<b>An interpretable deep learning framework for genome-informed precision oncology</b>

Cancers result from aberrations in cellular signaling systems, typically resulting from driver somatic genome alterations (SGAs) in individual tumors. 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 signaling systems when perturbed by SGAs, using a biologically motivated and interpretable deep learning model. 2) A drug-response-prediction component, which predicts drug response by leveraging the information of the cellular state of the cancer cells derived by the first component. Our cell-state-oriented framework significantly improves drug response prediction accuracy 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 response to chemotherapy agents based on SGAs, thus expanding genome-informed precision oncology beyond molecularly targeted drugs.癌症源于细胞信号系统的异常，此类异常多由个体肿瘤内的驱动性体细胞基因组变异（somatic genome alterations, SGAs）引发。精准肿瘤学需要明晰癌细胞的细胞状态，并筛选可在该状态下诱导癌细胞产生治疗脆弱性的药物。为此，我们构建了一套包含两个模块的计算框架：其一为表征学习模块，该模块采用具备生物学先验依据且可解释的深度学习模型，学习体细胞基因组变异扰动下的细胞信号系统表征；其二为药物响应预测模块，该模块借助第一模块得到的癌细胞细胞状态信息，完成药物响应的预测。相较于直接使用细胞系体细胞基因组变异数据的模型，本细胞状态导向框架显著提升了药物响应预测的准确率。此外，本模型在真实患者数据集上同样表现优异。尤为关键的是，本框架可基于体细胞基因组变异预测化疗药物的响应，从而将基于基因组信息的精准肿瘤学应用范畴拓展至分子靶向药物之外。