Breast Cancer Research最新文献

{"title":"Unraveling breast cancer response to neoadjuvant chemotherapy through integrated genomic, transcriptomic, and circulating tumor DNA analysis.","authors":"Menghao Dong, Jian Chen, Nannan Lu, Song Wang, Wenhui Wei, Ziming Wang, Jinnan Wang, Jinguo Zhang, Xinghua Han, Fufeng Wang, Qiuxiang Ou, Hua Bao, Xiaopeng Ma, Benjie Shan, Yueyin Pan","doi":"10.1186/s13058-025-02026-5","DOIUrl":"https://doi.org/10.1186/s13058-025-02026-5","url":null,"abstract":"<p><strong>Introduction: </strong>Neoadjuvant chemotherapy (NAC) is a standard treatment for breast cancer (BC) to shrink tumors and facilitate surgery. However, the molecular underpinnings of response to NAC and prognosis have not been well characterized.</p><p><strong>Methods: </strong>We enrolled 73 stage II/III BC patients who received NAC followed by surgery. Tumor tissue samples were available from 36 patients at baseline and 38 at the time of surgery. Plasma circulating tumor DNA (ctDNA) was collected at three time points: before NAC (n = 63), during NAC (n = 42), and after NAC (n = 40). Comprehensive genomic, transcriptomic, and ctDNA analyses were performed to identify biomarkers associated with pathological complete response (pCR) and survival outcomes.</p><p><strong>Results: </strong>Nine baseline mutations, including DNHD1 and PLEC, along with HIPPO pathway alterations, were associated with pCR. Responsive tumors exhibited immune activation and downregulated PI3K-Akt and AGE-RAGE pathways, while non-pCR tumors showed reduced cytokine and immune receptor activity. Undetectable ctDNA during and after NAC was predictive of treatment efficacy and correlated with improved survival. Baseline mutations in USH2A were associated with shorter disease-free survival (hazard ratio: 11.9; 95% confidence interval: 2.8-50.8; P < 0.001), with a consistent trend observed for overall survival. Elevated NHSL1 expression in baseline tumors indicated an initial treatment response but was later associated with tumor relapse and poor overall survival (P = 0.026 and P = 0.023, respectively), findings that were validated in an independent clinical cohort (N = 30) through immunohistochemistry staining.</p><p><strong>Conclusion: </strong>Our comprehensive multi-omics analysis identified promising biomarkers predictive of treatment response and survival in BC patients receiving NAC followed by surgery. These findings underscore the importance of early tumor assessment for improved patient stratification and prognostication.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"64"},"PeriodicalIF":7.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12044986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neutrophil extracellular traps induced by neoadjuvant chemotherapy of breast cancer promotes vascular endothelial damage.","authors":"Linghui Kong, Song Hu, Ying Zhao, Yan Huang, Xiaobing Xiang, Yang Yu, Xiaochun Mao, Kangjie Xie, Xiaoyan Zhu, Pingbo Xu","doi":"10.1186/s13058-025-02011-y","DOIUrl":"https://doi.org/10.1186/s13058-025-02011-y","url":null,"abstract":"<p><strong>Background: </strong>The mechanisms underpinning neoadjuvant chemotherapy-induced vascular endothelial injury in breast cancer remain elusive. Our study aims to demonstrate that Neutrophil Extracellular Traps (NETs) play a pivotal role in neoadjuvant chemotherapy-induced vascular endothelial injury in breast cancer, elucidating that chemotherapy-induced upregulation of Solute Carrier 11a1 (Slc11a1) modulates Reactive Oxygen Species (ROS) generation, which may be critical for NETs formation.</p><p><strong>Methods: </strong>We investigated the impact of neoadjuvant chemotherapy for breast cancer on NETs formation and vascular endothelial injury by analyzing NETs dsDNA and serum markers in patients, cells, and chemotherapy mouse models. RNA sequencing of neutrophils from chemotherapy mouse models was performed to identify the potential NETs formation-associated gene Slc11a1, which was further validated through cellular and animal experiments by assessing Slc11a1 expression, intracellular ferrous ion content, and ROS levels. Knockdown of Slc11a1 in human neutrophils and mouse models were also performed to further confirm the phenotypic results.</p><p><strong>Results: </strong>Our study revealed that plasma NETs formation and endothelial injury markers were significantly elevated in breast cancer patients undergoing docetaxel & carboplatin (TCb) neoadjuvant chemotherapy, compared to controls. In these patients, NETs formation was associated with the augmentation of endothelial injury markers. Chemotherapy mouse models demonstrated that TCb treatment markedly elevated NETs formation and endothelial injury, which can be mitigated by CI-amidine, a protein-arginine deiminase inhibitor. In human neutrophils, we demonstrated that the TCb chemotherapeutic agents (combination of docetaxel and carboplatin) induced the formation of NETs, which subsequently facilitated damage to human umbilical vein endothelial cells in vitro. RNA sequencing of mouse neutrophils identified Slc11a1 as a key NETs formation-related gene, which was upregulated by TCb chemotherapy in neutrophils, leading to increased intracellular ferrous ion content and ROS generation. Knockdown of Slc11a1 in human neutrophils and mouse models demonstrated its reversal effect on TCb-induced ferrous ion upregulation, ROS generation, and NETs formation.