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PubMed2026

Liquid biopsy: a new window on the BRCA genes.

The Breast Cancer Susceptibility Gene (BRCA)-associated tumors represent a constantly evolving and intriguing scenario in oncology, in which the availability of novel systemic treatment, mainly including the poly (ADP-ribose) polymerase (PARP) inhibitors, has enabled an improved survival benefit in clinical subgroups. The expanding regulatory approvals of PARP inhibitors have inevitably reshaped the clinical indications for BRCA testing, moving the BRCA1/2 profiling from the traditional and preventive workflows to therapeutic paths. Despite advances in technology and treatment, substantial limitations remain in current genetic and genomic tools for the detection of deleterious BRCA1/2 variants. Germline and tumor tissue testing provide only a snapshot of a patient's disease, failing to capture the dynamic and longitudinal aspects of tumor clonal evolution. In this scenario, liquid biopsy (LB) profiling of BRCA1/2 genes, primarily as circulating tumor DNA, represents a highly active area of research potentially affecting many aspects of cancer screening, diagnosis, and monitoring in individuals who are carriers of BRCA1/2 deleterious variants. Beyond the attractive potential to surrogate the tumor tissue testing, to overcome the cancer spatial and temporal heterogeneity, and to monitor the tumor mutational profile over time, accurately detecting all clinically relevant BRCA genetic variants and epigenetic modifications using LB remains technically challenging.

BRCA1/2↗

PubMed2026

[Effect of Cancer Antigen-125 Elimination Rate Constant K and BRCA Mutation Status on the Prognosis of Interval Debulking Surgery in Advanced High-Grade Serous Ovarian Cancer].

OBJECTIVE: To investigate the predictive value of the cancer antigen-125 elimination rate constant K (KELIM) for treatment response and prognosis in patients with advanced high-grade serous ovarian cancer (HGSOC) undergoing neoadjuvant chemotherapy followed by interval debulking surgery (NACT-IDS), and to analyze the combined prognostic significance of KELIM and the mutation status of breast cancer susceptibility gene (BRCA). METHODS: A total of 106 patients with advanced HGSOC who had undergone NACT-IDS were retrospectively enrolled. The KELIM values during neoadjuvant chemotherapy were calculated, and patients were divided into high- and low-KELIM groups using a cutoff value of 1.0. Clinicopathological characteristics, R0 resection rates, and platinum sensitivity rates were compared between the two groups. Logistic regression analysis was performed to identify predictive factors for R0 resection, while Kaplan-Meier survival analysis and Cox proportional hazards regression were performed to evaluate factors associated with progression-free survival (PFS). Furthermore, the patients were stratified according to both KELIM and BRCA status to assess the risk of platinum-resistant recurrence in each subgroup. RESULTS: The R0 resection rate was higher in the KELIM ≥ 1 group than in the KELIM < 1 group (77.1% vs 55.2%), and the difference was statistically significant (P = 0.024). Multivariate logistic regression analysis showed that KELIM was an independent predictor of R0 resection (odds ratio [OR] = 2.922, 95% CI: 1.112-7.678). Survival analysis demonstrated longer PFS in the KELIM ≥1 group compared with that in the KELIM <1 group (33.0 months vs 18.0 months), and the difference was statistically significant (P < 0.001). Multivariate Cox regression analysis showed that KELIM ≥ 1 was associated with a reduced risk of disease progression (hazard ratio [HR] = 0.481, 95% CI: 0.280-0.826). Combined stratification analysis revealed that no platinum-resistant recurrence was observed in the subgroup with both KELIM ≥1 and a BRCA-positive status (0/21). Compared with patients with KELIM <1 and a BRCA-negative status, this subgroup exhibited a lower risk of platinum-resistant recurrence (OR = 0.053, 95% CI: 0.003-0.932, P = 0.006). CONCLUSION: KELIM is an effective dynamic biomarker for predicting surgical outcomes and PFS in patients undergoing NACT-IDS. Combined stratification by KELIM and BRCA status allows more precise identification of the patient population with both KELIM ≥1 and BRCA-positive status, who have an extremely low risk of platinum-resistant recurrence, thereby providing an important basis for individualized treatment and risk stratification management in patients with advanced HGSOC.

