  The study of high-throughput genomic profiles from a pharmacogenomics
viewpoint has provided unprecedented insights into the oncogenic features
modulating drug response. A recent screening of ~1,000 cancer cell lines to a
collection of anti-cancer drugs illuminated the link between genotypes and
vulnerability. However, due to essential differences between cell lines and
tumors, the translation into predicting drug response in tumors remains
challenging. Here we proposed a DNN model to predict drug response based on
mutation and expression profiles of a cancer cell or a tumor. The model
contains a mutation and an expression encoders pre-trained using a large
pan-cancer dataset to abstract core representations of high-dimension data,
followed by a drug response predictor network. Given a pair of mutation and
expression profiles, the model predicts IC50 values of 265 drugs. We trained
and tested the model on a dataset of 622 cancer cell lines and achieved an
overall prediction performance of mean squared error at 1.96 (log-scale IC50
values). The performance was superior in prediction error or stability than two
classical methods and four analog DNNs of our model. We then applied the model
to predict drug response of 9,059 tumors of 33 cancer types. The model
predicted both known, including EGFR inhibitors in non-small cell lung cancer
and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive
analysis further revealed the molecular mechanisms underlying the resistance to
a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer
potential of a novel agent, CX-5461, in treating gliomas and hematopoietic
malignancies. Overall, our model and findings improve the prediction of drug
response and the identification of novel therapeutic options.
