Vachon, Celine M.

Abstract Artificial intelligence (AI)-scores estimated from digital mammograms predict future breast cancer (BC) risk. MIRAI is a deep learning BC risk model that provides a continuous 5-year risk of overall BC from four screening full field digital mammograms. Common germline genetic variation in the form of a polygenic risk score (BC-PRS) is associated with increased BC risk and may improve MIRAI’s 5-year risk prediction. Our goal was to develop an updated MIRAI 5-year risk model that incorporates the BC-PRS (MIRAI+PRS) and to evaluate its discriminatory accuracy and calibration for both overall and invasive BC compared to MIRAI alone. We developed the MIRAI+PRS model by multiplying each woman’s 5-year MIRAI risk estimate by their relative risk based on their BC-PRS relative to the population mean. We evaluated the models within the Mayo Clinic Biobank mammography cohort, comprised of 12,307 women without a prior history of BC; 176 invasive and 250 overall BC were diagnosed within 5 years. MIRAI was estimated on screening mammograms closest to enrollment but at least 6 months prior to BC. Discriminatory accuracy, assessed by C-statistic, was high and similar for MIRAI+PRS vs. MIRAI models, for overall BC and invasive BC (Table). Calibration assessed by observed to expected (O/E) ratios was also similar for MIRAI+PRS compared to MIRAI predictions for BC outcomes (Table), although there was improvement in decile-specific O/E ratios across the lowest risk deciles (<1.67%) for MIRAI+PRS. Calibration for invasive cancer was poor for MIRAI with or without PRS. In summary, the MIRAI+PRS risk model did not result in significant difference of discriminatory accuracy or overall calibration compared to the MIRAI model, but there was evidence for improved calibration for women with 5-year risk below 1.67%. For invasive BC, the model had poor calibration regardless of whether PRS was included, underscoring the importance of training AI models for BC outcomes that are associated with a clinical intervention. Citation Format: Christopher G. Scott, Peter Kraft, Imon Banerjee, Ramon Correa Medero, Aaron D. Norman, Fergus J. Couch, Karla Kerlikowske, Stacey J. Winham, Celine M. Vachon. Development and evaluation of a MIRAI 5-year risk model with a breast cancer polygenic risk score [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2779.

Abstract Background: Hypogammaglobulinemia at the time of CLL diagnosis predicts shorter time to first therapy (TTFT). However, the depth of immunoparesis and its impact on TTFT in newly diagnosed MBL/CLL remains unexplored. Methods: We identified previously untreated MBL/CLL patients from Mayo Clinic CLL Database (2000-2024) who had serum immunoglobulin (Ig; IgG, IgM and IgA) and monoclonal protein assessed within 1 year of diagnosis. Immunoparesis was evaluated: a) qualitatively by classifying patients into preserved, partial and full Ig suppression, based on the number of suppressed Igs; and b) quantitatively by calculating the average relative difference (ARD), defined as the mean percentage below normal for all Igs. For both methods, only uninvolved Igs were considered for those with monoclonal protein. TTFT was estimated by Kaplan-Meier with competing risk of death. Cox models estimated hazard ratios (HR) and 95% confidence intervals (CI). Results: Among 1420 patients, median age at diagnosis was 65 (range 28-96); 954 (67%) were male; 493 (44.8%) had unmutated IGHV, and 86 (6.8%) had TP53 disruption. The median [range, mg/dL] serum IgG, IgA, and IgM were 889 [111-5290], 132 [1-4880], and 49 [4-9480]. Low IgG was present in 472 (33.2%), low IgA in 223 (16.4%), and low IgM in 530 (37.3%) patients. An M-spike was detected in 122 (8.6%) patients; distribution was: IgG (73), IgM (30), IgA (6), or multiple (13). By qualitative assessment, preserved Ig were seen in 664 (46.8%), partial Ig suppression in 604 (42.5%), and full suppression in 152 (10.7%) patients. By quantitative assessment, median ARD was 0.6 (-0.8 to 11.3); 280 (19.7%) patients had negative ARD, and 1140 (80.3%) had positive ARD. In those with negative ARD, 128 (9%) had partial Ig suppression and 152 (10.7%) had full Ig suppression; in those with positive ARD, 664 (46.8%) had preserved Ig and 476 (33.5%) had partial Ig suppression. Median follow-up was 14.7 years; 510 patients progressed requiring therapy. Median TTFT was 9.6 years. Median TTFT for patients with preserved Ig was 12.6 years, 8.0 years for partial Ig suppression (HR 1.4, 95%CI 1.1-1.7), and 1.8 years for full Ig suppression (HR 3.1, 95%CI 2.4-4.0). The median TTFT was 2.3 and 12.1 years for patients with negative vs positive ARD; negative ARD was associated with shorter TTFT (HR 2.4, 95%CI 2.0-2.9). After adjusting for sex and CLL-International Prognostic Index (CLL-IPI) score, full Ig suppression (HR 2.9, 95%CI 2.2-3.9) and partial Ig suppression (HR 1.6, 95% CI 1.3-2.0) were associated with shorter TTFT (model 1, c-stat 0.77); and negative ARD (HR 2.4, 95%CI 2.0-2.9) was associated with shorter TTFT (model 2, c-stat 0.76). Negative ARD identified an additional 9% of patients at higher risk for shorter TTFT beyond full Ig suppression. Conclusions: The depth of immunoparesis at diagnosis predicts TTFT and enhances risk stratification in newly diagnosed MBL/CLL. Citation Format: Yuan Yao, Kari G. Rabe, Eli Muchtar, Paul Hampel, Yucai Wang, Lindsey Roeker, Saad Kenderian, Amber Koehler, Catherine Wagner, Amy Behnken, Jose F. Leis, Mazie Tsang, Talal Hilal, Ricardo Daniel Parrondo, Susan M. Schwager, Min Shi, Curtis A. Hanson, Celine M. Vachon, Shaji Kunnathu Kumar, Esteban Braggio, Neil E. Kay, Susan Slager, Sameer A. Parikh. Depth of immunoparesis predicts time to first therapy in newly diagnosed monoclonal B-cell lymphocytosis and chronic lymphocytic leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1191.