CIBERSORT analysis of TCGA and METABRIC identifies subgroups with better outcomes in triple negative breast cancer

• Type of Material: Article
• Authors: Kelly E. Craven, Johns Hopkins University; Yesim Polar, Emory University; Sunil Badve, Emory University
• Language: English
• Date: 2021-02-25
• Publisher: NATURE RESEARCH
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2021
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41598-021-83913-7
• Title of Journal or Parent Work: SCIENTIFIC REPORTS
• Volume: 11
• Issue: 1
• Start Page: 4691
• End Page: 4691
• Grant/Funding Information: This work was supported by the NIH under Grant CA-194600 (Sunil S. Badve) and CA-200301 (Kelly E. Craven).
• Keywords: Science & Technology - Other Topics; Multidisciplinary Sciences; Science & Technology
• Research Categories: Health Sciences, Pathology

Abstract

Studies have shown that the presence of tumor infiltrating lymphocytes (TILs) in Triple Negative Breast Cancer (TNBC) is associated with better prognosis. However, the molecular mechanisms underlying these immune cell differences are not well delineated. In this study, analysis of hematoxylin and eosin images from The Cancer Genome Atlas (TCGA) breast cancer cohort failed to show a prognostic benefit of TILs in TNBC, whereas CIBERSORT analysis, which quantifies the proportion of each immune cell type, demonstrated improved overall survival in TCGA TNBC samples with increased CD8 T cells or CD8 plus CD4 memory activated T cells and in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) TNBC samples with increased gamma delta T cells. Twenty-five genes showed mutational frequency differences between the TCGA high and low T cell groups, and many play important roles in inflammation or immune evasion (ATG2B, HIST1H2BC, PKD1, PIKFYVE, TLR3, NOTCH3, GOLGB1, CREBBP). Identification of these mutations suggests novel mechanisms by which the cancer cells attract immune cells and by which they evade or dampen the immune system during the cancer immunoediting process. This study suggests that integration of mutations with CIBERSORT analysis could provide better prediction of outcomes and novel therapeutic targets in TNBC cases.