Christopher Yau

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

A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes

Bulk whole genome sequencing (WGS) enables the analysis of tumor evolution but, because of depth limitations, can only identify old mutational events. The discovery of current mutational processes for predicting the tumor’s evolutionary trajectory requires dense sequencing of individual clones or single cells. Such studies, however, are inherently problematic because of the discovery of excessive false positive (FP) mutations when sequencing picogram quantities of DNA. Data pooling to increase the confidence in the discovered mutations, moves the discovery back in the past to a common ancestor. Here we report a robust WGS and analysis pipeline (DigiPico/MutLX) that virtually eliminates all F results while retaining an excellent proportion of true positives. Using our method, we identified, for the first time, a hyper-mutation (kataegis) event in a group of ∼30 cancer cells from a recurrent ovarian carcinoma. This was unidentifiable from the bulk WGS data. Overall, we propose DigiPico/MutLX method as a powerful framework for the identification of clone-specific variants at an unprecedented accuracy.