overwhelming.

The output of data from such an array includes CNAs, LOH and CN-LOH, and in addition SNP data; the latter was not examined in our study due to our relatively small sample size being inadequate for SNP association. A future objective of our study is to validate our findings in another larger subset of tumors from young and older patients.

This could be carried out using quantitative PCR or fluorescent in situ hybridization

(FISH) studies for candidate genes/regions for CNAs. Regions of LOH can also be

187 validated using microsatellite markers (see Chapter 4). In addition, once validated genes have been confirmed for gains or losses, the role of the particular gene(s) can be examined in young and older patient samples using quantitative RT-PCR and immunohistochemistry. Functional analysis of each gene can then be performed depending on the gene identified (e.g. tumor suppressor, oncogene, etc.).

Other genome-wide platforms can also be used on the same samples used on the SNP array to gain further genetic information from these tumors, as we have RNA,

DNA, and additional FFPE samples from these tumors and adjacent normal tissues.

For instance, additional experiments comparing tumors from young and older patients can be examined using gene expression arrays (e.g. Affymetrix Human Genome U133

Plus 2.0), methylation arrays (e.g. Illumina HumanMethylation27 DNA Analysis

BeadChip) and microRNA arrays (e.g. Applied Biosystems TaqMan Low Density array

(TLDA)) could be compared to the results of our copy number analysis. The Partek

Genomics Suite software contains algorithms that can compare results from multiple platforms analyzing copy number, LOH, gene/microRNA expression, and methylation status. By combining the information and overlapping genes/regions from these different analyses, we will gain further insight regarding the molecular mechanisms of early-onset carcinogenesis.

Another interesting approach would be to analyze germ-line CNVs from young and older patients with oral cancer to investigate a possible predisposition mechanism for young patients to develop OSCC. In our study, we used adjacent normal oral mucosa tissue from each patient, thereby giving us information regarding copy number.

One constricting factor in our future analysis is that we were not able to obtain lymphocyte DNA from young patients to use as a normal DNA comparison from the

188 same patient. However, we could consider the effect of genetic damage resulting from risk factors use (e.g. smoking) by specific groups of patients when performing our analysis. Due to the field cancerization effect, normal tissue surrounding the tumor may harbor genetic changes that are not phenotypically present.33 Furthermore, we could compare our data to the publicly available HapMap data to examine CNVs in our data set or through collaboration with Dr. Scherer’s laboratory at the Medical and Related

Sciences (MaRS) centre. They have >1000 healthy patient DNA controls that have been analyzed on the Affymetrix SNP 6.0 array.

Lastly, the role of SNPs in OSCC development is an important field that can be investigated, as SNP genotyping information is present on these arrays for each patient sample. However, due to the large number of experimental samples required to assess

SNPs using a genome-wide platform, we may be constrained by the required number of young patient normal DNA samples. We thus would have to collaborate with another head and neck research group and perform additional Affymetrix SNP 6.0 analysis using a large sample set for each patient group and controls (e.g. >100s in each group), and obtain detailed clinicopathological information and family history.

189 5.3 Mismatch Repair in Oral Cancers from Young and Older Patients

Our laboratory has previously shown that head and neck tumors from young patients harbor a higher prevalence of microsatellite instability (MSI) compared to those from older patients.120 This previous analysis was completed using a southern blot gel electrophoresis method. In our study, we were interested in defective MMR in oral cancers from young and older patients. We used a more sensitive MSI detection method, utilizing fluorescent-based PCR and capillary electrophoresis.335, 336

We observed deregulated levels of hPMS1 mRNA expression in adjacent normal mucosa in young (≤ 40 years old) compared to paired oral tumors, and older (≥ 60 years old) patient adjacent normal mucosa. hPMS2 mRNA expression was increased in oral tumors from young patients compared to older patients. Lastly, hMLH1 mRNA expression was highly expressed in young patient tumors compared to their paired adjacent normal. The stochiometric levels of these mRNAs were deregulated in young patient adjacent normal oral tissue and older patient oral tumors. However, the role of deregulation of these genes is not clear, considering the redundant functions of MMR genes (see Chapter 4).

The next step was the analysis of hPMS1, hPMS2 and hMLH1 protein levels.

MMR proteins levels were decreased in more than half of the oral tumors analyzed, but no significant differences were observed between oral tumors of young vs. older patients. When we examined stochiometric differences in protein expression we did find expression level differences in hPMS1 vs. hMLH1 in young patient tumors; and hPMS2 vs. hMLH1 in older patient tumors. Given redundancies in MMR protein function, the results of our protein expression analysis are not clear.

190 We also examined the mutational status of hPMS1 and hPMS2 in oral tumors from young and older patients. We did not find any mutations in our analysis, although one SNP was observed in hPMS2 in one oral tumor from an older patient. MMR SNPs and head and neck cancers have been previously been shown to be associated,355 however, probably due to the relatively small sample size in our dataset for SNP analysis, we did not obtain similar conclusions. An interesting study would be to assess oral tumors from young and older patients for MMR SNP associations using the information gathered from the Affymetrix SNP 6.0 array, plus additional patient samples

(see previous section) to increase the statistical power in this proposed study. For the past 2 years, Dr. Geoffrey Liu’s laboratory at the Ontario Cancer Institute has been collecting head and neck cancer patient blood samples for genome-wide SNP array associations. Collaborations, such as this could be useful in integrating our dataset with other head and neck cancer patients for SNP-genome wide analysis in early-onset carcinoma.

Analysis of MSI at the genomic level in our study using the same microsatellite primers from our previous analysis,120 did not show any statistical differences in tumors from young vs. older patients. Approximately 18.3% (13/71) of tumors showed high levels of MSI (MSI-H), suggesting that deregulated mismatch repair is present in only a subset of oral cancers and that it is age-independent. Differences from our previous analysis may be attributed to a different patient subset than previously analyzed, the utilization of a more sensitive MSI detection method, and the analysis of only oral cavity cancers. Our analysis suggests that defective MMR is not involved in early-onset oral carcinoma and is only involved in a subset of oral cancers.

191 We also compared LOH across tumors from young and older patients, and did not find any significant differences. However, we found higher rates of LOH in tumors of older patients using the Affymetrix SNP 6.0 array (see Chapter 3). These differences between the different analyses are likely because of the very small number of genomic loci analyzed using our microsatellite primer technique compared to our SNP array analysis. We also found an increased level of LOH at the hPMS1 locus in tumors from young patients, but did not find any significant associations of MMR loci in our DNA array analysis.

The decreased protein levels of hPMS1, hPMS2, and hMLH1 in the majority of oral cancers analyzed suggest that these proteins have an involvement in oral cancer.

These proteins have been recently observed to be involved in chemotherapeutic resistance,362 and have been shown to bind to other proteins involved in other cellular processes besides MMR,364 see Chapter 4. These data further suggest that these proteins may have an alternative role in oral cancer.

192 5.4 Final Conclusion

The data obtained from this Ph.D. thesis suggests that HPV is not involved in oral tumors from young and older patients, and remains highly associated with oropharyngeal cancer. There are genetic regions that are specific to early-onset carcinoma that may be responsible for tumorigenesis in young patients. Furthermore, the role of MMR was not found to be different in oral tumors from young and older patients, but MMR appeared to have a role in a subset of oral cancers. Studies similar to ours will hopefully further elucidate the role of particular genetic mechanisms involved in oral tumors of young patients. With the increasing rate of oral tumors observed in young patients, further research is required for improved molecular understanding of early-onset carcinoma.

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APPENDICES

219

CHAPTER 3 APPENDIX - FIGURES

220

Appendix Figure 1: Gene ontology analysis of significantly altered GO biological processes between tumors of young and older patients.

221

Appendix Figure 2: Gene ontology analysis of significantly altered GO molecular functions between tumors of young and older patients.

222

APPENDIX FIGURE 3

Appendix Figure 3: Gene ontology analysis of significantly altered GO biological processes between tumors of smokers and non-smokers.

223

APPENDIX FIGURE 4

Appendix Figure 4: Gene ontology analysis of significantly altered GO molecular functions between tumors of smokers and non-smokers.

224

Appendix Figure 5: Representative figure of LOH on chromosome 9 in tumors from young patients. Red shaded bars represent regions of LOH for each tumor sample. Pink cytoband represents centromere.

225

Appendix Figure 6: Representative figure of LOH on chromosome 9 in tumors from older patients. Red shaded bars represent regions of LOH for each tumor sample. Pink cytoband represents centromere.

226

Appendix Figure 7: Copy number neutral LOH in at least 3 tumors from young patients. Red shaded bars to the right of each chromosome represent regions of cnLOH. Pink cytoband represents centromere.

227

Appendix Figure 8: Copy number neutral LOH in at least 4 tumors from older patients. Red shaded bars to the right of each chromosome represent regions of cnLOH. Pink cytoband represents centromere.

228 CHAPTER 3 APPENDIX - TABLES

Appendix Table 1: Significant copy number alterations in tumors from young and older patients. Significantly altered copy number and associated genes were examined using a Chi-square analysis between tumors of young and older patients. Chr – Chromosome; Ab – Abnormal; O – Older; Y – Young; Amp – Amplification; Del – Deletion. p≤0.01.

# O O Y Y Chr p-value Gene ID(s) Gene Name(s) Ab Amp Del Amp Del Discs, large (Drosophila) homolog- 8 0.0005 DLGAP2 17 1 11 5 0 associated protein 2 8 0.0005 ERICH1 Glutamate-rich 1 17 1 11 5 0 Rho guanine nucleotide exchange 8 0.0007 ARHGEF10 18 2 11 5 0 factor (GEF) 10 Rho guanine nucleotide exchange ARHGEF10; 8 0.0007 factor (GEF) 10; Kelch repeat and BTB 18 2 11 5 0 KBTBD11 (POZ) domain containing 11 Ceroid-lipofuscinosis, neuronal 8 8 0.0007 CLN8 (epilepsy, progressive with mental 18 2 11 5 0 retardation) Kelch repeat and BTB (POZ) domain 8 0.0007 KBTBD11 18 2 11 5 0 containing 11 8 0.0007 PTK2B PTK2B protein tyrosine kinase 2 beta 16 0 12 3 1 7 0.0008 CALCR Calcitonin receptor 17 10 1 0 6 RAP1, GTP-GDP dissociation 4 0.0009 RAP1GDS1 14 7 0 0 7 stimulator 1 8 0.0009 CCDC25 Coiled-coil domain containing 25 15 0 12 2 1 3-hydroxymethyl-3-methylglutaryl- 6 0.0012 HMGCLL1 17 8 1 0 8 Coenzyme A lyase-like 1 Zinc finger and BTB domain containing 3 0.0012 ZBTB20 17 8 1 0 8 20 8 0.0012 KIF13B Kinesin family member 13B 16 0 11 4 1 229 8 0.0012 SCARA5 Scavenger receptor class A, member 5 16 0 11 4 1 Leucine-rich repeats and IQ motif 12 0.0014 LRRIQ1 13 6 0 0 7 containing 1 Cholinergic receptor, nicotinic, alpha 2 CHRNA2; 8 0.0016 (neuronal); PTK2B protein tyrosine 15 0 11 3 1 PTK2B kinase 2 beta 8 0.0016 EXTL3 Exostoses (multiple)-like 3 15 0 11 3 1 F-box protein 16; Frizzled homolog 3 8 0.0016 FBXO16; FZD3 15 0 11 3 1 (Drosophila) 8 0.0016 FZD3 Frizzled homolog 3 (Drosophila) 15 0 11 3 1 Leptin receptor overlapping transcript- 8 0.0016 LEPROTL1 15 0 11 3 1 like 1 Prepronociceptin; Zinc finger protein 8 0.0016 PNOC; ZNF395 15 0 11 3 1 395 8 0.0016 TMEM66 Transmembrane protein 66 15 0 11 3 1 8 0.0016 ZNF395 Zinc finger protein 395 15 0 11 3 1 Activated leukocyte cell adhesion 3 0.0019 ALCAM 16 8 1 0 7 molecule 3 0.0021 EPHA6 EPH receptor A6 12 8 0 0 4 Thrombospondin, type I, domain 7 0.0024 THSD7A 19 8 1 1 9 containing 7A 4 0.0025 WHSC1 Wolf-Hirschhorn syndrome candidate 1 12 0 7 5 0 4 0.0025 C4orf37 Chromosome 4 open reading frame 37 12 6 0 0 6 Potassium voltage-gated channel, 12 0.0025 KCNC2 12 6 0 0 6 Shaw-related subfamily, member 2 Chromosome 8 open reading frame 80; 8 0.0025 C8orf80; ELP3 15 0 10 4 1 Elongation protein 3 homolog 8 0.0025 HMBOX1 Homeobox containing 1 15 0 10 4 1 8 0.0025 PBK PDZ binding kinase 15 0 10 4 1 9 0.0025 PBX3 Pre-B-cell leukemia homeobox 3 18 8 0 2 8

