Breast Cancer Research

PurposeTriple-negative breast cancer (TNBC) exhibits substantial clinical heterogeneity, with some patients experiencing early recurrence and poor survival despite similar clinicopathologic features. We sought to determine whether quantitative measures of intratumoral collagen architecture and composition derived from standard histopathologic specimens can identify patients at risk of recurrence and adverse survival outcomes. Experimental DesignWe analyzed a retrospective cohort of 79 TNBC tumors assembled into a tissue microarray using a multimodal computational pathology framework integrating Massons Trichrome staining with COL1 and COL3 immunohistochemistry. Collagen architecture was quantified using fiber-based image analysis and unsupervised clustering, while collagen composition was assessed using a normalized COL3:COL1 ratio. Associations with recurrence-free interval (RFI) and overall survival (OS) were evaluated using Kaplan-Meier analysis, restricted mean survival time (RMST), and Cox proportional hazards modeling. ResultsUnsupervised analysis identified four distinct collagen architectural states, which were consolidated into low-risk and high-risk groups based on recurrence patterns. High-risk collagen architecture was associated with significantly worse long-term RFI (log-rank p=0.025; RMST difference 10.1 months). Independently, a higher COL3:COL1 ratio was associated with improved OS (log-rank p=0.042; RMST difference 9.4 months). Integration of architectural and compositional biomarkers further refined risk stratification, identifying a subgroup with high-risk architecture and low COL3:COL1 ratio that exhibited the poorest survival outcomes. Notably, collagen-based stratification identified patients with divergent outcomes not readily predicted from tumor stage alone. ConclusionsQuantitative assessment of intratumoral collagen architecture and composition provides clinically meaningful prognostic information in TNBC and enables stratification of recurrence and survival risk. These findings support extracellular matrix phenotyping as a practical and scalable computational pathology approach for refining risk assessment in TNBC. Translational RelevanceTriple-negative breast cancer (TNBC) remains clinically challenging due to heterogeneous outcomes that are not fully captured by standard clinicopathologic variables. In this study, we demonstrate that quantitative features of intratumoral collagen architecture and composition, derived from routine pathology specimens, provide clinically meaningful prognostic information. Collagen-based biomarkers, including distinct collagen architectural phenotypes and the COL3:COL1 ratio, identify patient subgroups with distinct recurrence and survival outcomes, particularly among individuals whose risk is not adequately predicted by conventional staging. Importantly, these features can be extracted from widely available histological stains and immunohistochemistry, supporting the potential integration into existing pathology workflows. These findings support the tumor microenvironment as an underutilized source of biomarkers and suggest that extracellular matrix-based phenotyping may improve risk stratification and inform clinical decision-making in TNBC.

Purpose: The prognostic role of tumor-infiltrating lymphocytes in luminal breast cancer remains uncertain, partly because density-based metrics do not capture spatial interactions between immune cell subsets. We developed a density-independent spatial metric quantifying macrophage-T cell proximity and assessed its prognostic value. Experimental Design: Using multiplex immunohistochemistry across three breast cancer cohorts (exploratory, n = 17; discovery, n = 687; validation, n = 305), we measured nearest-neighbor distances from T cells to M1-like and M2-like macrophages, benchmarked against a randomly subsampled total macrophage pool. We defined the Macrophage Spatial Polarity Index (MSPI) as the difference between M2-to-T cell and M1-to-T cell affinity scores, where higher values reflect an M2-dominated spatial phenotype. Cox regression was used to assess associations with distant disease-free survival (discovery) and overall survival (validation). Results: M2-like macrophages preferentially localized near T cells, independent of cell density. Higher MSPI was associated with shorter survival in luminal cancers (discovery: HR = 1.45, p < 0.001), with the strongest effect in young women with early-stage disease (HR = 2.16, p < 0.0001). MSPI remained independently prognostic after adjustment for stage, systemic treatment, and diagnosis period (HR = 2.31, 95% CI 1.73-3.09, p < 0.0001) and was non-significant in HER2-positive and triple-negative subtypes. Validation in an independent ER-positive cohort confirmed the finding (HR = 1.30, p = 0.004). Pooled analysis yielded HR = 2.13 (95% CI 1.68-2.70, p = 3.45 x 10-10). Conclusions: MSPI is a robust prognostic biomarker in luminal breast cancer, particularly in young women with early-stage disease, warranting further validation for risk stratification and therapeutic guidance.

