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Original research
A novel algorithm to implement the molecular classification according to the new ESGO/ESTRO/ESP 2020 guidelines for endometrial cancer
  1. Ilaria Betella1,
  2. Caterina Fumagalli2,3,
  3. Paola Rafaniello Raviele4,
  4. Gabriella Schivardi1,5,
  5. Luigi Antonio De Vitis1,
  6. Maria Teresa Achilarre1,
  7. Alessia Aloisi1,
  8. Annalisa Garbi1,
  9. Matteo Maruccio1,
  10. Vanna Zanagnolo1,
  11. Giovanni Aletti1,6,
  12. Elena Guerini-Rocco4,6,
  13. Andrea Mariani5,
  14. Angelo Maggioni1,
  15. Massimo Barberis4,
  16. Nicoletta Colombo1,7 and
  17. Francesco Multinu1
  1. 1 Department of Gynecology, European Institute of Oncology (IEO), IRCCS, Milan, Italy
  2. 2 Clinical Unit of Oncogenomics, European Institute of Oncology (IEO), IRCCS, Milan, Italy
  3. 3 Department of Diagnostic Services, Division of Pathology, Azienda Socio Sanitaria Territoriale della Valle Olona, Gallarate, Italy
  4. 4 Department of Pathology, European Institute of Oncology (IEO), IRCCS, Milan, Italy
  5. 5 Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, New York, USA
  6. 6 Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
  7. 7 Faculty of Medicine and Surgery, Universita degli Studi di Milano-Bicocca, Milan, Italy
  1. Correspondence to Dr Ilaria Betella, Department of Gynecology, European Institute of Oncology (IEO), IRCCS, Milan, Lombardy, Italy; ilaria.betella{at}ieo.it

Abstract

Objective To compare the risk class attribution with molecular classification unknown to those with molecular classification known, according to the European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology (ESGO/ESTRO/ESP) 2020 guidelines on endometrial cancer, with a focus on risk group migration. Additionally, to evaluate the capability of a novel molecular analysis algorithm to reduce the number of required tests.

Methods We conducted a retrospective study including all consecutive patients with endometrial cancer undergoing surgery and comprehensive molecular analyses between April 2019 and December 2021. Molecular analyses including immunohistochemistry for p53 and mismatch repair (MMR) proteins, and DNA sequencing for POLE exonuclease domain were performed to classify tumors as POLE-mutated (POLE), MMR-deficient (MMR-d), p53 abnormal (p53abn), or non-specific molecular profile (NSMP). The two risk classifications of the ESGO/ESTRO/ESP 2020 guidelines were compared to estimate the proportion of patients in which the molecular analysis was able to change the risk class attribution. We developed a novel algorithm where the molecular analyses are reserved only for patients in whom incorporation of the molecular classification could change the risk class attribution.

Results A total of 278 patients were included. Molecular analyses were successful for all cases, identifying the four subgroups: 27 (9.7%) POLE, 77 (27.7%) MMR-d, 49 (17.6%) p53abn, and 125 (45.0%) NSMP. Comparison of risk class attribution between the two classification systems demonstrated discordance in the risk class assignment in 19 (6.8%, 95% CI 4.2% to 10.5%) cases. The application of our novel algorithm would have led to a reduction in the number of POLE sequencing tests by 67% (95% CI 61% to 73%) and a decrease of p53 immunohistochemistry by 27% (95% CI 22% to 33%), as compared with the application of molecular classification to all patients.

Conclusion Molecular categorization of endometrial cancer allows the reallocation of a considerable proportion of patients in a different risk class. Furthermore, the application of our algorithm enables a reduction in the number of required tests without affecting the risk classification.

  • Endometrial Neoplasms
  • Pathology

Data availability statement

Data are available upon reasonable request.

https://doi.org/10.1136/ijgc-2022-003480

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    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • The ESGO/ESTRO/ESP 2020 guidelines for endometrial cancer introduced into routine clinical practice the need for molecular analysis, which has not yet been implemented in many institutions, mainly due to the high cost of POLE sequencing.

    WHAT THIS STUDY ADDS

    • The integration of the molecular classification evaluation to the clinicopathologic features allowed reallocation of 6.8% of cases of endometrial cancer in a different risk class. We proposed a novel algorithm where the molecular analyses are reserved for patients in whom incorporation of the molecular classification could change the risk class attribution. The application of this algorithm enables a reduction in the number of required POLE tests by 67% and p53 tests by 27%, without affecting the risk classification.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

    • In resource-limited settings, the application of this novel algorithm enables assignment of the proper risk class and, consequently, the appropriate indication for adjuvant treatment, according to ESGO/ESTRO/ESP guidelines.