</p><p><strong>Conclusions: </strong>Our research underscores the capacity of TCb neoadjuvant chemotherapy in breast cancer to augment NETs formation in neutrophils through Slc11a1-mediated ROS generation, which is linked to vascular endothelial injury. Our study elucidates the potential mechanisms underlying perioperative vascular endothelial injury in breast cancer patients undergoing neoadjuvant chemotherapy, offering novel insights into perioperative therapeutic management strategies for these patients.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"61"},"PeriodicalIF":7.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12016159/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144009422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer learning drives automatic HER2 scoring on HE-stained WSIs for breast cancer: a multi-cohort study.","authors":"Xiaoping Li, Zhiquan Lin, Chaoran Qiu, Yiwen Zhang, Chuqian Lei, Shaofei Shen, Weibin Zhang, Chan Lai, Weiwen Li, Hui Huang, Tian Qiu","doi":"10.1186/s13058-025-02008-7","DOIUrl":"https://doi.org/10.1186/s13058-025-02008-7","url":null,"abstract":"<p><strong>Background: </strong>Streamlining the clinical procedure of human epidermal growth factor receptor 2 (HER2) examination is challenging. Previous studies neglected the intra-class variability within both HER2-positive and -negative groups and lacked multi-cohort validation. To address this deficiency, this study collected data from multiple cohorts to develop a robust model for HER2 scoring utilizing only Hematoxylin&Eosin-stained whole slide images (WSIs).</p><p><strong>Methods: </strong>A total of 578 WSIs were collected from five cohorts, including three public and two private datasets. Each WSI underwent adaptive scale cropping. The transfer-learning-based probabilistic aggregation (TL-PA) model and multi-instance learning (MIL)-based models were compared, both of which were trained on Cohort A and validated on Cohorts B-D. The model demonstrating superior performance was further evaluated in the neoadjuvant therapy (NAT) cohort. Scoring performance was assessed using the area under the receiver operating characteristic curve (AUC). Correlation between the model scores and specific grades (HER2 levels, pathological complete response (pCR) status, residual cancer burden (RCB) grades) were evaluated using Spearman rank correlation and Dunn's test. Patch analysis was performed with manually defined features.</p><p><strong>Results: </strong>For HER2 scoring, the TL-PA significantly outperformed the MIL-based models, achieving robust AUCs in four validation cohorts (Cohort A: 0.75, Cohort B: 0.75, Cohort C: 0.77, Cohort D: 0.77). Correlation analysis confirmed a moderate association between model scores and manual reader-defined HER2-IHC status (Coefficient_(Spearman) = 0.37, P_(Spearman) = 0.001) as well as RCB grades (Coefficient_(Spearman) = 0.45, P_(Spearman) = 0.0006). In Cohort NAT, with the non-pCR as the positive control, the AUC was 0.77. Patch analysis revealed a core-to-peritumoral probability decrease pattern as malignancy spread outward from the lesion's core.</p><p><strong>Conclusion: </strong>TL-PA shows robust generalization for HER2 scoring with minimal data; however, it still inadequately capture intra-class variability. This indicates that future deep-learning endeavors should incorporate more detailed annotations to better align the model's focus with the reasoning of pathologists.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"62"},"PeriodicalIF":7.4,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020254/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144028138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breast cancer-specific survival by molecular subtype in different age groups of women in Scotland.","authors":"Ines Mesa-Eguiagaray, Sarah H Wild, Linda J Williams, Kai Jin, Sheila M Bird, David H Brewster, Peter S Hall, Jonine D Figueroa","doi":"10.1186/s13058-025-02012-x","DOIUrl":"https://doi.org/10.1186/s13058-025-02012-x","url":null,"abstract":"<p><strong>Background: </strong>Age and molecular subtypes are important prognostic factors in breast cancer (BC). Here, we explore how age and molecular subtypes influence BC survival in Scotland.</p><p><strong>Methods: </strong>We analysed data from 71,784 women diagnosed with invasive BC in Scotland between 1997 and 2016, with follow-up until 31st December 2018 (median follow-up time = 5.5 years). Cox models estimated Hazard Ratios (HR) for BC-specific death by age group (with women of screening age, 50-69 years old, as the reference) within each molecular subtype, adjusting for prognostic factors. The cumulative incidence function was plotted to account for competing risks.