Medicine, Faculty of
Obstetrics and Gynaecology, Department of
DSpace
UBCV
Johal, Parveen
Master of Science - MSc
University of British Columbia
Background: In endometrial cancer (EC), histopathological parameters, often unreliably assessed from diagnostic sampling, are used to guide surgical decisions. Surgical decisions, like lymph node (LN) assessment, have implications for post-operative adjuvant therapy decisions, which often vary based on nodal status. The Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) subtypes are prognostically distinct, can be assigned on biopsy and have demonstrated exceptional reproducibility. Thus, they may be better suited to guide decision-making pre- and post-hysterectomy. As per the current literature, it is not yet well understood how ProMisE can impact decision-making. Objectives: This thesis explores ProMisE in the context of other pre-operative prognostic features that may influence nodal assessment and adjuvant therapy decision-making, measures the concordance of histopathological and molecular features available pre- and post-operatively, explores how ProMisE subtypes could tailor nodal assessment decisions to minimize the variability in adjuvant therapy decisions, and finally, proposes a model for a dynamic treatment regime (DTR) that simultaneously optimizes pre- and post-operative decision-making in EC. Methods: EC patients who underwent surgery, and have a ProMisE subtype assigned, were identified from two institutions. The dataset was generated using retrospective data from previous studies with additional pre-operative parameters collected via chart reviews. Univariate associations among ProMisE, treatment decisions and prognostic factors were measured. Multivariate analyses were used to assess association of survival with LN testing within ProMisE subgroups. A DTR optimizing nodal assessment and adjuvant therapy was estimated using all the parameters available at the time of each decision. To account for the confounders and mediators in our observational data, a counterfactual framework and dynamic weighted survival modelling was used. Results: 903 patients were included. Pre-operative histopathology, age, body mass index and co-morbidities are associated with ProMisE and nodal assessment. ProMisE assigned pre- and post-operatively demonstrates better concordance than histopathology assigned pre- and post-operatively or at surgery and after expert review. Nodal assessment is significantly associated with longer survival in p53abn ECs, but not in POLEmut or MMRd/NSMP. The methods and data used in the identification of the optimal DTR are insufficient due to the high rate of censoring for an accelerated failure time model.
CAUSAL MACHINE LEARNING TO OPTIMIZE TREATMENT DECISIONS FOR PATIENTS WITH ENDOMETRIAL CANCER by PARVEEN JOHAL B.Sc., The University of British Columbia, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Reproductive and Developmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2022 © PARVEEN JOHAL, 2022 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Causal Machine Learning to Optimize Treatment Decisions for Patients with Endometrial Cancer submitted by Parveen Johal in partial fulfillment of the requirements for the degree of Master of Science in Reproductive and Developmental Sciences Examining Committee: Dr. Aline Talhouk, Assistant Professor, Obstetrics and Gynecology, UBC Supervisor Dr. Mathias Lécuyer, Assistant Professor, Computer Science, UBC Supervisory Committee Member Dr. Tim Rowe, Associate Professor Emeritus, Obstetrics and Gynecology, UBC Supervisory Committee Member Dr. Janice Kwon, Professor, Obstetrics and Gynecology, UBC Additional Examiner Additional Supervisory Committee Members: Dr. Jessica McAlpine, Professor, Obstetrics and Gynecology, UBC Supervisory Committee Member Dr. Mohamed Bedaiwy, Professor, Obstetrics and Gynecology, UBC Supervisory Committee Member iii Abstract Background: In endometrial cancer (EC), histopathological parameters, often unreliably assessed from diagnostic sampling, are used to guide surgical decisions. Surgical decisions, like lymph node (LN) assessment, have implications for post-operative adjuvant therapy decisions, which often vary based on nodal status. The Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) subtypes are prognostically distinct, can be assigned on biopsy and have demonstrated exceptional reproducibility. Thus, they may be better suited to guide decision-making pre- and post-hysterectomy. As per the current literature, it is not yet well understood how ProMisE can impact decision-making. Objectives: This thesis explores ProMisE in the context of other pre-operative prognostic features that may influence nodal assessment and adjuvant therapy decision-making, measures the concordance of histopathological and molecular features available pre- and post-operatively, explores how ProMisE subtypes could tailor nodal assessment decisions to minimize the variability in adjuvant therapy decisions, and finally, proposes a model for a dynamic treatment regime (DTR) that simultaneously optimizes pre- and post-operative decision-making in EC. Methods: EC patients who underwent surgery, and have a ProMisE subtype assigned, were identified from two institutions. The dataset was generated using retrospective data from previous studies with additional pre-operative parameters collected via chart reviews. Univariate associations among ProMisE, treatment decisions and prognostic factors were measured. Multivariate analyses were used to assess association of survival with LN testing within ProMisE subgroups. A DTR optimizing nodal assessment and adjuvant therapy was estimated using all the parameters available at the time of each decision. To account for the confounders and mediators in our observational data, a counterfactual framework and dynamic weighted survival modelling was used. Results: 903 patients were included. Pre-operative histopathology, age, body mass index and co-morbidities are associated with ProMisE and nodal assessment. ProMisE assigned pre- and post-operatively demonstrates better concordance than histopathology assigned pre- and post-operatively or at surgery and after expert review. Nodal assessment is significantly associated with longer survival in p53abn ECs, but not in POLEmut or MMRd/NSMP. The methods and data used in the identification of the optimal DTR are insufficient due to the high rate of censoring for an accelerated failure time model. iv Lay Summary In EC, post-surgical treatment decisions are often based on whether or not spread of the disease to the lymph nodes was observed during surgery. The decision to assess lymph nodes must be made pre-surgically, but the parameters available to inform that decision are unreliably assessed pre-surgically. ProMisE provides more reliable classification of ECs and is available at the time of diagnosis. The timely and reliable information provided by ProMisE may be useful in decision-making, but it is not yet well understood how to best incorporate it in guidelines or the impact it will have. This thesis shows that ProMisE is associated with other pre-surgical parameters that may influence decision-making and that some ProMisE subtypes may benefit more from nodal assessment. However, since we use observational data, inferring which patients will have improved outcomes under each treatment requires causal modelling approaches that can adjust for the effect of confounding variables. v Preface This statement is to certify that the work in this thesis is original, produced by Parveen Johal, under the supervision of Dr. Aline Talhouk. The research for this thesis was conducted at the Research Pavilion on the Vancouver General Hospital (VGH) campus. This research was approved by the BC Cancer Agency’s Research Ethics Board (REB) (H19-03789-A008). All analyses were performed on de-identified data. The study proposal was originally drafted by Dr. Aline Talhouk and Dr. Jessica McAlpine. I adapted the original proposal and worked alongside Dr. Talhouk to conceptualize the aims and methods for this thesis. Drs. Amy Jamieson, Stefan Kommoss, Marcel Grube, and Jessica McAlpine provided clinical insights and guidance to ensure clinical relevance of this work. I conducted literature reviews and summarised the existing knowledge on this topic. I presented the research proposal at the Gynecologic Cancer Initiative Trainee Research Day 2021 and the Canadian Cancer Research Conference 2021. Chart reviews for the Vancouver data cohort were conducted by Daniella Sare, Rachel Wan, Jessica Kwon and Dr. Abigail Lee. Samuel Leung and Shanzhao Wang worked together to generate the data packages. The German dataset was prepared by Dr. Grube. I worked on data verification and curation. The code for the data analysis in chapters 2-4 was written by me, with support from Derek Chiu. The dynamic treatment regime methods implemented in chapter 4 were devised through collaborative efforts by Derek Chiu, Dr. Mathias Lécuyer, Dr. Talhouk and me. The functions for chapter 4 were coded by Derek Chiu. I conducted the data analysis in this chapter, with guidance from Derek Chiu. I generated all the tables and figures and Dr. Talhouk assisted me in the interpretation of the findings. I wrote all chapters contained within this thesis. A version of chapter 2 and 3 will be submitted for publication. vi Table of Contents Abstract ......................................................................................................................................................iii Lay Summary..............................................................................................................................................iv Preface..........................................................................................................................................................v Table of Contents.........................................................................................................................................vi List of Tables...............................................................................................................................................ix List of Figures...............................................................................................................................................x List of Abbreviations.................................................................................................................................xi Acknowledgements....................................................................................................................................xii Dedication…..............................................................................................................................................xiii Chapter 1: Introduction..............................................................................................................................1 1.1 Etiology and Risk Factors ................................................................................................................1 1.2 Presenting Symptoms and Diagnosis................................................................................................2 1.3 Classification Systems.....................................................................................................................3 1.4 Prognosis..........................................................................................................................................5 1.5 Treatment……………………………………………………….…………………………………5 1.5.1 Staging and Surgical Treatment...........................................................................................5 1.5.2 Adjuvant Therapy................................................................................................................7 1.5.3 Immunotherapy.…………………………………………………………………..….……8 1.5.4 Fertility-Sparing Treatment………………………………………………………………9 1.6 Risk Stratification Systems and Treatment Decision-Making……………………………………..9 1.6.1 Pre-operative Risk Stratification …………………………………………………………9 1.6.2 Post-operative Risk Stratification and Adjuvant Therapy Recommendations………….12 vii 1.7 Precision Medicine for Endometrial Cancer.…….……………………………………………….15 1.8 Dynamic Treatment Regimes and Observational Data…………………………………………...16 1.9 Study Objectives………………………………………………………………………………….18 1.10 Thesis Roadmap………………………………………………………………………………….18 1.11 Data……………………………...……………………………………………………………….19 1.11.1 Cohort Descriptions……………………………………………………………………….19 1.11.2 Covariates…………………………………………………………………………………20 Chapter 2: Characterization of the Pre- and Post- Operative Clinico-Pathological Parameters…..25 2.1 Introduction……………………………………………………………….………………………25 2.2 Methods…………………………………………………………………………………………...28 2.2.1 Statistical Analyses…………………………………………………………………………29 2.3 Results…………………………………………………………………………………………….30 2.3.1 Analysis of Associations between LN Assessment and Pre-operative Parameters…..…30 2.3.2 Analysis of Associations Among Promise Subtypes and Pre-Operative Parameters…...35 2.3.3 Analysis of Associations Among Adjuvant Therapy and All Parameters Available at the Time of the Decision………………………………………………………………………..…..41 2.3.4 Analysis of Concordance in Features Assigned Pre- and Post-Operatively……………......47 2.3.4.1 Grade………………………………………………………………………………...48 2.3.4.2 Binary Grade………………………………………………………………………...50 2.3.4.3 Histotype…………………………………………………………………………….52 2.3.4.4 ProMisE Subtypes…………………………………………………………………...54 2.4 Discussion…………………………………………………………….………………..…………55 Chapter 3: Potential Impacts of ProMisE Molecular Subtypes on Decision-Making……………….60 3.1 Introduction………………...…………………………………………………...…………………60 3.2 Methods………………………………………...…………………………………………………62 viii 3.2.1 Treatment policies……………………………………………………………………………62 3.2.2 Statistical analyses…………………………………………………………………………....62 3.3 Results………………………………………………………………………..…………………...63 3.3.1 Treatment Policies………………………………………………………….………………...63 3.3.2 Univariate Analysis of Nodal Status and Pre-Operative Prognostic Factors………………...66 3.3.3 Nodal Assessment in POLEmut……………………………………………………………...67 3.3.4 Nodal Assessment in p53abn…………………………………………………………………68 3.3.5 Nodal Assessment in MMRd and NSMP/p53wt……………………………………………..70 3.4 Discussion…………..…………………………………………………………………………….72 Chapter 4: Causal Machine Learning to Estimate an Optimal Dynamic Treatment Regime for Pre- and Post-Surgical Treatment Decisions……………………………………………...…………………77 4.1 Introduction……………………………………………………………………………………….77 4.2 DTR Methods……………………………………………………………………………………..79 4.2.1 Algorithm…………………………………………………………………………………...79 4.2.2 Data and Modelling…………………………………………………………………………80 4.2.3 Evaluation of Models……………………………………………………………………….83 4.3 Results and Discussion…………………..………………………………………………………...83 Chapter 5: Conclusion…………………………………………………………………………………..87 5.1 Summary of Work………………...……………………………………………………………….87 5.2 Implications and Next Steps……………………………………………………………………….88 References………..…………………….…………………………………………………………………90 Appendices.……………………………………………………………………………………………...106 Appendix A: Summary of Cohort Characteristics…….……………………………………………...106 Appendix B: Missing Data Analysis………………………………………………………………....111 ix List of Tables Post-Operative Risk Stratification and Treatment Recommendations Based on 2020 ESGO -ESTRO-ESP Guidelines……………………………………………………..………..….13 Summary of the EC Prognostic Factors Analysed in this Thesis........................................21 Univariable Associations between Lymph Node Testing and the Clinicopathological Features Available at the Time of Diagnosis………………………..…………………….32 Univariate analysis of associations among ProMisE and Pre-operative Parameters……...36 Univariate analysis of associations among adjuvant therapy and all parameters available at the time of the decision……………………………………………………………….43 Summary statistics for concordance in features assigned pre- and post-operatively…...…47 Treatment guidelines for ESGO-ESTRO-ESP (2020) risk groups based on clinical expertise………………………………………………………………………………...…63 Associations among nodal status and pre-operative prognostic factors…………..………66 Cox proportional hazards model for OS in patients with the POLEmut subtype……...….68 Cox proportional hazards model for OS in patients with the p53abn subtype………...….69 Cox proportional hazards model for OS in patients with NSMP/p53wt or MMRd subtype……………………………...……...………………………………….………..71 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 x List of Figures Case examples of how ProMisE molecular classification could impact care…………..…11 Graphical representation of relationships among treatment decisions, the clinicopathological parameters available at the time of each decision, and survival outcomes in EC……………………………………….……………………………..…….17 Analysis of changes in grade (1 vs 2 vs 3 vs Suspected Pre-cancer) assigned at biopsy, surgery, and review……………………………………………………………….……….48 Analysis of changes in grade (1/2 vs 3 vs Suspected Pre-cancer) assigned at biopsy, surgery, and review………………………………………………………………………..50 Analysis of changes in histotype assigned at biopsy, surgery and review………...………52 Analysis of changes in ProMisE molecular subtype assigned at biopsy and surgery……..54 Confusion matrix of treatment received, and treatment recommended by guidelines, with and without molecular classification.…………………………………………..………….65 Evaluation of optimal DTR model…………………………………………...……………84 Boxplot of survival (in years) for adjuvant therapy in groups stratified by ProMisE.…….85 Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 xi List of Abbreviations ACE Adult Comorbidity Evaluation AEH Atypical Endometrial Hyperplasia ASA American Society of Anesthesiologists CAH Complex Atypical Hyperplasia CT Computed Tomography DSS Disease Specific Survival EBRT External Beam Radiation Therapy EC Endometrial Cancer ECOG Eastern Cooperative Oncology Group EIN Endometrial Intraepithelial Neoplasia ESGO European Society of Gynaecological Oncology ESMO European Society of Medical Oncology ESP European Society of Pathology ESTRO European Society for Radiotherapy and Oncology FIGO International Federation of Gynecology and Obstetrics HBSO Hysterectomy and Bilateral Salpingo-Oophorectomy LN Lymph Nodes LVI Lympho-Vascular Invasion MRI Magnetic Resonance Imaging PET Positron Emission Tomography PFS Progression Free Survival PORTEC Post Operative Radiation Therapy in Endometrial Carcinoma ProMisE Proactive Molecular Risk Classifier for Endometrial Cancer TVS Transvaginal Sonography VB Vaginal Brachytherapy xii Acknowledgements Firstly, I would like to express my profound gratitude to Dr. Aline Talhouk for providing excellent mentorship and guidance during my time as a Masters student. I also thank the entire Talhouk lab team for all their assistance, particularly Derek Chiu, Samuel Leung, Rachel Wan, Jessica Kwon and Shanzhao Wang. Without this amazing team, this project would not be possible. I would like to thank our collaborators, Dr. Stefan Kommoss and Dr. Marcel Grube, for all their efforts in collecting and sharing the German data, as well as, for providing their clinical expertise and feedback. Also, I would like to thank Dr. Amy Jamieson for sharing her expert knowledge with me. I am grateful to my supervisory committee, Dr. Mathias Lecuyer, Dr. Jessica McAlpine, Dr. Mohamed Bedaiwy, and Dr. Tim Rowe for their time, guidance, and patience. I would like to express a heartfelt thank you to my parents, Surjit and Sarabjit Johal, for showering me with their unwavering support and love. Finally, I thank MU for always believing in me and providing encouragement every step of the way. xiii Dedication To my parents. 1 Chapter 1: Introduction and Background Endometrial cancer (EC) is a gynecologic malignancy that begins in the endometrium, the inner lining of the uterus. This is the most common gynecologic malignancy in the developed world, and unlike most other cancers, both the incidence and the mortality rates for EC are on the rise (1). Between 1990 and 2019, there was a 0.69% annual increase in the incidence of EC globally, and the highest age-standardized incidence rate in 2019 was observed in North America at 27.82/100 000 (2). The United States has observed mortality rates increasing at approximately 2% per year from 2010 to 2019, however, the rate of increase is significantly higher in Black women, with a hysterectomy- corrected increase in mortality rates at 3.5% (3,4). Current predictions suggest that over 8,000 new EC diagnoses will be made in Canadian women and 1,500 will die of EC in 2022 (5). The International Agency for Research on Cancer predicts EC incidence rates will increase by over 50% globally by 2040 (6). The increasing prevalence of obesity and an ageing population are considered to be key risk factors contributing to the increase in EC incidence (7–9). 1.1 Etiology and Risk Factors An etiologically heterogenous disease, EC is associated with several endocrine, metabolic, reproductive, and genetic features. Un-opposed estrogen exposure, characterised by high estrogen with insufficient counteracting progesterone levels, leads to sustained proliferation and thickening of the endometrial lining(10,11). This can result in malignant transformation, presenting as atypical endometrial hyperplasia (AEH) also known as endometrial intra-epithelial neoplasia (EIN), which are precursor lesions for EC. An estimated 30-40% of AEH/EIN cases progress to EC and majority of patients with EIN have a co-existing cancer, making this a significant risk factor (12–15). 