230 HMBOX1; Homeobox containing 1; Integrator 8 0.0028 13 0 11 1 1 INTS9 complex subunit 9 8 0.0028 MCPH1 Microcephalin 1 17 1 11 4 1 Potassium voltage-gated channel, 11 0.0029 KCNQ1 15 1 8 6 0 KQT-like subfamily, member 1 16 0.0029 CNTNAP4 Contactin associated protein-like 4 15 8 1 0 6 5 0.0029 FGF10 Fibroblast growth factor 10 17 9 2 0 6 7 0.0030 HDAC9 Histone deacetylase 9 15 7 0 1 7 1 0.0030 DENND1B DENN/MADD domain containing 1B 15 6 1 0 8 Family with sequence similarity 19 12 0.0030 FAM19A2 (chemokine (C-C motif)-like), member 15 6 1 0 8 A2 8 0.0031 DLC1 Deleted in liver cancer 1 14 1 11 1 1 8 0.0031 INTS9 Integrator complex subunit 9 14 1 11 1 1 22 0.0031 EP300 E1A binding protein p300 11 0 4 7 0 17 0.0031 INTS2 Integrator complex subunit 2 11 0 4 7 0 Cas-Br-M (murine) ecotropic retroviral 3 0.0031 CBLB 11 8 0 0 3 transforming sequence b 6 0.0032 BAI3 Brain-specific angiogenesis inhibitor 3 18 7 2 0 9 Cholinergic receptor, nicotinic, alpha 2 CHRNA2; 8 0.0033 (neuronal); Epoxide hydrolase 2, 16 1 11 3 1 EPHX2 cytoplasmic LEPROTL1; Leptin receptor overlapping transcript- 8 0.0033 16 1 11 3 1 TMEM66 like 1; Transmembrane protein 66 3 0.0033 LEKR1 Leucine, glutamate and lysine rich 1 16 11 1 1 3 Catenin (cadherin-associated protein), 5 0.0034 CTNND2 delta 2 (neural plakophilin-related arm- 13 9 1 0 3 repeat protein) 8 0.0034 C8orf80 Chromosome 8 open reading frame 80 14 0 10 3 1

231 DCTN6; Dynactin 6; leptin receptor overlapping 8 0.0034 LEPROTL1; transcript-like 1; Membrane bound O- 14 0 10 3 1 MBOAT4 acyltransferase domain containing 4 8 0.0034 DUSP4 Dual specificity phosphatase 4 14 0 10 3 1 Elongation protein 3 homolog (S. 8 0.0034 ELP3 14 0 10 3 1 cerevisiae) 8 0.0034 FBXO16 F-box protein 16 14 0 10 3 1 Protein phosphatase 2 (formerly 2A), 8 0.0034 PPP2CB; TEX15 catalytic subunit, beta isoform; Testis 14 0 10 3 1 expressed 15 RNA binding protein with multiple 8 0.0034 RBPMS 14 0 10 3 1 splicing 8 0.0037 ADAM32 ADAM metallopeptidase domain 32 14 6 6 0 2 5 0.0038 CDH18 Cadherin 18, type 2 12 9 1 0 2 Olfactory receptor, family 5, subfamily 3 0.0038 OR5H6 12 9 1 0 2 H, member 6 Arylsulfatase G; Solute carrier family ARSG; 17 0.0038 16, member 6 (monocarboxylic acid 11 0 5 6 0 SLC16A6 transporter 7) 4 0.0038 ZNF721 Zinc finger protein 721 11 0 5 6 0 6 0.0038 BMP5 Bone morphogenetic protein 5 11 7 0 0 4 7 0.0038 DGKB Diacylglycerol kinase, beta 90kDa 11 7 0 0 4 Protein tyrosine phosphatase, receptor 12 0.0038 PPFIA2 type, f polypeptide (PTPRF), interacting 11 7 0 0 4 protein (liprin), alpha 2 12 0.0038 CNTN1 Contactin 1 11 5 0 0 6 2 0.0038 NRXN1 Neurexin 1 11 5 0 0 6 Sodium channel, voltage-gated, type I, 2 0.0038 SCN1A 11 5 0 0 6 alpha subunit

232 Chromosome 1 open reading frame 59; C1orf59; Family with sequence similarity 102, FAM102B member B; Fibronectin type III domain 1 0.0039 FNDC7; 9 2 7 0 0 containing 7; PRP38 pre-mRNA PRPF38B; processing factor 38 (yeast) domain STXBP3 containing B; Syntaxin binding protein 3 Vav 3 guanine nucleotide exchange 1 0.0039 VAV3 9 4 5 0 0 factor 15 0.0039 RAB27A Member RAS oncogene family 9 5 4 0 0 1 0.0041 KIFAP3 Kinesin-associated protein 3 11 6 0 0 5 Olfactory receptor, family 6, subfamily 12 0.0041 OR6C76 11 6 0 0 5 C, member 76 8 0.0043 ADRA1A Adrenergic, alpha-1A-, receptor 14 2 10 1 1 LON peptidase N-terminal domain and 8 0.0043 LONRF1 14 2 10 1 1 ring finger 1 7 0.0043 PHF14 PHD finger protein 14 14 6 0 1 7 NIMA (never in mitosis gene a)- related 3 0.0044 NEK11 10 8 0 0 2 kinase 11 18 0.0047 KIAA0427 KIAA0427 14 0 9 4 1 3 0.0047 CPNE4 Copine IV 14 9 0 1 4 Limbic system-associated membrane 3 0.0047 LSAMP 14 9 0 1 4 protein 7 0.0047 CDK6 Cyclin-dependent kinase 6 17 10 1 1 5 8 0.0047 WRN Werner syndrome, RecQ helicase-like 13 0 10 2 1 3 0.0047 ZBBX Zinc finger, B-box domain containing 19 12 1 2 4 Hyperpolarization activated cyclic 5 0.0050 HCN1 14 7 1 0 6 nucleotide-gated potassium channel 1 2 0.0050 FLJ44048 FLJ44048 protein 17 7 2 0 8 Low density lipoprotein-related protein 2 0.0052 LRP1B 14 6 1 0 7 1B (deleted in tumors) 6 0.0052 TINAG Tubulointerstitial nephritis antigen 14 6 1 0 7 233 ABI family, member 3 (NESH) binding 3 0.0052 ABI3BP 14 7 0 1 6 protein ATP-binding cassette, sub-family G 21 0.0053 ABCG1 14 0 8 5 1 (WHITE), member 1 Lanosterol synthase (2,3- 21 0.0053 LSS 14 0 8 5 1 oxidosqualene-lanosterol cyclase) NADH dehydrogenase (ubiquinone) 21 0.0053 NDUFV3 14 0 8 5 1 flavoprotein 3 LIM domain containing preferred 3 0.0054 LPP 14 11 1 2 0 translocation partner in lipoma DIP2 disco-interacting protein 2 21 0.0055 DIP2A 13 1 8 4 0 homolog A (Drosophila) DIP2 disco-interacting protein 2 21 0.0055 DIP2A; PCNT 13 1 8 4 0 homolog A (Drosophila); Pericentrin Insulin-like growth factor 2; Insulin-like IGF2; IGF2AS; 11 0.0056 growth factor 2 antisense; Insulin; INS- 16 2 8 6 0 INS; INS-IGF2 IGF2 readthrough transcript BRCA1 interacting protein C-terminal 17 0.0058 BRIP1 10 0 4 6 0 helicase 1 Glutamate-cysteine ligase, modifier 1 0.0058 GCLM 10 0 4 6 0 subunit 12 0.0058 HMGA2 High mobility group AT-hook 2 10 4 0 0 6 2 0.0058 VRK2 Vaccinia related kinase 2 10 4 0 0 6 8 0.0060 DPYSL2 Dihydropyrimidinase-like 2 16 1 10 4 1 V-erb-a erythroblastic leukemia viral 2 0.0062 ERBB4 9 2 0 0 7 oncogene homolog 4 (avian) Solute carrier organic anion transporter 11 0.0064 SLCO2B1 17 1 7 8 1 family, member 2B1 1 0.0065 CD53 CD53 molecule 11 4 6 1 0 13 0.0065 RFC3 Replication factor C (activator 1) 3 11 6 4 0 1

234 UDP-N-acetyl-alpha-D- galactosamine:polypeptide N- 2 0.0066 GALNT13 13 4 1 0 8 acetylgalactosaminyltransferase 13 (GalNAc-T13) Potassium channel, subfamily T, 1 0.0066 KCNT2 13 4 1 0 8 member 2 7 0.0067 AUTS2 Autism susceptibility candidate 2 10 0 5 5 0 Breast cancer anti-estrogen resistance 1 0.0067 BCAR3 10 0 5 5 0 3 FERM, RhoGEF and pleckstrin domain 2 0.0067 FARP2; SEPT2 10 0 5 5 0 protein 2; Septin 2 2 0.0067 HDLBP High density lipoprotein binding protein 10 0 5 5 0 Ral GEF with PH domain and SH3 1 0.0067 RALGPS2 10 5 0 0 5 binding motif 2 8 0.0068 CSMD1 CUB and Sushi multiple domains 1 13 0 9 3 1 8 0.0068 TEX15 Testis expressed 15 13 0 9 3 1 3 0.0068 SENP7 SUMO1/sentrin specific peptidase 7 13 9 0 1 3 3 0.0068 STXBP5L Syntaxin binding protein 5-like 13 9 0 1 3 13 0.0070 ESD Esterase D/formylglutathione hydrolase 11 5 5 0 1 15 0.0070 DET1 De-etiolated homolog 1 (Arabidopsis) 11 5 5 1 0 9 0.0070 PALM2 Paralemmin 2 15 8 2 0 5 11 0.0070 MRPL48 Mitochondrial ribosomal protein L48 20 2 8 9 1 Uncoupling protein 3 (mitochondrial, 11 0.0070 UCP3 20 2 8 9 1 proton carrier) PTK2B protein tyrosine kinase 2 beta; 8 0.0073 PTK2B; TRIM35 15 1 10 3 1 Tripartite motif-containing 35 8 0.0073 STMN4 Stathmin-like 4 15 1 10 3 1 Neurotrophic tyrosine kinase, receptor, 15 0.0073 NTRK3 11 6 4 1 0 type 3 Colony stimulating factor 1 1 0.0074 CSF1 13 1 7 5 0 (macrophage) 235 18 0.0074 ELAC1 ElaC homolog 1 (E. coli) 13 1 7 5 0 Epidermal growth factor receptor 1 0.0074 EPS8L3 13 1 7 5 0 pathway substrate 8-like protein 3 8 0.0074 HOOK3 Hook homolog 3 13 1 7 5 0 Potassium voltage-gated channel, 1 0.0074 KCNA2 13 1 7 5 0 shaker-related subfamily, member 2 4 0.0074 POLN Polymerase (DNA directed) nu 13 1 7 5 0 6 0.0074 GFRAL GDNF family receptor alpha like 13 7 1 0 5 7 0.0078 NXPH1 Neurexophilin 1 12 4 0 1 7 3 0.0079 RSRC1 Arginine/serine-rich coiled-coil 1 19 11 1 2 5 8 0.0079 C8orf79 Chromosome 8 open reading frame 79 16 2 10 3 1 Protein phosphatase 2 (formerly 2A), 8 0.0079 PPP2R2A 16 2 10 3 1 regulatory subunit B, alpha isoform 12 0.0081 CAPS2 Calcyphosine 2 13 5 1 0 7 GLI pathogenesis-related 1; small 12 0.0081 GLIPR1 KRR1 subunit (SSU) processome component, 13 5 1 0 7 homolog (yeast) Chromosome 13 open reading frame 13 0.0081 C13orf16 16 2 6 8 0 16 Major facilitator superfamily domain 17 0.0081 MFSD11 16 2 6 8 0 containing 11 13 0.0081 PCID2 PCI domain containing 2 16 2 6 8 0 18 0.0083 RNF165 Ring finger protein 165 13 0 8 4 1 18 0.0083 SIGLEC15 Sialic acid binding Ig-like lectin 15 13 0 8 4 1 7 0.0083 CCDC132 Coiled-coil domain containing 132 13 8 0 1 4 3 0.0083 KLHDC6 Kelch domain containing 6 13 8 0 1 4 Phosphatidylinositol-specific 5 0.0084 PLCXD3 phospholipase C, X domain containing 13 8 2 0 3 3 1 0.0084 STXBP3 Syntaxin binding protein 3 8 1 7 0 0

236 Family with sequence similarity 102, 1 0.0084 FAM102B 8 2 6 0 0 member B 15 0.0084 GJD2 Gap junction protein, delta 2 8 2 6 0 0 15 0.0084 NEO1 Neogenin homolog 1 (chicken) 8 2 6 0 0 15 0.0084 PRTG Protogenin homolog (Gallus gallus) 8 2 6 0 0 15 0.0084 PYGO1 Pygopus homolog 1 8 2 6 0 0 EA domain family member 1 (SV40 11 0.0084 TEAD1 8 2 6 0 0 transcriptional enhancer factor) 15 0.0084 C15orf15 Ribosomal L24 domain containing 1 8 3 5 0 0 15 0.0084 CALML4 Calmodulin-like 4 8 3 5 0 0 10 0.0084 KIAA1217 KIAA1217 8 3 5 0 0 Mitogen-activated protein kinase kinase 15 0.0084 MAP2K5 8 3 5 0 0 5 10 0.0084 NEBL Nebulette 8 3 5 0 0 Sema domain, transmembrane domain 15 0.0084 SEMA6D (TM), and cytoplasmic domain, 8 3 5 0 0 (semaphorin) 6D Thrombospondin, type I, domain 15 0.0084 THSD4 8 3 5 0 0 containing 4 15 0.0084 WDR72 WD repeat domain 72 8 3 5 0 0 Chromosome 15 open reading frame 15 0.0084 C15orf33 FGF7 8 4 4 0 0 33; fibroblast growth factor 7 Family with sequence similarity 135, 6 0.0084 FAM135A 8 4 4 0 0 member A 11 0.0084 OVCH2 Ovochymase 2 8 4 4 0 0 15 0.0084 AGBL1 ATP/GTP binding protein-like 1 8 5 3 0 0 15 0.0084 ARRDC4 Arrestin domain containing 4 8 5 3 0 0 Ubiquitin-conjugating enzyme E2C 6 0.0084 UBE2CBP 8 5 3 0 0 binding protein 5 0.0084 PRLR Prolactin receptor 8 7 1 0 0

