Estrogen targets multiple cell types that impact tumor progression. However, effects on metastatic processes and interplay of signals to cancer cells and stromal cells are poorly understood. Using an immunocompetent preclinical model of estrogen receptor alpha (ER + ) breast cancer and two matched CRISPR-Cas9 Esr1-ablated (ER-) cell lines, we demonstrate that supplemental 17β-estradiol fosters a pro-tumor immune environment for both ER+ and ER- mammary tumors, but exerts disparate effects on early pulmonary colonization following intravenous injection: promoting ER + , but inhibiting ER- tumor lesions. Interrogation of mammary tumor environments and splenic and pulmonary immune cells revealed marked effects of 17β-estradiol on immune subpopulations and activity. RNASeq analyses illuminated the potential of ER+ tumor cells for immune evasion, revealing a new facet of estrogen action in ER+ cancer. This context-specific interplay among estrogen responsive cells in the mammary and premetastatic pulmonary environments has implications beyond breast cancers, including ER- cancers of varying origins.
Accurate assessment of breast cancer risk is essential for risk-stratified screening strategies. We developed BREST-AI, a deep-learning model that predicts three-year breast cancer risk from negative screening mammograms. Model development used a curriculum-learning strategy and data from 65,419 women; the model was trained on 58,591 women (9020 cancer, 1633 benign, and 47,938 three-year cancer-free). In the external UK case–control validation (1899 cases and 5697 matched controls), BREST-AI achieved an AUC of 0.751 (95% CI 0.738–0.764), outperforming Mirai (0.700, 95% CI 0.687–0.714; p < 0.001). In a US external cohort (97 cases and 291 matched controls), AUCs were 0.670 (95% CI 0.609–0.731) for BREST-AI and 0.633 (95% CI 0.570–0.696) for Mirai (p = 0.199); this analysis is underpowered to detect modest differences and should be interpreted as exploratory. In a UK cross-vendor external cohort acquired on Siemens systems (400 cases and 1200 matched controls), cross-vendor domain shift modestly reduced discrimination, but BREST-AI still exceeded Mirai (AUC 0.719 versus 0.685; p = 0.019). When projecting case–control results to the English screening prevalence (8 cancers per 1000 examinations), BREST-AI yielded an estimated positive predictive value of 8.0% and a relative risk of 10.1 at a high-risk operating point defined by 99% specificity. These prevalence-adjusted estimates suggest that BREST-AI could help to identify a small subgroup of women at high three-year breast cancer risk within the NHS Breast Screening Programme.
Oncotype DX® (ODX) guides adjuvant chemotherapy decisions in early breast cancer (eBC), but its high-cost limits access in resource-constrained settings. We developed and evaluated an interpretable machine learning model using routinely available clinicopathologic variables to predict ODX recurrence score (RS) and optimize genomic testing. We analyzed 935 Brazilian patients with eBC from the GBECAM cohort (2005–2024). Ridge regression was selected for its predictive performance, interpretability, and computational efficiency. Patients were stratified into risk groups based on predicted probability of RS > 25, and recurrence-free survival was estimated using the Kaplan–Meier method. The model showed good discrimination for identifying RS > 25 (AUROC 0.818 [95% CI, 0.780–0.852]), with sensitivity of 0.556 and specificity of 0.859. The highest-risk quartile contained 60.8% of all RS > 25 cases (observed probability 42.8%), whereas the lowest-risk half had an observed probability of only 4.4%. These findings suggest that up to 75% of patients could be triaged without confirmatory ODX testing, reserving genomic assays for an intermediate-risk subgroup. The Ridge model outperformed previously published clinicopathologic models and may represent a cost-effective strategy to improve ODX testing prioritization and resource allocation.
Metastasis remains a major challenge in breast cancer, driven by disseminated tumor cells that persist in a dormant state. This review examines immune regulation of breast cancer dormancy across bone, lung, liver, and brain. CD8⁺ T cells and natural killer cells can promote dormancy, whereas immune suppression, niche remodeling, and systemic factors, such as aging and inflammation, may promote metastatic awakening. Understanding these mechanisms may inform strategies to prevent relapse.
Clinical trials in breast cancer have demonstrated reduced recurrence in bone and soft tissue in postmenopausal women following either neoadjuvant or adjuvant treatment with Zoledronic acid (Zol). In contrast however, the same treatment resulted in an increased risk of extra-skeletal recurrence in premenopausal women. The reason for these stark differences has remained unclear, until now, preventing thousands of young breast cancer patients from being able to benefit from the life prolonging effects of Zol. Here we demonstrate that Zol induces an anti-tumor response by increasing infiltration of CD8 + T, dendritic and NK cells into soft tissue metastases, but only when estrogen concentrations are low. Anti-tumor response to Zol appears to be CD8 + T and NK cell-dependent in soft tissue, but only NK cell-dependent in bone. Conversely, elevated levels of estrogen are associated with reduced systemic and tumor infiltrating CD8+ and CD4 + T cells, increased PD-1 expression on T and NK cells, and PD-L1 on tumor cells. We propose that premenopausal concentrations of estradiol antagonize Zol-induced anti-tumor immunity, which relies on CD8 + T-cells and NK cells—an effect that is reversed by PD-1/PD-L1 blockade.