Purpose: Beyond estrogen receptor (ER) positivity, no genomic biomarker reliably identifies ER+ breast cancer patients who derive differential benefit from endocrine therapy (ET). We performed an unbiased genomic screen to discover genes predicting ET response and characterized the top candidate across clinical settings, treatment modalities, and an independent validation cohort. Experimental Design: We screened 240 genes in 1,197 metastatic ET-treated patients from the MSK-CHORD clinical genomics database using Cox proportional hazards regression with false discovery rate (FDR) correction. The top candidate, core-binding factor subunit beta (CBFB), was characterized across four cohorts defined by disease setting (metastatic/adjuvant) and treatment (ET/chemotherapy), with multivariable adjustment, gene-by-treatment interaction testing, left-truncation sensitivity analysis for guarantee-time bias, and external validation in METABRIC (N = 1,499 ER+). Results: CBFB mutations (prevalence, ~5%) were the only gene associated with improved time to progression (TTP). In metastatic ET patients, CBFB-mutated tumors (n = 80) demonstrated significantly longer TTP (hazard ratio [HR], 0.44; 95% CI, 0.29-0.67; P = .0002, FDR q = .010) with no chemotherapy benefit (HR, 1.16; P = .65). The gene-by-treatment interaction was significant (HR, 0.37; P = .009). Effects were robust to multivariable adjustment (HR, 0.46-0.50), independent of histology, and preserved under left-truncated Cox regression (HR, 0.38). In the adjuvant setting, CBFB mutations predicted improved recurrence-free survival (HR, 0.52; 95% CI, 0.31-0.85; P = .010), with no effect under chemotherapy. In METABRIC, CBFB mutations predicted improved ER+ overall survival (HR, 0.52; P = 9.3e-5). Conclusions: CBFB mutations identify ~5% of ER+ breast cancers with exceptional ET benefit. As CBFB is included on all major cancer gene panels, this biomarker requires no additional testing infrastructure for clinical implementation.

BackgroundTriple-negative breast cancer (TNBC) is a clinically aggressive breast cancer subtype. It is a heterogeneous disease that remains difficult to stratify and that still lacks durable and biomarker-guided therapeutic options. Low expression of the tumour suppressor MTUS1 is associated with aggressive breast cancer features, but the biological properties of MTUS1-low TNBC remain insufficiently defined. Our goal was to determine whether low MTUS1 expression defines shared proliferative and stress-adaptation mechanisms that could guide candidate therapeutic strategies and corresponding target/drug pairs in MTUS1-low TNBC. MethodsWe labelled tumours from seven public TNBC RNA-seq cohorts based on the lowest and highest MTUS1 expression tertiles. Differential gene expression was analysed using gene set enrichment analysis (GSEA) on the Hallmark pathway database to identify deregulated biological pathways between MTUS1-low TNBC tumours and their MTUS1-high counterparts. Reproducibility was examined across independent TNBC cohorts and secondarily in broader breast cancer and selected TCGA tumour cohorts. Gene essentiality scores from CRISPR-Cas9 experiments in TNBC cell-line models were correlated to MTUS1 expression in these cell lines, to propose therapeutic strategies and their corresponding candidate target/drug pairs. ResultsMTUS1-low tumours showed a reproducible pathway-level proliferation mechanism driven by the MYC oncogene and sustained by up-regulated oxidative phosphorylation, combined with stress adaptation mechanisms involving unfolded protein response (UPR), and DNA repair Hallmark gene sets. Based on CRISPR data, we propose 3 therapeutic strategies: (1) targeting MYC to reduce its transcriptional activity, (2) targeting proteins from UPR, (3) targeting DNA-repair. We also propose corresponding candidate target/drug pairs to allow experimental validation of these strategies. ConclusionsProliferation in low MTUS1 TNBC is driven by MYC and stress-adaptation mechanisms. By linking this tumour profile to CRISPR-derived dependency signals, our analysis prioritises experimentally testable target-pathway hypotheses centred on MYC, UPR/proteostasis, and DNA-repair or checkpoint control. Although the proposed therapeutic strategies and candidate targets remain to be experimentally tested, the latter finding is consistent with published work showing that ATIP3-deficient TNBC cell line models are sensitive to inhibition of the WEE1 PKMYT1 G2/M checkpoint kinases.

BackgroundThe ketogenic diet is being explored as an adjuvant intervention in breast cancer because it lowers circulating glucose and elevates ketone bodies such as {beta}-hydroxybutyrate (BHB), but how individual ER+ breast cancer subtypes adapt to these conditions remains poorly characterized. We examined metabolic responses to BHB supplementation under glucose restriction in two ER+ breast cancer cell lines, asking whether metabolic adaptation patterns differ between models. MethodsMCF-7 and T47D cells were cultured under high glucose, glucose-restricted (5% of standard), or glucose-restricted with 10 mM BHB conditions and profiled by comprehensive two-dimensional gas chromatography-mass spectrometry (GCxGC-MS). Pairwise Welchs t-tests with Benjamini-Hochberg false discovery rate (FDR) correction were applied to identify treatment-responsive metabolites. Targeted assays quantified intracellular glycine, SHMT1 protein, and total branched-chain amino acid (BCAA) concentrations across a BHB dose range (2.5-15 mM). Patient tumor transcriptomic data from TCGA (n=1,084) and paired tumor-normal samples from GSE58135 (n=20) were analyzed for genes involved in one-carbon, ketone body, and BCAA metabolism. ResultsMCF-7 and T47D cells exhibited markedly divergent metabolic responses to BHB. In MCF-7 cells, BHB supplementation produced a broad pattern-level metabolic shift: 75% of detected metabolites trended upward when BHB was added to glucose-restricted cultures (C vs. B comparison), with 1,4-butanediol reaching nominal significance (FC=2.35, p=0.016) and a 4.1-fold trend increase in lactic acid (p=0.11), although no individual metabolite survived FDR correction. T47D cells showed essentially no metabolic response to BHB at the global level. Targeted assays detected an elevation in glycine at 5 mM BHB in both cell lines that did not follow a monotonic dose response and was not accompanied by changes in SHMT1 protein expression. Total BCAA levels were elevated by BHB in T47D cells but remained unchanged in MCF-7 cells. In paired patient samples, OXCT1 (log2FC = -1.41), SHMT1 (log2FC = -1.31), and ACAT1 (log2FC = -1.07) were significantly downregulated in ER+ tumors relative to matched normal tissue (adjusted p < 0.001 for all three). ConclusionsER+ breast cancer cell lines show heterogeneous metabolic responses to BHB supplementation under glucose restriction. The broad pattern of metabolite elevation in MCF-7 but not T47D cells suggests that capacity to utilize ketone bodies as metabolic substrate varies between ER+ models. The downregulation of OXCT1, ACAT1, and SHMT1 in ER+ tumors compared to normal tissue identifies these enzymes as candidate biomarkers that may help stratify which patients are likely to benefit from ketogenic interventions. Findings related to individual metabolites should be regarded as exploratory and require validation in larger, adequately powered cohorts.

