Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013

Correlation between ultrasonography and elastography parameters and molecular subtypes of breast cancer in young women

OBJECTIVE

To explore the differences of conventional ultrasound characteristics, elastic imaging parameters and clinicopathological characteristics of distinct molecular subtypes of breast cancer in young women, and to identify imaging parameters that exhibited significant associations with each molecular subtype.

METHODS

We performed a retrospective analysis encompassing 310 young women with breast cancer. Observations were made regarding the ultrasonography and elastography characteristics of the identified breast lesions. Subsequently, based on immunohistochemistry results patients were classified into five distinct molecular subtypes: luminal A, luminal B (HER2-), luminal B (HER2+), HER2+, and triple-negative breast cancer (TNBC). Clinical, pathological, and ultrasound imaging features were compared among these subtypes using binary logistic regression analysis.

RESULTS

Statistically significant differences were observed in various parameters across the five molecular subtypes (p < 0.05), including tumor size, morphology, margins, calcification, posterior echo features, blood flow (Adler grading), and tumor hardness. Specifically, luminal A subtype exhibited propensity for spiculated margins, lower blood flow grading, and decreased hardness; luminal B subtype was characterized by angular margins; HER2+ subtype manifested higher blood flow grading, calcification, and elevated hardness. Conversely, TNBC subtype displayed smooth margins, absence of calcification, and heightened hardness.

CONCLUSION

Specific molecular subtypes of breast cancer have unique ultrasonic and elastic imaging characteristics.

Research trends of neoadjuvant therapy for breast cancer: A bibliometric analysis

The approach of neoadjuvant therapy for breast cancer, which involves administering systemic treatment prior to primary surgery, has undergone substantial advancements in recent decades. This strategy is intended to reduce tumor size, thereby enabling less invasive surgical procedures and enhancing patient outcomes. This study presents a comprehensive bibliometric analysis of research trends in neoadjuvant therapy for breast cancer from 2009 to 2024. Using data extracted from the Web of Science Core Collection, a total of 3,674 articles were analyzed to map the research landscape in this field. The analysis reveals a steady increase in publication output, peaking in 2022, with the United States and China identified as the leading contributors. Key institutions, such as the University of Texas System and MD Anderson Cancer Center, have been instrumental in advancing the research on neoadjuvant therapy. The study also highlights the contributions of influential authors like Sibylle Loibl and Gunter von Minckwitz, as well as major journals such as the Journal of Clinical Oncology. Emerging research topics, including immunotherapy, liquid biopsy, and artificial intelligence, are gaining prominence and represent potential future directions for clinical applications. This bibliometric analysis provides critical insights into global research trends, key contributors, and future developments in the field of neoadjuvant therapy for breast cancer, offering a foundation for future research and clinical practice advancements.

Multi-task learning for joint prediction of breast cancer histological indicators in dynamic contrast-enhanced magnetic resonance imaging

OBJECTIVES

Achieving efficient analysis of multiple pathological indicators has great significance for breast cancer prognosis and therapeutic decision-making. In this study, we aim to explore a deep multi-task learning (MTL) framework for collaborative prediction of histological grade and proliferation marker (Ki-67) status in breast cancer using multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

METHODS

In the novel design of hybrid multi-task architecture (HMT-Net), co-representative features are explicitly distilled using a feature extraction backbone. A customized prediction network is then introduced to perform soft-parameter sharing between two correlated tasks. Specifically, task-common and task-specific knowledge is transmitted into tower layers for informative interactions. Furthermore, low-level feature maps containing tumor edges and texture details are recaptured by a hard-parameter sharing branch, which are then incorporated into the tower layer for each subtask. Finally, the probabilities of two histological indicators, predicted in the multi-phase DCE-MRI, are separately fused using a decision-level fusion strategy.

RESULTS

Experimental results demonstrate that the proposed HMT-Net achieves optimal discriminative performance over other recent MTL architectures and deep models based on single image series, with the area under the receiver operating characteristic curve of 0.908 for tumor grade and 0.694 for Ki-67 status.

CONCLUSIONS

Benefiting from the innovative HMT-Net, our proposed method elucidates its strong robustness and flexibility in the collaborative prediction task of breast biomarkers. Multi-phase DCE-MRI is expected to contribute valuable dynamic information for breast cancer pathological assessment in a non-invasive manner.

Olive oil consumption and risk of breast cancer: Prospective results from the Moli-sani Study, and a systematic review of observational studies and randomized clinical trials

BACKGROUND

Breast cancer (BC) is the leading cause of cancer-related death among women worldwide. Olive oil, rich in monounsaturated fats and polyphenols, has been linked to a reduced BC risk, but epidemiological evidence remains limited. This study examined the association between olive oil consumption and BC risk in a large cohort of adult Italian women and conducted a systematic review on this association.

METHODS

Longitudinal analyses were performed on 11,442 women (mean age 54.7 ± 11.6 years) enrolled in the Moli-sani Study (2005-2010). Cox proportional hazard models were used to estimate BC risk in relation to olive oil consumption. A systematic review was conducted by searching Scopus, EMBASE, PubMed, and MEDLINE databases up to October 2024 for observational studies and RCTs.

RESULTS

Compared with lower olive oil consumption (≤2 tbsp./day), multivariable-adjusted HRs associated with highest intake (>3 tbsp./d) for overall, premenopausal, and postmenopausal BC were 0.71(95 %CI 0.48-1.05), 0.80 (95 %CI 0.28-2.28), and 0.70 (95 %CI 0.46-1.08), respectively. An increase of 1-tbsp./d of olive oil was associated with a lower risk of ER and PR breast cancers (HR=0.32; 95 %CI 0.13-0.77), particularly ER cases (HR=0.32; 95 %CI 0.15-0.69); additionally, a lowered hazard of HER2- BC incidence was observed at highest consumption of olive oil compared to the bottom category (HR=0.54; 95 %CI 0.31-0.96). The systematic review included 13 observational studies (11 case-control and 2 prospective) and 1 RCT. While case-control studies and the RCT suggested a protective effect associated with olive oil consumption, longitudinal studies reported no association.

CONCLUSIONS

Findings from the Moli-sani Study suggest an inverse association between olive oil consumption and the risk of hormone receptor-negative breast cancers, particularly ER subtype, while results were inconclusive for overall BC risk. The systematic review revealed that case-control studies more frequently reported a protective association, whereas prospective studies did not consistently support this finding.

Pharmacological modulation of cellular senescence: Implications for breast cancer progression and therapeutic strategies

Senescence, defined by the cessation of cell proliferation, plays a critical and multifaceted role in breast cancer progression and treatment. Senescent cells produce senescence-associated secretory phenotypes (SASP) comprising inflammatory cytokines, chemokines, and small molecules, which actively shape the tumor microenvironment, influencing cancer development, progression, and metastasis. This review provides a comprehensive analysis of the types and origins of senescent cells in breast cancer, alongside their markers and detection methods. Special focus is placed on pharmacological strategies targeting senescence, including drugs that induce or inhibit senescence, their molecular mechanisms, and their roles in therapeutic outcomes when combined with chemotherapy and radiotherapy. By exploring these pharmacological interventions and their impact on breast cancer treatment, this review underscores the potential of senescence-targeting therapies to revolutionize breast cancer management.

Novel insights into the causal relationship between endocrine-disrupting chemicals and breast cancer mediated by circulating metabolites

The relationship between endocrine-disrupting chemicals (EDCs) and breast cancer has not been extensively investigated. Although EDCs can disrupt human endocrine system, the underlying mechanism of EDCs on breast cancer requires further exploration. This study aimed to investigate the causal relationship between EDCs and breast cancer through Mendelian randomization (MR) and Generalised Summary-data-based Mendelian Randomization (GSMR) approach. Our results demonstrated that Bisphenol F was associated with increased risk of breast cancer [odds ratio (OR) = 1.018 (95 % CI 1.004-1.031), P = 0.010)]. Mono-(2-ethyl-5-carboxypentyl) phthalate (MECPP) was associated with lower breast cancer risk (OR = 0.894, 95 %CI = 0.819-0.975, P = 0.012). In addition, we identified 4 EDCs (bisphenol F, MECPP, Mono-ethyl phthalate, and Methyl paraben) significantly associated with ER + breast cancer. Furthermore, 3-bromo-5-chloro-2,6-dihydroxybenzoic acid mediated 10.9 % of the influence of MECPP on breast cancer. In addition, enrichment analysis was used to identify the pathways related to EDCs. MR-Phenome Wide Association Study (PheWAS) analysis was used to explore potential treatable diseases and adverse outcomes of EDCs. These findings shed light on the potential impact of EDCs exposure on breast cancer, which offer novel perspectives for future mechanistic and clinical research of EDCs and breast cancer.

Contrast-enhanced mammography for breast cancer detection and diagnosis with high concentration iodinated contrast medium

OBJECTIVES

We assessed the diagnostic performance of contrast-enhanced mammography (CEM) using a high-concentration iodinated contrast medium (HCCM, 400 mgI/mL) to determine whether the reduced iodine dose and increased iodine delivery rate (IDR) achieved might offer a more sustainable alternative to CEM performed with lower iodine concentrations.

METHODS

This two-center retrospective study included 205 patients who underwent CEM between March 2021 and February 2022. Patients were injected with HCCM at 1.0 mL/kg bodyweight at an IDR of 1.2 gL/s. Standard cranio-caudal and mediolateral-oblique views were acquired. Images were reviewed independently by two experienced radiologists who were blinded to patient clinical and imaging information. Diagnostic performance, including sensitivity, specificity, and accuracy, was assessed based on histological or long-term imaging follow-up as the reference standard.

RESULTS

Among the 205 patients, 149 (72.7%) had malignant lesions, and 56 (27.3%) had benign findings. The sensitivity and specificity of CEM were 96-97% and 84-87.5%, respectively, with an overall accuracy of 93-95%. The IDR achieved with HCCM resulted in excellent contrast enhancement, particularly in patients with aggressive malignancies. ROC analysis confirmed the good diagnostic performance, with AUC values of 0.90-0.92. Compared to conventional mammography and ultrasound, CEM demonstrated significantly higher diagnostic accuracy, especially in patients with dense breast tissue.

