Abstract PS4-05-07: Integrated Genomic Profiling Identifies Predictive Biomarkers for Neoadjuvant Therapy Response in Chinese Breast Cancers

Abstract BACKGROUND Neoadjuvant therapy (NAT) has emerged as a standard treatment strategy for locally advanced breast cancer (BC), yet effective predictive markers are still lacking. This study aimed to investigate genomic biomarkers of NAT efficacy in a Chinese BC cohort and characterize subtype-specific molecular signatures associated with differential treatment responses. Methods A large-scale NAT cohort comprising 1,161 patients with primary BC was analyzed, including 1,145 patients who underwent comprehensive genomic profiling using the FUSCC-BC targeted panel. Mutational signatures predictive of pathological complete response (pCR) and metastatic recurrence were identified. Machine learning models were developed for NAT response prediction. RESULTS The FUSCC-BC neoadjuvant cohort study integrated clinicopathological analysis and targeted sequencing of breast cancer subtypes (36.9% HER2+, 39.8% HR+/HER2-, 23.3% triple-negative) to identify genomic biomarkers associated with differential pCR rates. We revealed shared resistance mechanisms across subtypes, notably PIK3CA mutations and PI3K pathway activation predicting non-pCR in both HR+/HER2− and triple-negative breast cancer (TNBC), while ERBB2 and GRINA alterations emerged as a HER2-enriched subtype-specific predictor of therapeutic resistance. In patients who fail to achieve pCR, multivariate analysis revealed that multiple genomic alterations (e.g., TP53 and TOP2A) were independently associated with elevated risk of metastatic recurrence. To optimize clinical applicability, a machine learning framework integrating somatic mutation profiles and clinicopathological variables demonstrated robust predictive performance, achieving area under the curve (AUC) values of 0.82 in the training set and 0.81 in the test set for neoadjuvant therapy response prediction. Conclusion Our study identified subtype-specific biomarkers predictive of the response of BC patients to neoadjuvant therapy and established a predictive framework to optimize NAT strategies, providing molecular guidance for personalized treatment regimens that may enhance clinical outcomes. 1 Citation Format: X. YingK. ZhangX. ZhuS. JiangX. HuC. ChenZ. Wang. Integrated Genomic Profiling Identifies Predictive Biomarkers for Neoadjuvant Therapy Response in Chinese Breast Cancers [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS4-05-07.