Stephanie Vrede

Further refinement in the prognostic evaluation of patients with endometrial cancer Knowing what to keep or to combine in the genomic era Stephanie Vrede

The research presented in this thesis was carried out at the department of Obstetrics and Gynaecology of the Radboud university medical center, Nijmegen, the Netherlands For reasons of consistency within the thesis, some terms and abbreviations have been standardized throughout the text and might therefore slightly differ from the original publications. ISBN: 978-94-6483-726-1 Cover design: Veronique Baur | VEER illustratie Lay-out: Bregje Jaspers | Printed by: Ridderprint | The complete version of this thesis including all supplementary information can be found online through this QR code: Copyright © 2024 by Stephanie W. Vrede All rights reserved. No part of this thesis may be reproduced or transmitted in any form or by any means without permission in writing from the author. The copyright of the publications remain with the publishers.

Further refinement in the prognostic evaluation of patients with endometrial cancer Knowing what to keep or to combine in the genomic era Proefschrift ter verkrijging van de graad van doctor aan de Radboud Universiteit Nijmegen op gezag van de rector magnificus prof. dr. J.M. Sanders, volgens besluit van het college voor promoties in het openbaar te verdedigen op woensdag 28 februari 2024 om 14.30 uur precies door Stephanie Willemina Vrede geboren op 22 februari 1991 te Utrecht

Promotoren: Prof. dr. R.F.P.M Kruitwagen Dr. J.M.A Pijnenborg Copromotoren: Dr. J. Bulten Dr. M.P.L.M. Snijders (Canisius Wilhelmina Ziekenhuis) Manuscriptcommissie: Prof. dr. M.J.L. Ligtenberg Prof. dr. M. Verheij Prof. dr. R.P. Zweemer (UMC Utrecht)

TABLE OF CONTENTS Chapter 1 General introduction and outline thesis Chapter 2 The amount of preoperative endometrial tissue surface in relation to final endometrial cancer classification; Gynecologic Oncology 2022 Chapter 3 Relevance of molecular profiling in patients with low-grade endometrial cancer; Jama Network Open 2022 Chapter 4 Pure and mixed clear cell carcinoma of the endometrium: A molecular and immunohistochemical analysis study; Cancer Medicine 2023 Chapter 5 Immunohistochemical biomarkers are prognostic relevant in addition to the ESMO-ESGO-ESTRO risk classification in endometrial cancer; Gynecologic Oncology 2021 Chapter 6 Hormonal biomarkers remain prognostically relevant within the molecular subgroups in endometrial cancer; submitted Chapter 7 Abnormal preoperative haematological parameters in endometrial cancer; worse prognosis reflecting tumour aggressiveness or reduced response to radiotherapy?; Journal of Obstetrics and Gynaecology 2024 Chapter 8 General discussion Chapter 9 Summary / Samenvatting Appendix Research Data Management PhD Portfolio Curriculum Vitae List of publications Dankwoord 9 27 49 77 109 135 159 181 201 213 215 217 219 223


GENERAL INTRODUCTION 11 1 INTRODUCTION Epidemiology Endometrial cancer (EC) is the most common gynecological malignancy in industrialized countries, like Europe and North America. The incidence is rising due to advanced life expectancy and increasing obesity.1 In 2020 worldwide, 417,367 women were diagnosed with EC and 97,370 people died from this cancer.2 Various risk factors are identified for EC and summarized in Table 1. Low-income countries have a lower incidence of EC, because most risk factors are less present. Within the Netherlands, the reported incidence of EC in 2020 was 2069, with a mortality rate of 559 women, both have increased over the past years.3 The majority of patients diagnosed with EC are between 60-74 years old (50%) and a third of the patients is older than 75 years.3 Table 1. Risk factors for endometrial cancer Increasing factors Long-term exposure to unopposed estrogens Increasing Age Obesity Nulliparity Polycystic ovary syndrome Early menarche/late menopause Hormone replacement therapy without progestogens Others Tamoxifen use for breast cancer Genetic First-degree relative with endometrial cancer Lynch-syndrome Decreasing factors Grand multiparity Increased physical activity Oral conceptive and/or hormone replacement therapy (combination of estrogen & progestogens) Smoking Diagnosis Among EC patients, most women present with abnormal or postmenopausal bleeding as an early symptom.4 The diagnostic work-up consists of gynecological examination including cervical cytology and transvaginal ultrasonography (TVU) to measure endometrial thickness. Thickened endometrium, defined as >4.0 mm, or recurrent abnormal postmenopausal bleeding that occurs six weeks after a first episode within a year, requires histological evaluation by either endometrial sampling, hysteroscopic biopsy or dilatation and

CHAPTER 1 12 curettage (D&C). Performing office-based endometrial sampling as a first-line diagnostic procedure is recommended. When histopathological findings are inconclusive to rule out cancer, hysteroscopic biopsy or D&C is recommended.5 The obtained tissue is subjected to histopathological evaluation including tumor typing and grading.6 Histology Historically, EC was subdivided into two histopathological subtypes, type 1 and type 2 EC (Figure 1). Type 1, comprising grade 1 and 2 endometrioid EC (EEC), is associated with high immunohistochemical (IHC) expression of estrogen receptor (ER) and a favorable prognosis. Type 2, comprising of grade 3 EEC and non-endometrioid EC (NEEC), generally shows low ER expression, mainly TP53 mutation and an unfavorable prognosis.7 Non-endometrioid histology includes most commonly serous and clear cell histology.8 Serous carcinoma has a poor prognosis with extra-uterine disease in 37% of EC patients.9 Uterine clear cell carcinoma (CCC) also often presents with extra-uterine disease (40-45%), has a high recurrence rate (50% at 3-years) and a 5-year overall survival of only 63%.8, 10 Besides pure also mixed EC occurs, being a tumor composed of two or more different histological types of EC, for example components of endometrioid, serous and/or clear cell histology.11 The mixed form of uterine CCC can display apart from clear cell, endometrioid and/or serous carcinoma histological components.12 It is questioned whether pure CCC presents with another molecular and IHC background compared to the mixed form of uterine CCC, and affecting clinical outcome. The most recent ESGO/ESTRO/ESP (European Society of Gynaecological Oncology/ European SocieTy for Radiotherapy and Oncology/European Society of Pathology guideline) and WHO (World Health Organization) Classification of Tumors, recommends a modified binary FIGO (Federation International of Gynecology and Obstetrics) grading, considering both FIGO grade 1 and 2 lumped as low-grade EC and FIGO grade 3 EEC, and NEEC as high-grade EC.6, 12, 13 Most patients (80%) are diagnosed with low-grade EC and an overall favorable prognosis with a 5-year survival rate of 85%. About 20% of the patients are diagnosed with high-grade EC, associated with increased risk of regional or distant metastases and have a poor prognosis with a 5-year survival rate of 58%.4 Numerous studies state that preoperative endometrial sampling is poorly to moderately correlated with final tumor grade and histological subtype.14-17 Within a meta-analysis, the lowest concordance was found for grade 2 EC (only 61.0%).17 Since the primary treatment of EC is mainly based on preoperative tumor histology, disagreement in grading between preoperative and final diagnosis may therefore result in either under- or overtreatment and subsequently impact outcome.18, 19 Currently, sampling errors and interobserver variability

