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

Elena Martinez-Garcia, Antoine Lesur, Laura Devis, Silvia Cabrera, Xavier Matias-Guiu, Marc Hirschfeld, Jasmin Asberger, Jan Van Oostrum, María De Los Angeles Casares de Cal, Antonio Gomez-Tato, Jaume Reventos, Bruno Domon, Eva Colas, Antonio Gil-Moreno

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24 Citations (Scopus)

Abstract

©2017 AACR. Purpose: 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. Experimental Design: 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. Results: The levels of 28 proteins were significantly higher in patients with EC (n ¼ 69) compared with controls (n ¼ 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 (n ¼ 49) compared with serous EC (n ¼ 20). The combination of CTNB1, XPO2, and CAPG achieved 95% sensitivity and 96% specificity for the discrimination of these subtypes. Conclusions: 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.
Original languageEnglish
Pages (from-to)6458-6467
JournalClinical Cancer Research
Volume23
Issue number21
DOIs
Publication statusPublished - 1 Nov 2017

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