Humans↗

PubMed2026

Genomic Landscape of 6597 Hong Kong HBOC Patients: Implications for Beyond-BRCA Multi-Gene Panel Testing and Cancer Surveillance.

Breast cancer remains highly prevalent, where the lifetime risk before age 75 is one in 13. However, known genetic factors were only identified in 14.7% of cases in our Hong Kong Hereditary Breast Cancer Family Registry. Our current local policy for genetic testing does not cover the detection of beyond BRCA1/2. Here we highlight the clinical value of extending testing to beyond BRCA susceptibility genes for improved prevention, diagnosis, and management. We recruited 6597 hereditary breast and ovarian cancer (HBOC) patients from our registry based on family history and clinical criteria. Germline mutations were identified by multi-gene sequencing analysis using next-generation sequencing (NGS). Clinical-pathological characteristics of BRCA and beyond BRCA carriers were compared and the real-world management and surveillance services adopted in Hong Kong were highlighted. In this multi-gene hereditary cancer cohort, germline mutations were identified in 10.9% of cases for BRCA1/2 and 3.5% for beyond BRCA susceptibility genes. These beyond BRCA mutations constitute a considerable proportion of actionable hereditary risk. Notably, PALB2 emerged as the most prevalent non-BRCA gene, followed by TP53, ATM, and BARD1. New cancers or recurrences were detected during their surveillance; the overall pick up rates were 8.3% (PALB2), 29.4% (TP53), 33.3% (PTEN) and 5.6% (BARD1). This study provides the first comprehensive characterization of the beyond BRCA germline landscape in a Hong Kong hereditary cancer cohort and highlights the current need for implementing multi-gene sequencing analysis and surveillance services for HBOC patients, establishing the predominant non-BRCA drivers and providing a robust empirical basis for expanding public genetic screening frameworks.

Humans↗

PubMed2025

Leveraging Pharmacy Education through a Train-the-Trainer Model to Enhance Breast Cancer Literacy in Rural Communities.

Rural versus urban communities experience disproportionate challenges in breast cancer outcomes, with higher breast cancer mortality and later stage disease presentation, despite similar diagnosis rates. These disparities are driven by structural barriers, including rural hospital closures, transportation difficulties, and limited access to oncology specialists. This study evaluated a train-the-trainer program designed to equip PharmD students located at a pharmacy school in a rural county in South Carolina with breast cancer education training, leveraging the pharmacists' position as accessible healthcare professionals in rural communities. Training focused on breast cancer risk factors, prevention, screening, genetics, staging, and treatment options. Effectiveness was measured through pre- and post-workshop confidence surveys and knowledge assessments. Results showed significant improvement in student confidence across educational domains, with average scores increasing from 6.30 to 8.59 (p < 0.0001). Understanding of screening guidelines (mean difference: 4.30; p-value: < .0001) and target therapy options showed the greatest improvement (mean difference: 3.65; p-value: < .0001), while knowledge of BRCA gene inheritance showed the smallest change (mean difference: 0.369; p-value: ns), suggesting some pre-existing awareness but limited understanding of its clinical applications. Overall, this pilot program demonstrates how pharmacy education can address healthcare disparities in rural communities. By preparing pharmacists to deliver accurate breast cancer education and to increase rural patient agency, this model creates a sustainable approach to improving health literacy in medically underserved areas. Future research could further expand this model to include diverse healthcare professionals and incorporate long-term impact assessments in community settings.

Humans↗

PubMed2026

Brazilian Society of Surgical Oncology Analysis in Cost-Effectiveness of Population-Based BRCA Testing for Ovarian Cancer in the Public Health System.

