A comparative study in left-sided breast cancer treated with moderate deep inspiratory breath hold versus free breathing.

BACKGROUND: The moderate deep inspiratory breath hold (mDIBH) is a modality famed for cardiac sparing. Prospective studies based on this are few from the eastern part of the world and India. We intend to compare the dosimetry between mDIBH and free-breathing (FB) plans. METHODS: Thirty-two locally advanced left breast cancer patients were taken up for the study. All patients received a dose of 50 Gy in 25 fractions to the chest wall/intact breast, followed by a 10-Gy boost to the lumpectomy cavity in the case of breast conservation surgery. All the patients were treated in mDIBH using active breath coordinator (ABC). The data from the two dose volume histograms were compared regarding plan quality and the doses received by the organs at risk. Paired t-test was used for data analysis. RESULTS: The dose received by the heart in terms of V5, V10, and V30 (4.55% vs 8.39%) and mean dose (4.73 Gy vs 6.74 Gy) were statistically significant in the ABC group than that in the FB group (all p-values < 0.001). Also, the dose received by the LADA in terms of V30 (19.32% vs 24.87%) and mean dose (32.99 Gy vs 46.65 Gy) were significantly less in the ABC group. The mean treatment time for the ABC group was 20 min, while that for the free-breathing group was 10 min. CONCLUSIONS: Incorporating ABC-mDIBH for left-sided breast cancer radiotherapy significantly reduces the doses received by the heart, LADA, and left and right lung, with no compromise in plan quality but with an increase in treatment time.

Hormone receptor mRNA and protein levels as predictors of premenopausal tamoxifen benefit.

BACKGROUND AND PURPOSE: Tamoxifen remains an important adjuvant treatment in premenopausal patients with hormone receptor-positive breast cancer. Thus, determination of hormone receptors is important. Here, we compare cytosol-based methods, immunohistochemistry (IHC), and gene expression (GEX) analysis for determining hormone receptor status in premenopausal breast cancer patients from a randomised tamoxifen trial, to evaluate their performance in identifying patients that benefit from tamoxifen. PATIENTS AND METHODS: Premenopausal patients (n=564) were randomised to 2 years of tamoxifen or no systemic treatment. Estrogen receptor (ER) and progesterone receptor (PR) status by protein expression measured by cytosol-based methods and IHC, and mRNA by GEX analysis were compared in 313 patients with available data from all methods. Kaplan Meier estimates and Cox regression were used to evaluate the treatment-predictive value for recurrence-free interval (RFi) and overall survival (OS). Median follow-up for event-free patients was 26 (RFi) and 33 (OS) years. RESULTS: The mRNA data of ESR1 and PGR distributed bimodally, patterns confirmed in an independent cohort. Kappa-values between all methods were 0.76 and 0.79 for ER and PR, respectively. Tamoxifen improved RFi in patients with ER-positive (ER+) or PR-positive (PR+) tumours (Hazard Ratio [HR] and 95% confidence interval [CI]), cytosol-ER+ 0.53 [0.36-0.79]; IHC-ER+ 0.55 [0.38-0.79]; GEX-ER+ 0.54 [0.37-0.77]; cytosol-PR+ 0.49 [0.34-0.72]; IHC-PR+ 0.58 [0.40-0.85]; GEX-PR+ 0.55 [0.38-0.80]). Results were similar for OS. INTERPRETATION: These methods can all identify patients that benefit from 2 years of tamoxifen with equal performance, indicating that GEX data might be used to guide adjuvant tamoxifen therapy.

Molecular subtyping improves breast cancer diagnosis in the Copenhagen Breast Cancer Genomics Study.