GENERAL INTRODUCTION 13 1 are considered the most important explanations for this disagreement.17, 20-23 Besides, it remains unclear whether the amount of diagnostic tissue impacts the concordance. Immunohistochemical biomarkers In recent years, several IHC biomarkers have been studied in EC to improve diagnosis and prognostication of which ER, progesterone receptor (PR), p53 and L1 cell adhesion molecule (L1CAM) appear the most relevant. Examples of expression patterns of these biomarkers are shown in Figure 2.30-37 Positive ER/PR expression is associated with favorable outcome and low risk of lymph node metastasis (LNM). Negative ER/PR expression is associated with the opposite.37, 38 Our research group recently demonstrated that a revised three-tiered ER and PR risk classification improves prognostication over the commonly used cutoff value of 10% for ER and/or PR positivity: 0-10% with most unfavorable outcome, 20-80% with intermediate outcome and 90-100% with most favorable outcome.39 TP53 is the most frequently mutated gene, causing dysfunction of p53 tumor suppressor protein, playing an important role in cell proliferation, apoptosis, DNA repair and genomic stability. Overexpression of p53 or null-expression is associated with an unfavorable outcome. 40-42 The transmembrane L1CAM is critical for epithelial to mesenchymal transition (EMT) and cancer initiating cell (CIC) formation which may result in chemotherapy resistance.32, 43 Positive L1CAM tumor expression is associated with a poor outcome in EC.32, 44-47 Currently, most of these easy accessible IHC biomarkers are not yet used in the risk classifications for primary and secondary treatment.

CHAPTER 1 14 Endometrioid Non-endometrioid Type 1 Type 2 Grade 1 EC Grade 2 EC Grade 3 EEC Serous Clear cell A. B. Low-grade High-grade Incidence 80% Incidence 20% Age 40-60 years Age 60-80 years Obesity High ER/PR expression Generally low ER/PR expression PTEN, PIK3CA or KRAS mutation (NSMP) C. Favorable prognosis Unfavorable prognosis Figure 1: A. Examples of type 1; grade 1 EC and grade 2 EC and examples of type 2; grade 3 EEC, serous and clear cell EC. B. The pie charts showing the distribution of the molecular subgroups within the different histology’s. C. The characteristics of type 1 EC and type 2 EC. Abbreviations: POLE, polymerase epsilon; TP53, tumor protein 53; MSI, microsatellite instable; NSMP, no specific molecular profile; ER, estrogen receptor; PR, progesterone receptor; PTEN, phosphatase and tensin homolog; PIK3CA, phosphatidylinositol 3-kinase; KRAS, kristen ras sarcoma viral oncogene

GENERAL INTRODUCTION 15 1 Immunohistochemical expression ER expression PR expression P53 expression L1CAM expression ER positive PR positive p53 wildtype L1CAM negative A. C. E. H. ER negative PR negative p53 overexpression L1CAM positive B. D. F. I. p53 null-expression G. Figure 2. Immunohistochemical expression of ER, PR, p53 and L1CAM. A/C. Positive ER/PR expression with a cutoff >1 or 10%. B/D. negative ER/PR expression with a cutoff ≤1 or 10%. E. p53wildtype when there is no TP53 mutation. F. p53 overexpression when there is nuclear accumulation of p53 protein caused by a missense mutation of TP53. G. p53 null-expression with a frameshift or nonsense mutation of TP53. H. Negative L1CAM expression with a cutoff <10%. I. Positive L1CAM expression with a cutoff ≥10%. Abbreviations: ER, estrogen receptor; PR, progesterone receptor; p53, protein 53; L1CAM, L1 cell adhesion molecule

CHAPTER 1 16 Molecular biomarkers Recently, The Cancer Genome Atlas (TCGA) defined four important prognostic molecular subgroups in EC based on integrated genomic data: I) ultramutated tumors with polymerase epsilon (POLE) mutations, II) hypermutated tumors with microsatellite instability (MSI), III) copy-number-high (CNH) with frequent tumor protein (TP53) mutations and, IV) copy-number-low (CNL) (also known as no-specific molecular profile (NSMP)). These four subgroups increase insight in biological tumor behavior based on molecular signature beyond the histological morphological classification of type 1 and 2 EC.7, 51 Several studies have shown that patients with POLE mutation have an excellent outcome in EC. Patients within the MSI or NSMP subgroup are known with intermediate outcome, and patients with TP53-mutant tumors have the worst outcome, the latter representing 15% of all EC diagnosis and responsible for 50-70% of all EC-related mortality.51-54 The diagnostic algorithm and prognostic relevance of these subgroups are shown in Figure 3A-B. These molecular subgroups have shown to improve prognostication mainly in patients with high-grade EC, probably due to poor interobserver reproducibility of morphological classification and the prognostic and intratumoral heterogeneity of high-grade ECs.53, 55, 56 Specifically in patients with low-grade EC, the prognostic relevance of molecular classification so far is lacking. Clinical biomarkers In addition to the tumor histology and immunohistochemical and/or molecular biomarkers, clinical biomarkers may contribute to an improved risk stratification by reflecting the tumor macro-environment. Endometrial carcinogenesis is characterized by chronic inflammation with elevated pro-inflammatory cytokines and acute phase proteins.57 Overexpression of inflammatory cytokines could contribute to the development of cancer-related anemia, thrombocytosis and leukocytosis, thus generating a pro-tumorigenic environment.58-61 Preoperative anemia, thrombocytosis and leukocytosis, as clinical hematological parameters, may contribute to the identification of patients with extended disease and/or aggressive tumor behavior.46, 62-64 Indeed, they have been associated with advanced-stage (FIGO stage III-IV) and therefore prognostic relevant, however results remain conflicting.59, 60, 65-70 If these often routinely obtained preoperative hematological parameters may also influence the response to adjuvant therapy still remains to be elucidated.46, 62, 63

GENERAL INTRODUCTION 17 1 A. B. Figure 3A-B. A. Diagnostic algorithm and final classification according the WHO (World Health Organization) classification of Female Genital tumors. B. Progression-free survival of the four molecular subgroups according to The Cancer Genome Atlas (TCGA).51 Abbreviations: POLE, polymerase epsilon mutant; MMRd, mismatch repair deficient; MSI, microsatellite instable; MSS, microsatellite stable; TP53, tumor protein 53; p53, protein 53; NSMP, No specific molecular profile.

