Winham, Stacey J.

Breast cancer incidence rises with age and peaks across the menopausal transition, yet why some postmenopausal lobules persist, and why that persistence predicts cancer risk, remains unresolved. Incomplete age-related lobular involution is one of the strongest tissue-level predictors of subsequent breast cancer, but it is still commonly viewed as passive failure of hormonally driven regression. This Review proposes a different framework: persistent lobules are maintained by an active reserve niche that outlasts its reproductive function. By integrating breast epidemiology, mammary stromal biology, cellular senescence, immune surveillance, and comparative reserve systems in skeletal muscle, hematopoiesis, and postmenopausal endometrium, we argue that menopause is a biological control point at which tissue fate diverges. Efficient clearance of senescent cells permits lobular regression to complete, whereas impaired immune surveillance may allow inflammatory paracrine signaling, macrophage reprogramming, and immune evasion to create a self-sustaining senescent-immune niche lock. This framework explains why persistent lobules are biologically active, shifts attention from epithelial quantity to microenvironmental state, and identifies the perimenopausal window as a promising interval for biomarker-guided risk stratification and prevention.

Breast cancer incidence rises with age and peaks across the menopausal transition, yet why some postmenopausal lobules persist, and why that persistence predicts cancer risk, remains unresolved. Incomplete age-related lobular involution is one of the strongest tissue-level predictors of subsequent breast cancer, but it is still commonly viewed as passive failure of hormonally driven regression. This Review proposes a different framework: persistent lobules are maintained by an active reserve niche that outlasts its reproductive function. By integrating breast epidemiology, mammary stromal biology, cellular senescence, immune surveillance, and comparative reserve systems in skeletal muscle, hematopoiesis, and postmenopausal endometrium, we argue that menopause is a biological control point at which tissue fate diverges. Efficient clearance of senescent cells permits lobular regression to complete, whereas impaired immune surveillance may allow inflammatory paracrine signaling, macrophage reprogramming, and immune evasion to create a self-sustaining senescent-immune niche lock. This framework explains why persistent lobules are biologically active, shifts attention from epithelial quantity to microenvironmental state, and identifies the perimenopausal window as a promising interval for biomarker-guided risk stratification and prevention.

Abstract Background Multiplexed immunofluorescence (MxIF) enables high-dimensional immune cell phenotyping and detailed characterization of the tumor immune microenvironment (TIME), but complex cyclic staining can introduce signal degradation and uncertainty in cell-level measurements. In routine practice, pathologists rely on hematoxylin and eosin (H&E) staining as the primary reference for tissue morphology and frequently cross-reference H&E to interpret MxIF findings. Methods We present a framework for aligning H&E and MxIF images at the tissue microarray (TMA) core level to support cross-modal analysis. Cell nucleus detections from each modality are used as anchor points, formulating the task as a point-set alignment problem. Coherent Point Drift (CPD) is applied for global alignment, followed by graph-matching–based refinement to improve local consistency. Evaluation on ovarian TMAs demonstrates consistent alignment performance for both restained and serial sections. We further explore the use of restained H&E as a reference for MxIF interpretation and investigate virtual H&E generation from MxIF data as a complementary approach when restained H&E is unavailable. Results The aligned images enable quantitative assessment of cross-modal feature concordance between MxIF-derived measurements and H&E morphology. The generated virtual H&E demonstrated similar cell population compared to the real H&E. Conclusions Cell-centric alignment can facilitate integrative multimodal histopathology analysis for TMA cores in spatial co-localization, cell feature validation, and virtual H&E generation.

ABSTRACT Objective This study assessed the impact of sex and age on weight loss outcomes in patients receiving tirzepatide in real‐world clinical practice. Methods We conducted a retrospective cohort study of adults with overweight or obesity who initiated tirzepatide at Mayo Clinic between June 2022 and May 2024 and completed ≥ 12 months of continuous therapy. Primary outcome was total body weight loss percentage (TBWL%) at 15 months, stratified by sex and age groups (≤ 45, 46–59, ≥ 60 years). Results Among 1039 patients (57% women; mean age 56 ± 11 years), women achieved significantly greater TBWL% than men after 15 months of treatment (15.1% vs. 10.7%, p < 0.001). Patients aged ≤ 45 years had greater weight loss than those ≥ 60 years (15.1% vs. 12.3%, p = 0.024). In multivariable analyses, greater weight loss was independently associated with female sex, absence of type 2 diabetes, no prior obesity medication use, no concomitant weight gain‐promoting medications, and higher tirzepatide dosage. Age was not an independent predictor. Conclusions Sex, but not age, predicted weight loss with tirzepatide among individuals using the medication continuously for ≥ 12 months. These findings underscore the importance of incorporating sex‐specific considerations into personalized obesity treatment strategies.