</p><p><strong>Results: </strong>During the study period, 37% of women died, with 53% of deaths attributed to BC. Women aged 70 + years had increased BC-specific death compared to women aged 50 to 69 years with the same subtype. HRs (95% CI) were 1.49 (1.23-1.80) for luminal A, 1.39 (1.14 to 1.69) for luminal B tumours and 1.49 (1.15 to 1.94) for triple negative breast cancer (TNBC). Women aged < 50 years had lower risk of BC death in luminal A subtype only, with HR of 0.66 (0.51-0.86) compared to women aged 50 to 69 years. Competing risks analysis showed higher cumulative incidence of death from non-BC causes, particularly for women aged 70 + years with hormone positive subtypes. Stage, treatment, and molecular subtype were the strongest prognostic factors for BC-specific mortality across all ages.</p><p><strong>Conclusions: </strong>Age influences BC-specific mortality particularly within luminal subtypes. In contrast, other tumour characteristics and treatment are key prognostic factors for non-luminal subtypes. Future studies should investigate other markers of BC mortality particularly among over 70-year-olds, who account for 60% of BC deaths in the UK.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"59"},"PeriodicalIF":7.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12013176/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Genomic alterations in normal breast tissues preceding breast cancer diagnosis.","authors":"Jiawei Dai, Mariya Rozenblit, Xiaoyue Li, Naing Lin Shan, Yueyue Wang, Shrikant Mane, Michal Marczyk, Lajos Pusztai","doi":"10.1186/s13058-025-02018-5","DOIUrl":"https://doi.org/10.1186/s13058-025-02018-5","url":null,"abstract":"<p><strong>Background: </strong>Normal breast tissues adjacent to cancer often harbor many of the same genomic alterations as the cancer itself. However, it remains unclear whether histologically normal breast tissues carry genomic changes related to cancer development years before a cancer diagnosis.</p><p><strong>Methods: </strong>Whole exome sequencing was performed to examine germline and somatic alterations in histologically normal breast tissues from women who subsequently developed breast cancer (n = 79, pre-diagnosis tissues) and compared these with results from breast tissues of women who did not (n = 81). No patient had germline mutations in cancer predisposition genes.</p><p><strong>Results: </strong>The pre-diagnosis tissues had significantly more high functional impact germline variants per sample than the healthy controls (P = 0.034), 36.5% of affected genes were cancer hallmark genes, among these 62.4% were involved with evading growth suppressors and 5.7% with genome instability. The average number of somatic mutations were similar between the two cohorts. Mutation signature analysis revealed COSMIC signatures 3 (associated with impaired homologous recombination) as a dominant signature more frequent in pre-diagnosis tissues. At gene and variant level, nine common germline polymorphisms in two immune regulatory genes, FCGBP and TPSBP2, and along with three somatic mutations in F13A1, FRY and TMLHE, were significantly more frequently mutated in the pre-diagnosis samples.</p><p><strong>Conclusions: </strong>Individuals who develop breast cancer have a higher germline variant burden in normal breast tissues leading to subtle deficiencies in DNA repair that in the context of other germline and somatic mutations could facilitate malignant transformation.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"60"},"PeriodicalIF":7.4,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12013151/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144046821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Nottingham grade in breast cancer digital pathology using a foundation model.","authors":"Jun Seo Kim, Jeong Hoon Lee, Yousung Yeon, Do-Yeon An, Seok Jun Kim, Myung-Giun Noh, Suehyun Lee","doi":"10.1186/s13058-025-02019-4","DOIUrl":"https://doi.org/10.1186/s13058-025-02019-4","url":null,"abstract":"<p><strong>Background: </strong>The Nottingham histologic grade is crucial for assessing severity and predicting prognosis in breast cancer, a prevalent cancer worldwide. Traditional grading systems rely on subjective expert judgment and require extensive pathological expertise, are time-consuming, and often lead to inter-observer variability.</p><p><strong>Methods: </strong>To address these limitations, we develop an AI-based model to predict Nottingham grade from whole-slide images of hematoxylin and eosin (H&E)-stained breast cancer tissue using a pathology foundation model. From TCGA database, we trained and evaluated using 521 H&E breast cancer slide images with available Nottingham scores through internal split validation, and further validated its clinical utility using an additional set of 597 cases without Nottingham scores. The model leveraged deep features extracted from a pathology foundation model (UNI) and incorporated 14 distinct multiple instance learning (MIL) algorithms.