Abstract GS1-05: Prognostic and predictive associations of manual, digital and AI-derived tumor infiltrating lymphocytes-scoring: A retrospective analysis from the Phase III APHINITY trial

Abstract Background: Stromal tumor-infiltrating lymphocytes (sTILs) are prognostic and predictive biomarkers for HER2-targeted therapy in early-stage HER2-positive breast cancer (BC). Manual sTIL scoring demonstrates high reproducibility but may underrepresent immune infiltration. Digital pathology and artificial intelligence (AI) offer automated sTIL quantification and spatial assessment but require validation against clinical endpoints. Aim: To compare manual, digital (non-AI), and AI-based sTIL quantification methods, including AI-derived spatial metrics in the phase III APHINITY trial. Objectives included interobserver reproducibility, method concordance, prognostic performance for invasive disease-free survival (iDFS) and overall survival, enhancement of prognostic models by AI, and identification of patients benefiting from adjuvant pertuzumab. Methods: Of 4,804 APHINITY trial participants, 4,306 (90%) had evaluable archival H all p 0.001). AI spatial immune hotspots outperformed percentage metrics (HR = 0.41; p 0.001) and, when combined with manual scores, provided the greatest additional discrimination (p 0.001). In high-TIL patients, pertuzumab reduced iDFS events by 64% (manual HR = 0.36; p int = 0.003), 52% (digital HR = 0.48; p int = 0.025), and 54% (AI HR = 0.46; p int = 0.01). Manual scoring alone identified 562/2,573 (22%) node-positive patients as high-TIL and likely pertuzumab-responsive, whereas AI-percentage lymphocyte identified 625 (24%) thus contributing to further detect 253 node-positive patients who would benefit from addition of pertuzumab (a 10% larger group). AI spatial metrics were not predictive. Conclusions: Despite modest concordance, manual, digital and AI-derived sTIL assessments independently demonstrated prognostic and predictive value for identifying HER2-positive BC patients benefiting from adjuvant pertuzumab. AI-driven sTIL quantification matches and slightly improves prognostic accuracy, importantly both AI and manual sTIL can identify a larger group of pertuzumab-responsive patients. Integrating AI-derived quantitative and spatial metrics into multiparameter models can further individualize HER2-targeting therapy. Citation Format: R. Salgado, L. E. Lara Gonzalez, F. Giudici, F. Rojo, L. Comerma, S. Wienert, J. Palacios, Z. Kos, S. L. De Haas, A. Rodriguez Lescure, G. Viale, Y. Zheng, D. Gao, A. Kiermaier, F. Andre, S. Loibl, M. J. PIccart, R. Gelber, D. Cameron, I. E. Krop, P. Savas, T. O. Nielsen, C. Denkert, S. Michiels, S. Loi, APHINITY Steering Committee and Investigators, The International Immuno-Oncology Biomarker Working Group..Prognostic and predictive associations of manual, digital and AI-derived tumor infiltrating lymphocytes-scoring: A retrospective analysis from the Phase III APHINITY trial 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 GS1-05.