Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learning
This work addresses the problem of predicting drug response for individual cells and patients, which is crucial for identifying drug-resistant tumor subpopulations, but it is incremental as it builds on existing domain adaptation techniques.
The paper tackled drug response prediction at the single-cell level, which is understudied and suboptimal, by proposing scAdaDrug, a multi-source domain adaptation model that achieved state-of-the-art performance on multiple independent datasets including cell lines, PDX models, and clinical patient cohorts.
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.