Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in nonresponding tumors in three immunotherapy treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature and human leukocyte antigen status provided an improved predictor of response to immunotherapy that was independently validated.

尽管免疫疗法取得了进展，但确定有反应的患者仍然是一个挑战。通过分析来自 5,449 个肿瘤的全外显子组和靶向序列数据，我们发现肿瘤突变负荷 (TMB) 与肿瘤纯度之间存在显着相关性，这表明低肿瘤纯度肿瘤可能具有不准确的 TMB 估计值。我们开发了一种新方法来估计校正的 TMB (cTMB)，该方法针对肿瘤纯度进行了调整，并更准确地预测了免疫检查点阻断 (ICB) 的结果。为了与 cTMB 一起确定改进的预测标志物，我们对用 ICB 治疗的 104 个肺肿瘤进行了全外显子组测序。通过对序列和结构变化的综合分析，我们发现在三个免疫疗法治疗的队列中，在无应答肿瘤中激活受体酪氨酸激酶 (RTK) 基因的突变显着富集。一个整合了 cTMB、RTK 突变、吸烟相关突变特征和人类白细胞抗原状态的综合多变量模型提供了一个经过独立验证的对免疫治疗反应的改进预测因子。