Following the advent of cancer immunotherapy, increasing insight has been gained on the role of mutational load and neoantigens as key ingredients in T cell recognition of malignancies. However, not all highly mutational tumours react to immune therapies, and initial success is often followed by eventual relapse. Heterogeneity in the neoantigen landscape of a tumour might be key in the failure of immune surveillance. In this work, we present a mathematical framework to describe how neoantigen distributions shape the immune response. The model predicts the existence of an antigen diversity threshold level beyond which T cells fail at controlling heterogeneous tumours. Incorporating this diversity marker adds predictive value to antigen load for two cohorts of anti-CTLA-4 treated melanoma patients. Furthermore, our analytical approach indicates rapid increases in epitope heterogeneity in early malignancy growth following immune escape. We propose a combination therapy scheme that takes advantage of preexisting resistance to a targeted agent. The model indicates that the selective sweep for a resistant subclone reduces neoantigen heterogeneity, and we postulate the existence of a time window before tumour relapse where checkpoint blockade immunotherapy can become more effective.

中文翻译：

---

肿瘤新抗原异质性阈值为联合免疫治疗提供了一个时间窗口

随着癌症免疫疗法的出现，人们越来越了解突变负荷和新抗原作为 T 细胞识别恶性肿瘤的关键成分的作用。然而，并非所有高度突变的肿瘤都对免疫疗法有反应，最初的成功往往伴随着最终的复发。肿瘤新抗原景观的异质性可能是免疫监视失败的关键。在这项工作中，我们提出了一个数学框架来描述新抗原分布如何塑造免疫反应。该模型预测抗原多样性阈值水平的存在，超过该阈值水平 T 细胞无法控制异质肿瘤。结合这种多样性标志物增加了对两组抗 CTLA-4 治疗的黑色素瘤患者的抗原负荷的预测价值。此外，我们的分析方法表明，免疫逃逸后早期恶性肿瘤生长的表位异质性迅速增加。我们提出了一种联合治疗方案，该方案利用了对靶向药物的预先存在的耐药性。该模型表明，对抗性亚克隆的选择性扫描降低了新抗原异质性，我们假设在肿瘤复发之前存在一个时间窗口，此时检查点阻断免疫疗法可以变得更有效。