  A key goal of computational personalized medicine is to systematically
utilize genomic and other molecular features of samples to predict drug
responses for a previously unseen sample. Such predictions are valuable for
developing hypotheses for selecting therapies tailored for individual patients.
This is especially valuable in oncology, where molecular and genetic
heterogeneity of the cells has a major impact on the response. However, the
prediction task is extremely challenging, raising the need for methods that can
effectively model and predict drug responses. In this study, we propose a novel
formulation of multi-task matrix factorization that allows selective data
integration for predicting drug responses. To solve the modeling task, we
extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF)
method with component-wise multiple kernel learning. In addition, our approach
exploits the known pathway information in a novel and biologically meaningful
fashion to learn the drug response associations. Our method quantitatively
outperforms the state of the art on predicting drug responses in two publicly
available cancer data sets as well as on a synthetic data set. In addition, we
validated our model predictions with lab experiments using an in-house cancer
cell line panel. We finally show the practical applicability of the proposed
method by utilizing prior knowledge to infer pathway-drug response
associations, opening up the opportunity for elucidating drug action
mechanisms. We demonstrate that pathway-response associations can be learned by
the proposed model for the well known EGFR and MEK inhibitors.