2 1.2 Presenting Symptoms and Diagnosis EC is most commonly diagnosed in post-menopausal women, with the median age of diagnosis at 60. Approximately 25% of cases are diagnosed in pre-menopausal women, with 5% occurring in women under the age of 40 (9). ECs are typically detected due to the presence of symptoms such as abnormal uterine bleeding or discharge (16). Patients with more advanced disease may also experience abdominal or pelvic discomfort, early satiety, and bladder irregularities (17). Common recommendations for the initial diagnostic evaluation include transvaginal ultrasonography (TVS), an endometrial biopsy and/ or dilation and curettage. A hysteroscopy can be performed in combination with the biopsy or curettage. The transvaginal ultrasonography is used to measure endometrial thickness and based on the American Committee of Gynecology guidelines, a normal endometrium has a thickness of 4mm or less in post-menopausal women. Age, persisting uterine bleeding and abnormal endometrial thickness serve as indications for an endometrial biopsy. The biopsy involves removing a sample of cells from the uterine lining for analysis, and the results can provide a definitive EC diagnosis (13). Endometrial biopsy with hysteroscopy is considered the current gold standard for EC diagnosis (18). In certain situations, such as when symptoms point toward potential metastatic disease, further imaging is warranted. This could include computed tomography (CT) scans, magnetic resonance imaging (MRI), and integrated positron emission tomography and computed tomography (PET/CT) scan (17). MRI is considered the top choice, with an 80% accuracy rate in determining the extent of tumour spread (19,20). Overall, pre-operative imaging is considered to be unreliable in assessing lymph nodes (LN) metastases, limiting its use in pre-operative triage (21). In this thesis, the term pre-surgical/ pre-operative is used to define the timeframe after diagnostic sampling and pre-hysterectomy or before surgical staging. 3 1.3 Classification Systems Histopathological Classification EC can be divided into various histotypes based on histopathological morphology. The most common histotype is endometrioid, which is found in approximately 80% of patients. Less common, non-endometrioid histotypes include endometrial serous carcinoma, clear-cell carcinoma, and carcinosarcoma (22). EC is also categorised by grade, which describes the degree of differentiation of cancer cells. As per the International Federation of Gynecology and Obstetrics (FIGO) grading system, ECs are given a grade from 1 to 3, with grade 1 tumours being well differentiated (5% or less of tumour tissue is solid tumour growth) and grade 3 tumours being poorly differentiated (50% or more of tumour tissue is solid tumour growth) (23). Current recommendations indicate grades 1 and 2 can be classified as low grade and grade 3 as high grade (24). Historically ECs were grouped into two types: type 1 tumours were typically endometrioid, low grade (grade 1-2), estrogen-dependent and comprise 70-80% of all cases and type 2 tumours were non-endometrioid histology, high-grade and less common (25). This classification system has been proven inadequate as there are variations within and overlap among the 2 types. Furthermore, several studies have found histotype and grade assigned at biopsy to be unreliable parameters as interobserver variation results in unreproducible categorisation (26,27). This limits their utility in treatment decision making. Molecular Classification In 2013, the Cancer Genome Atlas (TCGA) identified 4 molecular subtypes of EC based on integrated genomic features, including mutations in the DNA polymerase gene POLE (28). These subtypes, POLE ultramutated, microsatellite instability hypermutated, copy-number low, and copy-number high, can be reliably inferred from biopsy and correlate with progression-free survival (PFS) (28). The methodology employed by the TCGA to assign molecular subtypes is costly and requires frozen-tissue material, making this classification system impractical for clinical use. Since then, more pragmatic ways 4 to classify ECs have emerged, including the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE), a clinically applicable molecular classification system which can reproducibly stratify ECs into one of 4 distinct subgroups. This classification system employs a practical assay that uses three immunohistochemistry (IHC) stains and the targeted sequencing of a single gene to divide ECs into the following groups: POLEmut, with identified pathogenic mutations in polymerase epsilon exonuclease domain exons 9-14; MMRd, showing loss of one or more mismatch repair protein(s); p53abn demonstrating aberrant p53 IHC staining; and p53wt or no specific mutation (NSMP), characterised by no specific mutations in p53 nor other above-defined molecular features (29). From worst to best prognosis: • p53abn: Associated with aggressive disease and the worst survival outcomes. P53abn makes up approximately 15% of all EC cases but contains disproportionately high rates of of LN metastasis cases and advanced disease (30). • MMRd: Associated with aggressive pathology and relatively intermediate prognosis. Approximately 10% of MMRd cases have Lynch Syndrome and the remaining are mainly MLH1-hypermethylated. Post et al found that MMRd cases associated with Lynch Syndrome have a better PFS than MLH1-hypermethylated MMRd ECs (31). A retrospective cohort study indicates that MMRd ECs experience a greater proportion of locoregional recurrences than the other subtypes (32). • p53wt/ NSMP: Often low stage, low grade and endometrioid histotype and also known to have intermediate prognosis (33,34). • POLEmut: Associated with excellent survival outcomes, with consistently better overall survival (OS), progression free survival (PFS) and disease specific survival (DSS) than the other subtypes (32,34,35). 5 1.4 Prognosis Over 70% of EC cases are diagnosed at an early stage and have a favorable prognosis (21,36,37). However, 15-20% of patients will experience recurrence and the prognosis for recurrent disease is poor (38). The majority of the recurrences occur within 3 years of surgical treatment and the median survival typically does not exceed 12 months (39,40). Several clinicopathological parameters, comorbidities and demographic variables can influence the overall prognosis of EC. These prognostic factors are summarized in table 2. 1.5 Treatment 1.5.1 Staging and Surgical Treatment The initial management of EC involves surgery, typically a hysterectomy with bilateral salpingo-oophorectomy (HBSO), which is curative for most cases. Surgical staging is required to evaluate the extent of the disease and recommend post-surgical adjuvant therapy. There is currently a lack of consensus on what is deemed appropriate surgical staging. While some deem HBSO sufficient for staging, LN assessment, pelvic washings, and directed biopsies can also be added for more comprehensive staging based on pathologic features (17). The LN assessment allows for observation of nodal metastases and is typically done by pelvic and/or para-aortic lymphadenectomy. The indications for and the overall benefits of a LN assessment continue to be debated. Some studies recommend LN assessment for patients of all grade cancers (41), others recommend it only in grade 3 tumours (42), and some suggest that there is no apparent benefit of nodal assessment to survival outcomes in any clinical stage 1 tumours (43). Furthermore, there is conflicting evidence regarding whether or not lymphadenectomy has a therapeutic role in EC, on top of its role in surgical staging (41,43–47). On one hand, comprehensive surgical staging with LN assessment can provide valuable information regarding prognosis and contribute to the triage of patients for adjuvant therapy. On the other hand, lymphadenectomies are associated with higher long-term morbidity and bear the risk of complications 6 such as lymphedema, and potential damage of major vessels or nerves (48,49). An alternative to the more invasive lymphadenectomy is the sentinel lymph node dissection, which is now being adopted by some centres. Studies indicate that the sentinel LN dissection method of LN assessment has a diagnostic accuracy comparable to lymphadenectomy in patients with clinical stage 1 (50,51). However, both methods require surgical staging to be completed by a gynecologic oncologist in a tertiary care centre. In general, patients requiring LN assessment due to high risk of extra-uterine disease, along with patients with morbid obesity or other severe co-morbidities, have improved DSS under the care of the gynecologic oncologist (17,52). This means full surgical staging incurs additional costs to the healthcare system and to patients who must travel further for treatment. The policies regarding referral, surgery and extent of staging varies from centre to centre and decisions may be made based on histopathology, age, comorbidities, and physician preference. The stages assigned after surgical staging are described below: • Stage IA: Tumour is confined to the endometrium or has grown less than halfway through the myometrium. • Stage IB: Tumour has spread halfway or more into the myometrium. • Stage II: Tumour has grown into the cervix and is spreading into the cervical stroma. • Stage IIIA: Cancer has spread into the outer lining of the uterus (uterine serosa) and/or has grown into the fallopian tubes, and/or ovaries. • Stage IIIB: Tumour has grown into or spread to the vagina or the parametrium, but has not spread to the bladder or rectum. • Stage IIIC: Cancer has spread into the body of the uterus and to the pelvic and/or to the para-aortic lymph nodes. • Stage IVA: Cancer has grown into the inner lining of the rectum, bladder or intestines. It has not spread to distant sites. 7 • Stage IVB: Cancer has spread to distant parts of the body including bone, lungs, or liver. The tumour can be any size. 1.5.2 Adjuvant Therapy Options Some patients’ cancer only requires surgery, particularly if the cancer is confined to the uterus, but for a large proportion of patients, additional post-surgical treatment is required to eradicate disease and prevent recurrence. The need for post-surgical treatment is determined by pathological parameters from surgical specimens and can involve any combination of chemotherapy, vaginal brachytherapy or external beam radiation therapy. The decision for whether or not adjuvant therapy is required, and which technique would be optimal, depends on a combination of individual clinicopathologic features, physician’s preference and whether or not an LN assessment was done as part of the surgical staging procedure. In the absence of factors such as LVI, deep invasion, a high-grade tumour, and older age, women typically do not require post-surgical therapy. For those who do require adjuvant therapy, it is typically commenced 6-8 weeks post-surgery. The most common adjuvant therapy methods are described below. Vaginal Brachytherapy: Radiation is delivered directly to the target area through the vaginal insertion of a radioactive source. The area of the vagina directly in contact with the source receives the most radiation exposure, with minimal exposure to neighbouring parts such as the bladder and rectum. This internal delivery of radiation can result in side effects such as vaginal dryness and vaginal stenosis, which can cause pain during intercourse. Severe long- term complications are rare. External Beam Radiation Therapy: Radiation is delivered non-invasively from an external source rather than inserting a device into the body. This form of therapy is administered 5 days a week for 4-6 weeks. Side effects include dermatological and gastro-intestinal toxic effects and cystitis. Overall, EBRT is considered to have harsher side effects and is associated with higher long-term morbidity than vaginal brachytherapy. The need for EBRT is minimal in cases where LN assessment has identified node-negative patients (24). 8 Chemotherapy: Chemotherapy uses cytotoxic drugs to destroy cancer cells. This is a systemic therapy, which means the drugs act throughout the whole body, including at metastatic sites. It can be used post-operatively to kill remaining cancer cells or to relieve pain and control symptoms of advanced EC, in a palliative capacity. Common side effects include nausea, hair loss, fatigue, mouth sores, loss of appetite, and increased risk of infection. Depending on the drugs administered, patients may also experience neuropathy, hemorrhagic cystitis, bone marrow suppression, or damage to the heart. Commonly used chemotherapy drugs in EC are: • Carboplatin (Paraplatin, Paraplatin Aq) • Cisplatin (Platinol Aq) • Docetaxel (Taxotere) • Doxorubicin (Adriamycin) • Ifosfamide (Ifex) • Liposomal Doxorubicin (Myocet) • Paclitaxel (Taxol) Chemoradiation: A combination of chemotherapy and radiation therapy is used in higher risk cases. In general, chemoradiation is associated with more severe, long term side effects. The Post Operative Radiation Therapy in Endometrial Carcinoma (PORTEC)-3 trial found that 25% of patients receiving chemoradiation continued to experience sensory neurological symptoms 24 months after treatment (54). 1.5.3 Immunotherapy EC cells can activate the immune checkpoint pathway, PD-1 signalling, a mechanism to downregulate the immune response against harmful cells and prevent T-cell activation. In order to combat this in advanced stage EC, immune checkpoint inhibitors, such as PD-1 inhibitors can help slow the progression of the cancer. Unfortunately, this effect is not specific for cancer cells and can affect other cells of the body. This non-specificity contributes to side effects of immune checkpoint inhibitors including, fatigue, fevers, cough, loss of appetite, shortness of breath and even autoimmune reactions. FDA-approved 9 immunotherapies include Jemperli (dostarlimab) and Pembrolizumab (Keytruda, Merck), which are recommended for advanced or recurrent MMRd EC (55–57). 1.5.4 Fertility-Sparing Treatment While EC is typically diagnosed in post-menopausal women, approximately 14% of cases are diagnosed in women under the age of 50 (58). These women may be interested in pursuing fertility-sparing treatments and delaying the routine HBSO. For patients who would like to preserve their childbearing ability, fertility-sparing treatments involving endocrine treatments with oral progestins, gonadotropin-releasing hormone, medroxyprogesterone acetate, megestrol acetate or levonorgestrel intrauterine device have been used (59). However, the efficacy of such management strategies is based on retrospective studies and involves inherent risks (24). These risks include the treatment being ineffective, risk of relapse and potential misdiagnosis of concurrent ovarian cancers (59). Although the results from these studies are encouraging, this form of therapy is only recommended to be performed by experienced gynecological oncologists with detailed patient information and with frequent follow-ups (24). Fertility-sparing treatments are generally reserved for patients fitting the following criteria: young women with childbearing ability, those with significant medical co-morbidities, obese women where surgery is infeasible, those with presumed stage IA grade 1 endometrioid cancers and/or those with hereditary-linked cancers. Unfortunately, the decision to pursue fertility sparing options must be made pre-operatively, at which point the pathological features used to determine eligibility, such as stage, grade and histotype, are not reliably assessed. 1.6 Risk Stratification Systems and Treatment Decision-Making 1.6.1 Pre-operative Risk Stratification Current risk stratification systems cannot be used to guide pre-operative care (referral, surgery and extent of staging), due to their heavy dependence on parameters that are only reliably measured surgically. In approximately 40% of cases, histopathology available at the time of diagnosis disagrees with the surgical 10 assignment of histotype (𝜅𝑎𝑝𝑝𝑎=0.44) and grade (𝜅𝑎𝑝𝑝𝑎=0.55) (26,60,61). Furthemore, approximately 20% of metastatic cancers are missed during clinical assessment as they are believed to be early stage at the time of diagnosis, but are later found to have stage III-IV disease (62). The poorly reproducible parameters available pre-operatively limit our ability to accurately identify which patients require full surgical staging, including a LN assessment by a gynecologic onocologist, and which patients can safely undergo HBSO in the community. This puts patients at the risk of being undertreated, increasing chances for recurrence, or overtreated, increasing exposure to toxicities and burdens to the healthcare system. Besides resource usage and risks associated with surgery, the decisions made at this time also have implications for post-operative care, as adjuvant treatment decisions take into consideration whether or not LN were tested. Unlike grade and histotype, molecular classification assigned at biopsy has been proven to serve as an accurate prognostic marker, with excellent agreement between biopsy and surgical assessment (kappa 0.88) (34,61). While grade and histotype assigned at biopsy suffers from interobserver variability, with pathologists disagreeing in up to 1/3 of cases (60), ProMisE can be assessed objectively. To demonstrate the impact that ProMisE molecular classification could have on EC decision-making, we have described two case examples (figure 1). Based on standard of care in 2016, patient A would be required to travel to a tertiary cancer centre for treatment, and potentially overtreated. With ProMisE classification uncovering a POLE mutation, indicating a low likelihood of extra-uterine spread of disease, she might instead be able to undergo simple HBSO without LN assessment in her community. For some patients, especially those in rural and remote areas, this overtreatment increases the risk of adverse events, costs, and burden, often unnecessarily. Further, given the highly favourable survival outcomes associated with POLE mutated ECs, which appear to be independent of treatment (63), she may be spared chemoradiation and frequent follow-ups. In contrast, patient B would potentially be under-treated. The evaluation of ProMisE would reveal an MMRd subtype; this would lead to referral to the hereditary 11 cancer program for genetic testing (and if an inherited syndrome was found, testing of her family members), and surgery at a tertiary cancer centre with LN assessment and possibly also adjuvant therapy. Knowledge of the molecular subtype would further provide opportunities for immunotherapy as the Federal Drug Administration has deemed mismatch repair deficiency an approved indication for this therapy. With molecular classification, these patients would be better informed of their predicted risk classification, and this prognostic information would be incorporated into their treatment decision making. This highlights the need for a pre-operative risk stratification system that incorporates ProMise molecular classification to guide surgical decision making. Figure 1. Case examples of how ProMisE molecular classification could impact care Alongside ProMisE, incorportating other prognostic factors available pre-operatively, such as A B 12 comorbidities, hematological markers and pre-operative imaging may also improve risk stratification at the time of diagnosis, and consequently, management of EC (64). 1.6.2 Post-operative Risk Stratification and Adjuvant Therapy Recommendations Several systems of post-operative risk stratification exist for EC, mainly based on histotype, grade, LVI and surgical stage, which includes information on myometrial invasion and LN involvement. Commonly used risk stratification models in previous trials were the European Society for Medical Oncology - European Society of Gynaecological Oncology - European Society for Radiotherapy and Oncology (ESMO-ESGO-ESTRO) Consensus 2016 (65), the PORTEC-1 (66), or the Gynecologic Oncology Group (GOG)-99 (67) risk categories. Bendifallah et al found that none of these existing risk stratification models have high accuracy and additional biomarkers are needed to improve stratification (68). The most prominent risk stratification system has been the ESGO-ESMO-ESTRO 2016 model (65), which has recently been updated by the European Society of Gynaecological Oncology - European Society for Radiotherapy and Oncology - European Society of Pathology (ESGO-ESTRO-ESP) in 2020 (24). The 2020 guidelines have incorporated ProMisE molecular subtypes into its decision making framework and have outlined different criteria for each risk group based on whether or not molecular classification is known. These evidence-based guidelines integrate the findings of several large trials. Ramon-Patino et al (2022) compared the 2016 and 2020 guidelines and found that the 2020 guidelines, which incorporate ProMisE, provide better stratification than prior risk stratification models, but the difference is small and there is still significant overlap between the intermediate and high-intermediate risk groups, indicating room for further improvements (69). Table 1 summarises the risk groups and the recommended adjuvant therapies from the ESGO-ESTRO-ESP (2020) guidelines (24). Within the risk stratification model using molecular classification, stage III–IVA POLEmut EC and stage I–IVA MMRd or NSMP clear cell carcinoma with myometrial invasion have not yet been allocated to a risk group as the evidence on their prognosis is limited. 