</p><p><strong>Results: </strong>The best-performing model achieved an F1 score of 0.731 and a multiclass average AUC of 0.835. The top 300 genes correlated with model predictions were significantly enriched in pathways related to cell division and chromosome segregation, supporting the model's biological relevance. The predicted grades demonstrated statistically significant association with 5-year overall survival (p < 0.05).</p><p><strong>Conclusion: </strong>Our AI-based automated Nottingham grading system provides an efficient and reproducible tool for breast cancer assessment, offering potential for standardization of histologic grade in clinical practice.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"58"},"PeriodicalIF":7.4,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retraction Note: Association of Life's Essential 8 cardiovascular health with breast cancer incidence and mortality according to genetic susceptibility of breast cancer: a prospective cohort study.","authors":"Yan Zhao, Yang Song, Xiangmin Li, Ayao Guo","doi":"10.1186/s13058-025-02021-w","DOIUrl":"https://doi.org/10.1186/s13058-025-02021-w","url":null,"abstract":"","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"56"},"PeriodicalIF":7.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008858/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin-eosin-stained slides.","authors":"Jun Du, Jun Shi, Dongdong Sun, Yifei Wang, Guanfeng Liu, Jingru Chen, Wei Wang, Wenchao Zhou, Yushan Zheng, Haibo Wu","doi":"10.1186/s13058-025-01998-8","DOIUrl":"https://doi.org/10.1186/s13058-025-01998-8","url":null,"abstract":"<p><strong>Background: </strong>Treatment with HER2-targeted therapies is recommended for HER2-positive breast cancer patients with HER2 gene amplification or protein overexpression. Interestingly, recent clinical trials of novel HER2-targeted therapies demonstrated promising efficacy in HER2-low breast cancers, raising the prospect of including a HER2-low category (immunohistochemistry, IHC) score of 1 + or 2 + with non-amplified in-situ hybridization for HER2-targeted treatments, which necessitated the accurate detection and evaluation of HER2 expression in tumors. Traditionally, HER2 protein levels are routinely assessed by IHC in clinical practice, which not only requires significant time consumption and financial investment but is also technically challenging for many basic hospitals in developing countries. Therefore, directly predicting HER2 expression by hematoxylin-eosin (HE) staining should be of significant clinical values, and machine learning may be a potent technology to achieve this goal.</p><p><strong>Methods: </strong>In this study, we developed an artificial intelligence (AI) classification model using whole slide image of HE-stained slides to automatically assess HER2 status.</p><p><strong>Results: </strong>A publicly available TCGA-BRCA dataset and an in-house USTC-BC dataset were applied to evaluate our AI model and the state-of-the-art method SlideGraph + in terms of accuracy (ACC), the area under the receiver operating characteristic curve (AUC), and F1 score. Overall, our AI model achieved the superior performance in HER2 scoring in both datasets with AUC of 0.795 ± 0.028 and 0.688 ± 0.008 on the USCT-BC and TCGA-BRCA datasets, respectively. In addition, we visualized the results generated from our AI model by attention heatmaps, which proved that our AI model had strong interpretability.</p><p><strong>Conclusion: </strong>Our AI model is able to directly predict HER2 expression through HE images with strong interpretability, and has a better ACC particularly in HER2-low breast cancers, which provides a method for AI evaluation of HER2 status and helps to perform HER2 evaluation economically and efficiently. It has the potential to assist pathologists to improve diagnosis and assess biomarkers for companion diagnostics.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"57"},"PeriodicalIF":7.4,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12008878/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Central and peripheral adiposity and premenopausal breast cancer risk: a pooled analysis of 440,179 women.","authors":"Minouk J Schoemaker, Taylor Ellington, Hazel B Nichols, Lauren B Wright, Michael E Jones, Katie M O'Brien, Clarice R Weinberg, Hans-Olov Adami, Laura Baglietto, Kimberly A Bertrand, Yu Chen, Jessica Clague DeHart, A Heather Eliassen, Graham G Giles, Serena C Houghton, Victoria A Kirsh, Roger L Milne, Julie R Palmer, Hannah Lui Park, Thomas E Rohan, Gianluca Severi, Xiao-Ou Shu, Rulla M Tamimi, Lars J Vatten, Elisabete Weiderpass, Walter C Willett, Anne Zeleniuch-Jacquotte, Wei Zheng, Dale P Sandler, Anthony J Swerdlow","doi":"10.1186/s13058-025-01995-x","DOIUrl":"https://doi.org/10.1186/s13058-025-01995-x","url":null,"abstract":"<p><strong>Background: </strong>Among premenopausal women, higher body mass index (BMI) is associated with lower breast cancer risk, although the underlying mechanisms are unclear. Investigating adiposity distribution may help clarify impacts on breast cancer risk. This study was initiated to investigate associations of central and peripheral adiposity with premenopausal breast cancer risk overall and by other risk factors and breast cancer characteristics.