Resolving tumor microenvironment heterogeneity to forecast immunotherapy response in triple-negative breast cancer through multi-scale analysis.

Immunotherapy has been used in the clinical management of TNBC. While BRCA1 mutations are associated with immunotherapy response, the therapeutic outcomes in TNBC patients are not promising. This study integrated spatial, single-cell, and bulk RNA-seq data to explore the role of BRCA1 in reshaping the TNBC microenvironment. Through multi-scale analysis, phenotype changes and potential biomarkers in cancer-associated fibroblasts (CAF) were identified. To validate these findings at the protein level, we employed high-resolution, label-free proteomics sequencing in our in-house cohort, providing critical real-world validation. A predictive system for response to ICIs was constructed through the step-by-step machine learning pipeline. Compared to BRCA1 mutant patients, BRCA1 wild-type patients experienced increased T-cell exhaustion and dendritic cell tolerance. We identified a MEG3+ pre-CAF subgroup via pseudo-time analysis. Moreover, ISG15 may serve as an immunoregulatory biomarker, and the proposed predictive model demonstrated potential in forecasting immunotherapy response, although further validation is needed. This study highlighted the cellular heterogeneity of TNBC and identified ISG15 as a candidate biomarker potentially associated with treatment response. The ISG15-based predictive system might provide a robust framework for predicting ICI response.