13 Table 1. Post-Operative Risk Stratification and Treatment Recommendations Based on 2020 ESGO-ESTRO-ESP Guidelines (24) Risk Level Without Molecular Classification With Molecular Classification Adjuvant Therapy Recommended Low Stage IA endometrioid, low-grade, LVI negative or focal Stage I–II POLEmut, no residual disease Stage IA MMRd/NSMP, low-grade, LVI negative or focal None Intermediate Stage IB endometrioid + low-grade + LVI negative or focal Stage IA endometrioid + high-grade + LVI negative or focal Stage IA non-endometrioid without myometrial invasion Stage IB MMRd/NSMP endometrioid + low-grade + LVI negative or focal Stage IA MMRd/NSMP endometrioid + high-grade + LVI negative or focal Stage IA p53abn and/or non-endometrioid without myometrial invasion Adjuvant brachytherapy recommended Omission of adjuvant brachytherapy can be considered, especially for patients under the age of 60 For p53abn cancers without myometrial invasion, adjuvant therapy is not recommended High-Intermediate Stage I endometrioid + substantial LVI, regardless of grade and depth of invasion Stage IB low-grade endometrioid with substantial LVI Stage IB endometrioid high-grade regardless of LVI status Stage II Stage I MMRd/NSMP endometrioid + substantial LVI regardless of grade and depth of invasion Stage IB MMRd/NSMP endometrioid + high-grade regardless of LVI status Stage II MMRd/NSMP endometrioid When LN testing is done, and nodal status is negative: Adjuvant brachytherapy can be recommended EBRT can be considered for substantial LVI and /or stage II Adjuvant chemotherapy can be considered for high grade and/or substantial LVI Omission of adjuvant treatment is an option When LN testing is not done: Adjuvant EBRT recommended, especially for substantial LVI and/or for stage II Adding adjuvant chemotherapy can be considered for high-grade and/or substantial LVI Adjuvant brachytherapy alone can be considered for high-grade LVI negative and for stage II grade 1 endometrioid cancers 14 Risk Level Without Molecular Classification With Molecular Classification Adjuvant Therapy Recommended High Stage III–IVA with no residual disease Stage I–IVA non-endometrioid with myometrial invasion, and no residual disease Stage III–IVA MMRd/NSMP endometrioid with no residual disease Stage I–IVA p53abn with myometrial invasion, with no residual disease Stage I–IVA NSMP/MMRd serous, undifferentiated carcinoma, carcinosarcoma with myometrial invasion, with no residual disease EBRT with adjuvant chemotherapy is recommended Chemotherapy alone can be considered The low-risk group is estimated to have a risk of recurrence lower than 5% and a comparison of vaginal brachytherapy to no adjuvant therapy in this group found no differences in the recurrence free or overall survival (70). The PORTEC-1 (66), GOG-99 (67) and ASTEC/EN5 (71) studies confirm that radiation therapy can reduce risk of recurrence but does not provide a survival benefit in intermediate risk patients when compared to no adjuvant therapy. Several studies, including the PORTEC-2 trial, support the recommendation of vaginal brachytherapy for intermediate-risk cases, as it provides surival rates similar to EBRT and limits vaginal recurrence (72–78). In the high-intermediate risk group, stage I patients demonstrated no difference in OS or vaginal recurrance rates under EBRT or vaginal brachytherapy (75). However, lower pelvic recurrence rates are observed under EBRT in patients with additional risk factors, such as substantial LVI and/or in stage II ECs (78,79). Some studies suggest chemotherapy can also be considered in high-intermediate risk patients with myometrial invasion, grade 3 and/or substantial LVI (80–82). The high-intermediate risk group is particularly of interest as there is a different set of treatment recommendations where nodal status is unknown. In order to limit the risk of pelvic recurrence, EBRT is more strongly recommended where there is substantial LVI or high grade but nodal status is unknown. Trials comparing the benefit of chemoradiotherapy and EBRT alone within a group of high risk ECs 15 found that the chemoradiotherapy arm had a significantly longer recurrence-free and overall survival (83,84). The GOG-258 trial explored the outcomes in high risk patients receiving chemoradiotherapy or chemotherapy only and concluded that recurrence free and overall survival rates do not differ significantly. However, chemotherapy only was associated with a higher vaginal recurrence and pelvic and paraaortic lymph-node recurrence (85). The existing literature supports the inclusion of molecular subtypes alongside the other pathological features in risk stratification models. Talhouk et al (2017) demonstrated that the addition of ProMisE to ESMO risk stratification results in better discrimination between prognostically distinct groups (86). Several studies suggest risk stratification based on molecular classification can improve decision making and limit over- or under- treatment (35,87–90). 1.7 Precision Medicine for EC There is a growing interest in personalized medicine as more scientists and clinicians accept that individual heterogeneity, in pathologic features, background characteristics and co-morbidities, results in varying responses to treatment. Through personalized medicine approaches, we can stratify patients into subgroups that differ in their response to a treatment and use the distinct features of each subgroup to inform treatment decisions. By doing so, we are tailoring the treatment to the patient’s unique demographic and clinicopathologic features rather than exclusively to the disease. In EC management, a sequence of treatment decisions must be made: first to determine the type and extent of surgical intervention needed during staging, and second to determine which, if any, adjuvant therapy is needed. However, it is important to optimize both these decisions simultaneously to account for the mediating effect of adjuvant treatment. To address this problem, sequential clinical decision-making techniques can be used. Reinforcement learning (RL) is a machine learning technique that was developed 16 to teach an agent (algorithm) to interact with its environment and learn which action will maximize its reward. Clinical applications of RL are based on its ability to identify an optimal dynamic treatment regime (DTR), which is a sequence of decision rules (one per treatment decision) made based on evolving patient history. In the DTR framework, the RL term ‘action’ refers to the treatment, the ‘state’ refers to the covariate history and the ‘reward’ refers to the outcome. DTRs facilitate personalized decision making as they take into account all the patient information accrued up to the time of decision, including clinical characteristics and previous treatments, to select the treatment option expected to result in the most favourable outcome. The decision rules identified by the optimal DTR will allow us to learn which patients would benefit from various treatments and understand their characteristics using empirical data. This personalized medicine approach, in combination with the ability to classify cancers into unique molecular groups with distinct prognosis, can transform patient management in cancer (91,92). 1.8 Dynamic Treatment Regimes and Observational Data Statistically, DTRs aim to estimate and infer the sequence of treatment decision rules for each stage that will lead to the best expected outcome. DTRs provide a promising approach to optimize the treatment decision-making process in EC as they can be identified in both randomized and observational data. The ability to use these methods in observational data is a significant advantage as randomized data is not feasible due to the cost and ethical concerns of conducting a randomized trial for exploratory research projects. We have several treatment options available and randomizing them in cases where we believe one treatment may be a better option than another would be a gross violation of medical ethics. Observational data may allow us to overcome this barrier and evaluate a broader range of treatments in a more diverse population. Additionally, randomized trials require longer timeframes before completion and analysis of results. It can be argued that it is unethical to take this time if we can learn something from observational data. 17 Observational data also has its own drawbacks which must be addressed. Using observational data to estimate the effect of treatment on survival outcomes can be difficult due to confounding by indication. Since treatment is assigned based on patients’ covariates, we have systematic differences between the treatment groups, and this introduces treatment selection bias. In the context of EC management, several pre-operative factors can influence the propensity of receiving treatment and the final outcome (figure 2). Pre-operative factors such as body mass index (BMI) and co-morbidities can influence the propensity of receiving full surgical staging with LN testing at stage 1, which can in turn influence the available information when adjuvant therapy decisions are made at stage 2. The effect of the first stage treatment on outcome is also mediated by decisions at subsequent treatment stages. Thus, we require methods that can appropriately adjust for confounders and mediating effects of treatment on survival outcomes. Figure 2. Graphical representation of relationships among treatment decisions, all the clinicopathological parameters available at the time of each decision, and survival outcomes in EC Standard regression analyses and supervised learning should not be used to infer treatment decisions that optimize survival outcomes because they are unable to account for causal relationships and have been shown to be biased. A causal modelling framework is required instead to estimate counterfactual outcomes, which are the unobserved outcomes or the outcomes that would have been observed had another treatment been given. Histotype at diagnosis Grade at diagnosis ProMisE subtype Pre-operative imaging Extra-uterine Disease Age at diagnosis Body mass index ACE comorbidity score ASA physical status Hematological markers • Leukocytes • Platelets • Hemoglobin • CA125 Nodal Assessment Histotype Grade ProMisE subtype Myo-Invasion LVI Nodes Stage ESGO-ESTRO-ESP 2020 + Pre-op Factors Adjuvant Therapy • None • Vaginal brachytherapy • Any RT • Any chemo DSS OS PFS 18 Methods using propensity scores, including propensity score matching or inverse probability weighing, are an alternative to standard regression analyses (93–96). These methods can reduce the effect of the systematic differences in baseline characteristics caused by confounding by indication, making our treatment groups more comparable. A DTR identified using techniques based on the counterfactual framework, such as inverse probability weighting, can account for time-varying treatment effect mediators and confounders when estimating counterfactuals. DTR algorithms allow us to model evolving patient histories and better capture the interplay between the different treatments, outcomes and evolving covariates at each point in time as the models can be specified in a way that outlines which features are available at each stage. 1.9 Study Objectives 1) Measure the associations of clinicopathological parameters available at the time of diagnosis with ProMisE molecular subtypes. 2) Evaluate changes in histopathological and molecular features, used for decision-making, pre- and post-surgically. 3) Explore associations between treatment decisions (nodal assessment and adjuvant therapy) and clinicopathological parameters available at the time of each decision. 4) Develop a dynamic treatment regime model which integrates clinicopathological parameters with molecular classification to inform endometrial cancer treatment decisions on nodal assessment and adjuvant therapy. 1.10 Thesis Roadmap In chapter 2, we explore the treatment patterns for LN assessments and adjuvant therapies within the context of the clinicopathological parameters available, assess how these parameters are associated with ProMisE and characterize the level of concordance for the parameters available pre- and post-operatively. In chapter 3, we conduct an exploratory analysis to assess the potential impact of guidelines with and 19 without molecular classification and characterize the various subsets of patients that could benefit from nodal assessment. In chapter 4, we propose a DTR model to simultaneously optimize nodal assessment and adjuvant treatment decisions, while taking into account all the information available at the time of each decision, and evaluate the model fit. 1.11 Data 1.11.1 Cohort Descriptions I have a population-based retrospective cohort of 903 endometrial cancer patients. This is termed the combined cohort as it combines records from a Vancouver cohort and a German cohort. All patients have a ProMisE molecular subtype assigned, have undergone surgical management without neoadjuvant therapy, and have been followed up according to the guidelines at their respective centres. While the “no surgery” option should be considered in pre-operative decision-making, I excluded these records from the study cohort due to the small number of patients (n=7) receiving no surgery in our data. I have excluded patients who underwent neoadjuvant therapy due to its potential impact on surgical and adjuvant treatment decision making and clinical outcomes. Finally, I excluded a case that was identified as a recurrent case as this thesis focuses on treatment of primary diagnoses of EC. Vancouver Cohort Overview: We identified 484 EC patients diagnosed prior to 2016 and treated by a gynecologic oncologist at a tertiary referral centre in Vancouver, British Columbia. I included all records with a ProMisE molecular subtype assigned (n=472). Records that were for a recurrent EC (n=1), or patients who received neoadjuvant therapy (n=7), did not receive surgical treatment (n=4), or were assigned hyperplasia for grade, histotype and stage after surgical staging (n=1) were excluded. The reasons for no surgery included fertility preservation (n=2), young age with morbid obesity (n=1) and normal biopsy after progesterone treatment (n=1). The final Vancouver cohort includes 459 patients. Chart reviews were conducted to collect clinical data, including histopathology from biopsy and hysterectomy tissues, BMI, treatment, co-morbidities and hematological factors. This data was collected 20 and managed using REDCap 9.1.10 electronic data capture tools hosted at the BC Children’s Hospital Research Institute. From the BC Cancer Registry, a cancer database for all new diagnoses in BC residents, we retrieved clinical outcome data (OS, DSS, and PFS). Date of recurrence, date of death, date of last follow-up and status at last follow-up are periodically updated in the BC Cancer Registry through the BC Vital Statistics Agency and we have retrieved the most up-to-date data as of 01/2022. German Cohort Overview: We identified 514 EC patients diagnosed and treated for primary EC between January 2003 and December 2013. All patients were treated at the Tuebingen University Hospital, a gynecologic-oncology center. I included all records with a ProMisE molecular subtype assigned (n= 450). Patients who received neoadjuvant therapy (n= 3), and/or did not receive surgical treatment (n=3) were excluded. The reasons for no surgery included fertility preservation, poor general health condition and a synchronous breast cancer. The final German cohort includes 444 patients. 11/444 patients had a synchronous ovarian cancer. Data collection was conducted via chart reviews of electronic patient records. Data was collected and managed using REDcap (version 9.8.5) electronic data capture tools hosted at the Department of Women’s Health, Tuebingen University Hospital. Survival outcomes data was updated through contacting patients or their physicians, and the data presented in this thesis is up-to-date as of 05/2019. Both cohorts have been described in previous publications (29,34,86). We have completed additional chart reviews to collect variables such as medical history, co-morbidities, hematological parameters, and reproductive factors, which are analysed in this thesis, and we have updated the survival outcomes data. 1.11.2 Covariates Table 2 summarizes the clinicopathological features analysed in this thesis, with a definition and rationale for inclusion for each feature. 21 Table 2. Summary of the EC Prognostic Factors Analysed in this Thesis Characteristic Description Significance Age at diagnosis Age of patient at time of diagnosis calculated by subtracting date of diagnosis from date of birth. Where missing, age at surgery is used instead. Age at diagnosis is an independent prognostic factor for EC, as older women have worse survival outcomes and greater risk of recurrence, even after adjusting for histology and treatment (67,97). The 5-year DSS rate is estimated at approximately 70% in patients over the age of 75 and 85% in patients aged 50–75 years (98). BMI Body mass index computed as weight (kg) / height (m)2. There is conflicting evidence on the association between obesity and EC prognosis as some studies suggest there is a correlation between higher BMI and worse prognosis, while others conclude that there is no association or further studies are needed to confirm (99–101). CA125 Level of the pre-surgical cancer antigen 125 protein, measured in kU/L, from lab reports dated closest to date of surgery. Elevated levels of the cancer antigen protein (>35 U/mL) are associated with worse 5-year PFS rates and overall survival (OS) rates in women with EC. CA 125 levels are also known to predict extra-uterine disease and disease recurrence, however utility of this parameter is limited by low sensitivity (102–104). Race Not collected in this study. Despite a higher incidence of EC in the Caucasian population, Black women with EC have significantly higher mortality rates. Survival outcomes have been shown to be worse in Black women compared to White women for every histotype, even when stratified by stage, grade and age (4,105). Adult comorbidity evaluation-27 (ACE-27) Overall ACE-27 score calculated with levels none, mild, moderate, and severe. We collapsed the categories for the overall score and each of the individual disease categories to “none” for patients with no comorbidity and “any” for patients with mild-severe levels of disease. Co-morbidities help in estimating the prognosis of endometrial cancer patients, even after adjusting for prognostic factors like age, stage, grade, histotype and adjuvant treatment (106). In patients with a mild ACE-27 comorbidity score, there is a 15% higher risk of death and 19% higher risk of recurrence compared to patients with a comorbidity score of 0. This goes up to 52% higher risk of death and 38% higher risk of recurrence in patients with a moderate comorbidity score. 22 Characteristic Description Significance American Society of Anesthesiologist (ASA) physical status Assessment of patient’s pre-anesthesia medical co-morbidities based on the ASA Physical Status Classification System, with levels: I: Healthy II: Mild disease III: Severe disease IV: Life-threatening disease The ASA score independently predicts OS and DSS in endometrial cancer (107). Hematological Parameters Pre-operative hemoglobin: Indicates blood hemoglobin concentration of < 12 g/dL. Pre-operative leukocytes: Indicates leukocyte count at diagnosis > 10 × 109 cells/L. Pre-operative platelets: Platelet count at diagnosis > 400 × 109 platelets/L. Measurement of each parameter was retrieved from lab reports dated closest to date of surgery. Prospective and retrospective studies suggest that a lower hemoglobin count, higher leukocyte count and higher platelet count can serve as predictors for advanced stage disease. Furthermore, patients with pre-operative leukocytosis, anemia, or thrombocytosis tend to have lower 5-year disease-free survival rates (108–112). Reproductive Factors Gravidity: Number of pregnancies. Parity: Number of pregnancies >20 weeks; multiple gestation (twins) are counted as a single birth. Nulliparous and nulligravid women have been found to have worse prognosis (113–115). Menopause status Whether or not patient has reached menopause, with levels yes and no. Univariate analyses indicate an association between menopausal status and EC prognosis (116,117). Post-menopausal women have been found to have worse histopathology, stage, and survival when adjusted for age (118). Imaging Indicates which imaging modality was used by the clinicians at the time of diagnosis. Regrouped as follows: • TVS Only • TVS + Other: TVS with any combination of abdominal ultrasound, CT, PET, X-Ray, and/or MRI • Other Only: Any combination of abdominal ultrasound, CT, PET, X-Ray, and/or MRI, without TVS The various imaging modalities available at the time of diagnosis have been shown to provide prognostic information through detection of assorted clinicopathological features (119). 23 Characteristic Description Significance Extra-uterine Disease Indicator for pre-operative evidence of extra-uterine disease, with levels yes and no. Extra-uterine disease is a prognostic factor closely associated with serous histotype and lymph node metastasis (62,116). Patients with extrauterine spread at diagnosis are at higher risk for recurrence, regardless of adjuvant therapy (120). Tamoxifen use Ever usage of Tamoxifen, with levels yes and no. Several studies indicate worse survival outcomes, and histopathology in EC patients that are long-term Tamoxifen users (121–123). Histotype Tumour histotype, regrouped as follows: • Suspected Pre-Cancer: hyperplasia, benign, hyperplasia approaching adenocarcinoma • Endometrioid: endometrioid, endometrioid (mucinous), endometrioid (secretory), endometrioid (villo-glandular) • Non-endometrioid + other: clear cell, serous, any mixed types, undifferentiated, carcinosarcoma, adenocarcinoma (NOS), large cell, small cell, high grade NOS, squamous cell Histotype at diagnosis is assigned on biopsy tissue, retrieved from biopsy pathology report. Histotype at surgery is assigned on hysterectomy tissue, retrieved from surgical pathology report. If a malignant histotype was assigned at biopsy and a benign histotype was assigned at surgery, we replaced the surgical histotype with the malignant biopsy histotype, as this is the histotype used in post-operative decision making. Where synchronous ovarian cancer is indicated, only the endometrial cancer histotype is reported. Non-endometrioid cancers have a lower 5-year survival than endometrioid tumours, and account for almost 50% of all endometrial cancer-related deaths, despite being rarer (124,125). Non-endometrioid tumors have extrauterine disease in up to 37% of patients and higher rates of recurrence (126,127). Grade Tumour grade, with levels: grade 1, grade 2, and grade 3. “Grade 1-2” and “Grade 2-3” were reclassified as grade 2 and grade 3, respectively. Grade at diagnosis retrieved from biopsy pathology report. Grade at diagnosis retrieved from biopsy pathology report. Grade 1 and 2 tumors are associated with a good prognosis, whereas grade 3 tumors generally have an intermediate-to-poor prognosis with a greater risk of recurrence. It has been shown that as tumour grade increases, the 5-year survival decreases, for every stage of EC (36). 