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靶向蛋白质组学鉴定子宫内膜液活检中的蛋白质组学特征,以诊断子宫内膜癌并辅助预测最佳手术治疗。

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.

机构信息

Biomedical Research Group in Gynecology, Vall Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, CIBERONC, Barcelona, Spain.

Luxembourg Clinical Proteomics Center (LCP), Luxembourg Institute of Health (LIH), Strassen, Luxembourg.

出版信息

Clin Cancer Res. 2017 Nov 1;23(21):6458-6467. doi: 10.1158/1078-0432.CCR-17-0474. Epub 2017 Aug 8.

DOI:10.1158/1078-0432.CCR-17-0474
PMID:28790116
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. .

摘要

子宫内膜癌 (EC) 的诊断依赖于对通过抽吸获得的子宫内膜活检中肿瘤细胞的观察(即子宫抽吸物),但这种方法与 22%的未确诊患者和多达 50%的 EC 组织类型和分级错误有关。我们旨在确定这些活检液部分中的生物标志物特征,以克服这些局限性。通过 LC-PRM(新一代靶向质谱采集技术)测量了 116 名患者的子宫抽吸液液部分中的 52 种蛋白质的水平。使用逻辑回归模型评估蛋白质组来区分 EC 和非 EC 患者以及 EC 组织学亚型的能力。通过在同一患者队列和 38 名患者的独立队列中进行的“留一法”交叉验证程序评估了面板的稳健性。与对照组( = 47)相比,EC 患者( = 69)的 28 种蛋白质水平显着升高。MMP9 和 KPYM 的组合对检测 EC 病例具有 94%的敏感性和 87%的特异性。该面板与标准诊断完美互补,在该数据集上实现了 100%的正确诊断。与浆液性 EC( = 20)相比,子宫内膜样 EC( = 49)中 9 种蛋白质显着升高。CTNB1、XPO2 和 CAPG 的组合对这些亚型的区分具有 95%的敏感性和 96%的特异性。我们开发了两种基于子宫抽吸物的签名来诊断 EC 并对最常见的组织学亚型进行分类。这将改善诊断并有助于预测最佳手术治疗。

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