BackgroundBreast cancer exhibits extensive molecular heterogeneity across intrinsic subtypes, yet convergent metabolic reprogramming may represent an obligate feature of tumour initiation. We hypothesised that suppression of nuclear-encoded mitochondrial fatty acid oxidation (FAO) constitutes such a convergence point, defining a shared metabolic phenotype independent of subtype. MethodsRNA-seq data from 1,106 primary breast tumours and 113 normal-adjacent tissues (TCGA-BRCA) were intersected with 1,079 nuclear-encoded mitochondrial genes from MitoCarta 3.0. Differential expression was assessed using Welch t-test with Benjamini-Hochberg correction at all tumour stages, at Stage I specifically, and stratified across PAM50 subtypes. A 55-gene core FAO signature was derived by three-way intersection. Ten candidate genes were selected by pre-specified objective scoring, locked before any clinical testing. Gene set enrichment analysis (GSEA) was performed using MitoCarta 3.0 pathway annotations. Diagnostic performance, clinical associations, survival, and mutation independence were characterised. External validation used two independent GEO cohorts (GSE42568, n = 121; GSE109169, n = 50); prognostic validation used METABRIC (Molecular Taxonomy of Breast Cancer International Consortium; n = 1,980). DESeq2 was applied as methodological cross-validation. ResultsAmong 126 differentially expressed mitochondrial genes, fatty acid oxidation was the most significantly depleted pathway (normalised enrichment score -2.130; false discovery rate 0.001). The 55-gene core signature replicated in both external cohorts with 100% directional concordance (hypergeometric p < 10-{superscript 1}). All 10 candidate genes discriminated tumour from normal tissue (area under the curve 0.915-0.979) and demonstrated broad clinical associations. The composite FAO suppression score predicted overall survival in METABRIC (log-rank p = 7.82 x 10-) and MAOA achieved independent prognostic significance in multivariable Cox regression (hazard ratio 0.890; adjusted p = 0.009). DESeq2 cross-validation confirmed Spearman {rho} = 0.980 concordance. ConclusionsNuclear-encoded FAO suppression is a robust, pan-subtype feature of breast cancer detectable at Stage I and validated across independent platforms and cohorts. These 10 candidate genes constitute a consistent initiation-phase mitochondrial signature, implicating FAO suppression as a potential convergence point in breast cancer oncogenesis and motivating targeted functional investigation.

Objective Accurate prognostication of recurrence risk in HR+/HER2- early breast cancer is central for therapeutic decision-making, including identifying patients who may safely avoid adjuvant systemic therapy. However, the performance of existing prognostic tools remains insufficient for effective clinical stratification, motivating the development of artificial intelligence (AI)-based methods to improve risk stratification. Methods Ataraxis Breast CTX (ATX) is a multi-modal AI test that integrates H&E-stained whole-slide images with clinicopathologic features to predict risk of recurrence for individual patients. This study aims to validate ATX in an external dataset enriched for clinically low-risk patients from Dordrecht, the Netherlands. ATX scores were generated for 892 women diagnosed with early HR+/HER2- breast cancer. Of the 892 patients, 299 did not receive adjuvant systemic therapy. The discriminative performance of ATX was assessed using C-index and its stratification ability was evaluated by log-rank tests comparing Kaplan-Meier survival curves across risk groups. Results ATX achieved a C-index of 0.71 and a 5-year time-dependent AUC of 0.71, demonstrating strong discrimination in predicting recurrence-free survival (RFS). Among 299 patients who received no adjuvant therapy, ATX achieved a C-index and time-dependent AUC of 0.78 and 0.81 respectively, suggesting ATX retains prognostic information in the absence of systemic therapy. ATX scores were used to stratify patients into risk groups using a pre-specified threshold, where 656 (74%) were classified as ATX low-risk and 236 (26%) were classified as high-risk. Notably, untreated and treated ATX low-risk patients had comparable 5-year RFS (untreated: 5-year RFS = 96%, 95% CI = 92-97%; treated: 5-year RFS = 96%, 95% CI = 93-97%) with near identical 10-year RFS (86%, 95% CI = 83-92% for both), suggesting ATX low-risk status may identify a subgroup with favorable prognosis independent of treatment exposure. Conclusion ATX provides robust prognostic stratification in an external cohort of clinically low-risk HR+/HER2- early breast cancer and identifies a subgroup of patients who did not receive systemic therapy with favorable observed outcomes. These results support prospective validation of ATX as a decision-support tool for adjuvant therapy de-escalation in HR+/HER2- early breast cancer.

