npj Breast Cancer

PURPOSETo examine the ability of 5 artificial intelligence (AI)-based computer vision algorithms, most trained to detect visible breast cancer on mammograms, to predict future risk relative to the Breast Cancer Surveillance Consortium clinical risk prediction model (BCSC v2). PATIENTS AND METHODSIn this case-cohort study, women who had a screening mammogram in 2016 at Kaiser Permanente Northern California with no evidence of cancer on final imaging assessment were followed through September 2021. Women with prior breast cancer or a known highly penetrant gene mutation were excluded. From the 329,814 total eligible women, a random subcohort of 13,881 women (4.2%) were selected, of whom 197 had incident cancer. All 4,475 additional incident cancers were also included. Continuous AI-predicted scores were generated from the index 2016 mammogram. Risk estimates were generated with the Kaplan-Meier method and time-varying area under the curve [AUC(t)]. RESULTSFor incident cancers at 0-1 year (interval cancer risk), BCSC demonstrated an AUC(t) of 0.62 (95% CI, 0.58-0.66), and the AI algorithms had AUC(t)s ranging from 0.66-0.71, all significantly higher than BCSC (P < .05). For incident cancers at 1 to 5 years (5-year future cancer risk), BCSC demonstrated an AUC(t) of 0.61 (95% CI, 0.60-0.62), and the AI algorithms had AUC(t)s ranging from 0.63 to 0.67, all significantly higher than BCSC. Combined BCSC and AI models demonstrated AUC(t)s for interval cancer risk of 0.67-0.73 and for 5-year future cancer risk of 0.66-0.68. CONCLUSIONThe AI mammography algorithms we evaluated had significantly higher discrimination than the BCSC clinical risk model for interval and 5-year future cancer risk. Combined AI and BCSC models had slightly higher discrimination than AI alone.

Background: Imaging-based breast cancer risk prediction models primarily use full-field digital mammography (FFDM). As digital breast tomosynthesis (DBT) has become a predominant screening modality in the United States, its potential for long-term breast cancer risk prediction remains under-explored. Objective: To develop and evaluate a deep learning model that uses longitudinal DBT exams to predict long-term breast cancer risk. Methods: This retrospective study included 313,531 DBT exams from 161,165 women (mean age, 58.5, std 11.7 years) between January 2016 and August 2020 at Institute A. A risk prediction (DRP) model was developed to estimate 2-5 year breast cancer risk using longitudinal DBT exams, patient age and breast density. Model performance was compared with a single-time point DBT model, the Mirai model using same-day FFDM, and the Tyrer-Cuzick model using the area under the receiver operating characteristic curve (AUC), time-dependent concordance index, and integrated Brier score. Results: In an independent test set (n = 34,580), the longitudinal DRP model achieved a 5-year AUC of 0.720 (95% CI, 0.703-0.738), improving on the single time point DRP model (AUC, 0.706; 95% CI, 0.687-0.724; p < 0.001) and the Mirai model (AUC, 0.687; 95% CI, 0.668-0.705; p < 0.001). In a matched case-control cohort (n=432), the DRP model achieved a 5-year AUC of 0.676 (95% CI, 0.626-0.727), compared with 0.567 (95% CI, 0.514-0.621; p < 0.001) for the Tyrer-Cuzick model. The model reclassified 37.6% (705/1,877) of women with extremely dense breasts as average risk, with a 5-year cancer incidence of 0.7% (5/705), and identified 15.5% (404/2,605) of women with fatty breasts as high risk, with a 5-year cancer incidence of 2.5% (10/404). Conclusion: A deep learning model using longitudinal DBT examinations improved long-term breast cancer risk prediction compared with FFDM-based and clinical risk models. Clinical Impacts: Longitudinal DBT-based risk prediction may enable dynamic risk assessment using screening images, supporting personalized screening strategies and more targeted use of supplemental imaging.

