PT  - JOURNAL ARTICLE
AU  - Gregory M Chen
AU  - Lavanya Kannan
AU  - Ludwig Geistlinger
AU  - Victor Kofia
AU  - Zhaleh Safikhani
AU  - Deena M A Gendoo
AU  - Giovanni Parmigiani
AU  - Michael Birrer
AU  - Benjamin Haibe-Kains
AU  - Levi Waldron
TI  - Consensus on Molecular Subtypes of Ovarian Cancer
AID  - 10.1101/162685
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 162685
4099  - http://biorxiv.org/content/early/2017/07/12/162685.short
4100  - http://biorxiv.org/content/early/2017/07/12/162685.full
AB  - INTRODUCTION Various computational methods for gene expression-based subtyping of high-grade serous (HGS) ovarian cancer have been proposed. This resulted in the identification of molecular subtypes that are based on different datasets and were differentially validated, making it difficult to achieve consensus on which definitions to use in follow-up studies. We assess three major subtype classifiers for their robustness and association to outcome by a meta-analysis of publicly available expression data, and provide a classifier that represents their consensus.METHODS We use a compendium of 15 microarray datasets consisting of 1,774 HGS ovarian tumors to assess 1) concordance between published subtyping algorithms, 2) robustness of those algorithms to re-clustering across datasets, and 3) association of subtypes with overall survival. A consensus classifier is trained on concordantly classified samples, and validated by leave-one-dataset-out validation.RESULTS Each subtyping classifier identified subsets significantly differing in overall survival, but were not robust to re-fitting in independent datasets and grouped only approximately one third of patients concordantly into four subtypes. We propose a consensus classifier to identify the minority of unambiguously classifiable tumors across multiple gene expression platforms, using a 100-gene signature. The resulting consensus subtypes correlate with patient age, survival, tumor purity, and lymphocyte infiltration.CONCLUSIONS Our analysis demonstrates that most HGS ovarian cancers are not able to be subtyped. A minority of tumors can be classified and our proposed consensus classifier consolidates and improves on the robustness of three previously proposed subtype classifiers. It provides reliable stratification of patients with HGS ovarian tumors of clearly defined subtype, and will assist in studying the role of polyclonality in the majority of tumors that are not robustly classifiable.