Breast Cancer Research

Structured AbstractO_ST_ABSPurpose/ObjectivesC_ST_ABSThe role of postmastectomy radiotherapy (PMRT) in patients with pathologic N1 (pN1) breast cancer, including triple-negative breast cancer (TNBC), remains controversial in the era of modern systemic therapy. We evaluated the association between PMRT and recurrence-free survival (RFS) and overall survival (OS) and identified prognostic factors in a contemporary single-institution pN1 cohort. Materials/MethodsWe retrospectively reviewed female patients with pT1-2N1M0 breast cancer treated with mastectomy between 2016 and 2022. RFS and OS were estimated using Kaplan-Meier methods and compared by PMRT status with log-rank testing. Univariable Cox proportional hazards models assessed associations between clinical factors--including tumor laterality, receptor subtype (TNBC vs non-TNBC), nodal burden, and adjuvant therapies--and survival outcomes, with subgroup analyses by PMRT status and receptor subtype. ResultsFifty-seven patients were included; 22 (38.6%) received PMRT. With a median follow-up of 85 months, PMRT was not associated with improved RFS (median 133 vs 120 months; p=0.256) or OS (not reached vs 195 months; p=0.154). Hormone therapy was significantly associated with improved RFS (HR 0.43; p=0.026) and OS (HR 0.13; p=0.003), while having 2-3 positive lymph nodes predicted worse RFS (HR 2.86; p=0.007). No significant differential benefit from PMRT was observed in patients with TNBC or non-TNBC disease. ConclusionsPMRT was not associated with a survival benefit in this pN1 cohort, including patients with TNBC. Interpretation is limited by modest sample size and statistical power. Outcomes appeared driven by tumor biology, nodal burden, and systemic therapy, supporting individualized PMRT decision-making.

BackgroundThe management of residual axillary disease after neoadjuvant therapy (NAT) remains controversial, as current recommendations often treat ypN1 breast cancer as a homogeneous entity despite potential prognostic heterogeneity. Evidence supporting uniform axillary surgical strategies across different levels of residual nodal burden is limited. We investigated whether survival associations related to axillary surgical evaluation differ according to residual nodal burden in ypN1 disease, using an adjuvant cohort to validate a SEER-based proxy for surgical extent. MethodsPatients with 1-3 positive lymph nodes were identified in the SEER database (2000-2022) and stratified into neoadjuvant (NAT; n=30,560) and adjuvant (AT; n=197,586) cohorts. Axillary surgical evaluation was categorized as limited (2-3 examined nodes) or extensive ([≥]10 examined nodes). Survival was analyzed using Kaplan-Meier methods and log-logistic accelerated failure-time models, adjusted with inverse probability of treatment weighting. ResultsIn the ypN1 cohort, limited axillary evaluation was not associated with inferior overall survival among patients with a single residual positive node (IPTW-adjusted HR: 1.15, p=0.134; time ratio [TR]: 0.86, p=0.184). In contrast, limited evaluation was associated with worse survival in patients with two positive nodes (HR: 1.70, 95%CI 1.54-1.87; TR: 0.58, 95%CI 0.53-0.64). The findings were similar when using breast cancer-specific survival as the endpoint. ConclusionsSurvival associations related to axillary surgical evaluation after NAT vary according to residual nodal burden. Axillary de-escalation appears feasible in patients with a single residual positive node but cannot be extrapolated to those with multiple residual nodes, underscoring heterogeneity within ypN1 disease.

BackgroundThe genetic architecture of Breast Cancer (BC) in Arab populations remains largely understudied, limiting the precision of current prevention and screening programs. The Emirati Genome Program (EGP), one of the worlds first nation-wide sequencing initiatives, offers an unprecedented opportunity to delineate inherited BC risk across an entire population. MethodsWe analyzed 436,780 EGP individuals, including 229,309 women, integrating whole-genome sequencing (WGS) with electronic health records (EHRs). We quantified the prevalence and penetrance of pathogenic and likely pathogenic (P/LP) variants across 13 NCCN-recommended BC genes, evaluated the performance of established polygenic risk scores (PRS), and reconstructed >48,000 pedigrees to measure familial aggregation. ResultsP/LP variants were identified in 0.84% of women, accounting for 5.2% of BC cases (mean age of 45.9{+/-}11.1 years). Highly penetrant BRCA1 c.4065_4068del (p.Asn1355fs) and BRCA2 c.2808_2811del (p.Ala938Profs) variants showed age-specific cumulative risks of 37.6% and 31% by age 60, respectively, and allele frequencies up to tenfold higher in the Emirati population than in global reference datasets. The European-derived PRS model (PGS000004) demonstrated strong performance, advancing 10-year BC risk onset by a decade for women in the top decile. Family-based PRS discriminated affected from unaffected individuals, revealing higher polygenic risk even within sister pairs. Integration of monogenic, polygenic, and familial data defined a national framework for risk stratification, identifying disease-free women potentially eligible for targeted prevention. ConclusionsNation-scale genome sequencing reveals, for the first time, the comprehensive landscape of inherited BC susceptibility within a Middle Eastern population. The integration of monogenic, polygenic, and familial data establishes a national framework for genomic risk stratification--transforming population genomics into a foundation for precision prevention and early detection in the UAE and beyond.