BackgroundCopper is an essential nutrient required for energy production, antioxidant defense, and connective tissue maturation, yet has emerged as a metabolic vulnerability in cancer. CTR1 (SLC31A1), the high-affinity copper importer, mediates cellular copper uptake, and its upregulation may signal increased copper demand in tumor cells. The dynamics of copper regulation across tumor growth, aggressiveness, and treatment resistance remain poorly defined in breast cancer. We investigated whether CTR1 expression and systemic copper changes reflect a coordinated tumor-systemic copper axis MethodsA retrospective dataset of 1632 breast cancer patients receiving neoadjuvant chemotherapy was analyzed to compare CTR1 gene expression between responders and non-responders across molecular subtypes and tumor grades. Findings were extended to a prospective neoadjuvant cohort in which paired pre-and post-treatment serum copper levels were measured. {Delta}Copper (post-pre change) was correlated with subtype, grade, response, and tumor size ResultsCTR1 expression was significantly higher in triple-negative breast cancer (TNBC) non-responders than responders (P = 0.0021), particularly in grade 3 tumors (P = 0.0035), with no difference in luminal subtypes. In the prospective cohort, {bigtriangleup}Copper was positive predominantly in TNBC and strongly grade-dependent: all grade 3 TNBCs exhibited copper elevation post-therapy, whereas all grade 2 TNBCs showed negative {bigtriangleup}Copper (P = 0.034). The only relapse in the cohort, a TNBC non-responder, exhibited persistently positive {bigtriangleup}Copper at follow-up and relapse, whereas non-responders from other subtypes showed near-zero or negative {bigtriangleup}Copper (P = 0.011). Baseline serum copper was higher in patients with smaller (clinical T1) versus larger (T2-T3) tumors (P = 0.033) ConclusionsParallel CTR1 upregulation in tumors and systemic copper elevation post-therapy suggest a coordinated copper mobilization program in high-grade TNBC. These integrated retrospective and prospective findings link copper transport to therapy response and tumor aggressiveness, highlighting copper biology as a potential therapeutic axis in breast cancer.

There has been a long-standing interest in targeting the insulin-like growth factor-1 receptor (IGF-1R) signaling system in breast cancer due to its key role in neoplastic proliferation and survival. However, no IGF-1R targeting agent has shown substantial clinical benefit in controlled trials, and no treatment predictive biomarkers for IGF-1R targeting agents exist. IGFBP7 is an atypical insulin-like growth factor binding protein as it has a higher affinity for the IGF-1R than IGF ligands. We report that low IGFBP7 gene expression identifies a subset of breast cancers for which the addition of ganitumab (an anti-IGF-1R monoclonal antibody) to chemotherapy substantially improved the pathological complete response rate compared to neoadjuvant chemotherapy alone. Furthermore, high IGFBP7 expression predicted increased distant metastasis risk. If our findings are confirmed, decisions to halt the development of IGF-1 targeting drugs, which were based on disappointing results of prior trials that did not use predictive biomarkers, should be reviewed.

Nottingham histologic grading is essential for breast cancer prognostication but suffers from inter-observer variability in assessing mitotic activity, nuclear pleomorphism, and tubule formation. We developed MOSAIC (Mammary Oncology Spatial Analysis and Intelligent Classification), an explainable AI framework designed to perform component-wise grading by independently modeling these three histologic features. Model outputs were calibrated using a two-phase pathology study to establish clinically reproducible scoring thresholds and were subsequently evaluated across public datasets and multi-institutional Indian cohorts. MOSAIC demonstrated robust performance, with AI-derived grades providing independent prognostic information (HR >= 1.8 in two datasets, p = < 0.001) and improved survival stratification compared to traditional methods. In pathologist calibration studies, AI-assisted scoring significantly reduced variability, specifically achieving near-perfect agreement in mitotic scoring with a weighted {kappa} up to 0.98. Accuracy and Cohens kappa ({kappa}) analysis further characterized the models technical performance across components: Tubule formation showed the highest agreement (Accuracy >= 0.6607, {kappa} = 0.549), followed by overall Grade (Accuracy = 0.5637, {kappa} = 0.539) and Mitotic activity (Accuracy = 0.4985, {kappa} = 0.4), while Nuclear pleomorphism proved the most challenging (Accuracy = 0.3303, {kappa} = 0.271). Comparative survival models confirmed that AI-derived grades were more significant predictors of risk than manual pathologist-assigned grades, with the AI model yielding a superior global p-value (5.9 x 10-7) and lower AIC (769.61). These results indicate that MOSAIC enables reproducible, interpretable grading by decomposing assessment into pathology-aligned components. By enhancing consistency while preserving prognostic relevance, this framework supports explainable AI as a viable assistive tool for routine breast cancer pathology.

