Clinical implications of the intrinsic molecular subtypes of breast cancer

Identification of progression related LncRNAs in colorectal cancer aggressiveness

Colorectal cancer (CRC) progression involves complex molecular alterations, including the dysregulation of long non-coding RNAs (lncRNAs). In this study, we identified key progression-related lncRNAs in CRC by integrating transcriptomic data from TCGA and single-cell RNA sequencing (scRNA-seq). Differential expression analysis revealed numerous lncRNAs associated with CRC progression. To systematically prioritize these lncRNAs, we developed a scoring system incorporating multiple progression-related signatures, differential expression, and survival analysis. This approach identified 198 key lncRNAs, including both known (e.g., LINC01615) and novel candidates (e.g., AC007998.3). Experimental validation confirmed that LINC01615 was significantly upregulated in CRC tissues, whereas AC007998.3 was downregulated. Further analyses indicated that these lncRNAs influence CRC progression through cis-, trans-, and post-transcriptional regulation. Patients were classified into distinct molecular subgroups based on lncRNA expression, exhibiting significant differences in prognosis and immune microenvironment composition. The enrichment of progression-related lncRNAs among differentially expressed lncRNAs was statistically significant, reinforcing their functional relevance. Validation across independent datasets demonstrated the robustness of our findings. Our research provides novel insights into the molecular mechanisms underlying CRC progression and highlights the potential of progression-related lncRNAs as prognostic biomarkers and therapeutic targets.

ANP32E drives vulnerability to ATR inhibitors by inducing R-loops-dependent transcription replication conflicts in triple negative breast cancer

Oncogene-induced replicative stress (RS) drives tumor progression by disrupting genome stability, primarily through transcription-replication conflicts (TRCs), which promote R-loop accumulation and trigger the DNA damage response (DDR). In this study, we investigate the role of chromatin regulators in exacerbating TRCs and R-loop accumulation in cancer. We find that in breast cancer patients, the simultaneous upregulation of MYC and the H2A.Z-specific chaperone ANP32E correlates with increased genomic instability. Genome-wide analyses reveal that ANP32E-driven H2A.Z turnover alters RNA polymerase II processivity, leading to the accumulation of long R-loops at TRC sites. Furthermore, we show that ANP32E overexpression enhances TRC formation and activates an ATR-dependent DDR, predisposing cancer cells to R-loop-mediated genomic fragility. By exploiting the vulnerability of ANP32E-expressing cancer cells to ATR inhibitors, we find that tumors relied on this DDR pathway, whose inhibition halts their pro-metastatic capacity. These findings identify ANP32E as a key driver of TRC-induced genomic instability, indicating ATR inhibition as a potential therapeutic strategy for ANP32E-overexpressing tumors.

Development and validation of an LC-MS/MS method for simultaneous determination of XZP-3287(bireociclib) and its metabolites in human plasma, and its clinical pharmacokinetics application

XZP-3287(bireociclib) is a novel and selective inhibitor of the cell cyclin-dependent kinases 4/6 (CDK4/6), which is primarily employed for the treatment of breast cancer in clinical trials. In this study, a novel and sensitive liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated for the simultaneous determination of XZP-3287 and its metabolites XZP-5286, XZP-3584 and XZP-5736 in human plasma in accordance with international conference on harmonization of technical requirements for registration of Pharmaceuticals for Human use (ICH) guideline R3 (M10) guideline. The multiple reaction monitoring mode (MRM) of mass spectrometer was used and all compounds were monitored in electrospray ionization (ESI+) mode. The correlation coefficients (R2) of all calibration curves for linear regression were greater than 0.99. The intra- and inter-day precision of XZP-3287 and its metabolites XZP-5286, XZP-3584 and XZP-5736 were determined to be 5.2 %-5.5 %, 14.9 %-10.1 %, 6.9 %-13.8 % and 7.3 %-5.6 %, and their accuracy were determined to be 5.2 %-6.0 % 6.9 %-4.4 %, 11.1 %-5.0 % and 7.4 %-5.6 %, respectively. In conclusion, a method for the simultaneous detection of the pharmacokinetic profiles of XZP-3287 and its metabolites in human plasma had been successfully developed. The results demonstrated the efficacy, sensitivity, and reliability of this method.

Role of ferroptosis in breast cancer: Molecular mechanisms and therapeutic interventions

Ferroptosis, an iron-dependent cell death pathway distinct from apoptosis, is crucial in breast cancer (BC) research, especially for overcoming resistance in triple-negative breast cancer (TNBC). Unlike traditional apoptosis, ferroptosis involves the glutathione (GSH)/glutathione peroxidase 4 (GPX4) axis, iron-driven oxidative reactions, and phospholipid peroxidation. TNBC, characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), is particularly prone to ferroptosis due to acyl-coenzyme A synthetase (ACSL) 4-related lipid changes and solute carrier family 7 member 11 (SLC7A11)-mediated cystine transport. Recent advancements in biomarkers and therapeutic strategies targeting ferroptosis hold significant promise for the diagnosis and prognosis of TNBC. Notable innovations encompass the development of small-molecule compounds and various methodologies designed to enhance ferroptosis. Combination therapies have demonstrated improved antitumor efficacy by counteracting chemotherapy resistance and inducing immunogenic cell death. Nonetheless, challenges persist in optimizing drug delivery mechanisms and minimizing off-target effects. This review underscores the progress in ferroptosis research and proposes precision oncology strategies that exploit metabolic flexibility in BC, intending to transform TNBC treatment and enhance therapeutic outcomes.

Impact of the COVID-19 pandemic on breast cancer diagnosis and treatment trends in Japan

BACKGROUND

There is no comprehensive report regarding which patient groups were disrupted by the COVID-19 pandemic in Japan having universal health insurance system. To provide the guidance regarding how to act in future pandemics, we investigated the changes in breast cancer (BC) diagnosis and treatment during the COVID-19 pandemic.

METHODS

The trends of monthly data were calculated in relation to the variables of a total of 291,018 primary BCs registered on the Japanese National Clinical Database between January 2018 and April 2021.