237 Archaelysin family metallopeptidase 2; 17 0.0084 AMZ2; ARSG 13 1 6 6 0 Arylsulfatase G 16 0.0084 FOXL1 Forkhead box L1 13 1 6 6 0 Inhibitor of kappa light polypeptide gene 8 0.0084 IKBKB 13 1 6 6 0 enhancer in B-cells, kinase beta 10 0.0084 ATRNL1 Attractin-like 1 13 6 1 0 6 5 0.0084 C9 Complement component 9 13 6 1 0 6 15 0.0084 MCTP2 Multiple C2 domains, transmembrane 2 13 6 1 0 6 ATP-binding cassette, sub-family A 1 0.0086 ABCA4 9 0 3 6 0 (ABC1), member 4 Glycosyltransferase-like domain 2 0.0086 GTDC1 9 3 0 0 6 containing 1 LRRIQ1; Leucine-rich repeats and IQ motif 12 0.0086 9 3 0 0 6 TSPAN19 containing 1; Tetraspanin 19 2 0.0086 MAP2 Microtubule-associated protein 2 9 3 0 0 6 2 0.0086 SPAG16 Sperm associated antigen 16 9 3 0 0 6 Src kinase associated phosphoprotein 7 0.0087 SKAP2 13 7 0 1 5 2 8 0.0089 MMP16 Matrix metallopeptidase 16 16 9 1 1 5 6 0.0094 DST Dystonin 6 6 0 0 0 6 0.0094 HCRTR2 Hypocretin (orexin) receptor 2 6 6 0 0 0 ATPase, Ca++ transporting, type 2C, 3 0.0095 ATP2C1 12 9 0 1 2 member 1 3 0.0095 TMEM108 Transmembrane protein 108 12 9 0 1 2

238 Appendix Table 2: Significantly altered gene copy number in oral tumors from smokers and non-smokers. Significantly altered copy number of genes was examined using a Chi-square analysis between tumors of smoking and non-smoking patients. Chr – Chromosome; Ab – Abnormal; O – Older; Y – Young; Amp – Amplification; Del – Deletion. p≤0.05.

N N Y Y Chr p-value Gene ID(s) # Ab Amp Del Amp Del 1 0.019 FPGT TNNI3K 14 5 6 0 3 1 0.023 OLFM3 12 3 7 0 2 1 0.03 RGS7 4 0 0 4 0 1 0.032 TNNI3K 13 5 5 0 3 1 0.038 LRRIQ3 14 4 7 1 2 1 0.044 PRKACB 11 3 6 0 2 1 0.044 DAB1 6 0 2 4 0 1 0.044 FAF1 6 0 2 4 0 1 0.05 ELAVL4 6 0 3 3 0 2 0.015 SCN7A 10 4 5 0 1 2 0.028 ETAA1 9 5 3 0 1 2 0.029 KCNJ3 7 0 5 2 0 2 0.031 KCNH7 9 4 4 0 1 2 0.031 LRP1B 9 4 4 0 1 2 0.031 LRPPRC 9 4 4 0 1 2 0.034 PID1 6 2 4 0 0 2 0.034 PARD3B 6 3 3 0 0 2 0.034 SP140 6 4 2 0 0 2 0.04 ACSL3 4 4 0 0 0 2 0.04 SGPP2 4 4 0 0 0 2 0.044 RBMS1 7 6 0 1 0 3 0.021 ABI3BP 9 1 0 6 2 3 0.031 SLC2A2 15 4 0 8 3 3 0.031 BBX 7 1 0 6 0 3 0.031 BOC 7 1 0 6 0 3 0.031 C3orf26 7 1 0 6 0 3 0.031 C3orf26 FILIP1L 7 1 0 6 0 3 0.031 CBLB 7 1 0 6 0 3 0.031 KIAA1524 7 1 0 6 0 3 0.031 MORC1 7 1 0 6 0 3 0.031 MYH15 7 1 0 6 0 3 0.031 TRAT1 7 1 0 6 0 3 0.04 SPATA16 13 3 0 9 1 3 0.042 FAM19A4 14 0 4 4 6 3 0.045 ECT2 15 4 0 10 1 3 0.047 CCDC54 8 1 0 6 1 3 0.047 IFT57 8 1 0 6 1 3 0.047 KIAA1407 ZDHHC23 9 2 0 7 0 239 3 0.047 SIDT1 9 2 0 7 0 4 0.049 GABRA2 11 2 7 0 2 4 0.05 GPM6A 6 0 3 3 0 4 0.05 MMRN1 6 0 3 3 0 5 0.038 CDH18 14 8 3 2 1 6 0.017 TMEM170B 10 7 1 0 2 6 0.029 JARID2 7 5 0 0 2 6 0.029 FBXO5 7 3 0 0 4 6 0.044 FBXO5 MTRF1L RGS17 6 2 0 0 4 6 0.047 NT5DC1 9 5 1 0 3 6 0.049 EFHC1 5 0 0 4 1 6 0.05 DNAH8 6 4 0 0 2 6 0.05 PACRG 6 4 0 0 2 6 0.05 PEX3 6 4 0 0 2 6 0.05 PSMB8 PSMB9 TAP1 TAP2 6 4 0 0 2 6 0.05 PDE10A 6 3 0 0 3 7 0.003 ANKIB1 10 0 2 8 0 7 0.004 CALCR 10 0 3 7 0 7 0.011 PTPN12 7 0 0 4 3 7 0.011 SEMA3C 7 0 0 5 2 7 0.011 ABCA13 7 0 0 6 1 7 0.013 CDK6 11 1 2 8 0 7 0.014 AKAP9 8 1 0 7 0 7 0.014 CLDN12 8 1 0 7 0 7 0.014 MTERF 8 1 0 7 0 7 0.014 PFTK1 8 1 0 7 0 7 0.022 GNG11 9 1 0 7 1 7 0.024 BBS9 6 0 0 4 2 7 0.025 PPP1R9A 10 1 2 7 0 7 0.031 CASD1 7 1 0 6 0 7 0.031 CLDN12 GTPBP10 7 1 0 6 0 7 0.031 CYP3A43 7 1 0 6 0 7 0.031 GTPBP10 7 1 0 6 0 7 0.032 NXPH1 8 0 1 5 2 7 0.032 FAM3C 8 1 0 2 5 7 0.035 DYNC1I1 9 1 1 7 0 7 0.035 FZD1 9 1 1 7 0 7 0.035 PON3 9 1 1 7 0 7 0.035 SAMD9 9 1 1 7 0 7 0.035 SLC25A13 9 1 1 7 0 7 0.039 MAGI2 9 0 3 5 1 7 0.042 TMEM106B 8 0 1 3 4 7 0.044 CREB5 8 1 0 5 2 7 0.044 THSD7A 8 1 0 5 2 7 0.046 CADPS2 9 2 0 2 5 7 0.046 CYP3A7 10 1 1 7 1 240 7 0.046 LHFPL3 10 1 1 7 1 7 0.046 RELN 10 1 1 7 1 7 0.047 ABCB5 8 1 0 6 1 7 0.047 GLI3 8 1 0 6 1 7 0.047 ICA1 8 1 0 6 1 7 0.047 VWC2 8 1 0 6 1 7 0.047 AKAP9 CYP51A1 9 2 0 7 0 7 0.047 CD36 8 0 2 5 1 7 0.047 TMEM195 8 0 2 5 1 7 0.048 CYP3A5 9 1 2 6 0 7 0.049 CACNA2D1 5 0 0 2 3 7 0.049 ANKMY2 5 0 0 3 2 7 0.049 PCLO 5 0 0 3 2 7 0.049 SCIN 5 0 0 3 2 7 0.049 BMPER 5 0 0 4 1 7 0.049 DPY19L1 5 0 0 4 1 7 0.049 ITGB8 5 0 0 4 1 7 0.049 NPSR1 5 0 0 4 1 7 0.049 PHF14 5 0 0 4 1 7 0.049 RAPGEF5 5 0 0 4 1 9 0.018 JMJD2C 11 0 8 2 1 9 0.022 TEK 11 0 7 3 1 9 0.024 TMC1 6 0 0 2 4 9 0.024 C9orf41 6 0 0 3 3 9 0.033 SH3GL2 10 2 6 2 0 9 0.034 ADAMTSL1 10 0 7 2 1 9 0.034 ZCCHC7 10 0 7 2 1 9 0.04 MPDZ 13 1 7 4 1 9 0.049 TLE4 10 3 0 2 5 9 0.049 TRPM3 5 0 0 2 3 9 0.049 PCSK5 5 0 0 3 2 9 0.049 LINGO2 12 0 6 4 2 10 0.028 KCNMA1 9 3 5 1 0 10 0.031 CTNNA3 9 4 4 0 1 10 0.032 ATRNL1 13 5 5 0 3 10 0.034 NEBL 10 0 7 2 1 10 0.034 ADAM12 6 2 4 0 0 10 0.034 C10orf137 6 3 3 0 0 10 0.034 PDZD8 6 3 3 0 0 10 0.037 C10orf68 10 0 6 3 1 10 0.044 CCDC147 6 0 2 4 0 10 0.047 RET 11 7 2 1 1 10 0.049 FGFR2 10 5 3 0 2 10 0.05 SORCS3 6 0 3 3 0 11 0.028 C11orf46 12 4 6 1 1 11 0.031 HPS5 SAA1 9 4 4 1 0 241 11 0.031 NELL1 7 0 4 3 0 12 0.024 CD163L1 6 0 0 3 3 12 0.031 TMTC2 9 4 4 0 1 12 0.034 FLJ20674 6 4 2 0 0 12 0.034 PEBP1 TAOK3 6 4 2 0 0 12 0.04 HCFC2 4 4 0 0 0 12 0.04 MYBPC1 4 4 0 0 0 12 0.044 CNOT2 6 5 0 0 1 12 0.049 SOX5 5 0 0 2 3 12 0.049 ACSM4 5 0 0 3 2 12 0.049 GRIN2B 5 0 0 3 2 12 0.049 GUCY2C 5 0 0 3 2 12 0.05 TMBIM4 6 4 0 0 2 12 0.05 IFLTD1 6 3 0 0 3 12 0.05 PHC1 6 3 0 0 3 13 0.029 WASF3 10 0 4 5 1 13 0.038 TPP2 8 0 1 4 3 13 0.039 ITGBL1 9 0 3 5 1 13 0.039 MYO16 9 0 3 5 1 13 0.047 FAM155A 8 0 2 5 1 13 0.047 HS6ST3 8 0 2 5 1 13 0.047 UGCGL2 7 0 1 5 1 13 0.049 GPC6 10 0 3 5 2 14 0.031 KCNH5 7 0 4 3 0 14 0.035 NRXN3 10 1 4 5 0 14 0.039 NPAS3 9 0 3 5 1 14 0.044 MDGA2 6 0 5 1 0 14 0.044 ARHGAP5 6 0 2 4 0 14 0.044 MAP4K5 6 0 2 4 0 15 0.035 ALDH1A2 10 0 5 4 1 15 0.039 RORA 9 0 3 5 1 16 0.017 RBL2 7 5 2 0 0 16 0.034 CYB5B 6 4 2 0 0 16 0.044 LOC283867 6 0 2 4 0 16 0.05 A2BP1 6 0 3 3 0 17 0.049 MAP2K4 5 0 0 1 4 17 0.049 MMD 5 0 0 2 3 18 0.029 EPB41L3 7 3 0 0 4 18 0.029 L3MBTL4 7 3 0 0 4 18 0.029 ZFP161 7 3 0 0 4 18 0.033 PTPRM 10 6 2 0 2 18 0.047 ANKRD12 9 5 1 0 3 18 0.047 CEP192 9 5 1 0 3 19 0.008 ZNF507 8 6 2 0 0 19 0.017 AXL 10 7 2 1 0 19 0.017 HNRNPUL1 10 7 2 1 0 242 19 0.023 ARHGEF1 12 8 2 1 1 19 0.023 CEACAM21 9 6 2 0 1 19 0.023 CEACAM4 9 6 2 0 1 19 0.034 AXL HNRNPUL1 9 6 2 1 0 19 0.034 DPY19L3 9 6 2 1 0 19 0.034 UBA2 9 6 2 1 0 19 0.034 ZNF792 9 6 2 1 0 19 0.047 ZNF578 8 5 2 0 1 19 0.047 ARHGEF1 CD79A RPS19 11 7 2 1 1 ATP5SL B3GNT8 B9D2 BCKDHA 19 0.047 11 7 2 1 1 CCDC97 EXOSC5 HNRNPUL1 TGFB1 19 0.047 AXL CYP2S1 11 7 2 1 1 19 0.047 BLVRB PRX SERTAD1 SERTAD3 11 7 2 1 1 19 0.047 BLVRB SPTBN4 11 7 2 1 1 19 0.047 CYP2A13 11 7 2 1 1 19 0.047 CYP2F1 11 7 2 1 1 DLL3 MED29 PAF1 PLEKHG2 RPS16 19 0.047 11 7 2 1 1 SAMD4B SUPT5H TIMM50 ZFP36 19 0.047 DMRTC2 LYPD4 11 7 2 1 1 19 0.047 FLJ16165 11 7 2 1 1 19 0.047 FLJ16165 PAK4 11 7 2 1 1 19 0.047 IL28A IL28B LOC342897 PAK4 SYCN 11 7 2 1 1 19 0.047 LRFN1 11 7 2 1 1 19 0.047 LSM14A 11 7 2 1 1 19 0.047 SPTBN4 11 7 2 1 1 19 0.049 PBX4 12 4 5 3 0 19 0.049 ZNF253 ZNF506 12 4 5 3 0 19 0.049 ZNF682 12 4 5 3 0 20 0.012 PLCB4 8 0 2 6 0 20 0.017 MACROD2 7 0 1 6 0 20 0.017 XRN2 7 0 1 6 0 20 0.018 PCSK2 11 1 3 7 0 20 0.032 PAK7 10 1 3 6 0 20 0.032 SNRPB2 10 1 3 6 0 20 0.034 SPTLC3 6 0 1 5 0 20 0.034 TASP1 6 0 1 5 0 20 0.048 SNX5 12 2 3 7 0 20 0.048 ANKRD5 9 1 2 6 0 20 0.048 KIF16B 9 1 2 6 0

