Breast Cancer Cohort Research Articles

Threonine and tyrosine kinase (TTK) mRNA and protein expression in breast cancer; prognostic significance in the neoadjuvant setting.

Threonine and tyrosine kinase (TTK) is up-regulated in triple-negative breast cancer (TNBC), yet its expression in patients undergoing neoadjuvant chemotherapy (NACT) remains unexplored. This investigation aims to assess TTK protein expression in treatment-naïve pre-treatment cores and paired pre- and post-NACT breast cancer (BC) cohorts, as well as its correlation with microcephaly 1 (MCPH1) protein expression. Transcriptomic data were sourced from the Gene Expression Omnibus microarray database for mRNA expression analysis. TTK protein expression was evaluated using immunohistochemistry staining, employing receiver operating characteristic curve analysis to determine an optimal TTK expression cut-off point. The association between TTK expression, clinicopathological parameters and survival outcomes was examined. Additionally, MCPH1 protein expression was assessed in a pilot study. Analysis revealed a significantly elevated TTK mRNA expression in BC tissue compared to normal breast tissue, with high TTK mRNA levels predicting reduced overall survival. Notably, TTK protein expression increased significantly post-NACT in a paired cohort. Conversely, decreased TTK protein expression pre-NACT was correlated with improved overall survival. High TTK and low MCPH1 protein expression was significantly correlated, highlighting TTK's potential as a biomarker for BC and a therapeutic target for MCPH1-deficient cancer cells.

Disrupted Lipid Metabolism, Cytokine Signaling, and Dormancy: Hallmarks of Doxorubicin-Resistant Triple-Negative Breast Cancer Models.

Chemoresistance in triple-negative breast cancer (TNBC) presents a significant clinical hurdle, limiting the efficacy of treatments like doxorubicin. This study aimed to explore the molecular changes associated with doxorubicin resistance and identify potential therapeutic targets to overcome this resistance, thereby improving treatment outcomes for TNBC patients. Doxorubicin-resistant (DoxR) TNBC models (MDA-MB-231 and BT-549) were generated by exposing cells to increasing concentrations of doxorubicin. RNA sequencing (RNA-Seq) was performed using the Illumina platform, followed by bioinformatics analysis with CLC Genomics Workbench and iDEP. Functional assays assessed proliferation, sphere formation, migration, and cell cycle changes. Protein expression and phosphorylation were confirmed via Western blotting. Pathway and network analyses were conducted using Ingenuity Pathway Analysis (IPA) and STRING, while survival analysis was performed using Kaplan-Meier Plotter database. DoxR cells exhibited reduced proliferation, sphere formation, and migration, but showed enhanced tolerance to doxorubicin. Increased CHK2 and p53 phosphorylation indicated cellular dormancy as a resistance mechanism. RNA-Seq analysis revealed upregulation of cytokine signaling and stress-response pathways, while cholesterol and lipid biosynthesis were suppressed. Activation of the IL1β cytokine network was prominent in DoxR cells, and CRISPR-Cas9 screens data identified dependencies on genes involved in rRNA biogenesis and metabolism. A 27-gene signature associated with doxorubicin resistance was linked to worse clinical outcomes in a large breast cancer cohort (HR = 1.76, FDR p < 2.0 × 10-13). This study uncovers potential therapeutic strategies for overcoming TNBC resistance, including dormancy reversal and targeting onco-ribosomal pathways and cytokine signaling networks, to improve the efficacy of doxorubicin-based treatments.