BACKGROUNDIntrinsic molecular subtypes define distinct biological breast cancers and can be used to further improve diagnosis and risk allocation.METHODSThe Copenhagen Breast Cancer Genomics Study (CBCGS) prospectively included women diagnosed with breast cancer at Rigshospitalet from 2014 to 2021. Eligible patients were females with a primary invasive breast cancer (T1c, if N0M0; otherwise, any T, any N, or any M stage) and no prior malignancy. All patients underwent molecular profiling with the CIT256 and PAM50 molecular profile.RESULTSIn the study period, 2,816 patients were included in the CBCGS. Molecular subtyping showed an increase in nonluminal (molecular-apocrine, luminal C, and Basal-like) as compared with luminal (luminal A, luminal B, and Normal-like) subtypes with increasing stage from I to IV. Across all stages, we found a significant difference in survival among subtypes; 91% of patients with LumA were alive at 5 years compared with 91% for LumB, 84% for LumC, 82% for mApo, and 80% for Basal-like. We identified 442 tumors (16%) that were discordant in subtype between CIT256 and IHC. Discordant subtype proved to be a risk factor of death among patients with IHC luminal breast cancer (hazard ratio [HR], 2.08; 95% CI, 1.51-2.86) in a multivariable Cox regression analysis. Discordance occurred more often among patients with N3, stage IV, or grade III disease.CONCLUSIONOur findings indicate that molecular subtypes are a predominant classification for survival. Assessment is particularly crucial for patients with IHC luminal breast cancer with known high-risk factors, since they are at an increased risk of harboring an aggressive molecular subtype.

3D amide proton transfer-weighted imaging may be useful for diagnosing early-stage breast cancer: a prospective monocentric study.

BACKGROUND: We investigated the value of three-dimensional amide proton transfer-weighted imaging (3D-APTWI) in the diagnosis of early-stage breast cancer (BC) and its correlation with the immunohistochemical characteristics of malignant lesions. METHODS: Seventy-eight women underwent APTWI and dynamic contrast-enhanced (DCE)-MRI. Pathological results were categorized as either benign (n = 43) or malignant (n = 37) lesions. The parameters of APTWI and DCE-MRI were compared between the benign and malignant groups. The diagnostic value of 3D-APTWI was evaluated using the area under the receiver operating characteristic curve (ROC-AUC) to establish a diagnostic threshold. Pearson's correlation was used to analyze the correlation between the magnetization transfer asymmetry (MTRasym) and immunohistochemical characteristics. RESULTS: The MTRasym and time-to-peak of malignancies were significantly lower than those of benign lesions (all p < 0.010). The volume transfer constant, rate constant, and wash-in and wash-out rates of malignancies were all significantly greater than those of benign lesions (all p < 0.010). ROC-AUCs of 3D-APTWI, DCE-MRI, and 3D-APTWI+DCE to differential diagnosis between early-stage BC and benign lesions were 0.816, 0.745, and 0.858, respectively. Only the difference between AUCAPT+DCE and AUCDCE was significant (p < 0.010). When a threshold of MTRasym for malignancy for 2.42%, the sensitivity and specificity of 3D-APTWI for BC diagnosis were 86.5% and 67.6%, respectively; MTRasym was modestly positively correlated with pathological grade (r = 0.476, p = 0.003) and Ki-67 (r = 0.419, p = 0.020). CONCLUSIONS: 3D-APTWI may be used as a supplementary method for patients with contraindications of DCE-MRI. MTRasym can imply the proliferation activities of early-stage BC. RELEVANCE STATEMENT: 3D-APTWI can be an alternative diagnostic method for patients with early-stage BC who are not suitable for contrast injection. KEY POINTS: • 3D-APTWI reflects the changes in the microenvironment of early-stage breast cancer. • Combined 3D-APTWI is superior to DCE-MRI alone for early-stage breast cancer diagnosis. • 3D-APTWI improves the diagnostic accuracy of early-stage breast cancer.

Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer.

Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%.

Analysis of flap thickness to breast projection ratio correlating to body mass index and age in east Asian women: Considerations in flap selection in breast reconstruction.

