Neoadjuvant therapy(NAT)has become the standard treatment for patients with locally advanced breast cancer and stage II-III HER2-positive(HER2+)or triple-negative breast cancer(TNBC)1,2.It is essential to accurately m...Neoadjuvant therapy(NAT)has become the standard treatment for patients with locally advanced breast cancer and stage II-III HER2-positive(HER2+)or triple-negative breast cancer(TNBC)1,2.It is essential to accurately mark the primary breast tumor and positive axillary lymph nodes(ALNs)prior to NAT to ensure precise surgical excision,guide axillary downstaging,and guarantee reliable lesion retrieval for pathologic evaluation3.The false-negative rate of sentinel lymph node biopsy(SLNB)after NAT can be reduced to<10%by applying modalities,such as the identification of≥3 sentinel lymph nodes(SLNs)with dual-mapping techniques or removal of the marked lymph node with target axillary dissection(TAD)according to the ASCO,NCCN,and CBCS guidelines3-5.However,there is a lack of consensus regarding the optimal methods and materials for accurate marking6,7.Conventional techniques include clip placement,guidewire localization,and carbon or ink tattooing,whereas wireless technologies,such as MagseedR,radiofrequency identification tags,SAVI SCOUTR,and radioactive iodine-125(125I)seeds,have also been adopted.Traditional marking techniques have a localization failure rate of approximately 10%.In contrast,the use of 125I seeds(with a radiation dose of 0.1-0.3 mCi)has significantly improved localization accuracy8,9.Nevertheless,owing to radioactive properties,concerns have been raised regarding the potential impact of 125I seed marking on assessing the pathologic complete response(pCR)after NAT10.Moreover,whether the influence of 125I seed marking on pCR could lead to suboptimal adjuvant treatment decisions and potentially compromise long-term oncologic outcomes has not been established.To investigate the potential impact of 125I seed placement on the pCR rate and long-term outcomes in breast cancer patients receiving NAT,we conducted a retrospective cohort study utilizing propensity score matching(PSM).展开更多
Objective:Accurate detection of PIK3CA mutations is essential for guiding PI3K-targeted therapies in breast cancer,yet sequencing is not universally accessible,and single-modality prediction models have limited perfor...Objective:Accurate detection of PIK3CA mutations is essential for guiding PI3K-targeted therapies in breast cancer,yet sequencing is not universally accessible,and single-modality prediction models have limited performance.This study developed a multimodal deep learning framework integrating whole-slide imaging(WSI)and structured clinical data to improve mutation prediction.Methods:A total of 1,047 patients from TCGA and 166 patients from 3 external centers were included.The histopathology model used a transformer-based pretrained encoder(H-optimus-0)and a clustering-constrained attention multiple instance learning(CLAM-SB MIL)classifier to generate WSI-level representations.The clinical model incorporated engineered clinical variables and an extreme gradient boosting(XGBoost)model.A decision-level late fusion strategy(Multimodal PIK3CA Model,MPM)combined probabilistic outputs from both branches.Performance was evaluated with the area under the curve(AUC)and secondary metrics.Interpretability was assessed via attention heatmaps and shapley additive explanations(SHAP)analysis.Results:MPM outperformed single-modality models.It achieved an AUC of 0.745 on TCGA and maintained stable performance across external cohorts(0.695,0.690,and 0.680).SHAP analysis identified molecular subtype as the most influential clinical feature,whereas attention maps highlighted mutation-associated morphological regions.Conclusions:The developed multimodal framework effectively integrates complementary morphological and clinical information,and provides a robust and generalizable method for predicting PIK3CA mutation status.Strong multicenter adaptability and biological interpretability support its potential use as a clinical decision-support tool and an accessible alternative to molecular testing.展开更多
Objective To develop a prognostic prediction model for early-stage triple-negative breast cancer(TNBC)using H&E-stained pathological images and to investigate its underlying biological interpretability.Methods A d...Objective To develop a prognostic prediction model for early-stage triple-negative breast cancer(TNBC)using H&E-stained pathological images and to investigate its underlying biological interpretability.Methods A deep learning model was trained on 340 WSIs and externally validated using 81 TCGA cases.Image-derived features extracted through convolutional neural networks were integrated with clinicopathological variables.Model performance was assessed using ROC curve analysis,and interpretability was evaluated by correlating image features with mRNA-seq data and characteristics of the immune microenvironment.Results The model achieved AUCs of 0.86 and 0.75 in the training and validation cohorts,respectively.Analysis using HoVer-Net indicated that lymphocyte abundance was associated with recurrence risk.Texture-related features showed significant correlations with immune cell infiltration and prognostic gene expression profiles.Conclusion This study demonstrates that deep learning can enable accurate prognostic prediction in early-stage TNBC,with interpretable image features that reflect the tumor immune microenvironment and gene expression profiles.展开更多
Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT h...Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT has become an urgent world-wide clinical problem.Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system.Methods:In this study,we retrospectively collected longitudinal(pre-NAT and post-NAT)multi-parametric magnetic resonance imaging(MRI)and clinicopathologic data of a total of 1,315 breast cancer patients(clinical stageⅠ-Ⅲ)who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from January 2010 to January 2023.We used radiomics,3D convolutional neural network technology and clinical data statistical analysis methods to extract and screen multimodal features,and then developed and validated a Clinical-Radiomics-Deep-Learning(CRDL)model to predict patients'pCR outcomes based on multimodal fusion features.Results:We use the area under the receiver operating characteristic curve(AUC)in the primary cohort(PC)and3 external validation cohorts(VC_(1-3))to evaluate the model performance.The results showed that the AUC in the PC composed of 2 medical centers was 0.947[95%confidence interval(95%CI):0.931-0.960],and the AUC values in VC_(1-3)were 0.857(95%CI:0.810-0.901),0.883(95%CI:0.841-0.918)and 0.904(95%CI:0.860-0.941),respectively.Conclusions:The CRDL model demonstrated high accuracy and robustness in predicting pCR to NAT using multimodal fusion data.This study provides a strong foundation for non-invasive assessment of pCR status in breast cancer patients following NAT and offers critical insights to guide clinical decision-making in post-NAT treatment planning.展开更多
Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly c...Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly correlated with clinicopathological characteristics and prognostic outcomes of patients with breast cancer remains elusive.This study systematically investigated the clinical significance of ZWINT expression in breast cancer through integrated molecular subtyping and survival analysis.Methods We systematically characterized the spatial expression pattern of ZWINT across various breast cancer subtypes and assessed its prognostic significance using an integrated bioinformatics approach that involved multi-omics analysis.The approach included the Breast Cancer Gene-Expression Miner v5.1(bc-GenExMiner v5.1),TNMplot,MuTarget,PrognoScan database,and Database for Annotation,Visualization,and Integrated Discovery(DAVID).Results Our analysis revealed consistent upregulation of ZWINT mRNA and protein expression across distinct clinicopathological subtypes of breast cancer.ZWINT overexpression demonstrated significant co-occurrence with truncating mutations in cadherin 1(CDH1)and tumor protein p53(TP53),suggesting potential functional crosstalk in tumor progression pathways.The overexpression of ZWINT correlated with adverse clinical outcomes,showing 48%increased mortality risk(overall survival:HR 1.48,95%CI 1.23–1.79),66%higher recurrence probability(relapse-free survival:1.66,95%CI 1.50–1.84),and 63%elevated metastasis risk(distant metastasis-free survival:HR 1.63,95%CI 1.39–1.90).Multivariate Cox regression incorporating TNM staging and molecular subtypes confirmed ZWINT as an independent prognostic determinant(P<0.001,Harrell’s C-index=0.7827),which was validated through bootstrap resampling(1000 iterations).Conclusion ZWINT may serve as a potential biomarker for prognosis and a possible therapeutic target alongside TP53/CDH1 in breast cancer.展开更多
