Analysis identifies biomarkers of response to immunotherapy in triple-negative breast cancer, suggesting implications for treatment strategies.
Key Points
The proposed predictive model forecasts immunotherapy response in triple-negative breast cancer patients through multi-scale analysis and biomarker identification.
Increased T-cell exhaustion and dendritic cell tolerance were observed in BRCA1 wild-type patients compared to BRCA1 mutant patients.
Integration of single-cell and bulk RNA-seq data, alongside machine learning techniques, enabled comprehensive insight into tumor microenvironment heterogeneity.
Findings highlight the need for further validation of ISG15 as a potential biomarker for predicting responses to immune checkpoint inhibitors in clinical settings.