Jillian A. Hung

PURPOSE: The study aim to identify novel molecular subtypes of ovarian cancer by gene expression profiling with linkage to clinical and pathologic features. EXPERIMENTAL DESIGN: Microarray gene expression profiling was done on 285 serous and endometrioid tumors of the ovary, peritoneum, and fallopian tube. K-means clustering was applied to identify robust molecular subtypes. Statistical analysis identified differentially expressed genes, pathways, and gene ontologies. Laser capture microdissection, pathology review, and immunohistochemistry validated the array-based findings. Patient survival within k-means groups was evaluated using Cox proportional hazards models. Class prediction validated k-means groups in an independent dataset. A semisupervised survival analysis of the array data was used to compare against unsupervised clustering results. RESULTS: Optimal clustering of array data identified six molecular subtypes. Two subtypes represented predominantly serous low malignant potential and low-grade endometrioid subtypes, respectively. The remaining four subtypes represented higher grade and advanced stage cancers of serous and endometrioid morphology. A novel subtype of high-grade serous cancers reflected a mesenchymal cell type, characterized by overexpression of N-cadherin and P-cadherin and low expression of differentiation markers, including CA125 and MUC1. A poor prognosis subtype was defined by a reactive stroma gene expression signature, correlating with extensive desmoplasia in such samples. A similar poor prognosis signature could be found using a semisupervised analysis. Each subtype displayed distinct levels and patterns of immune cell infiltration. Class prediction identified similar subtypes in an independent ovarian dataset with similar prognostic trends. CONCLUSION: Gene expression profiling identified molecular subtypes of ovarian cancer of biological and clinical importance.

Importance: Cytotoxic CD8+ tumor-infiltrating lymphocytes (TILs) participate in immune control of epithelial ovarian cancer; however, little is known about prognostic patterns of CD8+ TILs by histotype and in relation to other clinical factors. Objective: To define the prognostic role of CD8+ TILs in epithelial ovarian cancer. Design, Setting, and Participants: This was a multicenter observational, prospective survival cohort study of the Ovarian Tumor Tissue Analysis Consortium. More than 5500 patients, including 3196 with high-grade serous ovarian carcinomas (HGSOCs), were followed prospectively for over 24 650 person-years. Exposures: Following immunohistochemical analysis, CD8+ TILs were identified within the epithelial components of tumor islets. Patients were grouped based on the estimated number of CD8+ TILs per high-powered field: negative (none), low (1-2), moderate (3-19), and high (≥20). CD8+ TILs in a subset of patients were also assessed in a quantitative, uncategorized manner, and the functional form of associations with survival was assessed using penalized B-splines. Main Outcomes and Measures: Overall survival time. Results: The final sample included 5577 women; mean age at diagnosis was 58.4 years (median, 58.2 years). Among the 5 major invasive histotypes, HGSOCs showed the most infiltration. CD8+ TILs in HGSOCs were significantly associated with longer overall survival; median survival was 2.8 years for patients with no CD8+ TILs and 3.0 years, 3.8 years, and 5.1 years for patients with low, moderate, or high levels of CD8+ TILs, respectively (P value for trend = 4.2 × 10−16). A survival benefit was also observed among women with endometrioid and mucinous carcinomas, but not for those with the other histotypes. Among HGSOCs, CD8+ TILs were favorable regardless of extent of residual disease following cytoreduction, known standard treatment, and germline BRCA1 pathogenic mutation, but were not prognostic for BRCA2 mutation carriers. Evaluation of uncategorized CD8+ TIL counts showed a near-log-linear functional form. Conclusions and Relevance: This study demonstrates the histotype-specific nature of immune infiltration and provides definitive evidence for a dose-response relationship between CD8+ TILs and HGSOC survival. That the extent of infiltration is prognostic, not merely its presence or absence, suggests that understanding factors that drive infiltration will be the key to unraveling outcome heterogeneity in this cancer.

Accurately identifying patients with high-grade serous ovarian carcinoma (HGSOC) who respond to poly(ADP-ribose) polymerase inhibitor (PARPi) therapy is of great clinical importance. Here we show that quantitative BRCA1 methylation analysis provides new insight into PARPi response in preclinical models and ovarian cancer patients. The response of 12 HGSOC patient-derived xenografts (PDX) to the PARPi rucaparib was assessed, with variable dose-dependent responses observed in chemo-naive BRCA1/2-mutated PDX, and no responses in PDX lacking DNA repair pathway defects. Among BRCA1-methylated PDX, silencing of all BRCA1 copies predicts rucaparib response, whilst heterozygous methylation is associated with resistance. Analysis of 21 BRCA1-methylated platinum-sensitive recurrent HGSOC (ARIEL2 Part 1 trial) confirmed that homozygous or hemizygous BRCA1 methylation predicts rucaparib clinical response, and that methylation loss can occur after exposure to chemotherapy. Accordingly, quantitative BRCA1 methylation analysis in a pre-treatment biopsy could allow identification of patients most likely to benefit, and facilitate tailoring of PARPi therapy.

BACKGROUND: Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC. PATIENTS AND METHODS: Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies. RESULTS: Expression levels of 276 genes were associated with OS (false discovery rate < 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P < 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P < 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years. CONCLUSION: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches.