  High-throughput screening technology has facilitated the generation of
large-scale drug responses across hundreds of cancer cell lines. However, there
exists significant discrepancy between in vitro cell lines and actual tumors in
vivo in terms of their response to drug treatments, because of tumors comprise
of complex cellular compositions and histopathology structure, known as tumor
microenvironment (TME), which greatly influences the drug cytotoxicity against
tumor cells. To date, no study has focused on modeling the impact of the TME on
clinical drug response. This paper proposed a domain adaptation network for
feature disentanglement to separate representations of cancer cells and TME of
a tumor in patients. Two denoising autoencoders were separately used to extract
features from cell lines (source domain) and tumors (target domain) for partial
domain alignment and feature decoupling. The specific encoder was enforced to
extract information only about TME. Moreover, to ensure generalizability to
novel drugs, we applied a graph attention network to learn the latent
representation of drugs, allowing us to linearly model the drug perturbation on
cellular state in latent space. We calibrated our model on a benchmark dataset
and demonstrated its superior performance in predicting clinical drug response
and dissecting the influence of the TME on drug efficacy.
