Patient Informed Domain Adaptation Improves Clinical Drug Response Prediction

In-silico modeling of patient clinical drug response (CDR) promises to revolutionize personalized cancer treatment. State-of-the-art CDR predictions are usually based on cancer cell line drug perturbation profiles. However, prediction performance is limited due to the inherent differences between cancer cell lines and primary tumors. In addition, current computational models generally do not leverage both chemical information of a drug and a gene expression profile of a patient during training, which could boost prediction performance. Here we develop a Patient Adapted with Chemical Embedding (PACE) dual convergence deep learning framework that a) integrates gene expression along with drug chemical structures, and b) is adapted in an unsupervised fashion by primary tumor gene expression. We show that PACE achieves better discrimination between sensitive and resistant patients compared to the state-of-the-art linear regularized method (9/12 VS 3/12 drugs with available clinical outcomes) and alternative methods. ### Competing Interest Statement The authors have declared no competing interest. * GCN : Graph Convolutional Network MorganFP : Morgan Fingerprint SMILES : Simplified Molecular Input Line Entry System for annotating chemical structures using character strings ML/DL : machine learning/deep learning CDR : Clinician Drug Response CDI : Cell-line-Drug-IC50 EM : Expression Module DM : Drug Module PM : Prediction Module OOD : Out of Distribution CL : Cell Line