The vision transformer(ViT)architecture,with its attention mechanism based on multi-head attention layers,has been widely adopted in various computer-aided diagnosis tasks due to its effectiveness in processing medica...The vision transformer(ViT)architecture,with its attention mechanism based on multi-head attention layers,has been widely adopted in various computer-aided diagnosis tasks due to its effectiveness in processing medical image information.ViTs are notably recognized for their complex architecture,which requires high-performance GPUs or CPUs for efficient model training and deployment in real-world medical diagnostic devices.This renders them more intricate than convolutional neural networks(CNNs).This difficulty is also challenging in the context of histopathology image analysis,where the images are both limited and complex.In response to these challenges,this study proposes a TokenMixer hybrid-architecture that combines the strengths of CNNs and ViTs.This hybrid architecture aims to enhance feature extraction and classification accuracy with shorter training time and fewer parameters by minimizing the number of input patches employed during training,while incorporating tokenization of input patches using convolutional layers and encoder transformer layers to process patches across all network layers for fast and accurate breast cancer tumor subtype classification.The TokenMixer mechanism is inspired by the ConvMixer and Token-Learner models.First,the ConvMixer model dynamically generates spatial attention maps using convolutional layers,enabling the extraction of patches from input images to minimize the number of input patches used in training.Second,the TokenLearner model extracts relevant regions from the selected input patches,tokenizes them to improve feature extraction,and trains all tokenized patches in an encoder transformer network.We evaluated the TokenMixer model on the BreakHis public dataset,comparing it with ViT-based and other state-of-the-art methods.Our approach achieved impressive results for both binary and multi-classification of breast cancer subtypes across various magnification levels(40×,100×,200×,400×).The model demonstrated accuracies of 97.02%for binary classification and 93.29%for multi-classification,with decision times of 391.71 and 1173.56 s,respectively.These results highlight the potential of our hybrid deep ViT-CNN architecture for advancing tumor classification in histopathological images.The source code is accessible:https://github.com/abimo uloud/Token Mixer.展开更多
Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limita...Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limitations that reduce their accessibility and accuracy.This study investigates the use ofConvolutionalNeuralNetworks(CNNs)to enhance the diagnostic process of BC histopathology.Utilizing the BreakHis dataset,which contains thousands of histopathological images,we developed a CNN model designed to improve the speed and accuracy of image analysis.Our CNN architecture was designed with multiple convolutional layers,max-pooling layers,and a fully connected network optimized for feature extraction and classification.Hyperparameter tuning was conducted to identify the optimal learning rate,batch size,and number of epochs,ensuring robust model performance.The dataset was divided into training(80%),validation(10%),and testing(10%)subsets,with performance evaluated using accuracy,precision,recall,and F1-score metrics.Our CNN model achieved a magnification-independent accuracy of 97.72%,with specific accuracies of 97.50%at 40×,97.61%at 100×,99.06%at 200×,and 97.25%at 400×magnification levels.These results demonstrate the model’s superior performance relative to existing methods.The integration of CNNs in diagnostic workflows can potentially reduce pathologist workload,minimize interpretation errors,and increase the availability of diagnostic testing,thereby improving BC management and patient survival rates.This study highlights the effectiveness of deep learning in automating BC histopathological classification and underscores the potential for AI-driven diagnostic solutions to improve patient care.展开更多
Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine lea...Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.展开更多
Objective:Recurrence continues to be a pivotal challenge among hormone receptor-positive(HR^(+))/human epidermal growth factor receptor 2^(−)negative(HER2^(−))breast cancers.In the international consensus guidelines,H...Objective:Recurrence continues to be a pivotal challenge among hormone receptor-positive(HR^(+))/human epidermal growth factor receptor 2^(−)negative(HER2^(−))breast cancers.In the international consensus guidelines,HR^(+)/HER2^(−)breast cancer relapse patterns are divided into three distinct types:primary resistant,secondary resistant,and endocrine sensitive.However,owing to the lack of cohorts with treatment and follow-up data,the heterogeneity among different recurrence patterns remains uncharted.Current treatments still lack precision.Methods:This analysis included data from a large-scale multiomics study of a HR^(+)/HER2^(−)breast cancer cohort(n=314).Through the analysis of transcriptomics(n=312),proteomics(n=124),whole-exome sequencing(n=290),metabolomics(n=217),and digital pathology(n=228)data,we explored distinctive molecular features and identified putative therapeutic targets for patients experiencing recurrence.Results:We explored distinct clinicopathological characteristics,biological heterogeneity,and potential therapeutic strategies for recurrence.Based on a shared relapse signature,we stratified patients into high-and lowrecurrence-risk groups.Patients with different relapse patterns presented unique molecular features in primary tumors.Specifically,receptor tyrosine kinase(RTK)pathway activation in the primary resistant group suggested the utility of RTK inhibitors,whereas mammalian target of rapamycin(mTOR)and cell cycle pathway activation in the secondary resistant group highlighted the potential of mTOR and CDK4/6 inhibitors.Interestingly,the endocrine-sensitive group displayed a quiescent state and high genomic instability,suggesting that targeting quiescent cells and using poly-ADP-ribose polymerase(PARP)inhibitors could be effective strategies.Conclusions:These findings illuminate the clinicopathological and molecular landscape of HR^(+)/HER2^(−)breast cancer patients with distinct recurrence patterns,highlighting potential targeted therapies.展开更多
Introduction,Breast cancer is the most common cancer type in adolescents and young adults<40 years of age,accounting for 30%of cancers in this age group1.Breast cancer in the young presents significant challenges f...Introduction,Breast cancer is the most common cancer type in adolescents and young adults<40 years of age,accounting for 30%of cancers in this age group1.Breast cancer in the young presents significant challenges for patients and society,including more aggressive tumor biology,poor prognosis,genetic susceptibility,fertility preservation,and complex psychosocial issues.Moreover,because of the markedly younger median age of breast cancer,the proportion of young breast cancer patients in China is significantly higher than Western countries2.The first Young Breast Cancer in China(YBCC)consensus meeting was held in Guangzhou,China in December 2021 to address exclusive challenges and requirements facing young patients with breast cancer.Chinese medical experts from multiple specialties had an extensive discussion and formulated a consensus over several hot topics in young patients with breast cancer.The“Expert Consensus on the Diagnosis and Treatment of Young Breast Cancer in China(2022 edition)”published in the Chinese Medical Journal has garnered significant attention3,highlighting enormous interest in the YBCC consensus in the medical community and public.展开更多
Background:Sclerosing adenosis(SA)and breast cancer(BC)often exhibit overlapping clinical,imaging,and pathological characteristics,making them difficult to differentiate.SA may also coexist with BC(SA+BC),including du...Background:Sclerosing adenosis(SA)and breast cancer(BC)often exhibit overlapping clinical,imaging,and pathological characteristics,making them difficult to differentiate.SA may also coexist with BC(SA+BC),including ductal carcinoma in situ(SA-DCIS)and invasive breast cancer(SA-IBC),which complicates diagnosis even when core-needle biopsy(CNB)suggests SA.This study aimed to develop interpretable AI-based binary and ternary classification models that leverage clinical and imaging features to distinguish SA-only from SA+BC and to further differentiate among SA-only,SA-DCIS,and SA-IBC.Methods:We retrospectively analyzed a cohort of 726 patients with SA(January 2006 to December 2021),comprising 537 SA-only and 189 SA+BC cases(90 SA-DCIS,99 SA-IBC).Multiple machine learning algorithms-logistic regression,support vector machine,decision tree,XGBoost,and random forest-were compared using AUC,accuracy,F1-score,and C-index.Model interpretability was assessed with SHAP to elucidate feature contributions and identify key predictors.Additionally,we incorporated an independent external validation cohort consisting of 113 patients to verify the model's effectiveness.展开更多
