  Predicting the response of a specific cancer to a therapy is a major goal in
modern oncology that should ultimately lead to a personalised treatment.
High-throughput screenings of potentially active compounds against a panel of
genomically heterogeneous cancer cell lines have unveiled multiple
relationships between genomic alterations and drug responses. Various
computational approaches have been proposed to predict sensitivity based on
genomic features, while others have used the chemical properties of the drugs
to ascertain their effect. In an effort to integrate these complementary
approaches, we developed machine learning models to predict the response of
cancer cell lines to drug treatment, quantified through IC50 values, based on
both the genomic features of the cell lines and the chemical properties of the
considered drugs. Models predicted IC50 values in a 8-fold cross-validation and
an independent blind test with coefficient of determination R2 of 0.72 and 0.64
respectively. Furthermore, models were able to predict with comparable accuracy
(R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage.
Our in silico models can be used to optimise the experimental design of
drug-cell screenings by estimating a large proportion of missing IC50 values
rather than experimentally measure them. The implications of our results go
beyond virtual drug screening design: potentially thousands of drugs could be
probed in silico to systematically test their potential efficacy as anti-tumour
agents based on their structure, thus providing a computational framework to
identify new drug repositioning opportunities as well as ultimately be useful
for personalized medicine by linking the genomic traits of patients to drug
sensitivity.