CHAPTER 1 18 Preoperative risk stratification model guiding primary surgical treatment of endometrial cancer Primary surgical treatment according the latest ESGO/ESTRO/ESP guideline is based on preoperative tumor grade, histology and, if indicated imaging. Besides hysterectomy and bilateral salpingo-oophorectomy, additional staging including lymph node surgery (i.e. sentinel lymph node (SLN), lymph node dissection (LND)) is recommended in patients at substantial risk of metastases.13, 71 Current models for preoperative prediction of LNM and survival in EC are not optimal.13, 72 Numerous studies proposed preoperative risk stratification models for LNM.73-77 However, preoperative risk models including IHC and/or molecular markers are only limited.46, 78, 79 Within our research group we developed a Bayesian network model, ENDORISK, by integrating easy accessible preoperative markers and patient characteristics showing improved preoperative risk classification in EC.46 ENDORISK includes preoperative markers like; thrombocytosis, Cancer Antigen 125 (CA125), tumor grade, lymphadenopathy on imaging, atypical endometrial cells in cervical cytology, and IHC expression of p53, L1CAM, ER and PR. It was established to predict preoperatively macro-LNM and outcome accurately.46 Postoperative risk stratification model guiding adjuvant treatment of endometrial cancer For postoperative adjuvant treatment different classifications are used in clinical practice: ESGO/ESTRO/ESP, Postoperative Radiation Therapy for Endometrial Carcinoma (PORTEC) and Gynecologic Oncology Group (GOG) criteria.13, 80, 81 According to the latest ESGO/ESTRO/ESP guideline, adjuvant treatment is based on risk classification groups incorporating FIGO stage, tumor grade and histology, lympho-vascular space invasion (LVSI), with or without molecular markers.13 With the integration of the TCGA-based molecular classification a postoperative risk stratification model appears promising for guidance of adjuvant treatment.82, 83 Adjuvant therapy tailored to the TCGA groups will be studied in the prospective randomized control RAINBO trial.83, 84

GENERAL INTRODUCTION 19 1 AIMS AND OUTLINE OF THE THESIS Aims With only a moderate concordance between pre- and postoperative diagnosis, the creation of a more objective molecular classification by the TCGA was most welcome. However, routine molecular profiling comes with high costs, especially for low-income countries. With the introduction of these molecular subgroups, the prognostic relevance of tumor grading has gained less attention, as well as the easily accessible clinical and IHC biomarkers. It is questioned if the use of molecular biomarkers can be optimized by combining with IHC and clinical biomarkers. In this thesis we aim to evaluate the prognostic relevance of the current histomorphology, IHC and clinical biomarkers within the new era of molecular profiling in EC. Outline In chapter 2, the amount of preoperative endometrial tissue surface is evaluated to the degree of concordance with final low- and high-grade EC. Furthermore, it is determined whether discordance is influenced by sampling method and may impact outcome. In chapter 3, the prognostic relevance of molecular profiling in patients with low-grade EC is assessed. In chapter 4, the molecular and immunohistochemical features within mixed and pure uterine CCC are investigated and whether this affects clinical outcome. In chapter 5, the added prognostic relevance of preoperative IHC biomarkers to the ESMOESGO-ESTRO risk classification groups is investigated. In chapter 6, the relevance of using a three-tiered ER/PR risk model is investigated including the possible additional prognostic relevance within the four molecular subgroups. In chapter 7, the prognostic and predictive relevance of preoperative abnormal hematological parameters in patients with EC is evaluated. In chapter 8, a summary of the results of this thesis and future implications for clinical practice are discussed .

CHAPTER 1 20 REFERENCES 1. Amant F, Moerman P, Neven P, Timmerman D, Van Limbergen E, Vergote I. Endometrial cancer. Lancet. 2005;366(9484):491-505. 2. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209-49. 3. Nederland Ik. NCR data [updated 2022. Available from: 4. Morice P, Leary A, Creutzberg C, Abu-Rustum N, Darai E. Endometrial cancer. The Lancet. 2016;387(10023):1094-108. 5. Morice P, Leary A, Creutzberg C, Abu-Rustum N, Darai E. Endometrial cancer. Lancet. 2016;387(10023):1094108. 6. Parra-Herran C. Endometrial carcinoma-general 2022 [updated 18 october 2022; cited 2023 January 27th]. Available from: 7. Bokhman JV. Two pathogenetic types of endometrial carcinoma. Gynecol Oncol. 1983;15(1):10-7. 8. Mendivil A, Schuler KM, Gehrig PA. Non-endometrioid adenocarcinoma of the uterine corpus: a review of selected histological subtypes. Cancer Control. 2009;16(1):46-52. 9. Lu KH, Broaddus RR. Endometrial Cancer. N Engl J Med. 2020;383(21):2053-64. 10. Cetinkaya N, Selcuk İ, Ozdal B, Meydanli MM, Gungor T. Prognostic factors in endometrial clear cell carcinoma. Arch Gynecol Obstet. 2017;295(1):189-95. 11. Roelofsen T, van Ham MA, Wiersma van Tilburg JM, Zomer SF, Bol M, Massuger LF, et al. Pure compared with mixed serous endometrial carcinoma: two different entities? Obstet Gynecol. 2012;120(6):1371-81. 12. WHO classification of Tumours 5th ed2020 2020. 13. Concin N, Matias-Guiu X, Vergote I, Cibula D, Mirza MR, Marnitz S, et al. ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int J Gynecol Cancer. 2021;31(1):12-39. 14. Eltabbakh GH, Shamonki J, Mount SL. Surgical stage, final grade, and survival of women with endometrial carcinoma whose preoperative endometrial biopsy shows well-differentiated tumors. Gynecol Oncol. 2005;99(2):309-12. 15. Frumovitz M, Singh DK, Meyer L, Smith DH, Wertheim I, Resnik E, et al. Predictors of final histology in patients with endometrial cancer. Gynecol Oncol. 2004;95(3):463-8. 16. Thomas S, Hussein Y, Bandyopadhyay S, Cote M, Hassan O, Abdulfatah E, et al. Interobserver Variability in the Diagnosis of Uterine High-Grade Endometrioid Carcinoma. Arch Pathol Lab Med. 2016;140(8):836-43. 17. Visser NCM, Reijnen C, Massuger L, Nagtegaal ID, Bulten J, Pijnenborg JMA. Accuracy of Endometrial Sampling in Endometrial Carcinoma: A Systematic Review and Meta-analysis. Obstet Gynecol. 2017;130(4):803-13. 18. Eggink FA, Mom CH, Bouwman K, Boll D, Becker JH, Creutzberg CL, et al. Less-favourable prognosis for low-risk endometrial cancer patients with a discordant pre- versus post-operative risk stratification. Eur J Cancer. 2017;78:82-90. 19. Werner HM, Trovik J, Marcickiewicz J, Tingulstad S, Staff AC, Engh ME, et al. A discordant histological risk classification in preoperative and operative biopsy in endometrial cancer is reflected in metastatic risk and prognosis. Eur J Cancer. 2013;49(3):625-32. 20. Gilks CB, Oliva E, Soslow RA. Poor interobserver reproducibility in the diagnosis of high-grade endometrial carcinoma. Am J Surg Pathol. 2013;37(6):874-81. 21. Han G, Sidhu D, Duggan MA, Arseneau J, Cesari M, Clement PB, et al. Reproducibility of histological cell type in high-grade endometrial carcinoma. Mod Pathol. 2013;26(12):1594-604.