Breast cancer(BC)remains the most frequently diagnosed malignancy worldwide,with an estimated 2.3 million new cases and approximately 685,000 deaths reported in 2020.Forecasts suggest a substantial rise in global inci...Breast cancer(BC)remains the most frequently diagnosed malignancy worldwide,with an estimated 2.3 million new cases and approximately 685,000 deaths reported in 2020.Forecasts suggest a substantial rise in global incidence,with new annual cases projected to reach 3.2 million by 2050,representing a 39%increase.Additionally,BC is expected to account for approximately 7.7%of the anticipated$25.2 trillion global economic burden associated with cancer by 2050.These trends underscore an urgent need for affordable,widely accessible and effective therapeutic strategies,particularly in low-and middle-income countries.Statins,commonly prescribed for the treatment of hypercholesterolaemia via inhibition of 3-hydroxy-3-methylglutaryl-coenzyme A(HMG-CoA)reductase,have garnered increasing interest for their potential anticancer properties.This review focuses on the mechanistic underpinnings and therapeutic implications of statin use,particularly simvastatin,in the context of BC.Statins exert their primary effect through inhibition of the mevalonate pathway,which is crucial for cholesterol and isoprenoid biosynthesis.Disruption of this pathway impairs the prenylation of key signalling proteins,including members of the Ras and Rho GTPase families,which are essential for cancer cell proliferation,survival and metastasis.Preclinical evidence has demonstrated that simvastatin can induce tumour cell apoptosis,arrest cell-cycle progression and inhibit oncogenic signalling pathways.These effects have been particularly pronounced in hormone receptor-negative and triple-negative breast cancer(TNBC)subtypes,which are often associated with poor prognosis and limited treatment options.Epidemiological and observational studies further support a potential association between statin use and reduced BC recurrence and mortality.Nevertheless,robust evidence from randomised controlled trials remains limited,and further investigation is required to establish causality and define optimal therapeutic regimens.Given their well-established safety profile,global accessibility and pleiotropic effects,statins,especially simvastatin,represent a promising class of repurposed drugs in the adjuvant treatment of BC.This review synthesises evidence from the past two decades,highlighting the need for continued clinical research to validate and optimise the use of statins as adjunctive agents in BC therapy.展开更多
Breast cancer remains a global health challenge with greater than 2.3 million new cases diagnosed annually 1,according to the World Health Organization1.Management of breast cancer is shaped by a complex interplay of ...Breast cancer remains a global health challenge with greater than 2.3 million new cases diagnosed annually 1,according to the World Health Organization1.Management of breast cancer is shaped by a complex interplay of international guidelines,regional adaptations,and the rapidly evolving fields of precision medicine and artificial intelligence(AI).展开更多
Breast cancer is the most common malignant tumor among women globally and poses a major public health challenge due to limitations in traditional diagnostic and treatment processes,such as subjective interpretation bi...Breast cancer is the most common malignant tumor among women globally and poses a major public health challenge due to limitations in traditional diagnostic and treatment processes,such as subjective interpretation biases and inefficient multidimensional data integration.Artificial intelligence(AI),particularly deep learning and machine learning technologies,has emerged as a transformative tool in addressing these issues.Clinically,AI has been widely applied in imaging screening to improve detection rates and reduce reading time,digital pathology for precise tumor typing and gene mutation prediction,treatment decisionsupport systems to enhance guideline compliance,and drug research and development to accelerate target identification and virtual screening.Despite these achievements,AI implementation faces challenges,such as data standardization issues,limited model generalization,low clinical accessibility,and unclear ethical-legal responsibilities,which require targeted solutions that include national data standards,multi-center training,hierarchical physician training,and explainable AI.Future directions involve multimodal data integration,human-AI collaborative multidisciplinary team models,and extension to full-cycle health management from prevention-to-rehabilitation.This review provides a systematic overview of the role of AI in breast cancer care,offering insights for clinical practice and scientific research innovation,and supporting the transition toward personalized and intelligent medicine in oncology.展开更多
Objectives:Progesterone(P4)is believed to inhibit breast cancer growth,but its role in counteracting estrogen(E2)-driven progression remains unclear.This study aimed to investigate the inhibitory effect of P4 on E2-in...Objectives:Progesterone(P4)is believed to inhibit breast cancer growth,but its role in counteracting estrogen(E2)-driven progression remains unclear.This study aimed to investigate the inhibitory effect of P4 on E2-induced cell proliferation,migration,and invasion in Estrogen receptor(ER)+/progesterone receptor(PR)+breast cancer cells by examining its regulatory role in the epithelial-mesenchymal transition(EMT).Methods:ER and PRpositive MCF-7 clonal variant(MCF-7 CV)breast cancer cells were treated with E2 and co-treated with various concentrations of P4.The effects on cell proliferation,migration,and invasion were assessed.The expression of key EMT markers(E-cadherin,N-cadherin,vimentin),transcription factors(Snail,Slug),and apoptosis-related genes(p53,B-cell lymphoma 2[BCL-2],BCL2-associated X[BAX])were analyzed.Results:P4 significantly inhibited E2-induced cell proliferation in a dose-dependent manner.In the presence of E2,P4 treatment reversed EMT characteristics by increasing E-cadherin while decreasing N-cadherin,vimentin,Snail,and Slug.Consequently,P4 inhibited E2-stimulated cell migration and invasion.Furthermore,P4 treatment promoted apoptosis by upregulating BAX and p53 and downregulating BCL-2.Conclusion:Progesterone can counteract estrogen-driven breast cancer progression in ER+/PR+cells by inhibiting proliferation,reversing the EMT process,and inducing apoptosis.These findings provide mechanistic insight into the protective role of PR signaling in breast cancer.展开更多
Breast cancer is one of the most prevalent malignancies among women and comprises a heterogeneous spectrum of molecular subtypes with distinct biological behaviors.Among various regulatory molecules,sphingolipids play...Breast cancer is one of the most prevalent malignancies among women and comprises a heterogeneous spectrum of molecular subtypes with distinct biological behaviors.Among various regulatory molecules,sphingolipids play pivotal roles in dynamically modulating fundamental cellular processes such as proliferation,apoptosis,and metastasis through metabolic interconversions,including phosphorylation,glycosylation,and the generation of sphingosine-1-phosphate.This review aims to elucidate the mechanisms through which sphingolipid metabolism orchestrates cancer cell fate and drives breast cancer progression.Particular emphasis is placed on the balance between proapoptotic ceramides and pro-survival metabolites,such as sphingosine-1-phosphate,which collectively influence tumor growth and the therapeutic response.Additional sphingolipid species,including glucosylceramide and gangliosides(GD2,GD3,GM1,and GM3),have also been implicated in promoting breast cancer development.Furthermore,sphingolipid-based therapeutic strategies,including immunotherapy and antibody therapy,are discussed.By providing a comprehensive overview of sphingolipid metabolism,this review aims to identify novel therapeutic targets that may help overcome treatment resistance and improve clinical outcomes in breast cancer.展开更多
