文章目录

多模态基因组特征预测非小细胞肺癌免疫检查点阻断的结果

原文发布日期：2020-01-13

英文摘要：

摘要翻译：

原文链接：

文章：

多模态基因组特征预测非小细胞肺癌免疫检查点阻断的结果

Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer

原文发布日期：2020-01-13

英文摘要：

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.

摘要翻译：

尽管免疫治疗取得了进展，但识别 responds 的患者一直是挑战。通过分析 5,449 个肿瘤的全基因组和靶向序列数据，我们发现突变负担（TMB）与肿瘤纯度之间存在显著关联，这表明低纯度肿瘤可能具有不准确的 TMB 估计值。为此，我们开发了一种调整了肿瘤纯度的新方法来计算校正后的突变负担（cTMB），该指标更精确地预测了免疫检查点阻断剂（ICB）的效果。为了同时识别 cTMB 和改进的预测标志物，我们对接受过 ICB 治疗的 104 个肺癌肿瘤进行了全基因组测序。通过综合分析序列和结构改变，我们在三个免疫治疗处理组中的非响应肿瘤中发现 RTK 基因中激活突变显著富集。将 cTMB、RTK 突变、吸烟相关的突变签名以及人类淋巴细胞抗原状态纳入的整合多变量模型，提供了改进的对免疫治疗反应的预测指标，并通过独立验证进行了验证。