Introduction: With recent approvals of multiple targeted therapies for triple-negative breast cancer (TNBC), including antibody-drug conjugates and immunotherapy in biomarker-selected populations, it is critical to define the temporal evolution of cell-surface target expression from early-stage to metastatic disease, the co-expression patterns across these markers, and optimal quantification methodologies. Here we report biomarker expression profiles measured by multi-omics and pathology-based platforms in patients with TNBC using a large cohort of matched longitudinal tumor samples. Methods: Patients who underwent neoadjuvant chemotherapy (NAC) for stage I-III TNBC or were diagnosed with any stage TNBC and developed metastatic recurrence were retrospectively identified from an institutional database and prospective research metastatic biopsy protocol. Tumor samples from diagnosis (DX), residual disease (RD) post-NAC (if applicable), and metastasis/recurrence (MR) were collected. Quantification of HER2, TROP2, and PD-L1 expression was performed by immunohistochemistry (IHC), whole-exome sequencing, transcriptome sequencing, and targeted mass spectrometry (MS). For HER2, TROP2, and stromal tumor-infiltrating lymphocytes (sTILs), both manual pathologist assessment and computational pathology quantification were obtained. HER2 status was categorized as HER2-0 or HER2-low by local (L-IHC) and central (C-IHC) review, TROP2 status was defined as low (H-score <100), medium (H-score 100-200) or high (H-score >200), and PD-L1 as low (tumor area positivity, TAP <5%) or high (TAP [&ge;]5%). Pathologist-assessed sTILs were classified as low (<10%), medium ([&ge;]10% and <40%) or high ([&ge;]40%). Biomarkers were compared between primary (DX/RD) and MR, and between pre- vs post-NAC (DX-RD) samples. Correlations between markers, quantification methods, inferred PAM50 subtype, and clinical variables of interest were evaluated. Results: A total of 359 samples from 110 patients with TNBC with data available from at least one platform were included in the analysis. HER2-low prevalence at DX, RD, and MR was: 51% (50/98), 40% (21/53), and 27% (16/60); TROP2 high/medium was 90% (47/52), 91% (42/46), and 88% (28/32); PD-L1-high was 51%, 50%, and 38% (9/24); and sTILs-high/medium was 88% (59/67), 80% (40/50), and 49% (17/35), respectively. While TROP2-high/medium vs low remained stable over time, HER2 IHC and sTILs significantly decreased from DX/RD to MR samples, both at the cohort-level (HER2, p=0.0081; sTILs, p=4.6x10e-5) and longitudinal patient-level (HER2, p=0.030; sTILs, p=0.0077), with a similar decreasing trend for PD-L1 that did not reach statistical significance. HER2 concordance (0 vs low) between L-IHC and C-IHC was 78% (91/116). ERBB2, TACSTD2 and CD274 mRNA expression were significantly correlated with IHC protein levels, though only TACSTD2 had limited overlap in distribution of gene expression between high/medium vs low groups. Strong correlation between protein membrane staining intensity from computational pathology, protein expression measured by MS, and pathologist-assed IHC was observed across all biomarkers tested by each method. In comparisons between biomarkers, pathologist-assessed PD-L1 IHC and sTILs were significantly correlated (p=0.0001); 94% (51/54) of PD-L1-high tumors were classified as sTILs high/medium. PAM50 subtype was not significantly correlated with time point or biomarker status, although there was a trend toward more HER2-enriched tumors in HER2-low (20%, 5/25) vs HER2-0 (6%, 3/52) (p=0.086). Across biomarkers and clinical variables, an association between age and sTILs was observed (p=0.038, FDR=0.42) due to a decrease in sTILs high/medium tumors with age, primarily driven by post-treatment (RD/MR) but not DX samples. Conclusions: Multi-platform and multi-omics profiling in this large unique cohort of longitudinal TNBC samples revealed distinct patterns of expression and dynamic changes of key biomarkers of interest for targeted therapies. Given variability with manual IHC scoring, improved methods for quantification of expression may help optimize treatment selection in an individualized manner.

INTRODUCIONTumor cell infiltration in regional lymph nodes is a strong prognostic marker, guiding treatment decisions in breast cancer. While the immune cell composition in primary tumors has been more widely explored in later years, the immune cell composition of the sentinel node (SN) and axillary lymph nodes (ALN) remains understudied. A better understanding of how primary tumor and metastatic tumor cells alter the nodal immune microenvironment can shed light on metastasis and cancer progression to unveil new treatment strategies. MATERIALS AND METHODSFrom a prospective clinical cohort of 458 treatment-naive patients with primary operable breast cancer, we performed comprehensive immunophenotypic analysis using mass cytometry analysis of non-metastatic (SN-) and metastatic (SN+) and ALN (ALN+) lymph nodes. RESULTSAs expected, patients with ALN+ cases had a shorter time to distant metastases than SN+ and SN- cases. We identified an exhausted T-cell phenotype and an increase in Germinal Center B (GC B) cells and plasma cells in ALN+ samples compared to SN- samples, both in the whole cohort as well as when investigating estrogen-receptor positive (ER+) patients only. There were no differences in immune cell composition across breast cancer (BC) subtypes within SN-samples. SN+ samples from triple negative BC (TNBC) showed a trend towards increased abundance of GC B and plasma cells, similar to more advanced ALN+, suggesting that smaller TN metastases may trigger an immune activation at an early stage of dissemination. Further analysis of SN- samples from ER+ patients revealed a subset of patients where the immune response had a more exhausted T-cell phenotype. This group was enriched for lymph nodes that were deemed negative by ordinary pathology examination (microscopy) but had detectable tumor cells by CyTOF analysis. CONCLUSIONThe immune profiles of SN and ALN samples from breast cancer patients are highly diverse, showing limited associations to BC subtype, clinical parameters or patient outcome. Metastatic tumor cells play a significant role in driving T-cell exhaustion and immunosuppression. Notably, in approximately 50% of the ER+ samples, T-cell exhaustion was detectable. This coincides with the presence of tumor cells identified by CyTOF, which were likely missed by conventional pathological examination. These findings suggest that small tumor deposits alter the immune composition, and the immune profile reveals the presence of tumor cells.