Although ovarian cancer is the most lethal among gynecological cancers, access to massive BRCA testing is still limited. Its cost-effectiveness is still a topic of discussion in several countries. In Brazil, olaparib was recently incorporated into the public health system, access to BRCA testing is still limited. In this article, we aim to review the cost-effectiveness of offering BRCA testing to the at-risk population. A working group composed of 14 specialists in surgical oncology and cancer genetics was established to discuss the cost-effectiveness of population-based BRCA testing for ovarian cancer. The project was divided into five main areas, each with subtopics assigned among the 14 participants. They were: the existing clinical testing guidelines, the current healthcare infrastructure in the Brazilian public health system, cost-effectiveness analysis, challenges in implementing prophylactic surgeries, and family counseling and risk communication. A comprehensive literature review was conducted, followed by a series of meetings among the article's contributors to reach consensus on unresolved issues. These discussions aimed to build recommendations based on the best available scientific evidence. Using as a basis the current structure already existing within the Brazilian public health service (SUS [Sistema Único de Saude]), and based on the testing of the at-risk population chosen by our experts, we estimated savings. The net savings for a population of 100 000 women would range from BRL 7030.30 (US$1255.41) to BRL 1853.92 (US$331.05). And these costs could have an even greater impact when public service PARP inhibitors are incorporated. The working group of the Brazilian Society of Surgical Oncology understands that large-scale BRCA testing is cost-effective, especially when risk-reducing surgery is implemented. Other measures are important, such as training teams of non-specialists to recognize the population at risk, in addition to creating an entire line of care for patients with ovarian cancer in the SUS.

Humans↗

PubMed2026

Prevalence of homologous recombination repair genes alterations in metastatic castration-resistant prostate cancer, a multicentric study.

INTRODUCTION: Homologous Recombination Repair (HRR) genes alterations are a resistance mechanism to therapies by taxanes or Androgen Receptor Signalling inhibitors in Metastatic Castration Resistant Prostate Cancer (mCRPC). BRCA-mutated mCRPC patients are eligible to poly adenosine diphosphateribose polymerase inhibitors (PARPi). Therefore, assessing the population-specific prevalence of HRR-related genes alterations is of public healthcare importance. METHODS: This retrospective, non-interventional, multicentric study was conducted across 6 reference French centers in a "real-life" setting. 788 paraffin-embedded mCRPC patient-samples were included and submitted to testing for BRCA1/2 in six different centers; additionally, non-BRCA HRR-related genes were investigated in two different centers. RESULTS: Among the samples, n=602 (76.4%) were contributive for molecular testing. In multivariate analysis by logistic regression and sensitivity analysis, only sample age (P<0.01), sample surface area (P=0.02) and institution (P=0.018) remained statistically significant. BRCA alterations were detected in n=39/602 (6.5%) of contributive samples, with n=35 and n=4 alterations of BRCA2 and BRCA1 respectively. Non-BRCA HRR-related genes alterations were detected in n=12/157 (7.6%) of contributive samples, with alterations of mainly ATM (n=6, 3.8%), CDK12 (n=4, 2.5%) and CHEK2 (n=2, 1.3%). DISCUSSION: In this study, testing contributivity was similar or higher that of other studies in the literature, and observed mutations prevalences were similar to that of other screenings of western populations. Harmonising per-centres protocols and enhancing molecular testing contributivity with the screening of circulating DNA samples and expanding its range by including non-BRCA HRR-related genes in all reference centres will enable more patients to be accurately treated by targeted therapies. LEVEL OF EVIDENCE: 3 (grade C).

Male↗

PubMed2026

Unveiling novel transcriptomic prognostic biomarkers for specific breast cancer subtypes and treatment regimens.