BackgroundAlthough DCIS has a relatively low recurrence rate, many patients still receive adjuvant radiotherapy or endocrine therapy, raising concerns about overtreatment. Reliable biomarkers are therefore needed to predict an individual patients risk and guide treatment decisions. Recent studies suggest that the composition of the tumour-associated stroma (TAS) affects progression and outcome, highlighting TAS-derived biomarkers as promising candidates for further investigation. MethodsWe trained AI models for cell and tumour segmentation using whole slide digital pathology images acquired as part of a retrospective cohort study. We investigated the effects of cell density within both the tumour and the TAS to determine how they correlated with recurrence in the ipsilateral breast. ResultsWe found that the concentration of DCIS lesions on the slide and the density of mitotic figures inside the TAS region were significantly associated with recurrence risk. Additionally, we found some predictive value in the lymphocyte and red blood cell densities in different tumour regions. Stromal composition was shown to associate with recurrence risk, and density-based biomarkers were identified and used to cluster patients into phenotypes with significantly different risk profiles. ConclusionOur findings highlight the prognostic relevance of stromal composition in DCIS, and we identify novel density-based biomarkers that can be used to identify patients who are more likely to experience a local recurrence after breast-conserving surgery alone. These results may aid in developing future risk-stratification tools for breast cancer patients, thereby reducing overtreatment and improving patient care.

BackgroundTamoxifen is a cornerstone of endocrine treatment for hormone receptor-positive breast cancer, reducing recurrence and breast cancer-specific mortality. However, its use is associated with a small, yet clinically relevant, increase in uterine cancer. As diagnosis of this cancer remains symptom-triggered, it is essential for patients to be aware of this risk and report symptoms promptly for optimal outcomes. We therefore assessed risk awareness among breast cancer survivors while exploring their attitudes towards potential future endometrial surveillance strategies. MethodsOver a 10-month period, a web-based survey was conducted among breast cancer survivors with/without tamoxifen treatment. The mixed-format questionnaire included closed-ended questions and optional free-text comments. Quantitative data were summarized descriptively and analyzed statistically; qualitative responses were reviewed thematically to contextualize survey findings. ResultsOf 163 respondents, 154 breast cancer survivors were included in the analysis, 128 of whom had received tamoxifen. Among tamoxifen-associated participants, 60% reported insufficient awareness of the associated uterine cancer risk, and half expressed uncertainty about the adequacy of the current symptom-triggered endometrial evaluation. Despite this, acceptance of tamoxifen therapy was high; only one patient declined treatment over concerns about side effects. Almost all participants (96%) were willing to adopt endometrial surveillance methods, if developed and validated. ConclusionAs evaluation of tamoxifen-associated uterine pathology is symptom-triggered, our data highlight the need for improved and standardized risk communication to promote timely symptom recognition, reporting, and diagnostic evaluation. Moreover, our findings support incorporating patient-reported preferences into the development of future endometrial detection strategies to improve survivorship care.

BackgroundGenomic assays such as Oncotype DX, MammaPrint, and Prosigna have transformed risk stratification and treatment selection in early-stage, estrogen receptor-positive (ER+), HER2-negative breast cancers by enabling more precise prognostication and chemotherapy de-escalation in selected patients. However, their clinical utility is limited in lymph nodes positive disease. A major unmet need is the development of compact, mechanistically grounded biomarkers that extend risk and treatment-response prediction to clinically challenging ER+/HER2- subgroups, including lymph node-positive patients. MethodsBuilding on a cell-of-origin framework, we previously established a 70-gene triple hormone receptor (THR; ER, AR, VDR) signature (THR-70) that reflects luminal epithelial differentiation programs and is prognostic across breast cancer subtypes. Here, we refined this framework using interactome-guided clustering to derive a six-gene cell-of-origin signature (THR-6E: KIF4A, KIF2C, CDC20, FAM64A, TPX2, and LMNB2). We evaluated the prognostic and predictive performance of THR-6E across >7,000 breast cancer cases from multiple independent cohorts, assessed treatment-response prediction using endocrine- and chemotherapy-annotated datasets, and performed independent validation in the I-SPY2 adaptive clinical trial. FindingsTHR-6E robustly stratifies relapse-free survival (RFS) in ER+/HER2- breast cancer independent of tumor grade, proliferation status, and subtype. Hazard ratios for RFS were 2.41 (p<1x10-{superscript 1}), 1.61 (p=4.9x10-), and 1.50 (p=6.2x10-3) for grades 1, 2, and 3, respectively, and 2.16 and 1.33 for Luminal A and Luminal B subtypes. THR-6E maintained predictive value across endocrine- and chemotherapy-treated, untreated, lymph node-positive, and lymph node-negative subgroups. Beyond prognosis, THR-6E predicted endocrine therapy response in ER+/HER2-, node-negative disease and chemotherapy response in ER+/HER2-, node-positive disease, achieving approximately 70% sensitivity and specificity (AUC=0.714, p=3.6x10-), with consistent performance across taxane-, anthracycline-, and FEC-based regimens (AUCs 0.71-0.72). Single-cell transcriptomic and proteomic analyses demonstrated that THR-6E expression is specific to normal and malignant breast glandular epithelium, preserved during transformation, and further enriched in metastatic disease. Consistent with a cell-of-origin program, THR-6E genes were rarely mutated in breast cancer and retained normal tissue-like co-expression patterns. In the I-SPY2 trial, THR-6E achieved robust prediction of pathologic complete response across multiple treatment arms (AUCs 0.72-0.94), with an overall AUC of 0.741. InterpretationThese results support a cell-of-origin-anchored approach to biomarker development and challenge purely tissue-agnostic models of therapeutic response. THR-6E represents a compact, biologically interpretable signature that extends prognostic and predictive assessment to clinically relevant ER+/HER2- subgroups, including lymph node-positive disease. Its mechanistic grounding and stable performance across cohorts position THR-6E as a complementary tool to existing multigene assays, warranting prospective diagnostic accuracy studies to define its clinical utility and impact on treatment decision-making.

