Wu Jiashuo, Lai Jiyin, Zhao Xilong, Wang Ziyi, Zhang Yongbao, Wang Liqiang, Su Yinchun, He Yalan, Li Siyuan, Jiang Ying, Han Junwei
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
College of Basic Medical Science, Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
Adv Sci (Weinh). 2025 Jun;12(23):e2416958. doi: 10.1002/advs.202416958. Epub 2025 May 21.
Accurate characterization of cellular states is the foundation for precise prediction of drug sensitivity in cancer cell lines, which in turn is fundamental to realizing precision oncology. However, current deep learning approaches have limitations in characterizing cellular states. They rely solely on isolated genetic markers, overlooking the complex regulatory networks and cellular mechanisms that underlie drug responses. To address this limitation, this work proposes DeepCCDS, a Deep learning framework for Cancer Cell Drug Sensitivity prediction through Characterizing Cancer Driver Signals. DeepCCDS incorporates a prior knowledge network to characterize cancer driver signals, building upon the self-supervised neural network framework. The signals can reflect key mechanisms influencing cancer cell development and drug response, enhancing the model's predictive performance and interpretability. DeepCCDS has demonstrated superior performance in predicting drug sensitivity compared to previous state-of-the-art approaches across multiple datasets. Benefiting from integrating prior knowledge, DeepCCDS exhibits powerful feature representation capabilities and interpretability. Based on these feature representations, we have identified embedding features that could potentially be used for drug screening in new indications. Further, this work demonstrates the applicability of DeepCCDS on solid tumor samples from The Cancer Genome Atlas. This work believes integrating DeepCCDS into clinical decision-making processes can potentially improve the selection of personalized treatment strategies for cancer patients.
准确表征细胞状态是精确预测癌细胞系药物敏感性的基础，而这反过来又是实现精准肿瘤学的根本。然而，当前的深度学习方法在表征细胞状态方面存在局限性。它们仅依赖于孤立的遗传标记，忽略了药物反应背后复杂的调控网络和细胞机制。为了解决这一局限性，本研究提出了DeepCCDS，这是一个通过表征癌症驱动信号来预测癌细胞药物敏感性的深度学习框架。DeepCCDS在自监督神经网络框架的基础上，纳入了一个先验知识网络来表征癌症驱动信号。这些信号可以反映影响癌细胞发育和药物反应的关键机制，从而提高模型的预测性能和可解释性。与之前在多个数据集上的最先进方法相比，DeepCCDS在预测药物敏感性方面表现出了卓越的性能。受益于先验知识的整合，DeepCCDS展现出强大的特征表示能力和可解释性。基于这些特征表示，我们识别出了可能用于新适应症药物筛选的嵌入特征。此外，本研究还证明了DeepCCDS在来自癌症基因组图谱的实体瘤样本上的适用性。本研究认为，将DeepCCDS整合到临床决策过程中可能会改善癌症患者个性化治疗策略的选择。