24 Characteristic Description Significance Myometrial Invasion Myometrial invasion describes the extent to which the tumour has invaded the myometrium. The depth of invasion is divided into the following levels: >50%, 1-50% or none. The greater the depth of invasion, the higher the risk for recurrence and/or death in EC patients (128). Myometrial invasion also correlates with other prognostic factors such as higher grade and lymph node metastases (19). Lympho-Vascular Invasion (LVI) LVI is characterized by the presence of tumour cells in lymphatic or vascular spaces, with levels positive and negative. LVI is considered to be one of the first steps for tumour metastasis in EC. LVI is an independent prognostic factor for EC survival, as well as an independent predictor for LN and distant metastases (129–132). Stage FIGO 2009 stage, describing the spread of the disease, regrouped to have levels I, II and III-IV. Higher stages indicate greater spread of the tumour, and metastatic disease has a much lower 5-year survival rate (17%) compared to local disease (90%). Thus, stage I-II cancers are associated with better prognosis, while stage III-IV are associated with worse prognosis (133). The recurrence rate also increases as stage increases with 7%, 20%, 38% and 67% in stage I, II, III and IV tumours respectively (40). Lymph Node Status Nodal status indicating whether nodes were “positive”, “negative” or “not tested”. The 5-year disease-free survival is estimated at 54% in patients testing positive for nodal involvement, compared with 90% for patients testing negative. Furthermore, the recurrence rate is estimated to be 48% with positive LN, whereas it is 8% with negative LN (98). ProMisE Molecular Subtypes Molecular classification assigned to biopsy sample, 2019 version. With levels: • POLEmut: mutations in polymerase epsilon exonuclease domain exons 9-14 • MMRd: loss of one or more mismatch repair protein(s) • p53abn: aberrant p53 IHC staining • NSMP/p53wt: no specific mutations in p53 nor other above-defined molecular features Where subtype assigned on biopsy is missing, subtype assigned at surgery is used. ProMisE has been shown to be strongly associated with OS, PFS and DSS, even after adjusting for known risk factors (29,34,134). 25 Chapter 2: Characterization of the pre- and post- operative clinico-pathological parameters 2.1 Introduction Endometrial cancer is typically treated in multiple stages: primarily surgery followed by adjuvant therapy, if needed. There are further stages of treatment if a patient recurs, however this thesis focusses on the first two stages. The set of clinicopathological parameters available at the time of decision-making for each of these stages varies as additional information is collected during surgery. Surgery alone is often curative in EC patients with low risk of recurrence. However, in higher risk cases, including where LN assessments yield positive results, adjuvant radiation therapy and/or chemotherapy may also be needed. While conducting a nodal assessment during the surgery can inform a patient’s risk status, and consequently assist in making better adjuvant therapy decisions, many EC patients are elderly and obese, which makes them more vulnerable to the risks associated with LN assessments. The existing literature indicates vast variation in EC treatment practices for LN assessment and adjuvant therapy (135). Deviation from guideline concordant treatment is associated with lower overall survival and has been found to be more common in non-Hispanic Black women (136). A survey of over 400 gynecologic oncologists demonstrated variation in treatment of grade 1 ECs, with 35% of gynecologic oncologists performing full surgical staging on all grade 1 ECs, and 65% selectively performing lymphadenectomy in this group (137). Sharma et al (2011) investigated the effect of LN assessment on the utilization of adjuvant therapy in patients with stage I-II EC. They found that lymphadenectomy is more common in women who are non-white and/or have a grade 3 EC, and patients who receive a lymphadenectomy are less likely to receive EBRT (138). Finally, while co-morbidities are not often mentioned in adjuvant therapy guidelines, Boll et al (2011) found that patients with co-morbidities are significantly less likely to receive adjuvant RT (139). 26 While some suggest all patients should undergo LN testing, the general criteria used by gynecologic oncologists includes high grade, non-endometrioid histotype, elevated serum CA 125 levels and evidence of spread on pre-operative imaging. The heavy use of histotype and grade, which are unreliably assigned from biopsy tissues for pre-operative risk stratification, makes nodal testing a contentious decision. There is known interobserver variation in grade and histotype assignment as several studies report lack of consensus in approximately 30% of cases (60,140). Furthermore, the literature suggests that histotype and grade assignments are discordant between pre- and post-operative specimens, with reports of discrepancies in 10-35% of cases (26,27,141–144). Evaluations of outcomes indicate that patients with discordant diagnostic and surgical histology tend to have significantly worse prognosis (145,146). The lack of reliable pre-operative parameters to inform the LN assessment decision can have severe implications in EC management as future treatment decisions regarding adjuvant therapy will vary based on nodal status. Overall, the current state of the literature suggests a strong need for accurate and reproducible parameters that can inform treatment decisions and limit the variation of treatment practices in EC. ProMisE molecular classification is a novel classification system that can be used to stratify endometrial cancers into distinct subtypes and its availability at diagnosis supports its usefulness in pre-operative clinical decision making. The prognostic value of ProMisE has been reported in several studies, with excellent prognosis observed in POLEmut, intermediate prognosis in p53wt and MMRd, and poor prognosis in the p53abn subtype (34,86,134). Unlike grade and histotype, comparison of ProMisE subtypes assigned pre- and post- operatively found high concordance among assigned subtypes, with a reported 92% accuracy rate (34). Talhouk et al (2016) were the first to explore histotype, grade and ProMisE concordance in biopsy and hysterectomy specimens all within the same population of patients (61). While this study reported significant differences, the sample size was small, at 57 patients, and very few ECs were non-endometrioid. They found excellent agreement in ProMisE subtypes (kappa = 0.86), and moderate agreement in both, grade (kappa = 0.55) and histotype (kappa = 0.44). They also found moderate 27 agreement between biopsy and hysterectomy specimens when histology was assigned by gynecologic subspecialty pathologists. They did not evaluate concordance among the original histopathology assigned at hysterectomy and the histopathology later assigned by specialist gynecologic pathologists, or the concordance in binary FIGO grades. ProMisE subtypes have been integrated into the ESMO-ESTRO-ESP (2020) treatment guidelines for adjuvant therapy, however, little progress has been made in developing guidelines for LN assessments using pre-operative factors other than histopathology. Alongside ProMisE, grade and histotype, other factors available at the time of diagnosis include co-morbidities, age, BMI, hematological markers, reproductive factors, and extra-uterine disease on imaging. These parameters are of particular interest as they are both, available at the time of pre-operative decision making and significantly associated with EC prognosis (36,67,97,102,103,106–112,124,125,127,147–149). The associations among ProMisE subtypes and some of these clinicopathological parameters have been explored in discovery, confirmation and validation cohorts, and a meta-analysis pooling data from those analyses, plus data from subsequent studies, has been completed by Raffone et al (34,86,134,150). The findings suggest that the POLEmut group is comprised of younger women with lower BMI, the p53wt group has the highest mean BMI, the MMRd group has intermediate-high mean BMI, and the p53abn group has the oldest mean age (150). While the association of ProMisE subtypes with BMI and age at diagnosis, as well as histopathology, are documented in the existing literature (34,86,134,150), molecular subtypes have yet to be studied in the context of the various other prognostic factors available pre-operatively. In this chapter, we first describe the associations of all the factors available pre-operatively with nodal testing. We also conduct univariate analyses to explore the associations among ProMisE molecular subtypes and the pre-operative clinicopathological parameters. Next, we characterize the pre- and post-operative factors associated with adjuvant therapy decisions. Finally, we compare the concordance in pre- and post-operative assignments of ProMisE, grade, and histotype. We conduct additional assessments of 28 concordance among original surgical assignment of histopathology and assignment after an expert review, as well as concordance among binary FIGO grade assignments. We conduct all analyses on a retrospective cohort of 903 EC patients, comprised of 459 patients from Vancouver, Canada and 444 patients from Germany. All patients were treated by a gynecologic oncologist and have ProMisE molecular subtype assigned. 2.2 Methods Univariate analyses: Analyses were conducted on the combined cohort (N=903). The clinicopathological variables were processed as described in table 2. The treatment variables are as described below. Lymph Nodes Assessment indicates whether or not nodes were tested, with levels tested and not tested. All nodal assessment methods, pelvic and aortic lymphadenectomy, and sentinel biopsy were considered. Adjuvant Therapy indicates the type of adjuvant treatment, if any, received post-surgery. Regrouped as follows: • None • Vaginal Brachytherapy (VB) Only • EBRT +/- VB: EBRT alone or with VB • Any Chemotherapy: chemotherapy alone, chemotherapy with VB, EBRT, or both Analysis of concordance in features assigned pre- and post-operatively: Patients with both biopsy and surgical assignments of ProMisE subtypes were included for the analysis of concordance in molecular subtypes. Patients with biopsy, surgical, and reviewed assignments of histotype and grade were included for the analysis of concordance in histotype and grade, respectively. An additional analysis was done using binary grades. All diagnostic parameters were assigned from biopsy tissues. Surgical and reviewed 29 parameters were assigned from hysterectomy specimens. The reviewed factors indicate the grade and histotype assigned after review by an expert gynecologic pathologist. Grades were coded into 3 levels: grade 1, grade 2, and grade 3, where “Grade 1-2” and “Grade 2-3” were recoded as grade 2 and grade 3, respectively. Binary grades were generated by combining grade 1 and 2 into one level, termed ‘grade 1/2’. Histotypes were recoded into the following levels: • Suspected Pre-Cancer: hyperplasia, benign, hyperplasia approaching adenocarcinoma • Endometrioid: endometrioid, endometrioid (mucinous), endometrioid (secretory), endometrioid (villoglandular) • Non-endometrioid + other: clear cell, serous, any mixed types, undifferentiated, carcinosarcoma, adenocarcinoma (NOS), large cell, small cell, high grade NOS, squamous cell ProMisE molecular subtypes were assigned with the following levels: • POLEmut: mutations in polymerase epsilon exonuclease domain exons 9-14 • MMRd: loss of one or more mismatch repair protein(s) • p53abn: aberrant p53 IHC staining • NSMP/p53wt: no specific mutations in p53 nor other above-defined molecular features 2.2.1 Statistical analyses Univariate analyses: We used the Kruskal-Wallis rank sum test for continuous variables and the Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates) for categorical variables. The median is reported for all continuous variables. Percentages are calculated across rows for the categorical variables, unless otherwise noted. The number of missing values for each variable are also reported. 30 Analysis of change in features assigned pre- and post-operatively: We created Sankey plots to visualize the changes occurring in each parameter. We generated confusion matrices with both column-wise and row-wise percentages to quantitatively summarize the changes between biopsy and surgery assignments, and surgery and reviewed assignments. The percentage agreement and kappa statistics were calculated for grade, histotype and molecular subtype between biopsy and surgical assignments. The percent agreement and kappa statistics were also calculated for surgery and reviewed assignments for grade and histotype. 2.3 Results We have a cohort of 903 patients, aged between 29-94 years, with a median follow up time of 6.5 years (range: 0.05-17 years). The distribution of ProMisE molecular subtypes within this cohort is as follows: 85 (9.4%) POLEmut, 438 (48%) NSMP/p53wt, 229 (25%) MMRd, and 152 (17%) p53abn. 2.3.1 Analysis of associations among LN assessment and pre-operative parameters The associations between LN assessment and the various pre-operative parameters available at the time of the decision are summarized in table 3. Within this cohort, 284 patients did not undergo nodal testing, and 615 underwent a lymphadenectomy and/or sentinel LN biopsy. Within the tested group, majority of the patients underwent nodal assessment during the primary hysterectomy and 32 patients underwent a secondary staging surgery. The patients receiving a secondary staging surgery comprised 2.6% (6/230) of MMRd, 4.8% (21/434) of NSMP/p53wt, 2.6% (4/155) of p53abn and 1.2% (1/84) of POLEmut. 2/21 of the p53wt patients and 2/4 of the p53abn patients tested in the secondary staging surgery have positive nodal status. Older median age, lower BMI, and absence of comorbidities are all associated with nodal testing. The IQR of Ca125 levels is 13-170 kU/L for patients who did not get tested and 11-29 kU/L for those that did, despite a higher Ca125 indicating potentially greater spread of disease. Among patients who had 31 high Ca125 (>35 kU/L) and underwent testing, one third had nodal metastases. We noted a lower proportion of patients with hemoglobin levels low enough to indicate anemia among those undergoing testing. Among the reproductive factors, both gravidity and menopause status are significantly associated with nodal testing. Significant associations among the histopathological variables, grade and histotype, and nodal testing are observed (p<0.001). Patients assigned grade 1 at diagnosis comprise 54% of the group not receiving nodal testing and 22% of the patients receiving nodal testing. We observe that nodal assessment is omitted in approximately one third of patients with endometrioid histotype, and in one fifth of non-endometrioid or other patients. Patients with low grade non-endometrioid/other ECs, or high grade endometrioid ECs, comprise greater proportions of the group that receiving nodal testing than the group that did not. Among the non-endometrioid/ other ECs, nodal testing is more common in low grade tumours (91%) than high grade (76%). Approximately 50% of patients with a suspected pre-cancer at diagnosis received nodal testing. The decision to test nodes in these patients was often made after observing malignant features during surgery. The distribution of nodal testing within ProMisE subtypes differed significantly (p<0.001). The patients with nodes tested comprised 62%, 73%, 75% and 77% of NSMP/p53wt, p53abn, MMRd and POLEmut patients, respectively. Furthermore, we observe nodal assessment to be more common among patients who undergo imaging via TVS. However, this may be confounded due to differences in the German and Vancouver cohorts as there is a greater prevalence of both TVS imaging and nodal testing in Germany (table A.1, Appendix A). Finally, we note that vaginal surgical access and hysterectomy-only surgeries are less likely to include a nodal assessment. 32 Table 3. Univariable Associations between Lymph Node Testing and the Clinicopathological Features Available at the Time of Diagnosis LN Assessment Characteristic N Not Tested, N = 284 Tested, N = 615 p-value1 Age 899 60 (48, 73) 64 (56, 72) 0.002 Body Mass Index 837 29 (24, 40) 28 (24, 32) <0.001 Missing 31* 31 ACE Comorbidity Score 874 0.035 None 55 (21%) 165 (27%) Any 213 (79%) 441 (73%) Missing 16 9 ASA Comorbidity Score 813 <0.001 I (healthy) 53 (21%) 85 (15%) II (mild disease) 111 (44%) 382 (68%) III (severe disease) 88 (35%) 90 (16%) IV (life-threatening disease) 3 (1.2%) 1 (0.2%) Missing 29* 57 Ca125 (kU/L) 558 25 (13, 170) 16 (11, 29) <0.001 Missing 139* 202* Hemoglobin <12g/dl 643 55 (34%) 85 (18%) <0.001 Missing 121* 135* Leukocytes >10x109/L 639 31 (19%) 79 (17%) 0.5 Missing 123* 137* Platelets >400x109/L 639 22 (14%) 51 (11%) 0.3 Missing 123* 137* Menopause Status 879 163 (60%) 497 (82%) <0.001 Missing 11 9 33 LN Assessment Characteristic N Not Tested, N = 284 Tested, N = 615 p-value1 Gravidity 834 0.010 0 77 (30%) 112 (19%) 1-2 103 (40%) 252 (44%) 3-4 62 (24%) 171 (30%) 5+ 16 (6.2%) 41 (7.1%) Missing 26 39 Parity 850 0.13 0 73 (29%) 140 (23%) 1-2 123 (49%) 293 (49%) 3-4 46 (18%) 147 (25%) 5+ 9 (3.6%) 19 (3.2%) Missing 33* 16 Histotype at Diagnosis 790 <0.001 Suspected Pre-Cancer 22 (9.3%) 20 (3.6%) Endometrioid 169 (71%) 357 (65%) Non-Endometrioid /+ Other 46 (19%) 176 (32%) Missing 47* 62* Grade at Diagnosis 738 <0.001 Suspected Pre-Cancer 3 (1.4%) 4 (0.8%) Grade 1 115 (54%) 115 (22%) Grade 2 42 (20%) 226 (43%) Grade 3 53 (25%) 180 (34%) Missing 71* 90* 34 LN Assessment Characteristic N Not Tested, N = 284 Tested, N = 615 p-value1 Histotype and Grade 726 <0.001 Endometrioid + Grade 1/2 148 (70%) 285 (55%) Endometrioid + Grade 3 17 (8.1%) 62 (12%) Non-Endo / Other + Grade 1/2 5 (2.4%) 50 (9.7%) Non-Endo / Other + Grade 3 36 (17%) 115 (22%) Suspected Pre-Cancer 5 (2.4%) 3 (0.6%) Missing 73* 100* Promise Subtype at Diagnosis 899 <0.001 POLEmut 19 (6.7%) 65 (11%) NSMP/p53wt 166 (58%) 266 (43%) MMRd 57 (20%) 172 (28%) p53abn 42 (15%) 112 (18%) Pre-operative Imaging 832 <0.001 Other Only 128 (54%) 132 (22%) TVS Only 19 (8.0%) 88 (15%) TVS + Other 90 (38%) 375 (63%) Missing 47* 20 Extrauterine Disease 867 52 (20%) 65 (11%) <0.001 Missing 23 9 Surgery Type 893 <0.001 Hysterectomy Only 31 (11%) 8 (1.3%) Hysterectomy + Other 247 (89%) 607 (99%) Missing 6 0 35 LN Assessment Characteristic N Not Tested, N = 284 Tested, N = 615 p-value1 Surgical Access 441 <0.001 Laparoscopy 30 (52%) 260 (68%) Open surgery 18 (31%) 110 (29%) Vaginal 10 (17%) 13 (3.4%) Missing 226* 232* 1 Wilcoxon rank sum test; Fisher's exact test; Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates) *Greater than 10% missing Median (IQR) reported for all numerical variables Column-wise percentages reported 2.3.2 Analysis of associations among promise molecular subtypes and pre-operative parameters In Table 4, I summarize the associations among various clinicopathological features available at the time of diagnosis and ProMisE molecular subtypes. Prognostically worse subtypes are associated with older ages as the median age is 57 in POLEmut, 60 in p53wt, 64 in MMRd and 71 in p53abn. The prevalence of obesity is significantly lower in patients with POLEmut ECs (9% obese) and higher in patients with p53wt ECs (30% obese), as seen in the differences in body mass index and the ACE obesity score. There is a significant difference in the overall ACE comorbidity scores, with a greater absence of co-morbidities in the POLEmut subgroup and a higher prevalence of co-morbidities observed in the p53abn subgroup. Within the various ACE categories, aside from obesity, only hypertension, peripheral artery disease, diabetes mellitus, and rheumatic diseases are significantly different among subtypes, with all of them more commonly observed in the p53abn subtype and less commonly observed in the POLEmut subtype. The ASA physical status mirrors the results of the ACE scores. Both scores indicate similarly intermediate levels of comorbidities and frailty in the MMRd and p53wt patients, with approximately 16%, 60% and 20% of patients in the healthy, mild disease and 36 severe disease categories, respectively. We also observe an association between Tamoxifen use and ProMisE with 10% (6/60) of patients with p53bn ECs using Tamoxifen compared to 2-4% in the other groups. There was no significant difference in the various hematological parameters (hemoglobin, platelets, leukocytes and CA125) among the molecular subtypes. Most of the patients within each subtype have reached menopause, with the lowest proportion in POLEmut (63%) and the highest in p53abn (91%). The proportion of nulliparous women is highest in the p53wt group, with 29% of the patients within this group being nulliparous. On average, the p53abn group reports having greater gravidity and parity. Within this cohort, grade 3 cancers comprise 28%, 16%, 30% and 78% of patients in the POLEmut, p53wt, MMRd and p53abn subgroups, respectively. Non-endometrioid cancers comprise 22%, 18%, 26% and 62% of patients in the POLEmut, p53wt, MMRd and p53abn subgroups, respectively. High grade and severe histotypes make up the largest proportion of patients in the p53abn group. These severe histotypes and grades comprise a smaller proportion of the p53wt subgroup, relative to all other subtypes. Suspected pre-cancers (benign or hyperplasia) at diagnosis are most common within the p53wt group. Table 4. Univariate analysis of associations among ProMisE and Pre-operative Parameters ProMisE Subtype Variable N POLEmut, N = 84 p53wt, N = 434 MMRd, N = 230 p53abn, N = 155 p-value1 Age 903 57 (49, 64) 60 (49, 69) 64 (56, 73) 71 (65, 78) <0.001 Body Mass Index 841 26 (22, 30) 29 (24, 37) 28 (24, 33) 28 (23, 33) <0.001 Missing 7 40 11 4 ACE Comorbidity 878 <0.001 None 38 (47%) 99 (24%) 58 (26%) 25 (16%) Any 43 (53%) 320 (76%) 168 (74%) 127 (84%) Missing 3 15 4 3 37 ProMisE Subtype Variable N POLEmut, N = 84 p53wt, N = 434 MMRd, N = 230 p53abn, N = 155 p-value1 ACE Obesity 863 7 (8.9%) 123 (30%) 41 (18%) 32 (21%) <0.001 Missing 5 22 7 6 ACE Coronary Artery Disease 857 1 (1.2%) 18 (4.4%) 13 (5.9%) 10 (6.8%) 0.2 Missing 4 26 8 8 ACE Congestive Heart Failure 863 1 (1.3%) 17 (4.1%) 9 (4.0%) 8 (5.3%) 0.5 Missing 5 23 7 5 ACE Cardiac Arrhythmias 860 7 (9.2%) 26 (6.3%) 17 (7.8%) 20 (13%) 0.076 Missing 8 19 12 4 ACE Hypertension 642 14 (21%) 81 (27%) 46 (29%) 55 (48%) <0.001 Missing 17* 133* 71* 40* ACE Venous Disease 866 4 (5.0%) 20 (4.8%) 13 (5.9%) 5 (3.3%) 0.7 Missing 4 21 8 4 ACE Peripheral Artery Disease 867 0 (0%) 2 (0.5%) 1 (0.5%) 5 (3.3%) 0.025 Missing 4 20 8 4 ACE Respiratory Disease 870 8 (10%) 43 (10%) 12 (5.3%) 13 (8.7%) 0.2 Missing 4 19 5 5 ACE Hepatic Disease 863 0 (0%) 2 (0.5%) 0 (0%) 0 (0%) 0.8 Missing 4 24 7 5 ACE Stomach and Intestinal Disease 867 3 (3.8%) 16 (3.9%) 9 (4.1%) 8 (5.3%) 0.9 Missing 4 19 9 4 ACE Pancreatic Disease 869 0 (0%) 0 (0%) 1 (0.4%) 0 (0%) 0.5 Missing 5 19 6 4 38 ProMisE Subtype Variable N POLEmut, N = 84 p53wt, N = 434 MMRd, N = 230 p53abn, N = 155 p-value1 ACE End-stage Renal Disease 863 2 (2.5%) 9 (2.2%) 2 (0.9%) 7 (4.7%) 0.11 Missing 5 23 7 5 ACE Diabetes Mellitus 854 5 (6.2%) 71 (18%) 39 (18%) 31 (21%) 0.023 Missing 4 30 8 7 ACE Stroke 862 2 (2.5%) 6 (1.5%) 5 (2.2%) 3 (2.0%) 0.8 Missing 5 23 7 6 ACE Dementia 862 0 (0%) 4 (1.0%) 3 (1.3%) 4 (2.7%) 0.3 Missing 5 24 6 6 