Prognostic relevance of the molecular classification in high-grade endometrial cancer for patients staged by lymphadenectomy and without adjuvant treatment

León‐Castillo, Alicia; Horeweg, Nanda; Peters, Elke E.M.; Rutten, Tessa; Haar, Natalja T. ter; Smit, Vincent T.H.B.M.; Kroon, Cor D. de; Boennelycke, Marie; Høgdall, Estrid; Høgdall, Claus; Nout, R.; Creutzberg, Carien L.; Ørtoft, Gitte; Bosse, Tjalling

doi:10.1016/j.ygyno.2022.01.007

Cited by 59 publications

(62 citation statements)

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“…Furthermore, endometrioid-type EC is the dominant histological subtype of MMRd EC (5, 15, 17-19, 77, 78). Endometrioid EC with solid growth (FIGO grade 3) is relatively more frequent in MMRd EC than in NSMP EC but less frequent than in POLEmut EC (5,14,15,18). However, a variety of other non-endometrioid histological subtypes have also been reported within the MMRd EC subclass.…”

Section: Mismatch Repair-deficient Endometrial Cancersmentioning

confidence: 97%

“…Furthermore, a brisk mitotic activity is consistently found (14, 18, 78, 83-85). The p53abn EC molecular subgroup, however, has a broader histological spectrum, as it also includes uterine carcinosarcomas (78), clear cell carcinomas (14, 18), and FIGO grade 3 endometrioid carcinomas (5,14,18,84,85). Intriguingly, some studies described that p53abn EC can also present with low-grade endometrioid morphology (15,70,84).…”

Section: P53 Abnormal Endometrial Cancersmentioning

confidence: 99%

“…A lower abundance of TILS and TLS in the NSMP EC group than in MMRd EC and POLEmut EC has also been reported (69,70,72). Finally, non-endometrioid or high-grade NSMP ECs are uncommon but have been described (14, [17][18][19]78).…”

Section: No Specific Molecular Profile Endometrial Cancersmentioning

confidence: 99%

“…Yet, at the same time, some morphological features may arguably no longer carry prognostic value in some molecular subgroups. For instance, evidence showed that a broad range of histological subtypes and grades can be found in POLEmut EC and p53abn EC while carrying distinct genomic alterations and having excellent and poor prognosis, respectively (5,14,15,(17)(18)(19)(20). Likewise, the prognostic relevance of other morphological features is under investigation such as the presence of LVSI within POLEmut EC and MMRd EC (15,66,77) and the lymphocyte density in NSMP EC and p53abn EC (69,70,72).…”

Section: The Current Role Of Morphology Within the Molecular Ec Class...mentioning

confidence: 99%

“…The molecular EC classification has also shown a prognostic value across cohorts of different risk populations and is predictive of response to treatment, specifically in p53abn EC, which has a poor outcome and may benefit from addition of adjuvant chemotherapy (14), and in POLEmut EC, which has an excellent outcome regardless of adjuvant treatment, whereas MMRd and NSMP EC have intermediate prognoses (5,(14)(15)(16)(17)(18)(19)(20). This has been the rationale for adapting the clinical risk stratification system of EC patients (21) wherein it is encouraged to apply the molecular classification in the management of EC, especially high-risk EC (1, 2); ongoing and new trials such as PORTEC-4a (22) and RAINBO (23) exploit the molecular classification as a basis for targeted adjuvant therapy (Figure 1) (24).…”

Section: Introductionmentioning

confidence: 99%

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The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

et al. 2022

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Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into DNA polymerase epsilon mutated (POLEmut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients.

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Section: Mismatch Repair-deficient Endometrial Cancersmentioning

confidence: 97%

Section: P53 Abnormal Endometrial Cancersmentioning

confidence: 99%

Section: No Specific Molecular Profile Endometrial Cancersmentioning

confidence: 99%

Section: The Current Role Of Morphology Within the Molecular Ec Class...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