243 Appendix Table 3: Regions of LOH in oral tumors. Loss of heterozygosity of oral tumors was examined in oral tumors in at least 7/39 (18%) samples. Chr – Chromosome.

Chr # Samples Gene ID(s) Gene Name 11 10 CTTN; SHANK2 Cortactin; SH3 and multiple ankyrin repeat domains 2

11 10 FADD; TMEM16A Fas (TNFRSF6)-associated via death domain; Anoctamin 1, calcium activated chloride channel

chromosome 9 open reading frame 97; Nuclear cap binding protein subunit 1; Xeroderma 9 9 C9orf97; NCBP1; XPA pigmentosum, complementation group A 9 9 TNFSF8 Tumor necrosis factor (ligand) superfamily, member 8 ANP32B; C9orf156; FOXE1; Acidic (leucine-rich) nuclear phosphoprotein 32 family, member B; Chromosome 9 open reading 9 8 HEMGN frame 156; Forkhead box E1; hemogen 9 8 TMEM38B Transmembrane protein 38B 9 8 NFIL3 Nuclear factor, interleukin 3 regulated 9 8 PTPN3 Protein tyrosine phosphatase, non-receptor type 3 AMAC1L3; C17orf74; Acyl-malonyl condensing enzyme 1-like 3; Chromosome 17 open reading frame 74; Cholinergic CHRNB1; FGF11; NLGN2; receptor, nicotinic, beta 1; Fibroblast growth factor 11; Neuroligin 2; Polymerase (RNA) II (DNA 17 8 POLR2A; SPEM1; directed) polypeptide A; Spermatid maturation 1; Transmembrane protein 102; zinc finger and BTB TMEM102; ZBTB4 domain containing 4 11 8 TMEM16A Anoctamin 1, calcium activated chloride channel 9 8 OR1L8 Olfactory receptor, family 1, subfamily L, member 8 9 8 BICD2 Bicaudal D homolog 2 (Drosophila) 11 8 SHANK2 SH3 and multiple ankyrin repeat domains 2 17 8 AURKB; C17orf59 Aurora kinase B; chromosome 17 open reading frame 59 9 8 C9orf97; TMOD1 Chromosome 9 open reading frame 97; tropomodulin 1 5 7 ODZ2 Odz, odd Oz/ten-m homolog 2 (Drosophila) 17 7 ELAC2 RICH2 ElaC homolog 2 (E. coli); Rho-type GTPase-activating protein RICH2 9 7 DAB2IP DAB2 interacting protein 17 7 USP43 Ubiquitin specific peptidase 43 3 7 VPS8 Vacuolar protein sorting 8 homolog 5 7 KCNIP1 Kv channel interacting protein 1 5 7 KCNN2 Potassium large conductance calcium-activated channel, subfamily M, beta member 1

244 Adenylate kinase 1; Dolichyl-phosphate mannosyltransferase polypeptide 2, regulatory subunit; AK1; DPM2; ENG; Endoglin; Family with sequence similarity 102, member A; Phosphatidylinositol-4-phosphate 5- FAM102A; PIP5KL1; 9 7 kinase-like 1; T6 (alpha-N-acetyl-neuraminyl-2,3-beta-galactosyl-1,3)-N-acetylgalactosaminide alpha- ST6GALNAC4; 2,6-sialyltransferase 4; ST6 (alpha-N-acetyl-neuraminyl-2,3-beta-galactosyl-1,3)-N- ST6GALNAC6 acetylgalactosaminide alpha-2,6-sialyltransferase 6 9 7 AKAP2; PALM2-AKAP2 A kinase (PRKA) anchor protein 2; PALM2-AKAP2 readthrough transcript 9 7 COL15A1 Collagen, type XV, alpha 1 9 7 PALM2 Paralemmin 2 KIAA0753; LOC388327; KIAA0753; Chromosome 17 open reading frame 100; mediator complex subunit 31; Thioredoxin 17 7 MED31; TXNDC17 domain containing 17 3 7 ERC2 ELKS/RAB6-interacting/CAST family member 2 3 7 SPATA16 Spermatogenesis associated 16 BICD2; CENPP; ECM2; Bicaudal D homolog 2 (Drosophila); Centromere protein P; Extracellular matrix protein 2; Inositol 9 7 IPPK 1,3,4,5,6-pentakisphosphate 2-kinase 9 7 JMJD2C Jumonji domain containing 2C

C9orf125; GRIN3A; Chromosome 9 open reading frame 125; Glutamate receptor, ionotropic, N-methyl-D-aspartate 3A; 9 7 PPP3R2; RNF20 Protein phosphatase 3 (formerly 2B), regulatory subunit B, beta isoform; Ring finger protein 20

9 7 C9orf9; C9orf98; TSC1 Chromosome 9 open reading frame 9; Chromosome 9 open reading frame 98; Tuberous sclerosis 1

9 7 SETX Senataxin 9 7 TGFBR1 Transforming growth factor, beta receptor 1 Ankyrin repeat and sterile alpha motif domain containing 6; UDP-N-acetyl-alpha-D- 9 7 ANKS6; GALNT12 galactosamine:Polypeptide N-acetylgalactosaminyltransferase 12 (GalNAc-T12) [ 11 7 MYEOV Myeloma overexpressed 13 7 TRPC4 Transient receptor potential cation channel, subfamily C, member 4

17 7 FLJ35773; PIK3R6 Major facilitator superfamily domain containing 6-like; Phosphoinositide-3-kinase, regulatory subunit 6

Actin-like 7A; Actin-like 7B; Inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase 9 7 ACTL7A; ACTL7B; IKBKAP complex-associated protein 17 7 DNAH9 Dynein, axonemal, heavy chain 9 9 7 CORO2A Coronin, actin binding protein, 2A

245 CD68 molecule; eukaryotic translation initiation factor 4A, isoform 1; Fragile X mental retardation, CD68; EIF4A1; FXR2; autosomal homolog 2; Mannose-P-dolichol utilization defect 1; polymerase (RNA) II (DNA directed) MPDU1; POLR2A; SAT2; polypeptide A; Spermidine/spermine N1-acetyltransferase family member 2; SUMO1/sentrin/SMT3 17 7 SENP3; SHBG; SOX15; specific peptidase 3; Sex hormone-binding globulin; SRY (sex determining region Y)-box 15; Tumor TNFSF12; TNFSF12- necrosis factor (ligand) superfamily, member 12; TNFSF12-TNFSF13 readthrough; Tumor necrosis TNFSF13; TNFSF13 factor (ligand) superfamily, member 13

9 7 BRD3 Bromodomain containing 3 9 7 RFX3 Regulatory factor X, 3 9 7 ASTN2 Astrotactin 2 9 7 AUH AU RNA binding protein/enoyl-Coenzyme A hydratase C9orf131; DNAJB5; Chromosome 9 open reading frame 131; DnaJ (Hsp40) homolog, subfamily B, member 5; KIAA1045; 9 7 KIAA1045; VCP Valosin-containing protein 9 7 KIAA2026; MLANA KIAA2026; melan-A 9 7 ABL1 C-abl oncogene 1, receptor tyrosine kinase 9 7 GRIN3A Glutamate receptor, ionotropic, N-methyl-D-aspartate 3A 3 7 IGF2BP2 Insulin-like growth factor 2 mRNA binding protein 2 9 7 FAM166B; RUSC2 Family with sequence similarity 166, member B; RUN and SH3 domain containing 2 9 7 NFIB Nuclear factor I/B

3 7 MASP1; RTP1 Mannan-binding lectin serine peptidase 1; Receptor (chemosensory) transporter protein 1

9 7 ZFAND5 Zinc finger, AN1-type domain 5 17 7 CCDC42 Coiled-coil domain containing 42 5 7 WWC1 WW and C2 domain containing 1 9 7 MPDZ Multiple PDZ domain protein 9 7 SH3GL2 SH3-domain GRB2-like 2 9 7 FAM125B Family with sequence similarity 125, member B 18 7 DCC Deleted in colorectal carcinoma 9 7 MUSK Muscle, skeletal, receptor tyrosine kinase 13 7 GTF2F2 General transcription factor IIF, polypeptide 2 Transient receptor potential cation channel, subfamily V, member 1; Transient receptor potential 17 7 TRPV1; TRPV3 cation channel, subfamily V, member 3 13 7 HS6ST3 Heparan sulfate 6-O-sulfotransferase 3 17 7 SMG6 Smg-6 homolog, nonsense mediated mRNA decay factor (C. elegans) 9 7 GLIS3 GLIS family zinc finger 3

246 17 7 CRK; MYO1C; TBC1D3B V-crk sarcoma virus CT10 oncogene homolog (avian); myosin IC; TBC1 domain family, member 3B

9 7 DBC1 Deleted in bladder cancer 1 9 7 FRMD3 FERM domain containing 3 ATP1B2; DNAH2; EFNB3; ATPase, Na+/K+ transporting, beta 2 polypeptide; Dynein, axonemal, heavy chain 2; Ephrin-B3; 17 7 TP53; WDR79 Tumor protein p53; WD repeat containing, antisense to TP53 3 7 RAD18 RAD18 homolog (S. cerevisiae) 3 7 TP63 Tumor protein p63 C9orf114; CCBL1; ENDOG; Chromosome 9 open reading frame 114; Cysteine conjugate-beta lyase; Endonuclease G; TBC1 9 7 TBC1D13 domain family, member 13 5 7 SGCD Sarcoglycan, delta 9 7 INSL6 Insulin-like 6 9 7 JAK2 Janus kinase 2 9 7 KIAA0020 KIAA0020 5 7 PFDN1 Prefoldin subunit 1 9 7 FKTN; TAL2 Fukutin; T-cell acute lymphocytic leukemia 2 9 7 GLT6D1 Glycosyltransferase 6 domain containing 1 9 7 ZNF618 Zinc finger protein 618 11 7 FGF3 Fibroblast growth factor 3 17 7 DHRS7C Dehydrogenase/reductase (SDR family) member 7C 17 7 SHPK; TRPV1 Sedoheptulokinase; Transient receptor potential cation channel, subfamily V, member 1 5 7 SH3TC2 SH3 domain and tetratricopeptide repeats 2 9 7 GAS1 Growth arrest-specific 1 9 7 OR1N2 Olfactory receptor, family 1, subfamily N, member 2 9 7 PTPRD Protein tyrosine phosphatase, receptor type, D

9 7 SMARCA2 SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 2

9 7 TRIM14 Tripartite motif-containing 14 13 7 FREM2 FRAS1 related extracellular matrix protein 2

FLJ44838; SGSM2; SMG6; FLJ44838; Small G protein signaling modulator 2; Smg-6 homolog, nonsense mediated mRNA decay 17 7 SRR; TSR1 factor (C. elegans); Serine racemase; TSR1, 20S rRNA accumulation, homolog (S. cerevisiae)

3 7 RAP2B Ras family small GTP binding protein RAP2 9 7 GNG10; LOC552891 Guanine nucleotide binding protein (G protein), gamma 10; LOC552891 9 7 PALM2; PALM2-AKAP2 Paralemmin 2; A kinase (PRKA) anchor protein 2