RESULTS

An analysis of the nationwide data during the pandemic showed 9% decrease of newly identified BC compared with before the pandemic. The impact was more relevant in the 40-50, 51-60 and 61-70-years age groups (13%, 8% and 9% decrease, respectively). The most substantial reduction was noted in patients identified through screenings without symptoms with a 17% decrease. These effects were also apparent in cT1, cN0, cStage 0, and cStage I (11%, 9%, 8% and 11% decrease, respectively). In breast surgery procedures, there was a notable decrease in breast-conserving surgery (13%) as well as post-operative radiation therapy (11%). During this period, strategies using neoadjuvant endocrine therapy or chemotherapy were implemented to avoid treatment delays for especially Stage I patients (1.5 folds increase).

CONCLUSIONS

We have identified the patient groups that are more vulnerable to the effects of the pandemic. The changes during the pandemic might provide the guidance regarding how to act in future emergencies to minimize disadvantages for BC patients.

Personalizing Neoadjuvant Chemotherapy: The Impact of BRCA Variants on Pathologic Complete Response in Luminal B Breast Cancer

Background: Neoadjuvant chemotherapy (NACT) is effective in downstaging locally advanced breast cancer, improving surgical and oncological outcomes. However, luminal B breast cancer typically exhibits a poorer response to NACT, with only 10-15% of patients achieving a pathologic complete response (pCR). This study investigates whether BRCA pathogenic variants (BRCA PVs) influence pCR rates in luminal B breast cancer patients, aiming to identify potential predictors for personalized treatment strategies. Materials and Methods: This retrospective study included luminal B breast cancer patients who underwent NACT at the Fondazione Policlinico Universitario Agostino Gemelli IRCCS between January 2014 and June 2023. Patients were stratified according to BRCA status: BRCA PVs and BRCA wild-type (WT). Primary endpoint was to evaluate pCR rates, while secondary endpoints included locoregional disease-free survival (LR-DFS), distant disease-free survival (DDFS), and overall survival (OS). Results: In total, 495 patients were enrolled, of whom 442 (89.3%) carried BRCA WT and 53 (10.7%) BRCA PVs. The pCR rate was significantly higher in the BRCA PVs group (20.8% PVs vs. 10.9% WT; p = 0.044). Specifically, the breast pCR rate was 28.3% in BRCA PVs versus 15.4% in BRCA WT (p = 0.030). BRCA WT patients had better 5-year LR-DFS (91.1% WT vs. 79.5% PVs; p = 0.003), while no significant differences were observed in 5-year DDFS or OS. Conclusions: BRCA PVs are associated with a higher pCR rate in luminal B breast cancer patients receiving NACT, suggesting a potential predictive role in tailoring treatment strategies.

Facing the Challenge to Mimic Breast Cancer Heterogeneity: Established and Emerging Experimental Preclinical Models Integrated with Omics Technologies

Breast cancer (BC) is among the most common neoplasms globally and is the leading cause of cancer-related mortality in women. Despite significant advancements in prevention, early diagnosis, and treatment strategies made over the past two decades, breast cancer continues to pose a significant global health challenge. One of the major obstacles in the clinical management of breast cancer patients is the high intertumoral and intratumoral heterogeneity that influences disease progression and therapeutic outcomes. The inability of preclinical experimental models to replicate this diversity has hindered the comprehensive understanding of BC pathogenesis and the development of new therapeutic strategies. An ideal experimental model must recapitulate every aspect of human BC to maintain the highest predictive validity. Therefore, a thorough understanding of each model's inherent characteristics and limitations is essential to bridging the gap between basic research and translational medicine. In this context, omics technologies serve as powerful tools for establishing comparisons between experimental models and human tumors, which may help address BC heterogeneity and vulnerabilities. This review examines the BC models currently used in preclinical research, including cell lines, patient-derived organoids (PDOs), organ-on-chip technologies, carcinogen-induced mouse models, genetically engineered mouse models (GEMMs), and xenograft mouse models. We emphasize the advantages and disadvantages of each model and outline the most important applications of omics techniques to aid researchers in selecting the most relevant model to address their specific research questions.

Optimizing Deep Learning Models for Luminal and Nonluminal Breast Cancer Classification Using Multidimensional ROI in DCE-MRI-A Multicenter Study

OBJECTIVES

Previous deep learning studies have not explored the synergistic effects of ROI dimensions (2D/2.5D/3D), peritumoral expansion levels (0-8 mm), and segmentation scenarios (ROI only vs. ROI original). Our study aims to evaluate the performance of multidimensional deep transfer learning models in distinguishing molecular subtypes of breast cancer (luminal vs. nonluminal) using DCE-MRI. Under two segmentation scenarios, we systematically compare the effects of ROI dimensions and peritumoral expansion levels to optimize multidimensional deep learning models via transfer learning for distinguishing luminal from nonluminal breast cancers in DCE-MRI-based analysis.

MATERIALS AND METHODS

From October 2020 to October 2023, data from 426 patients with primary invasive breast cancer were retrospectively collected. Patients were divided into three cohorts: (1) training cohort, n = 108, from SYSU Hospital (Zhuhai, China); (2) validation cohort 1, n = 165, from HZ Hospital (Huizhou, China); and (3) validation cohort 2, n = 153, from LY Hospital (Linyi, China). ROIs were delineated, and expansions of 2, 4, 6, and 8 mm beyond the lesion boundary were performed. We assessed the performance of various deep transfer learning models, considering precise segmentation (ROI only and ROI original) and varying peritumoral regions, using ROC curves and decision curve analysis.

RESULTS

The 2.5D1-based deep learning model (ROI original, 4 mm expansion) demonstrated optimal performance, achieving an AUC of 0.808 (95% CI 0.715-0.901) in the training cohort, 0.766 (95% CI 0.682-0.850) in validation cohort 1, and 0.799 (95% CI 0.725-0.874) in validation cohort 2.

CONCLUSION

The study highlights that the 2.5D1-based deep learning model utilizing the three principal slices of the minimum bounding box (ROI original) with a 4 mm peritumoral region is effective in distinguishing between luminal and nonluminal breast cancer tumors, serving as a potential diagnostic tool.

Unveiling the role of PANoptosis-related genes in breast cancer: an integrated study by multi-omics analysis and machine learning algorithms

BACKGROUND

The heterogeneity of breast cancer (BC) necessitates the identification of novel subtypes and prognostic models to enhance patient stratification and treatment strategies. This study aims to identify novel BC subtypes based on PANoptosis-related genes (PRGs) and construct a robust prognostic model to guide individualized treatment strategies.