Invasive lobular carcinoma (ILC) is the most frequently diagnosed special histological subtype of invasive breast cancer and accounts for 10 - 15% of all cases. The pathognomonic hallmark of ILC is the genetic loss of E-cadherin (CDH1) causing the disruption of adherens junctions and resulting in discohesive, linear growth. To better understand the role of E-cadherin in ILC metastasis, we generated three ILC cell lines, MDA-MB-134-VI, SUM44PE, and BCK4, with inducible E-cadherin expression, resulting in successful restoration of functional adherens junctions. E-cadherin expression reduced growth in 2D culture, and that effect was even greater in 3D ultra-low attachment (ULA) conditions where increased cell death was consistent with the previously described role of E-cadherin in anoikis. E-cadherin expression did not rescue the lack of migration and invasion of ILC cell line models; however, it decreased haptotaxis and increased adherence to Collagen I in SUM44 cells. There was no significant effect of E-cadherin expression on primary orthotopic tumor growth, but spontaneous metastasis to the reproductive tract, brain, and GI tract was reduced. Inhibition of metastasis to the reproductive tract and brain was also seen after tail vein injection of MDA-MB-134 E-cadherin-expressing cells. In summary, overexpression of functional E-cadherin in ILC models has some, but limited, effects on 2D growth in vitro and primary tumor growth in vivo, but there are pronounced effects on 3D ULA growth and metastases in vivo, with stronger effects on metastatic sites enriched in patients with ILC, especially the reproductive and GI tracts.

Background Background breast features are frequently noted in pathology reports alongside invasive breast cancer but rarely factor into prognosis or treatment decisions. Their relationship to tumor characteristics and patient outcomes remains incompletely characterised. Methods We conducted a retrospective cohort study of 7,603 patients with Stage I-III invasive breast cancer (diagnosed 1991-2022, age <80 years) from the Joint Breast Cancer Registry in Singapore. Natural language processing (NLP) was applied to 9,754 free-text pathology reports to extract co-existing background breast features, with accuracy validated by dual-reviewer assessment of 200 reports. Unsupervised hierarchical clustering grouped extracted features into three categories. Associations with tumor characteristics were assessed by multinomial logistic regression, and ten-year overall survival by Cox proportional hazards models (median follow-up 9.6 years; 620 deaths). Results Here we show that NLP-based extraction of background breast features from routine pathology reports achieves an accuracy of over 90% across features. Lobular neoplasia and benign proliferative changes are associated with less aggressive tumor characteristics, whereas early neoplastic and papillary lesions are more prevalent in HER2-enriched and luminal B tumor subtypes. Benign proliferative changes are associated with better survival in age- and year-adjusted models (hazard ratio 0.91, 95% CI 0.86-0.97), but this association is attenuated after adjustment for stage and subtype. Conclusions NLP-enabled extraction of background breast features from pathology text is feasible at scale. These features reflect tumor biology but do not independently add prognostic information beyond established clinical variables.

EMT associated transcription factors (EMTaTF) controls the epithelial-mesenchymal transition (EMT) process, but their detailed expression pattern in cancer is poorly documented at the cellular level. Here, we located two major EMTaTF: Snail and Slug by immunochemistry using validated antibodies in a cohort of 569 invasive breast carcinomas. We screened all tumor molecular types using TMA analysis in addition to full tumor sections, identifying cell types and structures involved in their expression, correlated to morphological, functional, and clinical characteristics. Briefly, Slug was expressed in Luminal A/B and HER2 breast cancer subtypes by almost all cells from the basal layer in normal-looking tubule structures and was very sporadically seen in transformed cells in the in situ or invasive components. In contrast, Slug was visibly expressed in 28% of our triple-negative (TN) tumor samples, significantly expressed by invasive tumor cells. Slug was also strongly expressed in a large subpopulation of stroma fibroblast-like cells in all breast carcinoma subtypes. Conversely, Snail was frequently expressed in transformed cells in all invasive breast carcinomas subtypes, up to 54 and 64% of tumor cells in TN and HER2 tumors respectively. Expression pattern was heterogeneous and included invasive areas and in situ component. Some stroma cells, particularly endothelial cells from the tumor microenvironment were also found to express Snail. Slug and Snail proteins were also detected in metastatic foci, sometimes with an increase in the expression level, particularly for Snail. Unexpectedly, we found a significant positive correlation between cell proliferation and Slug stroma cell expression in luminal A and B subtypes. This link bolstered the relative proximity we uncovered between CK- KI67- Slug+ stoma cells and CK8+ KI67+ Slug- tumor cells in LumB samples. However, survival long-term studies failed to demonstrate a link between slug tumor or stromal expression and long-term survival. On the other hand, Snail protein expression in tumor cells was surprisingly and significantly linked to a better survival fate overall. In contrast, Snail overexpression in stroma cells from primary tumors was significantly linked to a time-dependent tendency for relapses, metastasis and poorer survival, arising when post-surgery time lapse increased. In conclusion, these findings offer new and unsuspected understanding of the complex localization pattern and clinical involvement of Snail genes in breast cancer, beyond classic EMT pathways.