ACE Paralysis 870 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) >0.9 Missing 4 18 6 5 ACE Neuromuscular Disorder 871 1 (1.3%) 6 (1.4%) 4 (1.8%) 2 (1.3%) >0.9 Missing 6 17 5 4 ACE Psychiatric Disorder 868 4 (5.0%) 41 (9.9%) 15 (6.7%) 9 (6.0%) 0.3 Missing 4 20 7 4 ACE Rheumatologic Disorder 872 6 (7.5%) 28 (6.7%) 20 (8.9%) 31 (21%) <0.001 Missing 4 18 5 4 ACE Solid Tumor 872 6 (7.6%) 35 (8.4%) 16 (7.1%) 23 (15%) 0.067 Missing 5 17 6 3 ACE Lymphoma 867 0 (0%) 4 (1.0%) 2 (0.9%) 1 (0.7%) >0.9 Missing 6 21 4 5 ACE Alcohol 862 0 (0%) 3 (0.7%) 2 (0.9%) 2 (1.3%) 0.8 Missing 4 23 9 5 39 ProMisE Subtype Variable N POLEmut, N = 84 p53wt, N = 434 MMRd, N = 230 p53abn, N = 155 p-value1 ASA Physical Status 817 0.005 I (healthy) 23 (30%) 62 (16%) 33 (16%) 20 (14%) II (mild disease) 44 (58%) 239 (62%) 134 (64%) 80 (54%) III (severe disease) 9 (12%) 80 (21%) 42 (20%) 47 (32%) IV (life-threatening disease) 0 (0%) 3 (0.8%) 0 (0%) 1 (0.7%) Missing 8 50* 21 7 Ca125 (kU/L) 560 15 (12, 23) 17 (11, 31) 18 (12, 38) 20 (11, 58) 0.3 Missing 30* 173* 79* 61* Hemoglobin <12g/dl 645 11 (18%) 71 (22%) 31 (19%) 27 (26%) 0.4 Missing 24* 118* 63* 53* Leukocytes > 10x109/L 641 11 (18%) 56 (18%) 26 (16%) 17 (17%) >0.9 Missing 24* 120* 64* 54* Platelets > 400x109/L 641 8 (13%) 35 (11%) 21 (13%) 9 (8.8%) 0.8 Missing 24* 121* 64* 53* Extrauterine Disease 871 8 (10%) 55 (13%) 25 (11%) 30 (20%) 0.071 Missing 4 18 5 5 Tamoxifen Use 551 2 (3.6%) 7 (2.4%) 4 (2.8%) 6 (10%) 0.043 Missing 28* 141* 88* 95* Menopause Status 883 50 (62%) 286 (68%) 180 (79%) 145 (94%) <0.001 Missing 3 14 3 0 40 ProMisE Subtype Variable N POLEmut, N = 84 p53wt, N = 434 MMRd, N = 230 p53abn, N = 155 p-value1 Gravidity 838 0.002 0 16 (20%) 108 (27%) 55 (25%) 13 (8.9%) 1-2 35 (44%) 160 (40%) 97 (45%) 64 (44%) 3-4 22 (28%) 105 (26%) 54 (25%) 52 (36%) 5+ 6 (7.6%) 24 (6.0%) 10 (4.6%) 17 (12%) Missing 5 37 14 9 Parity 852 0.002 0 20 (26%) 116 (29%) 58 (26%) 20 (13%) 1-2 39 (51%) 195 (48%) 111 (50%) 72 (48%) 3-4 16 (21%) 80 (20%) 47 (21%) 50 (33%) 5+ 2 (2.6%) 12 (3.0%) 5 (2.3%) 9 (6.0%) Missing 7 31 9 4 Histotype at Diagnosis 794 <0.001 Suspected Pre-Cancer 1 (1.4%) 31 (8.1%) 6 (2.9%) 4 (2.9%) Endometrioid 53 (77%) 282 (74%) 147 (71%) 48 (35%) Non-Endo / Other 15 (22%) 68 (18%) 53 (26%) 86 (62%) Missing 15* 53* 24* 17* Grade at Diagnosis 742 <0.001 Suspected Pre-Cancer 0 (0%) 6 (1.7%) 1 (0.5%) 0 (0%) Grade 1 18 (27%) 154 (45%) 53 (27%) 6 (4.6%) Grade 2 30 (45%) 130 (38%) 86 (43%) 22 (17%) Grade 3 19 (28%) 56 (16%) 59 (30%) 102 (78%) Missing 17* 88* 31* 25* 41 ProMisE Subtype Variable N POLEmut, N = 84 p53wt, N = 434 MMRd, N = 230 p53abn, N = 155 p-value1 Histotype and Grade at Diagnosis 730 <0.001 Endometrioid + Grade 1/2 41 (62%) 251 (74%) 116 (59%) 26 (20%) Endometrioid + Grade 3 10 (15%) 22 (6.5%) 29 (15%) 21 (16%) Non-Endo / Other + Grade 1/2 7 (11%) 26 (7.6%) 20 (10%) 2 (1.6%) Non-Endo / Other + Grade 3 8 (12%) 34 (10%) 30 (15%) 79 (62%) Suspected Pre- Cancer 0 (0%) 7 (2.1%) 1 (0.5%) 0 (0%) Missing 18* 94* 34* 27* 1Kruskal-Wallis rank sum test; Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates) *Greater than 10% missing Median (IQR) reported for all numerical variables Column-wise percentages reported 2.3.3 Analysis of associations among adjuvant therapy and all parameters available at the time of the decision Within the combined cohort, we explored the distribution of adjuvant therapies across pre- and post-surgical parameters (table 5). Patients were administered either no adjuvant therapy (47%), vaginal brachytherapy only (18%), EBRT with or without brachytherapy (9%) or any chemotherapy (26%). Adjuvant therapy was not significantly associated with age, BMI, or co-morbidities. However, we do note that all 3 patients with life-threatening disease, as indicated by the ASA physical status, did not undergo any adjuvant therapy. The 207 patients receiving chemotherapy have the greatest proportion of patients with low hemoglobin (25%) and high platelets levels (15%). 42 Extra-uterine disease observed pre-operatively was also significantly associated with adjuvant therapy as 62% of patients with extra-uterine disease at diagnosis received chemotherapy, compared to 38% of patients without extra-uterine disease. Among patients with endometrioid, low grade ECs, the largest proportion (58%) received no adjuvant therapy and 24% received VB only. In patients with high-grade ECs, histotype made little difference to the administered adjuvant therapy. In high-grade Endometrioid ECs, 30% received no adjuvant therapy and 46% received chemotherapy. Similarly, 27% of patients with non-endometrioid / other histotype and high grade received no adjuvant therapy and 53% received chemotherapy. Among the ProMisE subtypes, NSMP/p53wt patients are more likely to receive no adjuvant therapy (56%). Approximately 22% of all subtypes receive VB only, except p53abn (5%). The MMRd group has a slightly more even distribution of adjuvant therapies than the other subtypes (39% none; 22% VB Only; 12% EBRT +/- VB; 27% any chemotherapy). Despite the confirmed absence of nodal metastasis, approximately 20% of patients with negative nodes still received chemotherapy. No adjuvant therapy was most commonly observed in the group of patients that did not undergo nodal assessment (61%). Approximately 27% of this group received chemotherapy. While two-thirds of p53abn ECs are treated with chemotherapy, 15% received no adjuvant treatment and there were no p53abn ECs treated with VB only. Other measures of the spread of disease, stage, myometrial invasion and LVI are also all significantly associated with adjuvant therapies. Stage II contains the greatest proportion of patients receiving EBRT +/- VB (43%). Approximately 20% of each of the myometrial invasion groups (none vs 1-50% vs >50%) receive VB only. Chemotherapy is given in 72%, 38% and 54% of patients with stage III-IV, >50% myometrial invasion and positive LVI, respectively. No adjuvant therapy is given to approximately 40% and 20% of the patients in the ESGO-ESTRO-ESP (2020) (24) high-intermediate risk and high-risk categories, respectively. 43 Table 5. Univariate analysis of associations among adjuvant therapy and all parameters available at the time of the decision. Adjuvant Therapy Characteristic N None, N = 377 VB Only, N = 150 RT +/- VB, N = 74 Any Chemo, N = 207 p-value1 Age 808 63 (51, 73) 66 (58, 72) 60 (51, 71) 62 (53, 71) 0.082 Body Mass Index 757 28 (24, 34) 28 (24, 34) 28 (24, 35) 28 (23, 33) 0.5 Missing 28 11 5 7 ACE Comorbidity Score 783 0.6 None 85 (43%) 38 (19%) 22 (11%) 54 (27%) Any 275 (47%) 111 (19%) 50 (8.6%) 148 (25%) Missing 17 1 2 5 ASA Physical Status 734 0.2 I (healthy) 61 (48%) 16 (13%) 14 (11%) 35 (28%) II (mild disease) 198 (44%) 92 (20%) 35 (7.7%) 128 (28%) III (severe disease) 80 (53%) 22 (14%) 15 (9.9%) 35 (23%) IV (life-threatening disease) 3 (100%) 0 (0%) 0 (0%) 0 (0%) Missing 35 20* 10* 9 Ca125 (kU/L) 497 15 (11, 24) 15 (11, 23) 18 (9, 36) 51 (19, 251) <0.001 Missing 164* 33* 32* 82* Hemoglobin <12g/dl 570 40 (33%) 18 (15%) 11 (9.0%) 53 (43%) <0.001 Missing 129* 6 30* 73* Leukocytes >10x109/L 566 32 (33%) 26 (27%) 6 (6.2%) 32 (33%) 0.063 Missing 133* 6 30* 73* Platelets >400x109/L 565 20 (31%) 6 (9.4%) 8 (12%) 30 (47%) <0.001 Missing 133* 5 30* 75* 44 Adjuvant Therapy Characteristic N None, N = 377 VB Only, N = 150 RT +/- VB, N = 74 Any Chemo, N = 207 p-value1 Menopause Status 789 263 (44%) 133 (22%) 51 (8.6%) 147 (25%) <0.001 Missing 11 4 1 3 Gravidity 749 0.8 0 83 (49%) 28 (16%) 15 (8.8%) 45 (26%) 1-2 143 (45%) 64 (20%) 24 (7.5%) 87 (27%) 3-4 99 (47%) 37 (18%) 24 (11%) 50 (24%) 5+ 20 (40%) 11 (22%) 3 (6.0%) 16 (32%) Missing 32 10 8* 9 Parity 764 0.5 0 85 (44%) 39 (20%) 21 (11%) 48 (25%) 1-2 172 (46%) 74 (20%) 30 (8.0%) 98 (26%) 3-4 80 (46%) 32 (18%) 20 (12%) 41 (24%) 5+ 9 (38%) 4 (17%) 0 (0%) 11 (46%) Missing 31 1 3 9 Extra-uterine Disease 779 24 (23%) 0 (0%) 16 (15%) 65 (62%) <0.001 Missing 17 0 1 11 Histotype 803 <0.001 Endometrioid 318 (52%) 126 (21%) 54 (8.8%) 113 (18%) Non-Endo / Other 55 (29%) 24 (12%) 20 (10%) 93 (48%) Missing 4 0 0 1 Grade 791 <0.001 Grade 1 192 (78%) 22 (8.9%) 10 (4.0%) 23 (9.3%) Grade 2 91 (37%) 99 (40%) 25 (10%) 32 (13%) Grade 3 84 (28%) 27 (9.1%) 38 (13%) 148 (50%) Missing 10 2 1 4 45 Adjuvant Therapy Characteristic N None, N = 377 VB Only, N = 150 RT +/- VB, N = 74 Any Chemo, N = 207 p-value1 Histotype and Grade at Surgery 790 <0.001 Endometrioid + Grade 1/2 275 (58%) 113 (24%) 33 (7.0%) 50 (11%) Non-Endo / Other + Grade 1/2 8 (35%) 8 (35%) 2 (8.7%) 5 (22%) Endometrioid + Grade 3 39 (30%) 12 (9.2%) 20 (15%) 60 (46%) Non-Endo / Other + Grade 3 44 (27%) 15 (9.1%) 18 (11%) 88 (53%) Missing 11 2 1 4 Promise Subtype 808 <0.001 POLEmut 37 (46%) 18 (22%) 5 (6.2%) 20 (25%) NSMP/p53wt 213 (56%) 79 (21%) 30 (7.9%) 60 (16%) MMRd 81 (39%) 46 (22%) 26 (12%) 56 (27%) p53abn 46 (34%) 7 (5.1%) 13 (9.5%) 71 (52%) Nodal Status 807 <0.001 Negative 212 (44%) 141 (29%) 39 (8.1%) 92 (19%) Not tested 153 (61%) 9 (3.6%) 22 (8.8%) 67 (27%) Positive 11 (15%) 0 (0%) 13 (18%) 48 (67%) Missing 1 0 0 0 Stage 808 <0.001 I 339 (57%) 143 (24%) 27 (4.5%) 86 (14%) II 16 (26%) 7 (11%) 26 (43%) 12 (20%) III-IV 22 (14%) 0 (0%) 21 (14%) 109 (72%) 46 Adjuvant Therapy Characteristic N None, N = 377 VB Only, N = 150 RT +/- VB, N = 74 Any Chemo, N = 207 p-value1 Myometrial Invasion 748 <0.001 None 126 (71%) 33 (19%) 4 (2.2%) 15 (8.4%) 1-50% 146 (48%) 65 (21%) 22 (7.2%) 73 (24%) >50% 71 (27%) 52 (20%) 40 (15%) 101 (38%) Missing 34 0 8* 18 LVI 730 <0.001 Negative 279 (54%) 131 (25%) 40 (7.7%) 71 (14%) Positive 53 (25%) 19 (9.1%) 25 (12%) 112 (54%) Missing 45* 0 9* 24* ESGO-ESTRO-ESP Risk w/ ProMisE 741 <0.001 Low 190 (65%) 70 (24%) 6 (2.1%) 26 (8.9%) Intermediate 47 (53%) 34 (38%) 2 (2.2%) 6 (6.7%) High-intermediate 41 (38%) 24 (22%) 27 (25%) 16 (15%) High 54 (22%) 16 (6.6%) 35 (14%) 137 (57%) Advanced 1 (10%) 0 (0%) 2 (20%) 7 (70%) Missing 44* 6 2 15 ESGO-ESTRO-ESP Risk 758 <0.001 Low 181 (72%) 60 (24%) 2 (0.8%) 10 (4.0%) Intermediate 58 (48%) 46 (38%) 4 (3.3%) 12 (10%) High-intermediate 54 (39%) 29 (21%) 31 (22%) 25 (18%) High 47 (19%) 15 (6.1%) 36 (15%) 147 (60%) Advanced 0 (0%) 0 (0%) 0 (0%) 1 (100%) Missing 37 0 1 12 1 Kruskal-Wallis rank sum test; Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates) *Greater than 10% missing Median (IQR) reported for all numerical variables Row-wise percentages reported 47 2.3.4 Analysis of concordance in features assigned pre- and post-operatively A summary of the changes observed for grade, binary grade, histotype and ProMisE assigned at different timepoints is reported in table 6. Table 6. Summary statistics for concordance in features assigned pre- and post-operatively. N % Agreement Kappa Grade Biopsy-Surgery 723 73.6 0.606 Surgery-Review 823 75.2 0.63 Binary Grade Biopsy-Surgery 723 87.7 0.733 Surgery-Review 823 100 1.00 Histotype Biopsy-Surgery 789 79.1 0.522 Surgery-Review 875 91.1 0.756 ProMisE Molecular Subtype Biopsy-Surgery 175 96.0 0.942 48 2.3.4.1 Grade Figure 3. Analysis of changes in grade (1 vs 2 vs 3 vs Suspected Pre-cancer) assigned at biopsy, surgery and review. (a) Sankey plot. (b) Confusion matrix for grade assigned at biopsy and surgery with row-wise and column-wise percentages reported. (c) Confusion matrix for grade assigned at surgery and review with row-wise and column-wise percentages reported. Biopsy Surgery Expert Review a. b. c. Biopsy Surgery Reviewed Grade 1 (216) Grade 1 (218) Grade 1 (313) Grade 2 (254) Grade 2 (225) Grade 2 (127) Grade 3 (196) Grade 3 (230) Grade 3 (233) Grade 3 Grade 2 Grade 1 Grade 1 Grade 2 Grade 3 Grade 1 Grade 2 Grade 3 Surgery Suspected Pre-cancer Grade 3 Grade 2 Grade 1 Suspected Pre-cancer (7) 49 The percentage agreement between biopsy and surgical assignment of grade is 73.6% and the kappa statistic is 0.61. The percentage agreement between surgical and reviewed assignment of grade is 75.2% and the kappa statistic is 0.63. Approximately 20% of cancers assigned grade 1 at biopsy, switch to grade 2 at surgery, and vice versa. However, 48% of patients assigned grade 2 at surgery switch to grade 1 after expert review. Grade 2 is upgraded to grade 3 in 17.7% of patients from biopsy to surgery, and 7.1% from surgery to review. Of the patients assigned grade 3 at biopsy, 90.1% stay grade 3 at surgery and of the patients assigned grade 3 at surgery, 93.5% stay grade 3 at review. Two thirds of the patients assigned suspected pre-cancer at biopsy changed to grade 1 and one third changed to grade 2 (figure 3). 50 2.3.4.2 Binary Grade Figure 4. Analysis of changes in grade (1/2 vs 3 vs Suspected Pre-cancer) assigned at biopsy, surgery and review. (a) Sankey plot. (b) Confusion matrix for grade assigned at biopsy and surgery with row-wise and column-wise percentages reported. (c) Confusion matrix for grade assigned at surgery and review with row-wise and column-wise percentages reported. Biopsy Surgery Reviewed Biopsy Surgery Surgery Expert Review a. b. c. Grade 3 (233) Grade 1/2 (483) Grade 1/2 (454) Grade 1/2 (454) Grade 3 (269) Grade 3 (269) Grade 1/2 Grade 3 Grade 1/2 Grade 3 Suspected Pre-cancer Grade 1/2 Grade 3 Grade 1/2 Grade 3 Suspected Pre-cancer (7) 51 The percentage agreement between biopsy and surgical assignment of grade (1/2 vs 3) is 88% and the kappa statistic is 0.73. 12% of cancers assigned grade 1/2 at biopsy were assigned grade 3 at surgery. 10% of the of cancers assigned grade 3 at biopsy were assigned grade 1/2 at surgery. The grade assignments of grade 1/2 and grade 3 did not change between surgery and review (figure 4). 52 2.3.4.3 Histotype Figure 5. Analysis of changes in histotype assigned at biopsy, surgery and review. (a) Sankey plot. (b) Confusion matrix for histotype assigned at biopsy and surgery with row-wise and column-wise percentages. (c) Confusion matrix for histotype assigned at surgery and review with row-wise and column-wise percentages reported. Biopsy Surgery Reviewed Surgery Surgery Reviewed b. a. Endometrioid (508) Endometrioid (569) Endometrioid (598) Non-Endo/Other (221) Non-Endo/Other (190) Non-Endo/Other (173) Suspected Pre-Cancer (42) Suspected Pre-Cancer (12) Endometrioid Non-Endo/Other Suspected Pre-Cancer Endometrioid Non-Endo/Other Biopsy Suspected Pre-Cancer Endometrioid Non-Endo/Other Suspected Pre-Cancer Non-Endo/Other Endometrioid c. 53 The percentage agreement between biopsy and surgical assignment of histotype is 79% and the kappa statistic is 0.52. The percentage agreement between surgical and reviewed assignment of histotype is 91% and the kappa statistic is 0.76. 91% and 97% of Endometrioid cancers go unchanged from biopsy to surgery and from surgery to review, respectively. 32% of cancers assigned non-endometrioid/other histotype changed to endometrioid from biopsy to surgery and 22% underwent this change from surgery to review (figure 5). 54 2.3.4.4 ProMisE Subtypes Figure 6. Analysis of changes in ProMisE molecular subtype assigned at biopsy, surgery and review. (a) Sankey plot. (b) Confusion matrix for subtypes assigned at biopsy and surgery with row-wise and column-wise percentages reported. Biopsy Surgery Biopsy Surgery a. b. POLEmut POLEmut NSMP/p53wt MMRd p53abn NSMP/ p53wt MMRd p53abn POLEmut (22) POLEmut (23) NSMP/p53wt (75) NSMP/p53wt (76) MMRd (52) MMRd (53) p53abn (26) p53abn (23) 55 The percentage agreement between biopsy and surgical assignment of ProMisE subtype is 96% and the kappa statistic is 0.941. Patients assigned POLEmut and p53wt at biopsy do not undergo any changes after surgery. Of the patients initially assigned MMRd, 2% change to POLEmut and another 2% change to p53wt. 88.5% of biopsy p53abn ECs remain p53abn at surgery, with 4% changing to MMRd and 8% changing to p53wt (figure 6). 2.4 Discussion: One of the biggest barriers to optimizing EC management is the lack of reliable prognostic factors available pre-operatively on which to base treatment decisions. ProMisE molecular classification has proven its ability to stratify ECs into one of four prognostically distinct subtypes at the time of diagnosis and can potentially be integrated with other prognostic factors to improve clinical decision making. In this chapter, I explored ProMisE molecular subtypes in the context of various pre-operative clinicopathologic parameters and two major treatment decisions – LN testing and adjuvant therapy. Similar to several previously published studies, we found that POLEmut patients tend to have lower BMI and age, the p53wt group has a higher median BMI and the p53abn patients are generally older (150,151). We report associations between ACE-27 co-morbidities and ProMisE subtypes for the first time. As co-morbidities have been shown to be associated with worse outcomes in EC, it is not surprising that p53abn patients have the highest prevalence of co-morbidities, while the POLEmut group has the least (87% vs 53% with at least one co-morbidity described in the ACE-27 index). Among the patients who have co-morbidities in both groups, the ASA physical status indicates a higher prevalence of severe disease in p53abn compared to POLEmut. There is a possibility that this association is confounded by age as p53abn patients may have a higher prevalence of co-morbidities like hypertension due to their older age, and POLEmut patients may have fewer co-morbidities due to their younger age. While prognostic hematological markers are not found to be significantly associated with ProMisE, we do notice a trend with Ca125 levels mirroring prognosis in ProMisE subtypes. As both, elevated Ca125 56 levels and p53abn subtype, are associated with higher risk of metastases, the highest median Ca125 level observed in the p53abn group is expected (102,103). Histotype and grade at diagnosis are both significantly associated with ProMisE, however, our analyses, and the existing literature, indicate that histopathology assigned at diagnosis has low reproducibility and is unreliable. In this cohort, grade (kappa = 0.61) demonstrated substantial agreement, and histotype (kappa = 0.52) demonstrated fair agreement among biopsy and surgical assignments. This is consistent with previous studies (60,61). Similar to the findings presented in Kommoss et al (2018), but with a larger cohort of patients, we show that ProMisE demonstrates nearly perfect agreement among biopsy and surgical assignments (kappa = 0.94) (34). The existing literature suggests using binary FIGO grades, where grades 1 and 2 are considered as one low-grade group, as the greatest discordance is observed among grade 1 and 2 ECs (152,153). Our analysis of concordance in binary FIGO grades shows better concordance than the 3-tiered grading system, which is consistent with the existing studies. However, we show that ProMisE maintains better agreement among biopsy and surgical assessments even when the binary grading system is used. We further highlight that ProMisE has better concordance even when compared to the substantial concordance of histopathology assigned at surgery and after expert review. Another study, exploring concordance among surgical and reviewed assignments of pathological features in EC, found only substantial agreement for both, LVI and histopathology (154). It is noted that in general, uterine pathologies have the highest rate of inconsistent assessments which can influence treatment decision-making (155,156) . While it is well documented that ProMisE is more reliably assigned at biopsy and might be useful in pre-operative management, these findings highlight the need for more reproducible parameters, like ProMisE, pre- and post-operatively. Although we hope to improve EC management using molecular classification, we must still take into account the unique contribution of other prognostic factors in treatment decision making. Our analysis of associations among LN testing and pre-operative parameters indicates strong associations with obesity 57 and co-morbidities. Patients not receiving nodal assessment have a higher median BMI and greater prevalence of co-morbidities, which can be explained by guidelines indicating greater risk of surgical and post-surgical complications, more difficult operations, and higher prevalence of co-morbidities in obese patients (157,158). Furthermore, we observe that NSMP/p53wt patients are less likely to receive nodal testing and this may also be related to the greater prevalence of obesity within this subtype. We also found extra-uterine disease observed on imaging to be significantly associated with nodal testing and Eriksson et al conducted a multivariable analysis which found that combination of ProMisE with sonographic findings enables better prediction of clinical outcomes pre-operatively (64). While our analysis indicates similar rates of nodal testing across POLEmut (77%), MMRd (75%), and p53abn (73%), a study by Jamieson et al indicates higher rates of testing in the p53abn group relative to all other subtypes (135). In contrast with other studies, which report LN testing most commonly in grade 3 ECs (135,138), we observe 84% and 77% of patients assigned grade 2 and 3 at diagnosis undergoing nodal testing, respectively. The difference in rates of nodal testing among grade 1 (50%) and grade 2 (84%) patients is interesting as we also revealed that 20% of grade 1 ECs at diagnosis are found to be grade 2 on hysterectomy specimens, and vice versa. Unlike LN testing, age, BMI, and co-morbidities are not significantly associated with adjuvant therapy decisions. Interestingly, high Ca125 levels, which suggest spread of disease, are significantly associated with chemotherapy in adjuvant treatment but are also associated with the absence of nodal testing. The literature suggests that high Ca125 levels can serve as an indication for full surgical staging (103,159). However, it is possible that the high Ca125 levels are leading to recommendations for chemotherapy regardless of nodal status, which negates the need for any nodal assessment. We observe 25% of POLEmut patients receive chemotherapy, and when compared to the 16% reported by Jamieson et al, this indicates potentially greater prevalence of over-treatment in our cohort. Similar to 58 other studies, which report 30-36% of the p53abn subgroup not receiving any adjuvant therapy (135,150), we report no adjuvant therapy in 34% of the p53abn patients. Our investigation of the treatments received in each of the 2020 ESGO-ESTRO-ESP risk groups (24) with and without molecular classification, reveals that each treatment option is received by patients within every risk group from low to high, again indicating over- and under-treatment of many patients and highlighting vast variations in treatment practices. As we compare the distribution of nodal testing and adjuvant therapies across various clinicopathological parameters in pre-existing studies and our own study, we observe variations in treatment, along with significant proportions of patients being potentially under- or over-treated. This is concerning as studies report worse outcomes in patients receiving guideline discordant treatment and this wide variation in practices may also be related to racial disparities (136,160,161). The strengths of this study include the large sample size (N=903) with patients from a local, Vancouver cohort and an international, German cohort. We conducted comparable analyses of changes across several relevant groups: in pre- and post- operative ProMisE, and grade, binary grade and histotype at biopsy, surgery and after review. We studied ProMisE in the context of features such as ACE-27 index co-morbidities for the first time. However, one weakness of our study is that we are missing the ACE-27 index scores which contain information regarding severity of co-morbidities. Other weaknesses of this study include the retrospective study design and lack of data on a key prognostic factor, race/ethnicity. We also lack data on patient decisions for treatments, which could explain some of the discordance among guideline-recommended treatment and received treatment as existing literature highlights the variations in patient and clinician preferences for EC management (162,163). Finally, we acknowledge that the two cohorts we have combined (German and Vancouver) differ in several key variables (table A.1, Appendix A). 