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A Retrospective Study on Multiple Classifier Endometrial Cancer

Fondazione IRCCS Istituto Nazionale dei Tumori, Milano · 2023
ClinicalTrials.gov · Datasets · License: Public Domain · 2023
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Endometrial cancer is not a single entity but rather a very heterogeneous group of diseases. Historically, endometrial cancer patients have been classified as endometrioid (type I) or non-endometrioid (type II) according to the dualistic Bokhman model- However, this approach has been limited in accurately predicting prognosis and guiding treatment owing to heterogeneity within subtypes, inadequate incorporation of molecular and genetic information, and high interobserver variability . In the last ten years, after the publication of The Cancer Genome Atlas (TCGA)\[5\], the molecular classification of endometrial cancer into four molecular subtypes \[(i) POLE/ultramutated group (POLE mutated), (ii) mismatch repair deficiency/microsatellite-instable, hypermutated group (MMRd/MSI-H), (iii) copy-number-high, TP53-mutant (CNH/p53abn), and (iv) copy-number-low, TP53-wild-type (CNL, or No Specific Mutational Profile \[NSMP\])\] has rapidly gained interest. Recently, the European Societies of Gynaecological Oncology, Radiotherapy and Oncology, and Pathology (ESGO-ESTRO-ESP), the European Society of Medical Oncology (ESMO), the National Comprehensive Cancer Network (NCCN), and the new 2023 International Federation of Gynecology and Obstetrics (FIGO) staging system have promoted the use of (surrogate) molecular classification. Retrospective studies supported the value of adopting molecular classification to offer reliable data on prognostication and adjuvant treatment decisions. Although no prospective data are available, current guidelines promote the use of molecular profiles to tailor adjuvant treatment after surgery. As only a few retrospective studies have investigated the association between molecular profiles and response to various adjuvant treatments, it is important to note that data are limited. Interestingly, the growing adoption of molecular profiling led to the detection of a subgroup of tumors called multiple classifiers, characterized by multiple (two or three
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