Abstract

Abstract High-Grade Serous Ovarian Carcinoma (HGSOC) is a highly aggressive and heterogeneous cancer with variable treatment responses. Current treatment strategies, including surgical resection and chemotherapy, are often ineffective due to a lack of molecular markers to predict drug sensitivity and patient prognosis. Mass spectrometry-based proteomics analysis is a powerful tool for biomarker discovery, but it is restricted by the availability and quality of archived clinical specimens. In this study, we developed a proteomics-based biomarker discovery pipeline that optimizes the extraction and sample preparation from archived tissue specimens of HGSOC patients. Macrodissection of the tumor rich tissue areas of interest ensured sample purity and statistical power for comparative analysis. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS), we conducted a comprehensive, global proteomics analysis of FF tissue specimens from 25 HGSOC patients to associate proteomic profiles with patient outcomes. Combining high-resolution mass spectrometry proteomics of clinical samples with advanced computational tools for clinical data mining, we were able to identify pathways associated with HGSOC that hold clinical significance. Our unique biomarker discovery platform provides a novel strategy for detecting cancer vulnerabilities from archived tissue samples and identifying proteomic biomarkers linked with treatment response and patient prognosis beyond BRCA mutation status. Citation Format: Anjana Shenoy, Gali Arad, Dimitri Kovalerchik, Amit Manor, Nitzan Simchi, Dina Daitchman, Kirill Pevzner, Eran Seger. Proteomics driven biomarker discovery for high-grade serous ovarian carcinoma from archived tissue specimens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1855.

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