59 Prevalence of other, clinically relevant, prognostic factors within each ProMisE subgroup are defined in this chapter. Characterizing the distribution of features such as age and co-morbidities within each subtype will allow us to make even more precise judgements of which treatment is most suitable for each distinct group of patients. For example, knowing that p53abn patients tend to have poor prognosis may compel physicians to recommend more intense therapies, but adding the knowledge that these patients are also more likely to have severe co-morbidities may invoke the need to reassess if the intense therapies are suitable. More work is required to assess the influence of other prognostic factors on ProMisE subtypes, and vice versa, before all the parameters can be optimally integrated into decision-making. We also confirm that ProMisE is reliably assigned on both, biopsy and hysterectomy specimens, while other pathological parameters like grade and histotype have inconsistent assignments even on hysterectomy specimens. Overall, we see that ProMisE can provide timely, reproducible and objective prognostic information which has the potential to improve EC management, so future work should focus on how to best integrate the prognostic information in ProMisE for pre- and post-operative decision making. 60 Chapter 3: Potential Impacts of ProMisE Molecular Subtypes on Decision-Making 3.1 Introduction There is wide variation in EC management globally, nationally and regionally, which exacerbates disparities. ProMisE molecular classification advances EC research by providing accurate and timely prognostic information that has potential for use in tailoring treatment decisions and to minimize deviations in care. The 2020 ESGO-ESTRO-ESP guidelines (24) have proposed how to incorporate ProMisE into risk stratification and provided treatment recommendations based on risk groups. This model still has some drawbacks: (1) the risk groups change only for a small number of patients due to low level of evidence available at the moment, (2) the guidelines provide several options within each risk group, leaving room for interpretability and potential variation in treatment practices and (3) the integration of ProMisE is only occurring post-operatively in this model, which means surgical decisions such as whether to test nodes, are still being based on the unreliable histopathology assigned at biopsy. Jamieson et al (2022) have highlighted the issue of variation in EC management with their investigation of the variation in treatment practices across Canadian centres (135). They observed all the wide-ranging adjuvant therapy options given within the low, intermediate, and high- risk categories, indicating clear deviations from the guidelines. Whether or not nodes are tested during the surgery has strong implications for post-operative risk stratification. Based on the 2020 ESGO-ESTRO-ESP guidelines (24), low risk patients are recommended no adjuvant therapy, and intermediate risk patients may be recommended to vaginal brachytherapy. These low and intermediate risk patients may be upstaged if nodal assessment is conducted and the results are positive, which would make them high risk patients and may drastically change the adjuvant therapy recommendations to include chemotherapy. For example, within the high-intermediate risk group, EBRT is recommended when LN staging is not done. This recommendation can change to vaginal brachytherapy if the nodal status is negative or to EBRT with chemotherapy if the nodal status is positive. 61 While nodal status can strongly influence a patient’s risk group, and subsequently, their recommended adjuvant therapy, nodal testing also requires longer, more resource-intensive operations performed by specialists, and are associated with risks such as lymphedema, and higher post-surgical morbidity (43). Thus, it is crucial to define the subsets of patients for whom LN assessment will be significantly beneficial. Some hypotheses have been developed for how to best use ProMisE to guide LN assessment decisions, however they are largely untested. For example, POLEmut ECs may not need nodal testing as metastatic disease is rare in these patients and they have excellent outcomes irrespective of adjuvant therapy (63,164). Castillo et al (2022) has also reported that patients with the p53abn subtype have poor prognosis even when they are confirmed to be stage I by lymphadenectomy (164). This suggests that undetected LN metastases are not significant contributors to the adverse survival outcomes in early stage p53abn cases. Survival outcomes in the MMRd and NSMP subgroups, on the other hand, are likely to be more influenced by LN testing and adjuvant therapy decisions as they comprise a greater proportion of the intermediate/ high-intermediate risk group, within which recommendations can vary greatly depending on nodal status. Further studies are still required to elucidate the impact of including ProMisE molecular classification in the 2020 ESGO-ESTRO-ESP risk stratification model and determine how to best map ProMisE subtypes to treatment decisions both pre- and post-operatively. It has been suggested that along with providing valuable prognostic information, lymphadenectomies may also have a therapeutic benefit as they may be facilitating the debulking of nodal metastases (165,166), but this is a topic of great debate. While 2 randomised controlled trials have reported no significant benefit of lymphadenectomy on survival, their study design and population have been criticized (43,45). These trials have several limitations, including that they evaluate only pelvic lymphadenectomies, and their study populations contain a large proportion of patients at low risk for nodal metastases or recurrence. In contrast, several retrospective studies have demonstrated a positive therapeutic influence of 62 pelvic and para-aortic lymphadenectomy on survival in EC patients (46,167,168). The survival benefits of lymphadenectomy have yet to be studied in the context of ProMisE molecular classification. In this chapter, we first evaluate the potential impact of prescriptive treatments based on the ESGO-ESTRO-ESP (2020) guidelines with and without molecular classification. We then explore for which subgroup of patients is LN assessment a significant contributor to adjuvant therapy decisions and who can forego LN assessment without impacting adjuvant therapy decisions. We also conduct multivariate analyses to determine whether LN testing is significantly associated with OS outcomes in subsets of each of the ProMisE subtypes, adjusting for all other pathologic parameters, including adjuvant therapy. The subsets for analysis were selected based on their sensitivity to changes in treatment recommendations based on nodal status. 3.2 Methods: 3.2.1 Treatment Policies Consultations with a gynecologic oncologist resulted in two treatment policies, one for risk groups assigned using molecular classification (Guidelines with ProMisE), and another for risk groups assigned without molecular classification (Guidelines w/o ProMisE). The various treatment options available for each risk group in the ESGO-ESTRO-ESP (2020) guidelines (24) were narrowed down to one treatment recommendation for each group to generate a set of modified policies based on clinical expertise. 3.2.2 Statistical Analyses Confusion Matrices: We generated confusion matrices to explore the difference in the actual treatment given in our cohort and the treatment recommended by the two sets of guidelines, ‘Guidelines with ProMisE’ and ‘Guidelines w/o ProMisE. The row-wise and column-wise percentages are reported. Effect of Nodal Assessment on Survival: We fit Cox proportional hazards models for OS, any event, as a function of nodal assessment (not tested vs tested) in various subsets of the data. These subsets include 63 POLEmut subtype, p53abn subtype, Endometrioid EC with NSMP/ MMRd subtype, and NSMP/ MMRd subtype with any histotype. The Firth's penalized maximum likelihood bias reduction method was applied where over 80% of cases are censored. We adjusted for surgical grade (1/2 vs 3), histotype (endometrioid vs non-endometrioid), stage (I vs II-IV), LVI (negative vs positive), myometrial invasion (none vs 1-50% vs >50%), adjuvant therapy (none vs VB only vs EBRT +/- VB vs any chemotherapy), age at diagnosis and BMI. 3.3 Results 3.3.1 Treatment Policies Consultations with a team of gynecologic oncologists led to a set of treatment recommendations for the two ESGO-ESTRO-ESP risk stratification models (with and without molecular classification) that recommend only one treatment for each distinct group of patients, based on clinical expertise. The recommended treatment option is the one believed to be most effective and/or most commonly used in practice by our team. Table 7 summarizes the recommended treatments within each risk group where molecular classification is known and unknown. Table 7. Treatment guidelines for ESGO-ESTRO-ESP (2020) risk groups based on clinical expertise. Risk Without Molecular Classification With Molecular Classification Recommended Treatment LOW Stage IA endometrioid, low-grade, LVI negative or focal Stage I–II POLEmut, no residual disease Stage IA MMRd/NSMP, low-grade, LVI negative or focal None 64 Risk Without Molecular Classification With Molecular Classification Recommended Treatment INTERMEDIATE Stage IB endometrioid + low-grade + LVI negative or focal Stage IA endometrioid + high-grade + LVI negative or focal Stage IA non-endometrioid (serous, clear cell, undifferentiated carcinoma, carcinosarcoma, mixed) without myometrial invasion Stage IB MMRd/NSMP endometrioid + low-grade + LVI negative or focal Stage IA MMRd/NSMP endometrioid + high-grade + LVI negative or focal Stage IA p53abn and/or non-endometrioid without myometrial invasion Vaginal Brachytherapy Only HIGH- INTERMEDIATE Stage I endometrioid + substantial LVI, regardless of grade and depth of invasion Stage IB low-grade endometrioid with substantial LVI Stage IB endometrioid + high-grade regardless of LVI status Stage II Stage I MMRd/NSMP endometrioid + substantial LVI regardless of grade and depth of invasion Stage IB MMRd/NSMP endometrioid + high-grade regardless of LVI status Stage II MMRd/NSMP endometrioid EBRT +/- Vagina Brachytherapy Unless: Stage IB, MMRd or NSMP, Endometrioid, High-grade, LVI negative, and nodes negative Vaginal Brachytherapy Only HIGH Stage III–IVA with no residual disease Stage I–IVA non-endometrioid with myometrial invasion, and no residual disease Stage III–IVA MMRd/NSMP endometrioid carcinoma with no residual disease Stage I–IVA p53abn endometrial + myometrial invasion, with no residual disease Stage I–IVA NSMP/MMRd serous, undifferentiated carcinoma, carcinosarcoma with myometrial invasion, with no residual disease Chemotherapy + EBRT ADVANCED Stage III–IVA with residual disease Stage IVB Stage III–IVA with residual disease of any molecular type Stage IVB of any molecular type Surgical Debulking + Chemotherapy 65 We explored the concordance among the treatment received in the combined cohort and the treatment recommended in the two guideline versions detailed in table 7 (figure 7). We included all patients with both a treatment received, and a treatment recommended, which resulted in a sample size of 757 for the analysis using guidelines without ProMisE and 741 for the analysis using guidelines with ProMisE. The percentage of patients receiving their recommended treatment ranges from 25% to 75% across the four treatment options. The best agreement between received and recommended treatment is observed in the patients receiving chemotherapy, and the worst is in patients receiving VB only. Among the patients receiving chemotherapy in our cohort, a greater percentage are recommended to no adjuvant therapy in the guidelines that use ProMisE (14%) compared to the guidelines without ProMisE (5%). Both sets of guidelines recommend more intense therapy to the patients receiving EBRT +/- VB, with approximately 50% of these patients being recommended to chemotherapy. The guidelines without ProMisE recommend a greater proportion of patients receiving VB only to EBRT +/- VB (19%) compared to the guidelines without ProMisE (15%). Overall, approximately 10% of patients are recommended to a different adjuvant treatment by the guidelines using ProMisE than the guidelines without ProMisE. Figure 7. Confusion matrix of treatment received, and treatment recommended by guidelines with and without molecular classification. Row-wise and column-wise percentages reported. Treatment Received Recommended by Guidelines With ProMisE Recommended by Guidelines Without ProMisE None VB Only EBRT +/-VB Any Chemo None VB Only EBRT +/-VB Any Chemo None VB Only EBRT +/-VB Any Chemo None VB Only EBRT +/-VB Any Chemo 66 3.3.2 Univariate Analysis of Nodal Status and Pre-operative Prognostic Factors Table 8 shows the associations among LN metastases and ProMisE, grade, histotype, and extra-uterine disease observed at diagnosis. LN metastases is reported in 86% of patients who underwent testing. Table 8. Associations among nodal status and pre-operative prognostic factors Nodal Status Characteristic N Negative, N = 5331 Not Tested, N = 2841 Positive, N = 851 p-value2 Histotype at Diagnosis 793 <0.001 Suspected Pre-Cancer 18 (3.8%) 22 (9.3%) 2 (2.6%) Endometrioid 323 (67%) 169 (71%) 37 (48%) Non-Endometrioid/ Other 138 (29%) 46 (19%) 38 (49%) Missing 54 47 8 Grade at Diagnosis 741 <0.001 Suspected Pre-Cancer 4 (0.9%) 3 (1.4%) 0 (0%) Grade 1 106 (23%) 115 (54%) 9 (12%) Grade 2 208 (46%) 42 (20%) 18 (25%) Grade 3 138 (30%) 53 (25%) 45 (62%) Missing 77 71 13 Promise Subtype at Diagnosis 902 <0.001 POLEmut 64 (12%) 19 (6.7%) 1 (1.2%) NSMP/p53wt 242 (45%) 166 (58%) 25 (29%) MMRd 147 (28%) 57 (20%) 26 (31%) p53abn 80 (15%) 42 (15%) 33 (39%) Extra-uterine Disease 870 43 (8.2%) 52 (20%) 23 (28%) <0.001 Missing 7 23 2 1 n (%) 2 Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates) Column-wise percentages reported 67 3.3.3 Nodal Assessment in POLEmut Experts suggest de-escalation of POLEmut patients, especially for stage I/II ECs. Nodal testing can upstage patients with POLEmut ECs resulting in recommendation of more intense treatments. In the combined cohort, 65/84 (77%) of the patients with POLEmut subtype tested for nodes and only one (1.5%) tested positive. This was a 73-year-old patient with a grade 3, non-endometrioid tumour with LVI and >50% myometrial invasion. They received chemotherapy but experienced a PFS event after 0.25 years and died due to their disease in less than 1 year. All 19 POLEmut ECs that were not tested are stage IA/IB. There was 1 recurrence and 0 DSS events in this group. There are 3 POLEmut ECs with stage III-IV. Two of these patients experienced a recurrence after approximately 1 year. One was 73 years old, had nodes positive and received chemotherapy, the other was 66 years old had nodes negative and received EBRT +/- VB. Both patients had a grade 3 EC, with LVI and >50% myometrial invasion. We conducted a survival analysis on the POLEmut subgroup (N = 70, 14 OS events). The Cox proportional hazards model indicates that nodal testing is not significantly associated with better prognosis in POLEmut patients when grade, histotype, LVI, myometrial invasion, stage, adjuvant therapy, age and BMI are adjusted for (table 9). 68 Table 9. Cox proportional hazards model for OS in patients with the POLEmut subtype. # of events / n Hazard Ratio (95% CI) LRT P-value OS 14 / 70 Nodal Testing (Reference group: Not Tested) Tested 0.76 (0.21-3.28)F 0.9102 Grade (Reference group: grade 1/2) Grade 3 0.3 (0.05-1.43)F 0.0902 Histotype (Reference group: Endometrioid) Non- Endometrioid / Other 0.12 (0-1.39)F 0.0316 LVI (Reference group: Negative) Positive 0.93 (0.19-3.85)F 0.8703 Myometrial Invasion (Reference group: None) 1-50% 1.43 (0.32-8.31)F 0.3542 >50% 3.95 (0.51-34.44)F Stage (Reference group: I) II-IV 0.82 (0-18.1)F 0.3640 Treatment (Reference group: None) VB Only 0.78 (0.11-4.21)F 0.4404 EBRT +/- VB 10.15 (0.66-129.05)F Any Chemotherapy 1.78 (0.24-12.67)F Body Mass Index 1.03 (0.93-1.13)F 0.5420 Age at Diagnosis 1.13 (1.05-1.22)F 0.0002 F Firth's penalized maximum likelihood bias reduction method applied. Confidence Interval (CI); External Beam Radiation Therapy (EBRT); Likelihood Ratio Test (LRT); Lympho-vascular Space Invasion (LVI); Vaginal Brachytherapy (VB) 3.3.4 Nodal Assessment in p53abn Positive nodes are found to be more common in p53abn, as the p53abn subgroup makes up only 18% of all tested ECs, but contributes to 39% of the ECs with positive nodal status. The treatment recommendation for all p53abn ECs includes chemotherapy, unless stage IA without myometrial invasion. Nodal assessment can upgrade stage 1A ECs to stage III-IV, which will impact the treatment recommendation within this subset. In the entire cohort, there are only 3 patients with positive nodes and no myometrial invasion, and all 3 are p53abn. These comprise 9% (3/33) of the p53abn subgroup with 69 positive nodes. 42 (15%) p53abn patients were not tested. Of these, 2 patients were p53abn, and stage IA, without myometrial invasion. The age of these patients was 78.5 and 76.5 years. Neither patient experienced any PFS or DSS events but both patients died with overall survival 4.2 and 2.8 years. Neither of the patients received any adjuvant therapy. We conducted a survival analysis on the p53abn subgroup (N = 125, 83 OS events). The Cox proportional hazards model indicates that nodal testing is significantly associated with better prognosis in p53abn patients when grade, histotype, LVI, myometrial invasion, stage, adjuvant therapy, age and BMI are adjusted for (table 10). Confidence Interval (CI); External Beam Radiation Therapy (EBRT); Likelihood Ratio Test (LRT); Lympho-vascular Space Invasion (LVI); Vaginal Brachytherapy (VB) Table 10. Cox proportional hazards model for OS in patients with the p53abn subtype # of events / n Hazard Ratio (95% CI) LRT P-value OS 83 / 125 Nodes Testing (Reference group: Not Tested) Tested 0.57 (0.34-0.94) 0.0327 Grade (Reference group: Grade 1/2) Grade 3 0.97 (0.37-2.57) 0.9565 Histotype (Reference group: Endometrioid) Non-Endometrioid / Other 1.54 (0.8-2.95) 0.1863 LVI (Reference group: Negative) Positive 1.61 (0.89-2.91) 0.1130 Myometrial Invasion (Reference group: None) 1-50% 1.16 (0.54-2.5) 0.5994 >50% 1.45 (0.65-3.23) Stage (Reference group: I) II-IV 1.6 (0.88-2.92) 0.1154 Treatment (Reference group: None) VB Only 0.77 (0.17-3.39) 0.8785 EBRT +/- VB 0.72 (0.3-1.72) Any Chemotherapy 0.82 (0.43-1.57) Body Mass Index 0.97 (0.94-1) 0.0565 Age at Diagnosis 1.01 (0.98-1.03) 0.6704 70 3.3.4 Nodal Assessment in MMRd and NSMP/p53wt In this cohort, 66% (440/663) of the MMRd/ NSMP subgroup underwent nodal testing. 12% (51/440) of these ECs were positive for nodal involvement. Among patients with the MMRd or NSMP subtypes, histotype, nodal status and stage play key roles in determining adjuvant therapy recommendations. While nodal status mainly influences treatment decision making in endometrioid cancers, over 30% of ECs assigned non-endometrioid histotype at biopsy changed to endometrioid histotype after surgery in this cohort. Thus, it may be worthwhile to recommend nodal testing for all patients in the MMRd/ NSMP subgroups. Within MMRd/ NSMP endometrioid cancers, treatment recommendations can differ greatly based on stage, nodal testing and other parameters. For example, if an MMRd/ NSMP patient has endometrioid histotype, high grade, negative LVI, and stage IB, they will receive brachytherapy if their nodes are negative, but EBRT if their nodal status is unknown. There are 11 MMRd/NSMP patients with endometrioid histotype, high grade, negative LVI, and stage IB in this cohort. 8 of these patients underwent nodal assessment and are negative for nodes. 3 patients were not tested. There is less variation in treatment recommendations for non-endometrioid MMRd/ NSMP cancers, all of which are recommended chemotherapy, unless stage IA without myometrial invasion, which are recommended brachytherapy. If these stage IA cancers test positive for nodes, they are upstaged to stage III. There are 14 MMRd/NSMP patients in this cohort with non-endometrioid histotype and no myometrial invasion that underwent testing. All 14 tested negative for nodal metastasis. 24 patients are in the MMRd/ NSMP subgroup, not tested and non-endometrioid. Of these, 2 patients with the NSMP/ p53wt subtype have no myometrial invasion. Both patients have a stage IA EC without lympho-vascular invasion. 1 of these patients, aged 72, had a recurrence and an overall survival event. The other, aged 77, had only an overall survival event. 