Radisky, Derek C.

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 Non-small cell lung cancer (NSCLC) exhibits strikingly different responses to antiangiogenic therapies between its two major histologic subtypes, lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), suggesting an histotype-specific regulation of angiogenesis. Cancer-associated fibroblasts (CAFs) commonly exhibit an activated/myofibroblast-like phenotype in NSCLC, and are emerging as key modulators of tumor progression; however, their contribution to angiogenic control remains undefined. Here, we investigated angiogenesis and hypoxia signatures in NSCLC and integrated bulk RNA-seq, scRNA-seq, CAF secretome profiling, genetic perturbations, and functional in vitro and in vivo assays to dissect the histotype-dependent production of pro-angiogenic factors by CAFs. We observed greater angiogenesis and reduced necrosis/hypoxia in LUAD compared to LUSC across multiple patient cohorts. The LUAD-CAF secretome was primed for angiogenesis through SMAD3-dependent overproduction of key regulators, most notably TIMP-1 and VEGF-A. We also uncovered a previously unrecognized role for TIMP-1 in promoting endothelial hyper-branching. In contrast, LUSC-CAFs displayed attenuated angiogenic activity despite robust HIF-1α upregulation and an hypoxia-associated transcriptional program, due to their epigenetic repression of SMAD3 and compensatory increase in SMAD2. Collectively, these results reveal that CAFs critically shape the distinct angiogenic landscapes of LUAD and LUSC through opposing SMAD2/3 regulation of TIMP-1, VEGF-A, and hypoxia signaling. These results further highlight the therapeutic potential of targeting stromal SMAD3/TIMP-1 in LUAD or microenvironmental stressors such as hypoxia and acidosis in LUSC. In addition, these findings provide a biological framework for understanding histotype-specific patterns of dissemination, immune evasion, and response to antiangiogenic therapies in NSCLC. Citation Format: Jordi Alcaraz, Natalia Isabel Diaz Valdivia, Paula Duch, Marselina Arshakyan, Amelia Parker, Alejandro Bernardo Suarez, Danielle Park, Erik Sahai, Noemi Reguart, Derek C. Radisky, Oriol Casanovas. Antagonistic SMAD2/3 control of TIMP-1, VEGF-A, and hypoxia signaling in myofibroblasts shapes histotype-specific angiogenesis in lung cancer [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 4797.

ABSTRACT The normal adult male breast has not been characterized at single-cell resolution, leaving the cellular basis of male breast cancer (MBC) biology undefined. Here we present an integrated single-cell RNA sequencing atlas of the adult human breast comprising 174,471 cells from 17 donors (3 male, 14 female), including 18,117 male-derived cells. This revealed that the male breast retains all three epithelial populations, basal (BC), luminal progenitor (LP), and luminal committed cells (LC), but with an increase in LC at the expense of BC and LP across all three male donors. Male LC were distinguished from female by elevated ESR1 and PGR mRNA, enrichment of RNA processing and ribosome biogenesis programs, reduced inflammatory cytokine and growth factor signaling, elevated estradiol gene set enrichment scores, and higher inferred activity of developmental patterning transcription factors. This pattern was observed across differential expression, gene ontology, ligand profiling, and regulon-based analyses, and was not restricted to sex chromosome-linked gene expression. This is consistent with the near-universal estrogen receptor (ER) positivity that characterizes MBC clinically. This atlas provides the first cellular and transcriptional reference for the normal male breast and a resource for investigating sex differences in mammary biology, germline susceptibility variant interpretation, and modeling breast malignancies.