Breast cancer is a heterogeneous disease. Tumor cells and the surrounding microenvironment form an ecosystem that determine disease progression and response to therapy. To characterize the breast cancer ecosystem and the changes induced by targeted treatment selective pressure, we analyzed 136 HER2-positive tumor samples for the expression of canonical BC tumor diagnostic proteins at a single cell level without disrupting the spatial context. The combined expression of HER2, ER, PR, and Ki67 in more than a million cells was evaluated using a tumor-centric panel combining the four biomarkers in a single tissue section by sequential immunohistochemistry to derive 16 tumor cell phenotypes. Spatial interactions between individual tumor cells and cytotoxic T cells were studied to determine the immune characteristics of the ecosystem and the impact on response to treatment. HER2-positive tumors displayed individuality in tumor cells and immune cells composition, including intrinsic phenotype dominance which only partially overlapped with molecular intrinsic subtyping determined by PAM50 analysis. This single cell analysis of canonical BC biomarkers deepens our understanding of the complex biology of HER2-positive BC and suggests that individual cell-based patient classification may facilitate identification of optimal responders or resistant individual to HER2-targeted therapies.

BackgroundStratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts. MethodsThis retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint. ResultsIn the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63-4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2-Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22-3.98, p-value = 0.009) between low- and high-risk groups. ConclusionThe results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2-subgroup and the NHG2/ER+/HER2-subgroup. Improved risk stratification of intermediate-risk ER+/HER2-breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

This hypothesis-generating study aims to examine the extent to which computed tomography-assessed body composition phenotypes are associated with immune and PI3K/AKT signaling pathways in breast tumors. A total of 52 patients with newly diagnosed breast cancer were classified into four body composition types: adequate (lowest two tertiles of total adipose tissue [TAT]) and highest two tertiles of total skeletal muscle [TSM] areas); high adiposity (highest tertile of TAT and highest two tertiles of TSM); low muscle (lowest tertile of TSM and lowest two tertiles of TAT); and high adiposity with low muscle (highest tertile of TAT and lowest tertile of TSM). Immune and PI3K/AKT pathway proteins were profiled in tumor epithelium and the leukocyte-enriched stromal microenvironment using GeoMx (NanoString). Linear mixed models were used to compare log2-transformed protein levels. Compared with the normal type, the low muscle type was associated with higher expression of INPP4B (log2-fold change = 1.14, p = 0.0003, false discovery rate = 0.028). Other significant associations included low muscle type with increased CTLA4 and decreased pan-AKT expression in tumor epithelium, and high adiposity with increased CD3, CD8, CD20, and CD45RO expression in stroma (P<0.05; false discovery rate >0.2). With confirmation, body composition can be associated with signaling pathways in distinct components of breast tumors, highlighting the potential utility of body composition in informing tumor biology and therapy efficacies.

Recurrence scores based on a 21-gene assay are clinically useful for predicting prognosis and chemotherapy benefit in postmenopausal node-positive breast cancer patients, but its performance in premenopausal patients is inconsistent. Here, we evaluated Ataraxis Breast RISK (ATX), an AI test that predicts recurrence risk, and compared it with the genomic assay. ATX identified high risk patients misclassified as low risk by the genomic assay and therefore may refine selection of patients for adjuvant chemotherapy.

