Abstract P3-05-21: A novel AI-digital Test with an automated approach for grading and phenotyping breast cancer enriches recurrence score risk prediction in an Oncotype evaluated cohort

Abstract Background: Genomic testing such as OncotypeDx remains an important component of the treatmentdecision process for many breast cancer (BC) patients. Evidence from Sparano et al. JCO 2021;39:557-564 demonstrated the importance of combining clinical features such as tumor grade, size and age with the 21-gene recurrence score (i.e., RSClin). Given the challenges associated with reliability of BC grading as a prognostic feature, we sought to develop a broadly accessible AI-digital test (PDxBr) which included a BC AI-grade combined with clinical features (i.e., age, tumor size, stage and lymph node status) to predict recurrence risk in an Oncotype categorized cohort. Methods: We evaluated performance of the PDxBr test along with the AI-grade, clinical feature (i.e., age, size, tumor stage and LN status) and histology grade models in a subgroup analysis from a retrospective longitudinal clinical development validation study utilizing samples from breast cancer (BC) patients in the Mount Sinai Health Care System (NYC, NY) from 2004-2016. Eligible participants were ≥23 years old with infiltrating ductal or mixed ductal and lobular carcinoma of the breast (IDC) and all with an Oncotype RS, and a median 6-year follow-up. All participants had H&E slides or paraffin blocks (for slide generation) from the resected BC specimen. H&E slides were digitized (40X magnification) using a Philips UltraFast Digital slide scanner (Netherlands) and a single whole slide image (WSI) was selected for model development. The AUC/C-index was used to demonstrate performance. Results: 599 patients with Oncotype RS results were interrogated: 57% white, mean age 57, mean tumor size 1.3cm, 100% T1/2 and HR+ve, 55% grade 2, 26% grade 3 and 18% grade 1; 95% pN0 with 36 events (6%). 21 (60%) of events were local-regional recurrences. Of note, there were 55% histologic Grade 2 cases in this population. Combining Oncotype RS with assorted sub-models including histologic grade, clinical features, AI-grade, or PDxBr model in a SVRc analysis demonstrated incremental improvement in the C-index for predicting BC recurrence (Table 1). Conclusionss: Both PDxBr test and AI-grade when combined with Oncotype were superior to Oncotype alone, or Oncotype with grade or clinical features suggesting that the incorporation of an improved BC grade with Oncotype RS enhances overall risk discrimination. PDxBr is the first digital BC test combining automated AI-BC prognostic grade with clinical-pathologic features to predict risk of early-stage BC recurrence. Additional validation studies are underway to confirm these results. AUC comparison of Oncotype alone and then combined with histology grade, clinical data (age, stage, tumor size and LN pos), AI-grade, and the PDxBr model. Citation Format: Gerardo Fernandez, Marcel Prastawa, Richard Scott, Bahram Marami, Nina Shpalensky, Abishek Madduri, Krystal Cascetta, Mary Sawyer, Monica S. Chan, Giovanni Koll, Rebecca E. DeAngel, Alexander Shtabsky, Aaron Feliz, Thomas Hansen, Brandon Veremis, Carlos Cordon-Cardo, Jack Zeineh, Michael Donovan. A novel AI-digital Test with an automated approach for grading and phenotyping breast cancer enriches recurrence score risk prediction in an Oncotype evaluated cohort. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-05-21.