Abstract Uptake of tamoxifen is limited to 5-15% of all eligible women due to concerns regarding medication side effects combined with the lack of ability to predict which women will benefit from the therapies. We used a unique retrospective sample of n=92 high-risk women with paired benign biopsies obtained before (PreTam) and during tamoxifen (OnTam) to discover tissue gene expression biomarkers of biologic response, defined as change (Δ) in lobular Ki67. Ki67 was digitally quantified in normal lobules from PreTam and OnTam benign tissue samples and samples with PreTam Ki67 = 0 were excluded. Data were analyzed from 51 women with both PreTam and OnTam Ki67 (median age = 49 years and median BMI = 25.5 kg/m2 at PreTam biopsy). OnTam biopsies were collected a median of 11.5 months after PreTam biopsy (median 6.5 months after starting tamoxifen; dose 20 mg/day in 86%). Median Ki67 significantly decreased overall (Δ Ki67 = -0.29%, p=0.003), but the effect was restricted to women with baseline Ki67 ≥ 0.5% (n=38, Δ Ki67 = -0.78%, p=0.0002); no significant change occurred with baseline Ki67 < 0.5 (Δ Ki67 = 0.19%, p=0.23). Δ Ki67 was inversely proportional to baseline Ki67. RNA-seq was successful for 171 benign tissue samples (including 53 PreTam-OnTam pairs). After edgeR normalization (>14,600 genes), PreTam vs OnTam comparison across 33 pairs with baseline Ki67 ≥ 0.5% yielded 66 differentially expressed genes (fold-change > 2 and p<0.01). At p<0.01, ranked gene set enrichment analysis revealed 27 gene sets enriched for reduced expression with Tam (with top-ranked gene set related to steroid hormone biosynthesis) and 59 gene sets enriched for increased expression with Tam (with top-ranking gene sets related to B cell and T cell receptor signaling, regulation of cell cycle, and other immune-related pathways). To identify predictors of response, we correlated baseline PreTam gene expression with Δ Ki67 in 37 women with available baseline PreTam RNAseq and Δ Ki67 data. Partial correlation analysis (via ‘ppcor’ package, adjusted for library qubit) revealed 73 genes with significant positive correlation (rho > 0.50) and 14 genes with significant negative correlation (rho < -0.50) with Δ Ki67 (p<0.01). At p<0.01, ranked gene set enrichment analysis yielded 67 gene sets enriched with negative correlation (i.e. increased expression associated with a reduction in Ki67), including pathways related to ribosome biogenesis, DNA recombination, and nucleic acid metabolic processes; there were 62 gene sets enriched for positive correlation (i.e. decreased expression associated with a reduction in Ki67), including pathways such as VEGF and ERBB signaling. Our data support (i) baseline Ki67 ≥ 0.5 % and (ii) an 87-gene baseline signature as complementary biomarkers of tamoxifen response. Prospective validation of this combined assay could target tamoxifen to the women most likely to benefit, potentially overcoming a major barrier to chemoprevention uptake. Citation Format: S. J. Winham, C. Wang, Y. Liu, B. M. McCauley, D. J. Tabrizi, D. L. Gehling, J. L. Fischer, L. R. Seymour, M. H. Solanki, M. E. Sherman, K. R. Lohani, T. L. Hoskin, D. C. Radisky, A. C. Degnim. Gene expression signatures of beneficial response to tamoxifen for breast cancer prevention [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 PS3-01-18.

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 The incidence of early onset breast cancers (BCs) has increased, paralleling rising trends in delayed childbearing. We hypothesize that a distinct postpartum BC (PPBC) subtype, identifiable by time since last birth (TSLB) and biomarker expression, contributes to this trend. Methods We applied GeoMx Digital Spatial Profiling (DSP) to measure associations between TSLB and 71 proteins in 640 BCs from women aged ≤40 years included in the Young Women’s Breast Cancer Study. We analyzed data using univariable linear regression and multivariable sliced inverse regression to account for higher order interactions among biomarkers. Results In keratin-rich segments, progesterone receptor (PR) (P = 1.00 × 10−4) and PTEN (P = 1.00 × 10−3) were associated with longer TSLB; multivariable analyses revealed positive associations for GZMB (P = 4.00 × 10−4), SMA (P = 1.00 × 10−4), and NF-1 (P = 1.00 × 10−3). In keratin-poor segments, univariable significant positive associations were found for PR (P = 2.00 × 10−4) and PTEN (P = 1.00 × 10−3), whereas CD20 (P = 3.00 × 10−4) and CTLA4 (P = 4.00 × 10−5) were negatively associated; multivariable significant associations were found for fibronectin (P = 3.00 × 10−5) and pan-Akt (P = 1.00 × 10−3). Associations persisted after adjustment for multiple comparisons and BC molecular subtypes. Associations including for PR, PTEN, and CD20 were strongest among women with the shortest TSLB. OPAL multiplex immunofluorescence assays for PR, PTEN, CD20, SMA, and CTLA4 replicated several DSP findings, particularly when stratified by subtype and with compartment matching. In TCGA, RNA species linked to proteins associated with TSLB correlated strongly with a T-cell exhaustion signature previously linked to poor prognosis among premenopausal women. Conclusion These data support PPBC as a biologically coherent phenotype defined by TSLB and biomarker profile, with potential implications for prevention and therapy.