METHODS

The transcriptome data along with clinical data of BC patients were sourced from the TCGA and GEO databases. Consensus clustering was performed on 12 PRGs to ascertain potential BC subtypes, and variances in survival, infiltration of immune cells, and functional pathways among them were examined. A prognostic model was generated through 101 combinations of machine learning algorithms and validated across multiple cohorts. The response of patients towards immunotherapy were analyzed using multiple frameworks.

RESULTS

Consensus clustering of 12 PRGs identified two distinct BC subtypes, with subtype B exhibiting significantly lower overall survival (OS) rates compared to subtype A. Immune cell infiltration analysis revealed higher immune activity in subtype A. Functional pathway analysis revealed that subtype A exhibited a significant enrichment in immune-related pathways, while subtype B was associated with cell cycle and metabolic processes. An integrated machine learning framework integrating CoxBoost and Random Survival Forest (RSF) algorithms was developed, demonstrating high predictive performance across multiple cohorts. A nomogram combining age and risk score was constructed, showing excellent predictive performance. Immune landscape analysis revealed that the high-risk group exhibited a suppressive tumor immune microenvironment (TIME). Immunotherapy response prediction suggested that low-risk patients were more likely to benefit from PD-1 and CTLA-4 inhibitors.

CONCLUSIONS

Our study provides a comprehensive framework for BC subtype classification and prognostic prediction, offering valuable insights for personalized treatment strategies.

Refining prognostic tools for luminal breast cancer: genetic insights and comprehensive analysis

BACKGROUND

Luminal breast cancer (BC) is generally associated with a lower risk of recurrence compared with other subtypes. However, patients with luminal BC can still experience recurrence, which remains a significant concern and contributes to BC-related mortality. Current clinical practice for recurrence risk prognosis relies on prognostic tests based on tumor gene expression profiles.

MATERIALS AND METHODS

In this study, we aimed to investigate the association between different genetic alterations with the likelihood of recurrence and gene expression prognostic prediction (Oncotype DX®, MammaPrint®, and PAM50-ROR) in luminal BC patients. We constructed three transcriptome-based predictive models, based on these widely used clinical tests, to evaluate the recurrence risk of patients with luminal BC, using RNA-seq data from 1527 samples across 11 datasets. We further classified 1780 patients from the TCGA and METABRIC datasets into risk groups and detected distinct recurrence risk patterns.

RESULTS

Our analysis revealed that low-risk groups had higher frequencies of mutations in PIK3CA, MAP3K1, CDH1, KMT2C, and CBFB, as well as co-mutations in PIK3CA-MAP3K1, PIK3CA-CBFB, and KMT2C-MAP3K1. In contrast, high-risk groups showed enrichment of TP53, RB1, and PTPN22 mutations compared with the whole cohort, with notable co-mutations in TP53-PIK3CA and TP53-KMT2C. Furthermore, mutations in TP53 and BRCA2, and deletions in the 7p22.3 region were at least threefold more frequent in high-risk patients compared with low-risk patients. Using an independent dataset, we validated our finding of higher frequency of BRCA2 mutations in Oncotype DX® high-risk patients. Notably, PIK3CA mutations had an unexpected negative impact on recurrence and survival among high-risk patients.

CONCLUSION

Our study reveals key genetic factors associated with recurrence risk in luminal BC. Identifying these mutations and copy number alterations provides a basis for refined prognostic models and suggests avenues for further research, potentially improving treatment strategies and follow-up care for patients with luminal BC.

Unveiling fine-scale spatial structures and amplifying gene expression signals in ultra-large ST slices with HERGAST

We propose HERGAST, a system for spatial structure identification and signal amplification in ultra-large-scale and ultra-high-resolution spatial transcriptomics data. To handle ultra-large spatial transcriptomics (ST) data, we consider the divide and conquer strategy and devise a Divide-Iterate-Conquer framework especially for spatial transcriptomics data analysis, which can also be adopted by other computational methods for extending to ultra-large-scale ST data analysis. To tackle the potential over-smoothing problem arising from data splitting, we construct a heterogeneous graph network to incorporate both local and global spatial relationships. In simulations, HERGAST consistently outperforms other methods across all settings with more than a 10% increase in average adjusted rand index (ARI). In real-world datasets, HERGAST's high-precision spatial clustering identifies SPP1+ macrophages intermingled within colorectal tumors, while the enhanced gene expression signals reveal unique spatial expression patterns of key genes in breast cancer.

Intratumoral and peritumoral radiomics signature based on DCE-MRI can distinguish between luminal and non-luminal breast cancer molecular subtypes

Distinguishing the luminal subtypes of breast cancer (BC) remaining challenging. Thus, the aim of this study was to investigate the feasibility of radiomic signature using intratumoral and peritumoral features obtained from dynamic contrast-enhanced MRI (DCE-MRI) in preoperatively discriminating the luminal from non-luminal type in patients with BC. A total of 305 patients with pathologically confirmed BC from three hospitals were retrospectively enrolled. The LASSO method was then used for selecting features, and the radiomic score (radscore) for each patient was calculated. Based on the radscore, Radiomic signature of intratumoral, peritumoral, and combined intratumoral and peritumoral were established, respectively. The performances of the radiomic signatures were validated with receiver operator characteristic (ROC) curve and decision curve analysis. For predicting molecular subtypes, the AUC for intratumoral radiomic signature was 0.817, 0.838, and 0.883 in the training set, internal validation set, and external validation set, respectively. AUC for the peritumoral radiomic signature was 0.863, 0.895, and 0.889 in the training set, internal validation set, and external validation set, respectively. The AUC for combined intratumoral and peritumoral radiomic signature was 0.956, 0.945, and 0.896 in the training set, internal validation set, and external validation set, respectively. Additional contributing value of combined intratumoral and peritumoral radiomic signature to the intratumoral radiomic signature was statistically significant [NRI, 0.300 (95% CI: 0.117-0.482), P = 0.001 in internal validation set; NRI, 0.224 (95% CI: 0.038-0.410), P = 0.018 in external validation set]. These results indicated that the radiomic signature combining intratumoral and peritumoral features showed good performance in predicting the luminal type of breast cancer.

