Higher Mutation Burden in High Proliferation Compartments of Heterogeneous Melanoma Tumors.
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ABSTRACT:
SUBMITTER: Grzywa TM
PROVIDER: S-EPMC8069012 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundImmunotherapy is a vital component in cancer treatment. However, due to the complex genetic bases of cancer, a clear prediction index for efficacy has not been established. Tumor mutation burden (TMB) is one of the essential factors that affect immunotherapeutic efficacies, but it has not been determined whether the mutation is associated with the survival of Skin Cutaneous Melanoma (SKCM) patients. This study aimed at evaluating the correlation between TMB and immune infiltration.MethodsSomatic mutation profiles (n = 467), transcriptome data (n = 471), and their clinical information (n = 447) of all SKCM samples were downloaded from The Cancer Genome Atlas (TCGA) database. For each sample, TMB was calculated as the number of variants per megabase. Based on K-M survival analysis, they were allocated into the high-TMB and low-TMB groups (the optimal cutoff was determined by the 'surv_cutpoint' algorithm of survival R package). Then, Gene ontology (GO) and Gene Set Enrichment Analyses (GSEA) were performed, with immune-associated biological pathways found to be significantly enriched in the low-TMB group. Therefore, immune genes that were differentially expressed between the two groups were evaluated in Cox regression to determine their prognostic values, and a four-gene TMB immune prognostic model (TMB-IP) was constructed.ResultsElevated TMB levels were associated with better survival outcomes in SKCM patients. Based on the cutoff value in OS analysis, they were divided into high-TMB and low-TMB groups. GSEA revealed that the low-TMB group was associated with immunity while intersection analysis revealed that there were 38 differentially expressed immune-related genes between the two groups. Four TMB-associated immune genes were used to construct a TMB-IP model. The AUC of the ROC curve of this model reached a maximum of 0.75 (95%CI, 0.66-0.85) for OS outcomes. Validation in each clinical subgroup confirmed the efficacy of the model to distinguish between high and low TMB-IP score patients.ConclusionsIn SKCM patients, low TMB was associated with worse survival outcomes and enriched immune-associated pathways. The four TMB-associated immune genes model can effectively distinguish between high and low-risk patients.
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Project description:Background: Melanoma is highly immunogenic and therefore suitable for immunotherapy, but the efficacy is limited by response rate. In several types of tumor, tumor mutation burden (TMB) and immune infiltration have been reported to predict the response to immunotherapy, although each has its limitations. In the current study, we aimed to explore the association of TMB with immune infiltration and prognosis in cutaneous melanoma. Methods: The data of cutaneous melanoma used for analyses was downloaded from The Cancer Genome Atlas (TCGA) database. The mutation data was sorted using "maftools" R package. TMB was estimated and then patients were divided into two groups based on TMB. The association of TMB with prognosis and clinical characteristics was explored. Differential analysis between two TMB groups was performed using "DESeq2" R package to identify differentially expressed genes (DEGs). The function enrichment analyses of DEGs were conducted to screen critical pathways. Besides, DEGs were further filtered to identify two hub genes, based on which a risk score model and nomogram for predicting prognosis were conducted, and the validation was performed using three datasets from Gene Expression Omnibus (GEO) database. Finally, CIBERSORT algorithm and TIMER database were used to assess the effect of TMB and hub genes on immune infiltration. Results: The most common mutation was C > T, and the top three frequently mutated genes were TTN, MUC16, and BRAF. Higher TMB indicated better survival outcomes and lower pathological stages. 735 DEGs were identified and mainly involved in immune-related and adhesion-related pathways. The risk score model and nomogram were validated using receiver operating characteristic (ROC) curves and calibration curves, and exhibited relatively high predictive capability. Decision curve analysis (DCA) was used to assess clinical benefit. As for immune infiltration, the proportion was higher for macrophages M1 and M2 in the high-TMB group, while lower for memory B cells and regulatory T cells. Conclusions: In cutaneous melanoma, TMB was positively correlated with prognosis. The risk score model and nomogram can be conveniently used to predict prognosis. The association of TMB with immune infiltration can help improve the predicting methods for the response to immunotherapy.
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