Citations

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 Deep mutational scanning (DMS) has proven effective for mapping protein–protein interactions (PPIs), but it cannot provide complete coverage of the mutation landscape, particularly for multi‐mutant variants. To address this limitation, we trained machine‐learning (ML) models on previously generated DMS data for a stabilized amyloid precursor protein inhibitor (APPI) binding to either of two serine proteases, mesotrypsin and kallikrein‐6 (KLK6), which are implicated in various human disorders. We combined the models to accurately predict the binding selectivity of APPI variants, including double‐mutant variants, for the two serine proteases. We achieved a Pearson correlation of 0.937 between predicted log 2 selectivity enrichment ratios and DMS‐derived values. We further validated the predictions of our combined model by yeast‐surface‐display measurements and inhibition assays of purified APPI variants and revealed epistatic interactions that shape protease selectivity. Guided by binding selectivity predictions, we identified highly selective APPI variants, including the most selective mesotrypsin inhibitor reported to date. Together, these findings support the use of DMS and ML as a framework for predicting PPI selectivity and prioritizing selective therapeutic protein variants.

Abstract Matrix metalloproteinases (MMPs) are key regulators of tumor progression, invasion, and metastasis; however, clinical efforts to inhibit MMPs have largely failed due to a lack of selectivity and unintended suppression of protective family members. In triple-negative breast cancer (TNBC), MMP-9 (gelatinase B) is strongly associated with tumor aggressiveness, extracellular matrix remodeling, and metastatic dissemination. In contrast, MMP-8 has been shown to exert anti-tumorigenic effects in breast cancer by limiting inflammation and suppressing metastasis. This functional divergence underscores the importance of selectively targeting MMP-9 while preserving MMP-8 activity as a rational therapeutic strategy in TNBC. To address this challenge, we engineered variants of the endogenous MMP inhibitor tissue inhibitor of metalloproteinases-1 (TIMP-1) with enhanced selectivity for MMP-9 over MMP-8. A yeast surface display library of human TIMP-1 was constructed and screened using fluorescence-activated cell sorting through iterative rounds of positive selection for recombinant MMP-9, followed by competitive counter-selection against MMP-8 to eliminate off-target binders. This strategy enabled enrichment of TIMP-1 variants with improved specificity for MMP-9 compared with wild-type TIMP-1. Several TIMP-1 variants exhibited enhanced binding and inhibitory selectivity toward MMP-9, with TIMP-1-C6.2 emerging as the most promising candidate. TIMP-1-C6.2 preferentially inhibited MMP-9 with a Ki of 0.3 ± 0.04 nM, while demonstrating substantially weaker inhibition of MMP-8 (Ki = 6.8 ± 0.27 nM), corresponding to an approximately 23-fold selectivity for MMP-9. Importantly, functional evaluation in Matrigel invasion assays using MDA-MB-231 TNBC cells revealed that TIMP-1-C6.2 significantly enhanced inhibition of tumor cell invasion compared with wild-type TIMP-1 in a dose-dependent manner (p < 0.0001). This improved anti-invasive activity reflects refined target selectivity rather than a generalized increase in inhibitory potency. Collectively, these findings demonstrate that selective engineering of TIMP-based inhibitors can overcome a central limitation of MMP-targeted therapies by suppressing pro-tumorigenic MMP-9 while sparing anti-tumorigenic MMP-8. This work provides a strong foundation for the development of selective MMP-9 inhibitors with improved safety and therapeutic potential for TNBC and other MMP-driven cancers. Citation Format: Alireza Shoari, Alexandra Hockla, Mathew A. Coban, Ella E. Aitchison, Alexandra M. Dimesa, Evette S. Radisky. Protein engineering of selective MMP-9 inhibitors reveals a therapeutic strategy for triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr LB233.

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.

Matrix metalloproteinases (MMPs) are secreted and cell membrane‐associated enzymes that remodel the extracellular matrix (ECM) and cleave extracellular proteins to promote tumor invasion, angiogenesis, immune evasion, and many other aspects of cancer progression. Among this family, the gelatinases MMP‐2 and MMP‐9, specialized for cleaving collagen IV, are tightly linked to metastatic progression in breast cancer and adverse clinical outcomes. Here, we summarize gelatinase biochemistry and regulation, including zymogen activation, TIMP control, cell‐surface trafficking and tethering, and receptor‐mediated clearance, and explain how crosstalk between ECM and proteases amplifies invasion and metastatic seeding. We then review therapeutic strategies in two parts: direct inhibition and indirect pathway modulation. Direct approaches emphasize selective small molecules (thiirane mechanism‐based inhibitors, allosteric blockers of pro‐MMP‐9 activation) and protein biologics (monoclonal antibodies, nanobodies, engineered TIMPs). Indirect strategies target upstream signals that drive MMP‐2/‐9, including MAPK/AP‐1, PI3K/Akt/mTOR, NF‐κB, EGFR/JAK/STAT, and nuclear receptor and Nrf2/HO‐1 programs, with agents that curb invasion, angiogenesis, and metastasis in breast cancer models. Together, these advances define a maturing toolkit for precise gelatinase control and support prospective evaluation in rational combinations to restrain metastatic progression in high‐risk breast cancer.