CONCLUSIONS

CEM with HCCM provides excellent diagnostic performance, achieving high sensitivity and specificity while allowing for a reduced iodine dose and increased IDR. This approach may offer a more sustainable alternative to conventional contrast media without compromising diagnostic accuracy, particularly for the detection and characterization of aggressive breast lesions.

CRITICAL RELEVANCE STATEMENT

This study demonstrates that reducing the volume of injected contrast media while increasing iodine concentration maintains the diagnostic benefits of CEM, further supporting its potential to improve early cancer detection, thereby advancing clinical radiology practices and optimizing screening strategies for women with dense breasts.

KEY POINTS

Currently, CEM protocols utilize a variety of iodine concentrations and flow rates. CEM with high-concentration contrast (400 mgI/mL) achieved 96% sensitivity and 87.5% specificity. High-concentration contrast in CEM improves early detection of aggressive breast cancers.

Body mass index and progesterone receptor in postmenopausal ER-positive/HER2-negative breast cancer: A nation-wide study in Korean breast cancer society and the multi-institutional cohort

BACKGROUND

Obesity is a risk factor for breast cancer and associated with increased estrogen levels that stimulate the progesterone receptor (PgR). Understanding interplay between obesity, PgR, and prognosis in estrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer (ER+/HER2-) is crucial. This study aimed to investigate the association between body mass index (BMI) and the prognostic value of PgR.

METHODS

Study included 10,125 postmenopausal patients with ER+/HER2-breast cancer between January 1991 to December 2019. Patients were categorized according to BMI (cutoff: 25 kg/m2) and PgR (positive/negative). The primary outcomes were the 6-year overall survival (OS) in the Korean Breast Cancer Registry (KBCR) cohort and 6-year recurrence-free survival (RFS) in the multi-institutional cohort.

RESULTS

In both cohorts, a greater proportion of patients with high BMI were PgR-positive, and the mean BMI was higher in the PgR-positive group. PgR-negativity was associated with worse 6-year OS in the KBCR cohort among patients with BMI ≥25 kg/m2 (hazard ratio [HR], 1.45; 95 % confidence intervals [CI], 1.06-1.97; P = .02), but not in those with BMI <25 kg/m2. Similarly, in the multi-institutional cohort, PgR-negativity was associated with worse 6-year RFS only in patients with BMI ≥25 kg/m2 (HR, 2.93; 95 % CI, 1.29-6.69; P = .01). The mean 21-gene recurrence score was higher in the PgR-negative group, regardless of the BMI.

CONCLUSIONS

In postmenopausal patients with ER+/HER2-breast cancer, the prognostic impact of PgR is modified by BMI. PgR-negativity is a strong predictor of poor outcomes in obese patients but not in non-obese patients.

PTPN2 inhibits TG-induced ERS-initiated TNBC apoptosis through the mitochondrial pathway

Triple negative breast cancer (TNBC) is the most malignant subtype of breast cancer that portends a poor prognosis and limited treatment. PTPN2 is a member of the non-receptor protein tyrosine phosphatase family that regulates biological processes by dephosphorylating various signaling molecules. Endoplasmic reticulum stress (ERS) plays a dual regulatory role by promoting both survival and apoptosis. This study aims to elucidate the role of PTPN2 in mediating the pro-apoptotic effects of ERS induced by Thapsigargin (TG), and its influence on the fate of TNBC cells, utilizing both loss-of-function and gain-of-function methodologies. Our findings indicate that PTPN2 modulates TG-induced ERS via the IRE1-XBP1 and PERK/EIF2α/ATF-4 signaling pathways. Furthermore, PTPN2 mitigates the TG-induced reduction in cell proliferation and the concomitant increase in apoptosis. Specifically, PTPN2 appears to inhibit several facets of TG-induced apoptosis, including: (1) Ca2+ elevation in mitochondria, (2) the production of reactive oxygen species (ROS), and (3) Bax/Bcl-2 augmentation which dictates mitochondria-mediated apoptosis. Additionally, we observed that the knockdown of PTPN2 enhances TG-induced autophagy; however, our results suggest that autophagy may serve a protective role against TG-induced apoptosis. Consequently, targeting PTPN2 in conjunction with ERS-inducing agents may represent a promising therapeutic strategy for the treatment of TNBC.

Study on the Differentiation of Infiltrating Breast Cancer Molecular Subtypes Based on Ultrasound Radiomics

OBJECTIVE

To establish and validate a 2-dimensional ultrasound (US) radiomics model for the noninvasive preoperative differentiation of various molecular subtypes of infiltrating breast cancer (IBC).

METHODS

A retrospective analysis of 210 female patients diagnosed with IBC through surgical operation or needle biopsy pathology at our hospital between May 2019 and February 2024 was conducted. Relevant data were collected to establish predictive models for different molecular subtypes of IBC.

RESULTS

Based on 5936 US radiomics features, 39, 25 and 19 optimal features were identified for the differentiation of luminal versus nonluminal types, luminal A versus luminal B types and human epidermal growth factor receptor 2 (HER2) overexpression versus triple-negative (TN) IBC subgroups, respectively. The corresponding areas under the curve for the training and validation sets were 0.901 and 0.752 (luminal vs. nonluminal), 0.931 and 0.773 (luminal A vs. luminal B) and 0.962 and 0.842 (HER2 overexpression vs. TN), respectively, indicating robust discriminatory performance of these models for different pathological molecular subtypes of IBC.

CONCLUSION

A radiomics model based on US images is capable of effectively differentiating between various molecular subtypes of IBC prior to surgery, holding promise in assisting medical professionals in crafting tailored diagnostic and therapeutic strategies.

Real-world utilization of aromatase inhibitors, tamoxifen, and ovarian function suppression in premenopausal patients with early hormone receptor-positive, HER2-negative breast cancer with increased recurrence risk

BACKGROUND

The optimal adjuvant endocrine treatment in premenopausal patients with hormone receptor-positive, HER2-negative (HRpos/HER2neg) early breast cancer (eBC) remains debated, particularly the choice between aromatase inhibitors plus ovarian function suppression (AI + OFS) or tamoxifen (TAM) with or without additional OFS. This study assessed the use of adjuvant endocrine therapies for premenopausal patients with intermediate/high-risk HRpos/HER2neg eBC.

METHODS

CLEAR-B (AGO-B-059; NCT05870813) was a retrospective study analyzing data, collected from January 2016 to June 2019 and from January 2022 to December 2023 during the certification process of breast centers in Germany. Premenopausal patients with HRpos/HER2neg intermediate/high-risk eBC were eligible. Patient and disease characteristics, in addition to recommended and received adjuvant treatments, were evaluated.

RESULTS

The number of registered patients was 3137, of whom 2789 had complete information on endocrine treatments (1717 for 2016-2019 and 1072 for 2022-2023). In 2016-2019, 8.4 % of the patients were recommended to be treated with AI + OFS, whereas in 2022-2023, the proportion of patients with a treatment recommendation for AI + OFS rose to 42.1 %. In 2016-2019, TAM monotherapy was most frequently recommended (80.8 %). Conversely, TAM + OFS was not commonly recommended (9.3 % in 2016-2019 and 16.5 % in 2022-2023). While no clear association between tumor stage and chosen endocrine therapy was found in 2016-2019, most patients with ≥stage IIA were recommended to be treated with AI + OFS in 2022-2023.

CONCLUSION

This analysis shows that treatment recommendation for AI + OFS in premenopausal patients with HRpos/HER2neg eBC increased relevantly in the past years, reflecting latest guideline recommendations.

The development and validation of a risk stratification system for assessing axillary status after neoadjuvant therapy in node-positive breast cancer: a multicenter, prospective, observational study

OBJECTIVE

It is not clear which procedure is most optimal for axilla after neoadjuvant therapy (NAT) in node-positive breast cancer patients. Accurately identifying patients with axillary pathologic complete response (pCR) is crucial to minimize the overtreatment of axilla. This study was designed to develop a risk stratification model for axillary pCR.

METHODS

In this multicenter, prospective, observational study, node-positive breast cancer patients who received NAT followed by axillary lymph node dissection (ALND) were enrolled between June 2021 and April 2024. We assessed the performance of breast shear wave elastography (SWE) utilizing virtual touch imaging quantification in determining axillary status across ultrasound (US) nodal stages following NAT. A predictive model incorporating axilla US nodal stage and breast SWE was developed using multivariate logistic regression analysis. Last, a simplified risk score was developed based on the calculated prediction probability from this model and validated in the external test cohort.

RESULTS

The axillary pCR rates were 52.53% in the training cohort ( n = 257) and 51.79% in the external test cohorts ( n = 195). Approximately 21.67% of US N0 cases were false negatives; 42.35% of US N1 cases were false positives. With SWE, the false negative rate was 11.53% in US N0 patients and false positive rate was 22.22% in US N1 patients. The model based on dual-modality US demonstrated strong discriminatory ability (AUC, 0.93), precise calibration (slope of calibration curve, 0.99), and practical clinical utility (probability threshold, 4.5-94.5%); the percentages of accuracy, sensitivity, and specificity were 87.94%, 88.52%, and 87.41%, respectively. Patients scoring 1 demonstrated a low axillary non-pCR rate (5.21%-6.97%), potentially reducing unnecessary ALND rate (17.12%-24.10%).

CONCLUSIONS

The risk stratification model integrating axilla US and breast SWE demonstrated good performance for assessing axillary status after NAT in node-positive breast cancer and might provide guidance for less aggressive management for specific individuals.

Beta blocker use and breast cancer survival by subtypes: A population-based cohort study

BACKGROUND

The associations between beta blocker (BB) use and breast cancer outcomes have been examined in previous observational studies, however the results are inconsistent. We examine these associations in a large population-based cohort of New Zealand (NZ) women with breast cancer.

