Exploration of the relationships between tumor mutation burden with immune infiltrates in clear cell renal cell carcinoma

Abstract

Background: Whether tumor mutation burden (TMB) correlated with improved survival outcomes or promotion of immunotherapies remained controversy in various malignancies. We aimed to investigate the prognosis of TMB and the potential association with immune infiltrates in clear cell renal cell carcinoma (ccRCC).

Methods: We downloaded the somatic mutation data of 336 ccRCC patients from the Cancer Genome Atlas (TCGA) database, and analyzed the mutation profiles with "maftools" package. TMB was calculated and we classified the samples into high-TMB and low-TMB group. Differential analysis was conducted to compare the expression profiles between two groups using "limma" package, and we identified the 9 hub TMB-related signature from batch survival analysis. Gene ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were performed to screen significantly enriched pathways between two groups. Based on the TIMER database, we further assessed the relationships of the mutants of 9 TMB-related signature with immune infiltration levels in ccRCC. Besides, we utilized the "CIBERSORT" package to estimate the abundance of 22 immune fractions between low- and high-TMB groups, and the significant difference were determined by Wilcoxon rank-sum test. Furthermore, Cox regression model combined with survival analysis were used to evaluate the prognostic value of immune cells. Last, we constructed a Tumor Mutation Burden Prognostic Index (TMBPI) from multivariate Cox results and Receiver Operating Characteristic (ROC) curve was drawn to assess the predictive accuracy.

Results: Single nucleotide polymorphism (SNP) occurred more frequently than insertion or deletion, and C>T was the most common of SNV in ccRCC. Higher TMB levels conferred poor survival outcomes, associated with higher tumor grades and advanced pathological stages. A total of 1,265 differentially expressed genes were obtained and top 19 immune-related genes were identified in Venn diagram. GSEA revealed that patients in higher TMB groups correlated with MAPK signaling pathway, Wnt signaling pathway and pathway in cancers. Moreover, we identified 9 hub TMB-related immune genes related with survival and mutants of 9 signature were associated with lower immune infiltrates. In addition, infiltration levels of CD8+ T cell, CD4+ memory resting T cell, M1 and M2 macrophages, as well as dendritic resting cells in high-TMB group were lower than that in low-TMB group, especially the level of CD8+ T cell and macrophage correlated negatively with prognosis of ccRCC. Last, the TMBPI was constructed and the AUC of ROC curve was 0.666.

Conclusions: Higher TMB correlated with poor survival outcomes and might inhibit the immune infiltrates in ccRCC. The mutants of 9 hub TMB-related immune signature conferred lower immune cells infiltration which deserved further validation.

Keywords: TCGA; Tumor mutation burden (TMB); clear cell renal cell carcinoma (ccRCC); immune infiltrate; survival.

Figure 1

Landscape of mutation profiles in ccRCC samples. Mutation information of each gene in each sample was shown in the waterfall plot, in which various colors with annotations at the bottom represented the different mutation types. The barplot above the legend exhibited the mutation burden. ccRCC, clear cell renal cell carcinoma.

Figure 2

Summary of the mutation information with statistical calculations. (A,B,C) Classification of mutation types according to different categories, in which missense mutation accounts for the most fraction, SNP showed more frequency than insertion or deletion, and C>T was the most common of SNV; (D,E) tumor mutation burden in specific samples; (F) the top 10 mutated genes in ccRCC; (G) the coincident and exclusive associations across mutated genes. SNP, single nucleotide polymorphism; SNV, single nucleotide variants; ccRCC, clear cell renal cell carcinoma.

Figure 3

Prognosis of TMB and associations with risk clinical characteristics. (A) Higher TMB levels correlated with poor survival outcomes with P=0.035; (B,C) higher TMB level was associated with advanced stages, and higher tumor grades; (D,E,F) no significant difference were observed with AJCC-TNM stages. TMB, tumor mutation burden.

Figure 4

Comparisons of gene expression profiles in low- and high-TMB samples. (A) Top 60 DEGs were shown in heatmap plot; (B) identification of TMB-related immune genes; (C) GO results revealed that these genes were involved in immune-related pathways; (D) besides, GSEA showed the top TMB-related crosstalk, including MAPK signaling pathway, Wnt signaling pathway, pathway in cancers and calcium associated crosstalk with FDR <0.25. DEGs, differentially expressed genes; TMB, tumor mutation burden.

Figure 5

Survival analysis of 6 hub TMB-related signature with P value of log-rank test. TMB, tumor mutation burden.

Figure 6

Associations of 6 hub TMB-related signature mutants with immune cells infiltration. (A,B,C,D,E,F) Mutants of 6 TMB-related genes conferred the low infiltration levels of immune cells. TMB, tumor mutation burden.

Figure 7

Comparisons of 22 important immune fractions between low- and high-TMB groups. (A) The specific 22 immune fractions represented by various colors in each sample were shown in barplot. (B) Wilcoxon rank-sum test revealed that the infiltration levels of CD8+ T cell, CD4+ memory resting T cell, M1 and M2 macrophages, as well as dendritic resting cells in high-TMB group were lower compared with that in low-TMB group. TMB, tumor mutation burden.

Figure 8

Survival analysis of differentially immune cells across two TMB groups. (A) Kaplan-Meier analysis revealed that lower infiltration levels of CD8+ T cell and Macrophage correlated with poor survival outcomes in ccRCC (P<0.05); (B) construction and assessment of TMBPI for ccRCC (AUC of 3-year OS prediction =0.666), where patients with higher TMBPI conferred poor survival outcomes (P<0.001). ccRCC, clear cell renal cell carcinoma; TMBPI, Tumor Mutation Burden Prognostic Index.

Figure S1

Genecloud plot showed mutation information of genes in ccRCC. ccRCC, clear cell renal cell carcinoma.

Figure S2

Differential analysis showed the distributions of immune cells in low- and high-TMB samples, where immune fractions were relatively lower in high-TMB groups from the heatmap. TMB, tumor mutation burden.