Metabolic dysfunction is increasingly recognized as a risk factor for poor outcomes in breast cancer, but whether incretin-based therapies confer survival benefit beyond weight loss remains unresolved. Using a federated electronic health record platform spanning nearly 29 million patients, we evaluated breast cancer survival after semaglutide and tirzepatide initiation in routine care. In 1:1 propensity-matched pooled-comparator analyses, semaglutide was associated with improved overall survival versus metformin, sodium-glucose cotransporter 2 (SGLT2) inhibitor, and dipeptidyl peptidase 4 (DPP4) inhibitor users, with 54 deaths among 2,433 semaglutide users (2.2%) versus 395 deaths among 2,433 comparators (16.2%) over 24 months (log-rank P < 0.001). Tirzepatide showed a favorable survival association relative to pooled anti-diabetic comparators that did not meet statistical significance (P = 0.24), with 3 deaths among 220 users (1.4%) versus 64 deaths among 220 comparators (29.1%). In a head-to-head propensity-score-matched comparison, overall survival did not differ significantly between semaglutide- and tirzepatide-treated patients with pre-existing breast cancer (2,117 per arm; P = 0.12). In semaglutide-treated patients alive and observable at the 1-year landmark, higher maximum dose achieved was significantly associated with lower post-landmark mortality (P = 0.034), with an event rate of approximately 1.0% in the high-dose group ([&ge;]1.7 mg) versus approximately 4.5% in the low-dose group (0.25-1.0 mg). Despite a linear dose-weight loss relationship for semaglutide, however, weight-loss strata did not separate survival outcomes (global P = 0.22). In tirzepatide-treated patients alive and observable at the same landmark, neither maximum dose achieved nor weight-loss strata separated post-landmark survival (P = 0.98 and P = 0.50, respectively). Structured EHR and AI-based clinical-note analyses further showed significantly lower frequency of documented metastatic disease in semaglutide-treated patients relative to pooled anti-diabetic comparators, including any metastasis (7.0% versus 15.0%, rate ratio 0.5, P < 0.001), bone metastasis (1.0% versus 5.2%, rate ratio 0.2, P < 0.001), and liver, lung, or brain metastases (all P < 0.001). LLM-derived cause-of-death extraction further showed a 60% lower relative proportion of cancer-associated deaths in semaglutide-treated patients (19% of ascertainable deaths) than in matched pooled anti-diabetic comparators (47% of ascertainable deaths), with comparator deaths more often attributed to cancer progression involving metastatic breast cancer, leptomeningeal carcinomatosis, and cancer-driven organ failure. Overall, this study demonstrates that semaglutide use in patients with pre-existing breast cancer is associated with a dose-correlated but weight-loss independent improvement in overall survival. These findings motivate prospective trials of GLP-1 receptor agonists in breast cancer across various stages and treatment settings.

BackgroundThe ability to predict future risk of cancer development in non-malignant biopsies is poor. Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumor-promoting microenvironmental mechanism that secretes pro-inflammatory paracrine factors. With most work done in non-human models and the heterogenous nature of senescence the precise role of senescent cells in the development of cancer in humans is not well understood. Further, more than one million non-malignant breast biopsies are taken every year that could be a major source of risk-stratification for women. MethodsWe applied single cell deep learning senescence predictors based on nuclear morphology to histological images of 4,411 H&E-stained breast biopsies from healthy female donors. Senescence was predicted in the epithelial, stromal, and adipocyte compartments using predictor models trained on cells induced to senescence by ionizing radiation (IR), replicative exhaustion (RS), or antimycin A, Atv/R and doxorubicin (AAD) exposures. To benchmark our senescence-based prediction results we generated 5-year Gail scores, the current clinical gold standard for breast cancer risk prediction. FindingsWe found significant differences in adipocyte-specific IR and AAD senescence prediction for the 86 out of 4,411 healthy women who developed breast cancer an average 4.8 years after study entry. Risk models demonstrated that individuals in the upper median of scores for the adipocyte IR model had a higher risk (OR=1.71 [1.10-2.68], p=0.019), while the adipocyte AAD model revealed a reduced risk (OR=0.57 [0.36-0.88], p=0.013). Individuals with both adipocyte risk factors had an OR of 3.32 ([1.68-7.03], p<0.001). Alone, 5-year Gail scores yielded an OR of 2.70 ([1.22-6.54], p=0.019). When combining Gail scores with our adipocyte AAD risk model, we found that individuals with both of these risk predictors had an OR of 4.70 ([2.29-10.90], p<0.001). InterpretationAssessment of senescence with deep learning allows considerable prediction of future cancer risk from non-malignant breast biopsies, something that was previously impossible to do. Furthermore, our results suggest an important role for microscope image-based deep learning models in predicting future cancer development. Such models could be incorporated into current breast cancer risk assessment and screening protocols. FundingThis study was funded by the Novo Nordisk Foundation (#NNF17OC0027812), and by the National Institutes of Health (NIH) Common Fund SenNet program (U54AG075932).

