Regional and temporal heterogeneity of epithelial ovarian cancer tumor biopsies: implications for therapeutic strategies.

Stage III/IV epithelial ovarian cancer (EOC) is a systemic disease. The clonal relationship among different tumor lesions at diagnosis (spatial heterogeneity) and how tumor clonal architecture evolves over time (temporal heterogeneity) have not yet been defined. Such knowledge would help to develop new target-based strategies, as biomarkers which can adjudge the success of therapeutic intervention should be independent of spatial and temporal heterogeneity. The work described in this paper addresses spatial and temporal heterogeneity in a cohort of 71 tumor biopsies using targeted NGS technology. These samples were taken from twelve high grade serous (HGS) and seven non HSG-EOC, both at the time of primary surgery when the tumor was naïve to chemotherapy and after chemotherapy. Matched tumor lesions growing in the ovary or at other anatomical sites show very different mutational landscapes with branched tumor evolution. Mutations in ATM, ATR,TGFB3,VCAM1 and COL3A1 genes were shared across all lesions. BRCA1 and BRCA2 genes were frequently mutated in synchronous lesions of non HGS-EOC. Relapsed disease seems to originate from resistant clones originally present at the time of primary surgery rather than from resistance acquired de novo during platinum based therapy. Overall the work suggests that EOC continues to evolve. More detailed mapping of genetic lesions is necessary to improve therapeutic strategies.

lncRNAs as Novel Indicators of Patients' Prognosis in Stage I Epithelial Ovarian Cancer: A Retrospective and Multicentric Study.

Purpose: Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs and is characterized by good prognosis with fewer than 20% of patients relapsing. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. We have demonstrated that in stage I EOC miR-200c-3p can predict patients' outcome. In the present study, we analyzed the expression of long non-coding RNAs (lncRNA) to enable potential definition of a non-coding transcriptional signature with prognostic relevance for stage I EOC.Experimental Design: 202 snap-frozen stage I EOC tumor biopsies, 47 of which relapsed, were gathered together from three independent tumor tissue collections and subdivided into a training set (n = 73) and a validation set (n = 129). Median follow up was 9 years. LncRNAs' expression profiles were correlated in univariate and multivariate analysis with overall survival (OS) and progression-free survival (PFS).Results: The expression of lnc-SERTAD2-3, lnc-SOX4-1, lnc-HRCT1-1, and PVT1 was associated in univariate and multivariate analyses with relapse and poor outcome in both training and validation sets (P < 0.001). Using the expression profiles of PVT1, lnc-SERTAD2-3, and miR-200c-3p simultaneously, it was possible to stratify patients into high and low risk. The OS for high- and low-risk individuals are 36 and 123 months, respectively (OR, 15.55; 95% confidence interval, 3.81-63.36).Conclusions: We have identified a non-coding transcriptional signature predictor of survival and biomarker of relapse for stage I EOC. Clin Cancer Res; 23(9); 2356-66. ©2016 AACR.

Circulating miRNA landscape identifies miR-1246 as promising diagnostic biomarker in high-grade serous ovarian carcinoma: A validation across two independent cohorts.

High-grade serous ovarian carcinoma (HGSOC) is the most lethal gynecologic neoplasm, with five-year survival rate below 30%. Early disease detection is of utmost importance to improve HGSOC cure rate. Sera from 168 HGSOC patients and 65 healthy controls were gathered together from two independent collections and stratified into a training set, for miRNA marker identification, and a validation set, for data validation. An innovative statistical approach for microarray data normalization was developed to identify differentially expressed miRNAs. Signature validation in both the training and validation sets was performed by quantitative Real Time PCR (RT-qPCR). In both the training and validation sets, miR-1246, miR-595 and miR-2278 emerged significantly over expressed in the sera of HGSOC patients compared to healthy controls. Receiver Operating Characteristic curve analysis revealed miR-1246 as the best diagnostic biomarker, with a sensitivity of 87%, a specificity of 77% and an accuracy of 84%. This study is the first step in the identification of circulating miRNAs with diagnostic relevance for HGSOC. According to its specificity and sensitivity, circulating miR-1246 levels are worthy to be further investigated as potential diagnostic biomarker for HGSOC.