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.

IntroductionKi-67 is a key biomarker of tumor proliferation in breast cancer, yet its clinical correlates in breast cancer screening populations, where disease is often detected at earlier stages, and remain underexplored. This study aimed to identify factors independently associated with high Ki-67 expression among women investigated for suspected breast cancer at Unilabs Laboratory, Lusaka, Zambia. MethodsA retrospective cross-sectional analysis was conducted on 208 women suspected with breast cancer through a laboratory-based screening program in Lusaka, Zambia (2019-2024). Demographic, clinical, and pathological data were extracted from laboratory records, and Ki-67 expression was dichotomized as low (<20%) or high (>20%). Variables significant in Bivariate analysis (p<0.05) were included in a multivariable logistic regression model to identify independent predictors of high Ki-67 expression. Variables significant in bivariate analysis (p<0.05) were included in multivariable logistic regression models to identify factors associated with high Ki-67 expression. ResultsThe median age was 48 years (IQR: 40-63), and 46.2% (n=96) exhibited high Ki-67 expression. In bivariate analysis, younger age (<40 years), invasive ductal carcinoma, right breast involvement, progesterone receptor (PR) positivity threshold [&ge;]10%, non-Quick Score scoring methods, and lack of fluorescence in situ hybridization (FISH) testing were associated with high Ki-67. However, in adjusted multivariable models, only younger age (>41 years) (aOR: 0.43-0.46, p<0.05), PR positivity threshold [&ge;]10% (aOR: 6.14-6.38, p<0.05), and use of non-Quick Score scoring methods (aOR: 10.5-14.9, p[&le;]0.002) remained significantly associated with high Ki-67, while other factors lost statistical significance after controlling for confounders. ConclusionIn this diagnostic cohort from Zambia, nearly half of women with breast cancer exhibited high Ki-67 expression, reinforcing its relevance even in early detection settings. The study identified younger age, higher PR positivity threshold, and alternative scoring methods as independent predictors of high Ki-67, highlighting the importance of standardized biomarker assessment. Future studies should consider prospective study design incorporating molecular subtyping to enhance the clinical interpretation of Ki-67 in similar populations.

