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Somatic cancer-driver mutations in endometriosis : implications beyond malignancy Lac, Vivian 2018

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  SOMATIC CANCER-DRIVER MUTATIONS IN ENDOMETRIOSIS: IMPLICATIONS BEYOND MALIGNANCY by Vivian Lac BMSc, The University of Western Ontario, 2016  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE  in  The Faculty of Graduate and Postdoctoral Studies  (Interdisciplinary Oncology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  October 2018  © Vivian Lac, 2018   ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis/dissertation entitled:  Somatic cancer-driver mutations in endometriosis: implications beyond malignancy  submitted by Vivian Lac  in partial fulfillment of the requirements for the degree of Master of Science In Interdisciplinary Oncology  Examining Committee: David G. Huntsman Co-supervisor Michael S. Anglesio Co-supervisor  Paul J. Yong Supervisory Committee Member Cathie Garnis Additional Examiner    Additional Supervisory Committee Members: Kelly McNagny Supervisory Committee Member  Supervisory Committee Member      iii Abstract  Introduction: Endometriosis is a chronic, inflammatory gynecological disease characterized by the ectopic growth of endometrial-like tissue.  Previous studies have established endometriosis as the precursor to clear cell and endometrioid ovarian carcinomas.  The presence of somatic driver mutations in endometriosis is believed to represent early events in transformation, however our group has recently described the presence of such mutations in nearly one-quarter of cases of deep infiltrating endometriosis (DE) – a form of endometriosis that rarely progresses to malignancy.  These mutations may play a fundamental role in the pathogenesis of endometriosis outside of the context of cancer, however it is unclear whether they occur in other forms of endometriosis or the eutopic endometrium – the likely tissue of origin for endometriosis.  The purpose of my study is to:  1) analyze and compare the mutational profiles of DE and incisional (iatrogenic; IE) endometriosis and 2) characterize somatic cancer-drivers that exist in the eutopic endometrium and determine whether the presence of such mutations reflect the aging of this tissue. Methods: I macrodissected endometriosis tissue from women with IE or DE.  Extracted DNA was analyzed by targeted sequencing and mutations were orthogonally validated by droplet digital PCR.  PTEN and ARID1A immunohistochemistry was also performed for each specimen.  Using the same protocol, I also analyzed hysterectomy and endometrial biopsy specimens obtained from cancer-free women. Results: Overall, we detected the presence of somatic alterations in 27.5% and 36.1% of IE and DE cases respectively.  These events affected canonical components of RAS/MAPK or PI3K-Akt signaling pathways.  Furthermore, over 50% of cancer-free women also harboured similar somatic alterations in their eutopic endometrial tissue.  The presence of somatic cancer-drivers in the eutopic endometrium are likely regional and are correlated with age (p = 0.048). Conclusions: My findings are consistent with a uterine origin of endometriosis.  Somatic cancer-driver alterations are commonly found in both endometriosis and the eutopic endometrium of cancer-free women and may reflect the accumulation of DNA damage   iv over time.  These somatic alterations alone are insufficient for malignant transformation and should be interpreted with caution in the early diagnosis of gynecologic malignancies given their common occurrence in cancer-free women.     v Lay Summary  Endometriosis is a common disease defined by the growth of endometrial tissue outside of the uterus.  Although endometriosis can develop into cancer, this is extremely rare.  We recently identified mutations commonly linked to cancer in deep infiltrating endometriosis – a form of endometriosis which virtually never progresses to cancer.  This finding suggests that mutations may play a fundamental role in the development of endometriosis itself.  However, it remains unclear if these mutations exist in other forms of endometriosis.  The main goals of this study were to determine whether mutations commonly linked to cancer exist in incisional endometriosis (another form of endometriosis) as well as the endometrium.  Through DNA sequencing we found that mutations are commonly found in both endometriosis and the endometrium of women and may be associated with aging.  Consequently, such mutations may be useful targets for endometriosis treatment yet should not be cause for concern for future cancer development.    vi Preface  This thesis was largely motivated by the findings published in Anglesio et al. (2017) in The New England Journal of Medicine, of which I am a contributing author.  A subset of samples from the work by Anglesio et al. were subject to more thorough mutational analysis in Chapter 3 of my thesis.  Collection of local specimens was approved by the UBC BC Cancer Agency Research Ethics Board [H05-60119] and the UBC Children’s and Women’s Research Ethics Board [H13-02563; H14-03040].  International specimens from The Referral Centre for Gynecopathology, University Hospital Tuebingen, and VU University Medical Centre were provided by our collaborators for Chapter 3.  Institutional review boards at these respective hospital sites approved specimen collection.  Experimental work including immunohistochemical experiments and next generation sequencing was approved by UBC BC Cancer Agency Research Ethics Board [H02-61375; H08-01411].  Chapter 3:  A version of Chapter 3 along with methodology detailed in Chapter 2 has been submitted and is currently under review.  I was the lead investigator for this work and along with Dr. Michael Anglesio, Dr. David Huntsman, and Dr. Paul Yong, I was responsible for study design.  I was also responsible for macrodissection, laser-capture microdissection, DNA processing, mutation calling based on targeted sequencing, orthogonal validation of mutations by droplet digital polymerase-chain-reaction (PCR), analyzing and interpreting the data, and writing the manuscript.  Dr. Leah Prentice, Dr. Jaswinder Khattra, and Amy Lum performed targeted sequencing of all samples in this study.  Dr. Rosalia Aguirre-Hernandez performed bioinformatics analysis of all targeted sequencing data.  Dr. Tayyebeh Nazeran and Dr. Basile Tessier-Cloutier reviewed cases and scored immunohistochemically stained slides.  Teresa Praetorius and Danielle Co provided assistance in sectioning and DNA extraction.  Dr. Martin Koebel from the University of Calgary (Calgary, AB) and the staff at the Genetic Pathology Evaluation Centre (Vancouver, BC) performed immunohistochemical staining.  Teresa Praetorius, Natasha   vii Orr, Heather Noga, Dr. Anna Lee, Dr. Jana Pasternak, Dr. Bernhard Kraemer, Dr. Sara Brucker, Dr. Friedrich Kommoss, and Dr. Stefan Kommoss were involved in the retrieval of specimens.    It is important to note that Chapter 3 represents a collaborative effort between our lab and the lab of Dr. Hugo Horlings at the National Cancer Institute (Amsterdam, The Netherlands) and co-led by Lisanne Verhoef (a subset of samples from her own master’s thesis have been included in our study).  Lisanne Verhoef, Dr. Hugo Horlings, Dr. Velja Mijatovic, and Dr. Maaike Bleeker conducted all data collection related to specimens from the VU Univeristy Medical Centre (Amsterdam, The Netherlands) aside from targeted sequencing analysis.  Targeted sequencing of these samples was performed at Contextual Genomics (Vancouver, BC) by Dr. Leah Prentice and Dr. Jaswinder Khattra.  All authors mentioned above provided manuscript edits.  Chapter 4: Chapter 4 represents original, unpublished work involving specimens retrieved solely from the local pathology archives at the Vancouver General Hospital (Vancouver, BC).  I am the lead investigator of this study and Dr. Michael Anglesio, Dr. David Huntsman, and myself have conceptualized this chapter of the study.  Dr. Arianne Albert also provided insight into statistical modelling and estimations of target sample size.  Similar to Chapter 3, my roles spanned macrodissection, laser-capture microdissection, DNA processing, mutation calling based on targeted sequencing, orthogonal validation of mutations by droplet digital polymerase-chain-reaction, data analysis, in addition to designing the statistical model.  Many of the authors mentioned in the contributions to work in Chapter 3 were also involved to similar capacities to the work in Chapter 4.      viii Appendix I: Appendix I represents original, unpublished work involving specimens retrieved solely from the local pathology archives at the Vancouver General Hospital or BC Women’s Hospital (Vancouver, BC).  This work involves further exploratory analysis of a subset of endometriosis specimens analyzed in Chapter 3 along with several additional cases.  I am the lead investigator of this study and Dr. Paul Yong, Dr. Michael Anglesio, and myself have conceptualized this chapter of the study.  My roles in this sub-analysis are identical to those outlined in Chapter 3.  Many of the contributing authors mentioned in Chapter 3 were also involved to similar capacities to the work in Appendix I.  In addition, Natasha Orr and Heather Noga were greatly involved in the identification and selection of cases for this study.    ix Table of Contents  Abstract ....................................................................................................................................................... iii Lay Summary ............................................................................................................................................... v Preface ........................................................................................................................................................ vi Table of Contents ....................................................................................................................................... ix List of Tables .............................................................................................................................................. xii List of Figures ............................................................................................................................................ xiii List of Abbreviations .................................................................................................................................. xiv Acknowledgements ................................................................................................................................... xvi 1. Introduction ............................................................................................................................................. 1 1.1 Overview ............................................................................................................................................ 1 1.2 Classification ...................................................................................................................................... 1 1.3 Pathogenesis ...................................................................................................................................... 4 1.3.1 Pathology of Endometriosis ....................................................................................................... 4 1.3.2 Etiology ....................................................................................................................................... 6 1.3.3 Genetic Risk ................................................................................................................................ 7 1.3.4 Environmental Risk ..................................................................................................................... 7 1.4 Endometriosis and Cancer ................................................................................................................. 7 1.4.1 Observations and Epidemiological Findings .............................................................................. 7 1.4.2 Genetic Risk for EAOCs ............................................................................................................... 8 1.4.3 Loss of Heterozygosity in Endometriosis ................................................................................... 9 1.4.4 Somatic Mutations in Cancer and Concurrent Endometriosis .................................................. 9 1.4.5 Somatic Mutations in Endometriosis without Cancer ............................................................. 10 1.5 Goals of Current Study .................................................................................................................... 12 2. Methodology ......................................................................................................................................... 14 2.1 DNA collection and extraction ........................................................................................................ 15 2.1.1 Tissue Fixation .......................................................................................................................... 15 2.1.2 Needle Macrodissection ........................................................................................................... 16 2.1.2 Laser-Capture Microdissection ................................................................................................. 18 2.1.3 DNA Extraction and Quantification .......................................................................................... 19 2.2 Targeted panel sequencing ............................................................................................................. 19 2.2.1 Library Construction ................................................................................................................. 21   x 2.2.2 Mutation Calling ....................................................................................................................... 21 2.3 Orthogonal Validation by ddPCR .................................................................................................... 21 2.3.1 ddPCR Primers, Assays, and Optimal Temperatures ............................................................... 22 2.3.2 Pre-Amplification ...................................................................................................................... 25 2.3.3 Droplet Generation and Quantification ................................................................................... 25 2.3.4 ddPCR Controls ......................................................................................................................... 26 2.4 Technical Validation ........................................................................................................................ 27 2.4.1 KRAS G12 Variant Screening .................................................................................................... 27 2.5 Immunohistochemistry ................................................................................................................... 34 2.5.1 ARID1A immunohistochemistry ............................................................................................... 34 2.5.1 PTEN immunohistochemistry ................................................................................................... 34 3. Somatic Cancer-Driver Mutations in Incisional Endometriosis ............................................................ 36 3.1 Patient Specimen Collection ........................................................................................................... 36 3.1.1 Overview of Incisional Endometriosis Specimens ................................................................... 36 3.1.2 Overview of Deep Infiltrating Endometriosis Specimens ........................................................ 37 3.1.3 Ethics Approval ......................................................................................................................... 37 3.1.4 Vancouver General Hospital Cohort ........................................................................................ 38 3.1.5 The Referral Centre for Gynecopathology Cohort ................................................................... 38 3.1.6 University Hospital Tuebingen Cohort ..................................................................................... 38 3.1.8 British Columbia Women’s Centre for Pelvic Pain and Endometriosis Cohort ....................... 39 3.2 Additional Details on Methods ....................................................................................................... 40 3.2.1 Tissue Fixation .......................................................................................................................... 40 3.2.2 Tissue Microarray Construction ............................................................................................... 40 3.2.3 DNA Collection and Extraction ................................................................................................. 40 3.2.4 Targeted Panel Sequencing ...................................................................................................... 41 3.2.5 ddPCR ........................................................................................................................................ 41 3.2.6 ARID1A IHC ............................................................................................................................... 41 3.2.7 PTEN IHC ................................................................................................................................... 42 3.2 Results .............................................................................................................................................. 43 3.2.1 Sample Description ................................................................................................................... 43 3.2.2 Targeted Panel Sequencing ...................................................................................................... 45 3.2.3 Immunohistochemistry ............................................................................................................ 47 3.2.4 Total Mutation Rates ................................................................................................................ 48   xi 3.3 Discussion ........................................................................................................................................ 51 4. Somatic Cancer-Driver Mutations in Eutopic Endometrium ................................................................ 52 4.1 Patient Specimen Collection ........................................................................................................... 53 4.1.1 Overview of Hysterectomy Cohort .......................................................................................... 53 4.1.2 Overview of Biopsy Cohort ...................................................................................................... 54 4.1.3 Ethics Approval ......................................................................................................................... 54 4.2 Additional Details on Methods ....................................................................................................... 54 4.3 Results .............................................................................................................................................. 54 4.3.1 Sample Description for Hysterectomy Cases ........................................................................... 54 4.3.2 Sample Description for Endometrial Biopsy Cases .................................................................. 55 4.3.3 Overview of Somatic Cancer-Driver Events ............................................................................. 57 4.3.4 Multiple Sampling Analysis in Hysterectomy Cases ................................................................ 61 4.3.5 Relationship Between Age and Presence of Somatic Cancer-Driver Events in Endometrial Biopsy Cases ...................................................................................................................................... 64 4.4 Discussion ........................................................................................................................................ 67 5. Concluding Chapter ............................................................................................................................... 69 5.1 Overview of Findings ....................................................................................................................... 69 5.2 Limitations ....................................................................................................................................... 72 5.3 Future Directions ............................................................................................................................. 73 5.4 Significance of Study ........................................................................................................................ 75 References ................................................................................................................................................. 77 Appendices ................................................................................................................................................ 93 Appendix A: Summary of clinical data for women with IE. .................................................................. 93 Appendix B: Summary of clinical data for women with DE. ................................................................. 96 Appendix C: Summary of somatic cancer-driver events in women with IE. ........................................ 99 Appendix D: Summary of somatic cancer-driver events in women with DE. .................................... 102 Appendix E: Summary of clinical data for women in Hx cohort. ........................................................ 105 Appendix F: Summary of clinical data for women in Bx cohort. ........................................................ 107 Appendix G: Summary of somatic cancer-driver events in Hx patients. ........................................... 112 Appendix H: Summary of somatic cancer-driver events in Bx patients. ............................................ 115 Appendix I: Is the Presence of Somatic Cancer-Driver Events in Deep Infiltrating Endometriosis Associated with Deep Dyspareunia? ................................................................................................... 118 Appendix J: Pelvic Pain Assessment Form  ......................................................................................... 129    xii List of Tables  Table 1.1: Major and minor forms of endometriosis. .................................................................................. 4 Table 2.1: Gene hotspots and exons analyzed by FIND ITTM version 3.4 assay. ......................................... 20 Table 2.2: Primers designed for ddPCR experiments. ................................................................................ 23 Table 2.3: ddPCR assay details and optimal extension temperatures. ...................................................... 24 Table 2.4: Positive controls used for ddPCR experiments. ......................................................................... 26 Table 2.5: KRAS G12/G13 variant calling by targeted sequencing and droplet digital PCR. ...................... 29 Table 3.1: Overview of clinical characteristics of women in IE cohort. ...................................................... 43 Table 3.2: Overview of clinical characteristics of women in DE cohort. .................................................... 