SCREP: Towards Single-Cell Drug Response Prediction by Pharmacogenomic Embedding Enhanced Meta-Pretraining and Few-Shot Transfer Learning

Abstract

Analyzing the drug response at the cellular level is crucial for identifying biomarkers and understanding the mechanisms of resistance. Although studies on the drug response of individual cells can provide novel insights into tumor heterogeneity, pharmacogenomic data related to single-cell (SC) RNA sequencing is often limited. Transfer learning provides a promising approach to translate the knowledge of drug response from bulk cell lines to SC analysis, potentially providing an effective solution to this challenge. Previous studies often use data from single drug-cell lines to pre-train specific models and adapt the models on SC datasets, which lack pharmacogenomic information from other drugs and hinder model generalization. In this work, we introduce MetaSCDrug as a unified meta pre-training framework that integrates molecular information with transcriptomic data to simultaneously modeling cellular heterogeneity in response to multiple pre-trained drugs and generalize to unseen drugs. Our model requires only one pre-training session, followed by fine-tuning on multiple single-cell datasets by few-shot learning, achieving an average of 4.58% accuracy increase in drug response prediction compared to the baselines. Furthermore, our meta pre-training strategy effectively captures transcriptome heterogeneity in the generalization of unseen drugs, achieving a 20% improvement over the model without meta pre-training. Case studies of our framework highlight its capability to identify critical genes for resistance, providing a method for exploring drug action pathways and understanding resistance mechanisms.