He Yujie, Li Shenghao, Lan Hao, Long Wulin, Zhai Shengqiu, Li Menglong, Wen Zhining
College of Chemistry, Sichuan University, Chengdu 610064, China.
Medical Big Data Center, Sichuan University, Chengdu 610064, China.
Int J Mol Sci. 2025 May 4;26(9):4365. doi: 10.3390/ijms26094365.
Chemotherapy is a fundamental therapy in cancer treatment, yet its effectiveness is often undermined by drug resistance. Understanding the molecular mechanisms underlying drug response remains a major challenge due to tumor heterogeneity, complex cellular interactions, and limited access to clinical samples, which also hinder the performance and interpretability of existing predictive models. Meanwhile, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for uncovering resistance mechanisms, but the systematic collection and utilization of scRNA-seq drug response data remain limited. In this study, we collected scRNA-seq drug response datasets from publicly available web sources and proposed a transfer learning-based framework to align bulk and single cell sequencing data. A shared encoder was designed to project both bulk and single-cell sequencing data into a unified latent space for drug response prediction, while a sparse decoder guided by prior biological knowledge enhanced interpretability by mapping latent features to predefined pathways. The proposed model achieved superior performance across five curated scRNA-seq datasets and yielded biologically meaningful insights through integrated gradient analysis. This work demonstrates the potential of deep learning to advance drug response prediction and underscores the value of scRNA-seq data in supporting related research.
化疗是癌症治疗中的一种基本疗法，但其有效性常常受到耐药性的影响。由于肿瘤异质性、复杂的细胞相互作用以及获取临床样本的机会有限，了解药物反应背后的分子机制仍然是一项重大挑战，这些因素也阻碍了现有预测模型的性能和可解释性。与此同时，单细胞RNA测序（scRNA-seq）已成为揭示耐药机制的有力工具，但scRNA-seq药物反应数据的系统收集和利用仍然有限。在本研究中，我们从公开可用的网络资源中收集了scRNA-seq药物反应数据集，并提出了一个基于迁移学习的框架来对齐批量和单细胞测序数据。设计了一个共享编码器，将批量和单细胞测序数据投影到一个统一的潜在空间中进行药物反应预测，而一个由先验生物学知识引导的稀疏解码器通过将潜在特征映射到预定义的途径来增强可解释性。所提出的模型在五个经过整理的scRNA-seq数据集上取得了优异的性能，并通过综合梯度分析产生了具有生物学意义的见解。这项工作展示了深度学习在推进药物反应预测方面的潜力，并强调了scRNA-seq数据在支持相关研究中的价值。