Introduction To assess and compare the accuracy, readability, and overall performance of large language models (LLMs) in answering questions about functional hypothalamic amenorrhea (FHA) for patients and healthcare professionals. Methods A total of 11 patient-level and 15 clinician-level FHA-related questions were entered separately into four LLMs: ChatGPT 3.5 (free version), ChatGPT 4.0 (updated, paid subscription), Gemini, and OpenEvidence. OpenEvidence was used only for clinician-based questions. Responses were evaluated by three expert reviewers blinded to the LLM used who rated them as accurate and complete, accurate but incomplete, or inaccurate. A fourth reviewer resolved discordant scores. Readability for patient-level questions was assessed using the Flesch Reading Ease Score (FRES) and word count. Lower FRES scores indicate more difficult reading. Accuracy and completeness were compared using odds ratios (95% CI) with ChatGPT 3.5 as the reference model, and differences in readability were analyzed using Friedman’s test. Results LLM performance varied across question types. For patient-level questions, ChatGPT 4.0 achieved the highest accuracy (9 of 11; 82%), followed by ChatGPT 3.5 and Gemini (each 8 of 11; 73%), with no statistically significant differences. Among clinician-level questions, OpenEvidence demonstrated perfect accuracy (15 of 15; 100%), compared with 93% for and 80% for ChatGPT 4.0 and Gemini. Completeness followed similar patterns, with OpenEvidence providing the most complete clinician responses (93%) and ChatGPT 4.0 the most complete patient-level responses (89%). Readability differed significantly among models ( p = 0.012), with Gemini producing the most readable patient-level content (median FRES 43.5 [IQR 36.8–53.4]) compared with ChatGPT 3.5 (30.6 [16.8–48.4]) and ChatGPT 4.0 (28.8 [22.1–37.6]). Word counts did not differ significantly ( p = 0.39). Discussion LLMs demonstrated good overall performance in answering FHA-related questions but often provided incorrect or incomplete information. Fine tuning field-specific data, engineered prompts, and obtaining human-in-the-loop feedback may help improve the accuracy of these models.

Objectives: Sexual minority women (SMW) have worse mental and physical health outcomes compared with heterosexual women. Limited research has explored menopause in SMW. This study compared menopause symptoms in cisgender SMW and heterosexual women. Methods: This cross-sectional analysis examined questionnaires from women 40-65 years, seen at women’s health clinics in Arizona, Florida, and Minnesota between December 2016 and February 2023. Menopause symptoms were evaluated using the Menopause Rating Scale (MRS). An MRS score ≥ 12 indicates moderate or greater symptoms. Multivariable logistic regression was used to compare scores between cisgender SMW and heterosexual women, adjusting for factors that may impact menopause symptoms. Results: Data from 2,273 women were included, with 69 SMW. The mean age was 53.2 (SD: 6.1), body mass index 25.7 kg/m 2 , and a majority were White (91.9%). The median MRS score among all participants was 15 (interquartile range=10, 20); 67.3% reported MRS scores ≥ 12. The likelihood of being on hormone therapy or selective serotonin reuptake inhibitors/serotonin-norepinephrine reuptake inhibitors was comparable between groups. In both univariate (OR: 0.85, 95% CI: 0.52-1.40) and multivariable (OR: 0.83, 95% CI: 0.47-1.48) analyses, no significant associations between sexual orientation and MRS ≥ 12 were found. Conclusions: Menopause symptoms and likelihood of treatment were similar for cisgender SMW and heterosexual women presenting to tertiary care centers. Limited diversity in the cohort and the small number of SMW reduce the generalizability of the results. The lack of adverse social determinants of health may have contributed to the findings, but additional research in larger cohorts is needed.