</p><p><strong>Methods: </strong>We used individual-level data from 14 prospective cohort studies to estimate hazard ratios (HRs) for premenopausal breast cancer using Cox proportional hazards regression. Analyses included 440,179 women followed for a median of 7.5 years (interquartile range: 4.0-11.3) between 1976 and 2017, with 6,779 incident premenopausal breast cancers.</p><p><strong>Results: </strong>All central adiposity measures were inversely associated with breast cancer risk overall when not controlling for BMI (e.g. for waist circumference, HR per 10 cm increase: 0.92, 95% confidence interval (CI): 0.90-0.94) whereas in models adjusting for BMI, these measures were no longer associated with risk (e.g. for waist circumference: HR 0.99, 95% CI: 0.95-1.03). This finding was consistent across age categories, with some evidence that BMI-adjusted associations differed by breast cancer subtype. Inverse associations for in situ breast cancer were observed with waist-to-height and waist-to-hip ratios and a positive association was observed for oestrogen-receptor-positive breast cancer with hip circumference (HR per 10 cm increase: 1.08, 95% CI: 1.10-1.14). For luminal B, HER2-positive breast cancer, we observed an inverse association with hip circumference (HR per 10 cm: 0.84, 95% CI: 0.71-0.98), but positive associations with waist circumference (HR per 10 cm: 1.18, 95% CI: 1.03-1.36), waist-to-hip ratio (HR per 0.1 units: 1.29, 95% CI: 1.15-1.45) and waist-to height ratio (HR per 0.1 units: 1.46, 95% CI: 1.17-1.84).</p><p><strong>Conclusions: </strong>Our analyses did not support an association between central adiposity and overall premenopausal breast cancer risk after adjustment for BMI. However, our findings suggest associations might differ by breast cancer hormone receptor and intrinsic subtypes.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"55"},"PeriodicalIF":7.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12001638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pharmacovigilance study of adverse reactions of anti-HER-2 drugs for the treatment of HER-2-positive breast cancer based on the FAERS database.","authors":"Jinming Han, Xiaohan Zhai, Xufeng Tao, Yunming Li, Ziqi Zhao, Zhan Yu, Deshi Dong, Shilei Yang, Linlin Lv","doi":"10.1186/s13058-025-02013-w","DOIUrl":"https://doi.org/10.1186/s13058-025-02013-w","url":null,"abstract":"<p><strong>Objective: </strong>There are three categories of drugs that treat human epidermal growth factor receptor type 2 (HER-2) positive breast cancer: monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), and tyrosine kinase inhibitors (TKIs). The purpose of this study is to analyze and compare the adverse reactions of three classes of anti-HER-2 drugs to various body systems in patients based on the FDA Adverse Event Reporting System (FAERS).</p><p><strong>Methods: </strong>All data reports were extracted from the FAERS between 2004 and 2024. Data mining of adverse events associated with anti-HER-2 drugs was carried out using disproportionality analysis. A multivariate logistic regression analysis was conducted to explore the risk factors associated with AEs leading to hospitalization.</p><p><strong>Results: </strong>A total of 47,799 patients were screened for the three classes of drugs, among which ADC drugs caused the largest proportion of deaths. MAb has the strongest ADR signals associated with \"cardiac disorders\". Moreover, trastuzumab was associated with a greater risk of cardiotoxicity. Logistic regression analysis revealed that the treatment with mAbs should be wary of serious adverse reactions in \"infections and infestations\" and \"metabolism and nutrition disorders\". Moreover, \"endocrine disorders\" were the factor associated with the highest risk of prolonged hospitalization due to trastuzumab deruxtecan (T-DXd). The safety of tucatinib among TKI drugs is greater than that of other drugs.</p><p><strong>Conclusion: </strong>In general, from the perspective of the effects of the three classes of drugs on the various body systems of patients, we should focus on mAb-associated \"cardiac disorders\", ADC-associated \"hepatobiliary disorders\", \"respiratory, thoracic and mediastinal disorders\", and TKI-associated \"gastrointestinal disorders.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"54"},"PeriodicalIF":7.4,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11983758/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}