44 Table 3.3: Somatic cancer-driver mutations detected in endometriosis specimens from women with IE or DE. .............................................................................................................................................................. 46 Table 3.4: Reported P-values for pairwise comparisons of somatic events affecting specific genes. ....... 50 Table 3.5: Reported P-values for pairwise comparisons of somatic events affecting canonical pathways. ................................................................................................................................................................... 50 Table 4.1: Overview of clinical characteristics of women in Hx cohort. ..................................................... 55 Table 4.2: Overview of clinical characteristics of women in Bx cohort. ..................................................... 56 Table 4.3: Reported P-values for pairwise comparisons of somatic events in Hx patients and Bx patients. ................................................................................................................................................................... 61 Table 4.4: Specific point mutations observed in samplings obtained from Hx patients. ........................... 63 Table 4.5: Somatic cancer-driver mutations detected in Bx patients. ....................................................... 66     xiii List of Figures  Figure 1.1: The r-ASRM classification of endometriosis. .............................................................................. 3 Figure 1.2: The gross presentation of the major forms of endometriosis. .................................................. 5 Figure 2.1: Overview of workflow for this study. ....................................................................................... 15 Figure 2.2: Light staining procedure for needle macrodissection of endometriosis. ................................. 17 Figure 2.3: Laser-capture microdissection (LCM) of endometriosis for epithelial and stromal cell fractions. .................................................................................................................................................... 18 Figure 3.1: Boxplot comparison of age of IE and DE patients. ................................................................... 44 Figure 3.2. ddPCR validation of ERRB2 c.929C>T (p.S310F) mutation in the epithelial component of endometriosis in Patient IE_16. ................................................................................................................. 47 Figure 3.3: IHC studies of endometriosis specimens showing (A) loss of ARID1a in epithelial endometriosis cells in a case of DE and (B) matching H&E staining. ......................................................... 48 Figure 3.4: Overview of somatic cancer-driver events in incisional endometriosis and deep infiltrating endometriosis. ........................................................................................................................................... 49 Figure 4.1: Theoretical and simplified model of the pathogenesis of endometriosis. ............................... 52 Figure 4.2: Boxplot comparison of age of Hx and Bx patients. .................................................................. 56 Figure 4.3: Overview of somatic cancer-driver events in endometrial tissue from women in the A) Hx cohort and B) Bx cohort. .............................................................................................................. 58 Figure 4.4: Proportion of Hx and Bx cases affected by somatic cancer-driver events. .............................. 59 Figure 4.5: PTEN IHC studies of Hx specimens showing (A,C,E) regional loss/heterogeneous expression of PTEN in epithelial endometriosis cells and (B,D,F) matching H&E stain. ................................................... 60 Figure 4.6: Concordance and discordance of point mutations observed among both samplings obtained from Hx patients. ....................................................................................................................................... 62 Figure 4.7: Logistic regression model depicting the correlation between age and presence of somatic cancer-driver events in Bx patients. ........................................................................................................... 64 Figure 4.8: Logistic regression model depicting the correlation between age and presence of somatic cancer-driver events in Bx patients with endometrium samplings with respect to the phase of the endometrium. ............................................................................................................................................ 65 Figure 5.1: A revised model of the pathogenesis of endometriosis lesions. .............................................. 72 Figure 5.2: Schematic of the BaseScope assay. .......................................................................................... 74       xiv List of Abbreviations  BDNF – brain-derived neurotrophic factor Bx – biopsy CCOC – clear cell ovarian carcinoma CI – confidence interval COSMIC – Catalogue of Somatic Mutations in Cancer ddPCR – droplet digital PCR DE – deep (infiltrating) endometriosis EAOC – endometriosis-associated ovarian cancer ENOC – endometrioid ovarian carcinoma EPHect – World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonization Project  FFPE – formalin-fixed and paraffin-embedded GWAS – genome-wide association study H&E – hematoxylin-eosin Hx – hysterectomy IE – incisional endometriosis IHC – immunohistochemistry IL - interleukin LCM – laser-capture microdissection LOH – loss of heterozygosity MFPE – molecular-fixed and paraffin-bedded   xv NGS – next-generation sequencing OCP – oral contraceptive pill  OR – odds ratio Pap – Papanicolaou r-ASRM – revised scoring system of the American Society for Reproductive Medicine SD – standard deviation TMA – tissue microarray VAF – variant allele frequency VGH – Vancouver General Hospital VUMC – VU University Medical Center     xvi Acknowledgements  I would like to express my deepest gratitude and appreciation towards my co-supervisors, Dr. David Huntsman and Dr. Michael Anglesio.  Without their ongoing guidance and encouragement, this study would not have been possible.  Dr. Huntsman has challenged me to think critically about my research and he continues to inspire me with his excitement regarding this research in this field.  Dr. Anglesio has been both an incredible supervisor and teacher and has provided me endless advice for success in the lab and beyond.    I would also like to thank my committee members, Dr. Paul Yong and Dr. Kelly McNagny, both of whom have provided key insight in the development of this study and have helped me set realistic goals for success as a graduate student.  This work would have been impossible without the assistance of my fellow colleagues in the Huntsman and Anglesio labs.  In particular, Dr. Tayyebeh Mehrane Nazeran and Dr. Basile Tessier-Cloutier, have helped me review all the cases included in this study.  I am extremely grateful for Amy Lum for her assistance and training for various laboratory techniques crucial to my research.  I am thankful for the Canadian Institutes of Health Research for providing me with financial support as a graduate student as well as support for the study itself.  I would also like to thank the Canadian Cancer Society and BC Cancer Foundation for providing the funding essential to perform the experimental work involved in this study.  Finally, I would like to thank my brother for providing me emotional support and wisdom over the course of my graduate studies.   1 1. Introduction  1.1 Overview Characterized by the growth of endometrial-like glands and stroma outside of the uterus, endometriosis is an estrogen-dependent, chronic, inflammatory gynecological disease affecting roughly 10% of reproductive-aged women and up to 50% of those with infertility or chronic pelvic pain1-3.  Clinical symptoms of endometriosis overlap with other gynecological conditions and can include: chronic (often cyclical) pain, dyspareunia (painful intercourse), dysmenorrhea, infertility, nausea, dyschezia (difficult or painful defecation), and dysuria2,3.  An estimated 176 million women are affected by endometriosis globally4, yet despite the prevalence of endometriosis, it is an underdiagnosed and poorly managed condition.  Firstly, there are currently no reliable biomarkers for the non-invasive detection of endometriosis and a definitive diagnosis requires surgery2,3, contributing to a mean latency period of 6.7 years from initial onset of symptoms to diagnosis with endometriosis. Secondly, recurrence rates of endometriosis following surgical resection are high; the estimated recurrence rate of endometriosis is 40-50% at 5-year follow-up5.  Consequently, many women continue to live with endometriosis for years and thus the condition represents a major burden to both affected individuals and society at large.  1.2 Classification First described by Carl Freiherr von Rokitansky in 18606, endometriosis is primarily found on the ovaries, surrounding pelvic peritoneum, and uterine ligaments, however it is not restricted to affecting sites in the pelvic region3,7,8. Rare cases of extra-pelvic endometriosis have been reported to affect the lungs, liver, pericardium, central nervous system9,10, and even the site of surgical incisions7,8,11. Pelvic endometriotic lesions can be further divided into three subtypes: ovarian endometriosis (also known as endometriomas, which appear as cystic masses of the ovary), superficial peritoneal endometriosis, and deep infiltrating endometriosis (which is surgically defined as lesions that penetrate >5mm into pelvic structures)2,12.  2 There are numerous ways that endometriosis can present itself clinically with respect to its histological appearance and potential anatomical sites affected. Professional organizations have developed a multitude of classification systems for endometriosis in efforts to stratify disease burden and guide disease management.  The revised scoring system of the American Society for Reproductive Medicine (r-ASRM) is one of the most widely used classification system for endometriosis13,14.  In the r-ASRM, a weighted point system based on the type, location, depth of invasion, and histologic appearance of endometriosis lesions, the extent of disease, and the presence of adhesions are used to determine the stage of endometriosis from Stage I (minimal disease) to Stage IV (severe disease)3.  Other classification systems exist such as the Enzian classification for deep infiltrating endometriosis15 and the endometriosis fertility index16, however none of the current classifications systems correlate well with severity of pain17 and they do not prognosticate for critical endpoints such as response to treatment, recurrence, or risk for malignant transformation14.   3  Figure 1.1: The r-ASRM classification of endometriosis.  [Figure adapted from American Society for Reproductive Medicine (1997) 13.]   4 1.3 Pathogenesis 1.3.1 Pathology of Endometriosis Endometriosis is a multi-factorial disease and its pathogenesis is not fully understood.   As mentioned in Section 1.2, endometriosis typically presents itself in three distinct forms: superficial peritoneal endometriosis, ovarian endometriotic cysts, or deep infiltrating endometriosis (Table 1.1; Fig. 1.2). These major forms of endometriosis as well as several minor forms (including iatrogenic, mass-forming, and extrapelvic endometriosis) are described in Table 1.1.  It is important to note that occurrences are not mutually exclusive and affected patients may have several lesions of different forms simultaneously.   Table 1.1: Major and minor forms of endometriosis.  Type of Endometriosis Brief Description superficial peritoneal lesions existing on the surface of pelvic peritoneum and ovaries2 ovarian (endometrioma) ovarian cysts lined by endometrioid mucosa2 deep infiltrating lesions that locally invade into pelvic structures2 iatrogenic occurs following surgical scars of obstetric or gynecological procedures such as caesarean sections and laparoscopies18  mass-forming benign-appearing endometriotic glands and stroma forming mass lesions and infiltrating organs, mimicking a malignant neoplasm19 extrapelvic lesions found outside of the pelvic cavity, including: lungs, liver, pericardium, the central nervous system, etc.8-11   Histologically and by definition, endometriotic lesions are comprised of epithelial and stromal components and consist of normal-appearing cells that closely resemble the eutopic endometrium.  Unlike the eutopic endometrium however, endometriotic lesions are characteristically progesterone resistant and locally produce estrogen, prostaglandins, and cytokines20,21. These alterations contribute to increased cell survival (and thus the persistence of endometriotic tissue over many menstrual cycles) and constant, localized inflammation, which may ultimately result in chronic pelvic pain and infertility experienced by women with endometriosis20-23.   5  Figure 1.2: The gross presentation of the major forms of endometriosis.  The three most prevalent forms of endometriosis – (A) superficial peritoneal endometriosis, (B) ovarian endometriosis (endometrioma), and (C) deep infiltrating endometriosis are shown.  [Figure adapted from Vercellini et al. (2014) 2.]    6 1.3.2 Etiology The origin of endometriosis is contentious and several theories on its etiology have been proposed.  Such theories include retrograde menstruation (the reflux of endometrial fragments through the fallopian tubes during menstruation), coelomic metaplasia, Müllerian remnants, and lymphatic or vascular dissemination2,20.  Although each theory is supported by at least circumstantial evidence, each theory has its limitations and no single theory can fully explain every incident case of endometriosis. The most popular theory, retrograde menstruation theory, posits that the development of endometriosis is attributed to the sloughing/reflux of eutopic endometrium through the fallopian tubes and into the peritoneal cavity during menstruation24.  This theory is supported by the high prevalence of endometriosis in females with congenital outflow obstruction25 as well as the anatomic distribution of endometriotic lesions24.  Although up to 90% of women exhibit this reflux of endometrial tissue24, a far smaller percentage of women (approximately 10%) have endometriosis – this may be explained by: 1) the impaired capacity of immune cells to mediate the clearance of refluxed endometrial cells and 2) other alterations in the cell-mediated and humoral immunity leading to increased levels of a variety of cytokines and growth factors, which may promote the implantation and growth of refluxed endometrial cells26.  However, this theory fails to explain why endometriosis has been observed in distant/extra-pelvic regions such as the lungs and pericardium. In contrast a second theory, coelomic metaplasia theory, posits that endometriosis arises from the transformation of normal peritoneal tissue to ectopic endometrial tissue24.  Metaplasia can explain why endometriosis can develop at distant sites that cannot physically contact refluxed endometrial tissue or why endometriosis can develop in males27,28.  This theory is supported by experimental models such as the mouse model developed by Dinulescu et al. (2004), wherein the expression of oncogenic KRAS or conditional deletion of PTEN within the ovarian surface epithelium was observed to give rise to preneoplastic lesions resembling endometriosis29.  However, causative agents of such transformation of normal peritoneal (or other bodily tissues) remain poorly defined24.    7 1.3.3 Genetic Risk Family and twin studies demonstrate the important contribution of genetic factors to the development of endometriosis.  The incidence of endometriosis in the first-degree relatives of women affected with endometriosis is nearly seven times greater than women lacking such family history30. Furthermore, based on a study on the concordance of endometriosis among 3096 Australian twins, the heritability component of endometriosis is estimated to be 51%31. A genome-wide linkage study of 1,176 families with two or more sister pairs affected by endometriosis has implicated a susceptibility locus for endometriosis on chromosome 10q2632.  More recent genome-wide association (GWAS) studies have revealed WNT4 (rs12037376)33,34,35, CDKN2BAS (rs10965235)33,34, rs12700667 on chromosome 7p15.2 (an intergenic region upstream of NFE2L3 and HOXA10)36,35, ESR1 (rs2206949)37, SYNE1 (rs17803970)37, FN1 (rs1250241)33,36,37,  VEZT (rs10859871)35, and GREB1 (rs13394619)35  among others as susceptibility loci for endometriosis. 1.3.4 Environmental Risk Lifestyle and environmental factors may also contribute to the development of endometriosis.  Consumption of red meat and trans-unsaturated fats is associated with an increased risk, whereas consumption of green vegetables and long-chain omega-3 fatty acid consumption is associated with decreased risk for endometriosis38,39.  Additionally, dioxin exposure as well as in utero diethylstibestrol exposure may also increase the incidence of endometriosis40,41.  1.4 Endometriosis and Cancer 1.4.1 Observations and Epidemiological Findings One of the more well-studied areas of endometriosis research focuses on its relationship with ovarian cancer. Endometriosis and ovarian cancer share several risk factors including early menarche and late menopause, nulliparity, and short intervals between menses, thereby hinting at a possible relationship between endometriosis and ovarian cancer42.  In 1925, Sampson first proposed a theory wherein endometriosis (specifically ovarian endometrioma) can transform into ovarian cancer43. This theory has   8 been supported by epidemiological studies, which demonstrate an association between endometriosis and ovarian cancers44-46.  It is important to note that epithelial ovarian carcinomas are comprised of five major histological subtypes: high-grade serous, low-grade serous, mucinous, clear cell (CCOC), and endometrioid (ENOC) ovarian carcinomas – the association between endometriosis and ovarian cancer is restricted to the CCOC and ENOC subtypes (known as the endometriosis-associated ovarian cancers; EAOCs).  In particular, women suffering from (surgically-confirmed) endometriosis have a 3 to 5-fold greater risk of developing CCOC and ENOC, wherein ovarian endometriomas are associated with the highest risk for EAOC development followed by superficial peritoneal endometriosis and then deep infiltrating endometriosis47.  Although the study by Pearce et al. (2012) shows a weak association between endometriosis and low-grade serous ovarian cancer45 , a smaller study by Merritt et al. (2013) does not46 .  Moreover, this association remains unexplained as low-grade serous ovarian cancers have not been found contiguous with endometriosis as with CCOCs and ENOCs.  Data pooled from 13 population-based case-control studies of over 10,000 women with ovarian cancer revealed that tubal ligation preferentially protects against CCOC and ENOC among ovarian cancer histotypes48, a finding which further suggests that retrograde menstruation may be a key factor in the genesis of these cancers or their precursor lesions. 1.4.2 Genetic Risk for EAOCs A limited number of studies have explored the subtype-specific genetic risk factors associated with the different ovarian cancer histotypes.  Epigenetic analysis has led to the identification of HNF1B as a susceptibility gene for CCOC49 , whereas a recent GWAS study identified rs555025179 on 5q12.3 as a susceptibility loci for ENOC50.  Interestingly, there is little overlap in susceptibility loci between endometriosis and EAOCs – the closest match appears to be around the ESR1/SYNE1 loci (rs2295190)43 (two genes involved in sex steroid hormone pathways), however this association does not reach genome-wide significance for either CCOC nor ENOC and the specific risk loci differs for endometriosis37,51 (see section 1.3.3).   9 1.4.3 Loss of Heterozygosity in Endometriosis Despite the classification of endometriosis as a benign disease, endometriotic lesions can harbor genetic aberrations.  Loss of heterozygosity (LOH) was one of the first genomic abnormalities observed in endometriosis52.  Microsatellite analysis studies revealed LOH in endometriosis affecting regions commonly lost in ovarian neoplasms including 1q, 9p, 11q, 17p, and 22q52,53.  Providing molecular evidence for the progression of endometriosis to EAOCs, Sato et al. (2000) found that 3/5 ENOC cases and 3/7 CCOC cases with concurrent endometriosis displayed LOH events on 10q23.3 (a region encoding PTEN) common to both the carcinoma and the endometriosis54.  Moreover, a separate study analyzing 82 microsatellite markers spanning the genome detected 63 LOH events among 10 EAOC samples, wherein 22 LOH events were subsequently detected in the corresponding endometriosis samples (yet LOH events were never detected in the endometriosis only)55.  This data further suggests the selection for/accumulation of LOH events as endometriosis progresses to malignancy.  It is important to note, however, that although these LOH events provide support for clonality between endometriosis and concurrent EAOCs, they are insufficient to conclude a clonal origin between the two since common LOH events may instead indicate that similar pathway aberrations are involved in endometriosis and cancer. 1.4.4 Somatic Mutations in Cancer and Concurrent Endometriosis Molecular studies focusing on somatic mutations found in EAOCs and contiguous endometriosis lesions have established endometriosis as the precursor of EAOCs.  Both CCOC and ENOC are characterized by a high prevalence of ARID1A mutations and frequent activation of the PIK3CA-mTOR pathway56,57 – such aberrations have been found in endometriosis, therefore implicating such events as early markers in malignant transformation.  Wiegand et al. described ARID1A mutations in 55 of 119 CCOC cases (46%) and 10 of 33 ENOC cases (30%)58.  Moreover, identical ARID1A mutations were found in the ovarian carcinoma and contiguous atypical endometriosis but not in distant lesions of endometriosis58.  Similarly, Anglesio et al. consistently observed ARID1A and PIK3CA mutations in concurrent endometriosis when present in primary CCOCs59.  Whole-genome shotgun sequencing in this study also revealed the presence of ancestral mutations in both distant and tumour-adjacent endometriotic lesions, with generally   10 increasing mutational burden the more central to the tumour site the endometriotic lesions were located59.  In short, recent molecular studies focusing on somatic mutations in EAOCs and concurrent endometriosis have led to the following observations regarding the nature of endometriosis: 1. Endometriotic lesions found adjacent to or contiguous with CCOC or ENOC harbour many of the same mutations, therefore some endometriotic lesions share origins with the cancer (i.e. a clonal relationship exists). 2. Both distant and adjacent endometriosis can share common mutations, therefore some endometriotic lesions (even without cytologic atypia) are capable of dissemination/metastasis. 3. Endometriosis contiguous with CCOC/ENOC typically share a larger number of mutations than more distant lesions, thereby supporting of a progression model wherein endometriosis is the direct precursor to CCOC/ENOC. 1.4.5 Somatic Mutations in Endometriosis without Cancer Although the studies outlined above have provided us with much insight into the relationship between endometriosis and EAOCs, it is crucial to also study the somatic mutations in endometriosis outside of the context of cancer.  Endometriosis is far more prevalent in the population than ovarian cancer (particularly CCOC and ENOC) and is estimated to progress to cancer in only approximately 1% of affected women2.  