Dong Yunyun, Zhang Yuanrong, Qian Yuhua, Zhao Yiming, Yang Ziting, Feng Xiufang
School of Software, Taiyuan University of Technology, Taiyuan, China.
Institute of Big Data Science and Industry, Shanxi University, Taiyuan, China.
PLoS Comput Biol. 2025 Jan 30;21(1):e1012748. doi: 10.1371/journal.pcbi.1012748. eCollection 2025 Jan.
Personalized cancer drug treatment is emerging as a frontier issue in modern medical research. Considering the genomic differences among cancer patients, determining the most effective drug treatment plan is a complex and crucial task. In response to these challenges, this study introduces the Adaptive Sparse Graph Contrastive Learning Network (ASGCL), an innovative approach to unraveling latent interactions in the complex context of cancer cell lines and drugs. The core of ASGCL is the GraphMorpher module, an innovative component that enhances the input graph structure via strategic node attribute masking and topological pruning. By contrasting the augmented graph with the original input, the model delineates distinct positive and negative sample sets at both node and graph levels. This dual-level contrastive approach significantly amplifies the model's discriminatory prowess in identifying nuanced drug responses. Leveraging a synergistic combination of supervised and contrastive loss, ASGCL accomplishes end-to-end learning of feature representations, substantially outperforming existing methodologies. Comprehensive ablation studies underscore the efficacy of each component, corroborating the model's robustness. Experimental evaluations further illuminate ASGCL's proficiency in predicting drug responses, offering a potent tool for guiding clinical decision-making in cancer therapy.
个性化癌症药物治疗正在成为现代医学研究中的一个前沿问题。考虑到癌症患者之间的基因组差异，确定最有效的药物治疗方案是一项复杂而关键的任务。针对这些挑战，本研究引入了自适应稀疏图对比学习网络（ASGCL），这是一种在癌细胞系和药物的复杂背景下揭示潜在相互作用的创新方法。ASGCL的核心是GraphMorpher模块，这是一个创新组件，通过策略性的节点属性掩码和拓扑修剪来增强输入图结构。通过将增强后的图与原始输入进行对比，该模型在节点和图层面都描绘出了不同的正样本集和负样本集。这种双层面的对比方法显著增强了模型在识别细微药物反应方面的辨别能力。利用监督损失和对比损失的协同组合，ASGCL实现了特征表示的端到端学习，大大优于现有方法。全面的消融研究强调了每个组件的有效性，证实了模型的稳健性。实验评估进一步阐明了ASGCL在预测药物反应方面的能力，为指导癌症治疗中的临床决策提供了一个有力工具。
