Multiomics Analysis Of Tumor Mutational Burden Across Cancer Types

Background Tumor mutational burden（TMB）is defined as the total number of somatic mutations per megabase（Mb）in the exon region,excluding synonymous mutations.It has been shown that TMB is associated with immunotherapy efficacy and prognosis in patients with some cancers,such as melanoma（SKCM）and bladder uroepithelial carcinoma（BLCA）.However,whether TMB is related to improved survival outcomes or the promotion of immunotherapy in various malignant tumors remains controversial,and we lack a comprehensive understanding of TMB across cancers.AimTo investigate multiomic features associated with TMB,explore the potential role of TMB in different tumors and dig out the related mechanisms through The Cancer Genome Atlas（TCGA）database.MethodsBased on data we obtained from TCGA database,the Tumor Immune Estimation Resource（TIMER）database and the Immune Cell Abundance Identifier（Immu Cell AI）tool,we conducted a multiomics analysis of TMB across 21 cancer types to identify characteristics related to TMB.Kaplan-Meier method,Cox risk regression,Wilcoxon rank-sum test,proportion test,edge R package and gene set enrichment analysis（GESA）were utilized to identify prognosis,driver gene mutations,differentially expressed genes,differentially methylated regions in promoter regions,immune cell infiltration and signaling pathways associated with TMB,respectively.We also attempted to explore the related mechanisms in multiple cancers.ResultsIn the analysis of the relationship between TMB and prognosis in 33 cancers,we found a significant relationship between TMB and overall survival（OS）in 21 tumors,and their relationship may be different in different tumors,which was corroborated in our later study of the relationship between TMB and progression-free survival（PFS）.By analyzing the correlation between TMB and OS in different molecular subtypes,we also found that the relationship between TMB and OS may differ even between different subtypes of the same tumor.TMB has also been shown to be associated with other clinical information,such as age,sex,clinical grade and stage,and the correlation in different cancer types is heterogeneous.Most driver gene mutations were found to be positively related to TMB in studies of somatic mutations,except for GATA3 and MAP3K1 in breast invasive carcinoma（BRCA）,TCF7L2 in colon adenocarcinoma（COAD）,NFE2L2 in esophageal cancer（ESCA）,CIC and IDH1 in brain lower grade glioma（LGG）,CDH1 in gastric adenocarcinoma（STAD）,and TP53 in uterine corpus endometrial carcinoma（UCEC）,in which mutations were ultimately determined to be correlated with lower TMB.In addition,we identified differentially expressed genes and differentially methylated regions in the high-and low-TMB groups（top/bottom tertiles by TMB）in 21 cancers and confirmed genes whose differential expression was related to differential DNA methylation.We also investigated the correlation between enrichment of signaling pathways,immune cell infiltration and TMB.It turned out that the relationship of them also varied according to different cancer types.ConclusionTaken together,our study presents a systematic analysis of TMB and its related factors across cancers and highlights the potential role of TMB in different cancers.And TMB can be used as a prognostic marker in pan-cancer.Our research further supports a comprehensive understanding of the role of TMB across cancers and has important clinical implications.