Background: Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and has the highest proportion of homologous recombination deficiency (HRD). HRD has been considered a biomarker of response to immune checkpoint inhibitors (ICIs), but the reality is more complicated. A comprehensive comparison of the tumor microenvironment (TME) in HRD and non-HRD TNBC samples may be helpful.
Methods: Datasets from single-cell, spatial, and bulk RNA-sequencing were collected to explore the role of HRD in the development of TME at multiple scales. Based on the findings in the TME, machine learning algorithms were used to construct a response prediction model in eleven ICI therapy cohorts.
Results: A more exhausted phenotype of T cells and a more tolerogenic phenotype of dendritic cells were found in the non-HRD group. HRD reprograms the predominant phenotype of cancer-associated fibroblasts (CAFs) from myofibroblastic CAFs to inflammatory-like CAFs. As interactions between myofibroblastic CAFs and other cells, DPP4-chemokines associated with reduced immune cell recruitment were unique in the non-HRD group. The prediction model based on DPP4-related genes had acceptable performance in predicting response, prognosis, and immune cell content. Higher HRD scores in bulk RNA-sequencing samples indicated more activated immune cell function, but not higher immune cell content, which may be affected by factors such as antigen-presenting capacity.
Conclusions: Based on multi-scale transcriptomics, our findings comprehensively reveal differences in the TME between HRD and non-HRD samples. Combining HRD with the prediction model or other methods for assessing immune cell content, may better predict response to ICIs in TNBC.
Keywords: Cancer-associated fibroblasts; Homologous recombination deficiency; Immune checkpoint inhibitor; Triple-negative breast cancer; Tumor microenvironment.
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