癌症免疫检查点抑制剂（ICI）的反应率因患者而异，因此很难预先确定特定患者是否会对免疫治疗产生反应。虽然基因突变对治疗结果至关重要，但尚未建立能够明确纳入生物学知识的框架。在这里，我们的目标是提出并验证基于突变的深度学习模型，用于对 1571 名接受 ICI 治疗的患者进行生存分析。与金标准 Cox-PH 模型 (0.52±0.10) 相比，我们的模型在九种癌症中的平均一致性指数为 0.59±±0.13。深度学习的“黑匣子”性质是医疗保健领域的一个主要问题。该模型的可解释性源于整合基因途径和蛋白质相互作用（即生物学感知），而不是依赖“黑匣子”方法，有助于患者分层并提供对新型基因生物标志物的洞察，从而增进我们对 ICI 治疗的理解。 © 2023。作者。

The response rate of cancer immune checkpoint inhibitors (ICI) varies among patients, making it challenging to pre-determine whether a particular patient will respond to immunotherapy. While gene mutation is critical to the treatment outcome, a framework capable of explicitly incorporating biology knowledge has yet to be established. Here we aim to propose and validate a mutation-based deep learning model for survival analysis on 1571 patients treated with ICI. Our model achieves an average concordance index of 0.59 ± 0.13 across nine types of cancer, compared to the gold standard Cox-PH model (0.52 ± 0.10). The "black box" nature of deep learning is a major concern in healthcare field. This model's interpretability, which results from incorporating the gene pathways and protein interaction (i.e., biology-aware) rather than relying on a 'black box' approach, helps patient stratification and provides insight into novel gene biomarkers, advancing our understanding of ICI treatment.© 2023. The Author(s).