    INTRODUCTION

    Endometrial cancer is the most common gynecologic cancer, with 417 367 new cases and 97 370 cancer-related deaths estimated worldwide in 2020.1 Although in the majority of patients the disease will present at an early stage (confined to the uterus) and has an excellent prognosis, approximately 15% of patients will experience a disease relapse.2

    Until recently, tumor type and histologic characteristics were the best available instruments for risk stratification and for predicting prognosis. Based on clinicopathological features, including age, histotypes, International Federation of Gynecology and Obstetrics (FIGO) stage, myometrial invasion, tumor grading, and lymphovascular space invasion, endometrial cancers were divided in four categories, defined as low, intermediate, high-intermediate, and high-risk, which had prognostic value and also guided adjuvant treatment indications.3–7 However, none of the risk stratification systems based on histology alone have shown high accuracy in stratifying the risk of recurrence.8

    In an attempt to overcome these limitations, a new molecular classification has been described by The Cancer Genome Atlas (TCGA) research network.9 In the clinical practice, the TCGA molecular classes can be determined with high accuracy by the evaluation of three surrogate markers: POLE gene, p53 expression, and mismatch repair (MMR) protein expression.10 11 Testing these three surrogates allows the classification of endometrial cancers in four categories: (1) POLE-mutant (POLE); (2) MMR-deficient (MMR-d); (3) p53-abnormal (p53abn); (4) non-specific molecular profile (NSMP) or p53-wild-type. These molecular categories have been related to prognosis and appear useful in predicting the response to adjuvant treatment.9 12–15

    The recently revised European Society of Gynaecological Oncology/European Society for Radiotherapy and Oncology/European Society of Pathology (ESGO/ESTRO/ESP) guidelines for the management of endometrial cancers, including two different risk classifications based on whether the molecular classification is either known or unknown, represent the first attempt to integrate the classic clinicopathological risk factors with the recently proposed molecular classification.16 However, these guidelines introduce into routine clinical practice a need for more extensive molecular analysis. In fact, although the evaluation of p53 by immunohistochemistry is widely available in the majority of institutions, and MMR evaluation has progressively entered into standard practice for Lynch syndrome screening17 18 and for guiding immune therapy in the metastatic or recurrent setting,19 POLE sequencing is a newly introduced molecular marker, which requires techniques that still have to be implemented in the majority of institutions.

    In the present study, we evaluate the extent of risk group migration associated with the addition of the molecular classification, according to ESGO/ESTRO/ESP guidelines. Furthermore, in order to meet the newly emerged clinical need, we propose a new algorithm for the molecular analysis that can be used in the presence of limited resources to reduce the number of required tests without affecting the allocation of risk class and, consequently, the indication for the appropriate adjuvant treatment.

    METHODS

    This single-institution retrospective study was approved by our institutional review board (UID2418). All participants provided written permission for use of their medical records for research studies. From our institutional electronic database, we identified all consecutive patients with endometrial cancer who underwent surgical staging between April 2019 and December 2021 at the European Institute of Oncology (IEO) in Milan, Italy.

    Demographic, clinicopathological, and surgical characteristics were abstracted from the electronic clinical records. Mucinous histotype was excluded. Based on histomorphologic features, every endometrial cancer was categorized according to ESGO/ESTRO/ESP guidelines in one of five risk groups with molecular classification unknown.16

    Immunohistochemistry and Molecular Analyses

    Representative formalin-fixed, paraffin-embedded tumor tissues were selected by a trained pathologist for molecular analyses, including (1) MMR proteins immunohistochemistry, (2) p53 immunohistochemistry, and (3) POLE sequencing. A detailed description of how immunohistochemistry and sequencing were performed is given in the supplemental materials (Online supplemental file 1).

    Supplemental material

    Briefly, after staining with antibodies for MSH6, PMS2, MSH2, and MLH1 proteins, MMR status was classified as proficient (MMR-p), deficient (MMR-d), and equivocal (MMR-e) in accordance with the expression of proteins (Online supplemental file 2). Cases with MMR-e or immunohistochemistry not feasible were further investigated using the Idylla microsatellite instability assay (Biocartis, Mechelen, Belgium). A tumor was considered MMR-d if either MMR proteins were not expressed or the microsatellite instability evaluation was positive.

    Immunohistochemical staining of p53 was classified as normal (wild-type) or abnormal (in the presence of one of the following aberrant patterns: overexpression, null phenotype, or cytoplasmic staining, in accordance with the literature20 21).