ERBB2/HOXB13 co-amplification with interstitial loss of BRCA1 defines a unique subset of breast cancers

BackgroundThe HOXB13/IL17RB gene expression biomarker has been shown to predict response to adjuvant and extended endocrine therapy in patients with early-stage ER+ HER2- breast tumors. HOXB13 gene expression is the primary determinant driving the prognostic and endocrine treatment-predictive performance of the biomarker. Currently, there is limited data on HOXB13 expression in HER2+ and ER- breast cancers. Herein, we studied the expression of HOXB13 in large cohorts of HER2+ and ER- breast cancers.MethodsWe investigated gene expression, genomic copy number, mutational signatures, and clinical outcome data in the TGGA and METABRIC breast cancer cohorts. Genomic-based gene amplification data was validated with tri-colored fluorescence in situ hybridization.ResultsIn the TCGA breast cancer cohort, HOXB13 gene expression was significantly higher in HER2+ versus HER2- breast cancers, and its expression was also significantly higher in the ER- versus ER+ breast cancers. HOXB13 is frequently co-gained or co-amplified with ERBB2. Joint copy gains of HOXB13 and ERBB2 occurred with low-level co-gains or high-level co-amplifications (co-amp), the latter of which is associated with an interstitial loss that includes the tumor suppressor BRCA1. ERBB2/HOXB13 co-amp tumors with interstitial BRCA1 loss exhibit a mutational signature associated with APOBEC deaminase activity and copy number signatures associated with chromothripsis and genomic instability. Among ERBB2-amplified tumors of different tissue origins, ERBB2/HOXB13 co-amp with a BRCA1 loss appeared to be enriched in breast cancer compared to other tumor types. Lastly, patients with ERBB2/HOXB13 co-amplified and BRCA1 lost tumors displayed a significantly shorter progression-free survival (PFS) than those with ERBB2-only amplifications. The difference in PFS was restricted to the ER- subset patients and this difference in PFS was not solely driven by HOXB13 gene expression.ConclusionsHOXB13 is frequently co-gained with ERBB2 at both low-copy number level or as complex high-level amplification with relative BRCA1 loss. ERBB2/HOXB13 amplified, BRCA1-lost tumors are strongly enriched in breast cancer, and patients with such breast tumors experience a shortened PFS.

Human papillomavirus E6 alters Toll-like receptor 9 transcripts and chemotherapy responses in breast cancer cells in vitro

BackgroundToll-like receptor 9 (TLR9) is a DNA recognizing receptor expressed also in several cancers. Decreased TLR9 expression is associated with poor prognosis in triple negative breast cancer (TNBC), but the role of TLR9 in breast cancer pathophysiology is currently unclear. Regulation of TLR9 expression in breast cancer is poorly understood. Human papillomavirus (HPV) infections suppress TLR9 expression in cervical cancers but the association between HPV and breast cancer has remained controversial. The aim of this study was to test if HPV16 can suppress TLR9 expression in breast cancer cells and affect cell behavior.Methods and resultsHuman T-47D and MDA-MB-231 breast cancer cells were transduced with lentivirus encoding HPV16 E6 oncoprotein. The effects of E6 on TLR9 mRNA and protein expression, and cell proliferation, migration, invasion and sensitivity to chemotherapy were studied in vitro. Breast cancer tissue samples (n = 37) were analyzed for the presence of HPV DNA. E6 expression decreased TLR9 mRNA expression in MDA-MB-231 and T-47D cells in hypoxia. E6 expression altered breast cancer cell proliferation and made cells significantly less sensitive to the growth inhibitory effects of chemotherapeutic agents. HPV L1 gene was not detected in a small pilot cohort of clinical breast cancer specimens.ConclusionHPV16 may influence breast cancer cell TLR9 transcription and chemotherapy responses and could thereby affect breast cancer prognosis. These results suggest that HPV may have a previously unrecognized role in breast cancer pathophysiology and warrant further studies on the topic.

A commonly inherited human PCSK9 germline variant drives breast cancer metastasis via LRP1 receptor.

Identifying patients at risk for metastatic relapse is a critical medical need. We identified a common missense germline variant in proprotein convertase subtilisin/kexin type 9 (PCSK9) (rs562556, V474I) that is associated with reduced survival in multiple breast cancer patient cohorts. Genetic modeling of this gain-of-function single-nucleotide variant in mice revealed that it causally promotes breast cancer metastasis. Conversely, host PCSK9 deletion reduced metastatic colonization in multiple breast cancer models. Host PCSK9 promoted metastatic initiation events in lung and enhanced metastatic proliferative competence by targeting tumoral low-density lipoprotein receptor related protein 1 (LRP1) receptors, which repressed metastasis-promoting genes XAF1 and USP18. Antibody-mediated therapeutic inhibition of PCSK9 suppressed breast cancer metastasis in multiple models. In a large Swedish early-stage breast cancer cohort, rs562556 homozygotes had a 22% risk of distant metastatic relapse at 15 years, whereas non-homozygotes had a 2% risk. Our findings reveal that a commonly inherited genetic alteration governs breast cancer metastasis and predicts survival-uncovering a hereditary basis underlying breast cancer metastasis.