Multiple instance learning-based prediction of programmed death-ligand 1 (PD-L1) expression from hematoxylin and eosin (H&E)-stained histopathological images in breast cancer

Programmed death-ligand 1 (PD-L1) is an important biomarker increasingly used as a predictive marker in breast cancer immunotherapy. Immunohistochemical quantification remains the standard method for assessment. However, it presents challenges related to time, cost, and reliability. Hematoxylin and eosin (H&E) staining is a routine method in cancer pathology, known for its accessibility and consistently reliability. Deep learning has shown the potential in predicting biomarkers in cancer histopathology. This study employs a weakly supervised multiple instance learning (MIL) approach to predict PD-L1 expression from H&E-stained images using deep learning techniques. In the internal test set, the TransMIL method achieved an area under the curve (AUC) of 0.833, and in an independent external test set, it achieved an AUC of 0.799. Additionally, since RNA sequencing results indicate a threshold that allows for the separation of H&E pathology images, we further validated our approach using the public TCGA-TNBC dataset, achieving an AUC of 0.721. These findings demonstrates that the Transformer-based TransMIL model can effectively capture highly heterogeneous features within the MIL framework, exhibiting strong cross-center generalization capabilities. Our study highlights that appropriate deep learning techniques can enable effective PD-L1 prediction even with limited data, and across diverse regions and centers. This not only underscores the significant potential of deep learning in pathological artificial intelligence (AI) but also provides valuable insights for the rational and efficient allocation of medical resources.

TMAN: A Triple Morphological Feature Attention Network for Fine-Grained Classification of Breast Ultrasound Images

Accurately diagnosing various types of breast lesions is critical for assessing breast cancer risk and predicting patient outcomes, which necessitates a fine-grained classification approach. While convolutional neural networks (CNNs) are predominantly employed in fine-grained classification tasks for breast lesions, they often struggle to effectively capture and model the intricate relationships between local and global features, an aspect that is vital for achieving high classification accuracy. Additionally, Color Doppler Flow Imaging (CDFI) and Strain Elastography (SE) are two important ultrasound imaging techniques widely used in the diagnosis of breast lesions. However, their specific contributions to fine-grained classification have not been thoroughly investigated. In this paper, we introduce a Triple Morphological Feature Attention Network (TMAN) designed to enhance fine-grained classification of breast ultrasound images. The TMAN architecture comprises three key modules: Local Margin Attention (LMA), Structured Texture Attention (STA), and Fusion Attention (FA), each focused on extracting distinct morphological features. TMAN achieved an average accuracy of 74.40%, precision of 73.18%, and specificity of 96.02%, surpassing state-of-the-art methods. The findings reveal that incorporating CDFI significantly improved classification for malignant subtypes with a 10% accuracy boost, while SE had a negligible impact. These findings highlight the effectiveness of TMAN in extracting nuanced morphological features and advancing precision in breast ultrasound diagnosis. The source code is accessible at https://github.com/windywindyw/TMAN .

Characterisation of HER2-Driven Morphometric Signature in Breast Cancer and Prediction of Risk of Recurrence

INTRODUCTION

Human epidermal growth factor receptor 2-positive (HER2-positive) breast cancer (BC) is a heterogeneous disease. In this study, we hypothesised that the degree of HER2 oncogenic activity, and hence response to anti-HER2 therapy is translated into a morphological signature that can be of prognostic/predictive value.

METHODS

We developed a HER2-driven signature based on a set of morphometric features identified through digital image analysis and visual assessment in a sizable cohort of BC patients. HER2-enriched molecular sub-type (HER2-E) was used for validation, and pathway enrichment analysis was performed to assess HER2 pathway activity in the signature-positive cases. The predictive utility of this signature was evaluated in post-adjuvant HER2-positive BC patients.

RESULTS

A total of 57 morphometric features were evaluated; of them, 22 features were significantly associated with HER2 positivity. HER2 IHC score 3+/oestrogen receptor-negative tumours were significantly associated with HER2-related morphometric features compared to other HER2 classes including HER2 IHC 2+ with gene amplification, and they showed the least intra-tumour morphological heterogeneity. Tumours displaying HER2-driven morphometric signature showed the strongest association with PAM50 HER2-E sub-type and were enriched with ERBB signalling pathway compared to signature-negative cases. BC patients with positive HER2 morphometric signature showed prolonged distant metastasis-free survival post-adjuvant anti-HER2 therapy (p = 0.007). The clinico-morphometric prognostic index demonstrated an 87% accuracy in predicting recurrence risk.

CONCLUSION

Our findings underscore the strong prognostic and predictive correlation between HER2 histo-morphometric features and response to targeted anti-HER2 therapy.

Predictive value of 18F-fluorodeoxyglucose uptake for axillary lymph node metastasis in operable breast cancer: impact of molecular subtypes

OBJECTIVES

To evaluate the predictive value of standardized uptake value (SUV) in both primary tumors and axillary lymph nodes (ALNs) using FDG PET/CT for lymph node metastasis in breast cancer patients, and to assess the influence of molecular subtypes on this predictive performance.

METHODS

This retrospective study included 287 patients with invasive ductal carcinoma (IDC) who underwent FDG PET/CT prior to surgery between September 2016 and December 2019. The maximum standardized uptake value (SUVmax) of primary tumors (SUV-B) and ALNs (SUV-LN) were analyzed. Molecular subtypes were classified as hormone receptor-positive, HER2-positive, and triple-negative breast cancer (TNBC). Receiver operating characteristic (ROC) curve analysis was performed to assess and compare the diagnostic performance of SUV-B and SUV-LN for predicting ALN metastasis.

RESULTS

Among the 287 patients, 62 (21.6%) had confirmed ALN metastasis. The median SUV-LN was significantly higher in patients with metastasis compared to those without metastasis (1.5 vs. 0.9; P < 0.001). SUV-LN demonstrated good discriminative performance for ALN metastasis (AUC: 0.796), whereas SUV-B did not show significant predictive value (AUC: 0.536). The SUV_LN demonstrated significantly lower predictive performance for ALN metastasis in the hormone-positive group (AUC: 0.796) compared to the excellent discriminative performance in the HER2-positive (AUC: 0.923, P = 0.018) and TNBC (AUC: 0.940, P = 0.004) groups. Hormone receptor-positive tumors also exhibited lower FDG uptake in metastatic lymph nodes compared to HER2-positive and TNBC subtypes (P = 0.031).

CONCLUSION

FDG PET/CT SUV-LN effectively predicts ALN metastasis in HER2-positive and TNBC subtypes. Hormone receptor-positive breast cancers show lower FDG uptake in metastatic ALNs, reducing diagnostic accuracy. This finding may aid in selecting the most appropriate diagnostic modality based on tumor characteristics in the era of personalized medicine.