    POLE analysis was performed by next generation sequencing. Mutations in exons 9–14 were classified in accordance with the literature.22

    All cases were subsequently placed in one of the following four categories: (1) POLE; (2) MMR-d; (3) p53abn; (4) NSMP. In the presence of ‘multiple classifiers’ (the combination of two or more positive molecular classifying surrogates), POLE-mutated–p53abn were classified as POLE, MMR-d–p53abn as MMR-d, and POLE-mutated–MMR-d–p53abn as POLE.23

    After molecular class attribution, every case was categorized according to the ESGO/ESTRO/ESP guidelines in one of five risk groups with molecular classification known.16 The turnaround time was defined as the time from the test request to the final molecular report.

    Molecular Analysis Algorithm

    In the algorithm we propose, molecular analysis is reserved only for endometrial cancers which might change their risk class categorization and post-operative management thanks to molecular analysis, according to ESGO/ESTRO/ESP guidelines.16 Specifically, the new molecular algorithm is applied only to early-stage (stage I–II) endometrial cancer, since advanced stages are considered at high risk and will receive adjuvant therapy regardless of the molecular class. In the presence of early-stage tumor, the analyses of both MMR proteins and p53 expression along with the histological features allows the identification of low-risk cases, which do not require any further molecular analysis. By contrast, all the patients not at low risk should be analyzed for POLE mutation (Figure 1).

    Figure 1
    Figure 1

    Novel algorithm for implementation of molecular analysis in clinical practice. LVSI, lymphovascular space invasion; MMR, mismatch repair; MMR-d, MMR-deficient; p53abn, p53 abnormal; POLE, polymerase epsilon gene).

    Statistical Analysis

    Study data were collected and managed using REDCap (Research Electronic Data Capture) tools.24 25 Data were summarized using standard descriptive statistics; categorical variables were presented as frequency and percentages, while continuous variables were visually tested for normality. For the continuous variables following a normal distribution such as age and body mass index, the mean and SD were reported; while for the turnaround time, which followed a positively skewed distribution, the median and IQR were reported. Clinicopathological characteristics of patients included in this study and the complete cohort of patients treated at our institution during the time period of the study were compared using t-test for continuous variables and Χ2 or Fisher’s exact test for all other categorical baseline characteristics. Clinicopathological characteristics between molecular subgroups were compared with one-way analysis of variance for continuous variables or Χ2 test for categorical variables. All calculated p values were two-sided and p values <0.05 were considered statistically significant. Statistical analysis was performed using JMP (JMP, Version 16. SAS Institute Inc.) statistical software. Graphs were designed with Excel 2016 (Microsoft). In accordance with the journal’s guidelines, we will provide our data for the reproducibility of this study in other centers if such is requested.

    RESULTS

    A total of 341 patients underwent surgery for endometrial cancer at our institution. Among them, 278 (81.5%) received a comprehensive molecular analysis and were included in the present analysis. Characteristics and statistical comparisons between included and excluded patients are presented in Online supplemental file 3. Table 1 shows the clinicopathological features of the overall included population and stratified by molecular class.

    View this table:
    Table 1

    Clinicopathological features and risk class attribution within the whole study population and stratified by molecular classes in accordance with ESGO/ESTRO/ESP guidelines

    Molecular Classes

    All the 278 formalin-fixed paraffin-embedded tumor specimens were suitable for the molecular analyses. The median turnaround time was 4 (IQR 2–7) days for immunohistochemical staining and 8 (IQR 6–10) days for next generation sequencing analysis. Collectively, the molecular data resulted in the definition of four molecular groups: POLE (n=27; 9.7%, 95% CI 6.5% to 13.8%), MMR-d (n=77; 27.7%, 95% CI 22.5% to 33.4%), p53abn (n=49; 17.6%, 95% CI 13.3% to 22.6%), and NSMP (n=125; 45.0%, 95% CI 39.0% to 51.0%), as depicted in Figure 2. Of note, multiple classifiers were found in 25 cases (9.0%, 95% CI 5.9% to 13.0%). Specifically, two were POLE-mutated and MMR-d, six were POLE- mutated and p53 abnormal, 15 were MMR-d and p53-abnormal, two were triple positive (POLE- mutated, MMR-d, and p53-abnormal) (Online supplemental file 4). Patient’s characteristics of each molecular class are presented in Table 1.