Cohort profile: Guangzhou breast cancer study (GBCS)

The Guangzhou Breast Cancer Study (GBCS) is a patient-based prospective cohort study designed to identify risk factors and underlying mechanisms for breast cancer (BC) incidence and prognosis, specifically addressing the need for individualized prevention in South China, where BC incidence is notably high. Based in Guangzhou, China, the GBCS began recruitment in 2008, comprises three complementary studies: the Guangzhou breast cancer cohort with 5471 breast cancer patients, a case–control study with 1551 cases and 1605 controls, and an immunohistochemistry (IHC) cohort with 1063 breast cancer patients. Participants are primarily aged 41–60 years. Cohort follow-up is conducted every three months in the first year, every six months in the second and third years, and annually thereafter. High follow-up rates have been achieved until 2023, with 73.5% for the Guangzhou breast cancer cohort and 98.6% for the IHC cohort still active. Baseline data collection included demographic characteristics and breast cancer risk factors, while follow-up data included survival, treatment details, disease history, occupational history, post-diagnostic lifestyle, and laboratory measures, including genetic markers, proteins, and environmental exposures. The study encourages global collaborations and invites interested researchers to contact the corresponding author at xulin27@ mail.sysu.edu.cn with specific research ideas or proposals.

Clinical and patient-reported outcomes after oncoplastic versus conventional breast conserving surgery-a longitudinal, multicenter cohort study.

Oncoplastic breast conserving surgery (OP-BCS) is becoming increasingly popular to avoid mastectomy or optimize cosmetic outcomes of breast conserving surgery (BCS). Few studies have compared clinical outcomes and patient-reported outcomes (PROs) of OP-BCS to conventional BCS (C-BCS). This study aims to compare clinical outcomes and short and long-term PROs after OP-BCS and C-BCS in a large prospective breast cancer cohort. Women in the prospective, multicenter UMBRELLA-breast cancer cohort who underwent OP-BCS or C-BCS were included. Clinical outcomes and PROs (measured by EORTC QLQ-C30/BR23) up to 24 months postoperatively were evaluated. Mixed-model analysis was performed to assess differences in PROs over time between groups. 1628 (84.9%) patients received C-BCS and 290 (15.1%) OP-BCS. After C-BCS and OP-BCS, free resection margins were obtained in 84.2% (n = 1370) and 86.2% (n = 250), respectively, reoperation for re-exision of margins <3 months occurred in 5.3% (n = 86) and 4.8% (n = 14), median time interval from surgery until adjuvant systemic therapy was 66 and 63 days, and 36 and 41 days until radiotherapy. Shortly postoperative, OP-BCS was associated with statistically significant lower mean scores for physical functioning (83.6 vs 87.2) and body image (82.8 vs 89.4) and more pain (19.8 vs 26.5) and breast symptoms (22.7 vs 30.3) than C-BCS. Body image scores remained significantly less favorable after OP-BSC than C-BCS up to 24 months postoperatively (87.8 vs 92.2). Oncoplastic surgery safely enables BCS, but may lead to less favorable long-term body image compared to C-BCS. These findings are important for patient education and shared decision-making.

Prognostic Significance of DSCC1, a Biomarker Associated with Aggressive Features of Breast Cancer.