BACKGROUND: Several alternative flaps have been introduced and used for autologous breast reconstruction. However, as body fat distribution is different among patients, the donor of choice for sufficient breast projection varies between patients. METHODS: Patients who underwent autologous breast reconstruction from Jan 2018 to Sep 2022 were included. Age, body mass index (BMI), smoking history and hypertension, and diabetes occurrence were collected as baseline demographic data. Breast projection with five types of flap thickness was measured based on computed tomography angiography. Analysis was performed for five major autologous flaps for breast reconstruction. RESULTS: A total of 563 patients were included in the study. The mean age of the patients was 47.4 ± 7.9 (standard deviation; SD) years. The mean BMI of the patients was 24.0 ± 3.4 kg/m2. Only the correlation between flap thickness to breast projection ratio and age in the PAP flap illustrated statistical significance (p = .039), but the correlation coefficient was quite low (r = -0.087). Slim patients who had lower BMIs (under 25 kg/m2) had significantly higher sufficient flap thickness for breast reconstruction than patients with higher BMIs over 25 kg/m2 in the profunda artery perforator (PAP) flap (p < .001), the lumbar artery perforator (LAP) flap (p < .001), and the superior gluteal artery perforator (SGAP) flap (p < .001). CONCLUSIONS: The deep inferior epigastric perforator flap provided sufficient thickness and was not usually affected by age and BMI. The PAP, LAP, and SGAP flaps tended to maintain the thickness of the flap even when BMI decreased, so they are advantageous for reconstruction in slim patients. This study contributes evidence in consideration of flap selection in autologous breast reconstruction.

[Comparative immunohistochemistry study of different antibodies clones for detection of breast cancer markers (estrogen receptor, progesterone receptor, HER2/neu, Ki-67)]. / Sravnitel'noe issledovanie antitel razlichnykh klonov dlya vyyavleniya markerov raka molochnoi zhelezy (retseptorov estrogenov, progesterona, HER2/neu, Ki-67) immunogistokhimicheskim metodom.

OBJECTIVE: A comparative study of detection of breast cancer markers (estrogen receptors, progesterone receptors, HER2/neu, Ki-67) by immunohistochemical method with antibodies produced by PrimeBioMed (Russia) and antibodies produced by Roche Ventana (USA). MATERIAL AND METHODS: Surgical specimens and biopsies from 37 patients with invasive breast cancer were used. Sections were stained with antibodies of clones ER SP1 and GM030, PR 1E2 and PBM-5B8, HER2/neu 4B5 and PBM-46A6, Ki-67 30-9 and GM010. RESULTS: There was a high positive and significant correlation between the immunohistochemistry results and antibodies of the clones ER-SP1 and GM030, PR1E2 and PBM-5B8, HER2/neu4B5 and PBM-46A6, Ki-67 30-9 and GM010. CONCLUSION: The study showed the possibility of using antibodies of clones GM030, HER2/neu 4B5, PBM-46A6, GM010 (PrimeBioMed) on the Ventana Bench Marck Ultra automatic immunostainer using the detection system UltraView Universal DAB Detection Kit.

Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening.

