Dataset Information

The Tumor Microenvironment of DLBCL in the Computational Era.

ABSTRACT: Among classical exemplifications of tumor microenvironment (TME) in lymphoma pathogenesis, the "effacement model" resembled by diffuse large B cell lymphoma (DLBCL) implies strong cell autonomous survival and paucity of non-malignant elements. Nonetheless, the magnitude of TME exploration is increasing as novel technologies allow the high-resolution discrimination of cellular and extra-cellular determinants at the functional, more than morphological, level. Results from genomic-scale studies and recent clinical trials revitalized the interest in this field, prompting the use of new tools to dissect DLBCL composition and reveal novel prognostic association. Here we revisited major controversies related to TME in DLBCL, focusing on the use of bioinformatics to mine transcriptomic data and provide new insights to be translated into the clinical setting.

SUBMITTER: Opinto G

PROVIDER: S-EPMC7136462 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The Tumor Microenvironment of DLBCL in the Computational Era.

Opinto Giuseppina G Vegliante Maria Carmela MC Negri Antonio A Skrypets Tetiana T Loseto Giacomo G Pileri Stefano Aldo SA Guarini Attilio A Ciavarella Sabino S

Frontiers in oncology 20200331

Among classical exemplifications of tumor microenvironment (TME) in lymphoma pathogenesis, the "effacement model" resembled by diffuse large B cell lymphoma (DLBCL) implies strong cell autonomous survival and paucity of non-malignant elements. Nonetheless, the magnitude of TME exploration is increasing as novel technologies allow the high-resolution discrimination of cellular and extra-cellular determinants at the functional, more than morphological, level. Results from genomic-scale studies and ...[more]

PMID: 32296632

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Project description:BackgroundImmunotherapeutic response failure of adrenocortical carcinomas highlights a need for novel strategies targeting immune cell populations in the tumor microenvironment to overcome tumor resistance and enhance therapeutic response. A recent study explored a new link between tumor mast cell infiltration and improved outcomes in patients with adrenocortical carcinomas. We further dissect the role of mast cells in the tumor microenvironment of adrenocortical carcinomas by examining the tumor mast cell expression signatures and mast cell activity within the tumor microenvironment to provide additional insight into potential novel immunotherapeutic targets.MethodsUsing the CIBERSORTx computational immunogenomic deconvolution algorithm to analyze adrenocortical carcinoma tumor gene messenger RNA expression data (The Cancer Genome Atlas, N = 79), we estimated the abundance of tumor immune infiltrating mast cells and assessed prognostic potential of mast cell signaling genes as pro or antitumor signatures, as well as examined the impact on overall and disease-free survival.ResultsWe stratified mast cell signaling genes with survival prognostic values (overall survival, disease-free survival, P < .05) into antitumor (ALOX5, CCL2, CCL5, CXCL10, HDC, IL16, TNF, TPSAB1, VEGFD) and protumor (CXCL1, CXCL3, CXCL8, IL4, IL13, PTGS3, TNSF4, VEGFD) groups. Antitumor mast cell signature, as the predominant phenotype, was associated with improved overall and disease-free survival.ConclusionThe deconvolution analysis of The Cancer Genome Atlas data identified mast cell infiltration in the adrenocortical carcinoma microenvironment as predominantly associated with antitumor activity. Future studies stemming from our findings may help define the role of mast cells in the tumor microenvironment and the impact on patient survival in patients with adrenocortical carcinomas. Modulation of tumor mast cell infiltration may serve as a potential target for novel synergistic immunotherapies for the treatment and improved survival of patients with adrenocortical carcinomas.

Dataset Information

The Tumor Microenvironment of DLBCL in the Computational Era.

Publications

The Tumor Microenvironment of DLBCL in the Computational Era.

Similar Datasets

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