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

Cross-domain feature disentanglement for interpretable modeling of tumor microenvironment impact on drug response