Luyao Wang

Oxford Classic–Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions

Abstract Purpose: The association between epithelial-to-mesenchymal transition (EMT) in high-grade serous ovarian cancer (HGSOC) and poor prognosis is known. However, molecularly defining a subset of tumors that reproducibly associates with poor prognosis has been an elusive goal in this disease. A molecular signature that can robustly identify patients with poor prognosis and guide treatment decisions, including surgical strategy and targeted therapies, can improve survival rates. Experimental Design: We carried out RNA sequencing of 139 tumor samples (Brescia cohort); an external validation on 362 and 126 patients from the Scottish and Garsed cohorts, respectively; and a meta-analysis of 1,023 tumors to develop clinically useful risk groups. Identification of therapeutic targets was carried out by transcriptomic analyses of fluorescence-activated cell sorted (FACS) tumor epithelial cells and multiplex immunofluorescence assessment of tissue sections. Results: In this study, we have validated the prognostic strength of the Oxford Classic–defined EMT in three independent patient cohorts: Brescia [HR = 3.6; 95% confidence interval (CI) of 1.59–7.97; P = 1.99e−03], Scottish (HR = 1.71; 95% CI of 1.08–2.70; P = 2.23e−02), and Garsed (Kruskal–Wallis P = 0.00071). OxC-based risk stratification of HGSOC could robustly identify poor-risk patients with a 5-year median survival for OxC high-risk and OxC low-risk groups of 13% and 50%, respectively (95% CI of 7.1%–23.5% vs. 36.1%–69.3%) in the Brescia cohort. Further analysis of the risk groups suggests that an alternative surgical strategy and a combination therapy involving EMT targeting drugs and immunomodulators could elicit improved clinical response in poor-risk patients. Conclusions: This study provides a clinically useful risk stratification strategy for HGSOC, as well as targeted treatment options for high-risk patients. See related commentary by Venegas et al., p. 10