BACKGROUND: Breast cancer (BRCA) is the most common cancer in women worldwide, yet current gene expression panels offer limited insight into treatment responses across different subtypes and therapies. This study aimed to identify reliable biomarkers for predicting treatment outcomes in specific BRCA subtypes and treatment regimens. METHODS: This study analyzed transcriptomic data from The Cancer Genome Atlas to identify differentially expressed genes (DEGs) in patient groups treated with different combinations of hormone therapy (H), chemotherapy (C), radiotherapy (R), and targeted therapy (T). Non-negative matrix factorization clustering was performed to stratify patients into clusters representing different BRCA subtypes. Functional enrichment analysis was performed, and survival assessments were conducted using the METABRIC dataset. RESULTS: A total of 1,148 DEGs were identified across treatment regimens, with 75 common DEGs shared across multiple regimens. Among these, 12 candidate biomarkers were associated with luminal subtypes treated with H, including LRP1B, of which high expression predicted cancer recurrence. In triple-negative breast cancer (TNBC) treated with C, 76 candidate biomarkers were identified, including TTYH1 for recurrence and ANXA8L1 and MPZ for non-recurrence. Functional analyses identified intermediate filament organization and keratinization as pathways associated with specific candidate biomarkers of TNBC following C. Survival analysis using METABRIC strengthened the prognostic ability of LRP1B and TTYH1 to predict worse survival and ANXA8L1 and MPZ to predict prolonged survival, with four additional prognostic biomarkers. CONCLUSION: This study identified gene expression prognostic biomarkers for luminal and TNBC subtypes, thereby supporting personalized therapies. Further experimental validation is required to confirm these findings for clinical application. CLINICAL TRIAL REGISTRY: No.

Breast cancer↗

PubMed2026

Primary Tumor Epigenetic and Transcriptomic Alterations Associated with Nodal Burden and Metastatic Risk in ER+/HER2- Breast Cancer.

De-escalation of axillary surgery has resulted in the loss of pathologic nodal information, yet the extent of lymph node involvement remains an important determinant of treatment decisions in estrogen receptor-positive (ER+)/HER2- disease. We examined whether primary tumors differed molecularly according to the extent of this regional dissemination. Genome-wide DNA methylation profiling of primary ER+/HER2- tumors from 47 patients with pN1 (n = 29) vs. >pN1 (n = 18) disease showed differences concentrated at promoters of developmental and cell-adhesion genes. By integrating methylomes with transcriptomes from the TCGA-BRCA cohort (n = 148) and clinical outcomes from KM Plotter (RFS, n = 1154; OS, n = 442; DMFS, n = 423), we identified four genes (ARL10, RIC3, CXCL14, KCNH2) showing concordant molecular and clinical associations, from which we derived the Lymph-node Involvement Outcome Numerator (LION) score. Lower LION scores were observed in metastatic lesions from the AURORA US cohort (n = 45). In SCAN-B (n = 3969), lower scores were associated with shorter distant recurrence-free intervals (HR = 0.38; 95% CI 0.23-0.62); this association persisted after adjustment for age, nodal and tumor category but was lost after adjustment for histological grade (HR = 0.83; 95% CI 0.48-1.44), indicating that the score and grade capture overlapping biology. These findings suggest that primary tumors already display coordinated epigenetic and transcriptional alterations associated with the extent of metastatic dissemination.

Humans↗

PubMed2026

TFPI-high myofibroblast states and a meta-program-related five-gene prognostic signature in breast cancer.

Intratumoral heterogeneity and tumor-microenvironment interactions limit prognostic stratification in breast cancer, but the prognostic relevance and cellular context of recurrent transcriptional meta-programs remain unclear. We aimed to derive a meta-program-related prognostic signature and characterize its component transcripts at single-cell resolution. Six paired institutional tumors and adjacent non-tumor tissues served as a proof-of-concept comparison. Univariable Cox screening and least absolute shrinkage and selection operator Cox regression were used to derive a five-gene score from a prespecified meta-program-related candidate set in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) training cohort; the score was tested internally and assessed in GSE20685 using fixed coefficients and cohort-specific median cutoffs. GSE161529 single-cell transcriptomic data were used to map signature transcripts across 136,526 quality-controlled cells, while donor-aware pseudobulk analysis compared upper- and lower-quartile TFPI expression states in annotated myofibroblasts. The score comprised TCN1, FOXJ1, PIGR, SLAIN1, and TFPI and was associated with overall survival in the training, testing, and external cohorts, with concordance indices of 0.782, 0.756, and 0.721, respectively. TFPI transcripts were detected across endothelial, fibroblast, and myofibroblast compartments. TFPI-high myofibroblasts showed transcriptional enrichment of extracellular matrix and collagen fibril organization, transforming growth factor beta signaling, epithelial-mesenchymal transition, and myogenesis, together with lower oxidative phosphorylation and fatty acid metabolism programs. In bulk TCGA-BRCA tissue, TFPI expression correlated positively with stromal (r = 0.48), immune (r = 0.25), and composite microenvironment scores (r = 0.40; all p < 0.001). These findings identify a hypothesis-generating five-gene bulk-tissue prognostic signature and an expression-associated TFPI-high myofibroblast state but do not establish a discrete lineage, the cellular source of bulk TFPI, a TFPI-dependent mechanism, or clinical utility. Independent prospective cohorts, spatial and protein-level validation, and functional perturbation studies are required.

