Deep Transfer Learning of Drug Sensitivity by Integrating Bulk and Single-cell RNA-seq data

ABSTRACT Massively bulk RNA sequencing databases incorporating drug screening have opened up an avenue to inform the optimal clinical application of cancer drugs. Meanwhile, the growing single-cell RNA sequencing data contributes to improving therapeutic effectiveness by studying the heterogeneity of drug responses for cancer cell subpopulations. Yet, the drug response information for single-cell data is scarcely obtained. Thus, there is an urgent need to develop computational pipelines to infer and interpret cancer drug response in single cells. Here, we developed scDEAL, a deep transfer learning framework integrating large-scale bulk and single-cell RNA sequencing data. The true innovation of scDEAL is to translate cancer cell line drug response into predicting clinical drug response through the transfer learning between bulk RNA-seq in cancer cells and single cell RNA-seq in clinical samples. The other innovation of scDEAL is the integrated gradient feature interpretation to infer a comprehensive set of signature genes to reveal potential drug resistance mechanisms. We benchmarked scDEAL on six single-cell RNA sequencing datasets and indicate its model interpretability by several case studies. scDEAL not only achieves accurate and robust performance in single-cell drug response predictions, but also can infer signature genes to reveal potential drug resistance mechanisms based on integrated gradient feature interpretation. This work may help study cell reprogramming, drug selection, and repurposing for improving therapeutic efficacy.