Background: Discovering risk biomarkers in small benign breast disease (BBD) biopsies is constrained by scarce tissue and microanatomic heterogeneity of terminal duct lobular units (TDLUs). We tested whether tissue-sparing tissue microarray (TMA)–based Digital Spatial Profiling (DSP) can deliver reproducible, biologically coherent protein measurements across benign lobules and breast cancers (BCs), and how well DSP aligns with standard immunoassays. Methods: We performed a pilot using tissues from the Mayo Clinic BBD cohort using TMAs representing four contexts: terminal duct lobular units (TDLUs) from BBD biopsies preceding BC and matched BBD-controls, subsequent BCs, and BC-associated TDLUs. We profiled 79 proteins by DSP (37 retained after QC) and benchmarked against chromogenic IHC and OPAL immunofluorescence. Reproducibility was evaluated using intraclass correlation coefficients (ICCs), cross-platform agreement (weighted kappa), marker correlations, and mixed-effects models with false-discovery-rate (FDR) control. Results: We analyzed 368 BBD-TDLU cores (88 cases; 88 controls), 204 BC cores and 110 BC-associated TDLU cores. ICCs were highest in BC tissues, and lower in BC-associated TDLUs and BBD-TDLUs. Agreement was slight–to-fair in TDLUs but moderate (ER/PR) to substantial (BCL2) in BC. DSP recapitulated expected immunologic correlations (CD45 with T-cell, B-cell, and macrophage markers) and tissue-type gradients (BC > BC-associated TDLUs > BBD-TDLUs). Exploratory case–control differences in BBD-TDLUs did not persist after FDR control. Conclusions: TMA-based DSP is feasible in archival breast tissues and yields biologically coherent, cross-platform-benchmarked profiles that are particularly robust in BC, while conserving scarce TDLUS and clarifying current limits of single-marker risk stratification from benign lobules. These data provide a foundation for refined sampling and expanded panels in future TDLU-focused studies.

Abstract Purpose: Black women are disproportionately affected by aggressive breast cancers compared to white women, suggesting underlying biological differences in normal breast tissue. This study aims to identify molecular disparities in normal breast tissue between premenopausal Black and white women that may contribute to increased breast cancer risk. Experimental Procedures: Visium spatial transcriptomic analysis was performed on a pilot cohort of normal breast tissue samples from premenopausal Black and white women. Tissue sections were spatially profiled, and capture spots were classified based on predominant cell types: epithelial, stromal, or endothelial. Differential gene expression analysis between racial groups was conducted for each cell type classification. Multiplex immunofluorescence (mIF) was used to validate selected markers in the pilot cohort. Meta-analysis compared differentially expressed genes with published gene sets associated with breast cancer subtypes. Results: Significant racial differences in gene expression profiles were identified in both epithelial and stromal spots. In epithelial spots, genes upregulated in Black women significantly overlapped with over 20 published gene sets associated with estrogen receptor-negative (ER−) breast cancer, including key genes involved in Wnt signaling inhibition (e.g., WIF1) and basal epithelial markers (e.g., KRT14, KRT15). This suggests alterations in pathways linked to more aggressive breast cancer phenotypes. In stromal spots, differentially expressed genes related to extracellular matrix remodeling and fibroblast activation (e.g., PI16, DPT, EFEMP1) were observed, indicating potential differences in stromal-epithelial interactions. Ongoing deconvolution and spatial analyses are being performed to further elucidate these findings. Preliminary mIF validation confirmed increased expression of PI16 in samples from Black women. Validation of additional markers is underway in a larger cohort of premenopausal Black and white women. Conclusions: Spatial transcriptomic profiling of normal breast tissue reveals distinct molecular differences between Black and white women, particularly in pathways associated with aggressive breast cancer subtypes and stromal interactions. These findings suggest that inherent variations in the normal breast microenvironment may contribute to the increased risk and aggressiveness of breast cancer in Black women. Further validation and analysis may identify potential biomarkers for risk assessment and inform targeted prevention strategies. Citation Format: Camila Pacheco Erak, Nicole Cruz-Reyes, Pria Westerman, Joshua Ogony, Jean R. Clemenceau, Tae Hyun Hwang, Sarah McLaughlin, Derek Radisky. Spatial transcriptomic profiling of normal breast tissue reveals potential mechanisms underlying increased breast cancer risk in Black women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 765.