PurposeTo test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. MethodsWe conducted a multi-cohort study including CALGB 9344 (anthracycline {+/-} paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrells C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. ResultsA total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. ConclusionHistology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit. HighlightsO_LIHistology-derived H&E signatures consistently predicted recurrence risk across randomized trials and real-world cohorts. C_LIO_LIA single cutoff of a low-risk histology signature predicted taxane benefit and dose-dense chemotherapy benefit. C_LIO_LICombined clinical-histology models identified low-risk groups with 2%-10% risk of distant recurrence. C_LI

BackgroundDuctal carcinoma in situ (DCIS) is a noninvasive breast lesion with variable risk of progression to invasive breast cancer (IBC). Current transcription and cell marker investigations suggest ECM decreases in later events but are limited in details of ECM proteomic composition, including post-translational modifications. We investigated whether the extracellular matrix (ECM) proteome alters with later breast events of DCIS or IBC. MethodsECM-targeted mass spectrometry imaging and liquid chromatography-tandem mass spectrometry (LC-MS/MS) were applied to ten tissue microarrays from the Resource of Archival Human Breast Tissue cohort (RAHBT). Primary DCIS specimens (n=136) were analyzed in relation to later events of DCIS (n=40) or IBC(n=30), with a mean follow-up of 192.1 months 95% CI [179.1,205.1]. Statistical modeling, survival analyses, and exploratory machine learning approaches were used to identify ECM peptide signatures associated with later events. ResultsDistinct ECM peptide profiles were associated with later events of DCIS or IBC. Fifteen peptides derived from fibrillar collagens (COL1A1, COL1A2, COL3A1) and elastin, showed significantly reduced abundance in patients who developed IBC. Lower expression of specific collagen peptides associated with overall 19.9% 95% CI [17.92, 21.81] decreased disease-free survival for IBC. Lower expression of these peptides was significantly associated with reduced disease-free survival (age-adjusted hazard ratio [HR] = 2.45, 95% CI: 2.33-2.57; P < 0.05). Patient-matched samples of primary DCIS, later DCIS, and later invasive breast cancer further demonstrated reduction in ECM peptide detection. Exploratory predictive modeling from patient-matched samples achieved high performance (AUROC >0.98, accuracy >93%) in distinguishing primary from later events. Following prior work in the RAHBT cohort, reduction of certain collagen peptides was also observed in primary DCIS samples from higher risk patient groups. ConclusionsECM proteomic remodeling, particularly decreases of specific collagen domains, is strongly associated with later events of DCIS and IBC. These findings highlight ECM proteome as a critical regulator of breast cancer emergence with potential as a prognosticator of risk stratification to guide clinical management of DCIS.

IntroductionThe mammogram risk score (MRS), an AI-driven texture feature derived from digital mammograms, strongly predicts breast cancer risk independently of breast density, though underlying mechanisms remain unclear. This study investigated relationships between established breast cancer risk factors, covering anthropometrics, reproductive factors, family history, and mammographic density metrics, and MRS. MethodsUsing data from the Nurses Health Study II (292 cases, 561 controls), we validated MRSs association with breast cancer using logistic regression and evaluated its relationships with risk factors through: linear regressions of MRS on observed risk factors and polygenic scores associated with risk factors, and Mendelian randomization (MR) analysis via two-stage least squares regression. We conducted two-sample MR of MRS using summary statistics from genome-wide association studies of risk factors. ResultsMRS was significantly associated with breast cancer risk before adjustment for BI-RADS density (OR=1.92 per SD increase in MRS; 95%CI:1.57-2.33; AUC=0.69) and after (OR=1.85; 95%CI:1.49-2.30). Early life body size and adult body mass index (BMI) were inversely associated with MRS, while history of benign breast disease and BI-RADS density showed positive associations; after adjusting for BI-RADS density, associations between MRS and the other three risk factors attenuated. Higher polygenic score for dense area was associated with increased MRS ({beta}=0.16 SD increase in MRS per SD increase in polygenic score; 95%CI: 0.06-0.25), as was percent density ({beta}=0.14; 95%CI:0.05-0.23). Two-sample MR identified associations between genetically predicted dense area ({beta}=0.83 SD increase in MRS per SD increase in dense area; 95%CI:0.39-1.27) and percent density ({beta}=1.14; 95%CI:0.55-1.74) with MRS. After adjusting for BI-RADS density and BMI, higher waist-to-hip ratio was significantly associated with increased MRS in polygenic score and two-sample MR analyses. No significant associations were observed with other risk factors. ConclusionWe validated MRSs association with breast cancer risk in cases diagnosed 0.5-10.1 years (median 2.6) after mammogram acquisition. Our findings reveal robust associations between breast density measures and MRS and suggest a potential impact of central obesity on MRS. Future larger-scale studies are crucial to validate these results and explore their potential to enhance our understanding of breast cancer etiology and refine risk prediction models.