    Figure 2
    Figure 2

    Molecular classes workflow. The evaluation of the three molecular surrogates allows the categorization in one of the four molecular classes. At the bottom, a representative case for each molecular class is shown: (from left to right) a POLE-mutated endometrioid carcinoma showing high-grade morphology, a MMR-d cancer characterized by negative immunohistochemistry staining for MLH1, a tumor with abnormal overexpression pattern of p53 immunohistochemisty, and a case of endometrioid carcinoma with non-specific molecular profile. POLE, polymerase epsilon gene; POLE mut, POLE mutated; MMR-d, mismatch repair-deficient; p53abn, p53 abnormal; NSMP, non-specific molecular profile.

    Correlation Between Risk Classification Systems

    The risk class migration, according to ESGO/ESTRO/ESP guidelines, is reported in Table 2.16 Figure 3 shows cross-tabulations between different risk stratification systems. Overall, comparing the two ESGO/ESTRO/ESP risk classifications, a risk class migration was observed in 19 (6.8%; 95% CI 4.2% to 10.5%) patients. Among them, 10 (3.6%; 95% CI 1.7% to 6.5%) patients were reclassified as high risk due to p53 aberrant (3 low risk, 2 intermediate risk, and 5 high-intermediate risk); 3 (1.1%; 95% CI 0.2% to 3.1%) low risk were reclassified as intermediate risk because p53 was abnormal and the neoplasia did not invade the myometrium; while 6 (2.2%; 95% CI 0.8% to 4.6%) POLE-mutated were reclassified as low risk from higher-risk classes (5 intermediate and 1 high-intermediate) (Table 2). More details regarding the clinicopathological features of the 19 patients who changed their risk class are reported in Online supplemental file 5.

    View this table:
    Table 2

    Risk class migration from the ESGO/ESTRO/ESP risk class with molecular classification unknown (rows) to the ESGO/ESTRO/ESP risk class with molecular classification known (columns).

    Figure 3
    Figure 3

    Cross-tabulation of ESGO risk classes without and with molecular classification. The cross-tabulation represents an imperfect overlap in classifying endometrial cancers using these two different modalities.

    Application of the New Molecular Algorithm to the Clinical Setting

    Following the ESGO/ESTRO/ESP guidelines,23 POLE, MMR, and p53 would have been requested in 278 patients (100%). According to the algorithm we propose (Figure 1, Online supplemental file 6), every patient with endometrial cancer undergoes screening for Lynch syndrome through evaluation of MMR protein expression (n=278, 100%). Then, p53 expression is evaluated only in patients with early-stage disease (n=203, 73%, 95% CI 67% to 78%). Finally, all patients that after MMR and p53 evaluation are categorized in a risk class different than low risk, will be tested for POLE (n=91, 33%, 95% CI 27% to 39%). Following the present algorithm, POLE analysis would have been spared in 187 (67%, 95% CI 61% to 73%) patients, of whom 112 (40%, 95% CI 34% to 46%) were already at low risk regardless of POLE sequencing and 75 (27%, 95% CI 22% to 33%) had advanced or metastatic disease. In addition, p53 evaluation would have been spared in 75 (27%, 95% CI 22% to 33%) patients with advanced and metastatic disease. No risk class would have been misclassified.

    DISCUSSION

    Summary of Main Results

    In the present study, the integration of the molecular categorization to the clinicopathologic features in a cohort of consecutive endometrial cancers allowed reallocation of 6.8% of cancers in a different risk class according to the ESGO/ESTRO/ESP guidelines, meaning that potentially 1 of 15 patients may be misclassified and consequently undertreated or overtreated. Indeed, the attribution to the correct risk class has a prognostic value, and could also potentially affect the recommendation of proper adjuvant management, thus improving oncologic outcomes.

    Furthermore, we proposed a molecular algorithm, which restricts the molecular analysis only to cases in which knowing the molecular classification might change their risk class categorization. Its application to our cohort would have led to a reduction in the number of POLE sequencing by 67% (95% CI 61% to 73%) and a decrease of p53 immunohistochemistry by 27% (95% CI 22% to 33%) compared with the application of molecular analysis to all patients. The reduction of tests, without affecting the accuracy of risk class attribution, would save the costs of the molecular analysis, and also those associated with unnecessary treatments.

    Results in the Context of Published Literature

    A new risk stratification system including molecular classification has been recently proposed by ESGO/ESTRO/ESP guidelines in an attempt to overcome the limits of previously adopted classifications.16 To the best of our knowledge, this large series represents one of the first applications of the new molecular classification in clinical practice. Consistent with our results, a recent Finnish study using the ProMisE schema for molecular classification observed a risk group migration in 6.0% of patients, who were downshifted or upshifted to another risk group due to a pathogenic POLE mutation or abnormal p53 staining, respectively.26 Similarly, in an earlier study from the Karolinska Institute the molecular classification according to the ESGO/ESTRO/ESP guidelines determined a 7% change in risk group as compared with the 2016 ESMO/ESGO/ESTRO classification system based only on clinicopathologic characteristics.27 Taken together with the results of our study, these studies further confirm that molecular categorization of endometrial cancer allows reallocation of a considerable proportion of patients to a different risk class.