Abstract Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with individual cancer risk, and polygenic risk scores (PRS) are typically derived from these cancer-specific variants. Although previous pan-cancer studies revealed shared genetic architectures across cancer types, latent factors contributing to cancer risk have not been explored. This may allow for improved prediction of cancer risk based on a latent cancer factor which leverages shared variants across cancers. Here, we conducted an integrative pan-cancer GWAS across seven solid tumors (breast, ovarian, endometrial, pancreatic, lung, colorectal, melanoma) to identify shared latent genetic factors and novel pleiotropic variants. We curated the most recent and large-scale GWAS summary statistics to maximize statistical power, comprising 398,917 cases and 1,501,715 controls of European ancestry. Shared genetic structures among 6,346,960 common variants were evaluated using Genomic Structural Equation Modeling (GenomicSEM). Three latent factors (Common Cancer Factor, Female-Specific Cancer Factor and Non-Sex Specific Cancer Factor) were constructed, and associations of individual variants with each latent factor were estimated. Genome-wide significant loci (p &lt; 5x10-8) were functionally annotated and subjected to pathway enrichment analysis. Models capturing shared genetic risk across cancers fit the data well. A single “Common Cancer Factor” explained much of the shared risk (SRMR = 0.064 [lower is better], CFI = 0.938 [closer to 1 is better]), and a correlated two-factor model—distinguishing female-specific from other cancers—showed even stronger fit and biological relevance (SRMR = 0.055, CFI = 0.988). The one-factor (Common Cancer Factor; SRMR: 0.064, CFI: 0.938, AIC: 54.7, p_chisq: 0.021) and the two-factor (Female-Specific Cancer Factor and Non-Sex Specific Cancer Factor; SRMR: 0.055, CFI: 0.988, AIC: 45.4, p_chisq: 0.281) models demonstrated robust model fit and biological relevance. Factor-specific GWAS identified 233 genome-wide significant loci (p &lt; 5x10-8) with 24 distinct loci showing shared susceptibility across cancers. More importantly, we identified eight novel variants that were not previously reported or related with any cancer risk, potentially regulating cancer-related genes such as PRC1, CEBPB, SMC2, KLF5 and ATXN2. Further analyses are needed to elucidate their roles in tumorigenesis. Our findings reveal a shared genetic architecture across major solid tumors and uncover novel pleiotropic variants with potential regulatory roles in oncogenesis. This study provides novel insights into the genetic basis of cancer susceptibility and supports the development of multi-cancer PRS to improve prediction in cancer prevention and prognosis. Citation Format: Xunxuan Chen, Gamaliel T. Taengwa, Brandon J. Coombes, Stacey J. Winham. Pan-cancer genome-wide study reveals shared genetic architecture and novel pleiotropic variants across seven solid cancers [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 3600.

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 (&lt;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 Ovarian endometrioid carcinoma (OEC) is the second most common histotype associated with the most favorable prognosis among ovarian carcinomas. Similar to endometrial carcinoma, OEC exhibit significant molecular diversity. We aimed to develop a practical immunohistochemical (IHC) prognosticator by integrating IHC biomarkers with clinical substage, and surgical outcomes. This multi-institutional study included participants with primary invasive OEC from the Alberta Cancer Registry (AOVT), Mayo Clinic, and Disease of the Ovary and their Evaluation Study (DOVE). Pathology review excluded misclassified high-grade serous and mesonephric carcinomas leaving 422 OEC cases (AOVT N=158; DOVE N=143; Mayo N=121) with available tumor biospecimens who did not receive neoadjuvant treatment. Clinical characteristics included age, stage, grade, residual disease and 5-year survival. Clinical risk group was defined by combining stage and residual disease: Low (FIGO stage IA/IB & no macroscopic disease), Intermediate (stage IC-II& no macroscopic disease), High (stage III/IV or macroscopic disease). Tissue microarrays were stained for TP53, PMS2, MSH6, PGR, and CTNNB1 using IHC and were scored by a single pathologist. Tumors were hierarchically categorized as TP53-abnormal (TP53abn)/PGR-loss, mismatch-repair deficient (MMRd, via PMS2 and MSH6), nuclear-CTNNB1 (nCTNNB1), or no-specific-immunohistochemical-profile (NSIP). Overall survival (OS) at 5 years was compared across clinical risk and biomarker groups using Cox regression, adjusted for age and site to generate hazard ratios (HR), and 5-year survival rates (5-YSR). High clinical risk group was associated with worse 5-year OS compared to low & intermediate risk (p&lt;0.0001). Hierarchical IHC biomarker groups were also associated with 5-year OS, adjusted for age and study site (p&lt;0.0001), with consistently worse survival for combined TP53abn/PGR-loss (n=17, HR=6.07, 95% CI 2.78-13.26), PGR-loss only (n=55, HR=4.17, 95% CI 2.19-7.94), TP53abn only (n=29, HR=2.04, 95% CI 0.85-4.90), and better survival for nuclear-CTNNB1 (n=146, HR=0.22, 95% CI 0.07-0.65) compared to NSIP (n=132). Within the low-risk group, MMRd, nCTNNB1, or NSIP had greater than 97% 5-YSR, while OEC with TP53abn/PGR-loss or PR-loss only had less than 75% 5-YSR. Within the intermediate group, only nCTNNB1 exceeded a 5-YSR of greater than 97%. Within the high-risk group, 5YSR for nCTNNB1 OEC was 90.9%, compared to only 16.7% for TP53abn/PGR-loss. This IHC-based algorithm refines prognosis beyond clinical substage. It identifies low-risk patients with unfavorable prognosis (TP53abn/PGRloss, PGR loss only) who may benefit from adjuvant therapy, and also patients with favorable prognosis within the intermediate group for whom adjuvant therapy could be de-escalated. Further patient selection for chemo vs. hormone therapy in the high-risk group may be improved by biomarkers. Citation Format: Gamaliel Taengwa, Hunter J. Atkinson, Bryan M. McCauley, Sebastian M. Armasu, Chen Wang, Jennifer A. Doherty, Holly R. Harris, Ellen L. Goode, Martin Koebel, Stacey J. Winham. IHC-based prognostic sub-stratification of ovarian endometrioid carcinoma [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 1184.