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Relevance of Molecular Profiling in Patients With Low-Grade Endometrial Cancer

et al. 2022

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ImportancePatients with low-grade (ie, grade 1-2) endometrial cancer (EC) are characterized by their favorable prognosis compared with patients with high-grade (ie, grade 3) EC. With the implementation of molecular profiling, the prognostic relevance of tumor grading might lose attention. As most patients present with low-grade EC and have an excellent outcome, the value of molecular profiling for these patients is unclear.ObjectiveTo determine the association of molecular profiling with outcomes among patients with low-grade EC.Design, Setting, and ParticipantsThis retrospective cohort study included a multicenter international European cohort of patients diagnosed with EC between 1994 and 2018, with a median follow-up of 5.9 years. Molecular subgroups were determined by next-generation sequencing using single-molecule molecular inversion probes and by immunohistochemistry. Subsequently, tumors were classified as polymerase epsilon (POLE)-altered, microsatellite instable (MSI), tumor protein p53 (TP53)-altered, or no specific molecular profile (NSMP). Patients diagnosed with any histological subtypes and FIGO (International Federation of Gynecology and Obstetrics) stages of EC were included, but patients with early-stage EC (FIGO I-II) were only included if they had known lymph node status. Data were analyzed February 20 to June 16, 2022.ExposuresMolecular testing of the 4 molecular subgroups.Main Outcomes and MeasuresThe main outcome was disease-specific survival (DSS) within the molecular subgroups.ResultsA total of 393 patients with EC were included, with a median (range) age of 64.0 (31.0-86.0) years and median (range) body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 29.1 (18.0-58.3). Most patients presented with early-stage (290 patients [73.8%]) and low-grade (209 patients [53.2%]) disease. Of all patients, 33 (8.4%) had POLE-altered EC, 78 (19.8%) had MSI EC, 72 (18.3%) had TP53-altered EC, and 210 (53.4%) had NSMP EC. Across all molecular subgroups, patients with low-grade EC had superior 5-year DSS compared with those with high-grade EC, varying between 90% to 100% vs 41% to 90% (P &lt; .001). Multivariable analysis in the entire cohort including age, tumor grade, FIGO stage, lymphovascular space invasion, and the molecular subgroups as covariates found that only high-grade (hazard ratio [HR], 4.29; 95% CI, 2.15-8.53; P &lt; .001), TP53-altered (HR, 1.76; 95% CI, 1.04-2.95; P = .03), and FIGO stage III or IV (HR, 4.26; 95% CI, 2.50-7.26; P &lt; .001) disease were independently associated with reduced DSS.Conclusions and RelevanceThis cohort study found that patients with low-grade EC had an excellent prognosis independent of molecular subgroup. These findings do not support routine molecular profiling in patients with low-grade EC, and they demonstrate the importance of primary diagnostic tumor grading and selective profiling in low-grade EC to increase cost-effectiveness.

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Racial and Ethnic Differences in Hysterectomy-Corrected Uterine Corpus Cancer Mortality by Stage and Histologic Subtype

et al. 2022

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IMPORTANCE Uterine cancer incidence has been increasing, particularly rates of aggressive, nonendometrioid subtypes, which are disproportionately higher among non-Hispanic Black women. The association of subtype-specific trends with uterine cancer mortality and with the role of tumor subtype and stage at diagnosis with racial disparities in uterine cancer deaths at the population-based level are not known.OBJECTIVE To estimate histologic subtype-and stage-specific uterine cancer mortality rates by race and ethnicity, corrected for hysterectomy. DESIGN, SETTING, AND PARTICIPANTSThis cohort study used the US Surveillance, Epidemiology, and End Results-18 Incidence-Based Mortality database, representing approximately 26% of the US population and including deaths that occurred from 2000 to 2017. Hysterectomy correction was based on hysterectomy prevalence data from the Behavioral Risk Factor Surveillance System. Uncorrected and corrected rates associated with uterine corpus cancer cases diagnosed between 2000 and 2017 and uterine corpus cancer deaths occurring between 2010 and 2017 were age-adjusted to the 2000 US standard population and are expressed per 100 000 person-years, and annual percent changes in rates were calculated using log-linear regression. Data analysis was performed from March 10 to May 20, 2021.EXPOSURES Tumor histologic subtype, cancer stage at diagnosis, and race and ethnicity.

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Prognostic relevance of the molecular classification in high-grade endometrial cancer for patients staged by lymphadenectomy and without adjuvant treatment

Cited by 59 publications

References 40 publications

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

Relevance of Molecular Profiling in Patients With Low-Grade Endometrial Cancer

Racial and Ethnic Differences in Hysterectomy-Corrected Uterine Corpus Cancer Mortality by Stage and Histologic Subtype

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