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

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.

Transcriptome-Based Molecular Networks Uncovered Interplay Between Druggable Genes of CD8+ T Cells and Changes in Immune Cell Landscape in Patients With Pulmonary Tuberculosis

“…This method relies on linear support vector regression (SVR), a machine learning approach to deconvolute the gene expression signatures, known as "signature matrix" for determining the relative fraction of immune cell proportions in blood or tissues (15). The CIBERSORT method has been widely used to infer immune cell types from transcriptomics data to predict outcomes of different cancers (9,43,44) and infectious diseases (45)(46)(47). In this study, the CIBERSORT output identified the downregulation of "CD8 + T cells" in patients with PTB.…”

“…Advances in statistical modeling and bioinformatics approaches have accelerated the identification of disease-centric genes by employing gene networking methods based on graph topological parameters for many infectious diseases (7,8). Moreover, the new bioinformatic methods like estimating relative subsets of RNA transcripts (CIBERSORT), Tumor Immune Estimation Resource (TIMER), and Estimating the Proportions of Immune and Cancer cells (EPIC) are developed to characterize immune cell composition using large-scale gene expression data (9,10). These bioinformatic methods implement functional enrichment scores based on the presence of the query genes over reference gene sets.…”

An Oxidative Stress-Related Gene Pair (CCNB1/PKD1), Competitive Endogenous RNAs, and Immune-Infiltration Patterns Potentially Regulate Intervertebral Disc Degeneration Development

“…Craven et al. ( 67 ) reported that PKD1 was more frequently mutated in a group enriched in both CD8 + T cells and CD4 memory-activated T cells in triple-negative breast cancer. Li et al.…”

Construction and Clinical Translation of Causal Pan-Cancer Gene Score Across Cancer Types

“…Following the CIBERSORTx manual, we set the number of permutations to 100. We uploaded RNA-seq FPKM data and set quantile normalization to discern the recommended setting ( Craven et al, 2021 ). We filtered out immune cell types with an average proportion lower than 2%, and 14 types of immune cells were included into the final analysis.…”