Immune landscape and a novel immunotherapy-related gene signature associated with clinical outcome in early-stage lung adenocarcinoma
Immune landscape and a novel immunotherapy-related gene signature associated with clinical outcome in early-stage lung adenocarcinoma
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DOI：
10.1007/s00109-020-01908-9
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发表时间：
2020-04-25
影响因子：
4.7
通讯作者：
Wang, Yanfang
中科院分区：
文献类型：
--
作者：
Bao, Xuanwen;Shi, Run;Wang, Yanfang
早期肺腺癌（LUAD）患者表现出不同的总生存率（OS）和免疫治疗反应。了解免疫景观有助于LUAD的个性化治疗。肿瘤组织中的免疫细胞群体被定量以描绘癌症基因组图谱（TCGA）中早期LUAD患者的免疫景观。由免疫景观确定的三个免疫簇中的早期LUAD患者表现出不同的生存潜力。建立了一个预后免疫相关基因标签来预测早期LUAD患者的生存率。应用几种机器学习方法（支持向量机、朴素贝叶斯、随机森林和基于神经网络的深度学习）来训练分类器，以基于基因签名识别早期LUAD中的免疫簇。这四个分类器在识别免疫簇方面表现出了鲁棒的效果。随机森林回归模型确定TP53是与免疫相关标签相关的最重要的基因突变。此外，根据免疫相关基因特征和临床病理特征构建决策树和诺模图，以改善个体患者的风险分层并量化风险评估。应用五个外部测试群组来验证免疫相关特征的准确性。本研究为早期LUAD的免疫治疗和个体化治疗提供了新的思路。免疫景观与早期腺癌（LUAD）的临床结局相关。机器学习方法识别预后基因特征以预测早期LUAD的生存和预后。TP53基因突变状态与早期LUAD的免疫景观相关。
Patients with early-stage lung adenocarcinoma (LUAD) exhibit different overall survival (OS) rates and immunotherapy responses. Understanding the immune landscape facilitates the personalized treatment of LUAD. The immune cell populations in tumour tissues were quantified to depict the immune landscape in early-stage LUAD patients in The Cancer Genome Atlas (TCGA). Early-stage LUAD patients in three immune clusters identified by the immune landscape exhibited different survival potentials. A prognostic immune-related gene signature was built to predict the survival of early-stage LUAD patients. Several machine learning methods (support vector machine, naive Bayes, random forest, and neural network-based deep learning) were applied to train the classifiers to identify the immune clusters in early-stage LUAD based on the gene signature. The four classifiers exhibited a robust effect in identifying the immune clusters. A random forest regression model identified that TP53 was the most important gene mutation associated with the immune-related signature. Furthermore, a decision tree and a nomogram were constructed based on the immune-related gene signature and clinicopathological traits to improve risk stratification and quantify risk assessment for individual patients. Five external test cohorts were applied to validate the accuracy of the immune-related signature. Our study might contribute to the development of immunotherapy and the personalized treatment of early-stage LUAD. Key messagesImmune landscape correlates with the clinical outcome of early-stage adenocarcinoma (LUAD). Machine learning methods identifies a prognostic gene signature to predict the survival and prognosis of early-stage LUAD. TP53 gene mutation status correlates with the immune landscape in early-stage LUAD.