  Predicting clinical outcomes to anti-cancer drugs on a personalized basis is
challenging in cancer treatment due to the heterogeneity of tumors. Traditional
computational efforts have been made to model the effect of drug response on
individual samples depicted by their molecular profile, yet overfitting occurs
because of the high dimension for omics data, hindering models from clinical
application. Recent research shows that deep learning is a promising approach
to build drug response models by learning alignment patterns between drugs and
samples. However, existing studies employed the simple feature fusion strategy
and only considered the drug features as a whole representation while ignoring
the substructure information that may play a vital role when aligning drugs and
genes. Hereby in this paper, we propose TCR (Transformer based network for
Cancer drug Response) to predict anti-cancer drug response. By utilizing an
attention mechanism, TCR is able to learn the interactions between drug
atom/sub-structure and molecular signatures efficiently in our study.
Furthermore, a dual loss function and cross sampling strategy were designed to
improve the prediction power of TCR. We show that TCR outperformed all other
methods under various data splitting strategies on all evaluation matrices
(some with significant improvement). Extensive experiments demonstrate that TCR
shows significantly improved generalization ability on independent in-vitro
experiments and in-vivo real patient data. Our study highlights the prediction
power of TCR and its potential value for cancer drug repurpose and precision
oncology treatment.