Journal Article↗

PubMed2026

Comprehensive Genomic Profiling Reveals the Mutational Spectrum and Clinical Significance of BRCA1/2 and Other Cancer-Susceptibility Genes in Breast Cancer Patients from Southern Tunisia.

BACKGROUND/OBJECTIVES: This study aims to investigate the mutational spectrum of BRCA1 and BRCA2 genes in a cohort of breast cancer (BC) patients from southern Tunisia, and to evaluate their clinical and prognostic significance. Additionally, this study explores the contribution of other cancer predisposition genes and the prevalence of variants of uncertain significance (VUS). RESULTS: Among the 165 patients included, pathogenic or likely pathogenic variants (P/LPVs) in BRCA1/BRCA2 were identified in 19 cases (11.51%), including 8 in BRCA1 and 11 in BRCA2. The presence of BRCA P/LPVs associated with young patients (p = 0.006) and those with TNBC (p = 0.036). Beyond BRCA1/2, PV/LPVs were detected in other cancer-related genes, including TP53 (n = 3), CHEK2, RAD50 (n = 2 cases each), and MUTYH, BARD1, and BRIP1 (one case each). Furthermore, 56 VUS were identified; among them, 7 were prioritized based on in silico predictive analyses, suggesting a potential deleterious effect. However, these VUS should not be used for clinical decision-making without additional evidence from functional and familial segregation studies. CONCLUSIONS: Our findings provide novel insights into the genetic landscape of breast cancer in southern Tunisia, highlighting the clinical relevance of BRCA1/2 mutations and the contribution of other susceptibility genes. These results support the personalized management of breast cancer patients and the implementation of expanded multigene panel testing in routine clinical practice to improve genetic counseling.

BRCA1↗

PubMed2026

PGR expression as a pharmacogenomic companion biomarker to GENE70-derived genomic risk in ER-positive/HER2-negative breast cancer.

BACKGROUND: The biology of the estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancers is heterogeneous even when they are categorized by their risk via genomics. Transcriptomic PGR expression reflects endocrine pathway activity and may provide complementary biological information within established GENE70-derived genomic-risk categories. Whether this molecular marker improves the biological interpretation of genomic-risk stratification beyond conventional clinicopathological assessment remains uncertain. OBJECTIVES: The aim of this study was to determine whether transcriptomic PGR expression provides complementary biological and prognostic information within reconstructed GENE70-derived genomic-risk categories and refines the characterization of endocrine-related tumour biology in ER-positive/HER2-negative breast cancer. METHODS: This study analysed publicly available transcriptomic and clinical data from three cohorts: METABRIC (discovery cohort), GSE96058/SCAN-B cohort (validation cohort) and TCGA-BRCA cohort (molecular validation cohort). The GENE70-derived genomic-risk score was reconstructed for each cohort using matched genes. Cox regression, Kaplan-Meier analysis and subgroup comparisons were used to assess relationships between PGR expression, clinicopathologic variables, molecular features and survival outcomes. RESULTS: Across the three independent cohorts, low transcriptomic PGR expression was consistently associated with higher GENE70-derived genomic risk, increased MKI67 expression, reduced ESR1 expression and enrichment of the Luminal B subtype. Survival findings differed between cohorts. In the discovery METABRIC cohort, transcriptomic PGR expression showed heterogeneous associations with survival, particularly within GENE70-derived high-risk subgroups, whereas the external GSE96058/SCAN-B validation cohort demonstrated consistent associations between low PGR expression and poorer overall survival in both the overall ER-positive/HER2-negative population and GENE70-derived high-risk subgroups. CONCLUSION: These findings suggest that transcriptomic PGR provides complementary biological and prognostic information within GENE70-derived genomic-risk categories. However, because treatment response was not evaluated in the present study, the findings should not be interpreted as evidence of predictive or pharmacogenomic utility and prospective studies incorporating treatment-response analyses are required before such applications can be established.

