Abstract A20: Integrating high-throughput technologies for the identification and validation of ovarian cancer biomarkers

Abstract

Abstract Background: Ovarian cancer (OvCa) is the most lethal gynecological malignancy in Canada. The 5-year survival rate for OvCa diagnosed at stage I or II is 80-95%, whereas diagnosis at more advanced stages is associated with survival rates of 10-30%. Proteomic analyses of OvCa fluid and conditioned media from cultured human OvCa cell lines has allowed for the identification of protein candidates as highly promising novel OvCa biomarkers. These candidates need further investigation to decipher their true potential to be reliable biomarkers for early stage OvCa. Hypothesis & Objectives: We hypothesize that through complementing our proteomic data with transcriptomics and bioinformatics, we will be able to identify and validate novel OvCa serum biomarkers. Specifically, we will establish a filtering algorithm using criteria based on biological soundness in order to prioritize the discovery candidates according to their potential to be novel OvCa biomarkers. Filtered candidates will then be assessed in the serum of healthy women and women with OvCa through quantitative assays to determine how effectively they distinguish between the cohorts. Methods: Filtering criteria with established scoring systems will be applied to the discovery candidates. As a proof-of-principle, the filtering algorithm will also be applied to known serum tumor markers. Filtered candidates will then be screened for their clinical relevance in serum through commercially-available enzyme-linked immunosorbent assays (ELISAs) and in-house developed mass spectrometry-based quantitative assays. Results: Sixteen high-priority candidates and known tumor markers were identified through filters based on cellular localization, differential mRNA expression, and computational prediction for secretion status. We have so far verified the sixteen candidates for their concentrations in a small cohort of healthy control and OvCa sera; kallikrein 6 (KLK6), folate receptor alpha precursor (FOLR1), granulin (GRN), basal cell adhesion molecule (BCAM), and Dickkopf-related protein 3 (Dkk-3) have displayed promising results. KLK6 and BCAM have retained their discriminatory power in subsequent independent validation cohorts and will be investigated further. Conclusions: In silico analyses can help highlight protein candidates from discovery-phase experiments with strong potential to be biomarkers for early OvCa diagnosis. However, proper verification and independent validation using gold-standard techniques is necessary to truly assess the ability of candidates to be clinically relevant biomarkers. Citation Format: Felix Leung, Apostolos Dimitromanolakis, Eleftherios P. Diamandis, Vathany Kulasingam. Integrating high-throughput technologies for the identification and validation of ovarian cancer biomarkers. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A20.