IMPORTANCEIn young-onset breast cancer, a diagnosis within 5-10 years of childbirth associates with increased mortality. Women with germline BRCA1/2 pathogenic variants (PVs) are more likely to be diagnosed with breast cancer at younger ages, but the impact of childbirth on mortality is unknown. OBJECTIVEDetermine whether time between recent childbirth and breast cancer diagnosis impacts mortality among young-onset breast cancer patients with germline BRCA1/2 PVs. DESIGN, SETTING, AND PARTICIPANTSThis prospective cohort study includes 903 women with germline BRCA1/2 PVs diagnosed with stage I-III breast cancer at [&le;]45 years of age, between 1950-2021 in the UK. MAIN OUTCOMES AND MEASURESThe primary outcome is all-cause mortality, censored at 20 years post-diagnosis. The primary exposure is time between most recent childbirth and breast cancer diagnosis, with recent childbirth defined as >0-<10 years post childbirth (n=419)], further delineated to >0-<5 years (n=228) and 5-<10 years (n=191). Mortality of nulliparous cases (n=224) was compared to the recent postpartum groups and the [&ge;]10 years postpartum (n=260) group. Cox proportional hazards regression analyses were adjusted for patient age, tumor stage, further stratified by tumor estrogen receptor (ER) and BRCA gene status. RESULTSFor all BRCA PV carriers, increased all-cause mortality was observed in women diagnosed >0-<10 years postpartum, compared to nulliparous and [&ge;]10 years groups, demonstrating the transient duration of postpartum risk. Risk of mortality was greater for ER-positive cases in the >0-<5 group [HR=2.35 (95% CI, 1.02-5.42)] and ER-negative cases in the 5-<10 group [HR=3.12 (95% CI, 1.22-7.97)] compared to the nulliparous group. Delineated by BRCA1 or BRCA2, mortality in the 5-<10 group was significantly increased, but only for BRCA1 carriers [HR=2.03 (95% CI, 1.15-3.58)]. CONCLUSIONS AND RELEVANCEYoung-onset breast cancer with germline BRCA PVs confers increased risk for all-cause mortality if diagnosed within 10 years of childbirth, with risk highest for ER+ cases at >0-<5 years postpartum, and for ER-cases at 5-<10 years postpartum. BRCA1 carriers are at highest risk for poor prognosis when diagnosed at 5-10 years postpartum. No such associations were observed for BRCA2 carriers. These results should inform genetic counseling, prevention, and treatment strategies for BRCA PV carriers. Key PointsO_ST_ABSQuestionC_ST_ABSIs a postpartum diagnosis an independent risk factor for mortality among young-onset breast cancer patients with germline BRCA1/2 PVs? FindingsA diagnosis <10 years postpartum associates with higher risk of mortality compared to nulliparous and [&ge;]10 years postpartum cases. Peak risk after childbirth varies for ER-positive (>0-<5 years) vs. ER-negative cases (5-<10 years). BRCA1 carriers had peak risk of mortality 5-10 years postpartum, with no associations observed for BRCA2 carriers. MeaningA breast cancer diagnosis within 10 years of childbirth independently associates with increased risk for mortality in patients with germline BRCA1/2 PVs, especially for carriers of BRCA1 PVs.