Most computational studies of the breast cancer immune microenvironment rely on a single deconvolution algorithm. Since different tools analyze different parameters, integrated information across a dataset can be missed. Here, we employed two complementary approaches, xCell and a CIBERSORT-style ssGSEA using LM22 signatures, on 1,099 PAM50-classified primary tumors from the TCGA-BRCA cohort. PD-L1 was highest in basal-like tumors (Kruskal-Wallis p < 0.001) and correlated with CD8+ T cells ({rho} = 0.65) and M1 macrophages ({rho} = 0.67) in that subtype, which fits the standard model of IFN-{gamma}-driven adaptive upregulation. In HER2-enriched cancers, PD-L1 tracked with both effector and regulatory populations simultaneously, while luminal tumors were largely immune-quiet. The most consequential finding involved {gamma}{delta} T cells and M2 macrophages: xCell showed a non-significant correlation ({rho} = 0.048, p = 0.11), whereas CIBERSORT-ssGSEA, using curated {gamma}{delta} gene signatures, produced a significant correlation ({rho}= 0.565, p < 2.2 x 10-{superscript 1}) that held across all five subtypes. A multivariate model explained 49% of PD-L1 variance (adjusted R{superscript 2} = 0.49), and Cox regression incorporating immune features gave a concordance of 0.60. These results suggest a baseline {gamma}{delta}-M2 immunosuppressive circuit in breast cancer, particularly in ER+ disease, that could be useful to set a therapeutic target.

Purpose: Unilateral breast cancer (BC) patients scheduled for mastectomy often choose to undergo contralateral prophylactic mastectomy (CPM), despite substantial declines in contralateral breast cancer (CBC) risk in recent decades. Models predicting absolute risk of future CBC can aid informed decision-making about CPM. CBCRisk is an existing CBC absolute risk prediction model trained on unilateral BC patients regardless of whether they had mastectomy. Here we developed CBCRisk-Mastectomy, tailored specifically to BC patients scheduled for mastectomy and considering CPM. Patients and Methods: We used data on BC patients who underwent mastectomy to treat their first BC from two nationally representative sources: Breast Cancer Surveillance Consortium (BCSC) and Surveillance, Epidemiology, and End Results (SEER) cancer registry. We imputed missing data in the BCSC sample and used conditional logistic regression models, trained on 2,660 BC patients (665 CBC cases) from BCSC, to identify predictors and estimate relative risks (RRs). These were combined with attributable risks and CBC incidence rates estimated from SEER to obtain absolute risk. Cross-validation was used to internally validate CBCRisk-Mastectomy and compare with CBCRisk. Results: CBCRisk-Mastectomy has nine predictors: first BC type, lobular carcinoma in situ status, estrogen receptor status, tumor stage, breast density, age at BC diagnosis, family history of BC, age at first birth, and body mass index. The areas under the curve and their 95% confidence intervals for 5-year predictions for CBCRisk-Mastectomy and CBCRisk were 0.62 (0.59, 0.65) and 0.58 (0.55, 0.61), respectively. Conclusions: CBCRisk-Mastectomy may aid clinicians in counseling BC patients scheduled for mastectomy, enabling improved decision-making regarding CPM.

Abstract Background Immune-related adverse events (irAEs) involving the breast remain rarely reported. Purpose To characterize clinical and imaging features of camrelizumab-associated breast lesions (CABLs). Materials and Methods This retrospective dual cohort study (October 2019 to February 2026) included 196 female patients. Cohort A comprised 180 non-breast cancer patients; Cohort B comprised 16 breast cancer patients receiving neoadjuvant camrelizumab. Baseline characteristics, treatment response, and CT/MRI features were compared between CABL-positive and CABL-negative groups using Mann-Whitney U and chi-square tests. Results CABLs developed in 34.4% (62/180) of Cohort A and 93.8% (15/16) of Cohort B. CABL-positive patients were younger (median 50.5 vs 54.5 years; P = 0.006) and more often premenopausal (46.8% vs 26.3%; P = 0.009). The objective response rate was relatively high among patients with positive lesions; in Group A, the disease progression rate was lower in the CABL-positive group than in the CABL-negative group (3.2% vs 17.8%), whilst in Group B, the pathological complete response rate was as high as 53.3% (8/15). On CT/MRI, CABLs were predominantly multiple (62.5%), with well-defined margins and unrestricted diffusion. The predominant time-intensity curve (TIC) pattern was washout (46.7%). Median time to onset was 2-3 cycles (the second MRI scan); most lesions disappeared (40.3%) and shrank (46.8%) during follow-up. ADC values of lesions were significantly higher than those of primary tumors (1.847+/-0.284 vs 0.976+/-0.055 x10[-3] mm[2]/s; P < 0.001). Histopathology of four lesions revealed lymphocytic infiltration and fibrosis without malignancy. Conclusion CABLs are benign reactive changes driven by multiple factors. Their recognition prevents misinterpretation as disease progression, thereby avoiding unnecessary treatment discontinuation or biopsy.

