Abundance of Regulatory T Cell (Treg) as a Predictive Biomarker for Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Abstract

Regulatory CD4+ T cell (Treg), a subset of tumor-infiltrating lymphocytes (TILs), are known to suppress anticancer immunity but its clinical relevance in human breast cancer remains unclear. In this study, we estimated the relative abundance of Tregs in breast cancer of multiple patient cohorts by using the xCell algorithm on bulk tumor gene expression data. In total, 5177 breast cancer patients from five independent cohorts (TCGA-BRCA, GSE96058, GSE25066, GSE20194, and GSE110590) were analyzed. Treg abundance was not associated with cancer aggressiveness, patient survival, or immune activity markers, but it was lower in metastatic tumors when compared to matched primary tumors. Treg was associated with a high mutation rate of TP53 genes and copy number mutations as well as with increased tumor infiltration of M2 macrophages and decreased infiltration of T helper type 1 (Th1) cells. Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) was significantly associated with low Treg abundance in triple negative breast cancer (TNBC) but not in ER-positive/Her2-negative subtype. High Treg abundance was significantly associated with high tumor expression of multiple immune checkpoint inhibitor genes. In conclusion, Treg abundance may have potential as a predictive biomarker of pCR after NAC in TNBC.

Annexin A1 Expression Is Associated with Epithelial–Mesenchymal Transition (EMT), Cell Proliferation, Prognosis, and Drug Response in Pancreatic Cancer

“…Furthermore, the xCell algorithm allows us to measure several types of cells, including immune cells and stromal cells, in the tumor microenvironment (TME). Our group previously reported the clinical relevance of CD8 + T cells [ 30 ], regulatory T cells [ 31 ], and dendritic cells (DC) [ 32 ], as well as fibroblasts [ 8 ], in multiple types of cancer using the xCell algorithm. The link between ANXA1 expression and several signaling pathways and several cells in TME was elucidated using these algorithms.…”

“…By comparison, bioinformatic approaches can estimate the quantity and function of cells in tens of thousands of samples with less cost and time [ 4 , 36 , 37 ]. We previously reported the clinical relevance of immune cells, including CD8 + T cells [ 30 ], regulatory T cells [ 31 ], and dendritic cells (DC) [ 32 ], as well as stromal cells such as fibroblasts [ 8 ], in the TME using the xCell algorithm, which allows us to estimate the fraction of 64 cells in the TME with the transcriptome of a bulk tumor. Cancer of the breast, colorectal, lung, and kidney with a low expression of ANXA1 is scarcely infiltrated by DC and cytotoxic T lymphocytes, supporting the idea that ANXA1 deficiency facilitates immune escape [ 38 ].…”

Abundance of Microvascular Endothelial Cells Is Associated with Response to Chemotherapy and Prognosis in Colorectal Cancer

“…In this study, we used the xCell algorithm to calculate the abundance of intra-tumoral mvE cells using the transcriptome of a bulk tumor. We previously reported the clinical relevance of several cells in the TME, such as CD8 + , regulatory T cells, dendritic cells, and fibroblasts using the xCell algorithm [ 20 , 21 , 22 , 26 ].…”

“…The xCell algorithm allows estimation of the relative proportions of 64 types of cells, such as immune cells and stromal cells in the TME, using the transcriptome of a bulk tumor [ 19 ]. We previously reported the clinical relevance of multiple types of cells in the TME using the xCell algorithm, including plasmacytoid dendritic cells, which correlate with better survival [ 20 ], and the association of regulatory T cells with the response to neoadjuvant chemotherapy in triple-negative breast cancer [ 21 ]. Cancer-associated fibroblasts were associated with curative resection in pancreatic ductal adenocarcinoma [ 22 ].…”

Organoids Are Limited in Modeling the Colon Adenoma–Carcinoma Sequence

“…Recently, our group has performed in silico translational research to identify biomarkers [17][18][19], clinically relevant immune cells [20][21][22][23], predictive genes [24], as well as microRNAs in breast and gastrointestinal cancers [25]. We employed a computational algorithm, referred to as gene set enrichment analysis (GSEA), which enables us to analyze the differences in biological pathways between two distinct groups [26].…”