College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China.
Key Laboratory of High Throughput Omics Big Data for Cold Region's Major Diseases in Heilongjiang Province, Harbin, Heilongjiang 150081, China.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac064.
Interaction between tumor cells and immune cells determined highly heterogeneous microenvironments across patients, leading to substantial variation in clinical benefits from immunotherapy. Somatic gene mutations were found not only to elicit adaptive immunity but also to influence the composition of tumor immune microenvironment and various processes of antitumor immunity. However, due to an incomplete view of associations between gene mutations and immunophenotypes, how tumor cells shape the immune microenvironment and further determine the clinical benefit of immunotherapy is still unclear. To address this, we proposed a computational approach, inference of mutation effect on immunophenotype by integrated gene set enrichment analysis (MEIGSEA), for tracing back the genomic factor responsible for differences in immunophenotypes. MEIGSEA was demonstrated to accurately identify the previous confirmed immune-associated gene mutations, and systematic evaluation in simulation data further supported its performance. We used MEIGSEA to investigate the influence of driver gene mutations on the infiltration of 22 immune cell types across 19 cancers from The Cancer Genome Atlas. The top associated gene mutations with infiltration of CD8 T cells, such as CASP8, KRAS and EGFR, also showed extensive impact on other immune components; meanwhile, immune effector cells shared critical gene mutations that collaboratively contribute to shaping distinct tumor immune microenvironment. Furthermore, we highlighted the predictive capacity of gene mutations that are positively associated with CD8 T cells for the clinical benefit of immunotherapy. Taken together, we present a computational framework to help illustrate the potential of somatic gene mutations in shaping the tumor immune microenvironment.
肿瘤细胞与免疫细胞的相互作用决定了患者之间高度异质的微环境,导致免疫疗法的临床获益存在很大差异。研究发现,体细胞基因突变不仅能引发适应性免疫,还能影响肿瘤免疫微环境的组成和抗肿瘤免疫的各种过程。然而,由于对基因突变与免疫表型之间关联的认识不完整,肿瘤细胞如何塑造免疫微环境并进一步决定免疫疗法的临床获益尚不清楚。为了解决这个问题,我们提出了一种计算方法,即通过整合基因集富集分析(MEIGSEA)推断基因突变对免疫表型的影响,以追溯导致免疫表型差异的基因组因素。MEIGSEA 被证明能够准确识别先前确认的与免疫相关的基因突变,模拟数据的系统评估进一步支持了其性能。我们使用 MEIGSEA 研究了驱动基因突变对 19 种癌症中 22 种免疫细胞浸润的影响。与 CD8 T 细胞浸润相关的最主要的关联基因突变,如 CASP8、KRAS 和 EGFR,也对其他免疫成分产生广泛影响;同时,免疫效应细胞共享关键的基因突变,共同塑造独特的肿瘤免疫微环境。此外,我们强调了与 CD8 T 细胞呈正相关的基因突变对免疫疗法临床获益的预测能力。总之,我们提出了一个计算框架,帮助阐明体细胞基因突变在塑造肿瘤免疫微环境中的潜力。