BackgroundTriple-negative breast cancer (TNBC) remains the most clinically challenging breast cancer subtype, in part due to the absence of validated molecular targets and the limited availability of non-invasive early detection strategies. Tumor-derived exosomes have emerged as promising liquid biopsy analytes, yet the functional organization of their protein cargo and the identification of biologically meaningful candidates remain incompletely characterized. MethodsWe present a Composite Driver Score (CDS) framework that integrates differential expression magnitude with protein-protein interaction network topology and Analytic Hierarchy Process (AHP)-based multi-criteria weighting to prioritize exosomal protein candidates in a systems-informed manner. The framework was applied to publicly available label-free quantitative proteomic datasets comparing MDA-MB-231 (TNBC) and MCF-10A (non-tumorigenic) exosomal fractions, with cross-dataset validation performed on an independent proteomic dataset. ResultsCDS prioritization demonstrated robustness to variations in proteome depth and parameter weighting, consistently recovering a functionally coherent set of extracellular matrix (ECM) and adhesion-associated proteins. Network and pathway analyses revealed coordinated co-enrichment of integrin receptors, cognate ECM ligands, and associated co-receptors -- consistent with selective packaging of a functionally integrated invasion module. Agrin (AGRN), a heparan sulfate proteoglycan with virtually limited prior characterization in TNBC exosome biology, emerged as a high-priority candidate through its network integration within this ECM program. ConclusionsThese findings support a model in which TNBC-derived exosomes carry coordinated molecular programs capable of modulating extracellular matrix architecture. The CDS framework offers a transferable strategy for integrative exosomal biomarker prioritization and a systems-level foundation for targeted liquid biopsy panel development.

Tumours reshape their surrounding extracellular matrix (ECM), creating a microenvironment with altered chemical and mechanical properties. Integrins detect these changes, linking the ECM to the intracellular cytoskeleton and promoting cell survival, motility, invasion and differentiation, and further ECM remodelling. However, the molecular mechanisms by which tumours remodel their ECM are not well understood. Here, we found that the cytokine oncostatin M (OSM) promotes breast cancer progression by activating ECM remodelling and integrin signalling in cancer cells, as shown by combining complementary in vitro, in ovo and in vivo models, and transcriptomic and proteomic analyses. We demonstrated that OSM induces fibrosis, characterized by increased collagen deposition and hydroxylation, together with activation of ECM and ECM-associated proteins and modifiers such as fibronectin, tenascin C, LOX, PLOD2 and collagen prolyl hydroxylases. OSM also promoted the expression of integrins. Integrin alpha 5 (ITGA5) was identified as an important mediator of OSM-effects. ITGA5 blockade, by means of small interference RNA and therapeutic inhibition with a blocking peptide, abrogated OSM-induced cancer cell migration, invasion and in vivo tumour growth. In addition, OSM blockade with a specific inhibitor reduced tumour growth in an immunocompetent mouse model. Our results are clinically relevant as the expression of integrins and matrisome genes strongly correlated with OSM and its receptor OSMR in breast cancer clinical samples; and co-expression of OSMR and ITGA5 associated with decreased survival in basal breast cancer patients. Collectively, our data reinforce the potential of the OSM-ITGA5 axis as a therapeutic target in this breast cancer subtype, which shows the highest mortality rates.

BackgroundRecent evidence suggests that cancer can exhibit splicing alterations that give rise to tumour-specific isoforms. One example is NUMB, which produces four isoforms (p72, p71, p66, and p65) through alternative splicing of exons 3 and 9. Traditionally considered a tumour suppressor, it also has been considered an oncogene. We propose that this duality is due to isoform-specific expression. ResultsUsing public databases, we identified a tumour-associated switch in NUMB isoform expression: p72/p71 are upregulated in tumours, whereas p66/p65 are more expressed in non-tumour tissues. These isoforms correlate differently with cellular processes. NUMBL, a NUMB homolog, behaves similarly to p65. We identified two transcriptional clusters: one characterized by high expression of p72/p71, and another by p66/p65/NUMBL. Each group was associated differently with the Notch, WNT/{beta}-catenin, Hedgehog, and Hippo signalling pathways, suggesting isoform-specific regulatory roles. Analysis of breast cancer cell lines (CCLE) led to a NUMB score based on isoform expression, which classified cell lines into biologically distinct groups. The p72/p71-enriched group showed distinct signatures, pathway activity, and drug sensitivity. Applying this score to TCGA-BRCA samples revealed a significant link between high NUMB-score and poor survival, confirmed by Kaplan-Meier analysis. ConclusionsNUMB emerges as a potential oncogenic contributor and biomarker in splicing-based personalised medicine, highlighting isoform-specific expression as a clinically relevant determinant of tumour behaviour, pathway activity, and therapeutic response.