Clinical interpretation of breast cancer sequencing is constrained not by a lack of data but by the absence of an organising framework that translates constellations of co-occurring mutations and copy-number alterations into tumour-level biology with prognostic and therapeutic meaning. This challenge is exemplified by PIK3CA, a clinically actionable alteration often treated as a single-label biomarker despite context-dependent associations with outcome. We analysed >5,000 breast tumours across multiple cohorts using integrated multi-omics (somatic mutations, copy-number, transcriptomic, proteomic and phosphoproteomic profiles) and quantified the directionality of downstream molecular consequences of recurrent alterations relative to TP53-associated trends to infer dominant tumour programmes. This revealed a robust functional organisation comprising (i) a canonical proliferative/replicative programme, enriched for cell-cycle, DNA replication and E2F signalling, and encompassing TP53 mutations and most recurrent CNAs, and (ii) a non-canonical signalling/cell-state programme marked by recurrent mutations including PIK3CA, CDH1, GATA3, MAP3K1 and AKT1, with opposing transcriptomic/proteomic directionality, comparatively lower proliferative output and a systematic tendency towards mutual exclusivity with TP53, consistent with alternative evolutionary routes. To operationalise these findings for clinical use, we developed T-OMICS (Tiered OMICS Classification System), which layers complementary readouts to deliver a single interpretable tumour profile: Tier 1 provides a continuous genomic-risk backbone via a DNA-anchored prognostic RNA signature capturing canonical proliferative/replicative output; Tier 2 assigns programme identity based on the dominant genomic context; Tier 3 quantifies within-programme activity along a continuum; and Tier 4 overlays non-redundant modifier mutations that refine phenotype, vulnerabilities and resistance liabilities, supported by orthogonal proteomic/phosphoproteomic pathway signals. In ER+/HER2- disease, T-OMICS resolves the prognostic ambiguity of PIK3CA by showing that "PIK3CA-mutant" is not a single biological entity: in a predominant low-genomic-score context, PIK3CA aligns with buffered luminal biology and favourable outcomes, whereas in high-score contexts--conditioned by TP53 background and modifier events--PIK3CA can mark adverse biology with distinct dependencies not captured by proliferation-centric readouts; notably, low-score PIK3CA tumours with CDH1 co-mutation shift to significantly worse outcomes. Together, these results establish a programme- and state-aware framework that converts sequencing reports into clinically legible tumour biology to support risk calibration, therapeutic prioritisation and evolution-aware sampling decisions from early-stage through metastatic ER+/HER2- breast cancer. Lay SummaryBreast cancer tumours often carry many genetic changes at the same time. While modern sequencing can identify these changes in detail, the results are frequently presented as long lists of mutations and DNA alterations that are difficult to interpret in terms of how a tumour behaves or how it should be treated. A well-known example is the PIK3CA gene: although it can be targeted with specific drugs, studies have reported mixed results on whether PIK3CA mutations are associated with better or worse outcomes, making it challenging to use this information confidently in clinical care. To address this problem, we analysed genomic (DNA-wide), RNA, and protein data from more than 5,000 breast tumours. We found that many common genomic changes cluster into two main biological "programmes" that reflect distinct ways tumours grow and survive. One programme is driven by rapid cell division and DNA replication and includes TP53 mutations and many common DNA copy-number changes; tumours following this programme tend to be more aggressive. The second programme is less focused on rapid growth and is defined by mutations such as PIK3CA, CDH1, GATA3, MAP3K1, and AKT1, which influence signalling and cell identity rather than directly accelerating proliferation. These programmes reflect broader tumour behaviours rather than the effects of single genes. Importantly, mutations in the second programme are usually not found alongside TP53 mutations, suggesting that breast cancers can develop through distinct biological routes--with some tumours following an alternative pathway (not overtly proliferation-dependent) that shapes their behaviour and may influence which treatments are most appropriate. Based on these findings, we developed a practical classification system, T-OMICS, for ER-positive, HER2-negative breast cancer. T-OMICS summarises which biological programme a tumour follows, how active or aggressive it is within that programme, and whether additional mutations are present that may influence treatment response or resistance. Using this framework, we show that PIK3CA mutations most often occur in a biologically buffered context associated with more favourable outcomes, but when they occur in more aggressive tumours--shaped by other key genetic changes--they can signal a higher-risk disease with different treatment needs. These findings indicate that treatment decisions should be based on the tumours overall biological pattern, not just the presence of a single mutation. By placing sequencing results in this broader context, T-OMICS supports more accurate risk assessment, better treatment planning, and more informed decisions about when to intensify therapy, from early-stage through advanced breast cancer. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/26346715v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@a602e7org.highwire.dtl.DTLVardef@108a6b1org.highwire.dtl.DTLVardef@f7ef9forg.highwire.dtl.DTLVardef@194b86d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical SummaryC_FLOATNO C_FIG

BackgroundEarly and accurate detection of breast cancer and differentiation from benign breast disease remains a substantial challenge, with about 70% of diagnostic breast biopsies having no malignant findings. Tumor-associated Autoantibodies represent the immune systems response to a neoplasm and are a promising biomarker group for the early diagnosis of breast cancer by liquid biopsy. MethodsIn this study, we quantified the IgM and IgG titers to 525 Tumor Associated Antigens in a prospectively-collected cohort of 50 serum samples from donors with benign breast disease and donors with early-stage breast cancer. The considerable number of antibodies analyzed enabled us to account for variations in individual immune profiles through z-score normalization of each donors total antibody distribution. Differentially expressed antibodies were identified using Mann-Whitney U tests (p < 0.05) and fold-change analysis (fold-change > {+/-} 1.2). For each donor, we calculated the total number of "high-titer" antibodies, defined as antibodies with relative concentrations > 3 SD above the cohort mean. Logistic regression classifiers were then built using differentially expressed biomarkers and high-titer antibody counts to distinguish benign breast disease from breast cancer. ResultsWe identified 25 differentially expressed antibodies between the benign and cancer groups. A down-selected panel of eight antibodies demonstrated good performance in a logistic regression classifier to distinguish benign disease from invasive carcinomas (AUC-ROC = 0.83 {+/-} 0.14). High-titer antibody analysis revealed that the benign group had a higher prevalence of donors with elevated IgG immune response, and donors displayed antibody signatures unique to their individual disease pathway. ConclusionsThis study identifies an eight-antibody panel with promising diagnostic potential to distinguish benign breast disease from early-stage breast cancer. The z-score normalization approach and analysis of individual donors high-titer antibody profiles represent a novel approach towards personalized cancer immunology. This study provides encouraging preliminary evidence supporting the promise of tumor-associated autoantibody profiling for distinguishing benign and malignant breast disease, warranting future studies in larger cohorts.