Clinical and Multiomic Features Differentiate Young Black and White Breast Cancer Cohorts Derived by Machine Learning Approaches

BACKGROUND

There are documented differences in Breast cancer (BrCA) presentations and outcomes between Black and White patients. In addition to molecular factors, socioeconomic, racial, and clinical factors result in disparities in outcomes for women in the United States. Using machine learning and unsupervised biclustering methods within a multiomics framework, here we sought to shed light on the biological and clinical underpinnings of observed differences between Black and White BrCA patients.

MATERIALS AND METHODS

We examined The Cancer Genome Atlas BrCA samples from stage II patients aged 50 or younger that are Black (BAA50) or White (W50) (n = 139 patients; 36 BAA50 and 103 W50) These patients were chosen because marked differences in survival were observed in an earlier study. A variety of multiomic data sets were analyzed to further characterize the clinical and molecular disparities for insights.

RESULTS

We coupled RNAseq data with protein-protein interaction as well as BrCA-specific protein co-expression network data to identify 2 novel biclusters. These biclusters are significantly associated with clinical features including race, number of lymph nodes involved with disease, estrogen receptor status, progesterone receptor status and menopausal status. There were also differentially mutated genes. Using DNA methylation data, we identified differentially methylated genes. Machine learning algorithms were trained on differential methylation values of driver genes. The trained algorithms were successful in predicting the bicluster assignment of each sample.

CONCLUSION

These results demonstrate that there was a significant association between the cluster membership and BAA50 and W50 cohorts, indicating that these biclusters accurately stratify these cohorts.

L-arginine dependence of breast cancer - molecular subtypes matter

L-arginine limits proliferation in highly proliferative tissues. It is a substrate for nitric oxide synthases, arginases; its methylation by protein-L-arginine methyltransferases (PRMTs) leads to asymmetric (ADMA) and symmetric dimethylarginine (SDMA). We measured L-arginine and its metabolites L-ornithine, L-citrulline, ADMA, and SDMA in a prospective cohort of 243 women with primary breast cancer (BC) and their associations with mortality and disease recurrence during 88 (IQR, 82-93) months of follow-up. We quantified these metabolites and expression of genes involved in L-arginine metabolic pathways in MCF-7, BT-474, SK-BR-3, MDA-MB-231, and MDA-MB-468 cells representing ER-positive, HER2-positive, and triple-negative BC compared to MCF-12 A cells. Plasma L-arginine and ADMA concentrations were elevated in 47 patients with recurrent disease and in 34 non-survivors. ADMA was significantly associated with mortality and recurrent disease in Luminal A patients; low L-citrulline was significantly associated with survival in triple-negative BC. In all BC cells except MCF-7, DDAH1 and DDAH2 expression was higher than in MCF-12 A (DDAH1: 32-44 fold, DDAH2: 1.7-4.2 fold; p < 0.05). By contrast, MCF-7 cells showed low DDAH1 and DDAH2, but high PRMT4 and PRMT6 expression and high L-arginine content. BT-474 and MDA-MB-468 cells showed high ARG2 expression and high L-ornithine concentrations, and MDA-MB-468 cells had the highest L-citrulline/L-arginine ratio. In conclusion, regulation of L-arginine metabolic pathways shows a complex and differential pattern between BC subtypes. ADMA is a prognostic biomarker in Luminal A patients; its metabolizing enzyme, DDAH, is highly overexpressed in BC cells. Thus, fingerprinting of L-arginine metabolism may offer novel personalized treatment options within BC subtypes.

Coding and regulatory somatic profiling of triple-negative breast cancer in Sub-Saharan African patients

The burden of triple-negative breast cancer (TNBC) may be shaped by genetic factors, particularly inherited and somatic mutation profiles. However, data on this topic remain limited, especially for the African continent, where a higher TNBC incidence is observed. In the age of precision medicine, cataloguing TNBC diversity in African patients becomes imperative. We performed whole exome sequencing, including untranslated regions, on 30 samples from Angola and Cape Verde, which allowed to ascertain on potential regulatory mutations in TNBC for the first time. A high somatic burden was observed for the African cohort, with 86% of variants being so far unreported. Recurring to predictive functional algorithms, 17% of the somatic single nucleotide variants were predicted to be deleterious at the protein level, and 20% overlapped with candidate cis-regulatory elements controlling gene expression. Several of these somatic functionally-impactful mutations and copy number variation (mainly in 1q, 8q, 6 and 10p) occur in known BC- and all cancer-driver genes, enriched for several cancer mechanisms, including response to radiation and related DNA repair mechanisms. TP53 is the top of these known BC-driver genes, but our results identified possible novel TNBC driver genes that may play a main role in the African context, as TTN, CEACAM7, DEFB132, COPZ2 and GAS1. These findings emphasize the need to expand cancer omics screenings across the African continent, the region of the globe with highest genomic diversity, accelerating the discovery of new somatic mutations and cancer-related pathways.

B cells and energy metabolism in HER2-positive DCIS: insights into breast cancer progression from spatial-omics analyses

During breast tumor progression, the transition from ductal carcinoma in situ (DCIS) to invasive breast cancer is a critical step with large implications for prognosis. However, the mechanisms of invasion are still largely unknown. At the DCIS stage, there is an over-representation of HER2-positive lesions compared with invasive breast cancer. In this study, we investigated the associations between gene expression profiles in cancer cells and the immune microenvironment of HER2-positive DCIS and invasive breast tumors with concurrent DCIS using spatial transcriptomics. We found distinctly more B cells in the vicinity of DCIS ducts than in invasive tumor areas. There was higher expression of genes involved in energy metabolism in DCIS cancer cells than in invasive cancer cells and a positive correlation between expression of metabolic genes and B-cell abundance in DCIS. In contrast were processes related to epithelial to mesenchymal transition negatively correlated with B-cell abundance in DCIS. We also found significant correlation between expression of the B-cell-attracting chemokines CCL19, CCL21 and CXCL13 in stromal cells and B cell abundance in DCIS. This study indicates that B cells may play a protective role in the progression of HER2-positive DCIS to invasive breast cancer and that increased metabolic activity in intraductal cancer cells in combination with chemokines produced by stromal cells may influence the immune microenvironment of DCIS. These findings have implications for understanding HER2-positive breast cancer progression.

