Abstract PD11-04: Comparative performance of an AI-based digital pathology tool and genomic signatures in early ER+/HER2- breast cancer

Background Prognostic tools are important for guiding adjuvant therapy in early breast cancer (EBC), particularly for patients with estrogen receptor-positive, HER2-negative (ER+/HER2-) tumors, where optimizing the treatment strategy, like making a de-escalation decision, remains a clinical challenge (1). Recent advances in artificial intelligence (AI) applied to whole-slide images have opened new possibilities for capturing prognostic information directly from routinely available H&E slides of invasive breast tumors. This may offer a scalable, cost-effective, and accessible approach to personalized risk assessment. This study evaluates the prognostic performance of RlapsRisk BC (RR) (2), an AI digital pathology-based tool, in comparison with EndoPredict (EP) (3) and Oncotype DX (ODX) (4), using five-years distant recurrence-free survival (dRFS) as the primary endpoint. Methods We conducted a retrospective analysis of ER+/HER2- EBC patients from two independent cohorts who underwent genomic testing as part of their routine clinical care. The first cohort comprised patients treated at Gustave Roussy (France) between 2016 and 2019, all of whom underwent the EP test (according to local guidelines) which combines gene expression with clinico-pathologic factors resulting in the EPclin sore (EP cohort). The second cohort included patients with node negative disease treated at MD Anderson Cancer Center (USA), who underwent ODX testing and received adjuvant endocrine therapy alone (ODX cohort). For both cohorts, a minimum of five years of clinical follow-up was available. Diagnostic H&E-stained slides from surgical resection specimens were digitized at x20 and analyzed using RR. Results In the EP cohort (n=381, 7 events), RR achieved an AUC of 0.73 versus 0.57 for EPclin, while in the ODX cohort (n=154, 42 events), its AUC reached 0.78 compared to 0.58 for ODX, indicating stronger discriminative ability in both settings. RR classified fewer patients as high risk than EPclin (33% vs. 66%) and more patients as high risk than ODX (47.4% vs. 37.7%), suggesting improved alignment with observed risk. Among patients classified as low risk by each tool, the 5-year dRFS was 99.6% with RR and 98.3% with EPclin in the EP cohort, and 95.3% with RR compared to 80.2% with ODX in the ODX cohort. In the high-risk groups, 5-year dRFS was 96.3% with RR and 98.6% with EPclin in the EP cohort, and 65.3% with RR versus 84.0% with ODX in the ODX cohort. Notably, RR high-risk classification captured the majority of observed events in both cohorts (6 of 7 in EP and 35 of 42 in ODX), reinforcing its utility for identifying patients at true elevated risk. Conclusion These findings suggest that RR may support treatment de-escalation by safely expanding the proportion of patients classified as low risk who experience excellent outcomes within their risk group, while also improving identification of patients with a genuinely higher likelihood of recurrence. This ability to improve risk stratification makes it a promising tissue-efficient alternative to current genomic assays such as ODX and EP in ER+/HER2- early breast cancer. References 1. Krop I, Ismaila N, Andre F, et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: American Society of Clinical Oncology clinical practice guideline focused update. J Clin Oncol. 2017;35(24):2838-2847. doi:10.1200/JCO.2017.74.04722. Garberis I, Gaury V, Saillard C, et al. Deep learning assessment of metastatic relapse risk from digitized breast cancer histological slides. Nat Commun. 2025;16:5876.3. Dubsky P, Filipits M, Jakesz R, et al. EndoPredict improves the prognostic classification derived from common clinical guidelines in ER-positive, HER2-negative early breast cancer. Ann Oncol. 2012;24(3):640-647. doi:10.1093/annonc/mds3584. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817-2826. Citation Format: V. Aubert, V. Gaury, I. Garberis, Z. Vaquette, E. Hocquet, D. Almaraz-Klippel, F. Daidj, D. Drubay, D. Jacobs, N. Arfaoui, L. Guillou, D. Lin, J. Guillon, C. Barcenas, F. Andre, S. Krishnamurthy, M. Lacroix-Triki. Comparative performance of an AI-based digital pathology tool and genomic signatures in early ER+/HER2- 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 PD11-04.