247 17 7 C17orf68 Chromosome 17 open reading frame 68 9 7 NANS; TRIM14 N-acetylneuraminic acid synthase; tripartite motif-containing 14 9 7 ROR2 Receptor tyrosine kinase-like orphan receptor 2 9 7 SIT1 Signaling threshold regulating transmembrane adaptor 1 5 7 COL23A1 Collagen, type XXIII, alpha 1 9 7 BNC2 Basonuclin 2 9 7 C9orf84 Chromosome 9 open reading frame 84 9 7 FREM1 FRAS1 related extracellular matrix 1 ALOX12; C17orf49; 17 7 Arachidonate 12-lipoxygenase; Chromosome 17 open reading frame 49; Ribonuclease, RNase K RNASEK 5 7 ARHGAP26 Rho GTPase activating protein 26 Kv channel interacting protein 1; Potassium large conductance calcium-activated channel, subfamily 5 7 KCNIP1; KCNMB1 M, beta member 1 5 7 RAPGEF6 Rap guanine nucleotide exchange factor (GEF) 6 9 7 DENND1A DENN/MADD domain containing 1A 9 7 KIAA1432 Connexin 43-interacting protein 150 9 7 PRG-3 Apoptosis-inducing factor, mitochondrion-associated, 2 12 7 DCP1B DCP1 decapping enzyme homolog B (S. cerevisiae)

17 7 C17orf61; NLGN2; PLSCR3 Chromosome 17 open reading frame 61; Neuroligin 2; Phospholipid scramblase 3

3 7 MED12L; P2RY12 Mediator complex subunit 12-like; Purinergic receptor P2Y, G-protein coupled, 12 3 7 PLS1 Plastin 1 5 7 ADAMTS2 ADAM metallopeptidase with thrombospondin type 1 motif, 2 5 7 DOCK2; LOC100131897 Dedicator of cytokinesis 2; LOC100131897 9 7 EPB41L4B Erythrocyte membrane protein band 4.1 like 4B 9 7 PAPPA Pregnancy-associated plasma protein A, pappalysin 1 9 7 PHF2 PHD finger protein 2 9 7 RALGPS1 Ral GEF with PH domain and SH3 binding motif 1 17 7 TRPV1 Transient receptor potential cation channel, subfamily V, member 1

248 Appendix Table 4: Regions of cnLOH in at least 5 oral tumors. Chr – Chromosome.

Chr Gene ID(s) Gene Name(s) 9 ABL1 c-abl oncogene 1 ACTL7A ACTL7B Actin-like 7A; actin-like 7B; inhibitor of kappa light polypeptide 9 IKBKAP gene enhancer in B-cells, kinase complex-associated protein 4 ADH4 Alcohol dehydrogenase 4 (class II), pi polypeptide 4 ADH5 Alcohol dehydrogenase 5 (class III), chi polypeptide AKAP2 PALM2- A kinase (PRKA) anchor protein 2; PALM2-AKAP2 9 AKAP2 readthrough transcript 9 ALDH1A1 Aldehyde dehydrogenase 1 family, member A1 9 ALDOB Aldolase B, fructose-bisphosphate ANKHD1 Ankyrin repeat and KH domain containing 1; ANKHD1- 5 ANKHD1- EIF4EBP3 readthrough EIF4EBP3 9 ANKS6 Ankyrin repeat and sterile alpha motif domain containing 6 Acidic (leucine-rich) nuclear phosphoprotein 32 family, 9 ANP32B member B 9 ASTN2 Astrotactin 2 9 BAAT Bile acid Coenzyme A: amino acid N-acyltransferase Bile acid Coenzyme A: amino acid N-acyltransferase; 9 BAAT MRPL50 mitochondrial ribosomal protein L50 15 C15orf26 Chromosome 15 open reading frame 26 4 C4orf22 Chromosome 4 open reading frame 22 9 C9orf125 Chromosome 9 open reading frame 125 9 C9orf135 Chromosome 9 open reading frame 135 9 C9orf156 Chromosome 9 open reading frame 156 C9orf156 9 Chromosome 9 open reading frame 156; hemogen HEMGN 9 C9orf40 Chromosome 9 open reading frame 40 9 C9orf41 Chromosome 9 open reading frame 41 9 C9orf61 Family with sequence similarity 189, member A2 9 C9orf84 Chromosome 9 open reading frame 84 9 C9orf85 Chromosome 9 open reading frame 85 9 C9orf97 Chromosome 9 open reading frame 97 9 C9orf97 NCBP1 Nuclear cap binding protein subunit 1 9 CDK5RAP2 CDK5 regulatory subunit associated protein 2 9 CHCHD9 Coiled-coil-helix-coiled-coil-helix domain containing 9 9 COL15A1 Collagen, type XV, alpha 1 9 DAB2IP DAB2 interacting protein 9 DBC1 Deleted in bladder cancer 1 6 DCBLD1 Discoidin, CUB and LCCL domain containing 1 9 DENND1A DENN/MADD domain containing 1A 249 DOCK2 5 Dedicator of cytokinesis 2; LOC100131897 LOC100131897 12 DRAM DNA-damage regulated autophagy modulator 1 9 EPB41L4B Erythrocyte membrane protein band 4.1 like 4B 9 FAM125B Family with sequence similarity 125, member B 12 FGD6 FYVE, RhoGEF and PH domain containing 6 9 FKTN Fukutin 13 FREM2 FRAS1 related extracellular matrix protein 2 9 FRMD3 FERM domain containing 3 9 FSD1L fibronectin type III and SPRY domain containing 1-like 9 GDA Guanine deaminase 9 GLIS3 GLIS family zinc finger 3 1 GLUL Glutamate-ammonia ligase 9 GNA14 Guanine nucleotide binding protein (G protein), alpha 14 9 GNAQ Guanine nucleotide binding protein (G protein), q polypeptide GNG10 Guanine nucleotide binding protein (G protein), gamma 10; 9 LOC552891 LOC552891 14 GPHN Gephyrin 9 GRIN3A Glutamate receptor, ionotropic, N-methyl-D-aspartate 3A Glutamate receptor, ionotropic, N-methyl-D-aspartate 3A; 9 GRIN3A PPP3R2 protein phosphatase 3 (formerly 2B), regulatory subunit B, beta isoform Glutamate receptor, ionotropic, N-methyl-D-aspartate 3A; ring 9 GRIN3A RNF20 finger protein 20 5 HBEGF Heparin-binding EGF-like growth factor Heparin-binding EGF-like growth factor; solute carrier family 5 HBEGF SLC4A9 4, sodium bicarbonate cotransporter, member 9 9 HEMGN Hemogen 12 IGF1 Insulin-like growth factor 1 9 INVS Inversin Inversin; thioredoxin domain containing 4 (endoplasmic 9 INVS TXNDC4 reticulum) 5 KCNIP1 Kv channel-interacting protein 1 Kv channel-interacting protein 1; potassium large KCNIP1 5 conductance calcium-activated channel, subfamily M, beta KCNMB1 member 1 1 KCNK2 Potassium channel, subfamily K, member 2 16 KIAA1576 Vesicle amine transport protein 1 homolog (T. californica)-like 13 KL Klotho 9 KLF9 Kruppel-like factor 9 12 LIN7A Lin-7 homolog A (C. elegans) LOC401152 Chromosome 4 open reading frame 3; ubiquitin specific 4 USP53 peptidase 53 250 9 MAMDC2 MAM domain containing 2 9 MRPL50 ZNF189 Mitochondrial ribosomal protein L50; zinc finger protein 189 9 MUSK Muscle, skeletal, receptor tyrosine kinase 13 N4BP2L2 NEDD4 binding protein 2-like 2 N-acetylneuraminic acid synthase; tripartite motif-containing 9 NANS TRIM14 14 13 NBEA Neurobeachin 9 NCBP1 Nuclear cap binding protein subunit 1 Nuclear cap binding protein subunit 1; xeroderma 9 NCBP1 XPA pigmentosum, complementation group A 9 NFIL3 Nuclear factor, interleukin 3 regulated Non-metastatic cells 7, protein expressed in (nucleoside- 1 NME7 diphosphate kinase) 14 NRXN3 Neurexin 3 9 OR1L8 Olfactory receptor, family 1, subfamily L, member 8 PALM2 PALM2- 9 Paralemmin 2; PALM2-AKAP2 readthrough transcript AKAP2 9 PAPPA Pregnancy-associated plasma protein A, pappalysin 1 12 PAWR PRKC, apoptosis, WT1, regulator 9 PCSK5 proprotein convertase subtilisin/kexin type 5 PDS5, regulator of cohesion maintenance, homolog B (S. 13 PDS5B cerevisiae) 5 PFDN1 Prefoldin subunit 1 9 PRG-3 Apoptosis-inducing factor, mitochondrion-associated, 2 9 PTPN3 Protein tyrosine phosphatase, non-receptor type 3 5 RANBP17 RAN binding protein 17 9 RASEF RAS and EF-hand domain containing 9 RFX3 Regulatory factor X, 3 (influences HLA class II expression) 5 RNF130 Ring finger protein 130 9 RNF20 Ring finger protein 20 9 ROR2 Receptor tyrosine kinase-like orphan receptor 2 9 RORB RAR-related orphan receptor B 6 SCGN Secretagogin, EF-hand calcium binding protein 1 SH3GLB1 SH3-domain GRB2-like endophilin B1 9 SLC44A1 Solute carrier family 44, member 1 9 SMC2 Structural maintenance of chromosomes 2 9 SMC5 Structural maintenance of chromosomes 5 Spermatogenesis and oogenesis specific basic helix-loop- 13 SOHLH2 helix 2 13 SPG20 Spastic paraplegia 20 5 STK32A Serine/threonine kinase 32A

251 Sushi, von Willebrand factor type A, EGF and pentraxin 9 SVEP1 domain containing 1 12 SYT1 Synaptotagmin I 1 SYT14 Synaptotagmin XIV 9 TAL2 T-cell acute lymphocytic leukemia 2 1 TEDDM1 Transmembrane epididymal protein 1 Transducin-like enhancer of split 4 (E(sp1) homolog, 9 TLE4 Drosophila) 9 TMC1 Transmembrane channel-like 1 9 TMEM2 Transmembrane protein 2 9 TMEM38B Transmembrane protein 38B 12 TMTC2 Transmembrane and tetratricopeptide repeat containing 2 9 TNFSF8 Tumor necrosis factor (ligand) superfamily, member 8 9 TRIM14 Tripartite motif-containing 14 Transient receptor potential cation channel, subfamily C, 13 TRPC4 member 4 Transient receptor potential cation channel, subfamily M, 9 TRPM3 member 3 9 TXNDC4 Thioredoxin domain containing 4 (endoplasmic reticulum) 9 VPS13A Vacuolar protein sorting 13 homolog A (S. cerevisiae) 1 WDR64 WD repeat domain 64 5 WWC1 WW and C2 domain containing 1 9 XPA Xeroderma pigmentosum, complementation group A 6 ZFAND3 Zinc finger, AN1-type domain 3 9 ZFAND5 Zinc finger, AN1-type domain 5 9 ZNF189 Zinc finger protein 189 9 ZNF618 Zinc finger protein 618

252 CHAPTER 4 APPENDIX - TABLES

Appendix Table 1: Clinical information of young and older patients with oral cancer and experiments used. A summary is given of each clinical sample used in the following experiments: Q-RT-PCR (Quantitative RT-PCR), IHC (Immunhistochemistry), MSI/LOH analyses, and sequencing. M= Male; F= Female; Y= Yes; N= No; FOM= Floor of Mouth; FU= Follow Up; NED= No Evidence of Disease; AWD= Alive with Disease; LFU= Lost to Follow Up; DOD= Died of Disease; DOC= Died of Other Causes; ANED= Alive with No Evidence of Disease; DNED= Died with No Evidence of Disease. Case numbers are provided for each sample and are repeated if the same sample was used multiple times. Dates are given as MM/DD/YY.

