Abstract: Somatic mutations in cancer have accumulated during its evolution and are caused by different exposures to carcinogens and therapeutic agents, as well as, intrinsic errors that occur during DNA replication. Analysing a set of cancer samples jointly allows to explain their somatic mutations as a linear combination of (to be learned) mutational signatures. In this presentation I will discuss the problem of learning mutational signatures from cancer data using probabilistic modelling and nonnegative matrix factorisation. I further describe our ongoing work using mutational signatures in the context of drug response prediction and extensions of the basic model to explicitly include DNA repair processes.

Speaker: Ville Mustonen

Affiliation: Professor of Mathematics and Natural Science, University of Helsinki

Place of Seminar: University of Helsinki