Furthermore, despite being considered a benign disease, endometriosis shares many notable features with cancer.  Unlike the eutopic endometrium, cells from endometriotic lesions are resistant to apoptosis and can stimulate angiogenesis20,60.  Particularly in deep infiltrating endometriosis, endometriosis is also capable of invading local tissue61. A small number of studies have identified somatic cancer-driver mutations in endometriotic lesions in patients without cancer.  Immunohistochemistry studies showed loss of ARID1A in a small percentage of benign ovarian endometriosis and deep infiltrating endometriosis cases62,63. Sato et al. (2000) reported somatic PTEN mutations in 7/34 (20.6%) endometriomas in cases without cancer through the sequencing of laser-  11 captured endometriotic lesions54.  The study of somatic mutations in endometriosis remains technically challenging since endometriotic lesions are often small and scattered among reactive and fibrotic (non-endometriotic) tissue.  Without specific enrichment of endometriotic cells (such as by laser-capture microdissection), mutations – particularly those at low allelic frequencies – may not be detectable among DNA contributed by surrounding, normal/non-endometriotic cells.  The dilution of the endometriotic cell fraction of interest (in whole-excised surgical specimens) is especially problematic combined with low-resolution detection methods (such as Sanger sequencing), which  generally detect variant allelic frequencies as low as only 10-20%64.  Such challenges may explain the extremely low rates of somatic KRAS mutations reported by two separate groups (1/23 patients and 0/19 patients)65,66, as well as the identification of somatic driver mutations in only 3/101 Chinese patients with ovarian endometriosis67 . In stark contrast, a whole-exome sequencing study by Li et al. (2014) reported on extremely high rates of somatic mutations (averaging over 1000 somatic mutations per case) within both endometriotic cells and eutopic endometrial cells68 , however no orthogonal validation was performed, and specimen curation, collection, and sequencing methods were ambiguously described.  It remains likely that many of the reported mutations represent sequencing artifacts, poor technical and/or analytical methods, or background noise and this study cannot be considered credible.  Compiling the findings of these studies, there has been little consensus on the extent to which somatic mutations exist in endometriosis outside of the context of cancer and the general belief maintains that such events are directly linked to malignant transformation69. In a recent study by our group along with collaborators at John Hopkins University, deep infiltrating endometriotic (DE) lesions were analyzed by lesions by means of exome-wide sequencing (24 cases), cancer-driver targeted sequencing (3 cases), and droplet digital PCR assay (12 cases)59.  Ten of 39 (26%) cases of DE harboured somatic mutations including known cancer-driver hotspots in KRAS, PIK3CA, and PPP2RIA as well as loss of function mutations in ARID1A59.  Since DE virtually never undergoes malignant transformation47, the function of these somatic mutations is unclear.  It is possible such mutations may contribute to the development and pathogenesis of DE unrelated to oncogenic change.  Another possibility is that the somatic mutation of one   12 allele of a given oncogene or tumour suppressor gene is insufficient for malignant transformation of endometriosis and further DNA damage rarely occurs in cases of DE compared to ovarian endometriomas.  Nonetheless, it is difficult to speculate on the functional role of somatic cancer-driver mutations in endometriosis found without associated cancer without elucidating the extent in which such mutations exist in other forms of endometriosis (to date, no equivalent analysis has been conducted on superficial peritoneal endometriosis, ovarian endometriomas, or even rarer forms of endometriosis).  Moreover, do such mutations already pre-exist in the eutopic endometrium (the presumed site of origin of endometriosis)?  To date, a limited number of studies have been published on the possible existence of somatic mutations in non-diseased (normal) eutopic endometrium, yet such studies have been largely restricted to immunohistochemistry studies of PTEN and PAX270-73.  1.5 Goals of Current Study The goal of my current research study is to explore the prevalence of somatic cancer-driver mutations among benign tissue types involved in EAOC pathogenesis (assuming the retrograde menstruation theory), particularly endometriosis and the eutopic endometrium.  I will seek to address the following questions: 1. “Do benign forms of endometriosis aside from DE harbour somatic cancer-driver mutations?”  In regards to Question 1, I hypothesize that other benign forms of endometriosis (i.e. forms of endometriosis with an exceptionally low likelihood of malignant transformation) harbour somatic cancer-driver mutations.  To test this hypothesis, I will compare the mutational profiles of DE to incisional endometriosis (IE – a rare, iatrogenic form of endometriosis) by means of targeted sequencing.  This comparison between DE and IE also serves to address a secondary question: does the mutational spectrum of iatrogenically-occurring endometriosis (IE) differ from endogenous forms of endometriosis (DE)?  I will compare the proportion of cases affected as well as the specific genes/signalling pathways involved in somatic mutations in both forms of endometriosis.    13 2. “Does the eutopic endometrium harbour somatic cancer-driver mutations?  If so, does the presence of somatic mutations reflecting aging of this tissue?”  In regards to Question 2, I hypothesize that the eutopic endometrium harbours somatic cancer-driver mutations and that age is associated with an increased likelihood of observing these somatic mutations.  To test this hypothesis, I will assess the presence of somatic cancer-drivers in the eutopic endometrium of women without evidence of gynecologic malignancy by means of targeted sequencing.  I will examine the presence of mutation and age of women studied to generate a logistic regression model to determine the effect of age on the likelihood of observing a somatic driver mutation in the eutopic endometrium (odds ratio).     14 2. Methodology  This chapter provides an overview of the common methods used throughout this work, specifically in the analysis of somatic alterations in benign forms of endometriosis (Chapter 3) and eutopic endometrium (Chapter 4).  Note that because of the small size of lesions and non-specific symptomology, the pathological confirmation and subsequent identification of endometriosis often requires formalin fixation of specimens.  Consequently, this work focuses on the analysis of archival endometriosis specimens since these are the most readily accessible endometriotic material.    Fig. 2.1 below illustrates a simplified version of the workflow.  The remainder of the chapter provides further details on the methodology.  Patient collection information and any deviations from the common methods presented here will be discussed in the relevant chapters.     15   Figure 2.1: Overview of workflow for this study.  Endometriosis and eutopic endometrium cases undergo pathology review to ensure lack of evidence of cancer or dysplasia and sufficient tissue for analysis.    Suitable cases undergo PTEN and ARID1A immunohistochemistry and are also macrodissected (in some cases laser-captured) to enrich for DNA of interest.  Extracted DNA is used in targeted sequencing and subsequently in orthogonal validation by means of droplet digital polymerase-chain-reaction.  2.1 DNA collection and extraction 2.1.1 Tissue Fixation Most endometriosis and all endometrial tissue specimens were formalin-fixed and paraffin-embedded (FFPE) tissues, which were fixed in (10%) neutral-buffered formalin and paraffin-embedded following standard methods.  A subset of endometriosis cases were molecular-fixed and paraffin-embedded (MFPE) tissues – endometriosis lesions from these cases were surgically removed and fixed in Tissue-Tek molecular fixative Pathology ReviewImmunohistochemicalStainingfor PTEN and ARID1ADNA Collection & Extractionmacrodissection and/or laser-capture of archival tissueDroplet Digital PCR Analysisfor orthogonal validationTargeted Sequencing for common somatic hotspot mutations  16 (Sakura Finetek, USA), processed, and embedded using the Tissue-Tek microwave rapid processing system (Sakura Finetek, USA)74.  2.1.2 Needle Macrodissection As defined, endometriosis tissue exists ectopically within other tissues.  Moreover, glands present are variable in number, shape, and size (although often miniscule compared to cancers) within a given tissue section.  Consequently, standard macrodissection or coring would result in collecting proportionally more cells from the surrounding, normal tissue than the endometriotic lesion itself and therefore poor resolution for the detection of mutations affecting endometriotic cells.  We have instead developed a protocol for stereo microscope-guided needle macrodissection of tissue specimens.  Firstly, specimens were sectioned at 8µm onto standard glass slides and baked at 60°C for 1 hour.  Next, slides were deparaffinized with xylene and stained with 10% diluted hematoxylin and eosin (which we will refer to as “light staining”) following the protocol below:   17  Figure 2.2: Light staining procedure for needle macrodissection of endometriosis.  Note that this protocol can be adapted for dissection of larger areas of interest such as endometrial tissue.  After light staining, using a standard hematoxylin-eosin (H&E) slide as a guide, I manually macrodissected tissue of interest (endometriosis or eutopic endometrium) under a stereo microscope using the tip of a 20-guage, bevel-tip needle.   18 2.1.2 Laser-Capture Microdissection (LCM) For technical validation of our targeted sequencing assay (see section 2.2) as well as orthogonal validation of mutations called by this assay, I collected laser-captured material for many of the cases we studied.  Samples were sectioned at 8µm onto PEN membrane slides (Leica Microsystems Inc., Switzerland).  Laser-captured specimens were also stained with the same light staining protocol as defined above for macrodissected specimens (Fig. 2.2).  I used serial H&E slides to identify areas of interest and performed LCM using the LMD7000 Laser Microdissection system (Leica Microsystems Inc., Switzerland).  Figure 2.3: Laser-capture microdissection (LCM) of endometriosis for epithelial and stromal cell fractions.  An area of endometriosis is shown (A) before LCM, (B) after separation of endometriotic epithelium, and (C) after subsequent separation of endometriotic stroma.    19 2.1.3 DNA Extraction and Quantification For all macrodissected and laser-captured tissue, I performed DNA extraction using the ARCTURUS® PicoPure® DNA Extraction Kit (ThermoFisher Scientific, USA).  Quantification of extracted DNA was performed using the Qubit 2.0 Fluorometer (ThermoFisher Scientific, USA).  2.2 Targeted panel sequencing We performed next-generation sequencing (NGS) of DNA extracted from macrodissected specimens using the FIND ITTM version 3.4 (Contextual Genomics, Canada) assay. The FIND ITTM version 3.4 assay is an Illumina-based, targeted sequencing assay which focuses on the detection of known mutations found in many solid tumour cancers (many of which are treatable with current therapies).  The assay can be performed on FFPE tissue to detect mutations at very low variant allele frequencies (VAFs) (< 1%).  Currently, over 120 hotspots and 17 exons in 33 known cancer genes are analyzed in the FIND ITTM panel.  These gene regions are outlined below in Table 2.1.        20 Table 2.1: Gene hotspots and exons analyzed by FIND ITTM version 3.4 assay.    Gene PositionAKT1 E17ALK T1151, L1152, C1156, F1174, L1196, L1198, G1202, D1203, S1206, R1275, G1269AR H875, F877, T878, S741, W742, V716BRAF G466, F468, G469, Y472, D594, G596, L597, V600, K601, Q201CTNNB1 D32, S33, G34, S37, T41, S45DDR2 I638,L239,S768EGFR Exon18,Exon19,Exon20,Exon21ERBB2 Exon20,G309,S310,L755ESR1 K303, S463, V534, P535, L536, Y537, D538FGFR1 N546, K656FGFR2 S252, P253, N549, K659GNA11 Q209GNAQ Q209GNAS R201HRAS G12,G13,Q61IDH1 R132IDH2 R140,R172JAK1 V658, S703KIT Exon9, Exon11, Exon13, T670, D816, D820, N822, Y823, A829KRAS G12, G13, A59, Q61, K117, A146MAP2K1 Q56, K57, K59, D67, P387MAP2K2 F57, Q60, K61, L119MET Exon13, Exon 14-50+25, Exon18, Y1253NRAS G12, G13, A59, Q61, K117, A146PDGFRA N659, R560-E571, D842, L839-Y849PIK3CA R88, E542, E545, Q546, D549, M1043, N1044, A1046, H1047, G1049PTCH1 W844, G1093PTEN R130, R173, I122_M134, S170_Y188, Y225_F243, K254_K267RET C634, V804, M918ROS1 L2026, G2032SMO D473, S533, W535STK11 Q37, P281TP53 Exon4, Exon5, Exon6, Exon7, Exon8, Exon9  21 2.2.1 Library Construction Libraries were constructed for panel-based sequencing for hotspot mutations in 33 genes (Table 2.1) using 45-75 ng of DNA input from endometriosis specimens.  DNA samples were barcoded and run using the 300-cyle MiSeq Reagent Kit v2 (Illumina Inc., USA).  Proprietary quality assurance methods based on DNA sequence barcodes, that were incorporated into the assay and the bioinformatics pipeline, were used to increase the sensitivity of called mutations.  The bioinformatics pipeline first removed poor quality reads based on sequence length and base mismatches in the primer region.  Good quality reads were then aligned to a reference genome.  2.2.2 Mutation Calling Mutations were called with a supervised classification method that returned the probability that a variant belongs to the mutation class (as opposed to the artifact class) based on the alignment, sequence composition and barcode information of the variant.  Germline mutations were removed from the list.  Candidate variants for orthogonal validation by ddPCR were selected as the ones with probability scores ≥ 0.8 and variant allele frequency (VAF) ≥ 0.8% for macrodissected samples (or VAF ≥ 5.0% for laser-captured samples).  Note that these VAF cut-offs were determined empirically following mutation calling thresholds for KRAS mutations I have established for macrodissected samples (see Chapter 2.4.1), since mutations (even if real) at lower VAFs would not be able to be orthogonally validated by droplet digital PCR (note that other mutations were reported far less often than KRAS and I assumed that such mutations would have similar detection thresholds).  Additionally, variants must have also been reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) as somatic, hotspot driver mutations75.  2.3 Orthogonal Validation by ddPCR DNA extracted from FFPE tissues is comparatively poor in quality.  Not only is DNA fragmented, but the process of this fixation results in the introduction of DNA lesions, which result in sequencing artifacts (most notably C>T changes)76.  Consequently, I used droplet digital PCR (ddPCR) to orthogonally validate mutations called by targeted   22 sequencing to determine whether mutations were real (true positives) or sequencing artifacts (false positives).  Macrodissected, and/or laser-captured, material was used to orthogonally validate hotspot mutations – in most cases, the same aliquot of DNA collected and extracted for targeted sequencing was used in these validations.  2.3.1 ddPCR Primers, Assays, and Optimal Temperatures Overall, I performed ddPCR for KRAS G12 hotspot mutations (G12S, G12A, G12V, G12D, G12R, and G12C) (technical validation purposes – see section 2.4) as well as mutations identified by targeted sequencing.  These included mutations in the following genes: PIK3CA (R88Q, H1047R), ERRB2 (S310F), CTNNB1 (G34V), FGFR2 (K659E) NRAS (G13D), as well as other mutations affecting KRAS (G13D).  The list of primers used for our ddPCR assays are provided in Table 2.2, whereas the specific details on each ddPCR assay and their corresponding optimal annealing/extension temperature are provided in Table 2.3.      23 Table 2.2: Primers designed for ddPCR experiments. Assay Description Primer Sequence Source KRAS G12/G13 (all) forward 5’-GCCTGCTGAAAATGACTGAATATAAACT -3’ Applied Biosystems, Inc., USA reverse 5’-GCTGTATCGTCAAGGCACTCTT -3’ Applied Biosystems, Inc., USA PIK3CA R88Q forward pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA reverse pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA PIK3CA H1047R forward pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA reverse pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA ERBB2 S310F forward pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA reverse pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA CTNNB1 G34V forward pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA reverse pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA FGFR2 K659E forward 5'-GCCAGAGATATCAACAATATAGACTATT -3' Integrated DNA Technologies, Inc., USA reverse 5'-CTGTGTTACTGCCATCGACTTA -3' Integrated DNA Technologies, Inc., USA NRAS G13D forward pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA reverse pre-designed; included in assay (see Table 2.3) Bio-Rad Laboratories, USA       24 Table 2.3: ddPCR assay details and optimal extension temperatures. Assay Description Probe Details Source Extension Temp. (⁰C) Assay Specifications KRAS G12S (c.34G>A)  Wildtype 5’-Yak Yellow-CGCC+A+C+CA+GCT-IABkFQ-3’ Integrated DNA Technologies, Inc., USA 60 500nM primer + 200nM probe Mutant 5’-FAM- CGC+CA+C+T+AGC-IABkFQ-3’ Integrated DNA Technologies, Inc., USA KRAS G12A (c.35G>C) Wildtype 5’-Yak Yellow-CGCC+A+C+CA+GCT-IABkFQ-3’ Integrated DNA Technologies, Inc., USA 60 500nM primer + 200nM probe Mutant 5’-FAM- CGCC+A+G+CA+GCT-IABkFQ-3’ Integrated DNA Technologies, Inc., USA KRAS G12V (c.35G>T) Wildtype 5’-Yak Yellow-CGCC+A+C+CA+GCT-IABkFQ-3’ Integrated DNA Technologies, Inc., USA 60 500nM primer + 200nM probe Mutant 5’-FAM-CG+CC+A+A+CAGC+TC-IABkFQ-3’ Integrated DNA Technologies, Inc., USA KRAS G12D (c.35G>A) Wildtype 5’-VIC-TTGGAGCTGGTGGCGTA-NFQ-3’ Applied Biosystems, Inc., USA 60 assay at 40X - use 1x primer/probe mix Mutant 5’-FAM-TTGGAGCTGATGGCGTA-NFQ-3’ Applied Biosystems, Inc., USA KRAS G12R (c.34G>C) Wildtype 5’-VIC-TTGGAGCTGGTGGCGTA-NFQ-3’ Applied Biosystems, Inc., USA 60 assay at 40X - use 1x primer/probe mix Mutant 5’-FAM-TTGGAGCTCGTGGCGTA-NFQ-3’ Applied Biosystems, Inc., USA KRAS G12C (C.34G>T)  Wildtype 5’-VIC-TTGGAGCTGGTGGCGTA-NFQ-3’ Applied Biosystems, Inc., USA 60 assay at 40X - use 1x primer/probe mix Mutant 5’-FAM-TTGGAGCTTGTGGCGTA-NFQ-3’ Applied Biosystems, Inc., USA KRAS G13D (c.38G>A) Wildtype  5’-Yak Yellow-TA+CG+C+C+ACCA-IABkFQ-3’ Integrated DNA Technologies, Inc., USA 60 500nM primer + 200nM probe Mutant 5’-FAM-CTA+C+G+T+CAC+CA-IABkFQ-3’ Integrated DNA Technologies, Inc., USA PIK3CA R88Q (c.263G>A) Wildtype PIK3CA WT for p.R88Q, Human (dHsaCP2500559) Bio-Rad Laboratories, USA 55 450nM primer + 250nM probe Mutant PIK3CA p.R88Q, Human (dHsaCP2500558) Bio-Rad Laboratories, USA     25 Assay Description Probe Details Source Extension Temp. (⁰C) Assay Specifications PIK3CA H1047R (c.3140A>G) Wildtype PIK3CA WT for p.H1047R, Human (dHsaCP2000078) Bio-Rad Laboratories, USA 55 450nM primer + 250nM probe Mutant PIK3CA p.H1047R, Human (dHsaCP2000077) Bio-Rad Laboratories, USA ERBB2 S310F (c.929C>T) Wildtype ERBB2 WT for p.S310F, Human (dHsaIS2501415) Bio-Rad Laboratories, USA 54 900nM primer + 450nM probe Mutant ERBB2 p.S310F, Human (dHsaIS250141) Bio-Rad Laboratories, USA CTNNB1 G34V (c.101G>T) Wildtype CTNNB1 WT for p.G34V, Human (dHsaCP2500551) Bio-Rad Laboratories, USA 55 450nM primer + 250nM probe Mutant CTNNB1 p.G34V, Human (dHsaCP2500550) Bio-Rad Laboratories, USA FGFR2 K659E (c.1975A>G) Wildtype 5'-HEX-CAA+A+A+AGA+C+C+ACC-IABkFQ-3' Integrated DNA Technologies, Inc., USA 53 500nM primer + 200nM probe Mutant 5'-FAM-CAA+A+G+AGA+C+CA-IABkFQ-3' Integrated DNA Technologies, Inc., USA NRAS G13D (c.38G>A) Wildtype NRAS WT for p.G13D, Human (dHsaCP2500527) Bio-Rad Laboratories, USA 55 450nM primer + 250nM probe Mutant NRAS p.G13D, Human (dHsaCP2500526) Bio-Rad Laboratories, USA  2.3.2 Pre-Amplification Since targeted sequencing requires a DNA input of 45-75 ng, DNA remaining for ddPCR validation was often limited.  Therefore, I performed DNA pre-amplification for 10 cycles.  Thermocycler conditions for pre-amplification were as follows: polymerase activation at 95⁰C for 10 minutes followed by 10 cycles of 94⁰C for 30 seconds and annealing/extension at an optimal temperature (for the specific ddPCR assay used) (see Table 2.3) for 4 minutes.  2.3.3 Droplet Generation and Quantification Using the QX200 Droplet Generator (Bio-Rad Laboratories, USA), droplets were generated in a 25uL reaction consisting of ddPCR™ Supermix for Probes (no dUTP) (Bio-Rad Laboratories, USA), diluted pre-amplification PCR product, and PrimePCR™ ddPCR™ Mutation Assays (Bio-Rad Laboratories, USA) for mutations of interest at primer/probe concentrations according to the manufacturer’s protocol (see Table 2.3).   26 The following conditions were used for PCR cycle amplification for ddPCR analysis: initial polymerase activation at 95⁰C for 10 minutes, then 40 cycles of denaturation at 94⁰C for 30 seconds followed by annealing/extension at an optimal temperature for the specific ddPCR assay used (see Table 2.2) for 90 seconds (with ramp of 2.5⁰C per second to reach temperature), and final denaturation of 98⁰C for 10 minutes.  After thermal cycling, the QX200 Droplet Reader (Bio-Rad Laboratories, USA) was used to quantify droplets.  2.3.4 ddPCR Controls For each assay, I ran the following controls alongside samples of interest: Positive control: Sheared DNA from the sources listed in Table 2.4 below Negative/wild type-only control: macrodissected, normal tissue No-Template control: ddH20  Table 2.4: Positive controls used for ddPCR experiments.  The wildtype DNA was supplied from Promega Corporation, USA.  All custom-designed oligos were designed by Integrated DNA Technologies, Inc., USA. Assay Description of Positive Control KRAS G12S 1:1 mix of A549 cell line and wildtype DNA KRAS G12A H2009 cell line KRAS G12V 1:1 mix of OvCar5 cell line and wildtype DNA KRAS G12D HEY cell line KRAS G12R PK-8 cell line KRAS G12C MIA PaCa-2 cell line KRAS G13D HCT116 cell line PIK3CA R88Q 1:1 mix of custom-designed oligo and wildtype DNA PIK3CA H1047R HCT116 cell line ERBB2 S310F 1:1 mix of custom-designed oligo and wildtype DNA CTNNB1 G34V 1:1 mix of custom-designed oligo and wildtype DNA FGFR2 K659E  1:1 mix of custom-designed oligo and wildtype DNA NRAS G13D 1:1 mix of custom-designed oligo and wildtype DNA  Mutations were determined to be “real” if the VAFs determined by ddPCR were at least 3X higher than the average background mutation rate determined from three different negative control specimens (macrodissected, normal tissue).   27 2.4 Technical Validation In our previous paper, we identified mutations by targeted sequencing (TruSeq Amplicon Cancer Panel and TruSeq Amplicon Custom Panel) using laser-captured tissue59.  LCM-based enrichment was necessary to achieve high enough VAFs for possible mutations in endometriosis lesions to be detected by TruSeq panel sequencing.  Laser-capturing tissue for every sample analyzed is a time-consuming, costly (due to labour and operation costs) task that may take up to a few days per sample.  Furthermore, the amount of sections required to obtain enough DNA for sequencing was high – most cases required 15-25 slides of 8µm tissue sections.  For these reasons, we performed targeted panel sequencing in this study using the FIND ITTM version 3.4 assay (Contextual Genomics, Canada).  Because this assay is capable of detecting mutations at very low frequencies (1% and possibly lower), I sought to determine whether FIND ITTM would be able to detect somatic mutations in macrodissected tissue (which is far less time consuming to collect and usually requires between 2-14 8µm sections only).  