BackgroundThe tumor microenvironment (TME) significantly impacts breast cancer progression, with stromal and immune components influencing tumor behavior. The scavenger receptor SCARA5 is recognized as a tumor suppressor in various cancers, but its role in breast cancer remains uncertain. MethodsWe performed integrative transcriptomic analyses of bulk RNA-seq data from TCGA-BRCA, validated with GTEx, to investigate SCARA5 expression and its clinical significance. Differential expression, PAM50 subtype classification, PCA/t-SNE clustering, and ROC analyses were used to assess diagnostic potential. Functional enrichment (GO, KEGG, Reactome), PPI networks, and co-expression analyses explored pathways related to SCARA5. Single-cell transcriptomic datasets (TISCH2, Broad Portal) and spatial profiling (Human Protein Atlas) were employed to examine cellular and spatial localization. The prognostic importance was assessed using GEPIA2 survival analysis. ResultsSCARA5 was significantly downregulated in breast tumors, especially in Her2-enriched and Luminal B subtypes, with ROC curves confirming its diagnostic importance. Enrichment and PPI analyses linked SCARA5 to lipid metabolism, immune regulation, and scavenger receptor pathways. Co-expression studies showed associations with lipid metabolism genes (FABP4, ADIPOQ, CD36) and immune-related genes (CLEC3B, LYVE1). Single-cell data indicated SCARA5 expression was limited to fibroblasts, endothelial cells, and immune subsets, with rare expression in malignant epithelial cells. Spatial analysis confirmed stromal enrichment, mainly in areas rich in fibroblasts and endothelial cells. Survival analysis demonstrated worse outcomes in patients with HER2+ and Luminal B breast cancers who had low SCARA5 expression. ConclusionSCARA5 is a stromal-enriched gene with potential tumor-suppressive and immunometabolic regulatory roles in breast cancer. Its diagnostic and prognostic significance, especially in aggressive subtypes, highlights its potential as a biomarker and therapeutic target within the tumor stroma.

The prognostic significance of tumor-infiltrating lymphocytes (TILs) in breast cancer has been recognized for over a decade. Although histology-based scoring recommendations exist to standardize visual TILs assessment, interobserver agreement and reproducibility are hampered by heterogeneous infiltration patterns, highlighting the importance of computational approaches. Despite advances to automate TILs quantification, adoption of computational models has been hindered by lack of consensus on scoring methods and lack of large-scale benchmarks. To address these limitations, we launched the international TIGER challenge, a public competition to build open-source computational TILs (cTILs) models in digital pathology. Here, we present the largest comprehensive multi-centric validation of multiple cTILs methods on surgical resections and biopsies using 3,708 Triple Negative Breast Cancer (TNBC) and human epidermal growth factor receptor 2 positive (HER2+) breast cancers from clinical practice and phase 3 clinical trials. We report benchmarks on image analysis performance of each method and show the strong agreement of cTILs with panels of pathologists. We show the positive association of cTILS with response after neoadjuvant therapy in HER2-positive, superior to visually scored TILs. We also show that cTILs add independent information to clinical variables in surgically resected TNBC but not in HER2-positive disease and breast biopsies.

Aggressive breast cancers portend a poor prognosis, but current polygenic risk scores (PRSs) for breast cancer do not reliably predict aggressive cancers. Aggressiveness can be effectively recapitulated using tumor gene expression profiling. Thus, we sought to develop a novel PRS for the risk of recurrence score weighted on proliferation (ROR-P), an established prognostic signature. Using 2,363 breast cancers with tumor gene expression data and single nucleotide polymorphism (SNP) genotypes, we examined the associations between ROR-P and known breast cancer susceptibility SNPs using linear regression models. We constructed PRSs based on varying p-value thresholds and selected the optimal PRS based on model r2 in 10-fold cross-validation. We then used Cox proportional hazards regression to test the ROR-P PRSs association with breast cancer-specific survival in two independent cohorts totaling 10,196 breast cancers and 785 events. In meta-analysis of these cohorts, higher ROR-P PRS was associated with worse survival, HR per SD = 1.13 (95% CI 1.05-1.21, p=0.001). The ROR-P PRS had a similar magnitude of effect on survival as a comparator PRS for estrogen receptor (ER)-negative versus positive cancer risk (PRSER-/ER+). Furthermore, its effect was minimally attenuated when adjusted for PRSER-/ER+, suggesting that the ROR-P PRS provides additional prognostic information beyond ER status. In summary, we used a novel approach based on integrated analysis of germline SNP and tumor gene expression data to construct a PRS associated with aggressive tumor biology and worse survival. These findings could potentially enhance risk stratification for breast cancer screening and prevention.

