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Accurate and robust prediction of patient's response to drug treatments iscritical for developing precision medicine. However, it is often difficult toobtain a sufficient amount of coherent drug response data from patientsdirectly for training a generalized machine learning model. Although theutilization of rich cell line data provides an alternative solution, it ischallenging to transfer the knowledge obtained from cell lines to patients dueto various confounding factors. Few existing transfer learning methods canreliably disentangle common intrinsic biological signals from confoundingfactors in the cell line and patient data. In this paper, we develop a CoherentDeconfounding Autoencoder (CODE-AE) that can extract both common biologicalsignals shared by incoherent samples and private representations unique to eachdata set, transfer knowledge learned from cell line data to tissue data, andseparate confounding factors from them. Extensive studies on multiple data setsdemonstrate that CODE-AE significantly improves the accuracy and robustnessover state-of-the-art methods in both predicting patient drug response andde-confounding biological signals. Thus, CODE-AE provides a useful framework totake advantage of in vitro omics data for developing generalized patientpredictive models. The source code is available athttps://github.com/XieResearchGroup/CODE-AE.

CODE-AE: A Coherent De-confounding Autoencoder for Predicting Patient-Specific Drug Response From Cell Line Transcriptomics