Among DE cases analyzed in our previous study, the most commonly observed mutations were KRAS G12 mutations (6 of 39 cases)59.  Therefore, I sought to perform technical validation of the analysis of macrodissected FFPE tissue by FIND ITTM by comparing KRAS mutations detected by the FIND ITTM to ddPCR (the gold standard for quantitative mutation detection).  2.4.1 KRAS G12 Variant Screening My technical validations were performed on endometriosis specimens from the IE and DE cases studied in Chapter 3.  Independent of mutation calling from targeted sequencing, one block containing endometriosis from each patient was tested for KRAS G12 (G12S, G12A, G12V, G12D, G12R, G12C) variants (a subset of cases were also tested for G13D) (see Table 2.5).  To be conservative on reporting KRAS mutations in macrodissected samples and to reflect mutation calling VAF thresholds by targeted sequencing, I set a cut-off of 0.80% VAF for all KRAS G12 variants.  This cut-off is above the empirically determined positive thresholds (defined as 3X the average background mutation rate) for all KRAS G12 variants as indicated below:     28 KRAS G12S (c.34G>S) assay > 0.673% KRAS G12A (c.35G>C) assay > 0.0243% KRAS G12V (c.35G>T) assay > undetermined (only positive control specimens recorded mutant droplet counts) KRAS G12D (c.35G>A) assay > 0.392% KRAS G12R (c.34G>C) assay > undetermined KRAS G12C (C.34G>T) assay > undetermined  Table 2.5 summarizes all KRAS G12 (and G13D) calls by both targeted sequencing and ddPCR.  True positive calls were considered those that were positive via targeted sequencing AND ddPCR assay, whereas false positive calls were considered those that were positive via targeted sequencing but not ddPCR.  Overall, with these thresholds, I observed a sensitivity of 88.9% (95% CI: 51.7% - 99.7%) and specificity of 97.0% (95% CI: 89.6% - 99.6%) for the detection of KRAS G12/G13 mutations by FIND ITTM version 3.4 (Contextual Genomics, Canada).     29 Table 2.5: KRAS G12/G13 variant calling by targeted sequencing and droplet digital PCR.  For readability purposes, variant allele frequencies (VAFs) are not stated in this table unless they surpassed the KRAS mutation calling threshold (VAF of 0.80%). Patient ID Block ID and Descriptor Targeted Sequencing Droplet Digital PCR Annotation Collection Method and Specimen Descriptor Mutation Identified and VAF (%) Collection Method and Specimen Descriptor ddPCR KRAS assay performed Mutation Identified and VAF (%) IE_1 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_2 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_3 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_4 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_5 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_6 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_7 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_8 A: index macrodissected - mixed KRAS G12V (3.04%) macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S KRAS G12V ( 2.53%) true positive for KRAS G12V IE_9 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_10 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_11 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_12 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_13 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_14 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_15 A: index macrodissected - mixed KRAS G12V (6.407) macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S   false positive for KRAS G12V   30 Patient ID Block ID and Descriptor Targeted Sequencing Droplet Digital PCR Annotation Collection Method and Specimen Descriptor Mutation Identified and VAF (%) Collection Method and Specimen Descriptor ddPCR KRAS assay performed Mutation Identified and VAF (%) IE_16 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_17 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_18 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_19 A: index macrodissected - mixed KRAS G12C (4.833%) macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S KRAS G12C (4.19%) true positive for KRAS G12C IE_20 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_21 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_22 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_23 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_24 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_25 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_26 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_27 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_28 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_29 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_30 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_31 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_32 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S       31 Patient ID Block ID and Descriptor Targeted Sequencing Droplet Digital PCR Annotation Collection Method and Specimen Descriptor Mutation Identified and VAF (%) Collection Method and Specimen Descriptor ddPCR KRAS assay performed Mutation Identified and VAF (%) IE_33 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_34 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_35 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_36 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_37 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     IE_38 A: index LCM - mixed   LCM - mixed multiplex (G12A, G12C, G12D, G12V, G12R, G12S, G13D)     IE_39 A: index LCM - mixed   LCM - mixed multiplex (G12A, G12C, G12D, G12V, G12R, G12S, G13D)     IE_40 A: index LCM - mixed   LCM - mixed multiplex (G12A, G12C, G12D, G12V, G12R, G12S, G13D)     DE_1 A: index macrodissected - mixed   LCM - mixed G12A, G12C, G12D, G12V, G12R     DE_2 A: index macrodissected - mixed KRAS G12D (2.807%) macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S KRAS G12D (2.14%) true positive for KRAS G12D DE_3 A: index macrodissected - mixed   LCM - mixed G12A, G12C, G12D, G12V, G12R     DE_4 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R     DE_5 A: index macrodissected - mixed KRAS G12D (0.932%) LCM - mixed G12A, G12C, G12D, G12V, G12R KRAS G12D (2.065%) true positive for KRAS G12D DE_6 A: index macrodissected - mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R     DE_7 A: index macrodissected – mixed   LCM - mixed G12A, G12C, G12D, G12V, G12R       32 Patient ID Block ID and Descriptor Targeted Sequencing Droplet Digital PCR Annotation Collection Method and Specimen Descriptor Mutation Identified and VAF (%) Collection Method and Specimen Descriptor ddPCR KRAS assay performed Mutation Identified and VAF (%) DE_8 A: index macrodissected – mixed   LCM - mixed G12A, G12C, G12D, G12V, G12R     DE_9 A: index macrodissected – mixed   LCM - mixed G12A, G12C, G12D, G12V, G12R     DE_10 A: index macrodissected – mixed   LCM - mixed G12A, G12C, G12D, G12V, G12R KRAS G12V (3.589%) false negative for KRAS G12V DE_11 A: index macrodissected – mixed KRAS G12D (1.108%) macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S KRAS G12D (1.03%) true positive for KRAS G12D DE_12 A: index macrodissected – mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     DE_13 A: index macrodissected – mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     DE_14 A: index macrodissected – mixed KRAS G12C (1.05%) macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S KRAS G12C (1.19%) true positive for KRAS G12C DE_15 A: index macrodissected – mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     DE_16 A: index macrodissected – mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     DE_17 A: index macrodissected – mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     DE_18 A: index macrodissected – mixed KRAS G13D (2.158%) macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S, G13D   false postive for KRAS G13D DE_19 B: separate macrodissected – mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     DE_20 A: index macrodissected – mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     DE_21 B: separate macrodissected – mixed KRAS G12V (2.627%) macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S KRAS G12V (2.81%) true positive for KRAS G12V DE_22 A: index macrodissected – mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S       33 Patient ID Block ID and Descriptor Targeted Sequencing Droplet Digital PCR Annotation Collection Method and Specimen Descriptor Mutation Identified and VAF (%) Collection Method and Specimen Descriptor ddPCR KRAS assay performed Mutation Identified and VAF (%) DE_23 A: index macrodissected – mixed   macrodissected - mixed G12A, G12C, G12D, G12V, G12R, G12S     DE_24 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_25 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_26 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_27 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_28 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_29 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_30 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_31 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_32 A: index LCM - mixed KRAS G12A (10.749%) LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D KRAS G12 (10.41%) true postive for KRAS G12A DE_33 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_34 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D     DE_35 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D       34 Patient ID Block ID and Descriptor Targeted Sequencing Droplet Digital PCR Annotation Collection Method and Specimen Descriptor Mutation Identified and VAF (%) Collection Method and Specimen Descriptor ddPCR KRAS assay performed Mutation Identified and VAF (%) DE_36 A: index LCM - mixed   LCM - mixed multiplex - G12A, G12C, G12D, G12V, G12R, G12S, G13D      2.5 Immunohistochemistry 2.5.1 ARID1A immunohistochemistry Loss of ARID1A is implicated as an early event in the malignant transformation of cancer and has been observed in endometriosis lesions both within and outside of the context of cancer58,77.  Especially since the FIND ITTM version 3.4 assay does not cover the ARID1A gene, we performed ARID1A immunohistochemistry (IHC) on endometriosis and eutopic endometrium cases analyzed in this study.  In particular, loss of nuclear ARID1A immunoreactivity was used as a surrogate for ARID1A inactivating mutations – studies suggest that there is good concordance between ARID1A IHC findings and ARID1A mutational status78.  Archival tissue sections were stained on the Dako Omnis (Agilent Technologies, USA) automated immunostainer with the use of a 1:150 dilution of ARID1A rabbit polyclonal antibody (HPA005456, Sigma-Aldrich, USA) for batch at 0.1mg/ml or 1:200 for batch at 0.2 mg/ml59.  Slides underwent a 10-X-10 incubation and detection was HRP based using 3,3’-diaminobenzidine (DAB).  A pathologist, Dr. Tayyebeh M. Nazeran, and a pathology resident, Dr. Basile Tessier-Cloutier, scored ARID1A immunostained slides.  2.5.1 PTEN immunohistochemistry PTEN mutations occur commonly in EAOCs and have also been observed in endometriosis54.  Furthermore, PTEN loss by IHC has been observed in normal, eutopic endometrial glands in nearly half of cases studied by Monte et al. (2010)73.  Although the FIND ITTM version 3.4 assay has partial coverage of PTEN, inactivating mutations in regions not covered by the assay (as well as loss of PTEN by other means such as large-scale deletion or epigenetic mechanisms) would not be detected79.  Therefore, as with   35 ARID1A, we performed PTEN IHC on endometriosis and eutopic endometrium cases analyzed in this study.  PTEN immunoreactivity was used as a surrogate for PTEN inactivating mutations as described in previous work80.  Immunostains were performed on the Ventana Discovery Ultra (Ventana Medical Systems, USA) immunostainer.  Slides underwent antigen retrieval with Cell Conditioning 1 (CC1, Ventana Medical Systems, USA) followed by 60 minutes of primary antibody incubation at room temperature and detected using UltraMap DAB anti-Rb Detection Kit (Ventana Medical Systems, USA).  PTEN antibody (rabbit clone 138G6, Cell Signaling, USA) was applied at dilution of 1:25.  Dr. Tayyebeh M. Nazeran and Dr. Basile Tessier-Cloutier, scored PTEN immunostained slides.    36 3. Somatic Cancer-Driver Mutations in Incisional Endometriosis  In this chapter, I sought to investigate the prevalence of somatic cancer-driver mutations in endometriosis by comparing DE to another form that is unlikely to undergo malignant transformation.  Specifically, I examined IE, an iatrogenic form of endometriosis that occurs in the resulting surgical scars of obstetric or gynecological procedures18.  Unlike other forms of endometriosis, the uterine origin of cells is well accepted for incisional endometriosis: endometrial cells, both stroma and epithelium, are mechanically transferred to the abdominal fascia or subcutaneous tissue around sites of incision following procedures such as caesarean sections, hysterectomies, myomectomies appendectomies, tubal ligations, and episiotomies11,81,82.  I compared somatic driver mutation profiles between deep infiltrating endometriosis and incisional endometriosis to determine whether there were differences in mutation profile between these two types of endometriosis with unique differences in their etiologies.  3.1 Patient Specimen Collection We obtained archival tissue specimens from four independent cohorts of women with IE and two independent cohorts of women with DE.  3.1.1 Overview of Incisional Endometriosis Specimens The Vancouver General Hospital in Vancouver, BC, Canada contributed tissue samples from 12 IE patients.  The Referral Centre for Gynecopathology in Mannheim, Germany contributed endometriotic tissue samples from 10 IE patients.  The University Hospital Tuebingen in Tuebingen, Germany contributed tissue samples from 15 IE patients.  Lastly, the VU University Medical Center (VUMC) in Amsterdam, The Netherlands contributed tissue samples from three IE patients.  Inclusion criteria for the IE cohort were diagnosis with incisional, umbilical, or post C-sectional endometriosis lesions containing both epithelial and stromal components by extensive pathology review, the absence of cancer or dysplasia, and a lesion size sufficient for tissue coring, macrodissection, and/or   37 laser-capture microdissection.  Details of prior surgery and the time interval between suspected inciting surgery and subsequent diagnosis with IE were available for most but not all patients (Appendix A).  Note that a few cases included in our IE cohort lacked surgical history or only had a history of surgical abortion and a diagnosis of spontaneous abdominal wall or subcutaneous endometriosis (rather than iatrogenic disease) cannot be ruled out.  Adjacent tissue blocks of endometriosis (same anatomical site) were available for sampling for some IE patients (Appendix A).  3.1.2 Overview of Deep Infiltrating Endometriosis Specimens We obtained FFPE or MFPE (Sakura Finetek, USA) tissue specimens from two independent cohorts of women with deep infiltrating endometriosis (DE).  Endometriotic tissue samples from 23 DE patients were retrieved from local pathology archives and the prospective tissue bank at BC Women’s Centre for Pelvic Pain and Endometriosis in Vancouver, BC, Canada.  Ten cases (Patients DE_1 to DE_10) overlap with our previous study (Appendix B) wherein they were analyzed by droplet digital PCR for KRAS mutations alone59.  Here we include them with a broader genomic analysis as noted below.  The VUMC contributed tissue samples from an additional 13 DE patients. Inclusion criteria for the DE cohort were local invasion > 5mm, pathologist-confirmed endometriosis, the absence of cancer or dysplasia, and a lesion size sufficient for tissue coring, macrodissection, and/or laser-capture microdissection.  Blocks of tissue representing DE at distant/anatomically distinct sites were available for several cases (Appendix B).  3.1.3 Ethics Approval Institutional review boards at each respective hospital approved tissue collection and collection of clinical data.  Detailed description of specimens collected from each hospital site are provided in sections 3.1.4 – 3.1.8.    38 3.1.4 Vancouver General Hospital (VGH) Cohort Tissue specimens from 12 women were obtained from the Department of Anatomical Pathology at the Vancouver General Hospital in Vancouver, Canada. These cases were identified using the search terms “endometriosis”, “scar”, “incisional”, “C-section” and “laparoscopic” between January 2004 and 2017.  Inclusion criteria were limited to the pathologic diagnosis of endometriosis in a laparoscopic or a C-section scar.  Patient were excluded if a malignancy was diagnosed in the same specimen.  Specimen collection and retrieval of clinical data was approved by the UBC BC Cancer Agency Research Ethics Board [H05-60119].  We also obtained ethics approval for immunohistochemical experiments and next generation sequencing to be performed on these specimens [H02-61375; H08-01411].  3.1.5 The Referral Centre for Gynecopathology (Mannheim) Cohort Tissue specimens from 10 women with IE were obtained from the Referral Centre for Gynecopathology in Mannheim, Germany.  These cases were identified using the search terms equivalent to “endometriosis”, “scar”, “incisional”, “C-section” and “laparoscopic” between January 2010 and 2016.  Most patients presented with painful nodules in C-section scars which were then surgically removed. No clinical data aside from age and site of endometriosis have been provided for these cases.  3.1.6 University Hospital Tuebingen (Tuebingen) Cohort Tissue specimens from 15 women with IE were obtained from the University Hospital Tuebingen in Tuebingen, Germany. These cases were identified using the search terms equivalent to “endometriosis”, “scar”, “incisional”, and “C-section” between January 2007 and June 2017.  Inclusion criteria were limited to the pathologic diagnosis of endometriosis in a laparoscopic or a C-section scar.  Patient were excluded if a malignancy was diagnosed in the same specimen.  Specimen collection and retrieval of clinical data was approved by the institutional research ethics committee.    39 3.1.7 VU University Medical Center (VUMC) Cohort Note that the VUMC contributed both IE and DE specimens.  Tissue specimens from 13 women with DE and 3 women with IE were retrieved from the Biobank Unit Pathology at the VU University Medical Center in Amsterdam, the Netherlands.  All women had histologically proven endometriosis without any known or diagnosed ovarian cancer during follow up.  For DE cases, inclusion criteria were deep endometriosis (within the pelvic cavity) and lack of cancer or dysplasia associated with the lesions.  Similarly, for IE cases, inclusion criteria were (extra-pelvic) endometriosis in C-section and lack of cancer or dysplasia associated with the lesions.  Specimen and data collection for these cases was approved by the Medical Ethical Committee (METc) of the VUMC, Amsterdam, the Netherlands.  3.1.8 British Columbia Women’s Centre for Pelvic Pain and Endometriosis (BC Women’s) Cohort Women with DE were seen at the British Columbia Women’s Centre for Pelvic Pain and Endometriosis in Vancouver, Canada.  Deep infiltrating endometriosis lesions were clinically defined as lesions with depths greater than 5 mm and were surgically excised with histopathological confirmation of endometriosis epithelial and stromal cells by Dr. Nazeran and Dr. Tessier-Cloutier.  Tissues were sourced from the OVCARE Tissue bank (banking performed in accordance with the World Endometriosis Research Foundation Endometriosis Phenome and Biobanking Harmonization Project (EPHect)83), with ethics approval from the UBC Children’s and Women’s Research Ethics Board [H13-02563; H14-03040] for the collection of archival tissue specimens.  We have obtained a waiver of consent for specimen collection.  Ten women from our previous study (whose DE lesions were analyzed KRAS mutations via ddPCR)59 were further analyzed in the current study for other potential somatic cancer-driver alterations.  Moreover, we identified and retrieved endometriosis specimens from an additional 13 women with DE.   40 3.2 Additional Details on Methods The analyses in this chapter follow the methodology described in Chapter 2.  Additional details on methodology specific for this chapter are detailed below.  In short, a subset of cases were MFPE tissues (see section 3.2.1) and cases from VUMC (Patients IE_38 to IE_40 and Patients DE_24 to DE_36, see Appendix A; Appendix B) underwent separate experimentation from all other cases (this experimental work was led by Ms. Lisanne Verhoef, a graduate student at the National Cancer Institute in Amsterdam, the Netherlands in a collaborative effort) and is described in sections 3.2.2 – 3.2.7.  3.2.1 Tissue Fixation A subset of endometriosis specimens analyzed in Chapter 3 were MFPE tissues (Patients DE_3 to DE_10, see Appendix B).  All other specimens were FFPE tissues.  3.2.2 Tissue Microarray Construction Cases from VUMC were used to build tissue microarrays (TMAs).  Using a tissue microarrayer (TMA) (Grand Master, Sysmex Europe GmbH, Norderstedt, Germany), a median of four cores (range, 1-15) of endometriosis (morphologically representative lesions, as determined by hematoxylin-eosin (H&E) slides from corresponding cases) with a median donor core height of 3.9 mm (range, 3-5), 2.0-mm diameter core biopsy of the area of interest in the donor block were punched and transferred to a recipient paraffin block. Note: All subsequent analyses of VUMC cases (including targeted sequencing, ddPCR analysis, and IHC staining) were carried out from TMA tissue sections.  3.2.3 DNA Collection and Extraction DNA collected for VUMC cases were obtained solely by means of LCM.  All cases from VUMC were sectioned at 5µm from TMAs and attached to PEN membrane slides (Leica Microsystems Inc., Switzerland).  Sections were deparaffinized applying Tuolidin blue staining by using the ST5020 multi-stainer (Leica Microsystems Inc., Switzerland).  Using   41 the Leica LCM laser microdissection system (Leica Microsystems Inc., Switzerland) endometriotic lesions were microdissected according the manufacturer’s instructions.  After laser-capture, DNA from VUMC specimens were extracted using the ARCTURUS® PicoPure® DNA Extraction Kit (ThermoFisher Scientific, USA).  Quantification of extracted DNA was performed using the Qubit 2.0 Fluorometer (ThermoFisher Scientific, USA).  3.2.4 Targeted Panel Sequencing Extracted DNA (75ng) from laser-captured VUMC specimens were sent to Contextual Genomics for targeted panel sequencing using the FIND ITTM version 3.4 assay.  In 11/16 (68.8%) cases the optimized 75ng input was not available and therefore a range as low as 45ng was used. The expected enrichment for endometriosis tissues is higher for laser-captured samples compared to macrodissected samples, therefore mutations were called in these laser-captured specimens if the VAF determined by targeted sequencing was 5.0% or higher.  3.2.5 ddPCR Independent of target panel sequencing findings, VUMC specimens were analyzed for potential KRAS mutations from laser-captured material using the ddPCR KRAS G12/G13 Screening Kit (catalog #1863506, Bio-Rad Laboratories, USA) following manufacturer’s instructions.  Positive references were from Horizon Discovery (Cambridge, UK) (15:15 wildtype/mutant) and negative control was H2O.  I have reported these results in our technical validation of the FIND ITTM assay in section 2.4 (see Table 2.5).  3.2.6 ARID1A IHC Archival TMA tissue sections from VUMC were stained on the BenchMark Ultra autostainer (Ventana Medical Systems, USA) with the use of a 1:100 dilution of ARID1A   42 rabbit polyclonal antibody (HPA005456, Sigma-Aldrich, USA).  