Abstract PS4-12-28: A novel approach for phenotyping triple negative breast cancer using an Artificial Intelligence digital pathology-based prognostic test to assess recurrence risk and response to therapy

Abstract Background Triple negative breast cancer (TNBC) accounts for approximately 15-20% of incident breast cancers and until recently, was the only BC subtype without targeted therapies. Recent therapeutic advances, including the use of immunotherapy (IO), have highlighted the importance of the spatial tumor microenvironment which includes both the number and location of infiltrating cancer immune cells. We previously reported on a redistribution of recurrence risk in TNBC patients from Mount Sinai using the PreciseBreast (PDxBR) test that includes AI-derived morphologic features of tumor architecture, nuclear size, stroma, mitotic figures and lymphocyte content combined with age, tumor size, anatomic stage and lymph node status. We conducted this study to gain a better understanding of the prognostic performance of PreciseBreast (PDxBR) for TNBC in an independent patient cohort. Methods Patients with H60 low risk, ≥60 high risk. Recurrence events included local regional (LR), second primary (SP), distant metastasis (DM), or death (D). Prognostic performance was analyzed using hazard ratios (HR), sensitivity (Se), specificity (Sp), negative predictive value (NPV) and positive predictive value (PPV). Results Characteristics of 77 patients with TNBC: median age 58 years (yrs), 66% 50yrs, 86% non-black; 62% tumor size ≤2.5cm and 30% 2.5cm-5cm; 87% Stage 1/2, 68% pN0, 32% pN1-3+, 88% Grade 3; 82% received chemotherapy. There were 14 events (18%) including 6 D, 4 DM, 2 LR, 2 SP with a median follow-up of 7.9 yrs. PDxBR identified 30 (39%) as high risk, 47 (61%) as low risk compared to the AI-grade model with 64 (83%) high risk, 13 (17%) low risk. PDxBR, which includes the AI-grade model + clinical features, was the only model with a significant HR of 3.72 (95% CI, 1.26-10.97, p = 0.0174), with Se 0.57 (95% CI 0.32-0.79), Sp 0.65 (95% CI, 0.53-0.76), PPV 0.27 (95% CI 0.14-0.45) and NPV 0.87 (95% CI, 0.75-0.94). PDxBR classified 8/14 events as high risk including 2 DM, 2LR, 1SP, and 3D. 4 of 14 events (3 D, 1 DM) included tumors that were ≥ 3cm of which 2 had 1-2pN. 6/14 patients with events did not have chemotherapy and 5 of 6 were high risk by PDxBR. The most significant PDxBR morphologic features for event identification included mitotic figure ‘hot spot’ quantification and tumor architecture (i.e. degree of differentiation). Conclusions PDxBR introduces a novel approach towards risk stratification and outcome prediction in a clinically high risk TNBC population. Standardized AI digital pathology-based platforms have the potential to accurately re-assess risk and response to chemotherapy and IO, especially in randomized clinical trials. Citation Format: N. Stanzione, G. Fernandez, S. Vaisman, A. Sainath Madduri, R. Scott, M. Prastawa, X. Zhang, M. Donovan. A novel approach for phenotyping triple negative breast cancer using an Artificial Intelligence digital pathology-based prognostic test to assess recurrence risk and response to therapy 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 PS4-12-28.