BackgroundLeptomeningeal disease (LMD) is a serious complication of metastatic breast cancer (MBC) with poor survival. This single-institution retrospective study compares overall survival (OS) among MBC patients with LMD based on CSF parameters (glucose, protein, and WBC count) MethodologyMBC patients who were diagnosed with LMD between 2010-2023 at Wilmot Cancer Institute were screened for eligibility. Only those with available data on CSF glucose, protein, and WBC count were included. OS was assessed via the Kaplan-Meier method and compared using the log-rank test. Cox models were used for multivariate analysis. ResultsOut of 69 patients with MBC LMD, 28 had CSF data and were included in the final analysis. The CSF cytology-positive cohort had significantly lower glucose levels vs the CSF cytology-negative cohort [median (IQR) 40 (18-58) vs 64 (53-92) mg/dl, p=0.006]. Median CSF WBC count was significantly higher in the CSF cytology positive cohort vs the CSF cytology negative cohort [median (IQR) 13 (6-44) vs. 2(2-4)cells/mm3, p=0.001]. When stratified by CSF cytology results and CSF glucose levels, the CSF cytology negative, glucose-low group was associated with the worst OS, while the CSF cytology negative, normal/high glucose group was associated with the best OS(p=0.03) in an unadjusted analysis. Multivariate analysis confirmed that low CSF glucose was independently associated with poorer survival [HR 4.64 (1.71, 13.2)]. Neither CSF protein levels nor CSF WBC counts were significantly associated with OS in unadjusted and multivariate analyses. ConclusionLow CSF glucose was associated with worse OS than normal/high CSF glucose. There was insufficient evidence to suggest that CSF protein or CSF WBC counts were associated with OS.

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.

BackgroundEarly and accurate prediction of pathological complete response (pCR) is essential for personalizing neoadjuvant chemotherapy (NACT) in invasive breast cancer. However, most high-performing predictive models rely on costly, multi-modal data that are not routinely available in standard clinical practice. PurposeTo develop and validate Breast Cancer Biological Multi-modal Information Transfer for Response Prediction Model (BC-BioMIXER), a biologically informed predictive model that transfers multi-omics-derived knowledge to routine clinical data, enabling accurate prediction of pathological complete response prior to neoadjuvant chemotherapy initiation. Material and MethodsBC-BioMIXER was developed in a multi-modality cohort of 648 patients with invasive breast cancer (T2-4, any N, M0) incorporating transcriptomic, proteomic, MRI, and clinical data. The model was externally validated in three independent cohorts (total N = 830), including one multi-modality cohort, one clinical trial cohort, and one contemporary real-world cohort. All patients received NACT followed by surgery. The framework employs a teacher-student knowledge-transfer paradigm in which a multi-omics teacher model learns biologically integrated representations that are subsequently transferred to a student model using only routine clinical data. Predictive performance for pCR was benchmarked against a multi-modality reference model and evaluated across cohorts, receptor-defined subgroups (HER2 and hormone receptor [HR]), and treatment groups (NACT with or without immune checkpoint inhibitors [ICI]). Prognostic value was assessed using distant recurrence-free survival (DRFS). The potential to inform immunotherapy decision-making was explored by comparing DRFS between NACT + ICI and NACT-alone groups within model-predicted pCR and non-pCR subgroups. ResultsBC-BioMIXER achieved pCR prediction performance comparable to the multi-modality benchmark (AUC 0.82 vs. 0.85; p = 0.271) and demonstrated consistent discrimination across all validation cohorts (AUCs 0.82, 0.81, and 0.80; all p < 0.001). Patients predicted to achieve pCR experienced significantly improved 3-year DRFS (HR = 0.36; 95% CI, 0.20-0.67; p < 0.001). In patients treated with NACT + ICI, BC-BioMIXER showed numerically superior pCR prediction compared with PD-L1 expression alone (AUC 0.84 vs. 0.72; p = 0.08). Notably, within the model-predicted non-pCR subgroup, patients receiving NACT + ICI had significantly inferior DRFS compared with those receiving NACT alone (HR = 2.70; p = 0.032), whereas no significant difference was observed in the predicted pCR subgroup. ConclusionBC-BioMIXER translates multi-omics-derived biological knowledge into a robust, routine-data-based predictive tool for breast cancer NACT. Its consistent validation across evolving clinical settings and its potential to inform personalized immunotherapy strategies highlight a step toward scalable and accessible precision oncology. HighlightsO_LIBrings multi-omics power to routine clinical practice: Through cross-modality knowledge transfer, BC-BioMIXER leverages transcriptomic and proteomic data during training to enable highly accurate pCR prediction using only standard MRI and clinical variables (AUC 0.82 vs. 0.85 for full multi-modality benchmark, p=0.271). C_LIO_LIConsistently strong and generalizable performance: Validated in three independent cohorts (total N=830), the model maintained robust pCR discrimination (AUC 0.80-0.82, all p<0.001) across receptor subtypes (HR/HER2) and treatment regimens, including with or without immune checkpoint inhibitors. C_LIO_LIGuides personalized immunotherapy de-escalation: In HER2-negative patients predicted as non-pCR, adding ICI to neoadjuvant chemotherapy was associated with significantly worse distant recurrence-free survival (HR 2.70, p=0.032) compared to chemotherapy alone. This effect was not seen in the predicted pCR group, suggesting the model may help identify patients unlikely to benefit from additional immunotherapy. C_LI