Heat-induced antigen retrieval was carried out using Cell Conditioning 2 (CC2, Ventana Medical Systems, USA) for BAF250a (ARID1a) followed by 60 minutes primary antibody incubation at room temperature.  Pathologist, Dr. Hugo M. Horlings, scored ARID1A immunostained slides from VUMC.  3.2.7 PTEN IHC PTEN IHC staining of TMA tissue sections from VUMC were executed on the BenchMark Ultra autostainer (Ventana Medical Systems, USA) followed by heat-induced antigen retrieval with Cell Conditioning 1 (Ventana Medical Systems, USA).  Slides then underwent primary antibody incubation with a rabbit monoclonal antibody (SP218, Spring Bioscience, USA) for 32 minutes at 36 degrees at a dilution of 1:100).  Dr. Horlings scored PTEN immunostained slides from VUMC.     43 3.2 Results 3.2.1 Sample Description I examined somatic mutations in common cancer hotspots in 40 women with IE (total of 59 specimens studied), and in 36 women with DE (total of 43 specimens studied).  The mean age of women with IE was 36.5 years with a standard deviation (SD) of 5.5 years (Table 3.1; Fig. 3.1; Appendix A).  Between one and four tissue blocks from each patient were collected and analyzed.  In four patients (Patients IE_5, IE_10, IE_12, and IE_28) eutopic endometrium samples were available for sequencing – we did not detect somatic cancer-driver mutations in the available eutopic endometrium specimens, although heterogeneous loss of PTEN was noted in 2/4 specimens.  Of IE cases with obtainable surgical history, the original surgical procedure performed was most often caesarean section and the interval between the most recent gynecological or obstetric surgery and subsequent diagnosis with IE ranged from 1 month to 11 years.    Table 3.1: Overview of clinical characteristics of women in IE cohort. Patient characteristics IE cohort (n = 40) Age (years; mean ± SD) 36.5 ± 5.5 Interval (years; mean ± SD) 5.1 ± 4.4 Diagnosis   incisional endometriosis 20 (50.0%)  post c-section endometriosis 11 (27.5%)  umbilical endometriosis 9 (22.5%) Surgical History   c-section 25 (62.5%)  laparascopy 4 (10.0%)  no prior surgery identified 3 (7.5%)  hysterectomy 2 (5.0%)  surgical abortion 2 (5.0%)  umbilical hernia 1 (2.5%)  adnexectomy 1 (2.5%)   n/a 2 (5.0%)      44 The mean age of women with DE was 33.9 years with SD of 7.0 years (Table 3.2; Fig. 3.1; Appendix B).  Although most women were affected with DE at a single anatomical site, several women had multiple DE lesions at distinct anatomical sites, these additional lesions were included when available.  The mean age of women in the IE and DE cohorts were not significantly different (p = 0.0765, Student’s t-test) (Fig. 3.1).  Table 3.2: Overview of clinical characteristics of women in DE cohort. Patient characteristics DE cohort (n = 36) Age (years; mean ± SD) 33.9 ± 7.0 ASRM Clinical Stage   I 1 (2.8%)  II 5 (13.9%)  III 10 (27.8%)   IV 20 (55.6%)   Figure 3.1: Boxplot comparison of age of IE and DE patients.  The difference in age of women with IE and women with DE in this study is not statistically significant (p = 0.0765, Student’s t-test).     45 3.2.2 Targeted Panel Sequencing Of 40 patients with IE, four patients (10.0%) harbored somatic COSMIC hotspot mutations in either KRAS (2), PIK3CA (1), or ERBB2 (1) (Table 3.3).  Of 36 patients with DE, nine patients (25.0%) harboured somatic cancer-driver mutations in either KRAS (7), CTNNB1 (1), or PIK3CA (1) (Table 3.3). To identify which cells in endometriosis lesions harboured the mutations I detected, I performed epithelial and stromal separation of endometriotic lesions by LCM.  We previously determined KRAS G12 mutations to be restricted to the epithelial component of endometriotic lesions using ddPCR from laser-captured epithelium and stromal compartments59 .  Similarly, I was able to confirm that CTNNB1, PIK3CA, and ERBB2 hotspot mutations were also enriched in the epithelium of endometriotic glands (Table 3.3; Fig. 3.2).  Hotspot KRAS mutations remained the most common somatic cancer-driver mutations detected in both IE and DE: there were KRAS mutations in 2 of 40 patients with IE (5%) compared to 7 of 36 patients with DE (19.4%) (p = 0.076, Fisher’s exact test).  Note: I also collected laser-captured tissue in a subset of cases (Patients IE_1 to IE_20 and Patients DE_1 to DE_10) to determine the differences in enrichment between macrodissected and laser-captured specimens when somatic mutations are detected.  Overall, I observe an enrichment of 6.7 times in mixed, laser-captured specimens over mixed, macrodissected specimens.      46 Table 3.3: Somatic cancer-driver mutations detected in endometriosis specimens from women with IE or DE.  The VAF of macrodissected or laser-captured specimens as determined by means of targeted panel sequencing and corresponding ddPCR assays are presented below.  “Adjacent” refers to tissue specimens obtained from a different archival tissue block yet the same anatomical site as the index block.  “Separate” refers to specimens obtained from an anatomically distinct site from the index block. Patient and Block Descriptor Driver Mutation Identified  Collection Method and Component VAF (%) - Targeted Sequencing VAF (%) - ddPCR IE_8A index KRAS G12V macrodissection: mixed 3.04 2.53 KRAS G12V LCM: mixed   28.7 IE_8B adjacent KRAS G12V macrodissection: mixed not detected 3.29 IE_16A index ERBB2 S310F macrodissection: mixed 3.336 3.97 ERBB2 S310F LCM: mixed   18.2 ERBB2 S310F LCM: epithelium   21.4 ERBB2 S310F LCM: stroma   1.36 IE_19A index KRAS G12C macrodissection: mixed 4.833 4.19 KRAS G12C LCM: mixed   29.5 IE_25A index PIK3CA H1047R macrodissection: mixed 5.359 5.79 PIK3CA H1047R LCM: epithelium   24.9 PIK3CA H1047R LCM: stroma   0.567 DE_1A index CTNNB1 G34V macrodissection: mixed 3.933 3.88 CTNNB1 G34V LCM: epithelium   19.5 CTNNB1 G34V LCM: stroma   0.664 DE_2A index KRAS G12D macrodissection: mixed 2.807 2.14 KRAS G12D LCM: epithelium   38.125 KRAS G12D LCM: stroma   0.002 DE_5A index KRAS G12D macrodissection: mixed 0.932 n/a KRAS G12D LCM: mixed   2.065 DE_10A index KRAS G12V macrodissection: mixed not detected 0.941 KRAS G12V LCM: mixed   3.589 DE_11A index KRAS G12D macrodissection: mixed 1.108 1.03 DE_14A index KRAS G12C macrodissection: mixed 1.05 1.19 DE_15A index PIK3CA E545A macrodissection: mixed 1.546 n/a DE_21B separate KRAS G12V macrodissection: mixed 2.627 2.81 DE_32A index KRAS G12A LCM: mixed 10.749 10.41     47  Figure 3.2. ddPCR validation of ERRB2 c.929C>T (p.S310F) mutation in the epithelial component of endometriosis in Patient IE_16.  Patient IE_16 harbours an ERBB2 S310F mutation as detected by ddPCR in a macrodissected endometriosis sample.  The VAF in the epithelium-enriched, LCM sample (from the same tissue block) is higher than in the stroma-enriched LCM sample (21.4% versus 1.36% respectively).  3.2.3 Immunohistochemistry  IHC staining revealed loss of ARID1A protein to be a rare event with only a single case of ARID1A-loss in a DE case (1/36; 2.78%) and no detectable loss in IE cases (Fig. 3.3A-B; Appendix C; Appendix D).  Conversely, 7 of 40 patients with IE (17.5%), and 5 of 36 patients with DE, (13.89%) exhibited a heterogeneous pattern on staining for PTEN wherein some, but not all, glands demonstrate loss of PTEN immunoreactivity (Fig. 3.3C-D, Appendix C; Appendix D).  As with cancer-driver mutations, we observed ARID1A-loss and PTEN-loss in only the epithelial component of endometriosis.   48  Figure 3.3: IHC studies of endometriosis specimens showing (A) loss of ARID1a in epithelial endometriosis cells in a case of DE and (B) matching H&E staining.  A case of IE with heterogeneous expression of PTEN IHC, (C) intermediate expression in some glands and (D) faint expression in others.  3.2.4 Total Mutation Rates Accounting for both ddPCR-validated somatic COSMIC hotspot mutations and the IHC findings, the overall frequency of somatic cancer-driver events in IE and DE was 27.5% (proportion) and 36.1% (proportion), respectively.  The pattern of somatic mutations compared between the IE and DE cases is illustrated in Fig. 3.4.   49  Figure 3.4: Overview of somatic cancer-driver events in incisional endometriosis and deep infiltrating endometriosis.  Somatic cancer-driver events were observed to co-occur in individual specimens and across specimens from the same patient.  Consequently, rather than conducting statistical analysis comparing the overall rates of mutation, I performed a Fisher’s exact test on each pairwise comparison of presence of a given somatic cancer-driver event in incisional endometriosis versus deep infiltrating endometriosis to assess whether the observed rates of cancer-driver events were significantly different between IE and DE patients.  All tests were two-sided, where a P-value <0.05 was considered to be statistically significant.  The P-value for each pairwise comparison is shown below in Table 3.4. ** It is important to note that because I am performing several statistical tests simultaneously (here and in subsequent analyses), it would be intuitive to correct for multiple comparisons (such as through the Bonferroni correction) to reduce the false discovery rate84.  However, given the exploratory/hypothesis-generating nature of this work and because the number of comparisons made is small, I chose not to formally apply these corrections.  Findings from this work should be interpreted with caution and should be validated in a second, independent cohort.   50 Table 3.4: Reported P-values for pairwise comparisons of somatic events affecting specific genes.   Most of the somatic cancer-driver events we observed affected components of the MAPK/RAS signalling pathway (includes KRAS and ERBB2) and PI3K-Akt signalling pathway (includes CTNNB1, PIK3CA, and PTEN).  The proportion of DE samples with activating RAS pathway alterations was higher than that observed for IE, however this difference was not quite significant (p = 0.0762).  I therefore also examined whether the collective rates of somatic events affecting these pathways differed in women with IE and women with DE.  As shown in Table 3.5, all P-values were > 0.05 and therefore the rate of somatic alterations affecting canonical components of the MAPK/RAS and PI3K-Akt pathway mutations are not significantly different.  Table 3.5: Reported P-values for pairwise comparisons of somatic events affecting canonical pathways.      IE patients affectedDE patients affected P -value2/40 7/36 0.07620/40 1/36 0.47371/40 1/36 1.0000ERBB2 in IE vs. DE 1/40 0/36 1.00000/39 1/34 0.4658PTEN-loss in IE vs. DE 7/37 5/25 1.0000Pairwise ComparisonKRAS in IE vs. DECTNNB1 in IE vs. DEPIK3CA in IE vs. DEARID1A-loss in IE vs. DEIE patients affectedDE patients affected P -valueMAPK/RAS in IE vs. DE 3/40 7/36 0.1773PI3K-Akt in IE vs. DE 8/40 6/36 0.7735Pairwise Comparison  51 3.3 Discussion Our previous study revealed the presence of recurrent somatic cancer-driver mutations (particularly KRAS) in DE59.  In the current study, I analyzed the prevalence of somatic cancer-driver events in IE, another form of endometriosis with little malignant potential, using a hypersensitive cancer hotspot assay combined with orthogonal validation by ddPCR or IHC staining.  We found that the overall rates of somatic cancer-driver events to be similar for IE and DE, moreover the spectrum of affected pathways was similar.  The similarity in the rates of mutation and mutational profile of IE and DE is consistent with endometriotic cells in both forms of endometriosis originating from the same source.  Because IE is accepted to originate from endometrial cells in the uterus via iatrogenic transplantation, this may support a uterine origin of deep infiltrating endometriosis as well (e.g. secondary to retrograde menstruation). Although our sample sizes were insufficiently large to conclude difference in either overall rates of somatic events or enrichment of particular alterations when comparing IE and DE, it is apparent that alterations resulting in upregulation of the MAPK/RAS or PI3K-Akt-mTOR signalling pathways are present in a substantial fraction of endometriosis cases.  Aberrant signalling of these pathways is known to affect cell growth/proliferation, differentiation, and apoptosis85,86 and therefore these mutations might be important in driving the growth and survival of endometriosis in ectopic regions of the body.  Interestingly, ddPCR assays and IHC staining revealed that all somatic cancer-driver events observed (hotspot mutations in KRAS, CTNNB1, and PIK3CA, loss of PTEN, or loss of ARID1A) affected only the epithelial compartment of endometriosis lesions. Moreover, visualization of lesions with PTEN-loss or ARID1A-loss revealed that only some glands were affected by these somatic events whereas other glands had normal expression.  It is unclear whether the hotspot mutations identified also affect only a few glands in endometriotic lesions or whether these mutations exist at low allelic frequencies throughout the entire lesion – though the prior would be more in line with clonal expansion and observed alterations seen by IHC.  If somatic cancer-driver alterations truly affect only certain endometriotic glands within lesions, do such alterations affect the fate of such glands over other glands where somatic events are absent?   52 4. Somatic Cancer-Driver Mutations in Eutopic Endometrium  As discussed in the previous chapter, I observed somatic cancer-driver alterations in over 25% of women with DE and women with IE studied, including hotspot, gain-of-function mutations in KRAS, CTNNB1, ERBB2, and PIK3CA as well as loss of PTEN protein.  Since a uterine origin of IE is well accepted, the similarity in mutation rates and spectra is consistent with a uterine origin of DE as well (perhaps through retrograde menstruation).  A proposed model for the pathogenesis of endometriosis (based on a uterine origin) is illustrated in Figure 4.1 below:     Figure 4.1: Theoretical and simplified model of the pathogenesis of endometriosis.  Endometrial cells from their native uterine site are transferred (via mechanical transplantation, retrograde menstruation, or other means) into a secondary site wherein endometriosis implants will establish themselves.  Additional DNA damage (such as through the accumulation of mutations), free iron (which results in iron-induced oxidative stress by the Fenton reaction)87, inflammation42 and/or hyperestrogenism42 occasionally lead to malignant transformation.   Although previous studies have documented somatic driver mutations in EAOCs and concurrent endometriotic lesions, our frequent findings of somatic mutations in cases of endometriosis very unlikely to progress to cancer prompt speculation on whether mutations independently arise in implanted endometrial cells or whether they already are already presenting in the originating cells.  To be put simply: “Which came first, the chicken (endometriosis) or the egg (somatic mutation)?”  Although the origin of eutopic endometrium endometriosis cancerimplantation (rare) transformation (rare)   DNA damage Free iron (Fenton Reaction) Inflammation Hyperestrogenism   53 (endogenous) endometriosis remains contentious, assuming a uterine origin of endometriosis and examining uterine tissue serves to potentially identify the originating cells of endometriosis as well as increase our understanding of latent mutations of unknown clinical significance (particularly in eutopic endometrial tissue, which often serves as “normal tissue” for molecular studies on endometriosis).  Therefore, are such cancer-driver events naturally present in the eutopic endometrium, perhaps merely reflecting the aging of that tissue88?  A study analyzing uterine lavage fluid has recently reported cancer-associated mutations, such as driver mutations in KRAS and PIK3CA, in roughly half of women analyzed (51 of 95) that lacked histopathological evidence of (endometrial) cancer89.  Likewise, peritoneal washing revealed TP53 mutations in 19 of 20 control women (women unaffected by cancer or reported benign pathology), albeit at ultra-low allelic frequencies (< 0.1%), with an apparent increase in mutational burden correlating with age90.  These studies suggest that somatic cancer-driver mutations may pre-exist in the eutopic endometrium.  To clarify these speculations, in this chapter I assessed the presence of somatic cancer-drivers in the eutopic endometrium of women without evidence of endometrial hyperplasia or gynecologic malignancy and whether the presence of such alterations was associated with age.  4.1 Patient Specimen Collection 4.1.1 Overview of Hysterectomy Cohort I examined somatic mutations in common cancer hotspots in endometrium specimens obtained from 25 women who underwent hysterectomies at VGH.  Hysterectomy (Hx) cases were identified using the search terms “endometrium” and “hysterectomy” between January 2004 and January 2018.  Inclusion criteria were limited to unremarkable proliferative or secretory endometrium between the age of 20 and 65 years, excluding malignant or premalignant disease and benign neoplasms.  Cases with previous history of endometrial hyperplasia or gynecologic malignancy were also excluded.     54 4.1.2 Overview of Biopsy Cohort I examined somatic mutations in common cancer hotspots in endometrium specimens obtained from 66 women who underwent endometrial biopsies at VGH.  These cases were identified using the search terms “endometrial” and “biopsy” between January 2015 and 2017.  Inclusion criteria were limited to the unremarkable proliferative or secretory endometrium between the age of 20 and 65 years, excluding malignant or premalignant disease and benign neoplasms. Cases with previous history of endometrial hyperplasia or gynecologic malignancy were also excluded.    4.1.3 Ethics Approval Specimen collection and retrieval of clinical data was approved by the UBC BC Cancer Agency Research Ethics Board [H05-60119].  We also obtained ethics approval for immunohistochemical experiments and next generation sequencing to be performed on these specimens [H02-61375; H08-01411].  4.2 Additional Details on Methods The analyses in this chapter follow the methodology described in Chapter 2.  The following difference should be noted: although Hx patients followed the same protocol for DNA collection by means of needle microdissection, DNA collected from Bx patients were derived from 2 – 4 10µm FFPE scrolls.  4.3 Results 4.3.1 Sample Description for Hysterectomy Cases The mean age of women in the Hx cohort (Patients Hx_1 to Hx_25) was 37.3 years (Table 4.1).  The most common reasons for women in this cohort to undergo hysterectomy was fibroids or pelvic pain (36% and 20% of women respectively).  Women noted as having “other/unclear” reasons for undergoing hysterectomy represented patients that were suspected (but not confirmed) to have pathology affecting the uterus, such as leiomyoma or endometrial polyps (Table 4.1; Appendix E).  We collected and subsequently analyzed   55 two blocks presumed to represent endometrial tissue from the posterior uterus and anterior uterus from each patient.  Note that in one patient (Patient Hx_25) anatomical distortion of the uterus resulted in the inability to define the location in the uterus in which the two blocks of endometrial tissue collected were sampled from (see Appendix E).  Table 4.1: Overview of clinical characteristics of women in Hx cohort. Patient characteristics Hysterectomy cohort (n = 25) Age (years; mean ± SD) 37.3 ± 6.5 Reason for procedure   fibroids 9 (36.0%)  pelvic pain 5 (20.0%)  leiomyoma 2 (8.0%)  prolapse 1 (4.0%)  sex reassignment 1 (4.0%)  abnormal bleeding 1 (4.0%)  dysmenorrhea 1 (4.0%)  other/unclear* 5 (20.0%) Endometrium State   proliferative 16 (64.0%)  secretory 7 (28.0%)   undetermined 2 (8.0%)  4.3.2 Sample Description for Endometrial Biopsy Cases The mean age of women in the endometrial biopsy (Bx) cohort (Patients Bx1 – Bx67, note Bx56 was not included in analysis) was 32.9 years (Table 4.2; Appendix F).  The difference in age between the Hx and Bx cohorts is not statistically significant (p = 0.5630, Student’s t-test).  The most common reasons for women in this cohort to undergo endometrial biopsy were abnormal uterine bleeding or not otherwise specified (48.5% and 22.7% of women respectively) (Table 4.2).   56 Table 4.2: Overview of clinical characteristics of women in Bx cohort. Patient characteristics Endometrial Biopsy cohort (n = 66) Age (years; mean ± SD) 38.6 ± 10.0 Reason for procedure   abnormal uterine bleeding 32 (48.5%)  not specified 15 (22.7%)  infertility 6 (9.1%)  rule out hyperplasia 4 (6.1%)  irregular menstrual cycle 3 (4.5%)  recurrent implant failure 2(3.0%)  submucosal fibroid 2 (3.0%)  postcoital spotting 1 (1.5%)  chronic anovulation 1 (1.5%) Endometrium State   proliferative 33 (50.0%)   secretory 31 (47.0%)  inactive 2 (3.0%)   Figure 4.2: Boxplot comparison of age of Hx and Bx patients.  The difference in age between the Hx and Bx cohorts is not statically significant (p = 0.5630, Student’s t-test).    57 4.3.3 Overview of Somatic Cancer-Driver Events Using the FIND ITTM version 3.4 assay, we performed targeted sequencing of macrodissected, endometrial tissue obtained from Hx women and Bx women for hotspots in the 33 genes listed in Table 2.1.  We also performed PTEN and ARID1A IHC as surrogates for PTEN and ARID1A inactivating mutations.  Targeted sequencing and IHC findings are summarized in Fig. 4.3.     58  Figure 4.3: Overview of somatic cancer-driver events in endometrial tissue from women in the A) Hx cohort and B) Bx cohort.  Cases are arranged by increasing age going downwards.  It is important to note that somatic events reported in Hx samples were derived from two specimens/blocks of endometrial tissue whereas Bx samples were   59 derived from a single biopsy specimen/block.  Somatic missense mutations are denoted in red and loss by IHC is denoted in blue.  I observed somatic-cancer driver events in 68.0% (17 of 25) of Hx cases including somatic hotspot, gain-of-function mutations in KRAS (8), ERBB2 (1), PIK3CA (4), and FGFR2 (3) as well as PTEN-loss by IHC (10) (Fig. 4.4; Fig. 4.5, Appendix G).  On the other hand, I observed somatic-cancer driver events in 50.0% (33 of 66) of Bx cases (Fig. 4.4, Appendix H).  These events included somatic hotspot mutations in KRAS (18), PIK3CA (8), and FGFR2 (3) as well as PTEN-loss by IHC (12).  I detected 0 – 3 somatic events in each patient, however I observed no abnormalities in ARID1A IHC in any Hx or Bx specimens.  Figure 4.4: Proportion of Hx and Bx cases affected by somatic cancer-driver events.  01020304050607080Total KRAS PTEN PIK3CA FGFR2 NRAS ERBB2 AKT1 CTNNB1 ARID1AAffected Cases (%)Hx Bx  60  Figure 4.5: PTEN IHC studies of Hx specimens showing (A,C,E) regional loss/heterogeneous expression of PTEN in epithelial endometriosis cells and (B,D,F) matching H&E stain.     61 I performed the Fisher’s exact test on the overall rate of somatic events in Hx patients and Bx patients as well as somatic events affecting each individual gene to assess whether the observed rates of cancer-driver events were significantly different between the two cohorts.  All tests were two-sided, where a P-value <0.05 was considered to be statistically significant.  The P-value for each pairwise comparison is shown below in Table 4.3.  Although the overall rate of somatic events was not significant (p = 0.1588), PTEN-loss seemed to occur more frequently in Hx patients compared to Bx patients (p = 0.0524).    Table 4.3: Reported P-values for pairwise comparisons of somatic events in Hx patients and Bx patients. Pairwise Comparison Hx patients affected Bx patients affected P-value Total Affected in Hx vs. Bx 17/25 33/66 0.1588 KRAS in Hx vs. Bx 8/25 18/66 0.7954 ERBB2 in Hx vs. Bx 1/25 0/66 0.2747 PIK3CA in Hx vs. Bx 4/25 8/66 0.7302 FGFR2 in Hx vs. Bx 3/25 3/66 0.3404 NRAS in Hx vs. Bx 0/25 2/66 1.0000 AKT1 in Hx vs. Bx 0/25 1/66 1.0000 CTNNB1 in Hx vs. Bx 0/25 1/66 1.0000 PTEN-loss in Hx vs. Bx 10/25 12/66 0.0524 ARID1A-loss in Hx vs. Bx 0/25 0/66 1.0000  *Note: Because of the exploratory nature of this study and because the number of comparisons was small, I did not adjust the P-value for multiple comparisons.  