Targeted Proteomics Identifies Proteomic Signatures in Liquid Biopsies of the Endometrium to Diagnose Endometrial Cancer and Assist in the Prediction of the Optimal Surgical Treatment

Abstract

Endometrial cancer (EC) diagnosis relies on the observation of tumor cells in endometrial biopsies obtained by aspiration (i.e., uterine aspirates), but it is associated with 22% undiagnosed patients and up to 50% of incorrectly assigned EC histotype and grade. We aimed to identify biomarker signatures in the fluid fraction of these biopsies to overcome these limitations. The levels of 52 proteins were measured in the fluid fraction of uterine aspirates from 116 patients by LC-PRM, the latest generation of targeted mass-spectrometry acquisition. A logistic regression model was used to assess the power of protein panels to differentiate between EC and non-EC patients and between EC histologic subtypes. The robustness of the panels was assessed by the "leave-one-out" cross-validation procedure performed within the same cohort of patients and an independent cohort of 38 patients. The levels of 28 proteins were significantly higher in patients with EC ( = 69) compared with controls ( = 47). The combination of MMP9 and KPYM exhibited 94% sensitivity and 87% specificity for detecting EC cases. This panel perfectly complemented the standard diagnosis, achieving 100% of correct diagnosis in this dataset. Nine proteins were significantly increased in endometrioid EC ( = 49) compared with serous EC ( = 20). The combination of CTNB1, XPO2, and CAPG achieved 95% sensitivity and 96% specificity for the discrimination of these subtypes. We developed two uterine aspirate-based signatures to diagnose EC and classify tumors in the most prevalent histologic subtypes. This will improve diagnosis and assist in the prediction of the optimal surgical treatment. .

Proteomics-Derived Biomarker Panel Improves Diagnostic Precision to Classify Endometrioid and High-grade Serous Ovarian Carcinoma

“…This notion is also supported by recent work by Martinez-Garcia and colleagues who employed targeted proteomics on uterine aspirates from patients with and without endometrial cancer, a disease sharing similarities with endometrioid carcinoma (43)(44)(45). Of note, SCGB2A1, CTNNB1, and PIGR were significantly elevated in patients with endometrial cancer compared benign conditions, and among others were the best single performing markers for discriminating endometrioid and serous histotypes in the endometrium as well (45,46). As proteomics technologies and protocols become more robust, easy to use and economical, for example, in the area of plasma biomarkers, there is potential for direct integration into current clinical work flows (47).…”

New perspectives on screening and early detection of endometrial cancer

“…Few proof‐of‐concepts in reduced sample sizes have shown feasibility to discriminate between endometrial cancer and benign conditions in Pap brush samples using genomics or epigenomics with high sensitivity (Table ). Two studies evaluated levels of the protein cancer antigen 125 and total proprotein convertase activity in cervical scrapings or swabs (Table ), while other proteins have been evaluated in uterine aspirates or lavages . In a large sample size (382 cancer cases and 714 controls), Wang et al .…”

Liquid Biopsy in Endometrial Cancer: New Opportunities for Personalized Oncology

“…This type of liquid biopsy can result in a comprehensive catalogue of proteins of the endometrial fluid during the secretory phase of the menstrual cycle [24], but also as a promising biological fluid in which to identify potential endometrial cancer biomarkers for its early diagnosis, such as costars family protein ABRACL and phosphoglycerate mutase 2 [25]. The fluid fraction of uterine aspirates are minimally invasive samples with an important value for the screening of EC protein biomarkers, leading to uterine aspirate-based signatures to diagnose EC and classify tumours in the most prevalent histologic subtypes [26]. This will improve diagnosis and assist in the prediction of the optimal surgical treatment.…”