Degnim, Amy C.

Abstract Background: Endocrine therapy (ET) with aromatase inhibitors (AIs) plus ovarian function suppression (OFS) is a standard endocrine strategy for premenopausal women with ER+/HER2- breast cancer. However, poor tolerability to AI+OFS limits its use, leaving tamoxifen as the only alternative. Prospective studies demonstrated that premenopausal women treated with neoadjuvant ET whose tumors exhibit Ki-67 ≤ 10% following 4 weeks of therapy achieve 5-year distant disease-free survival > 96%. Problematically, only 41% of patients reach this threshold with tamoxifen compared to 78% with AI+OFS (Nitz JCO 2022). (Z)-endoxifen (ENDX) is a potent selective estrogen receptor modulator that dually targets ERα and PKCβ1. At plasma concentrations of 3-5 ng/mL, it inhibits ERα; at ≥ 500 ng/mL, it targets PKCβ1, leading to AKT suppression. The EVANGELINE trial aims to examine endocrine sensitivity in terms of Week 4 Ki-67 in premenopausal women whose baseline Ki-67 > 10%, and objective response rate after 24 weeks of ENDX + goserelin in premenopausal women with baseline Ki-67≤ 10%. Methods: EVANGELINE (NCT05607004) is a multicenter, open-label, Phase 2 study comprised of three parts. Eligible patients are premenopausal women with Stage IIA/IIB ER+/HER2- breast cancer. • Part 1 (PK Run-in): assessed 40mg vs 80mg ENDX (+/- OFS) in 22 patients. • Part 2: patients with baseline Ki-67 >10% were randomized to 40 mg ENDX + goserelin or exemestane + goserelin for 6 months with the primary objective being 4-week ESD rate. Patients with a baseline Ki-67 ≤10% were assigned to a single-arm cohort of 40 mg ENDX monotherapy (no OFS) to evaluate the 24-week endocrine sensitivity disease rate. • Part 3 (replaces Part 2): all patients are treated with ENDX 40mg daily + goserelin every 28 days. A two stage Phase II design was chosen to assess outcomes as follows: Part 3a: Up to 45 patients with baseline Ki-67> 10% to assess Week 4 Ki-67≤ 10% rate is at least 65%. If 9 or more of these patients have a Week 4 Ki-67>10%, enrollment will be stopped due to futility. Otherwise, enrollment will continue. Part 3b: Up to 20 patients enrolled with baseline Ki-67≤ 10% to assess Week 24 objective response rate. Ki-67 levels are determined by central laboratory and images are centrally reviewed. Secondary objectives include safety, tolerability, surgical outcomes, and biomarker analysis using paired biopsies (baseline, week 4) and surgical specimens. Results: The PK run-in (completed Fall 2024) enrolled 22 patients and assessed ENDX doses (40 mg daily vs 80 mg daily (+/-OFS)). The Week 4 Ki-67 ≤ 10% rate with ENDX was 86% and did not differ according to dose. Early safety, PK, efficacy and compliance data supported the selection of 40 mg ENDX daily as the optimal dose. Enrollment in Part 2 began in May 2025. Part 3 will replace Part 2 with simplification of the trial design. Conclusions: EVANGELINE is the first trial to evaluate (Z)-endoxifen + OFS as neoadjuvant therapy in premenopausal ER+/HER2- breast cancer. By targeting both ER and PKC pathways, ENDX may offer a well-tolerated alternative to AI-based regimens and expand endocrine therapy options for this population. Clinical Trial Registration: NCT05607004 Citation Format: M. P. Goetz, V. J. Suman, C. Lopez, H. Erickson, L. Beaulieu, S. S. Hammer, L. Mina, R. Leon-Ferre, K. N. Hunt, M. Piltin, A. Degnim, J. N. Ingle, J. C. Boughey, B. A. Sarah, J. M. Reid, M. Schellenberg, J. R. Hawse, P. Advani, K. Giridhar, F. Batalini, D. Flora, J. M. Jones, N. A. Bagegni, J. Jakub, V. Abramson, V. Dabak, W. J. Irvin, E. Sima, K. Seema, S. C. Quay. A Phase 2 Trial of (Z)-endoxifen + Goserelin as Neoadjuvant Treatment for Premenopausal Women with ER+, HER2-, Breast Cancer (EVANGELINE) [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 PS5-12-16.

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.

Abstract Background:Breast terminal duct lobular units (TDLUs), the source of most breast cancer (BC) precursors, are shaped by age-related involution, a gradual process, and postpartum involution (PPI), a dramatic inflammatory process that restores baseline microanatomy after weaning. Dysregulated PPI is implicated in the pathogenesis of postpartum BCs. We propose that assessment of TDLUs in the postpartum period may have value in risk estimation, but characteristics of these tissues in relation to epidemiological factors are incompletely described.Methods: Using validated Artificial Intelligence and morphometric methods, we analyzed digitized images of tissue sections of normal breast tissues stained with hematoxylin and eosin from donors <45 years from the Komen Tissue Bank (180 parous and 545 nulliparous). Metrics assessed by AI, included: TDLU count; adipose tissue fraction; mean acini count/TDLU; mean dilated acini; mean average acini area; mean “capillary” area; mean epithelial area; mean ratio of epithelial area versus intralobular stroma; mean mononuclear cell count (surrogate of immune cells); mean fat area proximate to TDLUs and TDLU area. We compared epidemiologic characteristics collected via questionnaire by parity status and race, using a Wilcoxon rank sum test or Fisher’s exact test. Histologic features were compared between nulliparous and parous women (overall and by time between last birth and donation [ recent birth: ≤5 years versus remote birth: >5 years]) using multivariable regression models.Results:Normal breast tissues of parous women contained significantly higher TDLU counts and acini counts, more frequent dilated acini, higher mononuclear cell counts in TDLUs and smaller acini area per TDLU than nulliparas (all multivariable analyses p<0.001). Differences in TDLU counts and average acini size persisted for >5 years postpartum whereas increases in immune cells were most marked <5 years of a birth. Relationships were suggestively modified by several other factors, including demographic and reproductive characteristics, ethanol consumption and breastfeeding duration. Conclusions:Our study identified sustained expansion of TDLU numbers and reduced average acini area among parous versus nulliparous women and notable increases in immune responses within five years following childbirth. Further, we show that quantitative characteristics of normal breast samples vary with demographic features and BC risk factors.