GENERAL INTRODUCTION 21 1 22. Nielsen AL, Thomsen HK, Nyholm HC. Evaluation of the reproducibility of the revised 1988 International Federation of Gynecology and Obstetrics grading system of endometrial cancers with special emphasis on nuclear grading. Cancer. 1991;68(10):2303-9. 23. Hoang LN, Kinloch MA, Leo JM, Grondin K, Lee CH, Ewanowich C, et al. Interobserver Agreement in Endometrial Carcinoma Histotype Diagnosis Varies Depending on The Cancer Genome Atlas (TCGA)-based Molecular Subgroup. Am J Surg Pathol. 2017;41(2):245-52. 24. Soslow RA, Tornos C, Park KJ, Malpica A, Matias-Guiu X, Oliva E, et al. Endometrial Carcinoma Diagnosis: Use of FIGO Grading and Genomic Subcategories in Clinical Practice: Recommendations of the International Society of Gynecological Pathologists. Int J Gynecol Pathol. 2019;38 Suppl 1(Iss 1 Suppl 1):S64-s74. 25. Sharma A. LR. Endometrioid Carcinoma PathologyOutlines.com2020 [updated 3 september 2020. Available from: 26. Schulte JJ LR. Serous carcinoma PathologyOutlines.com2020 [updated 30 January 2020. Available from: 27. Huvila J, Gilks CB. Clear cell carcinoma 2020 [updated 09-03-2023. Available from: https://www. 28. Urick ME, Bell DW. Clinical actionability of molecular targets in endometrial cancer. Nat Rev Cancer. 2019;19(9):510-21. 29. Kim SR, Cloutier BT, Leung S, Cochrane D, Britton H, Pina A, et al. Molecular subtypes of clear cell carcinoma of the endometrium: Opportunities for prognostic and predictive stratification. Gynecol Oncol. 2020;158(1):3-11. 30. Ito K, Watanabe K, Nasim S, Sasano H, Sato S, Yajima A, et al. Prognostic significance of p53 overexpression in endometrial cancer. Cancer Res. 1994;54(17):4667-70. 31. Geisler JP, Geisler HE, Wiemann MC, Zhou Z, Miller GA, Crabtree W. p53 expression as a prognostic indicator of 5-year survival in endometrial cancer. Gynecol Oncol. 1999;74(3):468-71. 32. van der Putten LJ, Visser NC, van de Vijver K, Santacana M, Bronsert P, Bulten J, et al. L1CAM expression in endometrial carcinomas: an ENITEC collaboration study. Br J Cancer. 2016;115(6):716-24. 33. Bondong S, Kiefel H, Hielscher T, Zeimet AG, Zeillinger R, Pils D, et al. Prognostic significance of L1CAM in ovarian cancer and its role in constitutive NF-κB activation. Ann Oncol. 2012;23(7):1795-802. 34. Allory Y, Matsuoka Y, Bazille C, Christensen EI, Ronco P, Debiec H. The L1 cell adhesion molecule is induced in renal cancer cells and correlates with metastasis in clear cell carcinomas. Clin Cancer Res. 2005;11(3):1190-7. 35. Chen DL, Zeng ZL, Yang J, Ren C, Wang DS, Wu WJ, et al. L1cam promotes tumor progression and metastasis and is an independent unfavorable prognostic factor in gastric cancer. J Hematol Oncol. 2013;6:43. 36. Schröder C, Schumacher U, Fogel M, Feuerhake F, Müller V, Wirtz RM, et al. Expression and prognostic value of L1-CAM in breast cancer. Oncol Rep. 2009;22(5):1109-17. 37. Trovik J, Wik E, Werner HM, Krakstad C, Helland H, Vandenput I, et al. Hormone receptor loss in endometrial carcinoma curettage predicts lymph node metastasis and poor outcome in prospective multicentre trial. Eur J Cancer. 2013;49(16):3431-41. 38. van der Putten LJM, Visser NCM, van de Vijver K, Santacana M, Bronsert P, Bulten J, et al. Added Value of Estrogen Receptor, Progesterone Receptor, and L1 Cell Adhesion Molecule Expression to Histology-Based Endometrial Carcinoma Recurrence Prediction Models: An ENITEC Collaboration Study. Int J Gynecol Cancer. 2018;28(3):514-23. 39. van Weelden WJ, Reijnen C, Küsters-Vandevelde HVN, Bulten J, Bult P, Leung S, et al. The cutoff for estrogen and progesterone receptor expression in endometrial cancer revisited: a European Network for Individualized Treatment of Endometrial Cancer collaboration study. Hum Pathol. 2021;109:80-91. 40. Bell DW, Ellenson LH. Molecular Genetics of Endometrial Carcinoma. Annu Rev Pathol. 2019;14:339-67. 41. McDonald ME, Bender DP. Endometrial Cancer: Obesity, Genetics, and Targeted Agents. Obstet Gynecol Clin North Am. 2019;46(1):89-105. 42. Nakamura M, Obata T, Daikoku T, Fujiwara H. The Association and Significance of p53 in Gynecologic Cancers: The Potential of Targeted Therapy. Int J Mol Sci. 2019;20(21).

