  Accurate and robust prediction of patient's response to drug treatments is
critical for developing precision medicine. However, it is often difficult to
obtain a sufficient amount of coherent drug response data from patients
directly for training a generalized machine learning model. Although the
utilization of rich cell line data provides an alternative solution, it is
challenging to transfer the knowledge obtained from cell lines to patients due
to various confounding factors. Few existing transfer learning methods can
reliably disentangle common intrinsic biological signals from confounding
factors in the cell line and patient data. In this paper, we develop a Coherent
Deconfounding Autoencoder (CODE-AE) that can extract both common biological
signals shared by incoherent samples and private representations unique to each
data set, transfer knowledge learned from cell line data to tissue data, and
separate confounding factors from them. Extensive studies on multiple data sets
demonstrate that CODE-AE significantly improves the accuracy and robustness
over state-of-the-art methods in both predicting patient drug response and
de-confounding biological signals. Thus, CODE-AE provides a useful framework to
take advantage of in vitro omics data for developing generalized patient
predictive models. The source code is available at
https://github.com/XieResearchGroup/CODE-AE.
