Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA.
Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA.
Cancer Cell. 2020 Nov 9;38(5):672-684.e6. doi: 10.1016/j.ccell.2020.09.014. Epub 2020 Oct 22.
Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.
大多数进入临床试验的药物都以失败告终，这往往与对药物反应机制的不完全了解有关。机器学习技术在更好地预测药物反应方面具有巨大的潜力，但由于缺乏可解释性以及侧重于单药治疗，大多数技术尚未应用于临床实践。我们通过开发 DrugCell 来应对这些挑战，这是一种基于对 1235 种肿瘤细胞系对 684 种药物的反应进行训练的可解释深度学习模型的人类癌细胞。肿瘤基因型会诱导细胞子系统中的状态，这些状态与药物结构相结合，以预测对治疗的反应，同时还能学习药物反应背后的生物学机制。DrugCell 的预测在细胞系中是准确的，也可以对临床结果进行分层。对 DrugCell 机制的分析直接导致了协同药物组合的设计，我们通过组合 CRISPR、体外药物药物筛选以及患者来源的异种移植对其进行了系统验证。DrugCell 为构建预测医学的可解释模型提供了蓝图。
