Tumor microenvironment in triple-negative breast cancer: the correlation of tumor-associated macrophages and tumor-infiltrating lymphocytes

Purpose

Immune cells such as cytotoxic T cells, helper T cells, B cells or tumor-associated macrophages (TAMs) contribute to the anti-tumor response or pro-tumorigenic effect in triple negative breast cancer (TNBC). The interrelation of TAMs, T and B tumor-infiltrating lymphocytes (TILs) in TNBC has not been fully elucidated.

Methods

We evaluated the association of tumor-associated macrophages, T and B TILs in TNBC.

Results

TNBCs with a high CD68+, CD163+ TAMs and low CD4+, CD8+, CD20+ TILs had a significantly shorter relapse-free survival (RFS) and overall survival (OS) than those with low CD68+, CD163+ TAMs and high CD4+, CD8+, CD20+ TILs. TNBCs with high CD68+ TAMs/low CD8+ TILs showed a significantly shorter RFS and OS and a significantly poorer prognosis than those with high CD68+ TAMs/high CD8+ TILs, low CD68+ TAMs/high CD8+ TILs, and low CD68+/low CD8+. TNBCs with high CD163+ TAMs/low CD8+, low CD20 + TILs showed a significantly shorter RFS and OS and a significantly poorer prognosis than those with high CD163+ TAMs/high CD8+ TILs and high CD163+ TAMs /high CD20+ TILs.

Conclusions

Our study suggests that TAMs further create an optimal tumor microenvironment (TME) for growth and invasion of cancer cells when evasion of immunoreactions due to T and B TILs occurs. In TNBCs, all these events combine to affect prognosis. The process of TME is highly complex in TNBCs and for an improved understanding, larger validation studies are necessary to confirm these findings.

An immune-related risk gene signature predicts the prognosis of breast cancer

BACKGROUND

Accurate prediction of the outcome of breast cancer remains as a challenge due to its heterogeneous nature. We aimed to construct an immune-related risk signature to predict the overall outcome of breast cancer using bioinformatic approaches.

METHODS

In this study, transcriptome and survival data obtained from The Cancer Genome Atlas database and the Gene Expression Omnibus database were used to identify differentially expressed genes between breast cancer and normal samples. A regulatory network was constructed based on the immune-related prognostic genes and transcription factors screened from the differently expressed genes. The immune-related risk gene signature was obtained using the least absolute shrinkage and selection operator (LASSO) method and Cox regression model. The immune-related prognostic scores of breast cancer (IPSBC) calculated from the risk signature were used to group breast cancer patients by risk levels. The accuracy of IPSBC was evaluated by survival analysis and receiver operating characteristic curve analysis. The independency and the relationship of IPSBC with clinicopathological characteristics and abundance of tumor-infiltrated immune cells were also investigated.

RESULTS

A total of 4296 differentially expressed genes between breast cancer and normal samples were identified, and a total of 13 prognostic immune-related genes were eventually selected as the risk gene signature, which was an independent prognostic factor of the overall survival of breast cancer. The IPSBC stratified breast cancer patients into low- and high-risk groups. Breast cancer patients in the high-risk group were associated with worse overall outcomes, more advanced stage and less abundance of tumor-infiltrated immune cells, including B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells compared to low-risk group.

CONCLUSION

In this study, an immune-related gene signature of breast cancer was identified, which could be used as potential prognostic and therapeutic targets of breast cancer.