METHODS

Postmenopausal women diagnosed with a first primary early invasive breast cancer between 2006 and 2020 were identified from the NZ Breast Cancer Foundation National Register and linked to national pharmaceutical data, hospital discharges, and death records. Cox proportional hazard models were used to estimate hazards of breast cancer-specific death (BCD), recurrence free interval (RFI), and distant recurrence free interval (DRFI) associated with BB use at diagnosis. Analyses were stratified by subtype.

RESULTS

Of the 13,535 women included in analyses, 2,238 (17 %) were using a BB at diagnosis and the median follow up time with BCD as the outcome was 5.6 years. BB use (vs non-use) was not associated with BCD (adjusted hazard ratio: 1.03; 0.86-1.23), RFI (HR = 0.94; 0.81-1.09), or DRFI (HR = 0.98; 0.83-1.15) overall. In women with triple negative breast cancer (TNBC), BB use was associated with a significantly longer RFI (HR = 0.71; 0.52-0.98) and DRFI (HR = 0.70; 0.50-0.98), and there was a suggestion of a decreased risk of BCD (HR = 0.74; 0.52-1.06). BB use was also associated with a significantly longer RFI in women with Luminal B HER2+ cancers (HR = 0.52; 0.29-0.92).

CONCLUSIONS

Our findings suggest that any protective effect on breast cancer prognosis associated with BB use may be confined to specific subtypes, particularly TNBC.

Clinical treatment score post-5 years and survival benefit from extended endocrine therapy for breast cancer patients under and over 50 years of age

BACKGROUND

This study aimed to determine whether clinical treatment score post-5 years (CTS5) could predict the clinical benefits of extended endocrine therapy (ExET) in young and old patients.

METHODS

We reviewed 2495 hormone receptor-positive breast cancer patients treated between 2001 and 2012 who were free from recurrence or death during the 5 years post-surgery in South Korea. The cohort was analyzed separately based on age (≤ 50 years and > 50 years). Multivariable analysis was conducted, and a cutoff of CTS5 < 3.13 was defined as the low group and CTS5 ≥ 3.13 as the intermediate/high (int/high) group.

RESULTS

The median follow-up duration was 115 months. Regardless of young and old age at diagnosis, the low group displayed considerably enhanced disease-free survival. Multivariate analysis revealed that the low group emerged as an independent and favorable prognostic factor for disease-free survival after adjusting for ExET use and prognostic parameters. Patients in the low group demonstrated a trend toward improved overall survival compared to those in the int/high group, reaching marginal statistical significance. ExET use demonstrated a significant correlation with improved disease-free survival, particularly in patients aged ≤ 50 years.

CONCLUSIONS

ExET should be considered in premenopausal and postmenopausal breast cancer patients with high CTS5 levels.

Is Progesterone Receptor a Neglected Feature in Breast Cancer? A Retrospective Study Analysing the Clinicopathological Characteristics of Breast Cancer Based on Progesterone Receptor Status

PURPOSE

The aim of this study is to analyse PR independently and its relationship with demographic and clinicopathological information.

INTRODUCTION

Steroid hormones, particularly estrogen and progesterone, play a crucial role in breast cancer (BC) etiology. Research attention has focused mainly on estrogen while the progesterone impact on breast cancer has yet to be fully uncover. Hormone receptors, including those for estrogen and progesterone, are crucial in BC molecular classification, shaping prognosis and treatment strategies. Beyond its metabolic effects, progesterone and its receptor (PR) have significant clinical implications, impacting clinical outcomes.

MATERIALS AND METHODS

The study comprised 2223 women who were diagnosed with BC at the Comprehensive Cancer Centre in Portugal (IPO-Porto) between 2012 and 2016. Variables, including age at diagnosis, body mass index (BMI), laterality, topographic localization, histological type, differentiation grade, tumor stage, estrogen receptor (ER) and Human Epidermal growth factor Receptor 2 (HER2) expression, were stratified according to the expression of Progesterone Receptor. Statistical analysis included Pearson's Chi-squared test, binary and multinomial regression, and Cox proportional hazard model. Statistical significance was set for P < .05.

RESULTS

The results reveal a statistical association between PR and BMI, histological type, differentiation grade, tumour stage, ER and HER2. Progesterone receptor negativity is associated with adverse clinical outcomes, including advanced tumor stages, and diminished overall survival.

CONCLUSION

Further research is needed to elucidate the precise contributions of progesterone to breast cancer progression and to optimize therapeutic approaches for improved patient outcomes.

Molecular targets and therapies associated with poor prognosis of triple‑negative breast cancer (Review)

Triple‑negative breast cancer (TNBC) is a highly aggressive and heterogeneous subtype of BC characterized by the absence of estrogen, progesterone and human EGFR2 receptors. This lack of receptors renders it unresponsive to standard targeted therapies. Despite advances made in understanding the molecular landscape of TNBC, its poor prognosis and high recurrence rates underscore the urgent need for innovative therapeutic approaches. This review explores the effects of key prognostic markers, such as Ki‑67, programmed cell death ligand 1, BRCA1/2 mutations, E‑cadherin loss and EGFR alterations. It also examines critical pathways, including the PI3K/AKT/mTOR and mutant p53 pathways, which are prerequisites for TNBC progression and therapy resistance, and discusses the therapeutic potential of directly targeting these key molecules and their associated signaling pathways. In addition, recent advances in targeted therapies were highlighted, such as immune checkpoint inhibitors, and the statuses of emerging strategies were presented, such as chimeric antigen receptor‑T cell therapy and small inhibitory RNA‑based treatments. Given the molecular heterogeneity of TNBC, the importance of precision medicine was also discussed and it was emphasized that this approach is becoming an increasingly critical aspect of personalized treatment strategies. Resistance to existing therapies presents a major challenge to the effective treatment of TNBC, and thus, the development of future therapeutic strategies requires technical innovations. By integrating these insights, this review aims to provide a comprehensive overview of current and future means of improving TNBC outcomes.

Development of radiomics-based models on mammograms with mass lesions to predict prognostically relevant characteristics of invasive breast cancer in a screening cohort

BACKGROUND

Optimizing breast-screening performance involves minimizing overdiagnosis of prognostically favorable invasive breast cancer (IBC) that does not need immediate recall and underdiagnosis of prognostically unfavorable IBC that is not recalled timely. We investigated whether mammographic features of masses predict prognostically relevant IBC characteristics.

METHODS

In a screening cohort, we obtained pathological information of 1587 IBCs presenting as a mass through the nationwide cancer registry and pathology databank. We developed models based on mammographic tumor appearance to predict whether IBC was prognostically favorable (T1N0M0 luminal A-like) or unfavorable. Models were based on 1095 positive screening mammograms (possible overdiagnosis), or on 603 last negative mammograms with in retrospect visible masses (possible underdiagnosis). We calculated performance metrics using cross-validation.

RESULTS

23.5% of masses were prognostically favorable IBC. Using 1095 positive mammograms, the model's predictions to have prognostically favorable IBC (10th-90th percentile range 8.7-47.0%) yielded AUC 0.75 (SD across repeats 0.01), slope 1.16 (SD 0.07). Performance in 603 last negative screening mammograms with masses was poor: AUC 0.60 (SD 0.02), slope 0.85 (SD 0.28).

CONCLUSIONS

Mammography-based models from masses representing IBC at time of recall (possible overdiagnosis) predict prognostically relevant characteristics of IBC. Models based on in retrospect visible masses (possible underdiagnosis) performed poorly.

Ki-67 and 21-gene recurrence score assay in decision making for adjuvant chemotherapy in breast cancer patients

Although significant advances have been made in the molecular subtyping of breast cancers, identification of patients who do not benefit from the chemotherapy is a major challenge. Pioneer studies have examined the predictive value of the clinicopathological factors, such as tumor size, disease stage, the expression levels of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2) molecules and more importantly tumor cells proliferation index (Ki-67) to help guide patients' treatment and predict their outcome in the adjuvant chemotherapy setting. However, despite their clinical importance, no consensus is reached on their validity for chemotherapy decision. These challenges have ignited researchers to evaluate genomic signatures, which has led to the introduction of several genomic tests that can now help oncologists to include/exclude chemotherapy from the treatment regimen with more confidence. The present review aims to look back over the literature on the clinical significance of Ki-67 as well as the 21-gene recurrence score assay in identification of breast cancer patients who may benefit from the adjuvant chemotherapy.

PLCH1 overexpression promotes breast cancer progression and predicts poor prognosis through the ERK1/2-EGR1 axis

Background

Phospholipase C η1 (PLCH1), a member of the phospholipase C superfamily, has been implicated in the development of multiple cancers. However, its specific role in breast cancer progression, its association with clinicopathological features, and its prognostic significance remain unclear.

Methods

PLCH1 expression was analyzed across multiple tumor types using the TNMplot database, which integrates RNA-seq, microarray, and normalized data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO), encompassing 40,442 tumor and 15,648 normal samples. Differential expression analysis was performed using boxplots and statistical tests to assess significance. DNA methylation and survival analyses were conducted using TCGA data, with Kaplan-Meier curves and Cox regression to evaluate prognostic value. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, were performed on differentially expressed genes using the clusterProfiler package. Mutation analyses were conducted using mutation annotation format (MAF) files, and pathway activities were correlated with PLCH1 expression via single-sample GSEA (ssGSEA). Experimental validation included immunohistochemistry (IHC) on 100 breast invasive ductal carcinoma samples, real-time quantitative PCR (RT-qPCR), and Western blotting. PLCH1 knockdown functional studies assessed cell proliferation and signaling pathways.

