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Updated: Sep 9, 2025
Enrichment and Characterization of the Tumor Immune and Non-immune Microenvironments in Established Subcutaneous Murine Tumors
Published on: June 7, 2018
Resolving tumor microenvironment heterogeneity to forecast immunotherapy response in triple-negative breast cancer
Shihao Sun1, Shuang Chen2, Kaiyuan Li3
1Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
Insights
BRCA1 status influences the tumor microenvironment in triple-negative breast cancer (TNBC). This study identifies ISG15 as a potential biomarker for predicting immunotherapy response in TNBC patients.
Area of Science:
- Investigating the tumor microenvironment in triple-negative breast cancer (TNBC).
- Utilizing multi-omics data integration for comprehensive analysis.
- Exploring the role of BRCA1 mutations in immunotherapy response.
Background:
- Triple-negative breast cancer (TNBC) immunotherapy outcomes remain suboptimal.
- BRCA1 mutations are linked to immunotherapy response, but clinical results are limited.
- Understanding TNBC heterogeneity is crucial for improving treatment efficacy.
Purpose of the Study:
- To elucidate the impact of BRCA1 status on the TNBC tumor microenvironment.
- To identify novel biomarkers and cellular phenotypes associated with immunotherapy response.
- To develop a predictive model for immunotherapy response in TNBC.
Main Methods:
- Integration of spatial, single-cell, and bulk RNA sequencing data.
- Multi-scale analysis to identify cancer-associated fibroblast (CAF) phenotypes and biomarkers.
- High-resolution, label-free proteomics for protein-level validation.
- Machine learning pipeline for constructing a predictive system for immune checkpoint inhibitor (ICI) response.
Main Results:
- BRCA1 wild-type TNBC exhibits increased T-cell exhaustion and dendritic cell tolerance compared to BRCA1 mutant.
- Identification of a MEG3+ pre-CAF subgroup using pseudo-time analysis.
- ISG15 emerges as a potential immunoregulatory biomarker.
- A predictive model shows promise in forecasting immunotherapy response.
Conclusions:
- TNBC exhibits significant cellular heterogeneity.
- ISG15 is a candidate biomarker for predicting immunotherapy response in TNBC.
- An ISG15-based predictive system offers a potential framework for forecasting ICI response.