CHAPTER 1 22 43. Chen J, Gao F, Liu N. L1CAM promotes epithelial to mesenchymal transition and formation of cancer initiating cells in human endometrial cancer. Exp Ther Med. 2018;15(3):2792-7. 44. Bosse T, Nout RA, Stelloo E, Dreef E, Nijman HW, Jürgenliemk-Schulz IM, et al. L1 cell adhesion molecule is a strong predictor for distant recurrence and overall survival in early stage endometrial cancer: pooled PORTEC trial results. Eur J Cancer. 2014;50(15):2602-10. 45. Zeimet AG, Reimer D, Huszar M, Winterhoff B, Puistola U, Azim SA, et al. L1CAM in early-stage type I endometrial cancer: results of a large multicenter evaluation. J Natl Cancer Inst. 2013;105(15):1142-50. 46. Reijnen C, Gogou E, Visser NCM, Engerud H, Ramjith J, van der Putten LJM, et al. Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study. PLoS Med. 2020;17(5):e1003111. 47. Dellinger TH, Smith DD, Ouyang C, Warden CD, Williams JC, Han ES. L1CAM is an independent predictor of poor survival in endometrial cancer - An analysis of The Cancer Genome Atlas (TCGA). Gynecol Oncol. 2016;141(2):336-40. 48. Köbel M, Ronnett BM, Singh N, Soslow RA, Gilks CB, McCluggage WG. Interpretation of P53 Immunohistochemistry in Endometrial Carcinomas: Toward Increased Reproducibility. Int J Gynecol Pathol. 2019;38 Suppl 1(Iss 1 Suppl 1):S123-s31. 49. Monsur M, Yamaguchi M, Tashiro H, Yoshinobu K, Saito F, Erdenebaatar C, et al. Endometrial cancer with a POLE mutation progresses frequently through the type I pathway despite its high-grade endometrioid morphology: a cohort study at a single institution in Japan. Med Mol Morphol. 2021;54(2):133-45. 50. Van Gool IC, Stelloo E, Nout RA, Nijman HW, Edmondson RJ, Church DN, et al. Prognostic significance of L1CAM expression and its association with mutant p53 expression in high-risk endometrial cancer. Mod Pathol. 2016;29(2):174-81. 51. Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, Shen H, et al. Integrated genomic characterization of endometrial carcinoma. Nature. 2013;497(7447):67-73. 52. Jamieson A, Thompson EF, Huvila J, Gilks CB, McAlpine JN. p53abn Endometrial Cancer: understanding the most aggressive endometrial cancers in the era of molecular classification. Int J Gynecol Cancer. 2021;31(6):907-13. 53. Leon-Castillo A, Horeweg N, Peters EEM, Rutten T, Ter Haar N, Smit V, et al. Prognostic relevance of the molecular classification in high-grade endometrial cancer for patients staged by lymphadenectomy and without adjuvant treatment. Gynecol Oncol. 2022;164(3):577-86. 54. Talhouk A, McConechy MK, Leung S, Li-Chang HH, Kwon JS, Melnyk N, et al. A clinically applicable molecular-based classification for endometrial cancers. Br J Cancer. 2015;113(2):299-310. 55. Bosse T, Nout RA, McAlpine JN, McConechy MK, Britton H, Hussein YR, et al. Molecular Classification of Grade 3 Endometrioid Endometrial Cancers Identifies Distinct Prognostic Subgroups. Am J Surg Pathol. 2018;42(5):561-8. 56. Piulats JM, Guerra E, Gil-Martín M, Roman-Canal B, Gatius S, Sanz-Pamplona R, et al. Molecular approaches for classifying endometrial carcinoma. Gynecol Oncol. 2017;145(1):200-7. 57. Modugno F, Ness RB, Chen C, Weiss NS. Inflammation and endometrial cancer: a hypothesis. Cancer Epidemiol Biomarkers Prev. 2005;14(12):2840-7. 58. Birgegård G, Aapro MS, Bokemeyer C, Dicato M, Drings P, Hornedo J, et al. Cancer-related anemia: pathogenesis, prevalence and treatment. Oncology. 2005;68 Suppl 1:3-11. 59. Nie D, Yang E, Li Z. Pretreatment thrombocytosis predict poor prognosis in patients with endometrial carcinoma: a systematic review and meta-analysis. BMC Cancer. 2019;19(1):73. 60. Ye Q, Wu Z, Xia T, Liu D, Yang Y, Tang H. Pre-treatment thrombocytosis predicts prognosis of endometrial cancer: A meta-analysis of 11 studies. Exp Ther Med. 2020;19(1):359-66. 61. Worley MJ, Jr., Nitschmann CC, Shoni M, Vitonis AF, Rauh-Hain JA, Feltmate CM. The significance of preoperative leukocytosis in endometrial carcinoma. Gynecol Oncol. 2012;125(3):561-5. 62. Koukourakis MI, Giatromanolaki A, Sivridis E, Fezoulidis I. Cancer vascularization: implications in radiotherapy? Int J Radiat Oncol Biol Phys. 2000;48(2):545-53.

GENERAL INTRODUCTION 23 1 63. Cho Y, Kim KH, Yoon HI, Kim GE, Kim YB. Tumor-related leukocytosis is associated with poor radiation response and clinical outcome in uterine cervical cancer patients. Ann Oncol. 2016;27(11):2067-74. 64. Reijnen C, IntHout J, Massuger L, Strobbe F, Kusters-Vandevelde HVN, Haldorsen IS, et al. Diagnostic Accuracy of Clinical Biomarkers for Preoperative Prediction of Lymph Node Metastasis in Endometrial Carcinoma: A Systematic Review and Meta-Analysis. Oncologist. 2019;24(9):e880-e90. 65. Njolstad TS, Engerud H, Werner HM, Salvesen HB, Trovik J. Preoperative anemia, leukocytosis and thrombocytosis identify aggressive endometrial carcinomas. Gynecol Oncol. 2013;131(2):410-5. 66. Tamussino KF, Gücer F, Reich O, Moser F, Petru E, Scholz HS. Pretreatment hemoglobin, platelet count, and prognosis in endometrial carcinoma. Int J Gynecol Cancer. 2001;11(3):236-40. 67. Bai YY, Du L, Jing L, Tian T, Liang X, Jiao M, et al. Clinicopathological and prognostic significance of pretreatment thrombocytosis in patients with endometrial cancer: a meta-analysis. Cancer Manag Res. 2019;11:4283-95. 68. Salem H, Abu-Zaid A, Aloman O, Abuzaid M, Alsabban M, Elhassan T, et al. Preoperative Leukocytosis as a Prognostic Marker in Endometrioid-Type Endometrial Cancer: A Single-Center Experience from Saudi Arabia. Gulf J Oncolog. 2020;1(32):51-8. 69. Abu-Zaid A, Alomar O, Baradwan S, Abuzaid M, Alshahrani MS, Allam HS, et al. Preoperative leukocytosis correlates with unfavorable pathological and survival outcomes in endometrial carcinoma: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2021;264:88-96. 70. Abu-Zaid A, Alomar O, Abuzaid M, Baradwan S, Salem H, Al-Badawi IA. Preoperative anemia predicts poor prognosis in patients with endometrial cancer: A systematic review and meta-analysis. Eur J Obstet Gynecol Reprod Biol. 2021;258:382-90. 71. Colombo N, Creutzberg C, Amant F, Bosse T, Gonzalez-Martin A, Ledermann J, et al. ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer: Diagnosis, Treatment and Follow-up. Int J Gynecol Cancer. 2016;26(1):2-30. 72. Bendifallah S, Canlorbe G, Collinet P, Arsène E, Huguet F, Coutant C, et al. Just how accurate are the major risk stratification systems for early-stage endometrial cancer? Br J Cancer. 2015;112(5):793-801. 73. Kang S, Kang WD, Chung HH, Jeong DH, Seo SS, Lee JM, et al. Preoperative identification of a low-risk group for lymph node metastasis in endometrial cancer: a Korean gynecologic oncology group study. J Clin Oncol. 2012;30(12):1329-34. 74. Wang Z, Zhang S, Ma Y, Li W, Tian J, Liu T. A nomogram prediction model for lymph node metastasis in endometrial cancer patients. BMC Cancer. 2021;21(1):748. 75. Jiang P, Huang Y, Tu Y, Li N, Kong W, Di F, et al. Combining Clinicopathological Parameters and Molecular Indicators to Predict Lymph Node Metastasis in Endometrioid Type Endometrial Adenocarcinoma. Front Oncol. 2021;11:682925. 76. Berg HF, Ju Z, Myrvold M, Fasmer KE, Halle MK, Hoivik EA, et al. Development of prediction models for lymph node metastasis in endometrioid endometrial carcinoma. Br J Cancer. 2020;122(7):1014-22. 77. Koskas M, Fournier M, Vanderstraeten A, Walker F, Timmerman D, Vergote I, et al. Evaluation of models to predict lymph node metastasis in endometrial cancer: A multicentre study. Eur J Cancer. 2016;61:52-60. 78. Weinberger V, Bednarikova M, Hausnerova J, Ovesna P, Vinklerova P, Minar L, et al. A Novel Approach to Preoperative Risk Stratification in Endometrial Cancer: The Added Value of Immunohistochemical Markers. Front Oncol. 2019;9:265. 79. Lee JY, Jung DC, Park SH, Lim MC, Seo SS, Park SY, et al. Preoperative prediction model of lymph node metastasis in endometrial cancer. Int J Gynecol Cancer. 2010;20(8):1350-5. 80. Keys HM, Roberts JA, Brunetto VL, Zaino RJ, Spirtos NM, Bloss JD, et al. A phase III trial of surgery with or without adjunctive external pelvic radiation therapy in intermediate risk endometrial adenocarcinoma: a Gynecologic Oncology Group study. Gynecol Oncol. 2004;92(3):744-51. 81. Creutzberg CL, van Putten WL, Koper PC, Lybeert ML, Jobsen JJ, Wárlám-Rodenhuis CC, et al. Surgery and postoperative radiotherapy versus surgery alone for patients with stage-1 endometrial carcinoma: multicentre randomised trial. PORTEC Study Group. Post Operative Radiation Therapy in Endometrial Carcinoma. Lancet. 2000;355(9213):1404-11.