Humans↗

PubMed2026

Tertiary lymphoid structure transcriptomic signatures show limited and cohort-dependent value for predicting axillary nodal involvement in oestrogen receptor-positive luminal breast cancer.

Tertiary lymphoid structures (TLS) are associated with prognosis in solid tumours. Their value for predicting axillary nodal involvement in oestrogen receptor-positive luminal breast cancer remains uncertain. Three published TLS signatures were scored by single-sample gene set enrichment analysis in oestrogen receptor-positive luminal tumours. The Cancer Genome Atlas Breast Invasive Carcinoma cohort (TCGA-BRCA) included 632 cases, of which 379 met strict consensus. METABRIC included 1086 cases, of which 663 met strict consensus. Logistic models adjusted for age and pathological tumour stage. Strict consensus, majority vote, and continuous scores were compared. Performance assessment included bootstrapped changes in area under the receiver-operating-characteristic curve, Brier scores, calibration, and decision-curve analysis. Survival was evaluated in METABRIC and explored in TCGA-BRCA. Strict-consensus TLS status was not associated with nodal positivity in TCGA-BRCA (adjusted odds ratio: 0.95, 95% confidence interval: 0.62-1.45, P = 0.822). METABRIC was similar (odds ratio: 0.76, 95% confidence interval: 0.55-1.06, P = 0.105). Full-cohort METABRIC analyses detected small majority-vote and continuous-score associations, absent in TCGA-BRCA. Across specifications, bootstrapped changes in area under the receiver-operating-characteristic curve ranged from 0.0002 to 0.0089, with minimal Brier-score improvement and no stable decision-curve benefit. In METABRIC, the univariable overall survival association attenuated after age adjustment (hazard ratio: 1.33-1.10). TCGA-BRCA survival analyses were nonsignificant. TLS transcriptomic signals showed small, cohort-dependent associations with nodal status but no reproducible or clinically meaningful incremental predictive value. These data do not support replacing sentinel lymph node biopsy with a TLS signature in oestrogen receptor-positive luminal breast cancer.

breast cancer↗

PubMed2026

Histopathological evaluation of RPL5 expression in triple-negative breast cancer: an integrated immunohistochemical and transcriptomic study.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by high invasiveness, limited therapeutic options, and unfavorable clinical outcomes. Ribosomal protein L5 (RPL5), a component of the large ribosomal subunit, has been implicated in ribosome biogenesis, translational regulation, and p53-associated cellular processes. This study investigated the immunohistochemical expression pattern of RPL5 in TNBC tissues and explored its potential biological significance through integrated transcriptomic analyses. Tumor tissues from 37 patients with TNBC and 7 adjacent non-tumorous breast tissues were collected from the Affiliated Tumor Hospital of Xinjiang Medical University between December 2017 and December 2023. RPL5 protein expression was evaluated by immunohistochemistry, and its association with clinicopathological characteristics was analyzed. Public transcriptomic datasets from TCGA-BRCA and GEO were further used to validate RPL5 expression patterns in TNBC. Co-expression analysis and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate potential biological functions and signaling pathways associated with RPL5. Immunohistochemical analysis demonstrated significantly lower RPL5 protein expression in TNBC tissues compared with adjacent normal breast tissues (p=0.001). In contrast, transcriptomic analyses revealed significantly higher RPL5 expression in TNBC compared with non-TNBC breast cancer subtypes (p<0.001). No significant associations were observed between RPL5 expression and clinicopathological parameters, including age, tumor size, menopausal status, TNM stage, histological grade, or lymph node metastasis (all p>0.05). Survival analysis showed no significant difference in overall survival between patients with high and low RPL5 expression. Functional enrichment analyses indicated that RPL5-related genes were predominantly involved in ribosome biogenesis, translational regulation, and p53-related signaling pathways. These findings suggest that abnormal RPL5 expression may be associated with TNBC biology through ribosome-related programs, although causal roles require functional validation. RPL5 may represent a potential histopathological and molecular indicator associated with TNBC biology, although its precise functional role requires further experimental validation.