The landscape of breast cancer treatment has undergone a transformative shift with the integration of immunotherapy.Historically considered a“cold”tumor with limited immunogenicity,breast cancer management was domin...The landscape of breast cancer treatment has undergone a transformative shift with the integration of immunotherapy.Historically considered a“cold”tumor with limited immunogenicity,breast cancer management was dominated by surgery,chemotherapy,radiotherapy,and targeted therapies1.However,the advent of immune checkpoint inhibitors(ICIs)has challenged this paradigm,opening a new frontier.The initial breakthrough in triple-negative breast cancer(TNBC)demonstrated that a subset of patients could derive profound and durable clinical benefit from pembrolizumab and atezolizumab2,3.Today,precision immunotherapy aims to identify the patients most likely to respond,to convert immunologically silent tumors into responsive tumors,and to strategically combine immunotherapies with other modalities to overcome resistance.This evolution from empirical application to biomarker-driven strategies marks the critical juncture at which we stand,transitioning promising clinical trial data into refined,effective,and accessible clinical practice4.Recent key clinical studies on breast cancer immunotherapy are summarized in Table 1.展开更多
Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insi...Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insights needed for this purpose.Methods:To comprehensively capture breast cancer heterogeneity,we employed integrative clustering that incorporates six molecular features from 670 breast cancer samples.Ten distinct clustering algorithms were combined to ensure robust subtype identification,and the identified subtypes were validated in four independent datasets.Subsequently,we constructed a survival support vector machine prognostic model based on key molecular features to enhance survival prediction and clinical applicability.Results:Five novel subtypes were identified:consensus subtypes 1–5(CS1–CS5).CS2 was an aggressive subtype with elevated TP53 mutation rates,high tumor mutational burden,and strong sensitivity to YM-155 and ispinesib.Conversely,CS5 exhibited stable genomics with enhanced nucleotide excision repair and favorable prognoses.CS2 and CS4 showed enriched immune checkpoint expression,indicating potential immunotherapy responsiveness,while CS1 and CS5 exhibited immune-cold profiles.The survival support vector machine model effectively predicted survival outcomes across independent datasets.Conclusions:The refined breast cancer classification framework developed in this research uncovers new insights into molecular heterogeneity,enhances risk stratification,and enables the identification of promising therapeutic targets.The potential of this framework to optimize personalized treatment strategies warrants further clinical validation.展开更多
Objectives:Breast cancer(BC)is the leading cause of cancer-related mortality in women,largely due to metastasis.This study aims to explore the role of purine nucleoside phosphorylase(PNP),a key enzyme in purine metabo...Objectives:Breast cancer(BC)is the leading cause of cancer-related mortality in women,largely due to metastasis.This study aims to explore the role of purine nucleoside phosphorylase(PNP),a key enzyme in purine metabolism,in the aggressiveness and metastatic behavior of BC.Methods:A comprehensive analysis was performed using in silico transcriptomic data(n=2509 patients),immunohistochemical profiling of BC tissues(n=103),and validation through western blotting in multiple BC cell lines.Gene expression and survival analyses were conducted using Tumor Immune Estimation Resource(TIMER),Gene Expression Profiling Interactive Analysis 2(GEPIA2),and the cBioPortal for cancer genomics(cBioPortal)platforms.Correlations between PNP and key epithelial–mesenchymal transition(EMT)markers,molecular subtypes,tumor grades,and stages were examined.Results:PNP was significantly overexpressed in human epidermal growth factor receptor 2(HER-2)-positive and triple-negative BCs compared to luminal subtypes.High PNP levels were strongly associated with advanced BC stages,high-grade tumors,EMT phenotypes,and poor overall survival.Notably,HER-2 inhibition suppressed PNP expression,while PNP gene silencing induced HER-2 upregulation,revealing a reciprocal regulatory loop.Dual inhibition of PNP and HER-2 resulted in a significant reduction in cell viability compared to HER-2 inhibition alone.Conclusion:Collectively,PNP emerges as a promising biomarker of BC aggressiveness and progression.Its reciprocal interaction with HER-2 underscores its potential as a therapeutic target.Dual targeting of PNP and HER-2 may offer a novel strategy for improving outcomes in aggressive BC subtypes.展开更多
Breast cancer(BRCA)is characterized by high heterogeneity,with aggressive subtypes frequently showing poor prognosis and resistance to conventional therapies,making the discovery of new therapeutic targets and strateg...Breast cancer(BRCA)is characterized by high heterogeneity,with aggressive subtypes frequently showing poor prognosis and resistance to conventional therapies,making the discovery of new therapeutic targets and strategies imperative.Although elevated expression of discs large homolog 3(DLG3)has been reported in BRCA,its functional role in disease progression remains unclear.We performed bioinformatic analyses of clinical datasets to evaluate the prognostic significance of DLG3 expression in BRCA patients.In vitro gain-and loss-of-function experiments were conducted to assess the impact of DLG3 on BRCA cell proliferation,migration,and colony formation.Transcriptomic profiling,coupled with pharmacological inhibition,was employed to identify and validate downstream signaling pathways.Additionally,we extended our validation to an in vivo model to assess the role of DLG3 in tumor progression.We found that elevated DLG3 levels correlated with poor prognosis in breast cancer patients.Functionally,DLG3 overexpression significantly promoted cell proliferation and migration in estrogen receptor-positive MCF7 and triple-negative MDA-MB-231 breast cancer cells,whereas its knockdown suppressed these effects.Transcriptomic analyses revealed that DLG3 activates signal transducer and activator of transcription 3(STAT3)signaling,a finding further corroborated by Western blot.Critically,treatment with the STAT3 inhibitor Stattic attenuated DLG3-driven proliferation and migration,supporting a DLG3-STAT3 oncogenic axis.Furthermore,in vivo studies validated the role of DLG3 in promoting tumor growth and its correlation with elevated STAT3 signaling,consistent with our in vitro findings.Our findings establish DLG3 as a novel driver of breast cancer progression that directly activates STAT3 signaling.DLG3 thus represents both a potential prognostic biomarker and a promising therapeutic target for aggressive breast cancer subtypes,including triple-negative breast cancer.展开更多
基金supported by grants from the National Natural Science Foundation of China(Grant Nos.82573747,82172873,W2421095,and 82503888)National Science and Technology Major Project(Grant No.2025ZD0543900)+2 种基金Natural Science Foundation of Shandong Province(Grant Nos.ZR2024LMB011 and ZR2024QH058)Taishan Scholars Program of Shandong Province(Grant No.tsqn202211337)Collaborative Academic Innovation Project of Shandong Cancer Hospital(Grant No.GF003).
文摘Neoadjuvant therapy(NAT)has become the standard treatment for patients with locally advanced breast cancer and stage II-III HER2-positive(HER2+)or triple-negative breast cancer(TNBC)1,2.It is essential to accurately mark the primary breast tumor and positive axillary lymph nodes(ALNs)prior to NAT to ensure precise surgical excision,guide axillary downstaging,and guarantee reliable lesion retrieval for pathologic evaluation3.The false-negative rate of sentinel lymph node biopsy(SLNB)after NAT can be reduced to<10%by applying modalities,such as the identification of≥3 sentinel lymph nodes(SLNs)with dual-mapping techniques or removal of the marked lymph node with target axillary dissection(TAD)according to the ASCO,NCCN,and CBCS guidelines3-5.However,there is a lack of consensus regarding the optimal methods and materials for accurate marking6,7.Conventional techniques include clip placement,guidewire localization,and carbon or ink tattooing,whereas wireless technologies,such as MagseedR,radiofrequency identification tags,SAVI SCOUTR,and radioactive iodine-125(125I)seeds,have also been adopted.Traditional marking techniques have a localization failure rate of approximately 10%.In contrast,the use of 125I seeds(with a radiation dose of 0.1-0.3 mCi)has significantly improved localization accuracy8,9.Nevertheless,owing to radioactive properties,concerns have been raised regarding the potential impact of 125I seed marking on assessing the pathologic complete response(pCR)after NAT10.Moreover,whether the influence of 125I seed marking on pCR could lead to suboptimal adjuvant treatment decisions and potentially compromise long-term oncologic outcomes has not been established.To investigate the potential impact of 125I seed placement on the pCR rate and long-term outcomes in breast cancer patients receiving NAT,we conducted a retrospective cohort study utilizing propensity score matching(PSM).