Background Mammographic density measurements are used to identify patients who should undergo supplemental imaging for breast cancer detection, but artificial intelligence (AI) image analysis may be more effective. Purpose To assess whether AISmartDensity-an AI-based score integrating cancer signs, masking, and risk-surpasses measurements of mammographic density in identifying patients for supplemental breast imaging after a negative screening mammogram. Materials and Methods This retrospective study included randomly selected individuals who underwent screening mammography at Karolinska University Hospital between January 2008 and December 2015. The models in AISmartDensity were trained and validated using nonoverlapping data. The ability of AISmartDensity to identify future cancer in patients with a negative screening mammogram was evaluated and compared with that of mammographic density models. Sensitivity and positive predictive value (PPV) were calculated for the top 8% of scores, mimicking the proportion of patients in the Breast Imaging Reporting and Data System "extremely dense" category. Model performance was evaluated using area under the receiver operating characteristic curve (AUC) and was compared using the DeLong test. Results The study population included 65 325 examinations (median patient age, 53 years [IQR, 47-62 years])-64 870 examinations in healthy patients and 455 examinations in patients with breast cancer diagnosed within 3 years of a negative screening mammogram. The AUC for detecting subsequent cancers was 0.72 and 0.61 (P < .001) for AISmartDensity and the best-performing density model (age-adjusted dense area), respectively. For examinations with scores in the top 8%, AISmartDensity identified 152 of 455 (33%) future cancers with a PPV of 2.91%, whereas the best-performing density model (age-adjusted dense area) identified 57 of 455 (13%) future cancers with a PPV of 1.09% (P < .001). AISmartDensity identified 32% (41 of 130) and 34% (111 of 325) of interval and next-round screen-detected cancers, whereas the best-performing density model (dense area) identified 16% (21 of 130) and 9% (30 of 325), respectively. Conclusion AISmartDensity, integrating cancer signs, masking, and risk, outperformed traditional density models in identifying patients for supplemental imaging after a negative screening mammogram. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kim and Chang in this issue.

Recent developments in targeting breast cancer stem cells (BCSCs): a descriptive review of therapeutic strategies and emerging therapies.

Despite recent advancements in the diagnosis and treatment of breast cancer (BC), patient outcomes in terms of survival, recurrence, and disease progression remain suboptimal. A significant factor contributing to these challenges is the cellular heterogeneity within BC, particularly the presence of breast cancer stem cells (BCSCs). These cells are thought to serve as the clonogenic nexus for new tumor growth, owing to their hierarchical organization within the tumor. This descriptive review focuses on the evolving strategies to target BCSCs, which have become a pivotal aspect of therapeutic development. We explore a variety of approaches, including targeting specific tumor surface markers (CD133 and CD44), transporters, heat shock proteins, and critical signaling pathways like Notch, Akt, Hedgehog, KLF4, and Wnt/ß-catenin. Additionally, we discuss the modulation of the tumor microenvironment through the CXCR-12/CXCR4 axis, manipulation of pH levels, and targeting hypoxia-inducible factors, vascular endothelial growth factor, and CXCR1/2 receptors. Further, this review focuses on the roles of microRNA expression, strategies to induce apoptosis and differentiation in BCSCs, dietary interventions, dendritic cell vaccination, oncolytic viruses, nanotechnology, immunotherapy, and gene therapy. We particularly focused on studies reporting identification of BCSCs, their unique properties and the efficacy of various therapeutic modalities in targeting these cells. By dissecting these approaches, we aim to provide insights into the complex landscape of BC treatment and the potential pathways for improving patient outcomes through targeted BCSC therapies.

PDP1 promotes the progression of breast cancer through STAT3 pathway.

This study aimed to investigate the expression pattern and mechanisms of Pyruvate Dehydrogenase Phosphatase Catalytic Subunit 1 (PDP1) in the progression of breast cancer (BC). PDP1, known for its involvement in cell energy metabolism, was found to be overexpressed in BC tissues. Notably, low PDP1 expression aligns with improved overall survival (OS) in BC patients. In this study, we found that PDP1 was overexpressed among BC tissues and low PDP1 expression showed a better prognosis for the patients with BC. PDP1 knockdown suppressed cell amplification and migration and triggered cell apoptosis in BC cells. In vivo assessments through a xenograft model unveiled the pivotal role and underlying mechanisms of PDP1 knockdown. RNA sequencing and kyoto encyclopedia of genes and genomes analysis of RNAs from PDP1 knockdown and normal MCF7 cells revealed 1440 differentially expressed genes, spotlighting the involvement of the JAK/STAT3 signaling pathway in BC progression. Western blot results implied that PDP1 knockdown led to a loss of p-STAT3, whereas overexpression of PDP1 induced the p-STAT3 expression. Cell counting kit-8 assay showed that PDP1 overexpression significantly raised MDA-MB-231 and MCF7 cell viability while STAT3 inhibitor S3I-201 recovered the cell growth to normal level. To summarize, PDP1 promotes the progression of BC through STAT3 pathway by regulating p-STAT3. The findings contribute to understanding the molecular mechanisms underlying BC progression, and opening avenues for targeted therapeutic approaches.