CHAPTER 1 24 82. León-Castillo A, de Boer SM, Powell ME, Mileshkin LR, Mackay HJ, Leary A, et al. Molecular Classification of the PORTEC-3 Trial for High-Risk Endometrial Cancer: Impact on Prognosis and Benefit From Adjuvant Therapy. J Clin Oncol. 2020:Jco2000549. 83. Jamieson A, Bosse T, McAlpine JN. The emerging role of molecular pathology in directing the systemic treatment of endometrial cancer. Ther Adv Med Oncol. 2021;13:17588359211035959. 84. Bosse T, Powell M, Crosbie E, Leary A, Kroep J, Han K, et al. 595 Implementation of collaborative translational research (TransPORTEC) findings in an international endometrial cancer clinical trials program (RAINBO). International Journal of Gynecologic Cancer. 2021;31(Suppl 3):A108-A9.


CHAPTER 2 28 ABSTRACT Objective To evaluate whether the amount of preoperative endometrial tissue surface is related to the degree of concordance with final low- and high-grade endometrial cancer (EC). In addition, to determine whether discordance is influenced by sampling method and impacts outcome. Methods A retrospective cohort study within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC). Surface of preoperative endometrial tissue samples was digitally calculated using ImageJ. Tumor samples were classified into low-grade (grade 1-2 endometrioid EC (EEC)) and high-grade (grade 3 EEC + non-endometroid EC). Results The study cohort included 573 tumor samples. Overall concordance between pre- and postoperative diagnosis was 60.0%, and 88.8% when classified into low- and high-grade EC. Upgrading (preoperative low-grade, postoperative high-grade EC) was found in 7.8% and downgrading (preoperative high-grade, postoperative low-grade EC) in 26.7%. The median endometrial tissue surface was significantly lower in concordant diagnoses when compared to discordant diagnoses, respectively 18.7 mm2 and 23.5 mm2 (P=0.022). Sampling method did not influence the concordance in tumor classification. Patients with preoperative highgrade and postoperative low-grade EC showed significant lower DSS compared to patients with concordant low-grade EC (P=0.039). Conclusion The amount of preoperative endometrial tissue surface was inversely related to the degree of concordance with final tumor low- and high-grade. Obtaining higher amount of preoperative endometrial tissue surface does not increase the concordance between pre- and postoperative low- and high-grade diagnosis in EC. Awareness of clinically relevant down- and upgrading is crucial to reduce subsequent over- or undertreatment with impact on outcome.

AMOUNT OF PREOPERATIVE ENDOMETRIAL TISSUE 29 2 INTRODUCTION Endometrial cancer (EC) is the most common gynecological malignancy in industrialized developed countries with an increasing incidence.1-3 These carcinomas are histopathological classified as either endometrioid endometrial cancer (EEC) or non-endometrioid endometrial cancer (NEEC).4 Primary surgical treatment for EC consist of hysterectomy and bilateral salpingo-oophorectomy.5, 6 Additional lymph node surgery, i.e. sentinel lymph node mapping, lymph node dissection or algorithm-based approach for staging, is recommended in patients with increased risk of lymph node metastasis (LNM).7, 8 The recent ESGO-ESTRO-ESP guideline recommended a modified binary FIGO grading considering both grade 1 and 2 EC together as low-grade EC and grade 3 EC and NEEC as high-grade EC.9 Most patients are diagnosed with low-grade EC, and generally have a favorable prognosis with a 5-year survival rate of 85.6%.5 About 20.0% of the patients are diagnosed with high-grade EC, have an overall poor prognosis with a 5-year survival rate of 58.8% and are associated with increased risk of regional or distant metastases.5, 10 A meta-analysis has shown only moderate concordance of 67.0% between pre- and postoperative tumor grading.11 The lowest concordance was found for grade 2 EC (61.0%), and as these are generally classified as low-grade EC, disagreement in grading might impact treatment and outcome since performance of lymph node surgery is generally performed in high-grade EC only.9, 12, 13 Explanations for discordance on grade include 1) sampling errors leading to missed tumor components, 2) interobserver disagreement due to subjective interpretation of the defined criteria and 3) limited amount of tissue obtained by preoperative endometrial sampling, that might impair assessment of tumor characteristics. In 13-30% of the pipelle endometrial samples, insufficient material requires repeated biopsy for a reliable diagnosis, as in 7.3% of the failed samples women are subsequently diagnosed with EC.1417 Interestingly, Visser et al. showed that hysteroscopic biopsies had a higher concordance (89%) compared to samples obtained by dilatation and curettage (D&C) (70%), questioning whether in addition to the amount of tissue, the sampling method may also be relevant.11 In a previous study of our research group, we showed that the amount of endometrial tissue surface to classify an endometrial sample as conclusive with high diagnostic accuracy as malignant or non-malignant, was defined by a minimum cut-off level of 35 mm2.11, 14 However, this study was not designed to further specify the diagnosis on tumor grade and/ or histological subtype. Therefore, in the present study, we aim to evaluate the amount of preoperative endometrial tissue surface in relation to the degree of concordance with final low- and high-grade EC. Furthermore, we investigate whether discordancy in pre- and postoperative grading is influenced by the sampling method and whether discordancy impacts outcome.