基金financially supported by the Hebei Natural Science Foundation(Grant No.H2024206504)the Medical Science Research Project of Hebei(Grant No.20260484,20260530)the Fundamental Research Funds for the Central Universities(Grant No.20822041J4123).
文摘Objective:Accurate detection of PIK3CA mutations is essential for guiding PI3K-targeted therapies in breast cancer,yet sequencing is not universally accessible,and single-modality prediction models have limited performance.This study developed a multimodal deep learning framework integrating whole-slide imaging(WSI)and structured clinical data to improve mutation prediction.Methods:A total of 1,047 patients from TCGA and 166 patients from 3 external centers were included.The histopathology model used a transformer-based pretrained encoder(H-optimus-0)and a clustering-constrained attention multiple instance learning(CLAM-SB MIL)classifier to generate WSI-level representations.The clinical model incorporated engineered clinical variables and an extreme gradient boosting(XGBoost)model.A decision-level late fusion strategy(Multimodal PIK3CA Model,MPM)combined probabilistic outputs from both branches.Performance was evaluated with the area under the curve(AUC)and secondary metrics.Interpretability was assessed via attention heatmaps and shapley additive explanations(SHAP)analysis.Results:MPM outperformed single-modality models.It achieved an AUC of 0.745 on TCGA and maintained stable performance across external cohorts(0.695,0.690,and 0.680).SHAP analysis identified molecular subtype as the most influential clinical feature,whereas attention maps highlighted mutation-associated morphological regions.Conclusions:The developed multimodal framework effectively integrates complementary morphological and clinical information,and provides a robust and generalizable method for predicting PIK3CA mutation status.Strong multicenter adaptability and biological interpretability support its potential use as a clinical decision-support tool and an accessible alternative to molecular testing.
基金Supported by Capital’s Funds for Health Improvement and Research(CFH2024-1-4021)。
文摘Objective To develop a prognostic prediction model for early-stage triple-negative breast cancer(TNBC)using H&E-stained pathological images and to investigate its underlying biological interpretability.Methods A deep learning model was trained on 340 WSIs and externally validated using 81 TCGA cases.Image-derived features extracted through convolutional neural networks were integrated with clinicopathological variables.Model performance was assessed using ROC curve analysis,and interpretability was evaluated by correlating image features with mRNA-seq data and characteristics of the immune microenvironment.Results The model achieved AUCs of 0.86 and 0.75 in the training and validation cohorts,respectively.Analysis using HoVer-Net indicated that lymphocyte abundance was associated with recurrence risk.Texture-related features showed significant correlations with immune cell infiltration and prognostic gene expression profiles.Conclusion This study demonstrates that deep learning can enable accurate prognostic prediction in early-stage TNBC,with interpretable image features that reflect the tumor immune microenvironment and gene expression profiles.
基金supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(No.2023-JKCS-23)the Special Research Fund for Central Universities,Peking Union Medical College[No.2022-I2M-C&T-A-014,CAMS Innovation Fund for Medical Sciences(CIFMS)]。
文摘Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT has become an urgent world-wide clinical problem.Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system.Methods:In this study,we retrospectively collected longitudinal(pre-NAT and post-NAT)multi-parametric magnetic resonance imaging(MRI)and clinicopathologic data of a total of 1,315 breast cancer patients(clinical stageⅠ-Ⅲ)who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from January 2010 to January 2023.We used radiomics,3D convolutional neural network technology and clinical data statistical analysis methods to extract and screen multimodal features,and then developed and validated a Clinical-Radiomics-Deep-Learning(CRDL)model to predict patients'pCR outcomes based on multimodal fusion features.Results:We use the area under the receiver operating characteristic curve(AUC)in the primary cohort(PC)and3 external validation cohorts(VC_(1-3))to evaluate the model performance.The results showed that the AUC in the PC composed of 2 medical centers was 0.947[95%confidence interval(95%CI):0.931-0.960],and the AUC values in VC_(1-3)were 0.857(95%CI:0.810-0.901),0.883(95%CI:0.841-0.918)and 0.904(95%CI:0.860-0.941),respectively.Conclusions:The CRDL model demonstrated high accuracy and robustness in predicting pCR to NAT using multimodal fusion data.This study provides a strong foundation for non-invasive assessment of pCR status in breast cancer patients following NAT and offers critical insights to guide clinical decision-making in post-NAT treatment planning.
基金supported by the Research Project of Maternal and Child Health Hospital of Hubei Province(No.2023SFYM008)Key Project of Hubei Provincial Natural Science Foundation(No.JCZRLH202500304).
文摘Objective ZW10 interacting kinetochore protein(ZWINT)has been demonstrated to play a pivotal role in the growth,invasion,and migration of cancers.Nevertheless,whether the expression levels of ZWINT are significantly correlated with clinicopathological characteristics and prognostic outcomes of patients with breast cancer remains elusive.This study systematically investigated the clinical significance of ZWINT expression in breast cancer through integrated molecular subtyping and survival analysis.Methods We systematically characterized the spatial expression pattern of ZWINT across various breast cancer subtypes and assessed its prognostic significance using an integrated bioinformatics approach that involved multi-omics analysis.The approach included the Breast Cancer Gene-Expression Miner v5.1(bc-GenExMiner v5.1),TNMplot,MuTarget,PrognoScan database,and Database for Annotation,Visualization,and Integrated Discovery(DAVID).Results Our analysis revealed consistent upregulation of ZWINT mRNA and protein expression across distinct clinicopathological subtypes of breast cancer.ZWINT overexpression demonstrated significant co-occurrence with truncating mutations in cadherin 1(CDH1)and tumor protein p53(TP53),suggesting potential functional crosstalk in tumor progression pathways.The overexpression of ZWINT correlated with adverse clinical outcomes,showing 48%increased mortality risk(overall survival:HR 1.48,95%CI 1.23–1.79),66%higher recurrence probability(relapse-free survival:1.66,95%CI 1.50–1.84),and 63%elevated metastasis risk(distant metastasis-free survival:HR 1.63,95%CI 1.39–1.90).Multivariate Cox regression incorporating TNM staging and molecular subtypes confirmed ZWINT as an independent prognostic determinant(P<0.001,Harrell’s C-index=0.7827),which was validated through bootstrap resampling(1000 iterations).Conclusion ZWINT may serve as a potential biomarker for prognosis and a possible therapeutic target alongside TP53/CDH1 in breast cancer.
基金Deanship of Scientific Research at Northern Border University,Arar,KSA for funding this research work through the project number“NBU-FFR-2024-2439-05”.