    Restricting the molecular analysis only to patients for whom the molecular classification might change their risk class categorization allowed us to identify a new molecular algorithm which can be applied in a setting with limited resources without affecting the accuracy of risk grouping and therefore the correct post-operative management. An attempt to restrict the indications for molecular testing to a subgroup of patients with the potentially highest benefit from molecular analysis was made by the ESGO/ESTRO/ESP guidelines. When there is a lack of sufficient resources to perform this classification on all patients, ESGO/ESTRO/ESP guidelines recommend prioritizing patients with high-grade/high-risk disease. However, restricting the molecular analysis to these patients would determine a misclassification and, consequently, undertreatment of patients at low, intermediate, high-intermediate risk with p53abn.

    Strengths and Weaknesses

    Although our study is limited by its retrospective design, the prevalence of the four molecular types of endometrial cancer and the distribution of the clinicopathological features in the presented series are comparable with previous studies,11 12 suggesting that our results can be generalized. Another limitation of the present study is the lack of a validation cohort to confirm our results and demonstrate the applicability of our algorithm. To validate this study, we are prospectively collecting details of all consecutive patients with endometrial cancer undergoing surgical staging and molecular analysis at our institution. However, considering that the results of previous studies11 12 are consistent with our results, we expect similar results from the validation study.

    Implications for Practice and Future Research

    The routine application of molecular analysis to endometrial cancers is of paramount importance to guide the indications for proper management, as shown by recent evidence.9 12 13 15 16 28 As a consequence of the different survival between molecular classes, the ESGO/ESTRO/ESP working group has already introduced molecular classification as a parameter for guiding clinical decisions. Furthermore, as recently shown in a post hoc analysis of PORTEC-3 data,15 in high-risk patients adjuvant therapy appears to have different efficacy in each molecular subtype of endometrial cancer, suggesting that the indications for adjuvant therapy could potentially be guided by molecular classification. Specifically, while p53 cancers benefitted from adding adjuvant chemotherapy to radiotherapy, MMR-d class did not demonstrate any survival benefit of adding chemotherapy to adjuvant radiotherapy, indicating that chemotherapy might be omitted in this group of patients, as previously reported in another retrospective study.14 Moreover, POLE-mutated tumor prognosis was not improved by adjuvant therapy, implying that this molecular class displays intrinsically good prognosis and may not require any adjuvant treatment. However, while this study suggests that the knowledge of the molecular class might represent an instrument for guiding adjuvant treatment, the results of ongoing prospective trials including PORTEC-4a trial,29 TAPER trial (ClinicalTrials.gov: NCT04705649), and TransPORTEC RAINBO program30 are awaited and will provide stronger evidence on the role of molecular classification in tailoring the adjuvant treatment of these patients.

    One might argue that cancers without histomorphologic risk factors belonging to p53abn molecular class might be overtreated if receiving adjuvant treatment. However, even though this circumstance rarely occurs, there is some evidence demonstrating a worst prognosis in these early-stage, low-risk cancers harboring TP53 mutations compared with the non-TP53-mutated.31 32 For this reason, while waiting for strongest evidence based on ongoing prospective trials, we suggest adherence to the ESGO/ESTRO/ESP guidelines, discussing with every patient the therapeutic scenario with the possible implications.

    According to the ESGO/ESTRO/ESP guidelines, for advanced stage endometrial cancers the risk class attribution and the indication for adjuvant treatment do not change based on molecular classification. As a consequence, in the new proposed molecular algorithm the analyses of all three molecular surrogates would not be performed in advanced stage cases. However, advanced stage cases still represent an important area of research and we envision that in the future, post-operative management of these patients may change based on the molecular features. For this reason, wherever possible, we suggest application of a comprehensive molecular analysis to all patients and their inclusion in clinical trials.

    While the evaluation based on histomorphologic features has shown low inter-observer agreement,33–35 the molecular analysis is highly reproducible. The categorization of risk class based on molecular classification might overcome the limits in risk class attribution based exclusively on histomorphologic features and, subsequently, would allow more objective enrollment of patients in clinical trials.

    The introduction of a molecular algorithm more widely applicable in routine clinical practice might expedite innovations and advanced knowledge. The identification of rare molecular classes, such as POLE or so called ‘multiple classifiers’ endometrial cancers, would allow collection of more data about their behavior and therefore offer the correct management for these patients. Furthermore MMR-d class, which have a high tumor mutational burden, might expeditiously be enrolled in trials evaluating immunotherapy efficacy or other molecular tailored treatment.

