Gene expression and immune infiltration in melanoma patients with different mutation burden
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Published on: October 11, 2019
Liwei Wang1,2, Fu Chen3, Rui Liu1
1Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, 400010, China.
Background:
Immunotherapy 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.
Methods:
Somatic 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.
Results:
Elevated 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.
Conclusions:
In 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.
Insights
Tumor mutation burden (TMB) is linked to survival in Skin Cutaneous Melanoma (SKCM). A new four-gene model effectively predicts patient risk based on TMB and immune gene expression.
Area of Science:
- Oncology
- Genomics
- Immunotherapy
Background:
- Immunotherapy is crucial for cancer treatment, but predicting efficacy remains challenging.
- Tumor mutation burden (TMB) influences immunotherapy response, but its role in Skin Cutaneous Melanoma (SKCM) survival is unclear.
- This study investigates the relationship between TMB and immune infiltration in SKCM.
Purpose of the Study:
- To evaluate the correlation between TMB and immune infiltration in SKCM.
- To identify immune-related genes associated with TMB.
- To develop a prognostic model for SKCM patients based on TMB and immune markers.
Main Methods:
- Downloaded SKCM somatic mutation, transcriptome, and clinical data from TCGA.
- Calculated TMB and performed survival analysis to stratify patients into high-TMB and low-TMB groups.
- Utilized Gene Ontology, GSEA, and Cox regression to identify prognostic immune genes and construct a TMB immune prognostic (TMB-IP) model.
Main Results:
- Elevated TMB correlated with improved survival outcomes in SKCM patients.
- The low-TMB group showed enrichment in immune-associated pathways.
- A four-gene TMB-IP model demonstrated an AUC of 0.75 for predicting overall survival, validated across clinical subgroups.
Conclusions:
- Low TMB is associated with worse survival and altered immune profiles in SKCM.
- The developed four-gene TMB-IP model accurately distinguishes high-risk from low-risk SKCM patients.