Designing and implementing a bundle of care for patients with early-stage breast cancer: lessons from a pilot program.

We present a case study on the design and implementation of a value-based bundled package of care for patients with early-stage breast cancer treated in the private health sector in Australia. Value-based healthcare is an essential change to how we deliver healthcare, shifting the focus from paying for individual services provided to a focus on the health outcomes gained over a full cycle of care. The Australian health system has unintentionally created barriers to value-based cancer care through fragmented care pathways and complex funding arrangements where patients can unexpectedly encounter high out-of-pocket costs. A team of clinicians, service providers, health systems and funding experts, private health insurers and consumers have collaborated to design and pilot a complete bundled package of care for breast cancer patients which aims to address these challenges. With 40 patients recruited to date, early evaluation results show positive patient experience of 'joined-up' care and financial transparency. This case study provides a high-level overview of the approach taken to design and implement the Breast Cancer Bundle and the lessons learned for its expansion in both public and private settings.

TSPAN8+ myofibroblastic cancer-associated fibroblasts promote chemoresistance in patients with breast cancer.

Cancer-associated fibroblasts (CAFs) are abundant stromal cells in the tumor microenvironment that promote cancer progression and relapse. However, the heterogeneity and regulatory roles of CAFs underlying chemoresistance remain largely unclear. Here, we performed a single-cell analysis using high-dimensional flow cytometry analysis and identified a distinct senescence-like tetraspanin-8 (TSPAN8)+ myofibroblastic CAF (myCAF) subset, which is correlated with therapeutic resistance and poor survival in multiple cohorts of patients with breast cancer (BC). TSPAN8+ myCAFs potentiate the stemness of the surrounding BC cells through secretion of senescence-associated secretory phenotype (SASP)-related factors IL-6 and IL-8 to counteract chemotherapy. NAD-dependent protein deacetylase sirtuin 6 (SIRT6) reduction was responsible for the senescence-like phenotype and tumor-promoting role of TSPAN8+ myCAFs. Mechanistically, TSPAN8 promoted the phosphorylation of ubiquitin E3 ligase retinoblastoma binding protein 6 (RBBP6) at Ser772 by recruiting MAPK11, thereby inducing SIRT6 protein destruction. In turn, SIRT6 down-regulation up-regulated GLS1 and PYCR1, which caused TSPAN8+ myCAFs to secrete aspartate and proline, and therefore proved a nutritional niche to support BC outgrowth. By demonstrating that TSPAN8+SIRT6low myCAFs were tightly associated with unfavorable disease outcomes, we proposed that the combined regimen of anti-TSPAN8 antibody and SIRT6 activator MDL-800 is a promising approach to overcome chemoresistance. These findings highlight that senescence contributes to CAF heterogeneity and chemoresistance and suggest that targeting TSPAN8+ myCAFs is a promising approach to circumvent chemoresistance.

Breast Cancer Screening Recommendations for Transgender Patients: A Review.