    The median turnaround time of 12 days for completing the molecular analysis following the proposed algorithm (4 days for immunohistochemistry plus 8 days for POLE sequencing) is adequate. Moreover, considering that endometrial cancers are often treated in smaller centers, this short turnaround time may allow centralization of the molecular analysis (especially POLE sequencing) in referral centers, thus confirming its applicability to routine clinical practice.

    Finally, the evaluation of POLE status through next generation screening could simultaneously provide information on other molecular alterations that can be used to select patients for a targeted therapy, including mutations in the PIK3-AKT-mTOR pathway, HER2, and MET genes.

    CONCLUSIONS

    In conclusion, the incorporation of molecular categorization into the risk stratification system of endometrial cancer has been demonstrated as feasible and led to reallocation of patients to a different risk class, according to the ESGO/ESTRO/ESP guidelines. The application of our algorithm would reduce the number of required tests, and its application is suggested if resources are limited.

    Data availability statement

    Data are available upon reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    This study involves human participants and was approved by Comitato Etico degli IRCCS Istituto Europeo di Oncologia e Centro Cardiologico Monzino - UID 2418 Participants gave informed consent to participate in the study before taking part.

    References

    1. ↵
      International Agency for Research on Cancer (IARC) WHO, Globocan. Corpus uteri 2020. Available: https://gco.iarc.fr/today/data/factsheets/cancers/24-Corpus-uteri-fact-sheet.pdf
    2. ↵
      2. Heintz APM ,
      3. Odicino F ,
      4. Maisonneuve P , et al
      . Carcinoma of the ovary. FIGO 26th annual report on the results of treatment in gynecological cancer. Int J Gynaecol Obstet 2006;95 Suppl 1:S105–43.doi:10.1016/S0020-7292(06)60033-7 pmid:http://www.ncbi.nlm.nih.gov/pubmed/17161157
      OpenUrlPubMed
    3. ↵
      2. Creutzberg CL ,
      3. van Putten WL ,
      4. Koper PC
      . Post operative radiation therapy in endometrial carcinoma. Lancet 2000;355:1404–11.
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      2. Keys HM ,
      3. Roberts JA ,
      4. Brunetto VL , 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;92:744–51.doi:10.1016/j.ygyno.2003.11.048
      OpenUrlCrossRefPubMedWeb of Science
    5. ↵
      2. Nout RA ,
      3. Smit V ,
      4. Putter H , et al
      . Vaginal brachytherapy versus pelvic external beam radiotherapy for patients with endometrial cancer of high-intermediate risk (PORTEC-2): an open-label, non-inferiority, randomised trial. The Lancet 2010;375:816–23.doi:10.1016/S0140-6736(09)62163-2
      OpenUrl
    6. ↵
      2. Colombo N ,
      3. Creutzberg C ,
      4. Amant F , et al
      . ESMO-ESGO-ESTRO consensus conference on endometrial cancer: diagnosis, treatment and follow-up. Ann Oncol 2016;27:16–41.doi:10.1093/annonc/mdv484
      OpenUrlCrossRefPubMed
    7. ↵
      2. de Boer SM ,
      3. Powell ME ,
      4. Mileshkin L , et al
      . Adjuvant chemoradiotherapy versus radiotherapy alone for women with high-risk endometrial cancer (PORTEC-3): final results of an international, open-label, multicentre, randomised, phase 3 trial. Lancet Oncol 2018;19:295–309.doi:10.1016/S1470-2045(18)30079-2
      OpenUrlPubMed
    8. ↵
      2. Bendifallah S ,
      3. Canlorbe G ,
      4. Collinet P , et al
      . Just how accurate are the major risk stratification systems for early-stage endometrial cancer? Br J Cancer 2015;112:793–801.doi:10.1038/bjc.2015.35
      OpenUrlCrossRefPubMed
    9. ↵
      1. Cancer Genome Atlas Research Network,
      2. Kandoth C ,
      3. Schultz N , et al
      . Integrated genomic characterization of endometrial carcinoma. Nature 2013;497:67–73.doi:10.1038/nature12113 pmid:http://www.ncbi.nlm.nih.gov/pubmed/23636398
      OpenUrlCrossRefPubMedWeb of Science
    10. ↵
      2. Stelloo E ,
      3. Bosse T ,