CHAPTER 2 30 METHODS Patients The samples of patients were retrospectively collected within the European Network for Individualized Treatment of Endometrial Cancer (ENITEC) from a previous study including 1199 EC patients.15 Patients were only included when they were diagnosed by an expert gynecological pathologist of the participating hospitals, with complete data on treatment and histopathology. Clinical and pathological data were recorded from the patient files into a database; including patient age, date of diagnosis, preoperative sampling method, surgical treatment, original pre- and postoperative tumor grade and histological subtype, myometrial invasion (MI), cervical invasion (CI), lymphovascular space invasion (LVSI), FIGO (International Federation of Gynecology and Obstetrics) stage, adjuvant treatment, recurrent disease and death.15 The sole additional inclusion criterion used for this study was the availability of preoperative EC tissue samples, resulting in 598 patients. Tumor classification In addition to the FIGO three-tiered tumor grade, EC tissue samples were classified into low- and high-grade EC as recommended by the recent ESGO-ESTRO-ESP guideline and the World health organization (WHO) classification of tumors.9, 18 Low-grade EC was defined as grade 1 and 2 EEC, and included samples with mucinous histology as well, since prognosis and molecular characterization are similar to low-grade EECs.15 High-grade EC included grade 3 EEC and NEEC, i.e. serous, clear cell carcinoma, carcinosarcoma and mixed carcinomas.9, 18 Endometrial tissue samples were defined as upgraded if the preoperative sample was lowgrade and postoperative high-grade EC. Downgraded was defined as preoperative high-grade and postoperative low-grade EC. Biopsies initially diagnosed as premalignant, but EC on final hysterectomy specimen were included in this study. Scoring All the preoperative endometrial sampling slides were digitalized using Pannoramic Scanner 250 Flash III (3DHISTECH, Budapest, Hungary). As described previously by Reijnen et al., images were saved as a JPEG-compressed file and the area of endometrial tissue was digitally calculated using ImageJ software, selecting only benign, premalignant and malignant endometrial epithelium (Supplementary Figure S1).14 Thresholds 24-bit RGB images based on Hue Saturation and Brightness (HSB) were used to select the endometrial tissue surface, by adjusting the different threshold values to segment the image into the area of interest and the background. The Pannoramic Viewer software was used to examine the original-size digital slide in order to ensure ImageJ correctly selected the proper tissue. Subsequently, analysis was performed on the area selection to count and measure pixels in the threshold images and calculate the total area of endometrial tissue. A set of 50 slides were scored

AMOUNT OF PREOPERATIVE ENDOMETRIAL TISSUE 31 2 independently by two investigators (AH, CR) to assess the degree of inter-rater variability and intraclass correlation coefficient (ICC). A set of 90 slides were double-checked by a third investigator (SV) to ensure ImageJ selected the proper tissue. Statistical analysis All statistical analyses were performed using IBM Statistical Package for the Social Sciences (SPSS) statistics for Windows, version 25.0 (released 2017, Armonk, NY, United States) and P<0.05 was considered statistically significant. For observing within the low- and highgrade classification, the pre- and postoperative tumor diagnosis was specified in individual FIGO tumor grade and histological subtype. These included the original diagnosis (including premalignant tissue); grade 1, grade 2, grade 3 EEC or NEEC. For continuous data that were not normally distributed, the Mann-Whitney U and Kruskal Wallis test were used to compare the differences in median endometrial tissue surface and patient characteristics. Clinicopathological characteristics between dichotomous subgroups were compared using the χ2 or Fisher’s exact test for categorical data. Survival analyses were performed using the Kaplan Meier curves (first 10 years after diagnosis). Disease-specific survival (DSS) was defined as time from date of diagnosis to date of death from EC, all censored by date of last contact. RESULTS Patients From the original cohort of 1199 patients, 644 preoperative biopsies were available, of those 46 patients were excluded because absence of tumor tissue due to insufficient amount of tissue and benign endometrium and 25 because of an unspecified grade on preoperative biopsy, resulting in a total of 573 patients included in this study with a median follow-up of 5.7 years (Supplementary Figure S2). Excluded patients did not significantly differ from included patients with respect to tumor histology (data not shown). Baseline characteristics for all included patients, classified into postoperative low- and high-grade EC, are summarized in Table 1. Among these 573 patients, 462 patients (80.6%) were postoperative low-grade and 111 (19.4%) high-grade EC. The mean age at diagnosis was 64.8 years, most patients were preoperative diagnosed with grade 1 EEC (53.8%) and postoperative FIGO stage I (82.9%). The most used preoperative sampling method was the pipelle (45.2% ). Patients diagnosed with postoperative high-grade EC were significantly older, had lower Body Mass Index (BMI), more often LNM, subsequently resulting in more applied adjuvant chemotherapy and chemoradiotherapy compared to patients with low-grade EC.

CHAPTER 2 32 In Supplementary Table S1 detailed baseline information about patients diagnosed with postoperative NEEC (n=34) is shown. Most patients with NEEC had serous histology (n=14, 41.2%). Concordance pre- and postoperative tumor grade and histology Figure 1 shows the number and percentages of the pre- vs. postoperative individual tumor grade and histological subtype. Dark green shows the exact concordance between grading and histology, light green the concordance for the clinically relevant low- and high-grade classification and in red the clinically relevant discordancy. Overall, of the 573 EC tissue samples, 60.0% (n=345) showed concordant pre- and postoperative tumor grade and histological subtype (dark green). The lowest concordance was found for preoperative grade 3 EC (51.4%). Concordance between pre-and postoperative low- and high-grade EC was found in 88.8% (n=509) patients (light green + dark green). Patients with preoperative low-grade EC showed concordant diagnoses in 92.2% (n=435) and were upgraded to high-grade EC in 7.8% (n=37). Patients with preoperative high-grade EC showed concordant diagnoses in 73.3% (n=74) and were downgraded in 26.7% (n=27). Grade1 2 (7.4) Grade1 5 (6.8) Grade1 33 (21.2) Grade1 192 (62.3) Grade1 5 (62.5) Grade2 3 (11.1) Grade2 17 (23.0) Grade2 99 (63.5) Grade2 103 (33.4) Grade2 3 (37.5) Grade3 6 (22.2) Grade3 38 (51.4) Grade3 20 (12.8) Grade3 13 (4.3) NEEC 0 (0.0) NEEC 16 (59.3) NEEC 14 (18.8) NEEC 4 (2.5) NEEC 0 (0.0) Grade 3 0 (0.0) 0 20 40 60 80 100 NEEC Grade 3 EEC Grade 2 EEC Grade 1 EEC Premalignant Postoperative Preoperative Percentage Figure 1. Number and percentages (n (%)) of the pre- vs. postoperative individual tumor grade and histological subtype. Abbreviations: EEC, endometroid endometrial cancer; NEEC, non-endometroid endometrial cancer