Shifting societal attitudes trending toward increased acceptance of transgender people has contributed to a growth in the population of individuals who openly identify as transgender as well as an increase in utilization of gender-affirming surgeries. Therefore, health care providers should work toward increasing their knowledge with respect to the health care needs of the transgender population, particularly given that these individuals often face discrimination in the health care system. Moreover, transgender breast cancer screening rates lag behind breast cancer screening rates for cisgender individuals. We attempt to clarify important terminology about the transgender population and introduce the concept of equity-centered language. We review current transgender breast cancer screening guidelines by using PubMed, Google Scholar, and Google with search terms that included transgender breast cancer guidelines, transgender breast cancer screening guidelines, and transgender cancer screening guidelines. Searches were not filtered based on year of publication, language of publication, or geographic location. We compare the various guidelines as they pertain to both transgender men and transgender women and offer special considerations for upholding inclusivity in the health care field. An easy-to-reference summary of the current transgender breast cancer screening guidelines and the major points of this article were compiled into an original infographic that health care providers can reference to ensure that appropriate care is being offered.

'Just Google it'-A scoping review of online mental health resources for survivors of breast cancer.

OBJECTIVE: As the Internet is a ubiquitous resource for information, we aimed to replicate a patient's Google search to identify and assess the quality of online mental health/wellbeing materials available to support women living with or beyond cancer. METHODS: A Google search was performed using a key term search strategy including search strings 'cancer', 'wellbeing', 'distress' and 'resources' to identify online resources of diverse formats (i.e., factsheet, website, program, course, video, webinar, e-book, podcast). The quality evaluation scoring tool (QUEST) was used to analyse the quality of health information provided. RESULTS: The search strategy resulted in 283 resources, 117 of which met inclusion criteria across four countries: Australia, USA, UK, and Canada. Websites and factsheets were primarily retrieved. The average QUEST score was 10.04 (highest possible score is 28), indicating low quality, with 92.31% of resources lacking references to sources of information. CONCLUSIONS: Our data indicated a lack of evidence-based support resources and engaging information available online for people living with or beyond cancer. The majority of online resources were non-specific to breast cancer and lacked authorship and attribution.

Cellular plasticity in the breast cancer ecosystem.

The complex interplay between genetically diverse tumor cells and their microenvironment significantly influences cancer progression and therapeutic responses. This review highlights recent findings on cellular plasticity and heterogeneity within the breast cancer ecosystem, focusing on the roles of cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs). We discuss evidence suggesting that breast cancer cells exhibit phenotypic plasticity driven by both intrinsic genetic factors and external microenvironmental cues, impacting treatment responses and disease recurrence. Moreover, single-cell RNA sequencing studies reveal diverse subtypes of CAFs and TAMs, each with distinct functional gene expression programs and spatial organization within the tumor microenvironment. Understanding the hierarchical relationships and niche cues governing cellular phenotypes offers new opportunities for targeted therapeutic interventions. By elucidating the organizational principles of the tumor ecosystem, future therapies may target phenotypic states or entire cellular niches, advancing precision medicine approaches in breast cancer treatment.

Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study.

Background: Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC). Methods: This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF categories such as shape based, Gray level dependence matrix (GLDM), Gray level co-occurrence matrix (GLCM), First order, Gray level run length matrix (GLRLM), Gray level size zone matrix (GLSZM), NGTDM (Neighbouring gray tone difference matrix) were extracted from the DCE-MRI sequence using a 3D slicer. The maximum relevance and minimum redundancy (mRMR) was applied using Google Colab for identifying the top fifteen relevant radiomic features. The Mann-Whitney U test was performed to identify significant RF for differentiating IDC and ILC. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC. Results: Ten DCE MRI-based RFs used in our study showed a significant difference (p <0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC. Conclusions: MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.

Discovery of an independent poor-prognosis subtype associated with tertiary lymphoid structures in breast cancer.