Preoperative breast MRI in HER2-positive/hormone receptor-negative breast cancer: surgical outcomes using propensity score matching

OBJECTIVES

To examine the association between preoperative magnetic resonance imaging (MRI) and surgical outcomes in human epidermal growth factor receptor 2 (HER2)-positive/hormone receptor (HR)-negative breast cancer through a propensity score (PS)-matched analysis.

MATERIALS AND METHODS

Patients with HER2-positive/HR-negative invasive ductal carcinoma between 2007 and 2014 were retrospectively assessed and compared according to whether they underwent preoperative MRI. Inverse probability weighting (IPW) analysis and PS matching were used to adjust 17 covariates to control between the MRI and no-MRI groups. Surgical outcomes were compared between two groups and clinicopathologic variables were evaluated to determine who benefited from MRI.

RESULTS

Among 965 women (mean age ± standard deviation, 52 years ± 10), 423 (44%) underwent preoperative MRI and 542 (56%) did not. In the MRI group, a change in surgical management occurred in 48 patients (11%), and the change was appropriate in 31 of those patients (65%). The MRI group had a lower odds of initial mastectomy (odds ratio [OR], 0.63; 95% confidence interval [CI]: 0.47, 0.84; p = 0.002 and OR, 0.67; 95% CI: 0.48, 0.92; p = 0.01 for IPW and PS matching, respectively) and overall mastectomy (OR, 0.60; 95% [CI]: 0.45, 0.80; p = 0.001 and OR, 0.68; 95% CI: 0.49, 0.93; p = 0.02 for IPW and PS matching, respectively). In the subgroup analysis, asymptomatic patients or those with multifocal or multicentric lesions benefited more from MRI (61% vs 36%, p = 0.006 and 52% vs 31%, p = 0.02, respectively).

CONCLUSION

Patients with HER2-positive/HR-negative breast cancer who received preoperative MRI had a lower likelihood of undergoing mastectomy.

KEY POINTS

Question The role of preoperative MRI in predicting surgical outcomes in patients with HER2-positive/HR-negative breast cancer remains uncertain. Findings Preoperative MRI in HER2-positive/HR-negative breast cancer reduces mastectomy rates without increasing the positive resection margin or reoperation rate. Clinical relevance Preoperative MRI is beneficial in reducing mastectomy rates in women with HER2-positive/HR-negative breast cancer.

Synergistic Effect of Ribitol and Shikonin Promotes Apoptosis in Breast Cancer Cells

The mortality rate of breast cancer remains high, despite remarkable advances in chemotherapy. Therefore, it is imperative to identify new treatment options. In the present study, we investigated whether the metabolite ribitol enhances the cytotoxic effect of shikonin against breast cancer in vitro. Here, we screened a panel of small molecules targeting energy metabolism against breast cancer. The results of the study revealed that ribitol enhances shikonin's growth-inhibitory effects, with significant synergy. A significant (p < 0.01) increase in the percentage (56%) of apoptotic cells was detected in the combined treatment group, compared to shikonin single-treatment group (38%), respectively. The combined ribitol and shikonin treatment led to significant arrest of cell proliferation (40%) (p < 0.01) compared to untreated cells, as well as the induction of apoptosis. This was associated with upregulation of p53 (p < 0.05) and downregulation of c-Myc (p < 0.01), Bcl-xL (p < 0.001), and Mcl-1 (p < 0.05). Metabolomic analysis supports the premise that inhibition of the Warburg effect is involved in shikonin-induced cell death, which is likely further enhanced by dysregulation of glycolysis and the tricarboxylic acid (TCA) cycle, afflicted by ribitol treatment. In conclusion, the present study demonstrates that the metabolite ribitol selectively enhances the cytotoxic effect mediated by shikonin against breast cancer in vitro.

miR-1297 is frequently downmodulated in flat epithelial atypia of the breast and promotes mammary neoplastic transformation via EphrinA2 regulation

Breast cancer ranks as the most prevalent form of cancer globally. Currently, advanced screening methods have significantly improved early detection rates. These achievements have led to more non-invasive cancer diagnoses and underscored the clinical relevance of precursor lesions like flat epithelial atypia (FEA), a histological condition characterized by mild atypical changes in the normal epithelium lining the mammary ducts. Despite the increasing detection of FEA in mammary biopsy, our understanding of the biological behavior of this entity remains limited and, as a consequence, the clinical management of patients is still being debated. Evidence from the literature indicates that dysregulation of microRNAs contributes to all stages of breast cancer progression, potentially serving as valuable markers of disease evolution. In this study, through a comparison of the microRNA profiles of normal mammary epithelium, FEA, and non-invasive breast cancer in three cohorts of patients, we identified downregulation of miR-1297 as a common feature in both FEA and non-invasive breast cancer compared to the normal counterpart. Mechanistically, overexpression of miR-1297 inhibits the growth of breast cancer cells by targeting the oncogenic receptor tyrosine kinase EphrinA2. In contrast, downregulation of miR-1297 increases proliferation and alters the morphology of normal mammary epithelial cells in a three-dimensional context. These findings pinpoint the downregulation of miR-1297 as an early event in mammary transformation and suggest its potential role as a driver of progression in FEA, harboring the capacity to evolve into malignancy.

Deciphering the anti-neoplastic potential of Allium ascalonicum in averting the proliferation and epithelial-mesenchymal transition of triple-negative breast cancer through virtual docking and In Vitro approaches

BACKGROUND

Universally, Allium ascalonicum (Shallots) is a well-known flavouring agent in many cuisines. Though, it's been proved for its health benefits due to the presence of alkaloids, flavonoids, terpenoids, phenols and coumarins, its role as an anti-neoplastic agent still requires comprehensive investigation. In our study, we have investigated the presence of potential anti-neoplastic phytocompounds, anti-inflammatory, cytotoxicity and anti-metastatic activity of Shallots against Triple-Negative Breast Cancer cell line, MDA-MB-231.