Background and Objectives: Invasive breast cancer (BC) was traditionally investigated visually, and no technique could identify the key molecular drivers of patient survival. However, essential molecular drivers of invasive BC have now been discovered using innovative genomic, transcriptomic, and proteomic methodologies. Nevertheless, few evaluations of the prognostic factors of BC in Saudi Arabia have been performed. Evaluating the biomarkers associated with the development of early-stage BC could help determine the risk of metastasis and guide treatment decisions. In a previous study, using large BC cohorts and artificial neural network techniques, DNA replication and sister chromatid cohesion 1 (DSCC1) was found to be one of the principal genes in invasive BC samples. To date, no studies have addressed the prognostic significance of DSCC1 in invasive BC and its association with aggressive tumor behavior. This research aimed to address this gap. Materials and Methods: The association of clinicopathological features and patient outcomes with DSCC1 expression at the mRNA level was assessed using the Molecular Taxonomy Breast Cancer International Consortium (METABRIC; n = 1980) and The Cancer Genome Atlas (TCGA; n = 854) cohorts. DSCC1 was also evaluated at the protein level using immunohistochemistry on samples from invasive BC patients (n = 100) presenting to King Abdul Aziz Specialist Hospital in Saudi Arabia. The association of clinicopathological parameters (including patient age, tumor grade, tumor size, and patient outcome) with protein level was also evaluated. Results: In both METABRIC and TCGA cohorts, high expression of DSCC1 was significantly associated with high histological grade, large tumor size, lymphovascular invasion positivity, and hormone receptor negativity (all p < 0.001). A high DSCC1 mRNA level was associated with poor outcomes (p < 0.001 for METABRIC, p = 0.23 for TCGA). At the protein level, high DSCC1 expression was associated with high histological grade (p = 0.001), lymph node presence (p = 0.008), hormone receptor negativity (p = 0.005), high Ki67 expression (p = 0.036), and shorter survival (p = 0.008). Conclusions: This study confirmed the prognostic significance of DSCC1 in invasive BC patients. DSCC1 could be a therapeutic target in BC cases with poor outcomes.

LATS2 and FAT4 as key candidate genes of hippo pathway associated with the risk and progression of breast cancer: an in-silico approach

BackgroundThe 2020 cancer report states that breast cancer remains a significant cause of death for females, despite the use of various strategies for early detection and treatment. However, there are still gaps in the fight against this disease. Researchers are exploring the hippo pathway, one of eight significant pathways involved in cancer progression, for potential biomarkers to use in personalized therapeutics.MethodsThe current study used bioinformatic tools such as DEGs analysis, Methsurv, Km Plotter to generate data that can predict molecular biomarkers associated with hippo pathway in breast cancer development and treatment. The protein-protein interaction pathway was generated using the STRING database to find associations of hippo pathway genes with other dysregulated genes in breast cancer datasets. A disease enrichment study was also done to explore the potential of the hippo pathway in various aspects.ResultsLATS2 and FAT4 genes of the hippo pathway have shown an interesting association with overall survival, hypermethylation, genetic alterations, and decreased expression levels in the breast cancer cohort. Our findings suggest that both of these genes are associated with breast cancer progression and diagnosis and can be utilized as predictive biomarkers by oncologists for personalized therapy in patients.

METTL14 suppresses the expression of YAP1 and the stemness of triple-negative breast cancer

BackgroundTriple-negative breast cancer (TNBC) has pronounced stemness that is associated with relapse. N6-methyladenosine (m6A) plays a crucial role in shaping cellular behavior by modulating transcript expression. However, the role of m6A in TNBC stemness, as well as the mechanisms governing its abundance, has yet to be elucidated.MethodsWe analyzed proteomic and transcriptomic data derived from breast cancer cohorts, with an emphasis on m6A regulators. To unravel the role of m6A in TNBC, we employed RNA sequencing, methylated RNA immunoprecipitation sequencing, RNA immunoprecipitation, chromatin immunoprecipitation, and luciferase reporter assays with mesenchymal stem-like (MSL) TNBC models. The clinical relevance was validated using human tissue microarrays and publicly accessible databases.ResultsOur findings indicate that the global level of m6A modification in MSL TNBC is downregulated primarily due to the loss of methyltransferase-like 14 (METTL14). The diminished m6A modification is crucial for the maintenance of TNBC stemness, as it increases the expression of yes-associated protein 1 (YAP1) by blocking YTH domain-containing family protein 2 (YTHDF2)-mediated transcript decay, thereby promoting the activation of Hippo-independent YAP1 signaling. YAP1 is essential for sustaining the stemness regulated by METTL14. Furthermore, we demonstrated that the loss of METTL14 expression results from lysine-specific demethylase 1 (LSD1)-mediated removal of histone H3 lysine 4 methylation at the promoter region, which is critical for LSD1-driven stemness in TNBC.ConclusionThese findings present an epi-transcriptional mechanism that maintains Hippo-independent YAP1 signaling and plays a role in preserving the undifferentiated state of TNBC, which indicates the potential for targeting the LSD1-METTL14 axis to address TNBC stemness.

Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment.

Breast cancer, characterized by its heterogeneity, is a leading cause of mortality among women. The study aims to develop a Machine Learning-Derived Liquid-Liquid Phase Separation (MDLS) model to enhance the prognostic accuracy and personalized treatment strategies for breast cancer patients. The study employed ten machine learning algorithms to construct 108 algorithm combinations for the MDLS model. The robustness of the model was evaluated using multi-omics and single-cell data across 14 breast cancer cohorts, involving 9,723 patients. Genetic mutation, copy number alterations, and single-cell RNA sequencing were analyzed to understand the molecular mechanisms and predictive capabilities of the MDLS model. Immunotherapy targets were predicted by evaluating immune cell infiltration and immune checkpoint expression. Chemotherapy targets were identified through correlation analysis and drug responsiveness prediction. The MDLS model demonstrated superior prognostic power, with a mean C-index of 0.649, outperforming 69 published signatures across ten cohorts. High-MDLS patients exhibited higher tumor mutation burden and distinct genomic alterations, including significant gene amplifications and deletions. Single-cell analysis revealed higher MDLS activity in tumor-aneuploid cells and identified key regulatory factors involved in MDLS progression. Cell-cell communication analysis indicated stronger interactions in high-MDLS groups, and immunotherapy response evaluation showed better outcomes for low-MDLS patients. The MDLS model offers a robust and precise tool for predicting breast cancer prognosis and tailoring personalized treatment strategies. Its integration of multi-omics and machine learning highlights its potential clinical applications, particularly in improving the effectiveness of immunotherapy and identifying therapeutic targets for high-MDLS patients.

Characterization of Breast Cancer Intra-Tumor Heterogeneity Using Artificial Intelligence.

Intra-tumor heterogeneity (ITH) is a fundamental characteristic of breast cancer (BC), influencing tumor progression, prognosis, and therapeutic responses. However, the complexity of ITH in BC makes its accurate characterization challenging. This study leverages deep learning (DL) techniques to comprehensively evaluate ITH in early-stage luminal BC and provide a nuanced understanding of its impact on tumor behavior and patient outcomes. A large cohort (n = 2561) of early-stage luminal BC was evaluated using whole slide images (WSIs) of hematoxylin and eosin-stained slides of excision specimens. Morphological features of both the tumor and stromal components were meticulously annotated by a panel of pathologists in a subset of cases. A DL model was applied to develop an algorithm to assess the degree of heterogeneity of various morphological features per individual case utilizing defined patches. The results of extracted features were used to generate an overall heterogeneity score that was correlated with the clinicopathological features and outcome. Overall, 162 features were quantified and a significant positive correlation between these features was identified. Specifically, there was a significant association between a high degree of intra-tumor heterogeneity and larger tumor size, poorly differentiated tumors, highly proliferative tumors, tumors of no special type (NST), and those with low estrogen receptor (ER) expression. When all features are considered in combination, a high overall heterogeneity score was significantly associated with parameters characteristic of aggressive tumor behavior, and it was an independent predictor of poor patient outcome. In conclusion, DL models can be used to accurately decipher the complexity of ITH and provide extra information for outcome prediction.