Humans↗

PubMed2026

Hypernetwork-guided fusion with intra-class MixUp for breast cancer subtyping.

Accurate breast cancer subtyping guides treatment selection, yet histopathology captures morphology without molecular state, while genomic profiling captures molecular signatures without spatial context. Existing fusion methods rely on concatenation, or on attention applied only after each modality is encoded independently. This work identifies a scale-dependent asymmetry in the direction of cross-modal conditioning: the direction that performs best under limited samples is not the one that holds at scale, and the reversal is traced to the capacity of the modulation pathway rather than to the fusion principle. The comparison is carried out within a hypernetwork-guided framework in which an auxiliary network maps one modality to conditioning parameters that modulate the other's feature representation, shaping features at the parametric level rather than the decision stage; modulation is patient-specific rather than patch-specific. Both directions are instantiated-gene-to-image (HyperG2I) and image-to-gene (HyperI2G) - and trained under a label-aware MixUp strategy that interpolates within-class samples across both modalities, preserving the hard binary labels clinical decisions require. The framework is evaluated on two paired TCGA-BRCA cohorts-one limited-sample, one independently assembled at scale-under a single protocol spanning two whole-slide representations, multiple visual backbones, and both conditioning directions. On the limited-sample cohort, gene-to-image conditioning at its optimal augmentation setting exceeds early fusion and both unimodal baselines, giving the highest recall on the aggressive Basal/HER2 class of any configuration evaluated, and an ablation favours intra-class over inter-class mixing. At scale this ordering does not hold: image-to-gene conditioning sustains its performance whereas gene-to-image does not, recovering only partially under the full tissue bag and isolating the capacity of the modulation pathway as the binding constraint. Direction and capacity of cross-modal conditioning, rather than fusion depth alone, therefore govern how such frameworks scale.

Breast Neoplasms↗

PubMed2017

Predictors of BRCA1/2 genetic testing among Black women with breast cancer: a population-based study.

Evidence shows that Black women diagnosed with breast cancer are substantially less likely to undergo BRCA testing and other multipanel genetic testing compared to White women, despite having a higher incidence of early-age onset breast cancer and triple-negative breast cancer (TNBC). Our study identifies predictors of BRCA testing among Black women treated for breast cancer and examines differences between BRCA testers and nontesters. We conducted an analysis of 945 Black women ages 18-64 diagnosed with localized or regional-stage invasive breast cancer in Pennsylvania and Florida between 2007 and 2009. Logistic regression was used to identify predictors of BRCA 1/2 testing. Few (27%) (n = 252) of the participants reported having BRCA testing. In the multivariate analysis, we found that perceived benefits of BRCA testing (predisposing factor) ([OR], 1.16; 95% CI: 1.11-1.21; P < 0.001), income (enabling factor) ([OR], 2.10; 95% CI: 1.16-3.80; p = 0.014), and BRCA mutation risk category (need factor) ([OR], 3.78; 95% CI: 2.31-6.19; P < 0.001) predicted BRCA testing. These results suggest that interventions to reduce disparities in BRCA testing should focus on identifying patients with high risk of mutation, increasing patient understanding of the benefits of BRCA testing, and removing financial and other administrative barriers to genetic testing.