METHODS

Phytocompounds of aqueous Allium ascalonicum extract (AAE) derived from GC-MS and LC-MS analysis were docked with an inflammatory marker, Interleukin-18 (IL-18); anti-apoptotic proteins, B-cell Lymphoma-2 (BCL-2) and Myeloid Cell Leukemia-1 (MCL-1); and metastatic marker, Vimentin using PyRx (Version 0.9.9). Subsequently, the anti-inflammatory property of AAE was determined using Bovine Serum Albumin (BSA) Denaturation Assay and the chemotherapeutic potential of AAE was determined using 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assay on MDA-MB-231 and HEK293T cell lines. Additionally, to determine the synergistic effect of Doxorubicin Hydrochloride (Standard) and AAE, MTT assay was performed on MDA-MB-231 cell lines treated with the combination therapy. Furthermore, the anti-metastatic property of AAE was determined using cell migration and clonogenic assays. Finally, Dual Acridine Orange/Ethidium Bromide fluorescence staining method was used to determine if AAE has the ability to induce apoptosis and necrosis in MDA-MB-231 cells.

RESULTS

Molecular docking results using the compounds obtained from LC-MS and GC-MS with the target proteins revealed promising anti-neoplastic bioactive compounds. BSA Denaturation assay proved that AAE has anti-inflammatory property, with the highest, 85.78% observed at 2 mg/ml of AAE. Moreover, MTT assay proved that AAE exhibited cytotoxic effect on MDA-MB-231 in a dose-dependent manner, with an IC50 observed at 1.23 mg/ml (**p ≤ 0.005) and non-toxic to HEK293T cells. Combination therapy of the standard with AAE reduced the IC50 of the standard by 65.5%. Consecutively, the anti-metastatic property of AAE was proved using cell migration and clonogenic assays, suggesting suppression of epithelial-mesenchymal transition. Finally, Dual Acridine Orange/Ethidium Bromide fluorescence staining method displayed that, AAE has the ability to induce apoptosis and necrosis in TNBC cells.

CONCLUSION

The outcomes from in vitro assays corroborated with the molecular docking results and hence, on authenticating the potentiality of AAE's anti-neoplastic effect via. in vivo models, pre-clinical and clinical trials, Allium ascalonicum can be articulated to a prospective anti-neoplastic drug for treating TNBC.

The role of C1orf50 in breast cancer progression and prognosis

Although the prognosis of breast cancer has significantly improved compared to other types of cancer, there are still some patients who expire due to recurrence or metastasis. Therefore, it is necessary to develop a method to identify patients with poor prognosis at the early stages of cancer. In the process of discovering new prognostic markers from genes of unknown function, we found that the expression of C1orf50 determines the prognosis of breast cancer patients, especially for those with Luminal A breast cancer. This study aims to elucidate the molecular role of C1orf50 in breast cancer progression. Bioinformatic analyses of the breast cancer dataset of TCGA, and in vitro analyses, reveal the molecular pathways influenced by C1orf50 expression. C1orf50 knockdown suppressed the cell cycle of breast cancer cells and weakened their ability to maintain the undifferentiated state and self-renewal capacity. Interestingly, upregulation of C1orf50 increased sensitivity to CDK4/6 inhibition. In addition, C1orf50 was found to be more abundant in breast cancer cells than in normal breast epithelium, suggesting C1orf50's involvement in breast cancer pathogenesis. Furthermore, the mRNA expression level of C1orf50 was positively correlated with the expression of PD-L1 and its related factors. These results suggest that C1orf50 promotes breast cancer progression through cell cycle upregulation, maintenance of cancer stemness, and immune evasion mechanisms. Our study uncovers the biological functions of C1orf50 in Luminal breast cancer progression, a finding not previously reported in any type of cancer.

A novel micropeptide miPEP205 suppresses the growth and metastasis of TNBC

Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer and poses a treatment challenge due to high recurrence risk. Consequently, there is an urgent need for novel and efficacious therapies targeting TNBC. In this context, our study delineates the identification and characterization of a long non-coding RNA (lncRNA)-derived micropeptide miPEP205. Notably, the micropeptide exerts a significant inhibitory effect on the growth and metastasis of TNBC. Moreover, we observed a substantial down-regulation of micropeptide expression in clinical samples, which was markedly associated with a poor prognosis. Mechanistically, our research demonstrated that EGR3 governs lncRNA MIR205HG and the micropeptide expression, while miPEP205 boosts GSK-3β phosphorylation at Tyr216. This cascade causes β-catenin degradation, deactivating the GSK-3β/β-catenin signaling pathway and ultimately inhibits TNBC progression. Remarkably, our experiments in the spontaneous breast cancer mice model MMTV-PyMT demonstrated that the introduction of the miPEP205 gene or exogenous administration of the micropeptide miPEP205 significantly curtailed tumor growth and lung metastasis, and enhanced the overall survival among tumor-bearing mice. In conclusion, our study uncovers a previously uncharacterized micropeptide derived from a lncRNA, showcasing potent antitumor properties. These findings position miPEP205 as a promising novel target for therapeutic intervention in TNBC.

Role of circular RNAs in cancer therapy resistance

Over the past decade, circular RNAs (circRNAs) have gained recognition as a novel class of genetic molecules, many of which are implicated in cancer pathogenesis via different mechanisms, including drug resistance, immune escape, and radio-resistance. ExosomalcircRNAs, in particular, facilitatecommunication between tumour cells and micro-environmental cells, including immune cells, fibroblasts, and other components. Notably, micro-environmental cells can reportedly influence tumour progression and treatment resistance by releasing exosomalcircRNAs. circRNAs often exhibit tissue- and cancer-specific expression patterns, and growing evidence highlights their potential clinical relevance and utility. These molecules show strong promise as potential biomarkers and therapeutic targets for cancer diagnosis and treatment. Therefore, this review aimed to briefly discuss the latest findings on the roles and resistance mechanisms of key circRNAs in the treatment of various malignancies, including lung, breast, liver, colorectal, and gastric cancers, as well as haematological malignancies and neuroblastoma.This review will contribute to the identification of new circRNA biomarkers for the early diagnosis as well as therapeutic targets for the treatment of cancer.

The spatial landscape of cancer hallmarks reveals patterns of tumor ecological dynamics and drug sensitivity

Tumors are complex ecosystems of interacting cell types. The concept of cancer hallmarks distills this complexity into underlying principles that govern tumor growth. Here, we explore the spatial distribution of cancer hallmarks across 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. We show that hallmark activity is spatially organized, with the cancer compartment contributing to the activity of seven out of 13 hallmarks, while the tumor microenvironment (TME) contributes to the activity of the rest. Additionally, we discover that genomic distance between tumor subclones correlates with differences in hallmark activity, even leading to clone-hallmark specialization. Finally, we demonstrate interdependent relationships between hallmarks at the junctions of TME and cancer compartments and how they relate to sensitivity to different neoadjuvant treatments in 33 bladder cancer patients from the DUTRENEO trial. In conclusion, our findings may improve our understanding of tumor ecology and help identify new drug biomarkers.

