Immune checkpoint blockade (ICB) therapies have brought unprecedented advances in cancer treatment, but responses are limited to a fraction of patients. Therefore, sustained and substantial efforts are required to advance clinical and translational investigation on managing patients receiving ICB. In this study, we investigated the dynamic changes in molecular profiles of T-cell exhaustion (TEX) during ICB treatment using single-cell and bulk transcriptome analysis, and demonstrated distinct exhaustion molecular profiles associated with ICB response. By applying an ensemble deep-learning computational framework, we identified an ICB-associated transcriptional signature consisting of 16 TEX-related genes, termed ITGs. Incorporating 16 ITGs into a machine-learning model called MLTIP achieved reliable predictive power for clinical ICB response with an average AUC of 0.778, and overall survival (pooled HR = 0.093, 95% CI, 0.031–0.28, P < 0.001) across multiple ICB-treated cohorts. Furthermore, the MLTIP consistently demonstrated superior predictive performance compared to other well-established markers and signatures, with an average increase in AUC of 21.5%. In summary, our results highlight the potential of this TEX-dependent transcriptional signature as a tool for precise patient stratification and personalized immunotherapy, with clinical translation in precision medicine.

免疫检查点阻断（ICB）疗法为癌症治疗带来了前所未有的进步，但反应仅限于一小部分患者。因此，需要持续和大量的努力来推进治疗接受 ICB 的患者的临床和转化研究。在这项研究中，我们利用单细胞和批量转录组分析研究了 ICB 治疗期间 T 细胞耗竭 (TEX) 分子谱的动态变化，并证明了与 ICB 反应相关的不同耗竭分子谱。通过应用集成深度学习计算框架，我们确定了由 16 个 TEX 相关基因（称为 ITG）组成的 ICB 相关转录特征。P  < 0.001）在多个 ICB 治疗组中。此外，与其他成熟的标记和特征相比，MLTIP 始终表现出卓越的预测性能，AUC 平均增加 21.5%。总之，我们的结果强调了这种依赖于 TEX 的转录特征作为精确患者分层和个性化免疫治疗工具的潜力，并可在精准医学中进行临床转化。