Purpose: There are no clinically validated biomarkers to assess recurrence risk and guide treatment de-escalation in Basal-like and HER2-enriched breast cancer. Taxane-based chemotherapy remains a cornerstone of treatment despite significant toxicity. We evaluated the prognostic and predictive utility of the MHCII Immune Activation Score (IA Score) in these subtypes. Experimental Design: We retrospectively analyzed Basal-like and HER2-enriched breast cancers from the NCIC CTG MA.21 trial, which randomized patients with node-positive or high-risk node-negative disease to adjuvant chemotherapy with or without taxanes. MA.21 predated immune checkpoint inhibitors and routine HER2-targeted therapy. Subtype was previously assigned by PAM50. The 36-gene MHCII-IA assay used RNA from formalin-fixed, paraffin-embedded tissue. Multivariable Cox and Kaplan-Meier analyses evaluated associations between IA Score, clinicopathologic variables, tumor-infiltrating lymphocytes (TILs), relapse-free survival (RFS), and taxane benefit. Results: Among Basal-like (N=317) and HER2-enriched (N=155) tumors, higher IA Score was associated with improved RFS independent of lymph node status and provided stronger prognostic discrimination than TILs. Node-negative patients with high IA Score had excellent outcomes (8-year RFS >90%) versus those with low IA Score (8-year RFS <76%). In node-positive disease, high IA Score increased 8-year RFS by >10% relative to low IA Score. IA Score stratified taxane benefit: node-positive IA-low patients benefited, whereas IA-high tumors had favorable outcomes regardless of regimen. Conclusions: MHCII Immune Activation Score is a prognostic and predictive biomarker in Basal-like and HER2-enriched breast cancer. High IA Score identified patients with excellent outcomes before pembrolizumab, trastuzumab, and taxane-based treatment escalation, providing a rationale for prospective risk-adapted de-escalation strategies.

IntroductionWhile 2D mammography is the standard for breast cancer screening, its sensitivity is reduced in dense breasts, impacting early detection and extent assessment. This study, for the first time in New Zealand, evaluated the utility of supplementary multi-modality imaging (tomosynthesis, ultrasound, and MRI) in a risk-stratified population. MethodsA retrospective case study (May 2022 - September 2023, New Zealand private clinic) analysed patients by Tyrer-Cuzick (TC) v8 lifetime risk and by Volpara density categories. All patients in the screening pathway (n = 2171) underwent 2D mammography and tomosynthesis. Those patients with high breast density received supplementary ultrasound, and those with TC8 risk scores of [&ge;] 30% were offered abbreviated MRI. Symptomatic patients (n = 230) underwent standard diagnostic workup. Detection rates and extent of disease using multimodality imaging were compared. ResultsOf the 2401 patients, 205 were high-risk criteria ([&ge;]30%) and 19 breast cancers (16 invasive, 3 DCIS only) were diagnosed. Tomosynthesis identified 38% (6/16) more invasive cancers than 2D mammography alone. Ultrasound and MRI detected an additional 27% (4/16) invasive cancers occult on other modalities, predominantly in those women with density D breasts. Ultrasound and particularly MRI demonstrated superior accuracy in assessing disease extent, including identifying multifocal and multicentric disease that was not detected by 2D or Tomosynthesis. ConclusionSupplementary screening modalities, particularly MRI, significantly improve breast cancer detection and assessment of disease extent in high-risk women. These findings support a personalized screening approach integrating risk assessment and breast density to guide imaging modality selection.