Experiment Pt # Age Sex Tobacco Alcohol Tumor site Stage Grade Last FU Outcome Q-RT-PCR 1 62 M Y Former FOM II Moderate 01/29/07 NED 2 84 F N N Tongue IV Poor 06/24/07 LFU 3 70 M Y Y Tongue/FOM II Moderate 07/18/96 DNED 4 72 F Y Y FOM IV Moderate 06/08/99 NED 5 74 M Y Y Tongue IV Poor 05/12/99 DOD 6 87 F N N Tongue II Moderate 03/19/01 DOD 7 47 M Y Y FOM IV Moderate 03/02/06 DOD 8 80 F N N Tongue/FOM II Moderate 10/03/02 DOD 9 72 F N N Tongue III Moderate 08/07/06 LFU 10 59 F N N Tongue II Moderate 06/20/06 NED 11 64 F Y N Tongue III Well 10/18/06 NED 12 50 M Y Y FOM IV Moderate 04/30/08 NED 13 64 M Former Y Tongue III Moderate 03/20/07 NED 14 53 M N N Tongue IV Moderate 08/27/03 AWD 15 26 F N N Tongue IV Moderate 09/04/04 DOD 16 34 M Y Y Tongue IV Moderate 03/20/07 NED 17 39 M N N Tongue IV Moderate 12/05/00 DOD 18 40 M N N Tongue II Moderate 09/15/06 NED 19 22 M Y N Tongue IV Moderate 11/28/01 AWD 20 31 M Y Y Tongue III Moderate 05/06/06 NED 21 39 F N N Buccal mucosa IV Moderate 09/20/05 NED 22 39 F N N Tongue III Moderate 08/18/04 AWD 253 23 39 M Y Y Tongue IV Moderate 01/05/05 NED 24 27 M Y N Tongue/FOM II Moderate 12/18/06 NED 25 39 F N N Buccal mucosa IV Moderate 09/20/05 NED 26 35 F Y N Tongue II Moderate 03/06/07 NED 27 43 M N N Tongue/ FOM II Poor 04/04/07 NED 28 20 M Y N Tongue IV Moderate 12/15/06 AWD

Experiment Pt # Age Sex Tobacco Alcohol Tumor site Stage Grade Last FU Outcome IHC 29 43 F ? ? ? ? Moderate ? LFU 30 37 M ? ? Tongue/FOM ? Poor ? LFU 31 42 F ? ? ? ? Moderate ? LFU 32 38 F Y N Tongue IV Moderate 04/26/05 NED 33 30 F ? ? Tongue I Moderate ? NED 34 33 M Y Y Tongue III Moderate 03/08/00 LFU 35 39 F N N Tongue II Moderate 01/19/09 NED 36 41 M Y Y Tongue/FOM II Moderate 10/14/01 LFU 37 38 M Y Y FOM IV Moderate 03/08/04 DOD 38 40 M N N Tongue II Moderate 08/21/02 LFU 39 38 F Y Y Tongue II Well 12/12/06 NED 15 26 F N N Tongue IV Moderate 09/04/04 DOD 40 42 M Y Y Tongue I Moderate 11/17/03 NED 41 40 M ? ? Gingiva ? Moderate 07/20/03 LFU 42 24 M N N Tongue III Moderate 06/29/03 NED 43 29 M N N Tongue I Well NED 16 34 M Y Y Tongue IV Moderate 03/20/07 NED 44 30 M Y N Tongue I Moderate 01/14/08 NED 45 31 M Y N Tongue I Moderate ? NED 46 45 M ? ? Tongue ? Well ? LFU 47 40 M N N Tongue I Moderate ? NED 48 33 F N N Tongue IV Moderate 06/10/05 DOD 49 32 M Y N Tongue III Moderate ? NED 18 40 M N N Tongue II Moderate 09/15/06 NED

254 50 24 F ? ? Tongue ? Well ? LFU 51 21 F N N Tongue IV Poor 06/16/06 DOD 20 31 M Y Y Tongue III Moderate 05/06/06 NED 24 27 M Y N Tongue/FOM II Moderate ? NED 52 38 F Y N Tongue/FOM IV Moderate ? NED 53 32 M Y N Tongue I Moderate ? NED 54 39 F Y Y FOM IV Moderate ? NED 21 39 F N N Buccal mucosa IV Moderate ? NED 17 39 M N N Tongue IV Moderate 12/05/00 DOD 19 22 M Y N Tongue IV Moderate 11/28/01 AWD 22 40 F N N Tongue III Moderate 08/19/08 AWD 23 37 M Y Y Tongue IV Moderate 01/06/09 NED 27 43 M N N Tongue/FOM III Poor ? NED 26 35 F Y N Tongue II Moderate ? NED 55 75 F Y N Alveolus ? Well 03/10/09 NED 56 75 M ? ? Alveolus ? Well ? LFU 57 68 M ? ? Alveolus ? Moderate ? LFU 58 72 F ? ? Soft palate ? Well ? LFU 59 76 F ? ? Alveolus ? Well ? LFU 60 79 M ? ? FOM ? Well ? LFU 61 80 M Y N Alveolus I Well ? NED 62 60 M ? ? Tongue I Well 10/02/06 NED 63 73 F ? ? Alveolus ? Moderate ? LFU 64 61 F N N Retromolar III Moderate 08/14/08 NED 65 82 F Y N Retromolar I Moderate 06/06/06 NED Alveolus/Hard 66 84 F Y N palate IV Moderate 09/15/03 LFU Alveolus/Hard 67 69 M Y N Palate I Well 09/05/05 LFU 68 51 M ? ? Tongue ? Well ? LFU 69 78 M ? ? Alveolus IV Moderate 04/12/05 AWD 70 90 M ? ? Tongue ? Moderate ? LFU 71 62 M ? ? FOM ? Moderate ? LFU 255 72 79 M ? ? FOM/Alveolar IV Moderate 01/16/05 LFU 73 89 F N N FOM IV Moderate 03/19/06 DOD Alveolus/Hard 74 95 F N N palate IV Well 11/14/05 AWD 75 79 M N Y Buccal mucosa IV Moderate ? NED 76 84 F ? ? Gingiva ? Well ? LFU 77 62 F ? ? Soft palate ? Moderate ? LFU 78 62 M Y Y FOM I Moderate ? NED 79 70 M ? ? Tonsil ? Moderate ? LFU 80 73 M Y Y Alveolus IV Moderate 11/07/06 DOC Retromolar 81 60 M Y Y trigone IV Moderate ? NED 82 80 F ? ? Buccal mucosa ? Well 06/13/06 LFU 83 91 M N N FOM IV Well 07/04/06 LFU 84 72 F ? ? Soft palate ? Moderate 07/27/06 LFU 85 77 F ? ? Soft palate ? Well ? LFU 86 64 M Y Y Tongue/FOM I Moderate ? NED Alveolus/Hard 87 77 F N N palate IV Moderate 03/21/07 LFU 88 60 F N N Tongue III Moderate 11/11/07 LFU 89 83 M ? ? Hard palate ? Well ? LFU 90 62 F ? ? FOM ? Well ? LFU 91 69 F Y N FOM II Moderate ? NED 92 74 M ? ? FOM ? Moderate ? LFU 93 45 F N N Alveolus IV Moderate ? LFU 2 84 F N N Tongue IV Poor 06/24/07 LFU 8 80 F N N Tongue/FOM II Moderate 10/03/02 DOD 9 72 F N N Tongue III Moderate 08/07/06 LFU 12 50 M Y Y FOM IV Moderate 04/30/08 NED 13 64 M Former Y Tongue III Moderate 03/20/07 NED

256 Experiment Pt # Age Sex Tobacco Alcohol Tumor site Stage Grade Last FU Outcome MSI/LOH 20 31 M Y Y Tongue III Moderate 05/06/06 NED 24 26 M Y N Tongue/FOM IV Moderate 12/18/06 NED 23 37 M Y Y Tongue IV Moderate 01/05/05 NED 27 43 M N N Tongue/FOM II Poor 04/04/07 NED 26 35 F Y N Tongue II Moderate 03/06/07 NED 94 41 M Y Y Tongue I Well 01/09/07 NED 38 40 M N N Tongue II Moderate 8/18/98 AWD 95 39 M N N Alveolus IV Moderate 10/16/06 NED 49 32 M Y N Tongue III Moderate 05/04/05 NED 19 22 M Y N Tongue IV Moderate 11/28/01 AWD 96 43 M Y Y FOM II Moderate 01/31/97 NED 97 37 M Y Y FOM IV Moderate 07/12/04 NED 98 39 F Y Y Tongue III Well 8/16/01 LFU 99 37 F Y N FOM IV Moderate 12/15/98 LFU 100 32 M N N Tongue I Moderate 3/29/07 NED 101 43 M Y Y FOM I Moderate 11/06/99 NED 102 34 F N N Tongue IV Poor 08/04/00 AWD 17 39 M N N Tongue IV Moderate 12/05/00 DOD 22 39 F N N Tongue III Moderate 08/18/04 AWD 103 33 F ? ? Buccal Mucosa IV Poor 07/16/06 NED 104 37 M N N Tongue IV Moderate 08/15/07 NED 105 38 F Former N Tongue IV Moderate 03/11/05 NED 106 23 F N N Tongue I Moderate 10/01/06 NED 107 41 M N N Tongue I Well 11/25/04 DOD 16 34 M Y Y Tongue IV Moderate 03/20/07 NED 32 38 F Y N Tongue IV Moderate 04/26/05 NED 108 36 F N N Tongue II Moderate 10/28/98 LFU 27 43 M N N Tongue/FOM II Poor 04/04/07 NED 36 41 M Y Y Tongue/FOM II Moderate 10/14/01 LFU 37 38 M Y Y FOM IV Moderate 03/08/04 DOD 38 40 M N N Tongue II Moderate 08/21/02 LFU 109 43 F N N Tongue base IV Spindle 05/22/01 DOD 257 Cell 15 26 F N N Tongue IV Moderate 09/04/04 DOD 42 24 M N N Tongue III Moderate 06/29/03 NED 110 39 F Y N Tongue base IV Moderate 07/22/98 DOD 111 59 F N N Alveolus/FOM IV Moderate 12/10/03 NED 112 76 M Former N FOM IV Moderate 03/28/03 AWD 113 71 M Y Y Tongue IV Poor 12/22/04 AWD Well- 114 83 F Y N Alveolus/FOM III Moderate 4/23/07 NED 115 74 M Y Y Buccal mucosa IV Well 4/18/06 NED 116 65 F Y Former Alveolus III Moderate 08/18/03 NED 91 70 F Y N FOM II Moderate 1/15/07 NED 117 78 F N N Alveolus/FOM IV Well 07/15/03 AWD 118 82 F Y N Alveolus II Moderate 04/04/05 NED 119 59 M Y Y FOM IV Moderate 9/21/05 AWD 120 60 M Y Y FOM II Moderate 07/02/07 NED 121 81 M N Y Tongue II Moderate 06/07/04 NED Buccal mucosa+Retro 122 74 M N N molar trigone IV Moderate 10/02/06 NED 123 45 M ? ? Tongue/FOM IV Moderate 09/04/02 NED Alveolus/Hard 87 77 F N N palate IV Moderate 03/21/07 LFU Retromolar 124 77 F Y N trigone IV Moderate 04/12/06 AWD 2 84 F N N Tongue IV Poor 06/24/07 LFU 13 64 M Former Y Tongue III Moderate 03/20/07 NED 9 72 F N N Tongue III Moderate 08/07/06 LFU 125 73 F Y Y Tongue II Moderate 10/07/03 NED 126 56 M Y Y Tongue/FOM IV Moderate 09/15/05 DOD 8 80 F N N Tongue/FOM II Moderate 10/03/02 DOD Well- 127 71 M Y Y Alveolus/FOM IV Moderate 03/13/05 NED

258 128 73 M N N Upper palate IV Well 08/10/05 ANED 129 60 F Y Y Tongue base III Moderate 11/26/98 AWD 3 70 M Y Y Tongue/FOM II Moderate 07/18/96 DNED 130 66 M Y Y Alveolus/FOM IV Poor 08/16/97 DOC 131 58 M Y Y FOM IV Moderate 04/16/04 NED 132 64 M Y Y Tongue/FOM IV Moderate 06/25/98 LFU 5 74 M Y Y Tongue IV Poor 05/12/99 DOD Well- 133 44 M N N Tongue II Moderate 03/21/05 ANED 134 64 F Y N Tongue II Moderate 08/22/05 AWD 135 71 M Former Y Tongue IV Moderate 07/13/07 ANED 136 72 M Y Y Tongue/FOM IV Moderate 06/02/08 NED 137 65 M Y Y Tongue III Moderate 09/29/08 ANED

Pt Experiment # Age Sex Tobacco Alcohol Tumor site Stage GRADE Last FU Outcome MSI/LOH 20 31 M Y Y Tongue III Moderate 05/06/06 NED 24 26 M Y N Tongue/FOM IV Moderate 12/18/06 NED 23 37 M Y Y Tongue IV Moderate 01/05/05 NED 27 43 M N N Tongue/FOM II Poor 04/04/07 NED 26 35 F Y N Tongue II Moderate 03/06/07 NED 94 41 M Y Y Tongue I Well 01/09/07 NED 38 40 M N N Tongue II Moderate 8/18/98 AWD 95 39 M N N Alveolus IV Moderate 10/16/06 NED 49 32 M Y N Tongue III Moderate 05/04/05 NED 19 22 M Y N Tongue IV Moderate 11/28/01 AWD 96 43 M Y Y FOM II Moderate 01/31/97 NED 97 37 M Y Y FOM IV Moderate 07/12/04 NED 98 39 F Y Y Tongue III Well 8/16/01 LFU 99 37 F Y N FOM IV Moderate 12/15/98 LFU 100 32 M N N Tongue I Moderate 3/29/07 NED 101 43 M Y Y FOM I Moderate 11/06/99 NED

