Publications and Research

Document Type


Publication Date



Purpose: The majority of ovarian carcinomas are of high-grade serous histology, which is associated with poor prognosis. Surgery and chemotherapy are the mainstay of treatment, and molecular characterization is necessary to lead the way to targeted therapeutic options. To this end, various computational methods for gene expression-based subtyping of high-grade serous ovarian carcinoma (HGSOC) have been proposed, but their overlap and robustness remain unknown.

Experimental Design: We assess three major subtype classifiers by meta-analysis of publicly available expression data, and assess statistical criteria of subtype robustness and classifier concordance. We develop a consensus classifier that represents the subtype classifications of tumors based on the consensus of multiple methods, and outputs a confidence score. Using our compendium of expression data, we examine the possibility that a subset of tumors are unclassifiable based on currently proposed subtypes.

Results: HGSOC subtyping classifiers exhibit moderate pairwise concordance across our data compendium (58.9%-70.9%, p < 10-5) and are associated with overall survival in a metaanalysis across datasets (p < 10-5). Current subtypes do not meet statistical criteria for robustness to re-clustering across multiple datasets (Prediction Strength < 0.6). A new subtype classifier is trained on concordantly classified samples to yield a consensus classification of patient tumors that correlates with patient age, survival, tumor purity, and lymphocyte infiltration.

Conclusion: A new consensus ovarian subtype classifier represents the consensus of methods, and demonstrates the importance of classification approaches for cancer that do not require all tumors to be assigned to a distinct subtype.


This is the accepted manuscript of an article originally published in Clinical Cancer Research, available at doi: 10.1158/1078-0432.CCR-18-0784.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.