Adolescent↗

PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗

PubMed2026

Association of MPO Expression with the Immune Microenvironment in Breast Cancer: Insights from Bioinformatics and Single-Cell Analyses.

Breast cancer remains a major cause of cancer-related mortality, and exploratory computational workflows can help prioritize immune-associated markers for further investigation. Here, we used the cancer genome atlas breast invasive carcinoma (TCGA-BRCA) bulk transcriptomic data and the public single-cell dataset GSE161529 to examine associations between myeloperoxidase (MPO) expression, clinical outcomes, immune infiltration, methylation, upstream-regulator annotations, single-cell expression patterns, virtual knockdown sensitivity outputs, drug-gene interaction retrieval, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) annotation. MPO expression was lower in breast cancer tissues than in adjacent non-tumor tissues. Higher MPO expression was associated with a longer progression-free interval, whereas its associations with overall survival and disease-specific survival were not statistically significant. Receiver operating characteristic (ROC) analysis suggested tumor-normal separation within the analyzed public dataset, but this should not be interpreted as clinical diagnostic validation. Immune deconvolution and enrichment analyses indicated that MPO expression mainly tracked with immune- and myeloid-related transcriptional features, rather than establishing tumor-intrinsic regulation of the immune microenvironment. At single-cell resolution, the MPO signal was sparse, with only 85 MPO-positive cells detected before k-nearest neighbor (KNN)-based neighborhood expansion. Detectable MPO signal and MPO-associated scores were interpreted cautiously because they may be influenced by sparse expression, cell-type annotation uncertainty, dropout, doublets, or ambient RNA. In silico virtual knockdown suggested candidate immune- and inflammatory-related transcriptional changes, but these results were considered exploratory and require validation. Drug-gene interaction database (DGIdb)-based drug-gene retrieval and ADMET annotation were used only as preliminary chemical annotations and were not interpreted as therapeutic evidence. Overall, this study provides a reproducible in silico workflow for generating hypotheses about MPO-associated immune/myeloid features in breast cancer, which require external cohort validation and experimental confirmation.

Humans↗

PubMed2026

RCoxNet: A Deep Learning Framework Integrating Random Walk with Restart, Mutation, and Clinical Data for Cancer Survival Prediction.

Accurate survival prediction in cancer remains challenging due to the sparsity of somatic mutation profiles and the failure of existing models to capture higher-order gene-gene dependencies. Network diffusion methods such as Random Walk with Restart (RWR) can propagate mutation signals across protein-protein interaction (PPI) networks to address sparsity, yet their integration within a deep learning Cox survival framework has not been comprehensively benchmarked across multiple cancer cohorts. We present RCoxNet, a deep learning framework that maps somatic mutation profiles onto a ConsensusPathDB-derived PPI network via RWR, selects prognostic genes by log-rank filtering, and processes network-informed mutation scores through three fully connected hidden layers feeding into a Cox proportional hazards output. RCoxNet was evaluated on The Cancer Genome Atlas (TCGA) cohorts for four cancer types (breast invasive carcinoma [BRCA], lung adenocarcinoma [LUNG], glioblastoma multiforme [GBM], and ovarian serous cystadenocarcinoma [OV]) using 20 independent random splits. The model achieved mean C-index values of 0.807 ± 0.044 (BRCA), 0.750 ± 0.039 (LUNG), 0.704 ± 0.041 (GBM), and 0.668 ± 0.036 (OV), consistently outperforming DeepSurv, Cox-nnet, SurvivalNet, Cox Elastic-Net (Cox-EN), and DeepHit, with statistically significant gains over Cox-EN, Cox-nnet, SurvivalNet, and DeepHit across the majority of cohorts. RCoxNet demonstrates that embedding sparse mutation profiles into a PPI network context substantially improves cancer survival prediction and yields biologically interpretable prognostic features relevant to precision oncology.

cancer survival prediction↗