Introduction: Tertiary lymphoid structures (TLSs) are ectopic lymphoid formations that arise in non-lymphoid tissues due to chronic inflammation. The pivotal function of TLSs in regulating tumor invasion and metastasis has been established across several cancers, such as lung cancer, liver cancer, and melanoma, with a positive correlation between increased TLS presence and improved prognosis. Nevertheless, the current research about the clinical significance of TLSs in breast cancer remains limited. Methods: In our investigation, we discovered TLS-critical genes that may impact the prognosis of breast cancer patients, and categorized breast cancer into three distinct subtypes based on critical gene expression profiles, each exhibiting substantial differences in prognosis (p = 0.0046, log-rank test), with Cluster 1 having the best prognosis, followed by Cluster 2, and Cluster 3 having the worst prognosis. We explored the impact of the heterogeneity of these subtypes on patient prognosis, the differences in the molecular mechanism, and their responses to drug therapy and immunotherapy. In addition, we designed a machine learning-based classification model, unveiling highly consistent prognostic distinctions in several externally independent cohorts. Results: A notable marker gene CXCL13 was identified in Cluster 3, potentially pivotal in enhancing patient prognosis. At the single-cell resolution, we delved into the adverse prognosis of Cluster 3, observing an enhanced interaction between fibroblasts, myeloid cells, and basal cells, influencing patient prognosis. Furthermore, we identified several significantly upregulated genes (CD46, JAG1, IL6, and IL6R) that may positively correlate with cancer cells' survival and invasive capabilities in this subtype. Discussion: Our study is a robust foundation for precision medicine and personalized therapy, presenting a novel perspective for the contemporary classification of breast cancer.

BCAR4 facilitates trastuzumab resistance and EMT in breast cancer via sponging miR-665 and interacting with YAP1.

Breast cancer antiestrogen resistance 4 (BCAR4) has been suggested that can modulate cell behavior, resulting in tumorigenesis and chemoresistance. However, the underlying mechanisms of BCAR4 in trastuzumab resistance (TR) is still elusive. Here, we explored the function and the underlying mechanism of BCAR4 involving in TR. We found that BCAR4 is significantly upregulated in trastuzumab-resistant BC cells. Knockdown of BCAR4 could sensitize the BC cells to trastuzumab and suppress epithelial-mesenchymal transition (EMT). Mechanically, BCAR4 promotes yes-associated protein 1 (YAP1) expression by competitively sponging miR-665, to activated TGF-ß signaling. Reciprocally, YAP1 could occupy the BCAR4 promoter to enhance its transcription, suggesting that there exists a positive feedback regulation between YAP1 and BCAR4. Targeting the BCAR4/miR-665/YAP1 axis may provide a novel insight of therapeutic approaches for TR in BC.

Nucleosome reorganisation in breast cancer tissues.

BACKGROUND: Nucleosome repositioning in cancer is believed to cause many changes in genome organisation and gene expression. Understanding these changes is important to elucidate fundamental aspects of cancer. It is also important for medical diagnostics based on cell-free DNA (cfDNA), which originates from genomic DNA regions protected from digestion by nucleosomes. RESULTS: We have generated high-resolution nucleosome maps in paired tumour and normal tissues from the same breast cancer patients using MNase-assisted histone H3 ChIP-seq and compared them with the corresponding cfDNA from blood plasma. This analysis has detected single-nucleosome repositioning at key regulatory regions in a patient-specific manner and common cancer-specific patterns across patients. The nucleosomes gained in tumour versus normal tissue were particularly informative of cancer pathways, with ~ 20-fold enrichment at CpG islands, a large fraction of which marked promoters of genes encoding DNA-binding proteins. The tumour tissues were characterised by a 5-10 bp decrease in the average distance between nucleosomes (nucleosome repeat length, NRL), which is qualitatively similar to the differences between pluripotent and differentiated cells. This effect was correlated with gene activity, differential DNA methylation and changes in local occupancy of linker histone variants H1.4 and H1X. CONCLUSIONS: Our study offers a novel resource of high-resolution nucleosome maps in breast cancer patients and reports for the first time the effect of systematic decrease of NRL in paired tumour versus normal breast tissues from the same patient. Our findings provide a new mechanistic understanding of nucleosome repositioning in tumour tissues that can be valuable for patient diagnostics, stratification and monitoring.