Population based audit of heart radiation doses in 6925 high-risk breast cancer patients from the Danish breast cancer group RT Nation study

Background and purposeIn this study, we conducted a population-based retrospective audit of heart doses for high-risk breast cancer (BC) over a nine-year period in patients treated with adjuvant CT-based radiotherapy in a comprehensive and homogenized national BC cohort. Additionally, this serves as a demonstration of performing large scale audits with consistent delineations created by an auto-segmentation tool. Materials and methodsHigh-risk BC patients treated with adjuvant radiotherapy in the period 2008–2016 from all seven radiotherapy centres in Denmark were included. A homogenized cohort was created using an inhouse developed auto-segmentation tool. The homogenized cohort volume and planned doses (mean heart dose (MHD), V20Gy and V40Gy) were evaluated. Volumes and dose metrics were compared for clinical and homogenized heart volumes. ResultsAmong 6925 patients, 5589(81 %) had a clinical heart delineation. The median delineated heart volume increased from 531.9 ml (2008) to 638.5 ml (2016) (p < 0.01). The median MHD for the homogenized cohort was 1.58 Gy (2008–2016) with an overall decreasing trend, 2.14 Gy in left- and 1.08 Gy in right-sided patients. The median MHD in the clinically delineated hearts was 0.01 Gy lower than the planned median MHD in the homogenized cohort. ConclusionDuring 2008–2016 the planned heart dose has been low across the population. A volume increase was observed in the clinically delineated hearts, however the median MHD in the homogenized cohort was low, with 1.58 Gy. The study demonstrated the possibilities for full population-based and consistent dose audit by using auto-segmentation tools.

Deciphering the Role of ASPM in Breast Cancer: A Comprehensive Multicohort Study.

Assembly factor for spindle microtubules (ASPM) has gained significant attention in cancer research due to its association with tumor growth and progression. Through the analysis of large-scale genomic datasets, ASPM has been identified as the top upregulated gene in breast cancer (BC), characterized by high proliferation. This multicohort study aimed to investigate the clinicopathological and prognostic significance of ASPM mRNA and protein expression in BC. ASPM mRNA expression was assessed using the Cancer Genome Atlas (TCGA) BC cohort and has been further validated in the Molecular Taxonomy of BC International Consortium (METABRIC) (n = 1980), The Uppsala cohort (n = 249), in addition to the combined multicentric cohort (n = 7252). ASPM protein expression was evaluated in a large BC cohort (n = 1300) using immunohistochemistry. The correlations between ASPM expression, clinicopathological parameters, molecular subtypes and outcome were assessed. The response to taxane treatment was compared to the clinical prognosis of ASPM using the ROC plotter. High ASPM mRNA and protein expression were significantly associated with aggressive BC features and poor survival across all cohorts. The association with poor outcomes was maintained in the adjuvant chemotherapy and radio-therapy-treated patients. Responders to taxane treatment showed significantly elevated ASPM levels compared to non-responders. High ASPM expression predicts poor prognosis in BC. It may play a role in treatment resistance within a specific subgroup of patients. Further clinical trials are warranted to explore the potential of ASPM as a target for therapeutic interventions in cancer.

Prognostic and immune correlation of IDO1 promoter methylation in breast cancer

Indoleamine 2,3-dioxygenase 1 (IDO1) plays an important role in the initiation and progression of breast cancer. DNA promoter methylation status has the potential to be used as a biomarker for predicting the response to immunotherapy. This study aimed to investigate the biological and clinical significance of IDO1 promoter methylation in breast cancer. We analyzed IDO1 promoter methylation and its relationship with survival, patient prognosis, immune cell infiltration, immune-related pathways, and the expression of key immunomodulators via bioinformatics methods in The Cancer Genome Atlas (TCGA) breast cancer cohort (779 samples). Furthermore, the IDO1 promoter methylation status and expression of the IDO1 gene in the basal subtype of breast cancer were investigated in vitro via a methylation-specific PCR (MSP) assay and quantitative polymerase chain reaction (qPCR). The IDO1 promoter was significantly hypomethylated in the basal subtype of breast cancer tissues compared with normal adjacent tissues, and this effect was correlated with high expression of IDO1, resulting in abundant immune cell infiltration, activation of immune-related pathways, and upregulation of key immunomodulators. The influence of IDO1 promoter hypomethylation on the prognosis of patients with breast cancer was also investigated. The promoter hypomethylation of IDO1 in the basal subtype of breast cancer and its correlation with high expression of IDO1 were also investigated in vitro. Our results showed that IDO1 promoter methylation is vital for regulating its expression, which leads to the development of a tumor microenvironment in breast cancer. IDO1 promoter methylation and expression are associated with prognosis, immune cell infiltration, immune-related pathways, and immunomodulator expression in breast cancer. Our findings provide evidence for the validation of IDO1 promoter methylation as a promising biomarker to predict responses to immune checkpoint inhibitors in patients with breast cancer.

