Multiplatform Computational Analysis of Mast Cells in Adrenocortical Carcinoma Tumor Microenvironment

Abstract

IntroductionImmunotherapeutic response failure of adrenocortical carcinomas (ACC) highlights a need for novel strategies targeting immune cell populations in the tumor microenvironment (TME) to overcome tumor resistance and enhance therapeutic response. A recent study explored a new link between tumor mast cell (MC) infiltration and improved outcomes in patients with ACC. We further dissect the role of MC in TME of ACC by examining the tumor MC expression signatures and MC activity within TME to provide additional insight into potential novel immunotherapeutic targets. MethodsUsing CIBERSORTx computational immunogenomic deconvolution algorithm to analyze ACC tumor gene mRNA expression data (TCGA, N=79), we estimated the abundance of tumor immune infiltrating MC, and assessed prognostic potential of MC signaling genes as pro- or anti- tumor signatures, as well as the impact on overall (OS) and disease-free (DFS) survival. ResultsWe stratified MC signaling genes with survival prognostic values (OS, DFS, p<0.05) into anti-tumor (ALOX5, CCL2, CCL5, CXCL10, HDC, IL16, TNF, TPSAB1, VEGFD) and pro-tumor (CXCL1, CXCL3, CXCL8, IL4, IL13, PTGS3, TNSF4, VEGFD) groups. Anti-tumor MC signature, as the predominant phenotype, was associated with improved OS and DFS. ConclusionThe deconvolution analysis of TCGA data identified MC infiltration in ACC microenvironment as predominantly associated with anti-tumor activity. Future studies stemming from our findings may help define the role of MC in TME and the impact on patient survival in patients with ACC. Modulation of tumor MC infiltration may serve as a potential target for novel synergistic immunotherapies for the treatment and improved survival of patients with ACC.