259 102 34 F N N Tongue IV Poor 08/04/00 AWD 17 39 M N N Tongue IV Moderate 12/05/00 DOD 22 39 F N N Tongue III Moderate 08/18/04 AWD 103 33 F ? ? Buccal Mucosa IV Poor 07/16/06 NED 104 37 M N N Tongue IV Moderate 08/15/07 NED 105 38 F Former N Tongue IV Moderate 03/11/05 NED 106 23 F N N Tongue I Moderate 10/01/06 NED 107 41 M N N Tongue I Well 11/25/04 DOD 16 34 M Y Y Tongue IV Moderate 03/20/07 NED 32 38 F Y N Tongue IV Moderate 04/26/05 NED 108 36 F N N Tongue II Moderate 10/28/98 LFU 27 43 M N N Tongue/FOM II Poor 04/04/07 NED 36 41 M Y Y Tongue/FOM II Moderate 10/14/01 LFU 37 38 M Y Y FOM IV Moderate 03/08/04 DOD 38 40 M N N Tongue II Moderate 08/21/02 LFU Spindle 109 43 F N N Tongue base IV Cell 05/22/01 DOD 15 26 F N N Tongue IV Moderate 09/04/04 DOD 42 24 M N N Tongue III Moderate 06/29/03 NED 110 39 F Y N Tongue base IV Moderate 07/22/98 DOD 111 59 F N N Alveolus/FOM IV Moderate 12/10/03 NED 112 76 M Former N FOM IV Moderate 03/28/03 AWD 113 71 M Y Y Tongue IV Poor 12/22/04 AWD Well- 114 83 F Y N Alveolus/FOM III Moderate 4/23/07 NED 115 74 M Y Y Buccal mucosa IV Well 4/18/06 NED 116 65 F Y Former Alveolus III Moderate 08/18/03 NED 91 70 F Y N FOM II Moderate 1/15/07 NED 117 78 F N N Alveolus/FOM IV Well 07/15/03 AWD 118 82 F Y N Alveolus II Moderate 04/04/05 NED 119 59 M Y Y FOM IV Moderate 9/21/05 AWD 120 60 M Y Y FOM II Moderate 07/02/07 NED 121 81 M N Y Tongue II Moderate 06/07/04 NED 260 Buccal mucosa+Retromolar 122 74 M N N trigone IV Moderate 10/02/06 NED 123 45 M ? ? Tongue/FOM IV Moderate 09/04/02 NED Alveolus/Hard 87 77 F N N palate IV Moderate 03/21/07 LFU 124 77 F Y N Retromolar trigone IV Moderate 04/12/06 AWD 2 84 F N N Tongue IV Poor 06/24/07 LFU 13 64 M Former Y Tongue III Moderate 03/20/07 NED 9 72 F N N Tongue III Moderate 08/07/06 LFU 125 73 F Y Y Tongue II Moderate 10/07/03 NED 126 56 M Y Y Tongue/FOM IV Moderate 09/15/05 DOD 8 80 F N N Tongue/FOM II Moderate 10/03/02 DOD Well- 127 71 M Y Y Alveolus/FOM IV Moderate 03/13/05 NED 128 73 M N N Upper palate IV Well 08/10/05 ANED 129 60 F Y Y Tongue base III Moderate 11/26/98 AWD 3 70 M Y Y Tongue/FOM II Moderate 07/18/96 DNED 130 66 M Y Y Alveolus/FOM IV Poor 08/16/97 DOC 131 58 M Y Y FOM IV Moderate 04/16/04 NED 132 64 M Y Y Tongue/FOM IV Moderate 06/25/98 LFU 5 74 M Y Y Tongue IV Poor 05/12/99 DOD Well- 133 44 M N N Tongue II Moderate 03/21/05 ANED 134 64 F Y N Tongue II Moderate 08/22/05 AWD 135 71 M Former Y Tongue IV Moderate 07/13/07 ANED 136 72 M Y Y Tongue/FOM IV Moderate 06/02/08 NED 137 65 M Y Y Tongue III Moderate 09/29/08 ANED

261 Appendix Table 2: Primer sequences for quantitative RT-PCR of MMR genes. Forward and reverse primers are provided for hPMS1, hPMS2, and hMLH1. Primers are given in a 5’>3’ direction. A= adenine; C= cytosine; G= guanine; T = thymine.

Primer Name Forward Primer Reverse Primer 5’-GCACCAGCATCCAAGGAGTT- hPMS1 5’-TTGCCTGCGGCAACAGTT-3’ 3’ 5’-CCTCACAGCCCAAAGACTCC- 5’-ACGCCTTTGTCAGAGATGGC- hPMS2 3’ 3’ 5’-TGCAACATCTCCCGGAGAAC- hMLH1 5’-CTTGTACCCCCCGGAGAAG-3’ 3’

262 Appendix Table 3: Primers sequences for cDNA amplification of mismatch repair genes. Forward and reverse primers are provided for hPMS1, and hPMS2. Primers are given in a 5’>3’ direction. Amplicon sizes are provided in base pairs. A= Adenine; C= Cytosine; G= Guanine; T = Thymine. Frag= Fragment; Fwd= Forward; Rev= Reverse.

MMR Gene Primer Names Sequence Amplicon Size (base pairs) hPMS1 hPMS1 Frag A Fwd 5’-ATGAAACAATTGCCTGCGGCAAC-3’ 1725 hPMS1 Frag A Rev 5’-CATGGGTTTCTTGATTACTCG-3’ hPMS1 Frag B Fwd 5’-GCAGGTCTTGAAAACTCTTCG-3’ 1344 hPMS1 Frag B Rev 5’-TAAAAACAAGTCAGTGAATCCTC-3’ hPMS2 hPMS2 Frag A Fwd 5'-GGATCGGGTGTTGCATC-3' 1633 hPMS2 Frag A Rev 5’-CTTTCTCCTGAGAGTCCA CAT– 3’ hPMS2 Frag B Fwd 5'-GCAGCCACTGCTGGATGTTGAAG-3' 1624 hPMS2 Frag B Rev 5'-GGTTTAAAAGGGTTCTCAAGATCAC-3'

263 Appendix Table 4: Primers for sequencing of hPMS1 and hPMS2 cDNA products. Forward and reverse primers are provided for hPMS1, and hPMS2. Primers are given in a 5’>3’ direction. A = Adenine; C = Cytosine; G = Guanine; T = Thymine. Frag = Fragment; Fwd = Forward; Rev = Reverse.

Primer Name Fragment Primer Sequence hPMS1 Frag A Fwd hPMS1 Frag A 5’-ATGAAACAATTGCCTGCGGCAAC-3’ hPMS1 Frag A Rev hPMS1 Frag A 5’-CATGGGTTTCTTGATTACTCG-3’ hPMS1 Frag B Fwd hPMS1 Frag B 5’-GCAGGTCTTGAAAACTCTTCG-3’ hPMS1 Frag B Rev hPMS1 Frag B 5’-TAAAAACAAGTCAGTGAATCCTC-3’ hPMS1 C hPMS1 Frag A 5’-TGGAAGACATTGAGTGAAGAGG-3’ hPMS2/1 hPMS2 Frag A 5’ –GTAGAAATGGTGACATCGCTC– 3’ hPMS2/2 hPMS2 Frag A 5’ –ACTTACACGGATGCCTGCTG– 3’ hPMS2/3 hPMS2 Frag A 5’ –GACTCCGTGTGTGAAGAGTACGG– 3’ hPMS2/4 hPMS2 Frag A 5’ –CAACAAATGGATACTGGTGTCG– 3’ hPMS2A-R hPMS2 Frag A 5’ – CTTTCTCCTGAGAGTCCA CAT– 3’ hPMS2/6 hPMS2 Frag B 5’ –TCTGACAAAGGCGTCCTGAG– 3’ hPMS2/7 hPMS2 Frag B 5’ –TCTCAGGTTGATGTAGCTGTG– 3’ hPMS2/8 hPMS2 Frag B 5’ –GGCTCATAGCACCTCAGACTCTC– 3’

264 Appendix Table 5: Clinical characteristics of patients for quantitative RT-PCR. Summary of clinical characteristics of oral cancer patients used for mRNA expression detection by quantitative RT-PCR. Significant values are p≤0.05. Y = Yes; N = No.

Characteristic Young (<45) Older (≥45) p value Sex Male 9 7 0.71 Female 5 7

Tobacco Y 7 7 1 N 7 6

Alcohol Y 3 7 0.24 N 11 7

Stage Early 4 5 1 Late 10 9

Grade Poor 1 2 0.6 Moderate 13 11 Well 0 1

265 Appendix Table 6: Quantitative RT-PCR of hPMS1, hPMS2, and hMLH1in oral cancers from young and older patients.

Sample ID # Sample ID # hPMS1 hPMS2 hMLH1 hPMS1 hPMS2 hMLH1 (Older Pts.) (Older Pts.) 1N 0.80 0.00 1.13 1T 2.35 0.70 1.21 2N 0.78 0.10 1.05 2T 1.46 0.61 0.78 3N 3T 0.44 2.21 4N 0.93 0.00 0.08 4T 5N 0.75 0.92 0.53 5T 0.15 0.08 0.34 6N 6T 7N 0.68 0.01 0.26 7T 0.12 0.01 0.06 8N 8T 6.40 0.31 3.82 9N 0.08 0.02 0.02 9T 0.49 1.04 10N 0.12 0.00 0.25 10T 0.97 0.43 0.93 11N 2.42 0.61 1.86 11T 0.40 0.11 0.61 12N 24.36 0.20 0.08 12T 2.81 0.18 1.21 13N 0.18 0.38 0.38 13T 0.89 0.15 1.04 14N 79.23 0.54 0.90 14T 0.68 0.13 0.51 Sample ID # Sample ID # PMS1 PMS2 MLH1 PMS1 PMS2 MLH1 (Young Pts.) (Young Pts.) 15N 8.76 0.45 0.34 15T 0.30 0.07 0.34 16N 73.03 0.30 1.02 16T 2.05 1.69 1.11 17N 0.62 1.04 17T 3.22 2.94 2.58 18N 0.83 0.92 18T 1.05 0.71 0.93 19N 15.34 0.33 1.25 19T 2.38 2.10 20N 23.31 0.26 1.31 20T 0.49 0.17 0.58 21N 2.88 0.01 0.14 21T 0.24 0.60 22N 57.93 0.65 2.61 22T 4.39 1.23 4.33 23N 46.98 0.69 2.21 23T 2.39 0.75 1.95 24N 0.32 0.02 0.15 24T 0.76 0.68 25N 0.31 0.25 0.16 25T 0.89 1.00 26N 0.43 0.06 0.37 26T 1.42 0.67 27N 0.42 0.08 0.84 27T 0.42 0.02 0.06 28N 1.26 0.20 1.39 28T 1.36 1.44

266 Appendix Table 7: Clinical characteristics of patients for tumor immunohistochemistry analysis. Summary of clinical characteristics of oral cancer patients used for protein level intensity by immunohistochemistry. Significant values are p≤0.05 and are denoted (*). Y= Yes; N= No.

Characteristic Young (<45) Older (≥45) p value Sex Male 23 22 0.38 Female 15 22

Tobacco Y 17 11 0.78 N 13 11

Alcohol Y 9 7 1 N 21 15

Stage Early 14 8 0.42 Late 18 17

Grade Poor 3 1 Moderate 31 27 0.02* Well 4 16

267 Appendix Table 8: Immunohistochemistry results of MMR proteins in oral cancers from young and older patients. Values represent final scores from the sum of intensity (weak, moderate, strong) and percentage of tissue positive for MMR protein expression.

Young Pt. Sample ID hPMS1 Score Expression hPMS2 Score Expression hMLH1 Score Expression 29 5.0 Moderate 2.0 Weak 4.0 Weak 30 4.0 Weak 5.0 Weak 5.0 Moderate 31 7.0 Strong 32 5.0 Moderate 3.0 Weak Weak- 33 5.0 Moderate 4.5 moderate 34 5.0 Moderate 35 2.0 Weak 3.0 Weak 5.0 Moderate Weak- Moderate- 36 4.0 Moderate 6.5 strong Moderate- 37 6.0 Moderate 6.5 strong 38 7.0 Strong 39 6.0 Moderate 15 3.0 Weak 6.5 Mod-strong 40 5.0 Moderate 5.0 Moderate 4.0 Moderate 41 3.0 Weak 2.0 Weak 42 3.0 Weak 3.0 Weak 43 2.0 Weak 16 4.0 Weak 5.0 Weak 44 6.0 Moderate 6.0 Moderate 45 5.0 Weak 2.5 Weak 3.0 Weak Weak- Weak- 46 4.0 Weak 4.5 moderate 3.5 moderate 47 4.0 Weak 6.0 Moderate 48 4.0 Weak- 268 Moderate 49 5.0 Moderate 5.0 Moderate 18 3.0 Weak 5.0 Moderate Weak- 50 4.0 Moderate 2.5 moderate 51 3.0 Weak 20 3.0 Weak 24 3.0 Weak 4.5 Weak-mod Weak- 52 4.0 Moderate 5.0 Moderate Moderate- 53 5.0 Moderate 5.5 strong 54 6.0 Moderate 4.0 Weak 21 5.0 Weak 6.0 Moderate 17 Weak- 19 4.5 moderate 22 6.0 Moderate 23 5.0 Moderate Weak- 27 5.0 Moderate 5.5 moderate 26 3.5 Weak-mod 4.0 Moderate Older Pt. Sample ID hPMS1 Score Expression hPMS2 Score Expression hMLH1 Score Expression 55 3.0 Weak 2.0 Weak 3.0 Weak Weak- 56 6.0 Moderate 5.5 moderate 57 4.0 Weak 2.0 Weak 3.0 Weak 58 4.0 Weak 4.0 Weak 4.0 Moderate 59 6.0 Moderate 4.0 Moderate Weak- 60 3.0 Weak 2.0 Weak 4.5 moderate 61 3.0 Weak 2.5 Weak- 5.5 Mod-strong 269 moderate Weak- 62 0.0 Absent 4.5 moderate Weak- 63 5.0 Moderate 3.5 moderate 64 4.0 Weak 5.0 Moderate 65 7.0 Strong Weak- 66 5.0 Weak 3.5 moderate 5.0 Weak Weak- 67 5.0 Moderate 68 4.0 Weak 6.0 Moderate 69 6.0 Moderate 6.0 Moderate 70 6.0 Moderate 3.0 Weak 71 6.0 Moderate 3.0 Weak 4.0 Weak 72 6.0 Moderate Weak- 73 6.0 Moderate 5.5 moderate 6.0 Moderate Weak- Weak- 74 3.0 Weak 4.5 moderate 4.5 moderate 75 4.0 Weak 2.0 Weak 6.5 Mod-strong 76 4.0 Weak 2.0 Weak Weak- 77 3.0 Weak 3.5 moderate 78 6.0 Moderate Weak- 79 6.0 Moderate 2.0 Weak 3.5 moderate Weak- 80 4.0 Weak 4.5 moderate 81 4.0 Weak 82 3.0 Weak 83 4.0 Weak