4.3.4 Multiple Sampling Analysis in Hysterectomy Cases As mentioned, two FFPE blocks containing endometrial tissue were obtained from each Hx patient.  With the exception of Patient Hx25 (wherein the anatomical sites where samplings were obtained from are not specified), we collected anterior and posterior endometrial tissue samplings from each patient.  The presence of specific point mutations in the anterior versus posterior uterine samplings is outlined in Fig. 4.6 and Table 4.4.  Forty-four percent of Hx patients (11 of 25) harboured ≥1 point mutation between two endometrial samplings.  Of patients with ≥1 point mutation, 90.9% (10 of 11) had   62 different/discordant point mutations in their two samplings, whereas only 9.1% (1 of 11) had the same/concordant point mutations in both samplings.  Interestingly, the anatomical location of the uterus where samplings were obtained from is not known in the case with concordant mutations in both samplings (Patient Hx_25; Table 4.4).   Note: This analysis does not include IHC findings since the specific molecular event resulting in loss of protein expression cannot be inferred (i.e. if PTEN-loss is observed in both anterior and posterior samplings, we do not know if this is caused by the same point mutation/deletion or two separate events).  Figure 4.6: Concordance and discordance of point mutations observed among both samplings obtained from Hx patients.   58%25%13%4%no point mutation mutation in one samplingmutation in both samplings - discordant mutation in both samplings - concordant  63 Table 4.4: Specific point mutations observed in samplings obtained from Hx patients.  The VAF determined by targeted sequencing and ddPCR are provided.  Data for patients listed correspond to the colours in the legend key in Figure 4.6. Identifier Site of Uterus Sampled Driver Gene Mutation VAF (%) - Targeted Sequencing  VAF (%) - ddPCR Hx_2A anterior none identified     Hx_2B posterior ERBB2 S310F 0.974 0.792 Hx_6A anterior KRAS G12V 2.654 1.63 Hx_6B posterior KRAS G12A 1.408 1.08 Hx_9A anterior none identified     Hx_9B posterior KRAS G13D 2.16 pending Hx_12A anterior PIK3CA R88Q 1.337 1.23 Hx_12B posterior none identified   Hx_13A posterior PIK3CA H1047R 2.497 3.36 KRAS G12C 1.036 0.538 Hx_13B anterior none identified     Hx_16A anterior KRAS G12V 1.079 1.06 PIK3CA E542K 1.015 0.897 Hx_16B posterior PIK3CA H1047R 2.402 1.92 Hx_18A anterior FGFR2 K659E 5.505 7.77 KRAS G12V 1.551 1.24 Hx_18B posterior none identified     Hx_21A anterior KRAS G12D 5.587 4.71 PIK3CA M1043I 2.39 pending Hx_21B posterior KRAS G12V 6.112 pending PIK3CA H1047R 2.373 276.00% FGFR2 S252W 1.889 pending Hx_23A ant none identified     Hx_23B post KRAS G12D 11.866 pending Hx_24A ant none identified     Hx_24B post FGFR2 S252W 1.769 pending Hx_25A N/A KRAS G12A 1.584 1.6 Hx_25B N/A KRAS G12A 1.67 1.0      64 4.3.5 Relationship Between Age and Presence of Somatic Cancer-Driver Events in Endometrial Biopsy Cases To assess the relationship between age and presence of somatic cancer-driver events, I generated a logistic regression model based on 66 Bx specimens wherein the dependent variable is a binary variable: patients either harboured somatic events in their endometrial tissue (regardless of the specific number of events) or they did not harbour somatic events (based on our analysis).  The designed logistic regression model is shown below in Figure 4.7.  Despite our small sample size, the likelihood of harbouring a somatic event in an endometrial biopsy appears to increase by age (OR = 1.05, 95% CI = 0.99– 1.11, p = 0.06, Wald test).   Figure 4.7: Logistic regression model depicting the correlation between age and presence of somatic cancer-driver events in Bx patients.  The shaded regions represent the 95% confidence interval for the likelihood of harbouring somatic cancer-driver events at a given age.  Since we determined the phase of the endometrium (proliferative phase or secretory phase), I also generated a model considering both age and the phase of the endometrium,   65 as illustrated below in Figure 4.8.  In this model, the likelihood of harbouring a somatic event is demonstrated to increase significantly with age (OR = 1.06, 95% CI = 1.00 – 1.12, p = 0.048, Wald test).  Endometrial tissue in secretory phase is associated with nearly five times the likelihood of observing a somatic event compared to the proliferative phase (OR = 4.86, 95% CI = 1.67 – 15.6, p = 0.0051, Wald test).  Details on the somatic-cancer driver alterations observed in Bx patients are provided in Table 4.5.  Figure 4.8: Logistic regression model depicting the correlation between age and presence of somatic cancer-driver events in Bx patients with endometrium samplings with respect to the phase of the endometrium.  The shaded regions represent the 95% confidence interval for the likelihood of harbouring somatic cancer-driver events at a given age.     66 Table 4.5: Somatic cancer-driver mutations detected in Bx patients.  The age, phase of endometrium, and VAF as determined by means of targeted panel sequencing and corresponding ddPCR assays are presented. Identifier Age (years) Endometrium Phase Driver Gene Mutation VAF (%) - Targeted Sequencing  VAF (%) - ddPCR Bx_3 38 proliferative KRAS G12A 2.529 2.41 Bx_5 45 proliferative PIK3CA H1047R 1.536 1.71 Bx_6 32 secretory KRAS G12C 0.872 pending KRAS G12D 1.675 pending Bx_8 49 secretory FGFR2 S252W 1.177 pending Bx_9 44 secretory KRAS G12D 6.43 pending Bx_12 29 secretory KRAS G12V 2.437 2.65 PIK3CA H1047R 0.806 1.1 Bx_13 29 secretory KRAS G12D 1.158 pending Bx_15 34 secretory KRAS G12V 1.162 1.12 Bx_16 40 secretory NRAS G13D 3.064 1.46 PIK3CA E545K 1.456 1.35 Bx_18 34 secretory KRAS G12D 5.554 pending Bx_21 37 secretory KRAS G12V 1.424 1.05 NRAS Q61R 10.232 pending Bx_22 43 secretory KRAS G12D 3.546 pending Bx_23 29 secretory FGFR2 S252W 1.889 pending Bx_35 29 proliferative AKT1 E17K 1.193 pending Bx_37 29 secretory KRAS G12D 1.281 pending Bx_40 34 proliferative KRAS G12A 1.973 pending Bx_46 43 late secretory FGFR2 S252W 1.18 pending PIK3CA E542K 0.994 pending PIK3CA H1047R 1.084 pending Bx_50 44 secretory PIK3CA G1049S 0.983 pending Bx_51 46 proliferative PIK3CA H1047R 2.927 pending Bx_52 46 secretory KRAS G12D 1.464 pending Bx_53 49 inactive KRAS G12D 1.505 pending PIK3CA E545K 1.453 pending PIK3CA R88Q 0.965 pending Bx_58 51 proliferative PIK3CA H1047R 1.014 pending Bx_60 51 secretory KRAS G12V 1.686 pending Bx_61 52 proliferative KRAS G12D 9.421 pending Bx_64 55 secretory KRAS G12D 1.031 pending Bx_65 55 proliferative CTNNB1 T41I 0.825 pending KRAS G12V 1.018 pending Bx_67 61 proliferative KRAS G12D 1.131 pending   67 4.4 Discussion In Chapter 3, I observed that somatic cancer-driver events are a common feature of endometriosis.  The goal of this chapter was to assess the presence of such events in the histologically unremarkable, eutopic endometrium of women lacking evidence of gynecologic malignancy.  Analyzing 25 Hx cases and 66 Bx cases, I observed high rates of somatic cancer-driver events (68% and 50% respectively) including KRAS and PIK3CA gain-of-function mutations and loss of PTEN protein.  The differences in observed rates of somatic events likely result from my analysis of two samplings per case for Hx cases but only one sampling per case for Bx cases.  Nevertheless, these rates are comparable to the rates of somatic alterations we observed in benign forms of endometriosis (27.5% in IE and 36.1% in DE) in Chapter 3.  In addition, many Hx patients and Bx patients remarkably harboured multiple mutations in their endometrial samplings (Fig. 4.2).  Although the biological function/impact of these somatic cancer-driver events remains unclear, I have demonstrated that these alterations pre-exist in the eutopic endometrium prior to the development of endometriosis or associated malignancies. Consistent with my observations of PTEN loss in endometriotic glands in Chapter 3 as well as other studies focusing on PTEN-null glands in endometrial specimens70,73, I found that cases always exhibited regional PTEN-loss in a small cluster of glands within a section of endometrial tissue (Fig. 4.5).  I sought to clarify whether other somatic mutations I observed in the eutopic endometrium were present in a localized area or whether such mutations existed at low allelic frequencies throughout the uterus.  In our analysis of Hx patients, I selected one anterior and posterior endometrial sampling per case to ensure that I was analyzing endometrium from spatially separated areas of the uterus.  Nearly all Hx patients wherein I detected at least one somatic cancer-driver alteration harboured discordant mutations in their anterior and posterior endometrial samplings (> 90%).  Only one patient harboured concordant KRAS G12A mutations in both endometrial samplings, however the distortion of the uterus in this patient made it impossible to determine the location of the uterus in which samplings were obtained from (and thus whether they were spatially separated).  Although my analysis represents a gross snapshot of the localization of these mutations and does not exclude the possibility that somatic mutations may span a large area of the uterus (yet uncommonly so large as   68 to be detected in both an anterior and posterior sampling), they are consistent with the localization of somatic events to small clonal patches within the uterus as previously observed with PTEN-null glands70,73.  A prominent question I sought to investigate was whether somatic cancer-driver events reflect the aging of women and thus their endometrial tissue.  Somatic mutations have been described in normal tissue including blood91, skin92 , and peritoneal washings90 from patients and seemingly reflect tissue aging even in the absence of cancer73.  Moreover, Monte et al. (2010) found loss of PAX2 and PTEN proteins to be common events in normal endometrial tissue occurring in 36% and 49% of cases respectively and their occurrence increased significantly with age73.  Analyzing endometrial biopsy specimens obtained from 66 women ranging from 21-61 years of age, I observed that increasing age appears to be associated with an increased likelihood of harbouring somatic cancer-driver events in normal, eutopic endometrial tissue.  An increase of age by one year is associated with an increase in likelihood of harbouring such alterations by approximately 6% (p = 0.048).  Interestingly, however, at a given age endometrial tissue in the secretory phase is more likelihood to harbour detectable somatic alterations compared to tissue in the proliferative phase (p = 0.0051).  Generally, women go in for endometrial biopsies according to their own schedules, and thus the phase at which women have a biopsy is random.  However, certain conditions influence the observance of secretory or proliferative phases at the time of biopsy – for instance, women with polycystic ovarian syndrome have continuous estrogen stimulation and are therefore constantly in proliferative phase93, therefore it is likely that the population of women who receive biopsies in proliferative versus secretory phases may be slightly different.  Additionally, this association with endometrial phase may be a product of high rates of proliferation as endometrial gland cells transition from proliferative phase to late secretory phase94, which could allow glandular epithelial cells harbouring somatic events to clonally expand to levels (VAFs) detectable by our targeted sequencing assay.  Overall, regardless of the specific mechanism resulting in the observed differences, the phase of the endometrium should be considered in the analysis of somatic events affecting the eutopic endometrium.    69 5. Concluding Chapter  5.1 Overview of Findings Beyond the association of endometriosis and ovarian cancer, endometriosis is an understudied disease as its origin remains contentious and molecular pathogenesis poorly understood.  Despite the high prevalence of endometriosis in women across the world, millions of women continue to live with endometriosis and its associated morbidities for years before formal diagnosis and receiving appropriate medical treatment.  In a large multicenter, cross-sectional study of over 1,400 women, women with endometriosis were found to have a significantly reduced physical health-related quality of life compared to unaffected women and experienced a loss on average of 10.8 hours of work weekly95. With consideration of direct health care costs and indirect costs (predominated by productivity loss), the estimated cost of endometriosis in the United States in 2009 totaled roughly $69.4 billion USD96, thereby bringing to attention the importance of studying endometriosis.  Expanding recent finding of somatic molecular alterations across endometriosis types stands to benefit endometriosis classification and may lead to a novel and more biologically informative system of classification.  Widespread knowledge on the prevalence of mutations may highlight common pathway dysfunction.  Even with difficulties in targeting the RAS pathway97  and potential toxicities related to PI3K-Akt pathway inhibitors98, molecular characterization may justify the use of targeted therapies in select circumstances and will undoubtedly drive innovation for novel intervention strategies. The overall goal of my study was to explore the prevalence of somatic cancer-driver mutations in forms of endometriosis unlikely to progress to malignancy as well as the eutopic endometrium.  In Chapter 3, I addressed the question: “Does incisional endometriosis harbour somatic cancer-driver mutations?”  Comparing the mutation profiles of IE and DE, I observed comparable rates of somatic cancer-driver events in both forms of endometriosis (affecting 27.5% and 36.1% of cases respectively).  The somatic events largely affected the MAPK/RAS and PI3K-Akt signalling pathways, thereby suggesting that these pathways may play a key role in the pathogenesis of endometriosis even outside of the context of cancer.  Interestingly, all somatic events   70 affected only the epithelial component of endometriosis lesions.  Similar rates and mutation profiles of IE and DE are consistent with a uterine origin of endometriosis (which is likely facilitated by retrograde menstruation in endogenously-occurring forms of endometriosis).  Consequently, I sought to determine if somatic cancer-driver mutations can be traced back to the eutopic endometrium.  In Chapter 4, I addressed the question: “Are somatic cancer-driver mutations present in the eutopic endometrium and if so, does their presence correlate with increasing age?”  Analyzing either Hx or Bx specimens from women lacking evidence of endometrial hyperplasia or gynecologic malignancy, I determined that somatic cancer-drivers are at least as prevalent in the eutopic endometrium as they are in benign forms of endometriosis – occurring in roughly half of all patients – and similarly affect canonical components of the MAPK/RAS (KRAS, ERBB2, FGFR2, or NRAS) and PI3K-Akt (PIK3CA or PTEN) signalling pathways.  Additionally, these mutations appear to affect distinct regions of the uterus and the likelihood of finding somatic events increases with age.  (It is important to note, however, that alterations are more commonly observed in secretory phase endometrium compared to proliferative phase endometrium.) Conventionally, somatic cancer-driver mutations are regarded as early events in malignant transformation, however my findings demonstrate that cancer-drivers can be found in endometriosis not at-risk for malignant transformation and even in the eutopic endometrium.  Roughly 10% of women are believed to be affected by endometriosis, yet approximately half of women unaffected by endometriosis or malignancy harbour somatic cancer-drivers in their eutopic endometrium.  This mutation damage likely accumulates overtime as a product of aging of this tissue (DNA replication errors) and environmental exposures (such as dioxin or diethylstilboestrol).  Whether endometrial cells harbouring cancer-driver mutations are implanted at an ectopic site or these mutations arise de novo within endometriotic lesions themselves, driver mutations (even if present in a small fraction of epithelial cells within endometriotic lesions) may confer a survival advantage to such lesions and enable their persistence ectopically.  For instance, several studies have suggested endometriosis expresses high levels of VEGF and BCL-2 or BCL-xL, mediating angiogenesis and resistance to apoptosis respectively 60,99-101. Likewise, KRAS-transformed epithelial cell have been shown to upregulate VEGF and other pro-  71 angiogenic factors102,103.  Additionally, the expression of oncogenic Ras results in the upregulation of BCL-xL in colon cancer cells, and the upregulation of both BCL-2 and BCL-xL in hematopoietic cells in vitro104,105.  While these specific mechanisms have not been linked to KRAS alteration in endometriosis, Cheng et al. (2011) were able to develop a mouse model of endometriosis by transplanting endometrium from KRASG12V/+ donor mice into subcutaneous, abdominal pockets of immunocompetent recipient mice.  In this model, oncogenic KRAS promoted the formation of endometriosis and enabled the prolonged survival of endometriotic lesions but does not result in malignant transformation106.  In combination with other factors including the accumulation of more mutations or epigenetic events, hormone influence (particularly high levels of estrogen), cell-organ interactions contributing to the local microenvironment, and immune or inflammatory interactions, endometriotic lesions may undergo malignant transformation (see Figure 5.1).  Cancer risk is also dependent on the form of endometriosis: ovarian endometriosis is associated with the highest risk, whereas DE is associated with the lowest risk47.    72  Figure 5.1: A revised model of the pathogenesis of endometriosis lesions.   Cancer-driver mutations arise spontaneously in endometriotic cells or in the eutopic endometrium prior to seeding at an ectopic site and confer a survival advantage to cells, thereby allowing for the persistence of endometriotic lesions.  Additional factors such as further genetic or epigenetic events, hormone influence, cell-organ interactions, and immune or inflammatory interactions contribute to progression to malignancy.  5.2 Limitations It is crucial to note several limitations in my analyses, which are listed below: 1. Even though I have described somatic cancer-driver events in the tissues analyzed using the FIND ITTM version 3.4 assay, there remains challenges in ultra-low input sequencing from FFPE tissue.  Orthogonal validation by means of ddPCR resulted in our empirical determination of a targeted sequencing VAF cut-off of 0.8% for macrodissected FFPE tissue – it is possible that I failed to detect and report real mutations in endometriosis or endometrial tissue existing at lower VAFs. 2. We only conducted targeted panel sequencing for hotspot mutations in 33 genes in addition to PTEN IHC and ARID1A IHC.  It is possible that whole genome or exome sequencing, or epigenetic analysis may uncover additional somatic cancer-driver alterations missed by our methods. 3. Exploration of the relationship between age and the presence of somatic alterations in endometrial tissue is limited by my relatively small sample size of n = 66 (note that despite this, we found increased age to be correlated with an increased likelihood of harbouring mutation (p = 0.048).  This is particularly problematic if we want to simultaneously consider several variables (including menstrual state) in our logistic regression model. 4. Because of the descriptive nature of my study, the functional roles of identified mutations within the context of endometriosis remain unclear and causality cannot be established.   5. My study was also a retrospective, cross-sectional study by design.  The associated risks for development of malignancy if an endometriotic lesion harbours somatic alterations or the development of endometriosis if somatic alterations are   73 present in the eutopic endometrium remain undetermined by this study.  Furthermore, whether specific somatic alterations in the eutopic endometrium persist over time or disappear and emerge over the course of many menstrual cycles, as previously observed by Mutter et al. (2014) in PTEN-null endometrial glands72, is also unclear.  5.3 Future Directions The findings in my study give rise to more questions than those that were clarified in the process.  Firstly, the mutations I have identified in women with IE and DE (as well as the eutopic endometrium) are commonly mutated in clear cell and endometrioid ovarian cancers107,108, and therefore study of the prevalence of such mutations in endometriomas, which are most commonly linked to malignancy, is warranted.  In contrast to my studies on IE and DE, a recent targeted sequencing study including many of the genes we analyzed in our sequencing panel (such as KRAS, PIK3CA, and PTEN) identified somatic mutations in only 3 of 101 (3%) ovarian endometriosis samples67.  The sampling methods used to enrich for endometriosis within tissue specimens are not explicitly stated in this study, and therefore we are unsure whether such low frequencies of somatic alterations in ovarian endometriosis are truly reflective of a reduced burden compared to benign forms of endometriosis or whether endometriosis was insufficiently enriched for to be detected by targeted sequencing.  Analyzing ovarian endometriosis with methods consistent with my study will help clarify this contention and determine the relative mutation burden in this form of endometriosis. Regarding my analysis of eutopic endometrium, the distribution of glands harbouring somatic cancer-driven mutations is unknown aside that they are rarely ubiquitous throughout the uterus or span from one side of the uterus to the opposite site (i.e. from anterior to posterior).  PTEN IHC revealed PTEN-null glands exist in small clonal patches among many normally-expressing glands.  I suspect that the same is true for somatic mutations such as KRAS or PIK3CA driver mutations.  For the most part, mutation-specific antibodies such as those designed to target KRAS are unable to reliably distinguish mutant/aberrant versions of these proteins from wildtype (the exception is   74 BRAF, wherein an antibody achieving great sensitivity and specificity has been designed)109,110.   To clarify this matter, novel base-specific in situ hybridization may be an optimal mechanism to specifically indentify affected cells. The BaseScope assay (Advanced Cellular Diagnostics), is an RNA in situ hybridization assay based on the design of unique ‘Z’ probes which recognize RNA sequences of interest, followed by signal amplification (Fig. 5.2)111 .  Ultimately, this technique can discriminate between alterations as small as a single nucleotide change and enable the visualization of mutant subpopulations within archival tissue specimens while preserving morphological context111.  The BaseScope assay has been used successfully to precisely map the spatial and morphological context of subclones harbouring common point mutations in BRAF, KRAS, and PIK3CA in archival colorectal cancer specimens111 as well as splice junction visualization in metastatic castration-resistant prostate cancer112.  Figure 5.2: Schematic of the BaseScope assay.  Two custom-designed ‘Z’ probes recognize and bind to target mRNA sequence.  This binding allows for signal amplification followed by visualization of the point mutation of interest while preserving spatial context and cellular morphology of FFPE tissue. [Figure adapted from Baker et al., 2007111.]  