IntroductionBreast Cancer (BC) remains a significant clinical challenge, and despite well-established screening strategies, new biomarkers could improve BC detection, treatment and management. Urine represents a headily accessible liquid biopsy for diagnosis and extracellular vesicle (EV) transfer of oncogenic proteins, RNAs, and metabolites that promote tumor growth, invasion, metastasis, and immune evasion. AimsTo compare the whole urine and urinary EV metabolomes and identify BC specific metabolite changes. MethodologyUrine samples were collected from four participant groups: breast cancer (BC) patients (n = 42), individuals with breast benign disease (BBD; n = 3), symptom controls (SC; n = 4), and healthy controls (HC; n = 6). EVs were isolated using differential centrifugation, ultrafiltration, and size-exclusion chromatography (SEC), and their morphology was confirmed by transmission electron microscopy (TEM). Metabolites from whole urine and from EVs derived from the same samples were extracted using methanol-water (70:30, v/v) and analyzed by direct-infusion mass spectrometry (DI-MS) in both positive and negative ESI modes. Metabolic features were processed with BinneR and annotated using the HMDB and KEGG databases. Integrated multi-omics analysis of whole-urine and EV-associated metabolomes was performed using the DIABLO framework within the MixOmics package in R platform. ResultsDI-MS profiling detected a broad spectrum of metabolites in both whole-urine and EV-derived fractions. Multivariate analyses revealed a clear separation of breast cancer (BC) patients from healthy controls and non-cancer groups in both matrices. Whole EV metabolites with area under the curves (AUC) of > 0.7 included glyceryl phosphoryl derivatives, N-eicosapentaenoyl species, sphinganine-1-phosphate and tetracosahexaenoic acid. EV-enriched metabolites included carnitine, histidine and adenosine monophosphate. DIABLO-based integrative analysis suggested that urinary and EV metabolomes were broadly similar with the discrete putative metabolite biomarkers representing minor, but specific changes with BC. ConclusionsThe whole urine and EV metabolomes suggested a small number of metabolite changes that were specific to BC. This could indicate that the urinary EVs describe distinctive aspects of the breast carcinogenic process.

Selection of systemic therapy for breast cancer remains largely empirical, particularly for chemotherapy, due to the lack of robust biomarkers that predict treatment response at the individual patient level. We developed Oncology CoPilot, a real-world, transcriptomics-guided machine learning (ML) decision-support model designed to integrate heterogeneous tumor gene expression data and treatment response annotations to support treatment response stratification across therapeutic classes. Oncology CoPilot was trained on a pan-cancer cohort comprising 11,414 patients across 15 cancer types and 150 systemic drug regimens derived from publicly available and published datasets. Retrospective external validation was performed using five independent breast cancer cohorts comprising 503 patients, spanning multiple molecular subtypes, transcriptomic platforms, and six commonly used treatment settings, including chemotherapy, endocrine therapy, and targeted therapy. Across the external validation cohort, the model demonstrated an overall accuracy of 72.8%, with balanced sensitivity (71.5%) and specificity (73.4%), and an ROC-AUC of 0.783. Regimen-specific analyses demonstrated stable performance for chemotherapy-based regimens (accuracy 70.1%-79.1%), highlighting the potential of transcriptomics-guided modeling to inform treatment response stratification in clinical settings where therapy selection is often empirical. For endocrine therapy, the model achieved 95.0% accuracy for tamoxifen, suggesting that transcriptomic features may capture biologically relevant estrogen-responsive and resistance-associated programs beyond receptor status alone, although this result is exploratory and based on a small sample size. In contrast, HER2-targeted therapies showed lower and more variable predictive performance, with accuracies of 66.0% for trastuzumab monotherapy and 61.9% for anthracycline-taxane chemotherapy combined with trastuzumab, likely reflecting smaller cohort sizes and the biological heterogeneity characteristic of HER2-positive disease. Overall, these findings demonstrate the feasibility of leveraging real-world transcriptomic data and machine learning to achieve generalizable treatment response stratification across diverse cohorts, platforms, and therapeutic classes, supporting the potential role of transcriptomics-guided models as complementary decision-support tools in oncology.

Backgroundfollowing a first cutaneous squamous cell carcinoma (CSCC), one-third of patients develop new primaries, escalating their risk of metastasis and poor outcomes. However, current follow-up strategies are not risk-stratified, representing a critical gap in patient management. Objectiveto develop and validate a prognostic model to quantify individualized absolute risk of a first metachronous CSCC after an index tumor, accurately accounting for the high competing risk of mortality in this typically elderly population. Methodswe conducted a nationwide, population-based cohort study of 11,737 patients with a first histologically confirmed CSCC (Netherlands Cancer Registry, 2007-2008) with up to 10 years of follow-up. Data on subsequent tumors was retrieved via linkage to the Automated National Pathological Anatomy Archive (Palga). A Fine-Gray competing-risk model was developed using routinely available clinical and pathological predictors (age, sex, hematologic malignancy, basal cell carcinoma (BCC) and actinic keratosis (AK) history, presence of synchronous CSCC, primary tumor location, and differentiation). Model performance was assessed 10-fold cross-validation, quantifying discrimination (time-dependent C-index) and calibration. Resultsduring follow-up, 3,288 (28%) developed a first metachronous CSCC. The model identified key predictors: markers of cumulative UV-exposure (included AK history, [&ge;]5 prior BCCs), and immunosuppression (chronic lymphocytic leukaemia/small lymphocytic leukaemia). Male sex, presence of synchronous CSCC at baseline were also associated with higher risk. While discrimination was modest (cross-validated 5-year C-index: 0.64), the model demonstrated excellent calibration. Conclusionsthis competing-risk model provides individualized, well-calibrated absolute risk estimates for a first metachronous CSCC. Based on routinely available clinical features, it offers insight into how established predictors shape risk in this high-susceptibility population. External validation and the identification of novel predictors are necessary to further refine the model and support personalized dermatologic care.