Results

PLCH1 was significantly overexpressed in various cancers, including breast cancer, compared to normal tissues. PLCH1 expression was strongly correlated with the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) in breast cancer tissues, further linking PLCH1 to poor prognosis and adverse patient outcomes. Functional studies revealed that PLCH1 was highly expressed in breast cancer cell lines, and PLCH1 knockdown significantly inhibited cell proliferation, induced cell cycle arrest, and reduced cyclin-dependent kinase 1 (CDK1) expression in BT-474 cells. Mechanistically, PLCH1 silencing downregulated early growth response 1 (EGR1) expression by suppressing the extracellular signal-regulated kinases 1 and 2 (ERK1/2) signaling pathway, impairing tumor cell proliferation.

Conclusions

PLCH1 was overexpressed in breast cancer and was associated with worse patient outcomes. Its role in promoting cell proliferation via the ERK1/2-EGR1 axis highlighted PLCH1 as a potential therapeutic target for breast cancer. These findings offer new insights into the molecular mechanisms underlying breast cancer progression and suggest promising avenues for targeted therapy development.

Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions

Breast cancer (BC) is still the most diagnosed cancer for females with an increased focus on immunotherapy as a promising precise treatment. Selecting appropriate patients and monitoring patient treatments are crucial to ensure higher response rates with low adverse events. Various biomarkers were proposed to predict immunotherapy response, including tumor mutation burden, immune cell, and tumor microenvironment expression. However, traditional methods for evaluating immunotherapy are invasive and inaccurate, and their assessments could be biased due to the variability in quantification techniques. Artificial intelligence (AI) has emerged as a powerful technology that addresses these challenges, handling heterogeneous data to identify complex patterns and offering accurate and non-invasive solutions. In this paper, we review emerging AI-based models for immunotherapy prediction in BC using diverse biomarkers. We first discussed the application of AI models for each biomarker, highlighting both direct prediction of immunotherapy response and prognosis, as well as indirect approaches via the identification of immune subtypes or specific predictive biomarkers. Then, we investigated the integration of all biomarkers in multi-modal AI approaches for a precise and personalized prediction of immunotherapy response. We have also addressed the implication of integrating AI in the healthcare ecosystem with other new technologies, including nanodevices, and wearable technologies. We further elucidated the role of AI and healthcare providers with this convergence of personalized medicine and demonstrated its role in enhancing population health management and supporting personalized patient care.

Ki-67-Playing a key role in breast cancer but difficult to apply precisely in the real world

The Ki-67 index, which is a proliferative index, has become more important in making treatment decisions for patients with breast cancer (BC) and plays both a predictive role and a prognostic role. However, a few factors limit its use in clinical practice, particularly the assessment of the percentage of Ki-67-positive cells and the cutoff value of Ki-67. In this study, we examined the expression of Ki-67 via immunohistochemistry and systematically evaluated the value of the Ki-67 index in patients with BC. This was a retrospective study including 280 patients diagnosed with BC. There were marked differences in overall survival (OS) between patients with BC when the Ki-67 index ranged from 46 to 68% (χ2 = 5.87, P = 0.0154; χ2 = 7.64, P = 0.0057, respectively), and the same results were also found when the staining density was added to the Ki-67 index; however, the staining density alone has limited value in assessing the value of Ki-67. There were marked differences in disease-free survival (DFS) among BC patients when the Ki-67 index ranged from 50 to 58% (χ2 = 7.31, P = 0.0069; χ2 = 7.88, P = 0.005). When 14% was used as a cutoff point to classify the molecular type of BC, the luminal A-type patients were significantly different from patients with HER2-overexpressing subtype BC in terms of OS (χ2 = 5.33, P = 0.021). There was a significant difference in the OS of patients with human epidermal growth factor receptor 2 (HER-2)-overexpressing subtype BC when the Ki-67 index fell within the range of 49-60% (χ2 = 4.86, P = 0.0275; χ2 = 5.50, P = 0.019, respectively). There were significant differences between luminal A-type BC and HER2-overexpressing subtype BC in terms of OS (χ2 = 5.53, P = 0.019), according to suggestions of the 2019 CSCO consensus. There were significant differences between the two groups of luminal B HER-2(-) BC when the Ki-67 index was 52% (χ2 = 6.61, P = 0.0101). The differentiated Ki-67 index can be used to assess the OS and DFS of patients with BC, and the staining density of Ki-67 has little value in assessing prognosis in these patients. Different molecular classification methods may influence the assessment of prognosis and the results of molecular subtype in patients with BC. To predict the prognosis of BC patients, it is more scientifically feasible to use the interval values of Ki-67 than a specific value.

The correlation between obesity and the occurrence and development of breast cancer

This study reviews the mechanisms by which obesity affects the development and progression of breast cancer (BC). The association between obesity and BC is mainly due to three aspects: disruption of glycolipid metabolism, abnormal cell function and imbalance of adipokine levels. The dysregulation of glycolipid metabolism caused by obesity, including the accumulation of cholesterol and fatty acids and the reprogramming of glucose metabolism, promotes the growth and invasion of tumour cells. Obesity triggers multiple cellular abnormalities, particularly in lipid-associated macrophages and cancer-associated adipocytes, which promote tumour progression and immunosuppression by secreting inflammatory factors and various fatty acids into the tumour microenvironment. Obesity leads to an imbalance in the expression of several adipokines. Leptin upregulation is closely associated with BC metastasis and resistance to endocrine therapy, while reduced adiponectin levels attenuate the protective effect. At the same time, chronic inflammation and insulin resistance not only further increase the risk of BC, but also exacerbate tumour resistance. In terms of treatment, weight-loss drugs and metformin can improve the efficacy of obesity-related BC treatment to some extent. Intervention strategies targeting adipose tissue remodelling, lipid metabolism and leptin regulation also show potential clinical value, but more research is needed to clarify their safety and efficacy. This review provides systematic ideas and references for research into the mechanisms and clinical management of obesity-related BC.

Synthesis of carob honey loaded chitosan nanoparticles and determination of its antimicrobial potential and cytotoxic effect on breast cancer cell line

Embedding natural products into chitosan nanoparticles (CNP) is an effective way to produce a novel combination with better antimicrobial and anticancer activities. Therefore, this study aims to incorporate carob honey (CH) into CNP, determine its potential antimicrobial along with antiproliferative activities, by well diffusion and MTT cell viability assays, respectively. Successful loading of CH in CNP was confirmed after due characterization. The nanoparticles, synthesized by ionic gelation method, produced a small (101.3 ± 4.13 nm), stable (+27.27 ± 0.95 mV), and monodispersed (0.2265 ± 0.0027) CH-loaded CNP (CHCNP). The best antibacterial activity occurred in Klebsiella pneumoniae (K. pneumoniae) (23 ± 0 mm to 16 ± 1.7 mm) followed by Escherichia coli (E. coli) (18 ± 2.0 mm to 10 ± 1 mm). Meanwhile, Aspergillus niger (A. niger) and Aspergillus flavus (A. flavus) were evenly inhibited with inhibition zones in the range of 15 ± 3 mm to 7 ± 0.8 mm and 15 ± 5 mm to 9 ± 1.4 mm, respectively. CHCNP showed a remarkable cytotoxic effect on MDA-MB-231 according to concentration and time, with IC50 of 25 ± 5 to 18 ± 2.6 μg/mL within 24-72 h. These findings demonstrated the feasibility of loading CH in CNP to form a nanoformulation that could potentially serve as a target-specific therapeutic agent in the treatments of microbial infections and breast cancer. However, there is a need for further research on the safety, dosage optimization, in vivo studies and mechanisms of action of the nanoparticles.

Nonmass lesions on preoperative MRI in breast cancer patients: clinical implications and prognostic significance

This study aimed to investigate the clinical importance and prognostic value of nonmass lesions (NMLs) identified via preoperative magnetic resonance imaging (MRI) in patients with breast cancer, with an emphasis on understanding how these lesions affect treatment decisions and survival outcomes. A retrospective analysis was conducted on 6971 patients diagnosed with breast cancer who underwent surgery at Asan Medical Center, Seoul, between January 2000 and December 2021. Patients were categorized based on the presence or absence of NMLs on preoperative MRI. Various clinicopathological parameters were compared, and survival outcomes, such as overall survival (OS), distant metastasis-free survival (DMFS), regional recurrence-free survival (RFS), and local recurrence-free survival (LFS), were analyzed using Kaplan-Meier and Cox regression methods. Subgroup analyses were performed based on the type of surgery and the administration of neoadjuvant chemotherapy and adjuvant radiation therapy. Of the total cohort, 21.9% (n = 1524) had NMLs. The presence of NMLs was associated with a significant improvement in OS (P = 0.017) for the entire patient group. Multivariate analysis revealed the presence of NMLs as a favorable prognostic factor (hazard ratio 0.47, 95% confidence intervals 0.25-0.90, P = 0.022). Subgroup analyses demonstrated significantly improved OS, DMFS, and RFS outcomes for patients with NMLs who underwent mastectomy after neoadjuvant chemotherapy. NMLs on preoperative MRI in patients with breast cancer are associated with improved overall survival (OS) and serve as an independent prognostic factor. However, further research is needed to elucidate the underlying reasons for these outcomes.

Survival outcomes and prognostic factors of breast cancer spinal metastases: a retrospective study

PURPOSE

To investigate survival-related factors in patients with breast cancer spinal metastases (BCSM) within the context of multidisciplinary treatment.

METHODS

A retrospective cohort of 78 cases from July 2010 to December 2021 was recruited. These patients underwent surgery-based multidisciplinary treatment. Collected data included demographics, pathologies, symptoms, surgery-related data, adjuvant therapies, postoperative events, and survival data. The primary outcome was overall survival (OS). Kaplan-Meier survival curves were plotted. Univariate analysis employed the log-rank test, and post-hoc multivariate analysis utilized the Cox regression model.

RESULTS

The mean age was 50.9 years. 72 cases (92.3%) reported locoregional pain, and 30 cases (38.5%) presented with neurological dysfunction. The primary pathological subtype was invasive ductal carcinoma (83.3%).