270 84 6.0 Moderate 85 6.0 Moderate 86 4.0 Weak 6.0 Moderate Weak- 87 4.0 Weak 5.5 moderate 4.0 Weak 88 5.0 Weak 5.0 Weak 89 4.0 Weak 90 6.0 Moderate 4.0 Weak Weak- 91 6.0 Moderate 3.5 moderate Weak- 92 3.0 Weak 5.5 moderate 93 5.0 Weak Weak- 2 3.5 moderate 3.0 Weak Weak- 8 2.5 moderate 6.0 Moderate 9 4.0 Moderate 4.0 Moderate Weak- 12 2.0 Weak 3.5 moderate Weak- 13 5.5 moderate LEGEND % of cells Score Intensity Score Final Final Score 0 0 Absent 0 Absent 0 <10 1 Weak 1 Low 2-3 10-30 2 Moderate 2 Moderate 4-5 31-60 3 High 3 High 6-7 >60 4

271 Appendix Table 9: Clinical characteristics of patients for microsatellite instability (MSI) and loss of heterozygosity (LOH) analyses. Summary of clinical characteristics of oral cancer patients used for MSI and LOH. A) MSI detection as used in our previously published analysis; B) MSI detection using the NCI MSI guidelines; and C) LOH detection. Significant values are p≤0.05.

A) MSI = 0 MSI = 1 MSI > 1 FACTORS Variable p-value (n=18) (n=14) (n=38) Age Young 7 10 18 0.17 Older 11 4 20

Sex Female 5 8 15 0.24 Male 13 6 23

Tobacco No 6 9 11 0.23 Yes 10 5 23 Former 1 0 3

Alcohol No 9 11 19 0.08 Yes 8 2 18 Former 0 1 0

Site Tongue 11 9 15 0.51 FOM 1 1 7 Tongue/FOM 3 1 5 Other 3 3 11

Stage I 1 3 1 0.3 II 3 3 10 III 3 3 5 IV 11 5 22

Node + 8 7 15 0.78 - 10 7 23

Differentiation Poorly 1 3 3 0.43 Moderately 15 11 30 Well 2 0 4

Status Alive 14 12 25 0.1 Dead 2 0 9

272 0 < MSI MSI= 0 MSI >=4 B) FACTORS Variable <4 p-value (n=18) (n=39) (n=13)

Age Young 7 23 5 0.24 Older 11 16 8

Sex Female 5 18 5 0.42 Male 13 21 8

Tobacco No 6 18 2 0.37 Yes 10 18 10 Former 1 2 1

Alcohol No 9 24 6 0.65 Yes 8 13 7 Former 0 1 0

Site Tongue 11 3 5 FOM 1 9 5 0.03 Tongue/FOM 3 22 2 Others 3 5 1

Stage I 1 4 0 II 3 10 3 0.71 III 3 7 1 IV 11 18 9

Node + 8 17 5 0.94 - 10 22 8

Differentiation Poorly 1 30 11 0.91 Moderately 15 5 1 Well 2 3 1

Status Alive 14 29 8 0.61 Dead 2 6 3

273 C) Variable LOH = 0 LOH = 1 LOH > 1 p- FACTORS (n=36) (n=13) (n=21) value

Age Young 21 4 10 0.23 Older 15 9 11

Sex Female 14 4 10 0.61 Male 22 9 11

Tobacco No 15 4 7 0.56 Yes 17 7 14 Former 3 1 0

Alcohol No 23 4 12 0.18 Yes 12 8 8 Former 0 0 1

Site Tongue 3 2 4 0.36 FOM 8 2 7 Tongue/FOM 22 7 6 Others 3 2 4

Stage I 3 1 1 0.9 II 9 2 5 III 7 2 2 IV 17 8 13

Node + 16 4 10 0.6 - 20 9 11

Differentiation Poorly 29 8 19 0.08 Moderately 2 4 1 Well 4 1 1

Status Alive 28 10 13 0.99 Dead 6 2 3

274 Appendix Table 10: MSI and LOH analysis of genomic loci in oral cancer from young and older patients. Thirteen microsatellite markers were assessed across the genome for each patient sample. Empty fields were microsatellite markers that were not analyzed. MSI= Microsatellite Instability; LOH= Loss of Heterozygosity; NI= Non-informative; H= Heterozygous.

Young Pt. Sample ID MSI LOH D2S123 D3S1255 D3S1038 D3S1260 D3S1266 DS1285 D3S1284 D5S346 D14S67 D9S104 D9S171 D21S258 D17S794 20 1 0 NI NI NI NI MSI H H H H NI NI NI NI 24 3 3 LOH NI LOH LOH H H MSI H H MSI MSI H NI 23 2 1 MSI NI H H MSI H H H NI H H NI LOH 27 0 0 NI NI H H H H H H H H H H NI 26 0 2 NI NI NI H LOH NI H NI NI H LOH NI NI 94 0 0 H NI NI NI H NI H H H H H NI H 38 2 2 MSI NI MSI H LOH NI LOH H 95 0 0 NI H H NI NI H H NI H H H NI NI 49 3 0 NI MSI NI NI H H NI MSI NI MSI NI NI 19 2 0 H MSI H NI MSI NI NI H H H H NI NI 15 1 2 LOH H NI NI LOH NI H H NI MSI NI H H 96 2 0 H H H NI NI NI NI MSI MSI NI H NI H 97 2 2 NI H NI MSI LOH H MSI NI LOH NI 98 0 0 NI H H NI NI H H H NI H NI NI 99 4 2 MSI H MSI H MSI NI H H H H LOH MSI LOH 100 1 2 H LOH H NI H NI LOH H H MSI H 101 1 1 H NI H NI NI NI NI NI H NI MSI LOH H 102 1 1 H NI NI NI NI MSI NI H H NI H H LOH 17 0 0 NI H H H NI NI H NI H NI NI NI NI 22 0 0 H NI NI H H H H H H H NI NI NI 103 3 0 NI NI NI H MSI NI NI MSI H MSI H H NI 104 0 0 NI NI H H NI NI H NI H H NI H H 105 2 0 H H H NI MSI NI NI H H MSI H NI H 106 1 0 H NI H NI H H MSI H H H H NI NI 107 3 0 MSI H H MSI NI NI NI NI MSI H H NI 16 4 2 NI LOH LOH MSI MSI NI NI H H NI MSI MSI NI 32 1 0 NI MSI H NI H H NI H H H NI NI H 108 10 2 MSI MSI MSI MSI MSI LOH NI MSI LOH MSI MSI MSI MSI 27 1 0 NI H H MSI H H H H H H H H NI 36 3 1 H H MSI MSI H H MSI NI NI NI LOH H NI 275 37 4 4 MSI LOH MSI LOH NI NI MSI LOH H LOH NI MSI NI 38 1 0 NI H MSI NI NI NI NI NI H H NI H H 109 2 0 NI MSI H MSI NI H NI NI H H NI H H 15 3 0 H MSI NI NI H NI MSI H NI MSI NI H H 42 1 0 NI MSI NI H H H H H H H H H NI 110 4 0 H MSI MSI MSI NI NI MSI

Older Pt. Sample ID MSI LOH D2S123 D3S1255 D3S1038 D3S1260 D3S1266 DS1285 D3S1284 D5S346 D14S67 D9S104 D9S171 D21S258 D17S794 111 1 2 NI NI NI LOH MSI H NI NI LOH H H H 112 5 0 NI H MSI MSI MSI H H H MSI NI MSI 113 0 1 H H H H NI H LOH NI H NI NI H 114 3 2 H NI H MSI MSI NI H H LOH MSI LOH H H 115 3 0 NI NI MSI H H NI MSI H H NI MSI NI NI 116 1 3 H MSI LOH H LOH NI NI LOH NI NI 91 4 3 H MSI MSI MSI NI LOH NI H H LOH LOH MSI H 117 2 3 MSI H MSI H NI NI H H H LOH LOH H LOH 118 1 0 H H NI MSI H H NI H H NI H NI NI 119 0 1 NI NI NI H NI NI LOH H NI NI H NI NI 120 5 2 MSI MSI H MSI MSI LOH H H NI NI LOH MSI NI 121 3 0 NI H NI H NI H MSI H H NI MSI MSI NI 122 0 0 NI H H H NI H NI H NI H NI NI NI 123 0 1 H H NI NI H NI H NI H H H NI LOH 87 3 2 NI NI NI MSI NI H LOH H H MSI MSI NI LOH 124 0 0 H NI NI NI H H NI NI 2 1 1 NI NI NI H MSI NI NI H NI NI H NI LOH 13 2 1 H MSI LOH NI NI H MSI NI H H NI 9 3 0 NI NI MSI H MSI H NI MSI NI 125 2 0 NI H H MSI NI H MSI NI H NI NI NI H 126 0 5 LOH H H H LOH H LOH LOH NI LOH 8 2 1 MSI H NI NI MSI NI NI H H LOH NI H H 127 4 2 MSI MSI NI LOH MSI LOH NI H H NI H H MSI 128 4 1 LOH H MSI NI H MSI NI H MSI H MSI NI NI 129 5 1 NI LOH MSI MSI NI NI MSI NI NI MSI MSI H NI 3 3 0 H NI NI MSI H NI NI MSI MSI NI H H H 130 4 4 H MSI LOH LOH LOH MSI LOH H H NI MSI H MSI 131 5 0 H NI NI MSI MSI MSI NI MSI H NI NI MSI H 276 132 0 8 H NI LOH LOH LOH LOH LOH LOH H NI LOH LOH NI 5 3 1 MSI NI MSI LOH MSI NI H H NI NI H H NI 133 0 0 H H NI H NI NI H NI NI NI NI NI NI 134 0 0 H H NI NI NI NI NI H H NI H NI NI 135 0 0 H H H NI H H NI H H NI H NI H 136 2 0 NI MSI NI NI H H MSI NI H NI H H H 137 0 0 NI H NI H NI H H NI H H NI NI

277 Appendix Table 11: MSI and LOH analyses of MMR loci in oral cancer from young and older patients. Three microsatellite markers were assessed at hPMS1, hPMS2, and hMLH1 loci for each patient sample. Empty fields were microsatellite markers that were not analyzed. MSI= Microsatellite Instability; LOH= Loss of Heterozygosity; NI= Non- informative; H= Heterozygous. hPMS1 hPMS2 hMLH1 Young Pt. Sample ID D2S2027 D2S300 D2S118 D7S481 D7S472 G67272 D3S1260 D3S1561 D3S1611 20 MSI NI NI NI H MSI 24 H NI NI LOH MSI LOH 23 MSI NI NI H NI NI H H H 27 H H NI H LOH MSI 26 H NI NI MSI NI H H NI NI 94 H NI H H NI H NI H H 38 LOH LOH NI MSI NI 95 H NI NI NI LOH H 49 H NI NI NI MSI 19 MSI NI H NI H NI NI H NI 15 H NI NI H NI H NI NI NI 96 H H H H NI NI NI H MSI 97 LOH LOH MSI MSI MSI NI 98 H NI H H NI H 99 H NI H H H 100 LOH NI H NI NI LOH NI H H 101 H NI NI NI NI H NI H NI 102 H NI NI H H H NI NI NI 17 LOH H NI H 22 MSI H H H H NI 103 H NI H LOH NI H H NI H 104 H MSI NI NI NI H H H NI 105 H NI H H NI LOH LOH 106 LOH NI NI H H NI NI NI 107 MSI NI MSI 16 H NI NI H NI MSI NI NI NI

278 PMS1 PMS2 MLH1 Older Pt. Sample ID D2S2027 D2S300 D2S118 D7S481 D7S472 D7S481 D3S1260 D3S1561 D3S1611 111 H NI H NI H H 112 H NI NI NI MSI MSI 113 H NI MSI H NI NI NI MSI 114 MSI NI NI H H H H MSI H 115 H NI NI H NI H H H NI 116 H NI NI NI NI NI 91 MSI NI MSI NI NI LOH LOH LOH NI 117 H H H NI H H 118 MSI H MSI H NI LOH 119 H NI NI LOH NI H 120 MSI NI NI H H NI 121 H NI NI H H NI NI H H 122 H NI NI NI NI H NI NI H 123 NI NI NI NI H NI NI H H 87 MSI NI H NI NI H NI H H 124 NI MSI NI LOH 2 H H NI MSI H H H 13 MSI MSI NI NI 9 H MSI NI NI NI H 125 MSI NI NI H MSI H NI H NI 126 H MSI NI NI H NI 8 NI NI NI NI NI H NI 127 LOH NI H H NI H NI NI NI

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