BackgroundNeoadjuvant chemotherapy (NAC) is the standard of care for locally advanced breast cancer. However, the disconnect between efficacy in randomized trials and effectiveness in real-world practice--attributable to real-world treatment delays and adherence barriers--remains underexplored for early-stage (cT1-cT3) operable disease. MethodsWe applied the Target Trial Emulation (TTE) framework to a propensity-score matched cohort from the SEER database. To mitigate immortal time bias and staging migration, we reconstructed clinical baselines. Individualized Treatment Effects (ITE) were estimated using a Double-Robust Causal Forest algorithm. To rigorously cross-validate these estimates against model misspecification, we employed a DeepCox neural network as a non-linear sensitivity analysis tool, exposing complex risk structures (e.g., U-shaped hazards) that traditional linear assumptions might overlook. ResultsIn the matched cohort (N=26,946), Standard NAC was associated with an operational survival deficit (Absolute Risk Difference: 3.6%) compared to upfront surgery, corresponding to a hazard ratio of 1.32 (95% CI, 1.24-1.40; p < 0.001). Causal Forest analysis revealed a critical "Response-Survival Discordance": while young TNBC patients exhibited high nodal pathologic complete response (npCR) rates, they paradoxically faced the worst survival outcomes (Standard Cox HR 1.87). Even in the 6-month landmark analysis to account for immortal time bias, this survival detriment persisted (Landmark HR 1.39; 95% CI, 1.06-1.81; p = 0.016; Figure 3D). Crucially, node-positive (cN+) patients--traditionally considered ideal candidates for systemic downstaging--experienced a significant survival detriment with NAC (HR 1.39). This disadvantage was most pronounced in Luminal A subtype and Invasive Lobular Carcinoma (ILC), where NAC failed to provide effective source control. In contrast, HER2-positive status exhibited a trend towards survival benefit, diverging from the significant risks observed in other subtypes. Anatomically, while cT2 tumors identified a "window of minimal operational deficit" where the absolute risk difference was negligible, operational risk paradoxically resurged in cT3 tumors, challenging the conventional paradigm that larger burdens inherently mandate downstaging. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/25342768v1_fig3.gif" ALT="Figure 3"> View larger version (24K): org.highwire.dtl.DTLVardef@11c1cdforg.highwire.dtl.DTLVardef@aba647org.highwire.dtl.DTLVardef@131bf42org.highwire.dtl.DTLVardef@103bc02_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 3:C_FLOATNO The Heterogeneity Landscape of Treatment Effects.(A) Individualized Treatment Effect (ITE) Waterfall Plot Visualizes the distribution of treatment effects across the cohort. The prominent red area highlights that a significant proportion of patients incur a survival detriment from NAC, contradicting the "one-size-fits-all" assumption. (B) ITE by Molecular Subtype: Boxplots confirm biological heterogeneity; Luminal A and ILC subtypes show the deepest survival penalties, while TNBC exhibits high variance. (C) SHAP Summary Plot: AI-driven interpretability identifies Nodal Stage and Age as the top predictors influencing treatment efficacy. (D) Subgroup Analysis Forest Plot: Forest plot of Hazard Ratios (derived from 6-month landmark models) across key subgroups, confirming the significant survival disadvantage in Young TNBC (p = 0.016) and Node-Positive (p< 0.001) patients. C_FIG ConclusionOur causal analysis reveals a critical disconnect between biological risk and therapeutic efficacy. While SHAP modeling identified node-positive (cN+) status as a high-priority indicator for systemic therapy, the low real-world response rate (npCR 15.0%) rendered historical standard NAC regimens insufficient to counterbalance the risks of surgical delay (HR 1.39). Our findings indicate that without therapeutic escalation (e.g., immunotherapy) to ensure high pathologic response rates, the operational risks of deferring surgery may outweigh the benefits of downstaging in this subgroup. Our findings highlight a critical "Implementation Gap" where standard NAC regimens yield suboptimal real-world outcomes for high-risk subgroups. Our findings suggest that clinical prioritization should diverge based on subtype biology: for chemo-refractory subtypes (e.g., Luminal A, ILC), Upfront Surgery ensures immediate source control and should be prioritized; conversely, for high-risk TNBC, standard NAC is insufficient, warranting Therapeutic Escalation (e.g., immunotherapy) to minimize the risk of non-response.