      4. Nout RA , et al
      . Refining prognosis and identifying targetable pathways for high-risk endometrial cancer; a TransPORTEC initiative. Mod Pathol 2015;28:836–44.doi:10.1038/modpathol.2015.43
      OpenUrlCrossRefPubMed
    11. ↵
      2. Talhouk A ,
      3. McConechy MK ,
      4. Leung S , et al
      . Confirmation of promise: a simple, genomics-based clinical classifier for endometrial cancer. Cancer 2017;123:802–13.doi:10.1002/cncr.30496
      OpenUrlCrossRefPubMed
    12. ↵
      2. Stelloo E ,
      3. Nout RA ,
      4. Osse EM , et al
      . Improved risk assessment by integrating molecular and clinicopathological factors in early-stage endometrial cancer—combined analysis of the PORTEC cohorts. Clin Cancer Res 2016;22:4215–24.doi:10.1158/1078-0432.CCR-15-2878
      OpenUrlAbstract/FREE Full Text
    13. ↵
      2. Kommoss S ,
      3. McConechy MK ,
      4. Kommoss F , et al
      . Final validation of the promise molecular classifier for endometrial carcinoma in a large population-based case series. Ann Oncol 2018;29:1180–8.doi:10.1093/annonc/mdy058
      OpenUrlCrossRefPubMed
    14. ↵
      2. Reijnen C ,
      3. Küsters-Vandevelde HVN ,
      4. Prinsen CF , et al
      . Mismatch repair deficiency as a predictive marker for response to adjuvant radiotherapy in endometrial cancer. Gynecol Oncol 2019;154:124–30.doi:10.1016/j.ygyno.2019.03.097
      OpenUrlPubMed
    15. ↵
      2. León-Castillo A ,
      3. de Boer SM ,
      4. Powell ME , et al
      . Molecular classification of the PORTEC-3 trial for high-risk endometrial cancer: impact on prognosis and benefit from adjuvant therapy. JCO 2020;38:3388–97.doi:10.1200/JCO.20.00549
      OpenUrlPubMed
    16. ↵
      2. Concin N ,
      3. Matias-Guiu X ,
      4. Vergote I , et al
      . ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma. Int J Gynecol Cancer 2021;31:12–39.doi:10.1136/ijgc-2020-002230
      OpenUrlAbstract/FREE Full Text
    17. ↵
      2. Crosbie EJ ,
      3. Ryan NAJ ,
      4. Arends MJ , et al
      . The Manchester International Consensus Group recommendations for the management of gynecological cancers in Lynch syndrome. Genet Med 2019;21:2390–400.doi:10.1038/s41436-019-0489-y
      OpenUrlCrossRefPubMed
    18. ↵
      1. Network NCC
      . Uterine neoplasms (version 3.2021), 2021. Available: https://www.nccn.org/professionals/physician_gls/pdf/uterine.pdf
    19. ↵
      2. Marabelle A ,
      3. Le DT ,
      4. Ascierto PA , et al
      . Efficacy of pembrolizumab in patients with noncolorectal high microsatellite Instability/mismatch repair–deficient cancer: results from the phase II KEYNOTE-158 study. JCO 2020;38:1–10.doi:10.1200/JCO.19.02105
      OpenUrlCrossRefPubMed
    20. ↵
      2. Köbel M ,
      3. Ronnett BM ,
      4. Singh N , et al
      . Interpretation of p53 immunohistochemistry in endometrial carcinomas: toward increased reproducibility. Int J Gynecol Pathol 2019;38 Suppl 1:S123–31.doi:10.1097/PGP.0000000000000488 pmid:http://www.ncbi.nlm.nih.gov/pubmed/29517499
      OpenUrlPubMed
    21. ↵
      2. Singh N ,
      3. Piskorz AM ,
      4. Bosse T , et al
      . p53 immunohistochemistry is an accurate surrogate for TP53 mutational analysis in endometrial carcinoma biopsies. J Pathol 2020;250:336–45.doi:10.1002/path.5375
      OpenUrlPubMed
    22. ↵
      2. León‐Castillo A ,
      3. Britton H ,
      4. McConechy MK , et al
      . Interpretation of somatic POLE mutations in endometrial carcinoma. J Pathol 2020;250:323–35.doi:10.1002/path.5372
      OpenUrlCrossRefPubMed
    23. ↵
      2. León‐Castillo A ,
      3. Gilvazquez E ,
      4. Nout R , et al
      . Clinicopathological and molecular characterisation of ‘multiple‐classifier’ endometrial carcinomas. J Pathol 2020;250:312–22.doi:10.1002/path.5373
      OpenUrlPubMed
    24. ↵
      2. Harris PA ,
      3. Taylor R ,
      4. Thielke R , et al
      . Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42:377–81.doi:10.1016/j.jbi.2008.08.010
      OpenUrlCrossRefPubMedWeb of Science
    25. ↵
      2. Harris PA ,
      3. Taylor R ,
      4. Minor BL , et al
