Abstract PS3-04-25: Artificial intelligence (AI) based image analysis of PD-L1, TIL, immune signature and ctDNA for prediction of response to neoadjuvant chemotherapy in breast cancer

Abstract Background Tumor-infiltrating lymphocytes (TILs) have been recognized as prognostic and predictive biomarkers in breast cancer. However, challenges such as interobserver variability and lack of standardized cut-off values have limited their clinical implementation. Recent advancements in artificial intelligence (AI)-based pathology have shown potential to enhance reproducibility and objectivity in TIL quantification. Concurrently, circulating tumor DNA (ctDNA) has emerged as a minimally invasive biomarker capable of detecting treatment response after neoadjuvant chemotherapy (NAC). Objectives This study aims to: (1) evaluate the predictive value of AI-quantified stromal TILs from H and (2) validate the clinical utility of ctDNA-based biomarkers for detection of responses in breast cancer patients after NAC. Methods TILs were assessed from pre-treatment tumor H ctDNA, 46.4%), followed by PIK3CA (FFPE, 36.2%;ctDNA, 17.4%). The triple-negative breast cancer (TNBC) subtype exhibited the strongest association with ctDNA detection (odds ratio OR 209.50, p = 0.005) in multivariate analysis. Patients with inflamed TIL phenotype at diagnosis and ctDNA clearance after NAC had higher pathological complete response (pCR) rate (38.5% vs. 11.1%, p = 0.238). Conclusion Our integrated approach using AI-based TIL quantification and ctDNA analysis using NGS and ddPCR provides a feasible and sensitive approach for NAC response in breast cancer patients. These findings support the clinical utility of combining digital pathology and liquid biopsy for treatment monitoring and risk stratification in conjunction with precision oncology. Citation Format: E. Kim, I. Do, S. Chae. Artificial intelligence (AI) based image analysis of PD-L1, TIL, immune signature and ctDNA for prediction of response to neoadjuvant chemotherapy in breast cancer 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 PS3-04-25.