CXCL9 as a key feature for deep learning-based immune subtyping and prediction of immune checkpoint blockade response in triple-negative breast cancer - PubMed
3 hours ago
- #TNBC
- #Immunotherapy
- #CXCL9
- CXCL9 identified as a key biomarker for immune subtyping and predicting immune checkpoint blockade (ICB) response in triple-negative breast cancer (TNBC).
- Deep learning-based clustering (AE-K-means) classified TNBC into three distinct immune subtypes with significant survival and immune heterogeneity differences.
- The K2 subtype showed high PD-L1 expression and M1 macrophage infiltration, while K3 exhibited an immunosuppressive microenvironment.
- CXCL9 was highly expressed in myeloid cells, with CXCL9+ macrophages differing between ICB responders and non-responders.
- IDO1 inhibitors upregulated CXCL9 expression and secretion in macrophages, suggesting a regulatory link between IDO1 and CXCL9.
- The study provides a novel immune classification system for TNBC, supporting precision immunotherapy and IDO1 inhibitor exploration with ICB.