71 412 patients with MMRd or NSMP/p53wt subtype and Endometrioid histotype are selected for survival analysis. Among these patients, 137 OS events are observed. The Cox proportional hazards model indicates that nodal testing is not significantly associated with better prognosis in Endometrioid ECs with MMRd or NSMP/p53wt subtypes when grade, LVI, myometrial invasion, stage, adjuvant therapy, age and BMI are adjusted for (hazard ratio (HR) 0.66, 95% CI 0.44-1; p value = 0.0527; table 11a). The analysis was also done for MMRd / NSMP patients of all histotypes (N = 482, 172 OS events) and nodal testing was not significantly associated with OS here as well (table 11b). Table 11: Cox proportional hazards model for OS in patients with NSMP/p53wt or MMRd subtypes a) Endometrioid histotype # of events / n Hazard Ratio (95% CI) LRT P-value OS 137 / 412 Nodes Testing (Reference group: Not tested) Tested 0.66 (0.44-1)F 0.0527 Grade (Reference group: Grade 1/2) Grade 3 0.93 (0.59-1.45)F 0.7341 LVI (Reference group: Negative) Positive 1.29 (0.84-1.97)F 0.2470 Myometrial Invasion (Reference group: None) 1-50% 1.47 (0.83-2.74)F 0.0009 >50% 2.93 (1.54-5.79)F Stage (Reference group: I) II-IV 1.23 (0.75-2.02)F 0.4077 Treatment (Reference group: None) VB Only 0.55 (0.32-0.91)F 0.0170 EBRT +/- VB 0.96 (0.47-1.88)F Any Chemotherapy 1.55 (0.89-2.66)F Body Mass Index 1 (0.99-1.02)F 0.6687 Age at Diagnosis 1.03 (1.01-1.04)F 0.0036 F Firth's penalized maximum likelihood bias reduction method applied. Confidence Interval (CI); External Beam Radiation Therapy (EBRT); Likelihood Ratio Test (LRT); Lympho-vascular Space Invasion (LVI); Vaginal Brachytherapy (VB) 72 b) All histotypes # of events / n Hazard Ratio (95% CI) LRT P-value OS 172 / 482 Nodes Testing (Reference group: Not tested) Tested 0.72 (0.5-1.04)F 0.0800 Grade (Reference group: Grade 1/2) Grade 3 1.16 (0.79-1.7)F 0.4557 Histotype (Reference group: Endometrioid) Non-Endometrioid / Other 1.5 (0.98-2.24)F 0.0726 LVSI (Reference group: Negative) Positive 1.19 (0.81-1.75)F 0.3796 Myometrial Invasion (Reference group: None) 1-50% 1.32 (0.8-2.26)F 0.0058 >50% 2.2 (1.27-3.93)F Stage (Reference group: I) II-IV 1.68 (1.1-2.56)F 0.0154 Treatment (Reference group: None) VB only 0.57 (0.36-0.9)F 0.0926 EBRT +/- VB 0.78 (0.42-1.37)F Any Chemotherapy 0.97 (0.6-1.56)F Body Mass Index 1 (0.99-1.02)F 0.6393 Age at Diagnosis 1.03 (1.02-1.05)F < 0.0001 F Firth's penalized maximum likelihood bias reduction method applied. Confidence Interval (CI); External Beam Radiation Therapy (EBRT); Likelihood Ratio Test (LRT); Lympho-vascular Space Invasion (LVI); Vaginal Brachytherapy (VB) 3.4 Discussion EC management is burdened with several issues that can lead to sub-optimal treatment and poor survival outcomes. These issues include the poor reproducibility of histopathology assigned at biopsy, which limits the ability to make informed pre-operative decisions (60,154), and the multiple options recommended in EC guidelines for each risk group, which leaves room for interpretability by clinicians. In practice, this can result in inconsistent patterns of LN staging and adjuvant therapies in different 73 cohorts of patients, which may vary in their geographic location and/or distribution of baseline characteristics such as age, race, co-morbidities and histopathology (135,136,160). In this chapter, I conducted an exploratory analysis to understand the differences among treatments given in clinical practice and treatments recommended based on guidelines with and without ProMisE, define the subsets of patients for whom ProMisE and nodal status can influence treatment decisions, and investigate the impact of nodal assessment on survival within ProMisE subgroups. The treatment recommendations presented in this chapter are based on clinical expertise and they assign treatment believed to be most often received and/or recommended in current clinical practice for each distinct group of patients. We observe substantial differences in the received and recommended adjuvant treatments, particularly for patients who received VB only. We also note that using the guidelines with ProMisE results in a greater proportion of patients who originally received chemotherapy to be instead recommended no adjuvant therapy. We identified 3 specific groups for whom ProMisE molecular classification and nodal status may substantially influence treatment decisions: p53abn stage IA without myometrial invasion, MMRd/ NSMP endometrioid and POLEmut. We found that of the 65 POLEmut patients who underwent nodal testing in our cohort, only 1 tested positive for nodal metastases. This patient had a recurrence and died from the disease in less than one year, despite being treated with combined chemotherapy and RT. In general, this data aligns well with the existing literature which suggests that nodal metastases are rare in POLEmut ECs and the excellent outcomes of POLEmut patients are independent of adjuvant therapy (35,63,164,169,170). With the literature pointing towards de-escalation of POLEmut adjuvant therapies (171–173), and our survival analysis indicating no significant benefit of a nodal assessment, the reasons for conducting a nodal assessment in POLEmut seem to be diminishing. Unfortunately, the low sample size of POLEmut patients within most studies, including this one, does mean that the results should be taken with caution. 74 As the molecular analysis of the PORTEC-3 trial data indicates a significant PFS benefit under chemotherapy for p53abn patients, and it is well documented that the p53abn subtype is associated with poor prognosis, the treatment recommendations include chemotherapy for almost all p53abn patients (83). In our cohort, 18% of the high-risk patients with p53abn subtypes received no adjuvant therapy. This suggests that up to 1/5 of high-risk p53abn patients could be potentially undertreated when molecular classification is unknown. It is important to note here that there are other factors that may influence a patient’s decision to undergo chemotherapy, an intensive treatment with many side effects, so inclusion of molecular classification in treatment decision-making would likely not mitigate under-treatment in all these patients. We report a disproportionately higher rate of nodal metastasis in p53abn cancers which may explain their poor prognosis. As chemotherapy is recommended in the vast majority of p53abn cancers, the benefit of LN assessment in this subgroup is brought into question. Our results indicate improved OS in patients receiving LN assessment within the p53abn subgroup. The model used for this analysis adjusted for adjuvant therapy, thus the analysis was mainly assessing the therapeutic effects of the LN assessment procedure rather than the adjuvant-therapy-mediated influence of nodal assessment on outcomes. The only patients within the p53abn subgroup that would have adjuvant therapy treatment recommendations modified based on nodal status are those with p53abn stage IA ECs with no myometrial invasion. Our small sample of p53abn stage IA ECs (n= 47, number of events= 23) made it difficult to power an analysis of these patients. The wide variation of potential treatment recommendations for these patients depending on the nodal status (VB only to chemotherapy + EBRT) makes this a noteworthy issue. Within the MMRd and NSMP/p53wt subgroups, the implications of nodal testing on adjuvant therapy decisions are most influential in endometrioid cancers, where adjuvant therapy recommendations can change drastically based on nodal status. Although the 2020 ESGO-ESTRO-ESP treatment guidelines (24) refer to the histotype assigned at surgery, the decision for whether or not to test nodes in these 75 patients is made pre-operatively. The assignment of histotype at diagnosis has been shown to be unreliable and this limits its utility in pre-operative decision-making. With the strong influence of nodal status on adjuvant therapy options in this group and the low accuracy of the histotype available pre-operatively, it may be worthwhile to consider nodal assessment in all MMRd and NSMP ECs. Some other factors to consider before recommending nodal assessment in all MMRd and NSMP patients is the higher prevalence of obesity in the NSMP group, which is a known contraindication for nodal testing (157,158). The association of LN assessment with overall survival is not statistically significant in our analysis of endometrioid MMRd/ NSMP ECs. The literature suggests that the NSMP subgroup is a large heterogenous group of ECs, requiring further refinement to evaluate the effect of treatments and predict prognosis more accurately (174). LVI status, L1CAM, and CTNNB1 have been identified as potential biomarkers for further stratification of the NSMP/p53wt subgroup (175–177). Further studies are required to define a more specific subgroup of the intermediate-risk NSMP ECs that need nodal assessment. The strengths of this study include a large sample size and multivariate analyses with key factors such as histopathology, age, BMI, stage and adjuvant therapy accounted for. The exploratory analyses in this study were informed by clinical expertise to ensure clinical relevance and logic is maintained. The limitations of this study include the lack of data on patient wishes in treatment decision-making, missing details on the LN assessments performed, and our inability to adjust for unmeasured confounders. Our survival analyses did assess intermediate (NSMP/MMRd subtype) and high (p53abn subtype) risk patients, but we did not take into account the type or the extent of the nodal assessment. The survival effect of para-aortic lymphadenectomy in endometrial cancer (SEPAL) study compared the effects of pelvic and para-aortic lymphadenectomy with pelvic lymphadenectomy alone. They found that the addition of a para-aortic lymphadenectomy is significantly associated with better survival outcomes in intermediate and high-risk patients (178). A large retrospective cohort study found that more extensive 76 lymphadenectomies, with greater numbers of nodes resected, are also associated with better survival outcomes in patients with intermediate and high risk of recurrence (46). Seagle et al (2017) measured the association of lymphadenectomy with survival in stage I endometrioid ECs and they conducted matched cohort analyses adjusting for covariates and propensity scores (168). They adjusted for several covariates that are unmeasured confounders in our study, such as ethnicity and socioeconomic parameters. They also adjust for the type of cancer center that patients visit. In our study, all patients are treated by gynecologic oncologists at specialist centers which may make our cohort less representative of the EC patient population as a whole. This was an exploratory analysis of the effect of LN assessments on EC management and prognosis, within various subgroups of patients, mainly divided by molecular subtypes. This analysis did not consider the causal relationships among the various treatments, covariates, and outcomes. Without appropriately adjusting for the time-varying treatment confounding and mediating parameters, we cannot estimate the survival outcomes under different treatment options accurately. As such, the results presented in this chapter should be interpreted with caution. Overall, this study did elucidate which subgroups of patients may be most affected by the inclusion of ProMisE molecular subtypes in the management guidelines and suggests the need for a causal framework to better define how ProMisE molecular subtypes can be used to determine need for nodal assessment. 77 Chapter 4: Causal machine learning to estimate an optimal dynamic treatment regime for pre- and post-surgical treatment decisions 4.1 Introduction Background Dynamic treatment regimes (DTRs) can be identified using a counterfactual framework and allow us to optimize treatment decisions for sequential treatment stages. A key feature of DTRs is that they can account for time-varying covariates and an evolving patient history. They take into account all the information available at the time of each decision and this is crucial in EC treatment decision-making where our state of knowledge on the disease is changing as the patient goes through each stage of treatment. We are particularly interested in optimizing the sequence of 2 decisions in EC: whether or not to conduct LN assessment during surgery (stage 1) and which, if any, adjuvant therapy is needed (stage 2). The patient history is evolving as histopathological parameters are assigned at diagnosis and then re-assigned during or after surgery, surgical stage, LVI, and myometrial invasion are all determined after surgery in stage 1, and nodal status is known after surgery, but only if LN assessment is done in stage 1. Previously, optimizing surgical and post-surgical decisions was difficult due to the lack of reproducible and objective parameters available at the time of diagnosis. The inability to accurately assess risk pre-operatively led to inconsistent decisions at stage 1, which influenced the availability of a key prognostic factor, nodal status, when making adjuvant therapy decisions in stage 2. These decisions require evidence-based guidelines as both over- and under-treatment has serious consequences. Under-treatment in EC can lead to recurrent disease and while survival outcomes are typically favourable in EC, survival rates are extremely poor in recurrent EC. On the other hand, we wish to prevent over-treatment as nodal assessment and adjuvant therapies are associated with side effects and toxicities that lead to high morbidity. With the novel molecular-based classification system for ECs, ProMisE molecular classification (29,34), we have a 78 strong prognostic factor that can be reliably used to classify ECs into one of four molecular subgroups, at the time of diagnosis. The aim of this chapter is to use ProMisE, along with the all the other covariates collected at the time of each decision to personalize treatment in EC. We use a large observational dataset to identify a 2-stage DTR that optimizes time-to-event outcomes, disease-specific survival and progression-free survival. Approach The estimation of an optimal DTR from observational data is a novel approach implemented in the research of chronic diseases, such as HIV/AIDS, rheumatoid arthritis, diabetes and cancer. Many studies have implemented methods for identifying an optimal DTR with time-to-event outcomes in observational data (179–188). A key challenge noted when optimizing time-to-event outcomes is the occurrence of right-censoring. Methods such as propensity score models and inverse probability censoring weighting have been used to account for censoring in previous studies (189). Commonly used approaches for identifying optimal DTRs include Q-learning and G-estimation. Q-learning is simple to implement but is susceptible to model misspecification and assumes covariate-independent censoring (i.e. no relationship between censoring times and patient characteristics or outcomes) (190). On the other hand, G-estimation is doubly robust but has a complex theoretical framework and high computational burden (191). Another approach, using accelerated failure time (AFT) models, accounts for covariate-dependent censoring but is singly robust and fits models using only the data from patients who experience an adverse event (192). A novel approach published by Simoneau et al (2020), uses a dynamic weighted ordinary least squares (DWOLS) method (193) with modifications for use with time-to-event-outcomes in an observational study (194). This approach integrates the strong features of g-estimation, Q-learning and AFT models to generate an algorithm that allows for double-robustness, covariate-dependant censoring, and low computational complexity. The adaptation of DWOLS for time-to-event data uses balancing weights that 79 incorporate inverse probability censoring weighting. The following assumptions must be met when using this method to estimate an optimal DTR from observational data with time-to-event outcomes: 1. Stable unit treatment value assumption (SUTVA): each patient’s outcome is independent of other patients’ treatment 2. Sequential ignorability: no unmeasured confounders; the treatment is assigned independently of potential responses to treatment, conditional on the covariates 3. Coarsening at random: the probability of censoring is not influenced by outcomes, conditional on covariates 4. Positivity: all treatments have a non-zero probability of being assigned and there is a non-zero probability of avoiding censoring for all patients In this chapter, we adopt the approach proposed by Simoneau et al (2020) to identify a DTR that simultaneously optimizes nodal assessment and adjuvant therapy decisions with time-to-event outcomes from observational data. 4.2 DTR Methods 4.2.1 Algorithm: We use the dynamic weighted survival modeling (DWSurv) function with generalized overlap weights as an extension of dynamic treatment regimens (DTR) with time-to-event data (194), to estimate the expected survival time for each patient under each treatment policy. This algorithm uses an accelerated failure time model, meaning only the patients who experience an adverse event are used to fit the model to predict survival. We are optimizing decisions at two treatment stages: LN testing with binary treatment options (stage 1) and adjuvant therapy with multiple treatment options (stage 2). To avoid singularity issues in the model fitting and maintain the positivity assumption, we have collapsed the “none” and “brachytherapy only” adjuvant therapy options into one level. To extend the model for multiple 80 treatments, we implemented the generalized overlap weights described in Li et al (2019) (195). The propensity scores are calculated using a gradient boosted model (196) using 20000 iterations and the average treatment effect as the estimand of interest. The balance metric used is the effect size and the maximum is used to summarize across covariates. 4.2.2. Data and Modelling We include all records from the combined cohort with date of diagnosis, date of last follow up, and one of OS, PFS, or DSS time. Date of diagnosis: The earliest diagnosis date of class IV/V cytology, positive pathology, autopsy report or clinical diagnosis. Overall survival (OS): Time to death as in the interval from date of diagnosis until date of death from any cause. Where date of diagnosis is missing, date of surgery is used as the initial timepoint. Where the OS status (os.event or os.censored) indicates censoring, the overall survival time is the date of diagnosis until the date of last follow up. Disease specific survival (DSS): Time to death from disease as in the interval from date of diagnosis until death due to endometrial cancer. Where date of diagnosis is missing, date of surgery is used as the initial timepoint. The event status is considered unknown if patients die of an intercurrent disease or if the reason for death is unspecified. Where DSS status indicates censoring, the survival time is taken from date of diagnosis until the date of last follow up. Progression-free survival (PFS): Time to progression computed from date of diagnosis until the date of first evidence of recurrent or progressive disease (this is based on either clinical evidence of recurrence, or imaging confirmation of recurrence). Where date of diagnosis is missing, date of surgery is used as the initial timepoint. Where PFS status indicates censoring, the survival time is taken from date of diagnosis until the date of last follow up. 81 Where the given adjuvant therapy is missing, we used the recommended adjuvant therapy, if available. Where grade or histotype from surgery are missing, we used the grade and histotype after expert review, if available. For the remaining missing values, a general imputation framework was implemented to impute data using the LASSO algorithm for continuous variables and the random forest algorithm for categorical variables, except for CA125, which was imputed using predictive mean matching to truncate the result to non-negative values. The following model specifications, decided in consultation with gynecologic oncologists and residents in gynecologic oncology, are used to run DWSurv: Survival time: the time-to-event outcome measured during each stage. Stage 1: interval from date of diagnosis until 6 weeks after date of surgery. If date of surgery is within 6 weeks of date of last follow up, the interval is taken from date of diagnosis until date of last follow up. Stage 2: interval from 6 weeks after date of surgery until date of last follow up. Blip model: model to predict the effect of treatment and tailoring covariates. Includes all the variables that will drive decision-making. Stage 1: ProMisE subtype at diagnosis, grade at diagnosis (1/2 vs 3), histotype at diagnosis (suspected pre-cancer vs endometrioid vs non-endometrioid/other), ACE-27 (none vs any), age at diagnosis, extrauterine disease, BMI Stage 2: ESGO-ESTRO-ESP Risk (low vs intermediate vs high-intermediate vs high vs advanced), LVI (negative vs positive), myometrial invasion (none vs <50 vs >50), histotype at surgery (suspected pre-cancer vs endometrioid vs non-endometrioid/other), grade at surgery (1/2 vs 3), stage (I vs II vs III-IV), nodal status (positive vs negative), ProMisE subtype at surgery 82 Treatment model (propensity model): model to determine the probability of treatment as a function of covariates, using logistic regression. Covariates included all the variables associated with the propensity of receiving any treatment. Stage 1: Grade at diagnosis (1/2 vs 3), histotype at diagnosis (suspected pre-cancer vs endometrioid vs non-endometrioid/other), ACE-27 (none vs any), age at diagnosis, extrauterine disease, BMI, Ca125 level Stage 2: ESGO-ESTRO-ESP Risk (low vs intermediate vs high-intermediate vs high vs advanced), LVI (negative vs positive), myometrial invasion (none vs <50 vs >50), histotype at surgery (suspected pre-cancer vs endometrioid vs non-endometrioid/other), grade at surgery (1/2 vs 3), stage (I vs IA vs IB vs II vs III vs IV), nodal status (positive vs negative), ACE-27 (none vs any) Treatment-free model: model to predict the effect of covariates on the survival time, irrespective of treatment. Includes all the variables that will influence outcomes, regardless of the treatment. Stage 1: Promise subtype at diagnosis, Grade at diagnosis (1/2 vs 3), histotype at diagnosis (suspected pre-cancer vs endometrioid vs non-endometrioid/other), ACE-27 (none vs any), age at diagnosis, extrauterine disease, BMI, Ca125 level Stage 2: Promise subtype at surgery, ESGO-ESTRO-ESP Risk (low vs intermediate vs high-intermediate vs high vs advanced), LVI (negative vs positive), myometrial invasion (none vs <50 vs >50), histotype at surgery (suspected pre-cancer vs endometrioid vs non-endometrioid/other), grade at surgery (1/2 vs 3), stage (I vs IA vs IB vs II vs III vs IV), nodal status (negative vs not tested vs positive), ACE-27 (none vs any), age at diagnosis 83 Censoring model: model to predict the probability of any adverse event (OS, PFS or DSS) as a function of covariates, modeled using logistic regression. Includes all the variables associated with the probability of being censored. Stage 1: no censoring assumed as everyone survived until surgery Stage 2: Promise subtype at surgery, ESGO-ESTRO-ESP Risk (low vs intermediate vs high-intermediate vs high vs advanced) 4.2.3 Evaluation of Models: We generated a QQ plot, (theoretical quantiles vs sample quantiles and residual plots (residuals vs fitted values) to assess the fit of the survival time model. I also calculated the root mean square of the error (RMSE). 