Additionally, probes can be designed in various colours, therefore enabling the simultaneous visualization of cell populations harbouring different point mutations yet within the same FFPE tissue specimen (via multiplexing).  As noted in the overview of somatic events observed in eutopic endometrium in Fig. 4.2, several Hx and Bx cases exhibited multiple somatic point mutations.  It is unclear whether mutations observed in the same tissue specimen affect different clusters of glands, thereby representing   75 independent events, or whether affected glands overlap to some extent – the latter scenario would imply the accumulation of cellular damage.  My study is consistent with a uterine origin of endometriosis – operating under this premise implies that EAOCs ultimately arise from the eutopic endometrium.  The eutopic endometrium can also give rise to endometrial cancers113.  Oral contraceptive pill (OCP) use is known to reduce the incidence of both ovarian cancers114 and endometrial cancers115,116.  Lin et al. (2009) observed a significantly reduced frequency of latent precancers (defined as endometrial glands exhibiting PTEN-loss) in women with a history of OCP or non-hormonal intra-uterine device use and hypothesized that these glands may be targets of endometrial cancer risk modulating exposures 71.  Consequently, in my analysis of the relationship between age and the presence of somatic cancer-drivers in the eutopic endometrium, a possible confounding variable is OCP use.  OCP use should be assessed for significance in its association with the presence of somatic drivers in the endometrium and possibly included in a future, more comprehensive logistic regression model.    5.4 Significance of Study In short, despite the unclear role of somatic cancer-driver mutations outside the context of cancer, such mutations commonly exist in both endometriosis and eutopic endometrium.  Therefore, my findings have profound implications for basic researchers and society at large.   I discovered that somatic cancer-driver mutations are not restricted to specific anatomical forms of endometriosis – DE and IE harbour similar mutation profiles and therefore appear to be biologically equivalent despite differing mechanisms of dissemination.  The development of animal models for in vivo endometriosis research presents many challenges, particularly because endometriosis occurs spontaneously only in humans and some non-human primates117.  Ectopic rat and mouse models of endometriosis have been developed, however the usefulness of such models is contentious because these models represent induced disease in species that do not   76 naturally develop endometriosis 117.  Our findings serve as additional validation for the use of these ectopic animal models in endometriosis research since iatrogenically-caused endometriosis (IE) is similar to endogenously-occurring disease (DE) even on a molecular level. Perhaps the most important message from my study is that the mutational burden caused by the emergence of a single cancer-driver event in ectopic or eutopic endometrial tissue is clearly insufficient for malignant transformation and it is likely other additional events must occur for the development of EAOCs (or endometrial cancers).  Liquid biopsies represent exciting tools for the clinical management of disease – especially in the early, non-invasive detection of a variety of cancers and monitoring treatment response or residual disease following medical invention118.  Recently, Wang et al. (2018) reported on the detection of ovarian and endometrial cancers based on the genetic analysis (incorporating assays for mutation detection in 18 genes and an assay for aneuploidy) of DNA recovered from fluids obtained during routine Papanicolaou (Pap) tests119.  33% of ovarian cancer patients and 81% of endometrial cancer patients had positive Pap brush samples, whereas only 1.4% of women without cancer had positive results – when sampling with a Tao brush instead, the percentage of false positive women fell to 0%119.  Alongside findings of genetic abnormalities in uterine lavage samples88 and peritoneal washings89, my findings contradict these observations that somatic cancer-driver mutations nearly exclusively occur in cancer cases.  Indeed, the study by Wang et al. is deeply flawed as its control group were dissimilar to the patient population in a key way, age.   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Source Patient ID Age (years) Diagnosis Affected Site Interval (years) Number of IE Blocks Analyzed Surgical History Additional Notes VGH IE_1 37 incisional EMS subcutaneous 11 2 surgical abortion   VGH IE_2 36 incisional EMS subcutaneous n/a 1 no prior surgery identified   VGH IE_3 37 incisional EMS subcutaneous 3 2 TAH-BSO   VGH IE_4 31 incisional EMS subcutaneous n/a 1 c-section   VGH IE_5 45 incisional EMS fascia 17 1 surgical abortion eutopic endometrium sampling available VGH IE_6 32 incisional EMS subcutaneous 4 1 c-section   VGH IE_7 28 umbilical EMS subcutaneous n/a 2 no prior surgery identified   VGH IE_8 48 umbilical EMS subcutaneous 3 2 laparoscopy   VGH IE_9 44 umbilical EMS subcutaneous n/a 1 no prior surgery identified   VGH IE_10 49 incisional EMS abdominal wall 1 montha 4 c-section (earlier timepoint); laparoscopy eutopic endometrium sampling available VGH IE_11 37 incisional EMS abdominal wall 4 2 c-section   VGH IE_12 41 incisional EMS abdominal wall 6 3 c-section eutopic endometrium sampling available Mannheim IE_13 34 incisional EMS recto-uterine pouch n/a 3 n/a   Mannheim IE_14 29 post c-section EMS abdominal wall n/a 1 c-section     94 Source Patient ID Age (years) Diagnosis Affected Site Interval (years) Number of IE Blocks Analyzed Surgical History Additional Notes Mannheim IE_15 30 post c-section EMS abdominal wall n/a 2 c-section   Mannheim IE_16 28 post c-section EMS abdominal wall n/a 1 c-section   Mannheim IE_17 38 post c-section EMS abdominal wall n/a 2 c-section   Mannheim IE_18 32 post c-section EMS abdominal wall n/a 1 c-section   Mannheim IE_19 40 post c-section EMS abdominal wall n/a 1 c-section   Mannheim IE_20 38 post c-section EMS abdominal wall n/a 1 c-section   Mannheim IE_21 32 umbilical EMS subcutaneous n/a 1 n/a   Mannheim IE_22 36 post c-section EMS abdominal wall n/a 1 c-section   Tuebingen IE_23 39 incisional EMS n/a 9a 3 c-section   Tuebingen IE_24 41 incisional EMS n/a 12 1 c-section via vertical laparotomy   Tuebingen IE_25 43 incisional EMS n/a 2 1 c-section   Tuebingen IE_26 35 incisional EMS n/a 5 2 c-section   Tuebingen IE_27 37 umbilical EMS n/a 3a 2 c-section (earlier timepoint); umbilical hernia hx of endometriosis Tuebingen IE_28 39 incisional EMS n/a 3 1 c-section eutopic endometrium sampling available Tuebingen IE_29 45 umbilical EMS n/a 1 1 laparoscopic supracervical hysterectomy hx of endometriosis   95 Source Patient ID Age (years) Diagnosis Affected Site Interval (years) Number of IE Blocks Analyzed Surgical History Additional Notes Tuebingen IE_30 40 incisional EMS n/a 10 2 c-section   Tuebingen IE_31 28 umbilical EMS n/a 4 1 diagnostic laparoscopy   Tuebingen IE_32 38 umbilical EMS n/a 1 1 laparoscopy   Tuebingen IE_33 30 umbilical EMS n/a 2 1 adnexectomy hx of endometriosis Tuebingen IE_34 36 incisional EMS n/a 13 1 c-section hx of endometriosis Tuebingen IE_35 30 incisional EMS n/a 4a 1 c-section (earlier timepoint); c-section   Tuebingen IE_36 36 incisional EMS n/a 5 1 c-section hx of endometriosis Tuebingen IE_37 41 incisional EMS n/a 4 1 c-section hx of endometriosis VUMC IE_38 33 post c-section EMS abdominal wall 2 1 c-section   VUMC IE_39 31 post c-section EMS abdominal wall 3 1 c-section   VUMC IE_40 35 post c-section EMS abdominal wall 3 1 c-section    a Patients IE_10, IE_23, and IE_35 had multiple documented surgeries prior to resection of incisional endometriosis – the reported time interval between suspected inciting surgery and resection of endometriosis in these women reflect the time of the most recent surgery.    96 Appendix B: Summary of clinical data for women with DE. Source Patient ID Age (years) Clinical Stage (r-ASRM) Block ID and Descriptor Anatomical Site Additional Notes BC Women's DE_1 41 IV A: index peri-ureter   BC Women's DE_2 50 IV A: index recto-uterine   BC Women's DE_3 29 II A: index uterosacral ligament MFPE tissue BC Women's DE_4 28 IV A: index sigmoid colon MFPE tissue BC Women's DE_5 25 III A: index uterosacral ligament MFPE tissue BC Women's DE_6 34 IV A: index rectovaginal MFPE tissue BC Women's DE_7 41 IV A: index rectovaginal MFPE tissue BC Women's DE_8 40 II A: index uterosacral ligament MFPE tissue BC Women's DE_9 33 III A: index sigmoid colon MFPE tissue BC Women's DE_10 27 III A: index posterior cul-de-sac MFPE tissue BC Women's DE_11 27 II A: index central cul-de-sac   B: separate left uterosacral   BC Women's DE_12 39 IV A: index left uterosacral   B: separate bladder peritoneum   BC Women's DE_13 26 III A: index central cul-de-sac   B: separate left peri-ureteric nodule   BC Women's DE_14 22 I A: index left uterosacral   BC Women's DE_15 49 IV A: index right uterosacral   BC Women's DE_16 36 IV A: index Cul-de-sac   B: separate ovary     97 Source Patient ID Age (years) Clinical Stage (r-ASRM) Block ID and Descriptor Anatomical Site Additional Notes BC Women's DE_17 32 III A: index left ureterosacral area   BC Women's DE_18 36 IV A: index rectovaginal nodule   BC Women's DE_19 38 III A: index left ovarian fossa   B: separate bladder peritoneum   BC Women's DE_20 45 IV A: index posterior cul-de-sac   BC Women's DE_21 44 III A: index uterosacra right periureteric nodule   B: separate right pararectal   BC Women's DE_22 24 III A: index left uterosacral   B: separate right uterosacral   BC Women's DE_23 35 III A: index right vaginal-rectal nodule   VUMC DE_24 32 IV A: index Colon (rectosigmoid)   VUMC DE_25 33 IV A: index Colon (rectosigmoid)   VUMC DE_26 30 IV A: index colon, (rectosigmoid) bladder, left tube, endometriosis cyst (right side)   VUMC DE_27 37 IV A: index sigmoid colon   VUMC DE_28 26 IV A: index rectosigmoid   VUMC DE_29 35 IV A: index sigmoid   VUMC DE_30 33 IV A: index Colon (rectosigmoid),     98 Source Patient ID Age (years) Clinical Stage (r-ASRM) Block ID and Descriptor Anatomical Site Additional Notes bladder peritoneum VUMC DE_31 33 II A: index posterior bladder wall   VUMC DE_32 31 III A: index rectosigmoid   VUMC DE_33 29 IV A: index Bladder (posterior wall)   VUMC DE_34 31 IV A: index Colon (rectosigmoid), left , right tube   VUMC DE_35 43 II A: index Bladder (posterior wall)   VUMC DE_36 26 IV A: index Tubes left & right        99 Appendix C: Summary of somatic cancer-driver events in women with IE. Patient ID Block ID and Descriptor Driver Mutation Identified Material for IHC Staining PTEN IHC ARID1A IHC IE_1 A:index   whole section + + B: adjacent   whole section + + IE_2 A: index   whole section + + IE_3 A: index   whole section + + B: adjacent   whole section + + IE_4 A: index   whole section + + IE_5 A: index   whole section + + IE_6 A: index   whole section + + IE_7 A: index   whole section + + B: adjacent   whole section + + IE_8 A: index KRAS G12V whole section + + B: adjacent KRAS G12V whole section + + IE_9 A: index   whole section + + IE_10 A: index   whole section + + B: adjacent   whole section + + C: adjacent   whole section + + D: adjacent   whole section + + IE_11 A: index   whole section HET + B: adjacent   whole section + + IE_12 A: index   whole section + + B: adjacent   whole section + + C: adjacent   whole section + + IE_13 A: index   whole section HET + B: adjacent   whole section + + C: adjacent   whole section + +   100 Patient ID Block ID and Descriptor Driver Mutation Identified Material for IHC Staining PTEN IHC ARID1A IHC IE_14 A: index   whole section + + IE_15 A: index   whole section HET + B: adjacent   whole section HET + IE_16 A: index ERBB2 S310F whole section + + IE_17 A: index   whole section HET + B: adjacent   whole section HET + IE_18 A: index   whole section + + IE_19 A: index KRAS G12C whole section + + IE_20 A: index   whole section + + IE_21 A: index   whole section HET + IE_22 A: index   whole section + + IE_23 A: index   whole section + + B: adjacent   whole section + + C: adjacent   whole section + + IE_24 A: index   whole section + + IE_25 A: index PIK3CA H1047R whole section + + IE_26 A: index   whole section + + B: adjacent   whole section + + IE_27 A: index   whole section + + B: adjacent   whole section + + IE_28 A: index   whole section + + IE_29 A: index   whole section + + IE_30 A: index   whole section + + B: adjacent   whole section + +   101 Patient ID Block ID and Descriptor Driver Mutation Identified Material for IHC Staining PTEN IHC ARID1A IHC IE_31 A: index   n/a n/a n/a IE_32 A: index   whole section + + IE_33 A: index   whole section + + IE_34 A: index   whole section HET + IE_35 A: index   whole section + + IE_36 A: index   whole section n/a + IE_37 A: index   whole section n/a + IE_38 A: index   TMA + + IE_39 A: index   TMA LOSS + IE_40 A: index   TMA + +  *“HET” denotes loss of expression in some glands in a section, but not others. “LOSS” indicates loss of expression in all glands.  “Adjacent” refers to tissue specimens obtained from a different archival tissue block yet the same anatomical site as the index block.     102 Appendix D: Summary of somatic cancer-driver events in women with DE. Patient ID Block ID and Descriptor Anatomical Site Driver Mutation Identified Material for IHC Staining PTEN IHC ARID1A IHC DE_1 A: index peri-ureter   CTNNB1 G34V whole section + + DE_2 A: index recto-uterine KRAS G12D whole section + + DE_3 A: index uterosacral ligament   whole section n/aa + DE_4 A: index sigmoid colon   whole section n/aa + DE_5 A: index uterosacral ligament KRAS G12D whole section n/aa + DE_6 A: index Rectovaginal   whole section n/aa + DE_7 A: index Rectovaginal   whole section n/aa HET DE_8 A: index uterosacral ligament   whole section n/aa + DE_9 A: index sigmoid colon   whole section n/aa + DE_10 A: index posterior cul-de-sac KRAS G12V whole section n/aa + DE_11 A: index central cul-de-sac KRAS G12D whole section + + B: separate left uterosacral   whole section + + DE_12 A: index left uterosacral   whole section + + B: separate bladder peritoneum   whole section + + DE_13 A: index central cul-de-sac   whole section HET +   103 Patient ID Block ID and Descriptor Anatomical Site Driver Mutation Identified Material for IHC Staining PTEN IHC ARID1A IHC B: separate left peri-ureteric nodule   whole section + + DE_14 A: index left uterosacral KRAS G12C whole section + + DE_15 A: index right uterosacral PIK3CA E545A whole section HET + DE_16 A: index Cul-de-sac   whole section + + B: separate Ovary   whole section + + DE_17 A: index left ureterosacral area   whole section HET + DE_18 A: index rectovaginal nodule   whole section failed + DE_19 A: index left ovarian fossa   whole section + + B: separate bladder peritoneum   whole section + + DE_20 A: index posterior cul-de-sac   whole section + + DE_21 A: index uterosacra right periureteric nodule   whole section HET + B: separate right pararectal KRAS G12V whole section + + DE_22 A: index left uterosacral   whole section + + B: separate right uterosacral   whole section + + DE_23 A: index right vaginal-rectal nodule   whole section HET +   104 Patient ID Block ID and Descriptor Anatomical Site Driver Mutation Identified Material for IHC Staining PTEN IHC ARID1A IHC DE_24 A: index Colon (rectosigmoid)   TMA + + DE_25 A: index Colon (rectosigmoid)   TMA + + DE_26 A: index colon, (rectosigmoid) bladder, left tube, endometriosis cyst (right side)   n/a n/a n/a DE_27 A: index sigmoid colon   TMA + + DE_28 A: index Rectosigmoid   TMA + + DE_29 A: index Sigmoid   TMA + + DE_30 A: index Colon (rectosigmoid), bladder peritoneum   TMA + + DE_31 A: index posterior bladder wall   TMA + + DE_32 A: index Rectosigmoid KRAS G12A TMA + + DE_33 A: index Bladder (posterior wall)   n/a n/a n/a DE_34 A: index Colon (rectosigmoid), left , right tube   TMA + + DE_35 A: index Bladder (posterior wall)   TMA + + DE_36 A: index Tubes left & right   TMA + +  a All PTEN staining for MFPE sections (Patients DE_3 to DE_10) failed (no MFPE tissue retained PTEN stain). *“HET” denotes loss of expression in some glands in a section, but not others.  “Separate” refers to specimens obtained from an anatomically distinct site from the index block.     105 Appendix E: Summary of clinical data for women in Hx cohort. Patient ID Age (years) Reason for surgery Endometrium Phase Clinical History / Comments Hx_1 29 pelvic pain proliferative Hx of mature cystic teratoma Hx_2 29 pelvic pain proliferative   Hx_3 30 chronic pelvic pain proliferative   Hx_4 31 Chronic pelvic pain late proliferative   Hx_5 31 ? Leiomyoma irregular secretory   Hx_6 32 fibroid secretory   Hx_7 33 ? (endometrial polyp) proliferative   Hx_8 32 fibroid/pain proliferative   Hx_9 33 prolapse ??? (branching present...)   Hx_10 36 ? (leios)     Hx_11 36 sex reassignment proliferative   Hx_12 37 fibrods (leios) proliferative   Hx_13 38 r/o endometriosis (leios) secretory   Hx_14 37 fibroids (leios) early secretory   Hx_15 40 bleeding -on Fibristal (leios) secretory   Hx_16 44 Fibroid (leios) secretory   Hx_17 45 ? (leios) proliferative adenomyosis present Hx_18 48 Fibroid (leios) disordered proliferative   Hx_19 34 pelvic pain (leios) proliferative   Hx_20 35 dysmenorrhea proliferative   Hx_21 37 fibroid (leios) proliferative   Hx_22 51 fibroids weakly proliferative     106 Patient ID Age (years) Reason for surgery Endometrium Phase Clinical History / Comments Hx_23 44 degenerating leiomyoma proliferative endometriotic cyst left ovary Hx_24 49 fibroid disordered proliferative   Hx_25 42 leiomyoma secretory distortion of uterus     107 Appendix F: Summary of clinical data for women in Bx cohort. Patient ID Age (years) Clinical History Endometrium Phase Bx_1 37 Abnormal uterine bleeding Proliferative Bx_2 36 Irregular cycles Benign proliferative Bx_3 38   proliferative  Bx_4 32 Abnormal uterine bleeding Benign proliferative Bx_5 45 Menorrhagia proliferative endometrium Bx_6 32 infertility, polypoid endometrial lining late secretory Bx_7 27 1 year history of abnormal bleeding, Mother has endometrial cancer Proliferative endometrium Bx_8 49 R/O pathology, dysmenorrhea Secretory endometrium Bx_9 44 Irregular bleeding, R/O hyperplasia Secretory endometrium Bx_10 34 Menorrhagia Secretory Bx_11 29 R/O hyperplasia PCOS proliferative endometrium Bx_12 29 Chronic anovulation secretory endometrium Bx_13 29 recurrent implant failure early secretory Bx_14 38 recurrent implant failure proliferative  Bx_15 34 Infertility multiple polyps seen on hysteroscopy late secretory   108 Patient ID Age (years) Clinical History Endometrium Phase Bx_16 40 ?polyp, menorrhagia secretory Bx_17 38   proliferative  Bx_18 34 Abnormal uterine bleeding secretory Bx_19 36 submucosal fibroid -> reason for procedure late secretory Bx_20 49 menorrhagia proliferative  Bx_21 37 Irregular bleeding Irregular secretory Bx_22 43 Menorrhagia and fibroid uterus Postovulatory menstrual endometrium Bx_23 29 Postcoital spotting secretory endometrium Bx_24 21 irregular menses, rule out acute pathology Proliferative type endometrium Bx_25 23 menorrhagia secretory Bx_26 24 Irregular menstrual cycles Disordered proliferative endometrium Bx_27 25 Rule out hyperplasia, PCOS Proliferative endometrium Bx_28 25 PCOS, R/O hyperplasia Proliferative endometrium Bx_29 26 Abnormal uterine bleeding proliferative Bx_30 27 menorrhagia Secretory endometrium Bx_31 27 Menorrhagia, chronic anovulation Proliferative endometrium Bx_32 27   proliferative endometrium   109 Patient ID Age (years) Clinical History Endometrium Phase Bx_33 27   proliferative endometrium Bx_34 27 Abnormal uterine bleeding. R/O hyperplasia or malignancy Secretory endometrium Bx_35 29 Menorrhagia, R/O hyperplasia Menstrual endometrium Bx_36 29 infertility progesterone treatment changes Bx_37 29 Infertility, R/O luteal phase defect secretory endometrium Bx_38 29   proliferative endometrium Bx_39 34 Synechia seen in office hysteroscopy mid secretory Bx_40 34 Abnormal bleeding on Estrace plus Lupron disordered proliferative Bx_41 35 known fibroids plus menorrhagia weakly proliferative Bx_42 39 infertility late secretory Bx_43 39 abnormal bleeding and endometrial polyp inactive Bx_44 40 Infertility, endometrial synechiae, submucosal fibroid disordered proliferative Bx_45 43   Mildly disordered proliferative endometrium Bx_46 43 menorrhagia late secretory   110 Patient ID Age (years) Clinical History Endometrium Phase Bx_47 43   Irregular secretory endometrium Bx_48 44   Endometrium showing changes secondary to progestational Bx_49 44   Secretory endometrium Bx_50 44 Menorrhagia Irregular secretory endometrium Bx_51 46   Proliferative endometrium Bx_52 46   Menstrual endometrium Bx_53 49 Abnormal bleeding, fibroids inactive Bx_54 50 Abnormal uterine bleeding Proliferative endometrium Bx_55 50 Abnormal uterine bleeding Secretory endometrium Bx_57 51 Heavy menstrual bleeding Weakly proliferative Bx_58 51 Abnormal uterine bleeding, Pretreatment with Ulipristal proliferative endometrium Bx_59 51   secretory Bx_60 51 menorrhagia irregular secretory Bx_61 52 menorrhagia disordered proliferative Bx_62 53 Postmenopausal bleeding disordered proliferative endometrium Bx_63 53 Abnormal bleeding weakly proliferative Bx_64 55 Heavy bleeding late secretory endometrium Bx_65 55 Perimenopausal bleeding, fibroids Proliferative endometrium       111 Patient ID Age (years) Clinical History Endometrium Phase Bx_66 56 intermenstrual spotting. R/O hyperplasia or malignancy Proliferative endometrium Bx_67 61 PMB on HRT Weakly proliferative     112 Appendix G: Summary of somatic cancer-driver events in Hx patients. Patient ID Block ID and Descriptor Driver Mutation Identified PTEN IHC ARID1A IHC Hx_1 A:    + + B:    + + Hx_2 A: anterior   + + B: posterior ERBB2 S310F + + Hx_3 A:    + + B:    + + Hx_4 A:  H + B:  + + Hx_5 A:  H + B:  + + Hx_6 A: anterior KRAS G12V + + B: posterior KRAS G12A + + Hx_7 A:  H + B:  + + Hx_8 A:  H + B:  H + Hx_9 A: anterior  + + B: posterior KRAS G13D H + Hx_10 A:  + + B:  H + Hx_11 A:  + + B:  + + Hx_12 A: anterior PIK3CA R88Q + + B: posterior ERBB2 S310F + +   113 Patient ID Block ID and Descriptor Driver Mutation Identified PTEN IHC ARID1A IHC Hx_13 A: posterior PIK3CA H1047R, KRAS G12C + + B: anterior  + + Hx_14 A:  + + B:  + + Hx_15 A:  + + B:  + + Hx_16 A: anterior KRAS G12V, PIK3CA E542K + + B: posterior PIK3CA H1047R H + Hx_17 A:  + + B:  + + Hx_18 A: anterior FGFR2 K659E, KRAS G12V + + B: posterior  H + Hx_19 A:  + + B:  + + Hx_20 A:  + + B:  + + Hx_21 A: anterior KRAS G12D, PIK3CA M1043I + + B: posterior KRAS G12V, PIK3CA H1047R, FGFR2 S252W + + Hx_22 A: anterior  H +   114 Patient ID Block ID and Descriptor Driver Mutation Identified PTEN IHC ARID1A IHC B: posterior KRAS G12D N/A + Hx_23 A: anterior  FAILED + B: posterior  + + Hx_24 A: anterior FGFR2 S252W + + B: posterior  + + Hx_25 A: N/A KRAS G12A + + B: N/A KRAS G12A H +     115 Appendix H: Summary of somatic cancer-driver events in Bx patients. Patient ID Driver Mutation Identified PTEN IHC ARID1A IHC Bx_1 none identified + + Bx_2 none identified + + Bx_3 KRAS G12A + + Bx_4 none identified + + Bx_5 PIK3CA H1047R H (min) + Bx_6 KRAS G12C, KRAS G12D + + Bx_7 none identified + + Bx_8 FGFR2 S252W + + Bx_9 KRAS G12D H (min) + Bx_10 none identified H + Bx_11 none identified + + Bx_12 KRAS G12V, PIK3CA H1047R + + Bx_13 KRAS G12D H (min) + Bx_14 none identified + + Bx_15 KRAS G12V + + Bx_16 NRAS G13D, PIK3CA E545K H + Bx_17 none identified + + Bx_18 KRAS G12D + + Bx_19 none identified + + Bx_20 none identified + + Bx_21 KRAS G12V, NRAS Q61R + + Bx_22 KRAS G12D + + Bx_23 FGFR2 S252W + + Bx_24 none identified + + Bx_25 none identified + +   116 Patient ID Driver Mutation Identified PTEN IHC ARID1A IHC Bx_26 none identified H + Bx_27 none identified + + Bx_28 none identified + + Bx_29 none identified + + Bx_30 none identified + + Bx_31 none identified + + Bx_32 none identified + + Bx_33 none identified + + Bx_34 none identified H + Bx_35 AKT1 E17K + + Bx_36 none identified + + Bx_37 KRAS G12D + + Bx_38 none identified + + Bx_39 none identified + + Bx_40 KRAS G12A + + Bx_41 none identified + + Bx_42 none identified + + Bx_43 none identified H + Bx_44 none identified + + Bx_45 none identified H + Bx_46 FGFR2 S252W; PIK3CA E542K; PIK3CA H1047R + + Bx_47 none identified + + Bx_48 none identified + + Bx_49 none identified H + Bx_50 PIK3CA G1049S H + Bx_51 PIK3CA H1047R + +   117 Patient ID Driver Mutation Identified PTEN IHC ARID1A IHC Bx_52 KRAS G12D + + Bx_53 KRAS G12D; PIK3CA E545K; PIK3CA R88Q + + Bx_54 none identified H + Bx_55 none identified + + Bx_57 none identified + + Bx_58 PIK3CA H1047R + + Bx_59 none identified + + Bx_60 KRAS G12V + + Bx_61 KRAS G12D H + Bx_62 none identified + + Bx_63 none identified + + Bx_64 KRAS G12D + + Bx_65 CTNNB1 T41I; KRAS G12V + + Bx_66 none identified + + Bx_67 KRAS G12D + +      118 Appendix I: Is the Presence of Somatic Cancer-Driver Events in Deep Infiltrating Endometriosis Associated with Deep Dyspareunia?  