SURGICAL PROCEDURES

total en-bloc spondylectomy (6.4%), debulking surgery (61.5%), palliative surgery (32.1%). Postoperatively, both pain and neurological function significantly improved (P < 0.05). Radiotherapy, endocrine therapy, chemotherapy/targeted therapy were given to 56.4%, 60.3%, 61.5% patients, respectively. The estimated OS was 50.0 months. Tomita's scores (P = 0.355) and Tokuhashi's scores (P = 0.461) showed no significant OS association. Univariate analysis indicated that preoperative neurological dysfunction (P = 0.003), postoperative neurological dysfunction (P = 0.051), adjuvant endocrine therapy (P = 0.025), and hormone receptor expression status (P = 0.009) were associated with patient survival. Multivariate analysis identified endocrine therapy as an independent protective factor for prognosis (aHR = 0.070, 95% CI 0.007-0.727, P = 0.026).

CONCLUSIONS

Patients with BCSM have experienced prolonged survival. Neurological status, adjuvant anti-drugs, and expression of hormone receptors played crucial roles in predicting survival. Conventional prognostic systems may require modification to incorporate these factors. However, this study has limitations inherent to its retrospective design, single-center cohort, and relatively small sample size, which may affect generalizability.

Female breast cancer classification using immunohistochemistry biomarkers staining in Botswana

Breast cancer remains the most diagnosed cancer among women world-wide and a leading cause of cancer-related deaths accounting for 15% of deaths in 2018. Worldwide, the incidence increased from 1.4 million in 2011 to over 2 million in 2018 with a concomitant increase in mortality from 458,400 to 626,679 in the same period. Low- and middle-income countries, such as Botswana, have a disproportionate burden of breast cancer incidence and mortality and there is an urgent need to characterise the unique tumour molecular profiles that may be influencing mortality in these populations. Methods A retrospective study of 125 archived mastectomy specimens (from 2006 to 2009) from women with breast cancer in Botswana was conducted. We determined molecular characteristics of breast cancers by carrying out four immunohistochemistry (IHC)classification (PR, ER, HER2 receptors and Ki 67), cytokeratin 5/6 and EGFR1.Statistical software STATA and SPSS were used to determine the relationship between histology, IHC of biomarkers of interest. Results Out of 125 breast cancer tissues, the distribution of molecular subtypes were as follows: Luminal A (44/125; 35.2%), Luminal B (and TNBC (23/125; 18,4%), HER2 Enriched (17/125; 13.6%), and Luminal B HER2 Enriched (9/125; 7.2%), Basal (9/125; 7.2%), and CK5/6 was expressed by 12.8% (16/125) of tumours. Furthermore 6% of the tumours were basal positive luminal tumours. Morphological 76% of tumours were IDC-NOS and 24% were special type, majority were grade 2 (40%) followed by grade 1(30.4%), grade 3 (23.2%) was and mucinous types were 6.4%. Clinical staging and tumour involvement data were incomplete. Conclusion The discovery of basal positive luminal breast tumours in women from Botswana original not accounted for in the four distinct molecular subtype calls for an expanded antibody panel 6-IHC panel) in order to stratify women of African descent patients into good/poor prognostic groups. Characterising tumour subtypes will better inform optimal therapeutic regimens for women with breast cancer in Botswana.

Preoperative DBT-based radiomics for predicting axillary lymph node metastasis in breast cancer: a multi-center study

BACKGROUND

In the prognosis of breast cancer, the status of axillary lymph nodes (ALN) is critically important. While traditional axillary lymph node dissection (ALND) provides comprehensive information, it is associated with high risks. Sentinel lymph node biopsy (SLND), as an alternative, is less invasive but still poses a risk of overtreatment. In recent years, digital breast tomosynthesis (DBT) technology has emerged as a new precise diagnostic tool for breast cancer, leveraging its high detection capability for lesions obscured by dense glandular tissue.

PURPOSE

This multi-center study evaluates the feasibility of preoperative DBT-based radiomics, using tumor and peritumoral features, to predict ALN metastasis in breast cancer.

METHODS

We retrospectively collected DBT imaging data from 536 preoperative breast cancer patients across two centers. Specifically, 390 cases were from one Hospital, and 146 cases were from another Hospital. These data were assigned to internal training and external validation sets, respectively. We performed 3D region of interest (ROI) delineation on the cranio-caudal (CC) and mediolateral oblique (MLO) views of DBT images and extracted radiomic features. Using methods such as analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO), we selected radiomic features extracted from the tumor and its surrounding 3 mm, 5 mm, and 10 mm regions, and constructed a radiomic feature set. We then developed a combined model that includes the optimal radiomic features and clinical pathological factors. The performance of the combined model was evaluated using the area under the curve (AUC), and it was directly compared with the diagnostic results of radiologists.

RESULTS

The results showed that the AUC of the radiomic features from the surrounding regions of the tumor were generally lower than those from the tumor itself. Among them, the Signaturetuomor+10 mm model performed best, achieving an AUC of 0.806 using a logistic regression (LR) classifier to generate the RadScore.The nomogram incorporating both Ki67 and RadScore demonstrated a slightly higher AUC (0.813) compared to the Signaturetuomor+10 mm model alone (0.806). By integrating relevant clinical information, the nomogram enhances potential clinical utility. Moreover, it outperformed radiologists' assessments in predictive accuracy, highlighting its added value in clinical decision-making.

CONCLUSIONS

Radiomics based on DBT imaging of the tumor and surrounding regions can provide a non-invasive auxiliary tool to guide treatment strategies for ALN metastasis in breast cancer.

CLINICAL TRIAL NUMBER

Not applicable.

Mammary neoplasms in male dogs: A 24-year descriptive study

Mammary neoplasms in male dogs are as rare as in humans, accounting for <1% of all tumors. In a descriptive review of cases diagnosed between 2000 and June 2024, 2,172 mammary neoplasms were identified, of which only four (0.18%) occurred in male dogs. The patients ranged in age from 8 to 11 years, with a mean age of 9.7 years. Histopathological analysis revealed that 75% (3/4) of the cases were malignant, while 25% (1/4) were benign. Among the malignant cases, only one patient exhibited regional metastases. Two patients had concurrent testicular neoplasia, both diagnosed as Leydig cell tumors. Malignant histological subtypes included carcinosarcoma, tubular carcinoma, and carcinoma within a mixed tumor. The only benign tumor identified was a benign mixed tumor. All malignant neoplasms exhibited high expression of hormone receptors, highlighting their potential role in tumor development. Additionally, cyclooxygenase-2 (COX-2) expression, a potential therapeutic target and prognostic factor, was observed. Early diagnosis is crucial for improving prognosis; however, due to the rarity of this condition, diagnosis is often delayed. These findings emphasize the occurrence of these neoplasms in male dogs, and report on complementary techniques to improve therapeutic strategies.

The impact of carboplatin on pathologic complete response and survival based on HER2 low and HER2 zero status in triple negative breast cancer patients receiving neoadjuvant chemotherapy: a multicenter real-world analysis

BACKGROUND AND OBJECTIVES

Triple-negative breast cancer (TNBC) has a poor prognosis, and neoadjuvant chemotherapy (NACT) is the standard treatment for locally advanced TNBC. In this study, we aimed to evaluate the efficacy of adding carboplatin to NACT regarding pathological complete response (pCR) and survival in the HER2-low and HER2-zero subgroups of TNBC patients.

MATERIALS AND METHODS

The study included 269 patients from five medical oncology clinics. Patients were divided into two groups: HER2-low (n = 152, 56.5%) and HER2-zero (n = 117, 43.5%). Among HER2-zero patients, 30 (25.6%) received carboplatin, while 38 (25.0%) HER2-low patients received carboplatin. The benefit of adding carboplatin to NACT regarding pCR and survival was assessed in both HER2-zero and HER2-low groups.

RESULTS

When patients were evaluated according to HER2 status, the pCR rates were significantly higher in the HER2-zero group compared to the HER2-low group (45.2% versus 23.7%, p < 0.001). In the HER2-zero group, patients who received carboplatin had significantly higher pCR rates (63.3% versus 39.0%, p = 0.021). Similarly, in the HER2-low group, adding carboplatin significantly increased the pCR rates (36.8% versus 19.3%, p = 0.028). While carboplatin improved pCR rates in both HER2 subgroups, this benefit was not observed in patients with Grade 1 tumors, HER2 score 2-FISH negative tumors, or based on BRCA mutation status. Patients with pCR exhibited significantly prolonged DFS and OS (p = 0.002, p < 0.001, respectively).

CONCLUSIONS

Our research demonstrates that the addition of carboplatin increases pCR rates in both HER2-zero and HER2-low patient cohorts. We suggest that carboplatin should be considered as an addition to standard neoadjuvant chemotherapy for eligible TNBC patients, regardless of HER2-zero or HER2-low status, when appropriate based on individual patient factors and toxicity considerations.

Boron neutron capture therapy (BNCT) for left axillary lymph node metastasis of recurrent breast cancer

Here described for the first time is the efficacy of p-boronophenylalanine (BPA)-based BNCT in the clinical treatment of metastatic breast cancer. The patient was diagnosed with left axillary lymph node metastasis from breast cancer with comorbidity of neuropathy. Although several treatment strategies for suppressing the progression of the cancer were unavailing, BPA-based BNCT impeded the progression. This clinical case suggests the applicability of this new method of treatment to not only metastatic but also primary breast cancer.

