Endometrial Cancer Molecular Characterization: The Key to Identifying High-Risk Patients and Defining Guidelines for Clinical Decision-Making?

SIMPLE SUMMARY: Endometrial carcinomas (EC) have been traditionally classified based on histopathology (types I and II). Determining the risk of a patient to experience disease recurrence is important to decide which patients need adjuvant treatment. The current endometrial carcinoma risk assessment approaches fail to accurately classify patients into low-and high-risk groups. Several studies suggest that combining molecular characteristics with the current risk classification in EC may improve patients’ stratification and treatment decision-making. In this review, we describe how evolving molecular trends can be used as prognostic factors to identify high-risk EC subpopulations. We also look at how the most recent patient-derived models can help researchers find new possible targets and treatments for EC patients. ABSTRACT: Endometrial carcinomas (EC) are the sixth most common cancer in women worldwide and the most prevalent in the developed world. ECs have been historically sub-classified in two major groups, type I and type II, based primarily on histopathological characteristics. Notwithstanding the usefulness of such classification in the clinics, until now it failed to adequately stratify patients preoperatively into low- or high-risk groups. Pieces of evidence point to the fact that molecular features could also serve as a base for better patients’ risk stratification and treatment decision-making. The Cancer Genome Atlas (TCGA), back in 2013, redefined EC into four main molecular subgroups. Despite the high hopes that welcomed the possibility to incorporate molecular features into practice, currently they have not been systematically applied in the clinics. Here, we outline how the emerging molecular patterns can be used as prognostic factors together with tumor histopathology and grade, and how they can help to identify high-risk EC subpopulations for better risk stratification and treatment strategy improvement. Considering the importance of the use of preclinical models in translational research, we also discuss how the new patient-derived models can help in identifying novel potential targets and help in treatment decisions.