Simultaneous discovery of cancer subtypes and subtype features by molecular data integration

• Published in: Bioinformatics (Oxford, England) England), 2016-09, Vol.32 (17), p.i445-i454
• Main Authors: Le Van, Thanh, van Leeuwen, Matthijs, Carolina Fierro, Ana, De Maeyer, Dries, Van den Eynden, Jimmy, Verbeke, Lieven, De Raedt, Luc, Marchal, Kathleen, Nijssen, Siegfried
• Format: Article
• Language: English
• Subjects: Algorithms; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Breast Neoplasms - genetics; breast-cancer; Carcinogenesis; Cluster Analysis; Computer Science; copy-number alteration; cytoscape; data; Genetic Association Studies; genomic; glioblastoma; Humans; identification; interaction networks; Mathematical & Computational Biology; Mathematics; Medical Biotechnology; Medicinsk bioteknologi; Mutation; mutations; patterns; Prognosis; tumors
• ISSN: 1367-4803; 1367-4811; 1367-4811
• DOI: 10.1093/bioinformatics/btw434

Subtyping cancer is key to an improved and more personalized prognosis/treatment. The increasing availability of tumor related molecular data provides the opportunity to identify molecular subtypes in a data-driven way. Molecular subtypes are defined as groups of samples that have a similar molecular mechanism at the origin of the carcinogenesis. The molecular mechanisms are reflected by subtype-specific mutational and expression features. Data-driven subtyping is a complex problem as subtyping and identifying the molecular mechanisms that drive carcinogenesis are confounded problems. Many current integrative subtyping methods use global mutational and/or expression tumor profiles to group tumor samples in subtypes but do not explicitly extract the subtype-specific features. We therefore present a method that solves both tasks of subtyping and identification of subtype-specific features simultaneously. Hereto our method integrates` mutational and expression data while taking into account the clonal properties of carcinogenesis. Key to our method is a formalization of the problem as a rank matrix factorization of ranked data that approaches the subtyping problem as multi-view bi-clustering We introduce a novel integrative framework to identify subtypes by combining mutational and expression features. The incomparable measurement data is integrated by transformation into ranked data and subtypes are defined as multi-view bi-clusters We formalize the model using rank matrix factorization, resulting in the SRF algorithm. Experiments on simulated data and the TCGA breast cancer data demonstrate that SRF is able to capture subtle differences that existing methods may miss. The implementation is available at: https://github.com/rankmatrixfactorisation/SRF CONTACT: [e-mail], [e-mail] Supplementary data are available at Bioinformatics online.