Integrated genomic profiling identifies predictive biomarkers for neoadjuvant therapy response in Chinese breast cancer patients.

Neoadjuvant therapy (NAT) has emerged as a standard treatment strategy for locally advanced breast cancer (BC), yet robust biomarkers for response prediction remain elusive. Here, we established a real-world NAT cohort of 1161 Chinese BC patients, including 1145 cases with matched clinicopathological data and targeted sequencing, to systematically evaluate genomic features associated with NAT outcomes. We identified both cross-subtype and subtype-specific genomic associations with treatment response. PI3K-pathway alterations emerged as a consistent feature of resistance across subtypes, whereas mutations such as ERBB2 in HER2+ disease and MAP3K1 in triple-negative breast cancer were associated with subtype-specific response patterns. Regimen-level analyses further showed that some genomic associations were treatment-context dependent across chemotherapy-, endocrine-, anti-HER2-, and immunotherapy-containing regimens. Among patients with non-pathological complete response (non-pCR), genomic profiling further refined risk stratification for distant recurrence by revealing subtype-specific prognostic alterations, including TOP3B and SETD2. Furthermore, a machine-learning model integrating genomic and clinicopathological features showed favorable performance for NAT response prediction. Overall, our study provides a comprehensive genomic framework for response prediction and recurrence risk assessment, supporting more precise stratification and biomarker-guided treatment optimization in Asian breast cancer patients.