Cancers result from aberrations in cellular signalling systems, typically resulting from driver somatic genome alterations (SGAs) in individual tumours. Precision oncology requires understanding the cellular state and selecting medications that induce vulnerability in cancer cells under such conditions. To this end, we developed a computational framework consisting of two components: (1) a representation-learning component, which learns a representation of the cellular signalling systems when perturbed by SGAs and uses a biologically motivated and interpretable deep learning model, and (2) a drug-response prediction component, which predicts drug responses by leveraging the information of the cellular state of the cancer cells derived by the first component. Our cell-state-oriented framework notably improves the accuracy of predictions of drug responses compared to models using SGAs directly in cell lines. Moreover, our model performs well with real patient data. Importantly, our framework enables the prediction of responses to chemotherapy agents based on SGAs, thus expanding genome-informed precision oncology beyond molecularly targeted drugs.

癌症是由细胞信号系统的异常引起的，通常是由个体肿瘤中的驱动体细胞基因组改变（SGA）引起的。精准肿瘤学需要了解细胞状态并选择在这种条件下诱导癌细胞脆弱性的药物。为此，我们开发了一个由两个组件组成的计算框架：(1) 表示学习组件，它在受到 SGA 扰动时学习细胞信号系统的表示，并使用生物动机和可解释的深度学习模型，以及 (2 ）药物反应预测组件，它通过利用第一个组件衍生的癌细胞的细胞状态信息来预测药物反应。与直接在细胞系中使用 SGA 的模型相比，我们的面向细胞状态的框架显着提高了药物反应预测的准确性。此外，我们的模型在真实患者数据上表现良好。重要的是，我们的框架能够根据 SGA 预测化疗药物的反应，从而将基因组信息的精准肿瘤学扩展到分子靶向药物之外。