Proceeding talk – Theme: Data.
Abstract
Subtyping cancer is key to an improved and more personalized prognosis/treatment. 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 formalisation of the problem as a rank matrix factorisation of ranked data that approaches the subtyping problem as multi-view bi-clustering.
Authors
Thanh Le Van, KULeuven, Belgium
Matthijs van Leeuwen, Universiteit Leiden, Netherlands
Ana Carolina Fierro, Ghent University, Belgium
Dries De MaeyerBioinformatics Institute Ghent, Belgium
Jimmy Van den Eynden, University of Gothenburg, Sweden
Lieven Verbeke, Ghent University, Belgium
Luc De Raedt, KULeuven, Belgium
Kathleen Marchal, Ghent University, Belgium
Siegfried Nijssen, KULeuven, Belgium