4.3 Results and Discussion After excluding records with missing survival outcomes data, we have a cohort of 892 patients. 360/ 892 patients have a documented OS, PFS or DSS event and are used to fit the models specified in the methods section. The stage 1 decision, whether or not to test nodes, is made based on the ACE-27 index, age at diagnosis, BMI, and ProMisE, binary FIGO grade, and histotype at diagnosis. The stage 2 decision, which adjuvant therapy to give, if any, is made based on the ESGO-ESTRO-ESP risk score, LVI, myometrial invasion, histotype and grade at surgery, stage, nodal status, and ProMisE subtype at surgery. We explored the optimal treatments recommended by ProMisE molecular subtypes and observed that our optimal DTR recommends chemotherapy to 95% of POLEmut patients. This is inconsistent with all the existing literature and expert opinions stating de-escalation should be considered for POLEmut patients. We also observe 222 p53wt patients recommended to EBRT +/- VB. 10% of these patients tested negative for 84 nodes, and have an Endometrioid, low grade EC. Only 1 of these patients have myometrial invasion (1-50%) and positive LVI. I then tried specifying a simpler blip model, using only ESGO-ESTRO-ESP risk scores, stage and ProMisE to tailor treatment decisions. This resulted in 100% of POLEmut patients being recommended to chemotherapy. The recommendations for intensive therapies in groups of patients with relatively low-risk molecular and pathologic features compelled me to explore the fitted survival times further and find explanations for these results. Figure 8. Evaluation of optimal DTR model. (a) QQ plot showing sample quantiles vs theoretical quantiles. (b) Residual plot showing residual vs fitted values. The QQ plot, residuals plot and RMSE of 2.65 indicate our model cannot reliably predict survival time using our data (figure 8). We also generated a boxplot, plotting survival (in years) for adjuvant treatments in subgroups stratified by ProMisE (figure 9). We selected only the patients who experienced an event for this analysis (N=360), as the algorithm uses an accelerated failure time model which excludes all patients who have been censored when it fits models. Our data contains a significant amount of censoring as EC typically has excellent survival outcomes, which results in much of our cohort being excluded. Using a subset of data with only patients who have experienced an event shrinks our sample size such that it is no a. b. Sample Theoretical Residuals (log) Fitted Values (log) 85 longer representative of a large cohort. POLEmut patients have the best prognosis, which results in a sample size of 17 patients with an event being used to fit models and predict survival times in the DTR. The boxplot of survival for POLEmut patients with events shows only 3 patients who received chemotherapy and 1 of them happened to have the longest survival of all, at approximately 12.5 years. This explains why the DTR is recommending 95% of POLEmut patients to chemotherapy. The DTR is making recommendations based on these select few patients, which are not representative of the larger population, and this is resulting in biased, unreliable estimate of survival times. The same phenomenon explains the recommendation of p53wt patients with low-risk pathology to EBRT +/- VB as the boxplot shows that only about 10 patients comprise the total sample size of patients receiving EBRT +/- VB in each of the MMRd, NSMP/p53wt and p53abn subgroups. Figure 9. Boxplot of survival (in years) for adjuvant therapy in groups stratified by ProMisE. This evaluation of our models and data indicates that these results are not reliable and cannot be used to inform treatment decisions until the key issues are resolved. In order to address the problem in our approach we must either (a) increase our sample size so that we observe a sufficient number of patients Survival (Years) Adjuvant Therapy None or VB Only EBRT +/- VB Any Chemotherapy 86 who experience an event under each of the treatment options for all of the ProMisE subgroups and the other tailoring parameters or (b) replace the AFT model for another model which does not exclude censored records. Some other techniques we can try to incorporate include using non-linear models, and variable selection. Regardless of the drawbacks in our modelling and data, the motivation for identifying an optimal DTR for EC management remains and our team will continue to work on improving our modelling approach. As described earlier in this chapter, ProMisE molecular classification has the potential to revolutionize treatment decision making in EC, by providing reproducible risk stratification that is not possible using the traditional histopathological classification systems. The task of appropriately integrating ProMisE with other clinicopathological parameters and mapping the subtypes to the various treatment options at each stage remains a high priority. 87 Chapter 5: Conclusion 5.1 Summary of Work As ProMisE molecular classification is being implemented in guidelines for clinical decision making, we recognize the low level of evidence available to map ProMisE subtypes to sequences of pre- and post- operative treatments. In this thesis, we explore ProMisE molecular subtypes in the context of pre-operative prognostic factors and treatment decisions for LN testing and adjuvant therapy. The p53abn subgroup appears to have an older mean age, greater prevalence of comorbidities, higher Ca125 levels, and disproportionately high rates of nodal metastases. Regardless of the high-risk features in p53abn ECs, we observe 34% of patients in this subgroup do not receive adjuvant therapy. We note that stage IA p53abn ECs without myometrial invasion are the only p53abn ECs for which the adjuvant therapy recommendation will differ based on nodal status. We found that nodal assessment is significantly associated with better OS in the p53abn subgroup when adjuvant therapy, stage, BMI and histopathology are adjusted for. The POLEmut subgroup is found to be younger, and has lower BMI, fewer co-comorbidities, and lower prevalence of positive nodes, despite high rates of nodal testing. 25% of the patients with a POLEmut EC received chemotherapy. Among the 3 stage III-IV POLEmut ECs, we observe recurrences even where EBRT and/or chemotherapy is given. We do not find LN assessment to be significantly associated with OS within the POLEmut subgroup. The MMRd and NSMP/p53wt subgroups have similar distributions of co-morbidities, with the exception of obesity as the NSMP subtype is associated with a higher BMI. The NSMP ECs are also less likely to receive nodal testing, potentially due to their high BMI. The MMRd ECs are more likely than the other subtypes to receive EBRT +/- VB. Within the MMRd and NSMP/p53wt subgroups, the endometrioid ECs have the greatest variability in adjuvant therapy options based on nodal status. We found that LN 88 assessment is not significantly associated with better survival in this subset, after adjusting for confounders, including adjuvant therapy. We also explored the changes in pre- and post-operatively assigned ProMisE, grade, and histotype. We found that ProMisE has better reproducibility between biopsy and surgery assignment, than the grade and histotype assignments, even when comparing the concordance of histopathology assigned at surgery and after review. Condensing grade to a binary system also resulted in better concordance than the 3-tiered grade. Taking into consideration the time-varying confounding and mediating parameters for the EC treatment-outcome model, we identified a DTR model which allows us to input the information available at the time of each treatment decision. We used an AFT model to estimate survival times which resulted in a DTR that suggested chemotherapy to treat the vast majority of POLEmut ECs. This is inconsistent with the literature which suggests that treatment can be de-escalated in the POLEmut subgroup. Upon evaluation of our models and data, we found that the high rate of censoring in our data results in our analysis being conducted on a subset of patients that is not representative of the population. 5.2 Implications and Next Steps Molecular classification provides an exciting advance in EC as it can address the lack of reproducible parameters available at the time of diagnosis for surgical decision making. Ensuring that ProMisE subtypes and the other pre-operative prognostic markers are mapped to surgical treatment decisions accurately is crucial as the surgical treatment has implications for both patients and the healthcare system. Recommending a patient to nodal assessment means the patient must be treated at a tertiary cancer centre by a gynecologic oncologist, rather than in their community by a gynecologist. Furthermore, a lymphadenectomy is associated with longer, riskier surgeries and higher morbidity. Finally, the nodal assessment decision will have implications for adjuvant therapy decisions as the post-surgical decisions 89 can often be based on nodal status. In order to prevent undertreatment, which increases risk for recurrence and death, or overtreatment, which needlessly exposes patients to toxicities, we must accurately identify the best sequence of treatments for each distinct group of patients. Due to the evolving patient history in EC management and the influence of confounders and mediators on treatment outcomes, a causal modelling framework is required to appropriately adjust for the relevant factors to identify an unbiased estimation of an optimal DTR that simultaneously optimizes both the nodal assessment and the adjuvant therapy decisions. When the DTR is identified, key next steps would include a net benefit analysis. 90 References 1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. 2021;71(1):7–33. 2. 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Our combined cohort consists of 904 EC patients, aged between 29-94 years, with a median BMI of 28. The distribution of ProMisE molecular subtypes within this cohort is as follows: 85 (9.4%) POLEmut, 438 (48%) NSMP/p53wt, 229 (25%) MMRd, and 152 (17%) p53abn. 75% of patients have at least one ACE comorbidity. The median follow-up time is 6.5 years. In general, the Vancouver cohort appears to be significantly younger but has greater severity of disease, relative to the German cohort. This indicated by greater prevalence of high risk, p53abn ProMisE subtype, high grade, and non-endometrioid ECs in the Vancouver cohort. The rate of nodal testing differs among the 2 cohorts, with LN assessment occurring in 86% of the German patients and 51% of the Vancouver patients. The prevalence of positive nodal status is approximately 9% in both cohorts. The patients within the Vancouver cohort received significantly more intensive adjuvant therapies (1.9% Brachytherapy only, 11% RT +/- Brachytherapy, 38% Any Chemotherapy) compared to the German cohort (37% Brachytherapy only, 7% EBRT +/- Brachytherapy, 12% Any Chemotherapy). No significant differences are observed in leukocyte levels, platelet levels, gravidity, parity, and histotype at diagnosis. There is no significant difference in the overall prevalence of ACE comorbidities, however, the Vancouver cohort does have significantly higher rates of obesity, hypertension, rheumatologic 107 disease, psychiatric illness, and stomach/ intestine disorders. Meanwhile, the German cohort has higher rates of congestive heart failure and venous disease. There is also no significant difference in the following ACE variables: myocardial infarction, angina and coronary artery disease, cardiac arrhythmias, peripheral artery disease, respiratory disease, hepatic disease, pancreatic disease, end-stage renal disease, diabetes mellitus, stroke, dementia, paralysis, neuromuscular disorder, solid tumour, lymphoma, and alcohol use. Neither cohort contains any patients with AIDS, leukemia, myeloma, or use of illicit drugs reported in the medical history. Table A.1. Summary of Cohort Characteristics. Cohort Variable Combined, N = 9031 German, N = 444 Vancouver, N = 459 p-value2 Age <0.001 Median (IQR) 63 (53, 72) 65 (57, 73) 61 (49, 71) BMI 0.023 Median (IQR) 28(24, 34) 28(24, 33) 29(24, 37) Missing 62 20 42 ACE Obesity 203 (24%) 55 (13%) 148 (34%) <0.001 Missing 40 21 19 ACE Congestive Heart Failure 35 (4.1%) 29 (6.9%) 6 (1.4%) <0.001 Missing 40 23 17 ACE Hypertension 196 (31%) 16 (7.9%) 180 (41%) <0.001 Missing 261 241 20 ACE Venous Disease 42 (4.8%) 30 (7.1%) 12 (2.7%) 0.004 Missing 37 19 18 ACE Stomach and Intestinal Disease 36 (4.2%) 6 (1.4%) 30 (6.8%) <0.001 Missing 36 18 18 ACE Psychiatric disorder 69 (7.9%) 21 (4.9%) 48 (11%) 0.002 Missing 35 17 18 ACE Rheumatologic Disorder 85 (9.7%) 9 (2.1%) 76 (17%) <0.001 Missing 31 14 17 108 Cohort Variable Combined, N = 9031 German, N = 444 Vancouver, N = 459 p-value2 ASA Comorbidity Score <0.001 I (healthy) 138 (17%) 36 (9.5%) 102 (23%) II (mild disease) 497 (61%) 260 (68%) 237 (54%) III (severe disease) 178 (22%) 83 (22%) 95 (22%) IV (life-threatening disease) 4 (0.5%) 1 (0.3%) 3 (0.7%) Missing 86 64 22 Ca125 <0.001 Median (IQR) 17(11, 37) 16(10, 28) 24(13, 80) Missing 343 100 243 Hemoglobin <0.001 Median (IQR) 13.30 (12.10, 14.10) 13.70 (12.50,14.30) 12.80 (11.65, 13.65) Missing 258 18 240 Menopause Status 661 (75%) 378 (87%) 283 (63%) <0.001 Missing 20 10 10 Grade at Diagnosis <0.001 Suspected Pre-Cancer 7 (0.9%) 6 (1.7%) 1 (0.3%) Grade 1 231 (31%) 118 (33%) 113 (29%) Grade 2 268 (36%) 161 (45%) 107 (28%) Grade 3 236 (32%) 70 (20%) 166 (43%) Missing 161 89 72 Histotype at Diagnosis 0.081 Suspected Pre-Cancer 42 (5.3%) 25 (6.7%) 17 (4.0%) Endometrioid 530 (67%) 253 (68%) 277 (65%) Non-Endometrioid + Other 222 (28%) 93 (25%) 129 (30%) Missing 109 73 36 Extrauterine Disease 118 (14%) 36 (8.2%) 82 (19%) <0.001 Missing 32 7 25 Imaging <0.001 TVS Only 107 (13%) 106 (24%) 1 (0.3%) TVS + Other 466 (56%) 328 (75%) 138 (35%) Other Only 262 (31%) 2 (0.5%) 260 (65%) Missing 68 8 60 109 Cohort Variable Combined, N = 9031 German, N = 444 Vancouver, N = 459 p-value2 Hormone Replacement Therapy 88 (11%) 50 (15%) 38 (8.7%) 0.012 Missing 123 100 23 Nodes Test <0.001 Not Tested 284 (32%) 60 (14%) 224 (49%) Tested 615 (68%) 384 (86%) 231 (51%) Missing 4 0 4 Nodal Status <0.001 Negative 533 (59%) 343 (77%) 190 (41%) Not Tested 284 (31%) 60 (14%) 224 (49%) Positive 85 (9.4%) 41 (9.2%) 44 (9.6%) Missing 1 0 1 Promise Subtype <0.001 POLEmut 85 (9.4%) 43 (9.7%) 42 (9.2%) NSMP/p53wt 437 (48%) 230 (52%) 207 (45%) MMRd 229 (25%) 119 (27%) 110 (24%) p53abn 152 (17%) 52 (12%) 100 (22%) Histotype <0.001 Endometrioid 681 (76%) 367 (83%) 314 (69%) Non-Endometrioid + Other 217 (24%) 77 (17%) 140 (31%) Missing 5 0 5 Grade <0.001 Grade 1 276 (31%) 146 (34%) 130 (29%) Grade 2 285 (32%) 207 (48%) 78 (17%) Grade 3 321 (36%) 81 (19%) 240 (54%) Missing 21 10 11 Stage <0.001 I 662 (73%) 357 (80%) 305 (66%) II 68 (7.5%) 25 (5.6%) 43 (9.4%) III-IV 173 (19%) 62 (14%) 111 (24%) Myometrial Invasion <0.001 None 190 (23%) 127 (29%) 63 (17%) 1-50% 337 (41%) 167 (38%) 170 (45%) >50% 292 (36%) 150 (34%) 142 (38%) Missing 84 0 84 110 Cohort Variable Combined, N = 9031 German, N = 444 Vancouver, N = 459 p-value2 LVI <0.001 Negative 582 (73%) 384 (86%) 198 (55%) Positive 220 (27%) 60 (14%) 160 (45%) Missing 101 0 101 ESGO-ESTRO-ESP risk <0.001 Low 277 (33%) 208 (47%) 69 (18%) Intermediate 135 (16%) 90 (20%) 45 (12%) High-intermediate 147 (18%) 49 (11%) 98 (25%) High 275 (33%) 97 (22%) 178 (46%) Advanced 1 (0.1%) 0 (0%) 1 (0.3%) Missing 68 0 68 ESGO-ESTRO-ESP risk w/PROMISE <0.001 Low 317 (39%) 215 (50%) 102 (27%) Intermediate 99 (12%) 71 (17%) 28 (7.3%) High-intermediate 115 (14%) 43 (10%) 72 (19%) High 271 (33%) 96 (22%) 175 (46%) Advanced 12 (1.5%) 5 (1.2%) 7 (1.8%) Missing 89 14 75 Adjuvant Therapy <0.001 None 377 (47%) 169 (44%) 208 (49%) Brachytherapy Only 150 (19%) 142 (37%) 8 (1.9%) RT +/- VB 74 (9.2%) 27 (7.0%) 47 (11%) Any Chemotherapy 207 (26%) 45 (12%) 162 (38%) Missing 95 61 34 Overall Survival (Years) <0.001 Median (IQR) 6.5 (3.6, 10.4) 8.0 (5.2, 11.1) 5.5 (2.6, 9.3) Histotype at Surgery 0.049 Hysterectomy Only 39 (4.4%) 13 (2.9%) 26 (5.8%) Hysterectomy + Other 857 (96%) 431 (97%) 426 (94%) Missing 7 0 7 1 n (%) 2 Fisher's Exact Test for Count Data with simulated p-value (based on 2000 replicates) Column-wise percentages reported 111 Appendix B: Missing Data Analysis I conducted a missing data analysis to determine if missing data in any of the variables is associated with ProMisE molecular subtypes (table A.2). I used the Pearson’s chi-square test and Fisher’s exact test with statistical significance set to 0.05. Missing values in LVI, myometrial invasion, and ESGO-ESTRO-ESP risk with and without molecular classification are all significantly associated with ProMisE as there is a greater percentage of patients in the p53abn subgroup with missing data in these variables. Missing data in ESGO-ESTRO-ESP risk with and without molecular classification is correlated with missing LVI and myometrial invasion since I could not assign a risk score to patients with missing LVI and/or myometrial invasion. Missing values in BMI, menopause status, PFS status and PFS time are also significantly associated with ProMisE. However, fewer than 10% of patients are missing these factors within each of the ProMisE subtypes. Table B.1. Missing data across ProMisE molecular subtypes. Variable POLEmut, N = 851 NSMP/p53wt, N = 4371 MMRd, N = 2291 p53abn, N = 1521 p-value2 Histotype at Diagnosis 0.3 Missing 15 (18%) 54 (12%) 24 (10%) 16 (11%) Available 70 (82%) 383 (88%) 205 (90%) 136 (89%) Grade at Diagnosis 0.2 Missing 17 (20%) 88 (20%) 31 (14%) 25 (16%) Available 68 (80%) 349 (80%) 198 (86%) 127 (84%) Histotype at Surgery 0.090 Missing 2 (2.4%) 2 (0.5%) 0 (0%) 1 (0.7%) Available 83 (98%) 435 (100%) 229 (100%) 151 (99%) Grade at Surgery 0.3 Missing 4 (4.7%) 10 (2.3%) 3 (1.3%) 4 (2.6%) Available 81 (95%) 427 (98%) 226 (99%) 148 (97%) Nodal Status >0.9 Missing 0 (0%) 1 (0.2%) 0 (0%) 0 (0%) Available 85 (100%) 436 (100%) 229 (100%) 152 (100%) Stage Available 85 (100%) 437 (100%) 229 (100%) 152 (100%) 112 Variable POLEmut, N = 851 NSMP/p53wt, N = 4371 MMRd, N = 2291 p53abn, N = 1521 p-value2 Myometrial Invasion <0.001 Missing 1 (1.2%) 78 (18%) 1 (0.4%) 4 (2.6%) Available 84 (99%) 359 (82%) 228 (100%) 148 (97%) LVI <0.001 Missing 1 (1.2%) 84 (19%) 8 (3.5%) 8 (5.3%) Available 84 (99%) 353 (81%) 221 (97%) 144 (95%) ESGO-ESTRO-ESP w/PROMISE Risk <0.001 Missing 0 (0%) 75 (17%) 10 (4.4%) 4 (2.6%) Available 85 (100%) 362 (83%) 219 (96%) 148 (97%) ESGO-ESTRO-ESP Risk <0.001 Missing 2 (2.4%) 61 (14%) 4 (1.7%) 1 (0.7%) Available 83 (98%) 376 (86%) 225 (98%) 151 (99%) Surgery Type 0.3 Missing 1 (1.2%) 5 (1.1%) 0 (0%) 1 (0.7%) Available 84 (99%) 432 (99%) 229 (100%) 151 (99%) Age at Diagnosis Available 85 (100%) 437 (100%) 229 (100%) 152 (100%) BMI 0.022 Missing 7 (8.2%) 40 (9.2%) 11 (4.8%) 4 (2.6%) Available 78 (92%) 397 (91%) 218 (95%) 148 (97%) ACE Comorbidity Score 0.6 Missing 3 (3.5%) 15 (3.4%) 4 (1.7%) 3 (2.0%) Available 82 (96%) 422 (97%) 225 (98%) 149 (98%) ASA Physical Status 0.10 Missing 8 (9.4%) 50 (11%) 21 (9.2%) 7 (4.6%) Available 77 (91%) 387 (89%) 208 (91%) 145 (95%) Ca125 0.5 Missing 31 (36%) 174 (40%) 78 (34%) 60 (39%) Available 54 (64%) 263 (60%) 151 (66%) 92 (61%) Hemoglobin 0.3 Missing 24 (28%) 118 (27%) 63 (28%) 53 (35%) Available 61 (72%) 319 (73%) 166 (72%) 99 (65%) Leukocytes 0.3 Missing 24 (28%) 120 (27%) 64 (28%) 54 (36%) Available 61 (72%) 317 (73%) 165 (72%) 98 (64%) Platelets 0.4 Missing 24 (28%) 121 (28%) 64 (28%) 53 (35%) Available 61 (72%) 316 (72%) 165 (72%) 99 (65%) Menopause Status 0.041 Missing 3 (3.5%) 14 (3.2%) 3 (1.3%) 0 (0%) Available 82 (96%) 423 (97%) 226 (99%) 152 (100%) 113 Variable POLEmut, N = 851 NSMP/p53wt, N = 4371 MMRd, N = 2291 p53abn, N = 1521 p-value2 Gravidity 0.6 Missing 5 (5.9%) 37 (8.5%) 14 (6.1%) 9 (5.9%) Available 80 (94%) 400 (92%) 215 (94%) 143 (94%) Parity 0.075 Missing 7 (8.2%) 31 (7.1%) 9 (3.9%) 4 (2.6%) Available 78 (92%) 406 (93%) 220 (96%) 148 (97%) Imaging 0.2 Missing 7 (8.2%) 40 (9.2%) 11 (4.8%) 10 (6.6%) Available 78 (92%) 397 (91%) 218 (95%) 142 (93%) Extrauterine Disease 0.5 Missing 4 (4.7%) 18 (4.1%) 5 (2.2%) 5 (3.3%) Available 81 (95%) 419 (96%) 224 (98%) 147 (97%) Adjuvant Therapy 0.2 Missing 5 (5.9%) 55 (13%) 20 (8.7%) 15 (9.9%) Available 80 (94%) 382 (87%) 209 (91%) 137 (90%) Disease Specific Survival Status 0.5 Missing 1 (1.2%) 19 (4.3%) 12 (5.2%) 6 (3.9%) Available 84 (99%) 418 (96%) 217 (95%) 146 (96%) Progression Free Survival Status 0.033 Missing 0 (0%) 24 (5.5%) 7 (3.1%) 10 (6.6%) Available 85 (100%) 413 (95%) 222 (97%) 142 (93%) Overall Survival Status 0.056 Missing 0 (0%) 8 (1.8%) 0 (0%) 0 (0%) Available 85 (100%) 429 (98%) 229 (100%) 152 (100%) Disease Specific Survival Years 0.6 Missing 1 (1.2%) 18 (4.1%) 10 (4.4%) 6 (3.9%) Available 84 (99%) 419 (96%) 219 (96%) 146 (96%) Progression Free Survival Years 0.018 Missing 0 (0%) 25 (5.7%) 6 (2.6%) 10 (6.6%) Available 85 (100%) 412 (94%) 223 (97%) 142 (93%) Overall Survival Years 0.056 Missing 0 (0%) 8 (1.8%) 0 (0%) 0 (0%) Available 85 (100%) 429 (98%) 229 (100%) 152 (100%) 1 n (%) 2 Pearson's Chi-squared test; Fisher's exact test Column-wise percentages reported
Thesis/Dissertation
eng
Reproductive and Developmental Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Talhouk, Aline
Causal machine learning to optimize treatment decisions for patients with endometrial cancer
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