As discussed in Chapter 1, endometriosis represents a substantial burden to women affected by the disease.  Among other symptoms, approximately half of women with DE suffer from deep dyspareunia (defined as pelvic pain with intercourse)120,121.  Deep dyspareunia has profound negative impact on the quality of life of affected women and relationship with partners (resulting from the avoidance of sexual activity)122 .  However, the phenotype of deep dyspareunia is clinically heterogeneous and is seldom accounted for by endometriosis alone – for instance, one woman with cul-de-sac endometriosis may have severe dyspareunia, whereas another woman with endometriosis at the same site may exhibit minimal/no pain121.  This heterogeneity may be partly explained by differences in the extent of macrophage infiltration of endometriotic lesions and/or nerve bundle density.  A recent study has shown that interleukin (IL)-1B stimulates brain-derived neurotrophic factor (BDNF) production by endometriotic stromal cells in vitro – a growth factor known to stimulate nerve growth and angiogenesis123.  Moreover, compared to women without deep dyspareunia, women with deep dyspareunia have increased local PGP9.5 nerve bundle density around endometriotic lesions124.  This local nerve bundle density may interact with other biological factors (e.g. inflammation) or psychological factors to produce this heterogeneity in deep dyspareunia among patients.  Nevertheless, an understanding of contributing factors to deep dyspareunia and potential therapeutic strategies for such symptoms is still lacking. In Chapter 3, I found that 36.1% of DE cases examined harboured a somatic cancer-driver event.  It is plausible that such somatic alterations could help distinguish/stratify patients with severe deep dyspareunia (which will herein be referred to as “high-pain” patients) and those with minimal or no pain (which will herein be referred to as “low-pain” patients).      119 Methods Analyses generally follows the methodology described in Chapter 2 (Methodology) and the additional details described specifically in Chapter 3 (Somatic Cancer-Driver Mutations in Incisional Endometriosis).  For the purposes of this appendix, the DE women we studied were classified as follows:    Patient Classification We identified 28 women with DE (including 20/23 women from the BC Women’s cohort described in Chapter 3 (see section 3.1.8)) and collected deep dyspareunia data for each woman based on a self-report pelvic pain questionnaire by The International Pelvic Pain Society or an in-house questionnaire wherein the question regarding deep dyspareunia was similar (referred to as “pain with deep penetration”) (see Appendix J).  In short, women were asked to rate the extent of deep pain with intercourse from 0 (no pain) – 10 (worse pain imaginable).  Initially, women with a pain score ≥ 7 were classified as “high-pain” whereas women with a pain score of ≤ 3 were classified as “low-pain”.  However, in order to increase the number of cases included in the low-pain cohort, we extended low-pain criteria to a pain score of ≤ 5.  The breakdown of the deep dyspareunia classification of women with DE included in this sub-analysis is summarized below in Fig. 1:   120  Figure 1: Breakdown of DE cases included in deep dyspareunia study.  “N/A” refers to women wherein deep dyspareunia data was unavailable.  Results Sample Description As described in Chapter 3, I examined women with DE for somatic cancer-driver events by targeted sequencing (with orthogonal validation by means of ddPCR) as well as ARID1A and PTEN IHC.  A summary of clinical data relevant to this study and somatic alterations found is provided below in Table 1.  It is important to note that some women had multiple DE lesions at anatomically distinct/separated sites, which were collected and analyzed if available.  For the purposes of the deep dyspareunia study, I focused solely on the binary variable (i.e. yes or no) of whether at least one somatic alteration was found, either through hotspot sequencing or inferred from IHC findings, in at least one of the lesions analyzed from a given patient.  DE cases from BC Women’sn = 23high painn = 12 (+ 5 additional)low painn = 8 (+ 3 additional)N/An= 3  121 Table 1: Summary of clinical data for DE women included in deep dyspareunia study.  Mutations noted with (*) have not yet been orthogonally validated by ddPCR. Patient ID Age (years) Clinical Stage (r-ASRM) Deep Dyspareunia Classification Pain Score Somatic Alteration DE_1 41 IV low 4 CTNNB1 G34V DE_2 50 IV high 9 KRAS G12D DE_4 28 IV high 9 None identified DE_5 25 III high 8 KRAS G12D DE_6 34 IV high 9 None identified DE_7 41 IV low 4 ARID1A – loss DE_10 27 III high 10 KRAS G12V DE_11 27 II high 9 KRAS G12D DE_12 39 IV high 10 None identified DE_13 26 III high 8 PTEN – loss DE_14 22 I high 8 KRAS G12C DE_15 49 IV high 7 PTEN – loss DE_16 36 IV high 7 None identified DE_17 32 III low 0 PTEN – loss DE_18 36 IV high 9 None identified DE_19 38 III low 0 None identified DE_20 45 IV low 1 None identified DE_21 44 III low 0 KRAS G12V; PTEN – loss DE_22 24 III low 0 None identified DE_23 35 III low 0 PTEN – loss DE_37 35 IV high 8 None identified   122 DE_38 43 II high 10 KRAS G12D*; PTEN – loss DE_40 36 II high 10 None identified DE_42 27 I high 9 None identified DE_43 29 III high 8 None identified DE_45 25 IV low 5 None identified DE_46 39 III low 4 None identified DE_47 25 III low 5 PIK3CA E545K*  The mean age of women with DE in the “low-pain” group is 35.4 years (range: 24-45 years), whereas the mean age of women in the “high-pain” group is 33.5 years (range: 22-50 years) (Table 1; Fig. 2).  The mean age of women in these two groups is not statistically different (p = 0.5503, Student’s t-test) (Fig. 2).   123  Figure 2: Mean age of DE women included in deep dyspareunia study.  The difference in age of women in the low-pain and high-pain groups is not statistically significant (p = 0.5503, Student’s t-test).  The clinical stage of endometriosis (according to r-ASRM) was provided for women with DE included in this study.  The stage of endometriosis is also independent of whether patients experienced high or low pain in regards to deep dyspareunia (p = 0.13252, Fisher’s exact test).      124 Mutation Rates According to Deep Dyspareunia Severity Overall, 47.1% (8/17) high-pain and 54.5% (6/11) low-pain patients harboured a somatic alteration within their DE lesions (Fig. 3).  The breakdown of mutations according to gene affected is provided in Fig. 3 (note that alterations in PTEN and ARID1A were determined via IHC findings).  Figure 3: Breakdown of rates of somatic cancer-driver events by gene for high-pain and low-pain patients.  To determine whether overall somatic events or gene-specific somatic events were associated with either high-pain or low-pain groups, I performed Fisher’s exact tests on each pairwise comparison of interest.  All tests were two-sided, where a P-value <0.05 was considered to be statistically significant.  The P-value for each pairwise comparison is shown below in Table 2.   0.0%10.0%20.0%30.0%40.0%50.0%60.0%Overall KRAS PIK3CA CTNNB1 PTEN-loss ARID1A-lossAffected Cases (%)Somatic Events in Deep Infiltrating Endometriosis Caseshigh-pain low-pain  125 Table 2: Reported P-values for pairwise comparisons of somatic events affecting specific genes in patients with high-pain and low-pain.  Pairwise Comparison high pain patients affected low pain patients affected P-value Overall Affected 8/17 6/11 1.0000 KRAS 6/17 1/11 0.1914 PIK3CA 0/17 1/11 0.3929 CTNNB1 0/17 1/11 0.3929 PTEN-loss 3/17 3/11 0.6525 ARID1A-loss 0/17 1/11 0.3929  As shown in Table 2, all P-values were > 0.05 and therefore the rates of somatic events (both overall and affecting each gene specifically) did not significantly differ among high-pain or low-pain patients.  KRAS mutations appear to be more common in high-pain patients (occurring in 6/17 high-pain patients compared to 1/11 low-pain patients), however this observation is not statistically significant given our small sample size (p = 0.1914).  To better assess the relationship between the likelihood of harbouring a somatic alteration (regardless of gene since sample sizes are insufficiently large for gene-specific mutational analysis) in DE lesions and deep dyspareunia pain scores, I performed a logistic regression analysis with pain scores as a continuous variable (rather than as discrete high-pain and low-pain groups) (see Figure 4 below).  Given our sample sizes, the likelihood of harbouring a somatic alteration does not appear to be associated with pain scores (p = 0.71, Wald Test).   126  Figure 4: Logistic regression model illustrating the likelihood of harbouring a somatic cancer-driver alteration in DE lesions given self-reported deep dyspareunia pain scores.  The shaded regions represent 95% confidence intervals for the likelihood of harbouring somatic cancer-driver events at each pain score.  Discussion The analysis in Chapter 3 revealed that overall one-third of DE patients harboured detectable somatic cancer-driver events within their endometriotic lesions.  This observation prompts speculation over the role of such alterations within endometriosis.  Specifically, are there any phenotypic differences between patients harbouring such somatic events in endometriosis?  Exploring such questions may enhance our understanding of endometriosis pathophysiology and are potentially useful for the clinical   127 management of disease (via targeted therapies).  Deep dyspareunia continues to be a prevailing issue for women with DE – greatly affecting one’s self-esteem and relationship with others.  In this exploratory sub-analysis, I sought to interrogate whether the presence of somatic cancer-driver events within DE lesions could be used to distinguish patients with severe deep dyspareunia (“high-pain”) from those with minimal/no pain (“low-pain”). With consideration of our small sample sizes with respect to patients with high-pain (17 women) and low-pain (11 women), no somatic event was significantly enriched in either the high-pain or low-pain groups.  Note that this exploratory sub-analysis is intended to be hypothesis-generating and a larger, prospectively collected cohort with detailed clinicopathological data is being collected and has recruited over 250 women.  Based on this sub-analysis, of all genes wherein somatic mutations have been detected, KRAS appears to be the most enriched in the high-pain group (p = 0.1914), with 6/17 (47.1%) of high-pain patients and 1/11 (9.1%) of low-pain patients harbouring a KRAS mutation in their DE lesions.  Evidence suggests that there is a correlation between elevated concentration of inflammatory cytokines (especially TNF-a and glycodelin) and daily/chronic pain in endometriosis patients as well as greater central sensitization125,126 – such cytokines may enhance local nerve growth and angiogenesis, thereby potentially contributing to deep dyspareunia127,128.  Oncogenic KRAS may influence inflammatory cytokine production in endometriosis as this has been observed in early progression models of other diseases including pancreatic cancer and gastric cancer (wherein KRAS mutations are frequently observed)129,130 .  In support of this hypothesis, Cheng et al. (2011) developed an immunocompetent mouse model of endometriosis by transplanting endometrium from KRASG12V/+ donor mice into subcutaneous, abdominal pockets of immunocompetent recipient mice and observed numerous leukocytes and macrophages around endometriotic lesions (which would contribute to inflammatory cytokine production including TNF-a)106.  Oncogenic KRAS may also contribute to deep dyspareunia directly by causing a greater depth of invasion of local tissue by endometriotic lesions.  Although this has not been studied in endometriosis, studies suggest that oncogenic KRAS drives invasion in colorectal cancer131.   128 In short, assuming these rates of KRAS mutation are reflective of the true incidence of KRAS mutations among high-pain and low-pain patients (as defined by our pain score cut-offs), then at least 38 high-pain and 38 low-pain patients are required in the prospective study to show a statistical significance in the difference in KRAS mutation rates among the two groups at 80% power and a significance level of 0.05.     129 Appendix J: Pelvic Pain Assessment Form    Pelvic Pain Questionnaire  Date : ________________________  Please describe the pain you have been experiencing?        Please shade areas of pain and write a number from 1-10 at the site(s) of pain  (10 = most severe pain imaginable)     BC Women’s Centre for Pelvic Pain                            & Endometriosis,                                                               Women’s Health Centre, D6 – 4500 Oak Street      Vancouver, B.C., V6H 3N1                             Phone: (604)875-2445    Fax:(604)875-2569  Patient Label          Questionnaire # 1 (Initial visit)    130 Pain History:  How old were you when you first experienced pelvic pain? ____________  Did the pelvic pain become worse at some point?               No         Yes If yes; when? _________________________________ If yes; what triggered it? _________________________  Have you ever had painful menstrual cramps when bleeding?     No  Yes   If Yes, how painful were your menstrual cramps when bleeding?   Least pain 0       1        2        3        4        5        6        7        8        9        10        Average pain 0       1        2        3        4        5        6        7        8        9        10 Worst pain 0       1        2        3        4        5        6        7        8        9        10   1. Check any of the following health care providers you have seen in the past for this pain:  Anesthesiologist             Gynecologist       Gastroenterologist  General surgeon             Urologist       Neurosurgeon Rheumatologist             Psychiatrist       Psychologist/Counsellor Psychotherapist             Rehabilitation Medicine specialist   2. Check any of the Complementary Medicine providers you have seen in the past for this pain:   Homeopathic medicine  Naturopathic medicine  Biofeedback  A) Massage therapist  Physical therapy / physiotherapy Acupuncturist   Chiropractor    Hypnosis  3. Which of the following diagnosis, if any, have you been given for your pain (check all that apply)? Endometriosis     Vaginismus/pelvic floor muscle problem Pelvic Adhesions    Other muscle problem Pelvic Infection/vaginal infection  Nerve entrapment   Irritable Bowel Syndrome   Stress/ anxiety Bladder Pain/ Interstitial Cystitis  Depression Vestibulitis/vulvodynia   None of the above     131  4. Medical treatments • Column 1: lists available medical treatments for pelvic pain • Column 2: Check the treatments that you tried before, but didn’t help • Column 3: Check the treatments that you tried before, but had problems with side-effects • Column 4: Check the treatments that you have tried before and did help • Column 5: Check the treatments that you are trying now • Column 5: Check the treatments that you would like to try   • Column 6: Check the treatments that you do not want to try • Column 7: Check if you don’t know what the treatment is • Column 8: Circle how confident are you that the treatment will help your pelvic pain.  (If you don’t know what the treatment is, you can skip this step.) [Can choose more than one] 1 2 3 4 5 6 7 8 9 Treatments  for pelvic pain Tried before but did NOT help Tried before but side effects Tried before and did help Trying now I would like to try I would NOT  like to try I don’t know what  this is How confident are you this treatment will help your pain? 0…………………………10 Not at all                       Strongly                                      confident Tylenol, Advil, other anti-inflammatories (e.g. Ponstan, Voltaren)        0   1  2  3  4  5   6  7  8   9   10 Opioids/narcotics (e.g. Tylenol #3, Tramacet, oxycodone, morphine)         0   1  2  3  4  5   6  7  8   9   10 Birth control pill or patch or ring (w/ monthly period)         0   1  2  3  4  5   6  7  8   9   10 Hormone treatment to stop periods: Continuous birth control pill or patch or ring         0   1  2  3  4  5   6  7  8   9   10 Progestin ( Depo Provera, Norlutate, Visanne)         0   1  2  3  4  5   6  7  8   9   10 Mirena IUD         0   1  2  3  4  5   6  7  8   9   10 GnRH agonist (Lupron, Synarel)         0   1  2  3  4  5   6  7  8   9   10 Other treatments: Vaginal medications (e.g. danazol, diazepam)         0   1  2  3  4  5   6  7  8   9   10 Nerve medications (e.g. nortriptyline, gabapentin/Neurontin, pregabalin/Lyrica)          0   1  2  3  4  5   6  7  8   9   10  Surgery         0   1  2  3  4  5   6  7  8   9   10  Physiotherapy          0   1  2  3  4  5   6  7  8   9   10 Trigger point injections with local anesthetic         0   1  2  3  4  5   6  7  8   9   10 Nerve blocks         0   1  2  3  4  5   6  7  8   9   10   132 Botox injections        0   1  2  3  4  5   6  7  8   9   10   Dry needling or intra-muscular stimulation (IMS)          0   1  2  3  4  5   6  7  8   9   10  Neuro-prolotherapy          0   1  2  3  4  5   6  7  8   9   10 Small groups (Mindfulness, cognitive-behavioral therapy)          0   1  2  3  4  5   6  7  8   9   10 Individual counselling (Mindfulness, cognitive-behavioral therapy)         0   1  2  3  4  5   6  7  8   9   10 Education session (online)        0   1  2  3  4  5   6  7  8   9   10 Education session  (in-person, i.e. “Meet the Team” session         0   1  2  3  4  5   6  7  8   9   10 Other ____________________        0   1  2  3  4  5   6  7  8   9   10  5. If you have had surgery in the past, please check all surgeries that apply and how many times:  Laparoscopy (scope)  Laparotomy (open cut/incision) Diagnostic only (no treatment)  #/dates   #/dates  Cautery of endometriosis    #/dates         #/dates Excision of endometriosis   #/dates   #/dates Lysis (cutting) of adhesions   #/dates   #/dates Removal of ovarian cysts    #/dates   #/dates  Hysterectomy (removal of uterus)  date    date        Removal of right ovary   date                date              Removal of left ovary    date    date Removal of both ovaries (at same time) date    date   Please rate your pain from:  0 (no pain) to 10 (worst pain imaginable)    1) IN THE PAST 3 MONTHS, about how many days have you had menstrual (vaginal) bleeding?   0 1-10  10-20  20-40  40-60  60-80  >80      133  2) IN THE PAST 3 MONTHS, how painful were your menstrual cramps when bleeding?  No bleeding  Least pain 0       1        2        3        4        5        6        7        8        9        10        Average pain 0       1        2        3        4        5        6        7        8        9        10 Worst pain 0       1        2        3        4        5        6        7        8        9        10 [Least/average/worst amount of pain with menstrual cramps when bleeding] 3) IN THE PAST 3 MONTHS, how painful was deep penetration during sexual activity? No penetration Least pain 0       1        2        3        4        5        6        7        8        9        10        Average pain 0       1        2        3        4        5        6        7        8        9        10 Worst pain 0       1        2        3        4        5        6        7        8        9        10  4) IN THE PAST 3 MONTHS, how painful was initial penetration (entry) during sexual activity? No penetration Least pain 0       1        2        3        4        5        6        7        8        9        10        Average pain 0       1        2        3        4        5        6        7        8        9        10 Worst pain 0       1        2        3        4        5        6        7        8        9        10   5) IN THE PAST 3 MONTHS, how painful were bowel movements? Least pain 0       1        2        3        4        5        6        7        8        9        10        Average pain 0       1        2        3        4        5        6        7        8        9        10 Worst pain 0       1        2        3        4        5        6        7        8        9        10   6) IN THE PAST 3 MONTHS, how painful was other pelvic pain (that is, pelvic pain when not bleeding, not during sexual activity, and not during bowel movements)   Least pain 0       1        2        3        4        5        6        7        8        9        10        Average pain 0       1        2        3        4        5        6        7        8        9        10 Worst pain 0       1        2        3        4        5        6        7        8        9        10   Where is this other pelvic pain located?   Right  Left  Both sides        Not applicable Does this other pelvic pain get worse at certain times of your menstrual cycle?   Yes            No    Not applicable     134  7) IN THE PAST 3 MONTHS, have you had back pain?  Least pain 0       1        2        3        4        5        6        7        8        9        10        Average pain 0       1        2        3        4        5        6        7        8        9        10 Worst pain 0       1        2        3        4        5        6        7        8        9        10  Where is this back pain located?    Right  Left  Both sides        Not applicable Does this back pain get worse at certain times of your menstrual cycle?   Yes            No    Not applicable  Does your pain have one or more of the following characteristics? Burning  Yes   No Painful cold  Yes   No Electric shocks  Yes   No  Is your pain associated with one or more of the following symptoms in the same area? Tingling  Yes   No Pins and needles Yes   No Numbness  Yes   No Itching   Yes   No  In trying to deal with your pain, in the PAST 3 MONTHS, how many times have you:  1. Been to a physician’s office (other than this clinic)? ___________ 2. Been to the emergency room? __________   Bowels  Do you have pain or discomfort (at least 3 days of the month, for at least 3 months, starting at least 6 months before this visit) that is associated with the following:  Change in frequency of bowel movement?      Yes       No Change in appearance of stool or bowel movement?    Yes       No Does your pain improve after completing a bowel movement?   Yes       No Do these symptoms get worse at certain times of your menstrual cycle?   Yes       No                        135 Bladder  Do you have an unpleasant sensation (e.g. pain, pressure, discomfort)  that seems related to the bladder (lasting at least 6 weeks)?   Yes       No  Does the pain worsen as your bladder fills up?     Yes       No Does the pain improve when you urinate?        Yes       No Do you have a strong need (urge) to urinate because of the pain?    Yes       No Do you urinate more often than in the past?       Yes       No Do these symptoms get worse at certain times your menstrual cycle?   Yes       No    

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