Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learning

TL;DR

This paper introduces scAdaDrug, a multi-source domain adaptation model that uses importance-aware representation learning to accurately predict drug responses at the single-cell level across diverse datasets.

Contribution

The study presents a novel importance-aware multi-source domain transfer learning approach for single-cell drug response prediction, enhancing performance and generalization.

Findings

Achieved state-of-the-art drug response prediction accuracy across multiple datasets.

Effectively captured underlying response patterns from diverse sources.

Demonstrated robustness on cell line, PDX, and clinical tumor datasets.

Abstract

The advancement of single-cell sequencing technology has promoted the generation of a large amount of single-cell transcriptional profiles, providing unprecedented opportunities to identify drug-resistant cell subpopulations within a tumor. However, few studies have focused on drug response prediction at single-cell level, and their performance remains suboptimal. This paper proposed scAdaDrug, a novel multi-source domain adaptation model powered by adaptive importance-aware representation learning to predict drug response of individual cells. We used a shared encoder to extract domain-invariant features related to drug response from multiple source domains by utilizing adversarial domain adaptation. Particularly, we introduced a plug-and-play module to generate importance-aware and mutually independent weights, which could adaptively modulate the latent representation of each sample in element-wise manner between source and target domains. Extensive experimental results showed that our model achieved state-of-the-art performance in predicting drug response on multiple independent datasets, including single-cell datasets derived from both cell lines and patient-derived xenografts (PDX) models, as well as clinical tumor patient cohorts. Moreover, the ablation experiments demonstrated our model effectively captured the underlying patterns determining drug response from multiple source domains.