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

Kelly E Craven,Yesim Gökmen-Polar,Sunil S Badve

doi:10.1038/s41598-021-83913-7

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.

Highlights

Alterations, lower somatic mutations, and lower neoantigen loads, suggesting that the immune system eliminates some of the diversity seen in immune poor tumors[28]
Using the tumor infiltrating lymphocytes (TILs) cut off for lymphocyte-predominant breast cancer (LPBC) used by Loi et al.[9], we split the samples into two groups, those with > 30% TILs (n = 11) (Fig. 1a–c) or those with < 30% TILs (n = 92) (Fig. 1d–f)
We hypothesized that differences in survival might only be seen if we focused the analysis on specific immune cell types instead of TILs in general

Summary

Introduction

Alterations, lower somatic mutations, and lower neoantigen loads, suggesting that the immune system eliminates some of the diversity seen in immune poor tumors[28]. CIBERSORT is a deconvolution method that characterizes the cell composition of complex tissue from their gene expression p rofiles[29]. Its results have been shown to correlate well with flow cytometric analysis, and it has been referred to as “digital cytometry”[30]. This technique has been applied to solid tumors including breast c ancers[31,32,33,34], its usage has been relatively limited. We utilized the H&E images to identify TIL rich and TIL poor TNBC tumors, such that further molecular comparisons between the groups could be made. An additional TNBC dataset, Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), was utilized to determine the reproducibility of our findings

Methods

Results

Conclusion