The Negative Correlation Between&nbsp;TNBC Mutation Frequency and Immune Function: Is Immune Involvement in Regulating Mutations?
Authors: Wang Bo , Li Mengyan , Li Chao , Su Anna , Wang Hui , Liu Wenying , Gao Yongmei , Shi Yan , Ma Yuqing 
Published: April 2021

Introduction: Triple-negative breast cancer (TNBC) is a highly specific disease. It has been reported that TNBC has a higher gene mutation frequency than non-triple-negative breast cancer (nTNBC). Most TNBC gene mutation studies focus on BRCA1, BRCA2, TP53 and other highly mutated genes. However, few studies on extensive somatic mutations of TNBC have been conducted. Exploring the differences of TNBC mutations among individuals and the regulatory factors of gene mutations will help to find targeted treatment directions.Method: Using bioinformatics methods, we analyzed the gene expression in patients with TNBC, clinical pathologic features and mutation data. Mutation frequency analysis was performed on each TNBC sample, and differentially expressed genes were obtained. Unsupervised cluster classified TNBC patients into high mutation subtypes and low mutation subtypes. Subsequently, the enrichment pathways among different subtypes were evaluated by KEGG enrichment analysis. The TNBC data from TCGA were used as the train group to construct the prognosis model through Univariate Cox survival analysis, Lasso regression, multivariate Cox survival analysis and stepAIC. The GEO database TNBC dataset GSE58812 was used as the test group, and the ROC curve was used to verify its accuracy. Results: The frequency of gene mutation in TNBC samples was significantly higher than that in nTNBC samples, and there were significant differences in the frequency of mutations within TNBC. We compared the immune function among the different subtypes, and the results showed that there were significant differences in the level of immune cell infiltration and the expression of immune checkpoint markers (PD-L1, CTLA4) between the mutant subtypes. Therefore, the low mutant subtypes were more suitable for immunotherapy. Eventually, a prognostic risk model consisting of 6 mRNA (SEMA4B, IL22RA2, GPR25, TCF7, BACH2 and CLND5) is constructed. The verification is based on the calculated risk score by time-dependent receiver operating characteristic (ROC) curve analysis. The area under the curve (AUC) of the train group and test group was greater than 0.7. A survival analysis of SEMA4B using an online database showed that high expression of SEMA4B was associated with a worse prognosis (P<0.01). We found that the relationship between SEMA4B expression and prognosis was inconsistent in different tumors, but all of them were positively correlated with mutation. Therefore, through extensive differences in somatic mutations, we found the key genes involved in the regulation of mutations and proved the relationship between mutations and immune cell infiltration, providing a basis for TNBC immunotherapy.Conclusion: In this study, we investigated the pattern and prognostic role of gene mutation frequency and immune cell infiltration in TNBC, which provided a new perspective for tumor microenvironment and immunotherapy, and developed a new prognostic model for TNBC.