Tob negatively regulates NF-κB activation in breast cancer through its association with the TNF receptor complex

NF-κB mediates transcriptional regulation crucial to many biological functions, and elevated NF-κB activity leads to autoimmune and inflammatory diseases, as well as cancer. Since highly aggressive breast cancers have few therapeutic molecular targets, clarification of key molecular mechanisms of NF-κB signaling would facilitate the development of more effective therapy. In this report, we show that Tob, a member of the Tob/BTG family of antiproliferative proteins, acts as a negative regulator of the NF-κB signal in breast cancer. Studies with 35 human breast cancer cell lines reveal that Tob expression is negatively correlated with NF-κB activity. Analysis of The Cancer Genome Atlas (TCGA) database of clinical samples reveals an inverse correlation between Tob expression and NF-κB activity. Tob knockdown in human breast cancer cells promoted overactivation of NF-κB upon TNF-α treatment, whereas overexpression of Tob inhibited TNF-α stimulation-dependent NF-κB activation. Mechanistically, Tob associates with the TNF receptor complex I and consequently inhibits RIPK1 polyubiquitylation, leading to possible prevention of overwhelming activation of NF-κB.

Association of Breast Cancer and Selective Estrogen Receptor Modulators on the Risk of Meningioma: Insights from Mendelian Randomization

Considering the potential links between breast cancer (BC), selective estrogen receptor modulators, and meningioma in previous epidemiology studies, this study aimed to investigate them through the Mendelian randomization approach. We extracted instrumental variables (IVs) of different subtypes of BC from the largest genome-wide association study. Gene targets of SERMs were obtained from the Drug-Gene Interaction Database. Mendelian randomization (MR) analysis applied inverse variance weighted approach to evaluate causality. A series of sensitivity analyses and reverse MR were used to evaluate the stability of the MR results. Genetically determined estrogen receptor (ER) positive BC, luminal A-like breast cancer (OR 1.17, 95% CI 1.04 to 1.32, p = 0.01), and luminal B-like breast cancer (OR 1.20, 95% CI 1.04 to 1.37, p = 0.009) were associated with an increased odds ratio of meningioma (OR 1.18, 95% CI 1.05 to 1.32, p = 0.005). Among SERM-targeted genes, CYP2D6 (OR 1.37, 95% CI 1.23 to 1.54, p = 4.15 × 10- 8), NGR1 (OR 1.15, 95% CI 1.10 to 1.20, p = 2.59 × 10- 11), and MAPT (OR 10.20, 95% CI 2.90 to 35.84, p = 0.0003) were associated with increased meningioma risk, while BRCA1 (OR 0.67, 95% CI 0.57 to 0.80, p = 4.88 × 10- 6) showed negative causal association with meningioma risk. The outcome of the sensitivity analysis and reverse MR analysis corroborated the findings. These findings suggested a causal relationship between BC and meningioma, and identified potential target genes associated with meningioma, which was beneficial to early identification and prevention of meningioma risk.

Raman Peak Features Matching: Enhancing Spectral Analysis through Feature Augmentation

Raman spectroscopy has emerged as a pivotal technology in modern scientific research and industrial applications, offering nondestructive, high-resolution analysis with robust molecular fingerprinting capabilities. The extraction of Raman spectral features is a critical step in spectral data analysis, directly influencing sample identification, classification, and quantitative outcomes. However, integrating important data features from machine learning models with context-specific biosignatures to derive meaningful insights into spectral analysis remains a significant challenge. Herein, the Raman Peak Feature Matching (RPFM) method is proposed, which matches protein peak features with salient breast cell data features extracted from the machine learning models. Feature augmentation is subsequently applied to the matching-retained breast cell features, thereby enhancing spectral analysis capabilities. The RPFM method is applied to breast cell spectra for feature augmentation with a reclassification accuracy of 97.12% using a linear support vector machine model, achieving an 8.34% improvement over the model's performance without feature augmentation. The RPFM method has also been successfully implemented in generalized linear logistic regression and tree-based eXtreme gradient boosting, demonstrating its versatility across diverse machine learning algorithms. The RPFM method leverages data-driven machine learning models while compensating for the inability to take into account specific specialized background knowledge. This methodology significantly advances the accuracy and efficacy of spectral analysis in biological and medical applications, offering a novel framework for machine learning algorithms to perform augmented Raman spectral analysis.

Proteomics in Diagnostic Evaluation and Treatment of Breast Cancer: A Scoping Review

Objectives: The aim of this scoping review was to delineate the current role and possible applications of proteomics in personalized breast cancer diagnostic evaluation and treatment. Methods: A comprehensive search in PubMed/MEDLINE and Scopus/EMBASE was conducted, according to the PRISMA-ScR guidelines. Inclusion criteria: proteomic studies of specimens from breast cancer patients, clinically relevant studies and clinical studies. Exclusion criteria: in silico, in vitro and studies in animal models, review articles, case reports, case series, comments, editorials, and articles in language other than English. The study protocol was registered in the Open Science Framework. Results: In total, 1093 records were identified, 170 papers were retrieved and 140 studies were selected for data extraction. Data analysis and synthesis of evidence showed that most proteomic analyses were conducted in breast tumor specimens (n = 77), followed by blood samples (n = 48), and less frequently in other biologic material taken from breast cancer patients (n = 19). The most commonly used methods were liquid chromatography-tandem mass spectrometry (LC-MS/MS), followed by Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF), Surface-Enhanced Laser Desorption/Ionization Time-of-Flight (SELDI-TOF) and Reverse Phase Protein Arrays (RPPA). Conclusions: The present review provides a thorough map of the published literature reporting clinically relevant results yielded from proteomic studies in various biological samples from different subgroups of breast cancer patients. This analysis shows that, although proteomic methods are not currently used in everyday practice to guide clinical decision-making, nevertheless numerous proteins identified by proteomics could be used as biomarkers for personalized diagnostic evaluation and treatment of breast cancer patients.

The influence of socio-economic status on the fulfilment of Saint-Gallen recommendations for early-stage breast cancer

Socio-economic status (SES) is related to breast cancer diagnosis and prognosis. We study if SES is related to the adequacy of the treatment according to Saint Gallen consensus in Spanish women with incident breast cancer. Breast cancer cohort was assembled from incident cases from MCC-Spain and prospective followed-up afterwards. Participants were then classified according to the Saint-Gallen consensus in three categories (In Saint-Gallen, who received therapy accorded by Saint Gallen; Over Saint-Gallen, who received some additional therapy; or Under Saint-Gallen, who did not receive the complete therapy). Association between SES and Saint-Gallen fulfilment was analyzed using multinomial logistic regression, adjusting for clinicopathological and patient-related variables. 1115 patients in stages I and II were included. Women with university education were 58% more likely to receive over Saint-Gallen therapies (RRR = 1.68; 95%CI 0.84-3.33). In the simplified SES score, women with higher SES were over Saint-Gallen 52% more than those with lower SES (RRR = 1.52; 95%CI 0.88-2.64). Women with higher SES more often received over Saint-Gallen therapies. Further analyses are needed to understand the influence of these differences on the overall survival as well as its potential unwanted side effects.

Early detection of triple-negative breast cancer: evidence of a favourable prognostic impact in a comparative analysis of screen-detected versus symptomatic cases

PURPOSE

Mammographic screening is effective in reducing breast cancer mortality, but the impact of screening on triple-negative breast cancers (TNBCs) outcomes remains debated. This study aims to determine if screen detection is an independent prognostic factor for TNBCs and to analyse the radiological and pathological differences between screen-detected and symptomatic TNBCs.

METHODS

This retrospective cohort study analysed 353 histologically confirmed TNBC cases diagnosed between 2013 and 2020 at a single institution in Turin, Italy. Cases were categorized into screen-detected and symptomatic groups based on initial presentation. Clinical, radiological and pathological characteristics as well as disease-free survival (DFS) and overall survival (OS) were compared between groups. Statistical analyses included Kaplan-Meier survival curves and Cox proportional hazard models, adjusting for several clinical and biological variables.

RESULTS

50.1% of cases were screen-detected and 49.9% were symptomatic. Screen-detected cases were more commonly smaller (T1 or T2) (96.6%) than symptomatic cases (75%) (p < 0.001). Also, compared to symptomatic tumours, screen-detected ones were more often node negative (62.4% vs. 48%, p = 0.007) and diagnosed at a lower stage (85.4% vs. 63.8%, p < 0.001), with better DFS and OS. Detection method was not an independent prognostic factor, while stage at diagnosis, vascular invasion, histologic type and tumour-infiltrating lymphocytes (TILS) were more significant predictors of prognosis. Radiological and biological features were similar between the two groups.

CONCLUSIONS

TNBCs correlate with favourable pathological features and improved survival outcomes in univariate analyses, but these benefits diminish when accounting for traditional prognostic factors. Hence, the better prognosis observed among screen-detected cases is more likely due to stage shift rather than tumour biology.

HER2 (2+)/SISH-positive vs. HER2 (3+) Breast Cancer: Pre-treatment MRI Differences and Accuracy of pCR Prediction on Post-treatment MRI

RATIONALE AND OBJECTIVES

To evaluate whether HER2 (human epidermal growth factor receptor 2) (2+)/SISH (silver-enhanced in situ hybridization)+ and HER2 (3+) breast cancers exhibit distinct imaging characteristics on pre-treatment MRI and assess differences in pCR (pathologic complete response) prediction accuracy on post-treatment MRI, considering interobserver variability.

METHODS

This retrospective study included 301 HER2-positive breast cancer patients (mean age, 54 ± 10 years) who underwent NAC and surgery. Pre-treatment MRI features were analyzed in consensus. Two radiologists independently assessed post-treatment MRI for shrinkage patterns and response according to RECIST v1.1, further categorizing complete responses into rCR (radiologic complete response) and near-rCR. Interobserver agreement was measured (Cohen's kappa), and pCR was defined as no residual invasive or in situ tumor in the breast (ypT0) on the final pathology report. Sensitivity, specificity, and AUC were used to evaluate pCR prediction.

