Endometrial cancer (EC) is the sixth most common cancer in women worldwide and its mortality is directly associated with the presence of poor prognostic factors driving tumor recurrence. Stratification systems are based on few molecular, and mostly clinical and pathological parameters, but these systems remain inaccurate. Therefore, identifying prognostic EC biomarkers is crucial for improving risk assessment pre- and postoperatively and to guide treatment decisions. This systematic review gathers all protein biomarkers associated with clinical prognostic factors of EC, recurrence and survival. Relevant studies were identified by searching the PubMed database from 1991 to February 2020. A total number of 398 studies matched our criteria, which compiled 255 proteins associated with the prognosis of EC. MUC16, ESR1, PGR, TP53, WFDC2, MKI67, ERBB2, L1CAM, CDH1, PTEN and MMR proteins are the most validated biomarkers. On the basis of our meta-analysis ESR1, TP53 and WFDC2 showed potential usefulness for predicting overall survival in EC. Limitations of the published studies in terms of appropriate study design, lack of high-throughput measurements, and statistical deficiencies are highlighted, and new approaches and perspectives for the identification and validation of clinically valuable EC prognostic biomarkers are discussed.

Endometrial cancer (EC) mortality is directly associated with the presence of prognostic factors. Current stratification systems are not accurate enough to predict the outcome of patients. Therefore, identifying more accurate prognostic EC biomarkers is crucial. We aimed to validate 255 prognostic biomarkers identified in multiple studies and explore their prognostic application by analyzing them in TCGA and CPTAC datasets. We analyzed the mRNA and proteomic expression data to assess the statistical prognostic performance of the 255 proteins. Significant biomarkers related to overall survival (OS) and recurrence-free survival (RFS) were combined and signatures generated. A total of 30 biomarkers were associated either to one or more of the following prognostic factors: histological type (n = 15), histological grade (n = 6), FIGO stage (n = 1), molecular classification (n = 16), or they were associated to OS (n = 11), and RFS (n = 5). A prognostic signature composed of 11 proteins increased the accuracy to predict OS (AUC = 0.827). The study validates and identifies new potential applications of 30 proteins as prognostic biomarkers and suggests to further study under-studied biomarkers such as TPX2, and confirms already used biomarkers such as MSH6, MSH2, or L1CAM. These results are expected to advance the quest for biomarkers to accurately assess the risk of EC patients.

Endometrial cancer (EC) is the second most frequent gynecological cancer worldwide. Although improvements in EC classification have enabled an accurate establishment of disease prognosis, women with a high-risk or recurrent EC face a dramatic situation due to limited further treatment options. Therefore, new strategies that closely mimic the disease are required to maximize drug development success. Patient-derived xenografts (PDXs) are widely recognized as a physiologically relevant preclinical model. Hence, we propose to molecularly and histologically validate EC PDX models. To reveal the molecular landscape of PDXs generated from 13 EC patients, we performed histological characterization and whole-exome sequencing analysis of tumor samples. We assessed the similarity between PDXs and their corresponding patient’s tumor and, additionally, to an extended cohort of EC patients obtained from The Cancer Genome Atlas (TCGA). Finally, we performed functional enrichment analysis to reveal differences in molecular pathway activation in PDX models. We demonstrated that the PDX models had a well-defined and differentiated molecular profile that matched the genomic profile described by the TCGA for each EC subtype. Thus, we validated EC PDX’s potential to reliably recapitulate the majority of histologic and molecular EC features. This work highlights the importance of a thorough characterization of preclinical models for the improvement of the success rate of drug-screening assays for personalized medicine.