Wu Tao, Wang Guangshuai, Wang Xuan, Wang Shixiang, Zhao Xiangyu, Wu Chenxu, Ning Wei, Tao Ziyu, Chen Fuxiang, Liu Xue-Song
School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China.
Cancer Res. 2022 Jun 15;82(12):2226-2238. doi: 10.1158/0008-5472.CAN-21-3717.
Immunoediting includes three temporally distinct stages, termed elimination, equilibrium, and escape, and has been proposed to explain the interactions between cancer cells and the immune system during the evolution of cancer. However, the status of immunoediting in cancer remains unclear, and the existence of neoantigen depletion in untreated cancer has been debated. Here we developed a distribution pattern-based method for quantifying neoantigen-mediated negative selection in cancer evolution. The method can provide a robust and reliable quantification for immunoediting signal in individual patients with cancer. Moreover, this method demonstrated the prevalence of immunoediting in the immunotherapy-naive cancer genome. The elimination and escape stages of immunoediting can be quantified separately, where tumor types with strong immunoediting-elimination exhibit a weak immunoediting-escape signal, and vice versa. The quantified immunoediting-elimination signal was predictive of clinical response to cancer immunotherapy. Collectively, immunoediting quantification provides an evolutionary perspective for evaluating the antigenicity of neoantigens and reveals a potential biomarker for precision immunotherapy in cancer.
Quantification of neoantigen-mediated negative selection in cancer progression reveals distinct features of cancer immunoediting and can serve as a potential biomarker to predict immunotherapy response.
免疫编辑包括三个在时间上不同的阶段，即清除、平衡和逃逸，并且已被提出用于解释癌症演变过程中癌细胞与免疫系统之间的相互作用。然而，癌症中免疫编辑的状态仍不清楚，未经治疗的癌症中是否存在新抗原耗竭也一直存在争议。在此，我们开发了一种基于分布模式的方法，用于量化癌症演变过程中新抗原介导的阴性选择。该方法可为个体癌症患者的免疫编辑信号提供稳健且可靠的量化。此外，该方法证明了免疫编辑在未接受过免疫治疗的癌症基因组中的普遍性。免疫编辑的清除和逃逸阶段可以分别进行量化，其中具有强烈免疫编辑清除作用的肿瘤类型表现出较弱的免疫编辑逃逸信号，反之亦然。量化的免疫编辑清除信号可预测癌症免疫治疗的临床反应。总体而言，免疫编辑量化为评估新抗原的抗原性提供了一个进化视角，并揭示了一种用于癌症精准免疫治疗的潜在生物标志物。
量化癌症进展中新抗原介导的阴性选择揭示了癌症免疫编辑的独特特征，并可作为预测免疫治疗反应的潜在生物标志物。