RESULTS

Fifty-four patients had HER2 (2+)/SISH+ and 247 had HER2 (3+) tumors. pCR rates were significantly higher in HER2 (3+) (58.7% vs. 18.5%, p < 0.001). On pre-treatment MRI, HER2 (2+)/SISH+ tumors more often appeared as single masses, while HER2 (3+) tumors showed more NME (non-mass enhancement) (44.5% vs. 16.7%, p < 0.001) and mass with NME (33.6% vs. 9.3%, p = 0.005). Post-treatment MRI showed simple concentric shrinkage in HER2 (2+)/SISH+ and no enhancement in HER2 (3+). Agreement was moderate (κ = 0.541-0.588). For pCR prediction, rCR alone yielded AUCs ranging from 0.659 to 0.756. Adding near-rCR improved specificity but reduced sensitivity, with a significant AUC increase for one reader (p = 0.011).

CONCLUSION

Pre-treatment MRI revealed distinct imaging characteristics between subgroups. While pCR rates were higher in HER2 (3+), MRI-based pCR prediction showed similar performance, though near-rCR reduced sensitivity.

A large model for non-invasive and personalized management of breast cancer from multiparametric MRI

Breast Magnetic Resonance Imaging (MRI) demonstrates the highest sensitivity for breast cancer detection among imaging modalities and is standard practice for high-risk women. Interpreting the multi-sequence MRI is time-consuming and prone to subjective variation. We develop a large mixture-of-modality-experts model (MOME) that integrates multiparametric MRI information within a unified structure, leveraging breast MRI scans from 5205 female patients in China for model development and validation. MOME matches four senior radiologists' performance in identifying breast cancer and outperforms a junior radiologist. The model is able to reduce unnecessary biopsies in Breast Imaging-Reporting and Data System (BI-RADS) 4 patients, classify triple-negative breast cancer, and predict pathological complete response to neoadjuvant chemotherapy. MOME further supports inference with missing modalities and provides decision explanations by highlighting lesions and measuring modality contributions. To summarize, MOME exemplifies an accurate and robust multimodal model for noninvasive, personalized management of breast cancer patients via multiparametric MRI.

Intratumoral and peritumoral radiomics based on automated breast volume scanner for predicting human epidermal growth factor receptor 2 status

Purpose

To develop an intratumoral and peritumoral radiomics model using Automated Breast Volume Scanner (ABVS) for noninvasive preoperative prediction of Human Epidermal Growth Factor Receptor 2 (HER2) status.

Methods

This retrospective study analyzed 384 lesions from 379 patients with pathologically confirmed breast cancer across four hospitals. Two tasks were defined: Task 1 to distinguish HER2-negative from HER2-positive cases and Task 2 to differentiate HER2-zero from HER2-low status. For each classification task, three models were built: Model 1 included radiomics features from the tumor region alone; Model 2 included features from both the tumor region and a 5mm peritumoral region; and Model 3 incorporated features from the tumor region, the 5mm peritumoral region, and the 5-10mm peritumoral region. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, with key metrics including the area under the curve (AUC), sensitivity, specificity, and accuracy.

Results

In the classification tasks, Model 2 demonstrated superior predictive performance across multiple datasets. For Task 1, it achieved the highest AUC (0.844), exceptional sensitivity (0.955), and satisfactory accuracy (0.787) in the validation set, and outperformed other models in the test set with an AUC of 0.749 and sensitivity of 0.885, highlighting its robustness and clinical applicability. For Task 2, Model 2 exhibited the highest AUC (0.801), sensitivity (0.862), and accuracy (0.808) in the test set, with consistent performance across the training (AUC 0.850) and validation sets (AUC 0.801). Model 3, which combines intratumoral and peritumoral features, did not demonstrate significant improvements over the intratumoral-only model in the two classification tasks. These results underscore the value of incorporating peritumoral radiomics features, particularly within a 5mm margin, to enhance predictive performance compared to intratumoral-only models.

Conclusion

The radiomics model integrating intratumoral and appropriate peritumoral features significantly outperformed the model based on intratumoral features alone. This integrated approach holds strong potential for noninvasive, preoperative prediction of HER2 status.

Artificial intelligence-assisted multimodal imaging for the clinical applications of breast cancer: a bibliometric analysis

BACKGROUND

Breast cancer (BC) remains a leading cause of cancer-related mortality among women globally, with increasing incidence rates posing significant public health challenges. Recent advancements in artificial intelligence (AI) have revolutionized medical imaging, particularly in enhancing diagnostic accuracy and prognostic capabilities for BC. While multimodal imaging combined with AI has shown remarkable potential, a comprehensive analysis is needed to synthesize current research and identify emerging trends and hotspots in AI-assisted multimodal imaging for BC.

METHODS

This study analyzed literature on AI-assisted multimodal imaging in BC from January 2010 to November 2024 in Web of Science Core Collection (WoSCC). Bibliometric and visualization tools, including VOSviewer, CiteSpace, and the Bibliometrix R package, were employed to assess countries, institutions, authors, journals, and keywords.

RESULTS

A total of 80 publications were included, revealing a steady increase in annual publications and citations, with a notable surge post-2021. China led in productivity and citations, while Germany exhibited the highest citation average. The United States demonstrated the strongest international collaboration. The most productive institution and author are Radboud University Nijmegen and Xi, Xiaoming. Publications were predominantly published in Computerized Medical Imaging and Graphics, with Qian, XJ's 2021 study on BC risk prediction under deep learning frameworks being the most influential. Keyword analysis highlighted themes such as "breast cancer", "classification", and "deep learning".

CONCLUSIONS

AI-assisted multimodal imaging has significantly advanced BC diagnosis and management, with promising future developments. This study offers researchers a comprehensive overview of current frameworks and emerging research directions. Future efforts are expected to focus on improving diagnostic precision and refining therapeutic strategies through optimized imaging techniques and AI algorithms, emphasizing international collaboration to drive innovation and clinical translation.

Dynamic contrast-enhanced magnetic resonance imaging parameters combined with diffusion-weighted imaging for discriminating malignant lesions, molecular subtypes, and pathological grades in invasive ductal carcinoma patients

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters or diffusion-weighted imaging (DWI) findings provide prognostic information on breast cancer. However, the accuracy of a single MRI technique is unsatisfactory. This study intended to explore the combination of DWI and DCE-MRI parameters in discriminating molecular subtypes in invasive ductal carcinoma (IDC) patients. Eighty-two IDC patients who underwent breast DWI and DCE-MRI examinations were retrospectively analyzed. Eighty-six patients with benign masses were retrieved as benign controls. The combination of ADC value, Ktrans, Kep, Ve, and iAUC had a good ability to discriminate IDC patients (vs. benign controls) with an area under the curve (AUC) [95% confidence interval (CI)] of 0.961 (0.935-0.987). A nomogram-based prediction model with the above combination showed a good predictive value for IDC probability. The combination of ADC value, Ktrans, Kep, and iAUC also had a certain ability to discriminate pathological grade III (vs. I or II) [AUC (95% CI): 0.698 (0.572-0.825)] in IDC patients. Notably, ADC value (P=0.010) and Kep (P=0.043) differed in IDC patients with different molecular subtypes. Besides, ADC value was increased (P<0.001), but Ktrans (P=0.037) and Kep (P=0.004) were decreased in IDC patients with Lumina A (vs. other molecular subtypes). The combination of ADC value, Ktrans, Kep, had an acceptable ability to discriminate Luminal A (vs. other molecular subtypes) [AUC (95% CI): 0.845 (0.748-0.941)] in IDC patients. DWI combined with DCE-MRI parameters discriminates IDC from benign masses; it also identifies Luminal A and pathological grade III in IDC patients.

Central and peripheral adiposity and premenopausal breast cancer risk: a pooled analysis of 440,179 women

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

T1 Mapping-Derived Parameters in Breast Lesions: Diagnostic Accuracy and Correlation with Pathologic Features

RATIONALE AND OBJECTIVES

To evaluate the diagnostic potential of T1 mapping-derived parameters for distinguishing between benign and malignant breast tumors and their associations with pathologic prognostic indicators in invasive breast cancer.

MATERIALS AND METHODS

Patients who underwent breast surgery and quantitative magnetic resonance imaging (MRI), including apparent diffusion coefficient (ADC) and T1 mapping, between August 2023 and March 2024 were prospectively included. T1 parameters, including lesion T1 values before and after contrast agent injection (T10, T1c), reduction in T1 value (ΔT1), ratio of reduction (ΔT1%), extracellular volume fractions (ECVs), and ADC values were compared between benign and malignant breast lesions. The classification effect was evaluated via receiver operating characteristic (ROC) curves, and the correlation between MRI parameters and each prognostic indicator in invasive ductal carcinoma (IDC) was analyzed via Spearman correlation.

RESULTS

The ROC curves revealed that the area under the curve (AUC) of the ECV was slightly larger than that of the ADC (0.90 [95% CI: 0.84-0.95] vs 0.89 [95% CI: 0.83-0.94]). The combined diagnostic model of all parameters had the highest AUC (0.95 [95% CI: 0.90-0.98]). In IDC, ECV was positively correlated with the expression of estrogen receptor (r = 0.449, P < .001) and progesterone receptor (r = 0.433, P < .001) and negatively correlated with Ki-67 protein expression (r = -0.407, P < .001). No correlation was found between the ADC values and prognostic indicators.

CONCLUSION

T1 parameters can effectively differentiate benign and malignant breast lesions and have potential utility in predicting tumor invasiveness.

Caveolin-1: an ambiguous entity in breast cancer

Breast cancer (BC) is the most frequently diagnosed cancer in women and the second leading cause of death from cancer among women. Metastasis is the major cause of BC-associated mortality. Accumulating evidence implicates Caveolin-1 (Cav-1), a structural protein of plasma membrane caveolae, in BC metastasis. Cav-1 exhibits a dual role, as both a tumor suppressor and promoter depending on the cellular context and BC subtype. This review highlights the role of Cav-1 in modulating glycolytic metabolism, tumor-stromal interactions, apoptosis, and senescence. Additionally, stromal Cav-1's expression is identified as a potential prognostic marker, offering insights into its contrasting roles in tumor suppression and progression. Furthermore, Cav-1's context-dependent effects are explored in BC subtypes including hormone receptor-positive, HER2-positive, and triple-negative BC (TNBC). The review further delves into the role of Cav-1 in regulating the metastatic cascade including extracellular matrix interactions, cell migration and invasion, and premetastatic niche formation. The later sections discuss the therapeutic targeting of Cav-1 by metabolic inhibitors such as betulinic acid and Cav-1 modulating compounds. While Cav-1 may be a potential biomarker and therapeutic target, its heterogeneous expression and context-specific activity necessitates further research to develop precise interventions. Future studies investigating the mechanistic role of Cav-1 in metastasis may pave the way for effective treatment of metastatic BC.