Chemotherapeutic treatment of breast cancer with Doxorubicin (DOX) can induce tumor and stromal cell senescence leading to therapy-resistance. Senescence-associated secretory phenotype (SASP) promotes secretion of pro-inflammatory and tumorigenic factors causing systemic inflammation. Combined, this can result in immune suppression, tumor growth and secondary spread of cancer. Targeting and removing senescent and cancerous cells using a combination of chemotherapeutic and senolytic drugs may reduce systemic inflammation, improve therapeutic efficacy, and prevent metastasis. Exposure of triple-negative breast cancer (MDA-MB-231), hormone-responsive (MCF-7) and HER2+ (MDA-MB-453) cells, and primary spine osteoblasts to DOX showed significant induction of p21-positive senescent cells. DOX and senolytics (RG-7112, o-Vanillin) treatment of co-culture spheroids showed a significant additive effect in reducing tumor sphere viability and growth, indicating reduced metastatic potential. This was correlated with reduced SASP in triple-negative and hormone responsive lines and decreased levels of senescent cells in all subtypes and primary stromal cells, while proliferation was decreased, and apoptosis increased across all breast cancer subtypes. Future chemotherapeutic treatment in breast cancer models may be optimized by adding senolytic drugs to more effectively clear senescent tumor and stromal cells, reducing risk for relapse and metastatic potential, while allowing for tissue regeneration in the bone metastatic environment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/724653v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@c4cb8forg.highwire.dtl.DTLVardef@105219org.highwire.dtl.DTLVardef@17e0517org.highwire.dtl.DTLVardef@802bd2_HPS_FORMAT_FIGEXP M_FIG C_FIG Senolytics selectively eliminate senescent cancer and stromal cells and enhance Doxorubicin efficacy in a 3D bone-like tumor microenvironment model.

Purpose: Adjuvant endocrine therapy (AET) substantially reduces recurrence and mortality in hormone receptor-positive breast cancer but requires sustained daily adherence over 5 to 10 years. Approximately one-third of patients fall short of recommended adherence in the first year alone, largely due to distinct combinations of attitudes, barriers, and circumstances. Existing studies have catalogued individual risk factors but lack the scale and breadth to characterize how these factors co-occur within patients, or to distinguish behavioral drivers from confounding by clinical and demographic context. We sought to characterize the behavioral and social heterogeneity underlying AET adherence in a national real-world cohort. Moving beyond population-average risk factors, we identify the distinct patient profiles, and the differing drivers within them, that any effective adherence strategy must address. Methods: We conducted a retrospective cohort study of US women with invasive breast cancer diagnosed between 2016 and 2025, linking two large-scale, individual-level datasets through privacy-preserving tokenization: Surgo Health's BehavioralPulse, which provides modeled individual-level behavioral and attitudinal risk scores together with consumer sociodemographic attributes, and longitudinal medical and pharmacy claims from a claims data provider. Eligible patients underwent 1 to 2 breast surgeries, initiated oral AET (tamoxifen or aromatase inhibitors), and maintained continuous insurance coverage for 365 days following therapy initiation. The primary outcome was adherence, defined as medication possession ratio (MPR) [&ge;]80% in the first year. Mixed-effects logistic regression with a random intercept for ZIP3 estimated adjusted associations across behavioral, sociodemographic, and clinical predictors. To characterize how behavioral factors co-occur within patients, we identified the most prevalent configurations of the statistically significant behavioral predictors and estimated their relative association with adherence, holding clinical and demographic factors constant. Results: The final cohort included 401,450 women, of whom 280,595 (69.9%) achieved MPR [&ge;]80%. Several behavioral factors were independently associated with adherence after adjustment for clinical and demographic covariates, including comfort following medication instructions (aOR, 1.15; 95% CI, 1.06-1.24), geographic proximity to breast oncologists (aOR, 1.17; 95% CI, 1.04-1.32), tangible instrumental social support (aOR, 1.06; 95% CI, 1.00-1.13), religiosity (aOR, 1.04; 95% CI, 1.01-1.08), concern about sexual side effects (aOR, 0.96; 95% CI, 0.93-0.99), and cost-related access barriers (aOR, 0.97; 95% CI, 0.95-1.00). The 10 most common configurations of significant behavioral predictors accounted for over 70% of the cohort, with the two most prevalent representing more than 40% of patients. The most common profile, defined by an absence of behavioral barriers and the presence of social support, was associated with a positive behavioral contribution to adherence propensity (behavioral linear predictor OR = 1.18; 95% CI: 1.04-1.36) comparable in magnitude to several established clinical predictors. Compared against this referent profile, six of the nine remaining profiles had lower adherence, with relative odds ranging from approximately 0.92 (95% CI: 0.89-0.95) to 0.97 (95% CI: 0.94-0.99). One profile, similar to the reference but including high trust in doctors, was associated with higher adherence odds (1.04, 95% CI: 1.01-1.07). These profiles arose from substantively different underlying combinations of factors: segments dominated by cost barriers, by side-effect concerns, or by limited social support produced comparable overall adherence risk but through distinct pathways. Conclusion: In this national cohort, nearly one-third of women did not achieve recommended first-year adherence to AET. The pathways to non-adherence were heterogeneous, structured into recurring behavioral profiles rather than randomly distributed across patients. This heterogeneity is clinically meaningful: patients with similar adherence risk may benefit from substantially different forms of support, ranging from financial navigation to side-effect management to social support resources. Surfacing this structure required linking individual-level behavioral data to large-scale claims data, offering a practical foundation for optimal design of patient-centered adherence interventions that are tailored to the specific configurations of barriers patients actually face.