Breast tumor microbiome regulates anti-tumor immunity and T cell-associated metabolites.

Breast cancer, the most common cancer type among women, was recently found to contain a specific tumor microbiome, but its impact on host biology remains unclear. CD8+ tumor-infiltrating lymphocytes (TILs) are pivotal effectors of anti-tumor immunity that influence cancer prognosis and response to therapy. This study aims to elucidate interactions between CD8+ TILs and the breast tumor microbiome and metabolites, as well as how the breast tumor microbiome may affect the tumor metabolome. We investigated the interplay among CD8+ TILs, the tumor microbiome, and the metabolome in a cohort of 46 breast cancer patients with mixed subtypes (Cohort A). We characterized the tumor metabolome by mass spectrometry and CD8+ TILs by immunohistochemistry. Microbiome composition and T cell gene transcript levels were obtained from data from our previous study, which utilized 16S rRNA gene sequencing and a targeted mRNA expression panel. To examine interactions between intratumoral Staphylococcus and specific breast cancer subtypes, we analyzed RNA sequencing data from an independent cohort of 370 breast cancer patients (Cohort B). We explored the functions of the tumor microbiome using mouse models of triple-negative breast cancer (TNBC). In tumors from Cohort A, the relative abundance of Staphylococcus positively correlated with the expression of T cell activation genes. The abundances of multiple metabolites exhibited significant correlations with CD8+ TILs, of which NADH, γ-glutamyltryptophan, and γ-glutamylglutamate displayed differential abundance in Staphylococcus-positive versus Staphylococcus-negative breast tumors. In a larger breast cancer cohort (Cohort B), we observed positive correlations between tumoral Staphylococcus and CD8+ TIL activity exclusively in TNBC. Preclinical experiments demonstrated that intratumoral administration of S. aureus, the predominant species of Staphylococcus in human breast tumors, resulted in a depletion of total NAD metabolites, and reduced the growth of TNBC tumors by activating CD8+ TILs. We identified specific metabolites and microbial taxa associated with CD8+ TILs, delineated interactions between the breast tumor microbiome and metabolome, and demonstrated that intratumoral Staphylococcus influences anti-tumor immunity and TIL-associated metabolites. These findings highlight the role of low-biomass microbes in tumor tissues and provide potential biomarkers and therapeutic agents for breast cancer immunotherapy that merit further investigation.

Women with breast cancer exhibit a higher risk for periodontitis: A nationwide cohort study

Background/purposeEpidemiologic research has linked periodontitis to several types of cancer, particularly breast cancer. Although clinical evidence indicates a higher risk of breast cancer in women with periodontitis than in those without, few studies have explored whether the risk of periodontitis is higher in women with breast cancer than in those without. In this study, we examined the incidence of periodontitis in patients with breast cancer and identified potential interventions for its prevention. Materials and methodsThis retrospective cohort study included data from the National Health Insurance Research Database of Taiwan. We identified women who received a diagnosis of breast cancer between 2010 and 2019 and included a 1:1 matched control cohort with no breast cancer. Subsequently, we analyzed the risk of periodontitis by using Cox proportional-hazards models while adjusting for sociodemographic factors, comorbidities, and treatment regimens. ResultsIn 82,146 matched pairs, the breast cancer cohort was at a 51 % higher risk of periodontitis compared with the control cohort (adjusted hazard ratio = 1.51, 95 % confidence interval = 1.43–1.60). The stratified analysis revealed the same results. The risk of breast cancer was higher in younger patients than in older patients, whereas the risk of periodontitis was significantly lower in patients who underwent surgery, radiotherapy, chemotherapy, or hormone therapy compared with those who did not. ConclusionBreast cancer increases the risk of periodontitis, particularly in younger patients. These patients should receive regular dental care to prevent and manage periodontitis. Anticancer treatments may mitigate the risk of periodontitis in patients with breast cancer.

