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

• URL: http://arxiv.org/abs/2403.05260v2
• Date: Tue, 07 Jan 2025 00:53:48 GMT
• Title: Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learning
• Authors: Hui Liu, Wei Duan, Judong Luo,
• Abstract summary: scAdaDrug is a novel multi-source domain adaptation model powered by adaptive importance-aware representation learning. Our model achieved state-of-the-art performance in predicting drug response on multiple independent datasets.
• Score: 6.810949980810495
• License:
• 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.

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