文摘The vision transformer(ViT)architecture,with its attention mechanism based on multi-head attention layers,has been widely adopted in various computer-aided diagnosis tasks due to its effectiveness in processing medical image information.ViTs are notably recognized for their complex architecture,which requires high-performance GPUs or CPUs for efficient model training and deployment in real-world medical diagnostic devices.This renders them more intricate than convolutional neural networks(CNNs).This difficulty is also challenging in the context of histopathology image analysis,where the images are both limited and complex.In response to these challenges,this study proposes a TokenMixer hybrid-architecture that combines the strengths of CNNs and ViTs.This hybrid architecture aims to enhance feature extraction and classification accuracy with shorter training time and fewer parameters by minimizing the number of input patches employed during training,while incorporating tokenization of input patches using convolutional layers and encoder transformer layers to process patches across all network layers for fast and accurate breast cancer tumor subtype classification.The TokenMixer mechanism is inspired by the ConvMixer and Token-Learner models.First,the ConvMixer model dynamically generates spatial attention maps using convolutional layers,enabling the extraction of patches from input images to minimize the number of input patches used in training.Second,the TokenLearner model extracts relevant regions from the selected input patches,tokenizes them to improve feature extraction,and trains all tokenized patches in an encoder transformer network.We evaluated the TokenMixer model on the BreakHis public dataset,comparing it with ViT-based and other state-of-the-art methods.Our approach achieved impressive results for both binary and multi-classification of breast cancer subtypes across various magnification levels(40×,100×,200×,400×).The model demonstrated accuracies of 97.02%for binary classification and 93.29%for multi-classification,with decision times of 391.71 and 1173.56 s,respectively.These results highlight the potential of our hybrid deep ViT-CNN architecture for advancing tumor classification in histopathological images.The source code is accessible:https://github.com/abimo uloud/Token Mixer.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01096).
文摘Breast Cancer(BC)remains a leadingmalignancy among women,resulting in highmortality rates.Early and accurate detection is crucial for improving patient outcomes.Traditional diagnostic tools,while effective,have limitations that reduce their accessibility and accuracy.This study investigates the use ofConvolutionalNeuralNetworks(CNNs)to enhance the diagnostic process of BC histopathology.Utilizing the BreakHis dataset,which contains thousands of histopathological images,we developed a CNN model designed to improve the speed and accuracy of image analysis.Our CNN architecture was designed with multiple convolutional layers,max-pooling layers,and a fully connected network optimized for feature extraction and classification.Hyperparameter tuning was conducted to identify the optimal learning rate,batch size,and number of epochs,ensuring robust model performance.The dataset was divided into training(80%),validation(10%),and testing(10%)subsets,with performance evaluated using accuracy,precision,recall,and F1-score metrics.Our CNN model achieved a magnification-independent accuracy of 97.72%,with specific accuracies of 97.50%at 40×,97.61%at 100×,99.06%at 200×,and 97.25%at 400×magnification levels.These results demonstrate the model’s superior performance relative to existing methods.The integration of CNNs in diagnostic workflows can potentially reduce pathologist workload,minimize interpretation errors,and increase the availability of diagnostic testing,thereby improving BC management and patient survival rates.This study highlights the effectiveness of deep learning in automating BC histopathological classification and underscores the potential for AI-driven diagnostic solutions to improve patient care.
文摘Breast cancer is among the leading causes of cancer mortality globally,and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification.Existing machine learning(ML)methods struggle with intra-class heterogeneity and inter-class similarity,necessitating more robust classification models.This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning(DL)and Bat Swarm Optimization(BSO)hyperparameter optimization to improve breast cancer histopathology(BCH)image classification.A dataset of 804 Hematoxylin and Eosin(H&E)stained images classified as Benign,in situ,Invasive,and Normal categories(ICIAR2018_BACH_Challenge)has been utilized.ResNet50 was utilized for feature extraction,while Support Vector Machines(SVM),Random Forests(RF),XGBoosts(XGB),Decision Trees(DT),and AdaBoosts(ADB)were utilized for classification.BSO was utilized for hyperparameter optimization in a soft voting ensemble approach.Accuracy,precision,recall,specificity,F1-score,Receiver Operating Characteristic(ROC),and Precision-Recall(PR)were utilized for model performance metrics.The model using an ensemble outperformed individual classifiers in terms of having greater accuracy(~90.0%),precision(~86.4%),recall(~86.3%),and specificity(~96.6%).The robustness of the model was verified by both ROC and PR curves,which showed AUC values of 1.00,0.99,and 0.98 for Benign,Invasive,and in situ instances,respectively.This ensemble model delivers a strong and clinically valid methodology for breast cancer classification that enhances precision and minimizes diagnostic errors.Future work should focus on explainable AI,multi-modal fusion,few-shot learning,and edge computing for real-world deployment.
基金supported by the National Key Research and Development Program of China (No. 2020YFA0112304)the National Natural Science Foundation of China (No. 82373167, 82341003 and 92159301)+4 种基金the Natural Science Foundation of Shanghai (No. 22ZR1479200)the Shanghai Key Laboratory of Breast Cancer (No. 12DZ2260100)the SHDC Municipal Project for Developing Emerging and Frontier Technology in Shanghai Hospitals (No. SHDC12 021103)Shanghai Medical Innovation Research Project (No. 22Y11912700)Shanghai Anticancer Association EYAS PROJECT (No. SACA-CY22A05)
文摘Objective:Recurrence continues to be a pivotal challenge among hormone receptor-positive(HR^(+))/human epidermal growth factor receptor 2^(−)negative(HER2^(−))breast cancers.In the international consensus guidelines,HR^(+)/HER2^(−)breast cancer relapse patterns are divided into three distinct types:primary resistant,secondary resistant,and endocrine sensitive.However,owing to the lack of cohorts with treatment and follow-up data,the heterogeneity among different recurrence patterns remains uncharted.Current treatments still lack precision.Methods:This analysis included data from a large-scale multiomics study of a HR^(+)/HER2^(−)breast cancer cohort(n=314).Through the analysis of transcriptomics(n=312),proteomics(n=124),whole-exome sequencing(n=290),metabolomics(n=217),and digital pathology(n=228)data,we explored distinctive molecular features and identified putative therapeutic targets for patients experiencing recurrence.Results:We explored distinct clinicopathological characteristics,biological heterogeneity,and potential therapeutic strategies for recurrence.Based on a shared relapse signature,we stratified patients into high-and lowrecurrence-risk groups.Patients with different relapse patterns presented unique molecular features in primary tumors.Specifically,receptor tyrosine kinase(RTK)pathway activation in the primary resistant group suggested the utility of RTK inhibitors,whereas mammalian target of rapamycin(mTOR)and cell cycle pathway activation in the secondary resistant group highlighted the potential of mTOR and CDK4/6 inhibitors.Interestingly,the endocrine-sensitive group displayed a quiescent state and high genomic instability,suggesting that targeting quiescent cells and using poly-ADP-ribose polymerase(PARP)inhibitors could be effective strategies.Conclusions:These findings illuminate the clinicopathological and molecular landscape of HR^(+)/HER2^(−)breast cancer patients with distinct recurrence patterns,highlighting potential targeted therapies.
基金supported by the National Natural Science Foundation of China(Grant Nos.82230057 and 82272859)the National Key R&D Program of China(Grant No.2022YFC2505101).