Cell division cycle 6 ​is an independent prognostic biomarker in breast cancer

Cell division cycle 6 (CDC6) is a cell cycle protein involved in cell cycle control, DNA replication and cancer cell apoptosis. This study investigated the prognostic value of CDC6 in breast cancer (BC) utilising large well-characterised cohorts of early-stage ​BC. CDC6 mRNA was assessed using the Molecular Taxonomy of the Breast Cancer International Consortium (n = 1980), the Cancer Genome Atlas (n = 854) and Kaplan–Meier plotter (n = 4,929) cohorts. CDC6 protein expression was evaluated using immunohistochemistry in a large (n = 951) well-characterised Nottingham BC cohort. The associations between CDC6, clinicopathological parameters, molecular features and patient outcomes were assessed. High CDC6 expression positively correlated with dysregulation of key BC-related genes, including gene involved in cell cycle, DNA damage repair, epithelial cell migration ​and tumour microenvironment control, as well as with markers characteristic of the basal-like phenotype (CK5, CK14 and CK17). High CDC6 mRNA and protein expression were associated with clinicopathological parameters characteristic of aggressive behaviour, including high tumour grade, large tumour size, the presence of lymphovascular invasion and hormone receptor negativity. High CDC6 protein expression was an independent predictor of poor patients' outcome [p = 0.007; hazard ratio (HR) = 1.3; 95% confidence interval (CI): 1.2–1.9). This study indicates that CDC6 is an independent prognostic biomarker in BC. These results warrant further functional validation for CDC6 as a potential therapeutic target in BC.

Evaluation of breast-specific marker expression in metastatic breast cancers: Correlation with subtype switch.

This study evaluates the utility of breast specific markers in identifying breast cancer subtypes within metastatic settings. The subtype alteration in metastatic disease and its consequent impact on breast-specific marker expression is also examined. GATA-binding protein 3 (GATA3), mammaglobin (MMG), transcriptional repressor GATA binding 1 (TRSP1) and SRY-box transcription factor 10 (SOX10) expression were assessed in a large cohort of metastatic breast cancer (MBC) cases and correlated with the characteristics of both MBC and primary breast cancer (PBC). GATA3 was the most sensitive in MBC (83.1%), followed by TRPS1 (77.0%), MMG (58.5%) and SOX10 (7.1%). This trend was consistent in hormonal receptor (HR)+ and HR- MBC. Combining GATA3/TRPS1 yielded the highest detection rates in the overall cohort (90.1%) and HR+ MBC (97.1%), while TRSP1/MMG was most effective in HR- (76.2%) and TN (71.1%) MBC. Marker expression did not correlate with metastatic site, except SOX10 in lung metastases (P = 0.031). Subtype discordance between MBC and PBC occurred in 43 cases (24.4%), with GATA3 expression in HR- MBC significantly linked to subtype discordance (P = 0.005). Conversely, SOX10 expression was significantly associated with subtype concordance in HR- and TNBC (P ≤ 0.003). Despite a higher expression of GATA3 in all HR- cases, TRSP1 outperformed GATA3 in detecting concordant HR- cases (64.0% versus 38.5%). TRPS1 and SOX10 were expressed in more than 50% of concordant TNBC cases. The expression of breast-specific markers is mainly determined by the PBC subtype. GATA3 retains high sensitivity in HR+ cancers, even after HR loss during metastasis. TRPS1 and SOX10 are identified as valuable markers in TNBC metastasis.

Breast cancer survival prediction using an automated mitosis detection pipeline.

Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long-term follow-up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI-based MC and light-microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni- and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light-microscopic MC.