      . The REDCap Consortium: building an international community of software platform partners. J Biomed Inform 2019;95:103208.doi:10.1016/j.jbi.2019.103208
    26. ↵
      2. Loukovaara M ,
      3. Pasanen A ,
      4. Bützow R
      . Clinicopathologic vs. molecular integrated prognostication of endometrial carcinoma by European guidelines. Cancers 2022;14:651.doi:10.3390/cancers14030651
    27. ↵
      2. Imboden S ,
      3. Nastic D ,
      4. Ghaderi M , et al
      . Implementation of the 2021 molecular ESGO/ESTRO/ESP risk groups in endometrial cancer. Gynecol Oncol 2021;162:394–400.doi:10.1016/j.ygyno.2021.05.026
      OpenUrl
    28. ↵
      2. Talhouk A ,
      3. McConechy MK ,
      4. Leung S , et al
      . A clinically applicable molecular-based classification for endometrial cancers. Br J Cancer 2015;113:299–310.doi:10.1038/bjc.2015.190
      OpenUrlCrossRefPubMed
    29. ↵
      2. van den Heerik ASVM ,
      3. Horeweg N ,
      4. Nout RA , et al
      . PORTEC-4a: international randomized trial of molecular profile-based adjuvant treatment for women with high-intermediate risk endometrial cancer. Int J Gynecol Cancer 2020;30:2002–7.doi:10.1136/ijgc-2020-001929
      OpenUrlAbstract/FREE Full Text
    30. ↵
      2. Jamieson A ,
      3. Bosse T ,
      4. McAlpine JN
      . The emerging role of molecular pathology in directing the systemic treatment of endometrial cancer. Ther Adv Med Oncol 2021;13:17588359211035959.doi:10.1177/17588359211035959
    31. ↵
      2. Yano M ,
      3. Ito K ,
      4. Yabuno A , et al
      . Impact of TP53 immunohistochemistry on the histological grading system for endometrial endometrioid carcinoma. Mod Pathol 2019;32:1023–31.doi:10.1038/s41379-019-0220-1
      OpenUrlPubMed
    32. ↵
      2. Jamieson A ,
      3. Thompson EF ,
      4. Huvila J , et al
      . p53abn endometrial cancer: understanding the most aggressive endometrial cancers in the era of molecular classification. Int J Gynecol Cancer 2021;31:907–13.doi:10.1136/ijgc-2020-002256
      OpenUrlAbstract/FREE Full Text
    33. ↵
      2. Nedergaard L ,
      3. Jacobsen M ,
      4. Andersen JE
      . Interobserver agreement for tumour type, grade of differentiation and stage in endometrial carcinomas. APMIS 1995;103:511–8.doi:10.1111/j.1699-0463.1995.tb01399.x
      OpenUrlCrossRefPubMedWeb of Science
    34. ↵
      2. Lomo L ,
      3. Nucci MR ,
      4. Lee KR , et al
      . Histologic and immunohistochemical decision-making in endometrial adenocarcinoma. Mod Pathol 2008;21:937–42.doi:10.1038/modpathol.2008.97
      OpenUrlCrossRefPubMed
    35. ↵
      2. Gilks CB ,
      3. Oliva E ,
      4. Soslow RA
      . Poor interobserver reproducibility in the diagnosis of high-grade endometrial carcinoma. Am J Surg Pathol 2013;37:874–81.doi:10.1097/PAS.0b013e31827f576a
      OpenUrlCrossRefPubMed

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    Footnotes

    • NC and FM are joint senior authors.

    • Twitter @IBetella, @LuigiDEvitis, @Fmultinu

    • Contributors IB - Project development, data collection, data analysis, manuscript writing, guarantor. CF - Project development, data collection, data analysis. PRR - Project development, data collection, data analysis. GS - Project development, data collection, data analysis, manuscript writing. LADV - Data collection, data analysis, manuscript writing. MTA - Project development, data collection. AA - Project development, data collection. AG - Project development, data collection. MM - Project development, data collection. VZ - Project development, data collection, data analysis. GA - Project development, data collection, data analysis. EG-R - Data analysis. AMar - Project development, data analysis. AMag - Project development, data collection. MB - Project development, data analysis. NC - Project development, data collection, data analysis, manuscript writing. FM - Project development, data collection, data analysis, manuscript writing, guarantor.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

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