AMOUNT OF PREOPERATIVE ENDOMETRIAL TISSUE 33 2 Table 1. Baseline characteristics Total (n=573) Postoperative Low-grade (n=462) Postoperative High-grade (n=111) P Age (years) 64.8 ± 9.8 64.1 ± 9.6 66.6 ± 10.0 0.014* BMI (kg/m2) 30.2 ± 6.7 30.4 ± 6.5 28.7 ± 5.5 0.013* Preoperative grade Premalignant† 8 (1.4) 8 (1.7) 0 (0.0) <0.001* 1 EEC 308 (53.8) 295 (63.9) 13 (11.7) 2 EEC 156 (27.2) 132 (28.6) 24 (21.6) 3 EEC 74 (12.9) 22 (4.8) 52 (46.8) NEEC 27 (4.7) 5 (1.1) 22 (19.8) Preoperative sampling method Pipelle 259 (45.2) 199 (43.1) 60 (54.1) 0.002* D&C 77 (13.4) 63 (13.6) 14 (12.6) Hysteroscopic biopsy 213 (37.2) 189 (40.9) 24 (21.6) Not specified 24 (4.2) 11 (2.4) 13 (11.7) FIGO stage I 475 (82.9) 413 (89.4) 62 (55.9) <0.001* II 36 (6.3) 24 (5.2) 12 (10.8) III 45 (7.9) 22 (4.8) 23 (20.7) IV 17 (2.9) 3 (0.6) 14 (12.6) Positive nodes No 299 (52.2) 240 (52.0) 59 (53.2) <0.001* Pelvic 17 (3.0) 7 (1.5) 10 (9.0) Para-aortic 11 (1.9) 2 (0.4) 9 (8.1) Both 5 (0.9) 1 (0.2) 4 (3.6) Not specified 241 (42.0) 212 (45.9) 29 (26.1) Adjuvant treatment No 267 (46.7) 238 (51.5) 29 (26.1) <0.001* Radiotherapy 263 (46.0) 204 (44.2) 59 (53.2) Chemotherapy 17 (3.0) 5 (1.1) 12 (10.8) Chemoradiotherapy 25 (4.4) 14 (3.0) 11 (9.9) Missing 1 (0.2) 1 (0.2) Data is presented in number (%), mean ± standard deviation (SD) Abbreviations: EEC, endometrioid endometrial cancer; NEEC, non-endometroid endometrial cancer; BMI, Body Mass Index; FIGO, International Federation of Gynecology and Obstetrics * P<0.05 †including simple or complex hyperplasia, with or without atypia.

CHAPTER 2 34 Table 2. Overview of pre- vs. postoperative tumor grade and histological subtype. Median endometrial tissue surface (mm2) of endometrial cancer patients are shown. Displayed in dark green are the concordant diagnoses. Dark green shows the exact concordance between grading and histology, light green the concordance for the clinically relevant low- and high-grade classification and in red the clinically relevant discordancy. Postoperative Grade 1 EEC Grade 2 EEC Grade 3 EEC NEEC Total** Preoperative Premalignant 7.3 (0.8-8.4) 2.9 (1.0-3.5) NA NA 4.4 (0.8-8.4) Grade 1 EEC 17.2 (0.2-298.7) 15.6 (0.0-354.0) 16.1 (0.5-145.0) NA 16.6 (0.0-354.0) Grade 2 EEC 35.1 (1.0-251.4) 21.9 (0.6-278.7) 30.0 (1.9-110.9) 18.2 (10.1-30.6) 24.6 (0.6-278.7) Grade 3 EEC 42.4 (12.5-94.2) 38.6 (0.4-274.9) 29.7 (0.2-210.2) 16.1 (1.4-81.7) 24.4 (0.2-274.9) NEEC 26.7 (9.8-43.6) 16.6 (0.1-44.7) 11.5 (0.9-18.1) 21.1 (0.7-49.4) 14.7 (0.1-49.3) Total* 18.7 (0.2-298.7) 19.9 (0.0-354.0) 23.3 (0.2-210.2) 17.6 (0.7-81.7) Data is presented in median (range). Abbreviations: EEC, endometroid endometrial cancer; NEEC, non-endometroid endometrial cancer * P=0.888 between the total median postoperative endometrial tissue surface ** P=0.063 between the total median preoperative endometrial tissue surface

AMOUNT OF PREOPERATIVE ENDOMETRIAL TISSUE 35 2 Median endometrial tissue surface and degree of concordance An overview of the median endometrial tissue surface related to pre- vs. postoperative tumor grade and histological subtype is shown in Table 2. There was no significant difference between the median endometrial tissue surface of the individual tumor grade and histological subtype preoperatively, nor postoperatively, (P=0.063 and P=0.888, respectively). The median endometrial tissue surface between concordant (dark green) and discordant (light green + red) individual tumor grade and histological subtype showed no significant difference (19.6 mm2 vs. 18.6mm2, respectively, P=0.468). For the clinically relevant low- and high-grade classification, the median endometrial tissue surface for concordant diagnoses (dark green + light green) was significant lower compared to the discordant diagnoses (red) (18.7 mm2 vs. 23.5 mm2, respectively, P=0.022) (Table 2). In Supplementary Table S2 the correlation between median endometrial tissue and concordant and discordant diagnoses is shown per included center. Patients with concordant pre- and postoperative low-grade EC showed lower median endometrial tissue surface compared to preoperative low-grade and postoperative highgrade EC (upgraded), but not significantly (18.4 vs 20.1 mm2, P=0.335). Patients with concordant pre- and postoperative high-grade EC had significant lower endometrial tissue surface compared to patients with preoperative high-grade and postoperative low-grade EC (downgraded) (20.3 vs 38.6 mm2, P=0.044) (Figure 2). B. A. Figure 2 A-B. A. Patients with preoperative low-grade endometrial cancer (EC) and the median endometrial tissue surface for postoperative discordant or concordant diagnoses. B. Patients with preoperative high-grade EC and the median endometrial tissue surface for postoperative discordant or concordant diagnoses.