文摘Introduction,Breast cancer is the most common cancer type in adolescents and young adults<40 years of age,accounting for 30%of cancers in this age group1.Breast cancer in the young presents significant challenges for patients and society,including more aggressive tumor biology,poor prognosis,genetic susceptibility,fertility preservation,and complex psychosocial issues.Moreover,because of the markedly younger median age of breast cancer,the proportion of young breast cancer patients in China is significantly higher than Western countries2.The first Young Breast Cancer in China(YBCC)consensus meeting was held in Guangzhou,China in December 2021 to address exclusive challenges and requirements facing young patients with breast cancer.Chinese medical experts from multiple specialties had an extensive discussion and formulated a consensus over several hot topics in young patients with breast cancer.The“Expert Consensus on the Diagnosis and Treatment of Young Breast Cancer in China(2022 edition)”published in the Chinese Medical Journal has garnered significant attention3,highlighting enormous interest in the YBCC consensus in the medical community and public.
基金National High Level Hospital Clinical Research Funding,Grant/Award Numbers:2025-PUMCH-A-147,2022-PUMCH-B-039Chinese Academy of Medical Sciences(CAMS)Innovation Fund for Medical Sciences(CIFM),Grant/Award Number:2021-I2M-1-014。
文摘Background:Sclerosing adenosis(SA)and breast cancer(BC)often exhibit overlapping clinical,imaging,and pathological characteristics,making them difficult to differentiate.SA may also coexist with BC(SA+BC),including ductal carcinoma in situ(SA-DCIS)and invasive breast cancer(SA-IBC),which complicates diagnosis even when core-needle biopsy(CNB)suggests SA.This study aimed to develop interpretable AI-based binary and ternary classification models that leverage clinical and imaging features to distinguish SA-only from SA+BC and to further differentiate among SA-only,SA-DCIS,and SA-IBC.Methods:We retrospectively analyzed a cohort of 726 patients with SA(January 2006 to December 2021),comprising 537 SA-only and 189 SA+BC cases(90 SA-DCIS,99 SA-IBC).Multiple machine learning algorithms-logistic regression,support vector machine,decision tree,XGBoost,and random forest-were compared using AUC,accuracy,F1-score,and C-index.Model interpretability was assessed with SHAP to elucidate feature contributions and identify key predictors.Additionally,we incorporated an independent external validation cohort consisting of 113 patients to verify the model's effectiveness.
基金supported by Rural Health Research Institute,Charles Sturt University.
文摘Breast cancer(BC)remains the most frequently diagnosed malignancy worldwide,with an estimated 2.3 million new cases and approximately 685,000 deaths reported in 2020.Forecasts suggest a substantial rise in global incidence,with new annual cases projected to reach 3.2 million by 2050,representing a 39%increase.Additionally,BC is expected to account for approximately 7.7%of the anticipated$25.2 trillion global economic burden associated with cancer by 2050.These trends underscore an urgent need for affordable,widely accessible and effective therapeutic strategies,particularly in low-and middle-income countries.Statins,commonly prescribed for the treatment of hypercholesterolaemia via inhibition of 3-hydroxy-3-methylglutaryl-coenzyme A(HMG-CoA)reductase,have garnered increasing interest for their potential anticancer properties.This review focuses on the mechanistic underpinnings and therapeutic implications of statin use,particularly simvastatin,in the context of BC.Statins exert their primary effect through inhibition of the mevalonate pathway,which is crucial for cholesterol and isoprenoid biosynthesis.Disruption of this pathway impairs the prenylation of key signalling proteins,including members of the Ras and Rho GTPase families,which are essential for cancer cell proliferation,survival and metastasis.Preclinical evidence has demonstrated that simvastatin can induce tumour cell apoptosis,arrest cell-cycle progression and inhibit oncogenic signalling pathways.These effects have been particularly pronounced in hormone receptor-negative and triple-negative breast cancer(TNBC)subtypes,which are often associated with poor prognosis and limited treatment options.Epidemiological and observational studies further support a potential association between statin use and reduced BC recurrence and mortality.Nevertheless,robust evidence from randomised controlled trials remains limited,and further investigation is required to establish causality and define optimal therapeutic regimens.Given their well-established safety profile,global accessibility and pleiotropic effects,statins,especially simvastatin,represent a promising class of repurposed drugs in the adjuvant treatment of BC.This review synthesises evidence from the past two decades,highlighting the need for continued clinical research to validate and optimise the use of statins as adjunctive agents in BC therapy.
文摘Breast cancer remains a global health challenge with greater than 2.3 million new cases diagnosed annually 1,according to the World Health Organization1.Management of breast cancer is shaped by a complex interplay of international guidelines,regional adaptations,and the rapidly evolving fields of precision medicine and artificial intelligence(AI).
基金supported by the National Natural Science Foundation of China(Grant No.82404074)the Science and Technology Major Project(Grant No.2024ZD0519805).
文摘Breast cancer is the most common malignant tumor among women globally and poses a major public health challenge due to limitations in traditional diagnostic and treatment processes,such as subjective interpretation biases and inefficient multidimensional data integration.Artificial intelligence(AI),particularly deep learning and machine learning technologies,has emerged as a transformative tool in addressing these issues.Clinically,AI has been widely applied in imaging screening to improve detection rates and reduce reading time,digital pathology for precise tumor typing and gene mutation prediction,treatment decisionsupport systems to enhance guideline compliance,and drug research and development to accelerate target identification and virtual screening.Despite these achievements,AI implementation faces challenges,such as data standardization issues,limited model generalization,low clinical accessibility,and unclear ethical-legal responsibilities,which require targeted solutions that include national data standards,multi-center training,hierarchical physician training,and explainable AI.Future directions involve multimodal data integration,human-AI collaborative multidisciplinary team models,and extension to full-cycle health management from prevention-to-rehabilitation.This review provides a systematic overview of the role of AI in breast cancer care,offering insights for clinical practice and scientific research innovation,and supporting the transition toward personalized and intelligent medicine in oncology.
基金supported by the Regional Innovation System&Education(RISE)program through the(Chungbuk Regional Innovation System&Education Center),funded by the Ministry of Education(MOE)and the(Chungcheongbuk-do),Republic of Korea(2025-RISE-11-014-03)In addition,this work was also supported by the Sejong Fellowship through the NRF funded by the Ministry of Science and ICT(RS-2025-00557567)to HKL.
文摘Objectives:Progesterone(P4)is believed to inhibit breast cancer growth,but its role in counteracting estrogen(E2)-driven progression remains unclear.This study aimed to investigate the inhibitory effect of P4 on E2-induced cell proliferation,migration,and invasion in Estrogen receptor(ER)+/progesterone receptor(PR)+breast cancer cells by examining its regulatory role in the epithelial-mesenchymal transition(EMT).Methods:ER and PRpositive MCF-7 clonal variant(MCF-7 CV)breast cancer cells were treated with E2 and co-treated with various concentrations of P4.The effects on cell proliferation,migration,and invasion were assessed.The expression of key EMT markers(E-cadherin,N-cadherin,vimentin),transcription factors(Snail,Slug),and apoptosis-related genes(p53,B-cell lymphoma 2[BCL-2],BCL2-associated X[BAX])were analyzed.Results:P4 significantly inhibited E2-induced cell proliferation in a dose-dependent manner.In the presence of E2,P4 treatment reversed EMT characteristics by increasing E-cadherin while decreasing N-cadherin,vimentin,Snail,and Slug.Consequently,P4 inhibited E2-stimulated cell migration and invasion.Furthermore,P4 treatment promoted apoptosis by upregulating BAX and p53 and downregulating BCL-2.Conclusion:Progesterone can counteract estrogen-driven breast cancer progression in ER+/PR+cells by inhibiting proliferation,reversing the EMT process,and inducing apoptosis.These findings provide mechanistic insight into the protective role of PR signaling in breast cancer.