Differences in treatment response and survival between HER2(2+)/FISH-positive and HER2(3+) breast cancer patients after dual-target neoadjuvant therapy: a matched case-control study

Background

The efficacy of neoadjuvant therapy (NAT) comprising dual-target drugs has been confirmed among patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). Therefore, we explored the differences in responses to NAT and prognosis between patients with HER2(3+) and HER2(2+)/fluorescence in-situ hybridization (FISH)-positive BC after TCbHP-based dual-target NAT.

Methods

Data from patients with HER2-positive invasive BC who underwent NAT and radical surgery between January 2019 and December 2022 at the Peking University First Hospital and Cancer Hospital of Chinese Academy of Medical Sciences were retrospectively summarized. Propensity score matching (PSM) was used to reduce confounding effects. Pathological complete response (pCR) and invasive disease-free survival (IDFS) were evaluated to respectively reflect therapeutic response and patients' survival status.

Results

We selected 132 BC patients (66 pairs) through PSM form a cohort of 308 patients. The pCR rate of patients in the HER2(3+) group was significantly higher than that in the HER2(2+)/FISH-positive group after NAT (P<0.001). Univariate and multivariate logistic regression analyses determined that pCR was significantly affected by tumor grade, hormone receptor (HR) status, HER2 status (P<0.05). The 3-year IDFS rate of HER2(3+) BC patients was better than that of HER2(2+)/FISH-positive BC patient (P=0.083), although the difference was not statistically significant. Furthermore, multivariable Cox regression analysis exhibited that positive lymph node, HER2(3+), and pCR were independent prognostic factors for IDFS.

Conclusion

HER2(2+)/FISH-positive BC patients exhibited worse treatment response and prognosis than HER2(3+) BC patients after dual-target NAT, indicating that HER2 expression level is a crucial factor influencing the therapeutic efficacy and prognosis of BC patients after TCbHP-based dual-target NAT.

Proliferative Markers in Breast Cancer and Chemotherapy Implications: A Comprehensive Review

Background and Aims

Breast cancer is the most common cancer and a leading cause of cancer-related death among women globally. Determining which patients will benefit from chemotherapy remains challenging. Proliferative markers such as Ki-67, mini chromosome maintenance (MCM) proteins, and proliferating cell nuclear antigen (PCNA) offer valuable insights into tumor growth and treatment response. This review evaluates their clinical roles, with a focus on chemotherapy implications and emerging digital pathology techniques for marker quantification.

Methods

A narrative review was conducted by searching PubMed, Scopus, and Google Scholar for studies related to Ki-67, MCM, PCNA, breast cancer, and chemotherapy. Studies were thematically categorized into five areas. A bibliometric analysis of publications from 2000 to April 2023 was performed using the Bibliometrix R package and VOSviewer to assess research trends and thematic evolution.

Results

Eighty studies were included in the narrative synthesis. Ki-67 is the most commonly used marker, particularly useful in predicting response to neoadjuvant chemotherapy (NAC). MCM proteins show promise for identifying proliferative potential across tumor grades, while PCNA is associated with aggressive tumor features and poor prognosis. Post-chemotherapy changes in Ki-67 levels are linked to survival outcomes. Bibliometric analysis revealed a shift in research focus from basic mechanisms to clinical applications and digital quantification.

Conclusion

Proliferative markers play an essential role in breast cancer management. Ki-67 remains a key predictor of chemotherapy response, while MCM and PCNA offer complementary prognostic insights. Integration of these markers with digital pathology and AI-driven tools may enhance diagnostic accuracy and personalized treatment strategies. Standardization of assessment methods is crucial for broader clinical application.

Predicting molecular subtypes of breast cancer based on multi-parametric MRI dataset using deep learning method

PURPOSE

To develop a multi-parametric MRI model for the prediction of molecular subtypes of breast cancer using five types of breast cancer preoperative MRI images.

METHODS

In this study, we retrospectively analyzed clinical data and five types of MRI images (FS-T1WI, T2WI, Contrast-enhanced T1-weighted imaging (T1-C), DWI, and ADC) from 325 patients with pathologically confirmed breast cancer. Using the five types of MRI images as inputs to the ResNeXt50 model respectively, five base models were constructed, and then the outputs of the five base models were fused using an ensemble learning approach to develop a multi-parametric MRI model. Breast cancer was classified into four molecular subtypes based on immunohistochemical results: luminal A, luminal B, human epidermal growth factor receptor 2-positive (HER2-positive), and triple-negative (TN). The whole dataset was randomly divided into a training set (n = 260; 76 luminal A, 80 luminal B, 50 HER2-positive, 54 TN) and a testing set (n = 65; 20 luminal A, 20 luminal B, 12 HER2-positive, 13 TN). Accuracy, sensitivity, specificity, receiver operating characteristic curve (ROC) and area under the curve (AUC) were calculated to assess the predictive performance of the models.

RESULTS

In the testing set, for the assessment of the four molecular subtypes of breast cancer, the multi-parametric MRI model yielded an AUC of 0.859-0.912; the AUCs based on the FS-T1WI, T2WI, T1-C, DWI, and ADC models achieved respectively 0.632-0. 814, 0.641-0.788, 0.621-0.709, 0.620-0.701and 0.611-0.785.

CONCLUSION

The multi-parametric MRI model we developed outperformed the base models in predicting breast cancer molecular subtypes. Our study also showed the potential of FS-T1WI base model in predicting breast cancer molecular subtypes.

Cancer-associated fibroblasts in breast cancer in the single-cell era: Opportunities and challenges

Breast cancer is a leading cause of morbidity and mortality in women, and its progression is closely linked to the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), key components of the TME, play a crucial role in promoting tumor growth by driving cancer cell proliferation, invasion, extracellular matrix (ECM) remodeling, inflammation, chemoresistance, and immunosuppression. CAFs exhibit considerable heterogeneity and are classified into subgroups based on different combinations of biomarkers. Single-cell RNA sequencing (scRNA-seq) enables high-throughput and high-resolution analysis of individual cells. Relying on this technology, it is possible to cluster complex CAFs according to different biomarkers to analyze the specific phenotypes and functions of different subpopulations. This review explores CAF clusters in breast cancer and their associated biomarkers, highlighting their roles in disease progression and potential for targeted therapies.

Characteristics of premenopausal breast cancer patients with a midrange 21-gene recurrence score

Purpose

The results of the TAILORx trial have shown that premenopausal patients with intermediate Oncotype Dx (ODx) recurrence score of 16-25 may benefit from adjuvant chemotherapy. In addition, the clinicopathological features showed the information complementary to ODx results. However, the characteristics may vary depending on menopausal status even in the same score. This study aimed to analyze the differences in the clinical characteristics by menopausal status.

Methods

This study conducted a retrospective analysis of 756 patients with estrogen receptor-positive, human epidermal growth factor receptor 2-negative, and node-negative breast cancer who underwent the ODx test from July 2013 to December 2020 at the Severance Hospital.

Results

Of the 756 patients, 261 patients were postmenopausal, and 495 were premenopausal. The premenopausal patients with a midrange ODx had similar clinicopathological features as compared to those with a high ODx. Conversely, the postmenopausal patients with a midrange ODx did not show significantly different clinicopathological features from those with a low ODx, whereas a difference was seen as compared to those with a high ODx.

Conclusion

In this study, unlike the postmenopausal patients, some of the clinicopathological characteristics of the premenopausal patients with a midrange ODx were closer to those with a high ODx than those with a low ODx. In the premenopausal patients with a midrange ODx, considering the baseline characteristic itself, there was a significant difference between those with a low ODx when compared with postmenopausal patients. Therefore, more aggressive treatment decisions may be helpful in premenopausal patients with a midrange ODx.

Molecular Subtypes and Ki-67 index in Breast Carcinoma with Special Emphasis on Triple Negative Breast Cancer. A 3-year Study in a Tertiary Care Center

Purpose

Molecular subtyping of breast carcinoma and Ki-67 index has gained prominence in the recent past, as conventional factors such as surgical margins, tumor size, grade and lymph node involvement, are not sufficient to assess prognosis and make better therapeutic decisions. These subtypes include Luminal A, Luminal B, Triple Negative breast cancer (TNBC), and HER2-enriched subtypes. This study aimed to analyze the molecular subtypes and Ki-67 index in prognosis of breast carcinoma.

Method

This retrospective study was conducted in the department of Pathology in a tertiary care center over a period of 3 years. All invasive breast carcinomas (IDC) which were molecularly subtyped and Ki-67 indexed were included in the study. Statistical analysis was done using SPSS software.

Results and Discussion

Out of 253 cases, 231 cases (91.3%) were IDC-NST and 22 cases (8.7%) were special types. Metaplastic and papillary tumors were associated with higher grade and high Ki-67 value. TNBC (35.2%) showing a majority of high-grade tumors, was the most prevalent subtype followed by Luminal A (32%) showing low grade, unlike other studies which showed luminal A to be most common subtype. The rare PR positive subtype was also observed in our study.

Conclusion

TNBC and HER 2-positive subtypes exhibited bad prognosis with higher histological grade, high Ki-67 index and higher age at presentation whereas Luminal A subtype, with lower grade and low Ki-67 index showed better prognosis. Thus, this vast array of predictive and prognostic information obtained by molecular subtyping will help clinicians in not only distinguishing between low-risk and high-risk subtypes but also in customization of the treatment and follow-up of the patients.