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

子宫内膜癌 医学 浆液性液体 生物标志物 子宫内膜 子宫内膜活检 队列 活检 癌症 肿瘤科 蛋白质组学 内科学 逻辑回归 病理 生物 基因 生物化学
作者
Elena Martínez-García,Antoine Lesur,Laura Devis,Sílvia Cabrera,Xavier Matías‐Guiu,Marc Hirschfeld,Jasmin Asberger,Jan van Oostrum,M. Casares de Cal,Antonio Gómez‐Tato,Jaume Reventós,Bruno Domon,Eva Colás,Antonio Gil‐Moreno
出处
期刊:Clinical Cancer Research [American Association for Cancer Research]
卷期号:23 (21): 6458-6467 被引量:49
标识
DOI:10.1158/1078-0432.ccr-17-0474
摘要

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 = 69) compared with controls (n = 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 = 49) compared with serous EC (n = 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. Clin Cancer Res; 23(21); 6458-67. ©2017 AACR.
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