RT Journal Article
SR Electronic
T1 Consensus on Molecular Subtypes of Ovarian Cancer
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 162685
DO 10.1101/162685
A1 Gregory M Chen
A1 Lavanya Kannan
A1 Ludwig Geistlinger
A1 Victor Kofia
A1 Zhaleh Safikhani
A1 Deena M A Gendoo
A1 Giovanni Parmigiani
A1 Michael Birrer
A1 Benjamin Haibe-Kains
A1 Levi Waldron
YR 2017
UL http://biorxiv.org/content/early/2017/07/12/162685.abstract
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.