Comprehensive characterization of tumor mutation burden in clear cell renal cell carcinoma based on the three independent cohorts.

With the wide application of high-throughput sequencing and the development of multiomics analysis, somatic mutational profiling has demonstrated that there is heterogeneity across multiple malignancies. Meanwhile, tumor mutational burden (TMB) was proven to be effective predictors of immunotherapy response. However, the significance of TMB in predicting prognosis remains unclear.
In the present study, we analyzed a total of 1118 clear cell renal cell carcinoma (ccRCC) samples with somatic variation data, transcriptome profiles, copy number variation data and clinical data from three independent populations, which included the European Union (EU) cohort, and Tokyo cohort, as well as the Cancer Genome Atlas (TCGA)-KIRC cohort.
We identified the most common tumor mutation signature among these three ccRCC cohorts. In contrast to most tumors, higher TMB levels were correlated with poor survival outcomes and this association was consistent across the three cohorts. Furthermore, TMB was also significantly associated with VHL and BAP1 mutations’ genotypes, high pathological stages, and tumor grades. In addition, we discussed the potential relationships between TMB and the immune checkpoint signature (ICS) and found that TMB was negatively correlated with only programmed death-ligand 1 (PDL1) expression. Thus, in the TCGA-KIRC cohort, we constructed the integrative TMBICS model based on a multivariate Cox regression method to predict the prognosis of ccRCC. A receiver-operating characteristic (ROC) curve was utilized to assess the predictive accuracy of TMBICS. Kaplan-Meier analysis indicated that the high-TMBICS group suffered worse outcomes than the low-TMBICS group. Furthermore, we examined whether TMB was also associated with mutations in the DNA damage response (DDR) pathway. The Wilcoxon rank-sum test suggested that samples with a high TMB had a higher proportion of DDR mutations (P = 0.003) than samples with a low TMB. Finally, gene set enrichment analysis (GSEA) showed that the glycolysis and sucrose metabolism pathways were upregulated in the high-TMB group, while the MAPK signaling pathway, pathways in cancer, renal cell carcinoma and Wnt signaling pathways were downregulated in the low-TMB group.
In summary, a comprehensive characterization of TMB might provide additional insights into mutation-driven tumorigenesis, especially the implications of TMB for guidance on drug therapy.