Nyante, S.

Abstract Benign breast disease (BBD) confers elevated risk for breast cancer, yet current tools lack precision in identifying lesions likely to progress. Immune mechanisms may influence progression, but the spatial and molecular features of immune responses in BBD versus invasive cancer remain poorly characterized. We applied Digital Spatial Proteomics (DSP) to evaluate immune-related protein expression across 1,540 regions of interest (ROIs) from breast tumors (n=58, Carolina Breast Cancer Study [CBCS]), a UNC BBD cohort (n=20), and a Mayo BBD cohort (n=177). Using Consensus Clustering in CBCS and UNC BBD, we identified two stable immune phenotypes: “High Immune,” enriched in tumors and high-risk benign lesions, and “Quiet Immune,” predominant in low-risk BBD. ROIs were classified by immune marker expression, and patients were labeled as “High” if ≥50% of their ROIs were High Immune. In CBCS, High Immune profiles were associated with ER-negative status (OR=2.92 [0.99-8.65] participant-level; OR=3.28 [1.34-8.04] ROI-level). In BBD, High Immune profiles trended toward higher odds of high-risk lesions (OR=1.00 [0.15-6.70] participant-level; OR=1.68 [0.57-4.97] ROI-level), though this pattern was not replicated in the Mayo cohort. BBD samples exhibited substantial spatial heterogeneity in immune expression (40% within-patient ROI agreement vs. 74% in tumors), potentially limiting biomarker reliability. Nonetheless, four immune proteins (CD44, CD80, CD14, CD66b) were significantly associated with future cancer development in the Mayo cohort, though these findings require validation due to within-person variability. Our findings highlight the challenges posed by spatial heterogeneity in assessing immune biomarkers in BBD and suggest that immune activation may emerge later in disease progression. These results raise important questions about the timing and consistency of immune responses in the natural history of breast cancer. Citation Format: A. Zhang, A. Cozzo, L. Olsson, E. Kirk, X. Gao, S. Nyante, B. Calhoun, R. Vierkant, M. Sherman, M. Troester. Immune Heterogeneity in Breast Cancer Natural History: Benign Breast Disease to 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 PS2-05-23.

Abstract Background: Artificial intelligence (AI) algorithms based on deep learning approaches show promise in improving breast cancer (BC) detection on mammography and may also improve prediction of future BC risk compared with clinical risk prediction models. Historical clinical risk prediction models underperform among women with BBD; however, AI models for BC detection on mammography have not been tested among women at elevated BC risk due to benign breast disease (BBD). We evaluated if an AI cancer detection algorithm can aid BC risk prediction among women with BBD. Methods: We examined women without a prior BC who had an initial BBD biopsy at Mayo Clinic, Rochester, between 2002 and 2013. Incident BC occurring after BBD was identified using the Mayo Tumor Registry and supplemented by follow-up questionnaires. Only women with a full field digital mammogram at least 2 years prior to BC diagnosis were eligible. A breast pathologist evaluated BBD according to increasing severity, as non-proliferative (NP), proliferative disease without atypia (PDWA) or atypical hyperplasia (AH). AI malignancy scores derived from the Transpara detection algorithm (1-10) and volumetric percent density (VPD) from Volpara (per one standard deviation, SD) were assessed from mammograms close to BBD diagnosis. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) with 95% Confidence Intervals (CIs), adjusted for age and BMI. C-statistics were estimated to assess the contribution of each factor to BC risk prediction. Likelihood ratio tests (LRTs) and bootstrapping methods were used to compare model performance with the addition of AI malignancy score. Results: The BBD cohort with mammography consisted of 3,125 women followed for a median 12.9 years, with 250 incident BC. Of these, 221 BC occurred at least two years after the mammogram and were used in analyses. As expected, increased BC risk was associated with BBD severity [for AH HR=3.18 (95%CI: 2.04, 4.95) and for PDWA HR=1.59 (95%CI: 1.17, 2.14) compared to NP] and with higher VPD [HR=1.24 per SD (95% CI: 1.05, 1.45)] (Table ). The AI-malignancy score alone was also associated with BC risk [HR=1.16 per 1 unit score (95%CI: 1.09,1.23)] and showed similar discriminatory accuracy [C-statistic=0.626 (95%CI: 0.586, 0.665)] as the model with BBD severity and VPD combined [C-statistic=0.627, (95%CI: 0.585, 0.669)] (Table ). In models with BBD severity and VPD, the AI-malignancy score was an independent risk factor for BC [HR=1.14 (95%CI: 1.07, 1.21); PLRT<0.001] but only achieved a marginally significant improvement in discriminatory accuracy [C-statistic=0.651 (0.609, 0.693), ΔC-statistic=0.024 (95% CI: 0.000, 0.047)]. Conclusion: In this preliminary study, an AI BC detection algorithm achieved similar performance in predicting future BC risk compared to established risk factors. Citation Format: C. M. Vachon, S. Winham, M. Jensen, D. Hursh, L. Pacheco-Spann, A. Norman, J. Fischer, S. Schrup, L. Seymour, D. Gehling, S. Nyante, M. Troester, N. Karssemeijer, R. Vierkant, D. Radisky, C. Scott, S. I. Maimone, A. Degnim, M. Sherman. An AI algorithm for breast cancer detection improves future BC risk prediction among women with benign breast disease [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 PD4-02.