Biomarker prediction of immunotherapy response in breast cancer: from single markers to multi-omics integration

Immunotherapy has redefined treatment paradigms across several malignancies, yet its application in breast cancer remains limited to select subtypes, particularly triple-negative breast cancer. As immune checkpoint inhibitors advance into earlier disease stages and broader clinical use, the identification of predictive biomarkers is essential to optimize patient selection and therapeutic outcomes. This review critically examines the current landscape of immunotherapy biomarkers in breast cancer and discusses their predictive value across breast cancer subtypes and treatment settings, highlighting both their clinical relevance and limitations. While conventional single biomarkers have demonstrated clinical relevance, they remain limited in sensitivity and specificity. Recent data suggest that gene expression signatures may offer superior predictive power. Moreover, emerging evidence supports the utility of integrated, multi-parametric biomarker strategies that combine genomic, transcriptomic, spatial, and dynamic immune profiling. Ongoing endeavors in standardization, validation, and the incorporation of artificial intelligence-driven analytics are critical to translating these biomarkers into precision immuno-oncology for breast cancer.