基金supported by National Research Foundation(NRF)of Korea grants funded by the Korean government,the Ministry of Science and ICT[NRF-2022R1A2C1006737 to Joo-Won Park,NRF-2022R1I1A1A0106408112 to Min Hee Kim].
文摘Breast cancer is one of the most prevalent malignancies among women and comprises a heterogeneous spectrum of molecular subtypes with distinct biological behaviors.Among various regulatory molecules,sphingolipids play pivotal roles in dynamically modulating fundamental cellular processes such as proliferation,apoptosis,and metastasis through metabolic interconversions,including phosphorylation,glycosylation,and the generation of sphingosine-1-phosphate.This review aims to elucidate the mechanisms through which sphingolipid metabolism orchestrates cancer cell fate and drives breast cancer progression.Particular emphasis is placed on the balance between proapoptotic ceramides and pro-survival metabolites,such as sphingosine-1-phosphate,which collectively influence tumor growth and the therapeutic response.Additional sphingolipid species,including glucosylceramide and gangliosides(GD2,GD3,GM1,and GM3),have also been implicated in promoting breast cancer development.Furthermore,sphingolipid-based therapeutic strategies,including immunotherapy and antibody therapy,are discussed.By providing a comprehensive overview of sphingolipid metabolism,this review aims to identify novel therapeutic targets that may help overcome treatment resistance and improve clinical outcomes in breast cancer.
基金supported by the Non-communicable Chronic Diseases National Science and Technology Major Project(Grant No.2025ZD0544003).
文摘The landscape of breast cancer treatment has undergone a transformative shift with the integration of immunotherapy.Historically considered a“cold”tumor with limited immunogenicity,breast cancer management was dominated by surgery,chemotherapy,radiotherapy,and targeted therapies1.However,the advent of immune checkpoint inhibitors(ICIs)has challenged this paradigm,opening a new frontier.The initial breakthrough in triple-negative breast cancer(TNBC)demonstrated that a subset of patients could derive profound and durable clinical benefit from pembrolizumab and atezolizumab2,3.Today,precision immunotherapy aims to identify the patients most likely to respond,to convert immunologically silent tumors into responsive tumors,and to strategically combine immunotherapies with other modalities to overcome resistance.This evolution from empirical application to biomarker-driven strategies marks the critical juncture at which we stand,transitioning promising clinical trial data into refined,effective,and accessible clinical practice4.Recent key clinical studies on breast cancer immunotherapy are summarized in Table 1.
基金supported by the National Natural Science Foundation of China(Grant No.:82560497,82260502,82272656)Guizhou Provincial Basic Research Program(Grant No.:Natural Science,MS[2025]-495)Talent Fund of Guizhou Provincial People’s Hospital(Grant No.:2022-33).
文摘Background:As a heterogeneous disease,breast cancer requires refined classification frameworks that can effectively guide targeted therapies.However,traditional methods fail to capture the comprehensive molecular insights needed for this purpose.Methods:To comprehensively capture breast cancer heterogeneity,we employed integrative clustering that incorporates six molecular features from 670 breast cancer samples.Ten distinct clustering algorithms were combined to ensure robust subtype identification,and the identified subtypes were validated in four independent datasets.Subsequently,we constructed a survival support vector machine prognostic model based on key molecular features to enhance survival prediction and clinical applicability.Results:Five novel subtypes were identified:consensus subtypes 1–5(CS1–CS5).CS2 was an aggressive subtype with elevated TP53 mutation rates,high tumor mutational burden,and strong sensitivity to YM-155 and ispinesib.Conversely,CS5 exhibited stable genomics with enhanced nucleotide excision repair and favorable prognoses.CS2 and CS4 showed enriched immune checkpoint expression,indicating potential immunotherapy responsiveness,while CS1 and CS5 exhibited immune-cold profiles.The survival support vector machine model effectively predicted survival outcomes across independent datasets.Conclusions:The refined breast cancer classification framework developed in this research uncovers new insights into molecular heterogeneity,enhances risk stratification,and enables the identification of promising therapeutic targets.The potential of this framework to optimize personalized treatment strategies warrants further clinical validation.
基金funded by Al Jalila Foundation-Research Grant(AJF2023-078)to SSMS.
文摘Objectives:Breast cancer(BC)is the leading cause of cancer-related mortality in women,largely due to metastasis.This study aims to explore the role of purine nucleoside phosphorylase(PNP),a key enzyme in purine metabolism,in the aggressiveness and metastatic behavior of BC.Methods:A comprehensive analysis was performed using in silico transcriptomic data(n=2509 patients),immunohistochemical profiling of BC tissues(n=103),and validation through western blotting in multiple BC cell lines.Gene expression and survival analyses were conducted using Tumor Immune Estimation Resource(TIMER),Gene Expression Profiling Interactive Analysis 2(GEPIA2),and the cBioPortal for cancer genomics(cBioPortal)platforms.Correlations between PNP and key epithelial–mesenchymal transition(EMT)markers,molecular subtypes,tumor grades,and stages were examined.Results:PNP was significantly overexpressed in human epidermal growth factor receptor 2(HER-2)-positive and triple-negative BCs compared to luminal subtypes.High PNP levels were strongly associated with advanced BC stages,high-grade tumors,EMT phenotypes,and poor overall survival.Notably,HER-2 inhibition suppressed PNP expression,while PNP gene silencing induced HER-2 upregulation,revealing a reciprocal regulatory loop.Dual inhibition of PNP and HER-2 resulted in a significant reduction in cell viability compared to HER-2 inhibition alone.Conclusion:Collectively,PNP emerges as a promising biomarker of BC aggressiveness and progression.Its reciprocal interaction with HER-2 underscores its potential as a therapeutic target.Dual targeting of PNP and HER-2 may offer a novel strategy for improving outcomes in aggressive BC subtypes.
文摘Breast cancer(BRCA)is characterized by high heterogeneity,with aggressive subtypes frequently showing poor prognosis and resistance to conventional therapies,making the discovery of new therapeutic targets and strategies imperative.Although elevated expression of discs large homolog 3(DLG3)has been reported in BRCA,its functional role in disease progression remains unclear.We performed bioinformatic analyses of clinical datasets to evaluate the prognostic significance of DLG3 expression in BRCA patients.In vitro gain-and loss-of-function experiments were conducted to assess the impact of DLG3 on BRCA cell proliferation,migration,and colony formation.Transcriptomic profiling,coupled with pharmacological inhibition,was employed to identify and validate downstream signaling pathways.Additionally,we extended our validation to an in vivo model to assess the role of DLG3 in tumor progression.We found that elevated DLG3 levels correlated with poor prognosis in breast cancer patients.Functionally,DLG3 overexpression significantly promoted cell proliferation and migration in estrogen receptor-positive MCF7 and triple-negative MDA-MB-231 breast cancer cells,whereas its knockdown suppressed these effects.Transcriptomic analyses revealed that DLG3 activates signal transducer and activator of transcription 3(STAT3)signaling,a finding further corroborated by Western blot.Critically,treatment with the STAT3 inhibitor Stattic attenuated DLG3-driven proliferation and migration,supporting a DLG3-STAT3 oncogenic axis.Furthermore,in vivo studies validated the role of DLG3 in promoting tumor growth and its correlation with elevated STAT3 signaling,consistent with our in vitro findings.Our findings establish DLG3 as a novel driver of breast cancer progression that directly activates STAT3 signaling.DLG3 thus represents both a potential prognostic biomarker and a promising therapeutic target for aggressive breast cancer subtypes,including triple-negative breast cancer.