BackgroundBreast cancer susceptibility gene testing (BCSG-testing) is expanding in relation to both eligibility for testing and number of genes included on testing panels. However, uncertainty remains regarding the most effective testing strategies for identifying clinically actionable germline pathogenic variants (gPVs) while balancing increased burden on breast and genetics clinical services. Patients and MethodsThe North Thames Mainstreaming of Breast Cancer Genetic Testing (NT-MBGT) programme piloted unselected breast cancer (BC) patient BCSG-testing via a clinician-light BRCA-DIRECT mainstreaming pathway. We present real-world evaluation of (i) gPV pick-up rates according to BC characteristics and (ii) operational feasibility, acceptability, and satisfaction with the BRCA-DIRECT expanded testing pathway. ResultsThe BRCA-DIRECT pathway successfully tested 3,517 newly-diagnosed BC patients within 14 National Health Service (NHS) breast oncology units, with high levels of patient and breast healthcare professional (HCP) satisfaction, and genetics HCPs reporting concomitant decrease in service referrals. The overall pick-up rate of gPVs was 4.7%. Current NHS eligibility criteria would have offered testing to 20.6% of patients and identified 49.2% of observed gPVs in high penetrance (HP)-BCSGs (BRCA1/BRCA2/PALB2) and 18.2% of gPVs in intermediate penetrance (IP)-BCSGs (CHEK2/ATM/RAD51C/RAD51D). Ultra-simple eligibility criteria could improve detection (sensitivity) to 74.6% and 61.4%, respectively, whilst increasing testing to 50.2% of BC cases. ConclusionsEvidence from the NT-MBGT programme demonstrates that expanding BCSG-testing via a clinician-light pathway is acceptable and feasible, without increasing the burden on limited breast and genetics workforce, and has high satisfaction. Simplified testing criteria could improve identification of gPVs in HP-BCSGs. The concomitant increased pick-up of gPVs in IP-BCSGs warrants further consideration. highlightsO_LIIn this real-world evaluation we observed the successful rollout of the BRCA-DIRECT streamlined, clinician-light mainstreaming pathway for a pilot of germline breast cancer susceptibility gene testing in 3517 unselected breast cancer patients from 14 regional breast oncology/surgical units. C_LIO_LIPatients undergoing testing via the pathway reported high levels of satisfaction and low decisional regret, with breast and genetics healthcare professionals highly recommending the pathway for mainstream testing. C_LIO_LIDifferences were observed between breast healthcare professionals preferring unselected breast cancer patient testing and genetics healthcare professionals preferring restriction to current national testing criteria due to broader concerns around equity of access to testing. C_LIO_LIWe identified that current national testing criteria would have missed identifying 50.8% of germline pathogenic variants in high-penetrance, clinically actionable genes, likely having implications for treatment and surgical decision-making in the breast cancer patients. C_LIO_LIWe evaluated the performance of two additional approaches for establishing testing eligibility criteria to understand how we could best balance maximising identification of germline pathogenic variants (sensitivity) whilst limiting (unnecessary) testing within the breast cancer patient population (specificity). C_LI

Patient selection and enrolment into phase III randomized clinical trials (RCTs) of adjuvant cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitor therapies depend on accurate risk definition. However, standard clinicopathologic criteria incompletely capture recurrence risk, limiting their efficacy in treatment selection. To assess whether artificial intelligence (AI)-enhanced prognostication may enrich the clinical risk groups utilized in the adjuvant NATALEE trial, we evaluated Ataraxis Breast RISK (ATX), a multimodal AI test that integrates clinical data with morphological features from H&E-stained slides. ATX risk scores were generated for 2,228 patients with HR+/HER2- early breast cancer, of which 918 (41%) were classified as clinical high-risk and 1,310 (59%) were clinical low-risk. ATX was significantly associated with recurrence-free interval in both clinical risk groups and identified high-risk patients not captured by current clinical criteria, as well as individuals with limited benefit despite clinical high-risk classification. Consequently, integration of AI-enhanced risk assessment may improve selection of patients likely to benefit from adjuvant CDK4/6 inhibitors relative to current criteria.

Early and reliable discrimination between malignant and benign breast tumors is essential for clinical decision-making and for reducing unnecessary invasive procedures. This study presents a lightweight and reproducible machine-learning pipeline that integrates standard feature normalization with logistic regression to classify breast tumors using the Breast Cancer Wisconsin (Diagnostic) dataset (WDBC), which contains 569 samples described by 30 quantitative features derived from digitized fine-needle aspirate (FNA) images [1-3]. We implemented an end-to-end workflow in Python (scikit-learn), including stratified train-test splitting, model training, and evaluation with clinically meaningful metrics such as accuracy, sensitivity, specificity, and ROC-AUC [4-6]. On the held-out test set (n=114), the proposed approach achieved 98.25% accuracy and a ROC-AUC of 0.9954, with a confusion matrix indicating only two misclassifications (1 malignant predicted as benign, 1 benign predicted as malignant). Specifically, when treating malignant cases as the clinically critical positive class, the method yielded 97.62% sensitivity and 98.61% specificity. These results demonstrate that a simple, interpretable model can achieve near state-of-the-art performance on structured biomedical features while remaining computationally efficient and suitable for rapid prototyping.In addition, we benchmarked logistic regression against several classical baselines (SVM with RBF kernel, random forest, and kNN) under the same train-test split and evaluation protocol. The results indicate that increasing model complexity yields limited performance gains on WDBC, suggesting that an interpretable linear classifier can already approach the performance ceiling on this feature-engineered dataset. Future work will focus on external validation, calibration for risk estimation, and multimodal extensions to incorporate imaging or omics signals.