The Breast Cancer Classifier refines molecular breast cancer classification to delineate the HER2-low subtype

Current breast cancer classification methods, particularly immunohistochemistry and PAM50, face challenges in accurately characterizing the HER2-low subtype, a therapeutically relevant entity with distinct biological features. This notable gap can lead to misclassification, resulting in inappropriate treatment decisions and suboptimal patient outcomes. Leveraging RNA-seq and machine-learning algorithms, we developed the Breast Cancer Classifier (BCC), a unique transcriptomic classifier for more precise breast cancer subtyping, specifically by delineating and incorporating HER2-low as a distinct subtype. BCC also redefined the PAM50 Normal subtype into other subtypes, disputing its classification as a unique molecular group. Our statistical analysis not only confirmed the reproducibility and accuracy of BCC, but also revealed similarities in prognostic characteristics between the HER2-low and Basal subtypes. Addressing this gap in breast cancer classification is clinically significant because it not only improves treatment stratification, but also uncovers novel molecular and immunohistochemical features associated with the HER2-low and HER2-high subtypes, thereby advancing our understanding of breast cancer heterogeneity and providing guidance in precision oncology.

Combining KPNA2 and FOXM1 Expression as Prognostic Markers and Therapeutic Targets in Hormone Receptor-Positive, HER2-Negative Breast Cancer

Background/Objectives: Breast cancer remains the leading malignancy affecting women worldwide, with significant mortality rates. This study aimed to evaluate the prognostic significance of FOXM1 expression specifically in hormone receptor-positive, HER2-negative (HR+HER2-) breast cancer patients with high KPNA2 expression, and to identify potential FOXM1-targeted therapeutic strategies for this patient subgroup. Methods: We analyzed RNA sequencing and microarray data from three independent cohorts: Mackay Memorial Hospital patient samples, The Cancer Genome Atlas, and Gene Expression Omnibus databases. The expression levels of KPNA2, FOXM1, CCNB1, and CCNB2 were evaluated, with particular emphasis on stratifying patients based on KPNA2 expression levels. Their associations with clinical outcomes were assessed using Gene Set Enrichment Analysis and survival analyses. Results: While KPNA2 expression showed strong positive correlations with FOXM1, CCNB1, and CCNB2 across all datasets, our analysis revealed a distinct prognostic pattern in HR+HER2- breast cancer patients with high KPNA2 expressions. In this specific subgroup, low FOXM1 expression emerged as a favorable prognostic indicator, despite the generally poor prognosis associated with high KPNA2 levels. Gene Set Enrichment Analysis demonstrated significant enrichment of the G2/M checkpoint pathway in high KPNA2-expressing patients, suggesting potential therapeutic vulnerability to FOXM1 inhibition in this subgroup. Conclusions: This study establishes FOXM1 expression as a critical prognostic marker, specifically in KPNA2-high HR+HER2- breast cancer patients, where low FOXM1 levels correlate with improved survival outcomes. These findings suggest that FOXM1 inhibition could be particularly effective in patients with high KPNA2 expression, offering a novel therapeutic strategy for this specific molecular subtype. Several FOXM1 inhibitors, including thiostrepton and FDI-6, warrant investigation as potential targeted treatments for KPNA2-high HR+HER2- breast cancer patients.

Chromatin Remodulator CHD4: A Potential Target for Cancer Interception

Cancer initiation and progression are associated with numerous somatic mutations, genomic rearrangements, and structure variants. The transformation of a normal cell into a cancer cell involves spatio-temporal changes in the regulation of different gene networks. The accessibility of these genes within the cell nucleus is manipulated via nucleosome remodeling ATPases, comprising one of the important mechanisms. Here, we reviewed studies of an ATP-dependent chromatin remodulator, chromodomain helicase DNA-binding 4 (CHD4), in cancer. Multiple domains of CHD4 are known to take part in nucleosome mobilization and histone binding. By binding with other proteins, CHD4 plays a vital role in transcriptional reprogramming and functions as a key component of Nucleosome Remodeling and Deacetylase, or NuRD, complexes. Here, we revisit data that demonstrate the role of CHD4 in cancer progression, tumor cell proliferation, DNA damage responses, and immune modulation. Conclusively, CHD4-mediated chromatin accessibility is essential for transcriptional reprogramming, which in turn is associated with tumor cell proliferation and cancer development.

An assessment of breast cancer HER2, ER, and PR expressions based on mammography using deep learning with convolutional neural networks

Mammography is the recommended imaging modality for breast cancer screening. Expressions of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) are critical to the development of therapeutic strategies for breast cancer. In this study, a deep learning model (CBAM ResNet-18) was developed to predict the expression of these three receptors on mammography without manual segmentation of masses. Mammography of patients with pathologically proven breast cancer was obtained from two centers. A deep learning-based model (CBAM ResNet-18) for predicting HER2, ER, and PR expressions was trained and validated using five-fold cross-validation on a training dataset. The performance of the model was further tested using an external test dataset. Area under receiver operating characteristic curve (AUC), accuracy (ACC), and F1-score were calculated to assess the ability of the model to predict each receptor. For comparison we also developed original ResNet-18 without attention module and VGG-19 with and without attention module. The AUC (95% CI), ACC, and F1-score were 0.708 (0.609, 0.808), 0.651, 0.528, respectively, in the HER2 test dataset; 0.785 (0.673, 0.897), 0.845, 0.905, respectively, in the ER test dataset; and 0.706 (0.603, 0.809), 0.678, 0.773, respectively, in the PR test dataset. The proposed model demonstrates superior performance compared to the original ResNet-18 without attention module and VGG-19 with and without attention module. The model has the potential to predict HER2, PR, and especially ER expressions, and thus serve as an adjunctive diagnostic tool for breast cancer.

Divide and Conquer-Targeted Therapy for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive and malignant type of breast cancer with limited treatment options and poor prognosis. One of the most significant impediments in TNBC treatment is the high heterogeneity of this disease, as highlighted by the detection of several molecular subtypes of TNBC. Each subtype is driven by distinct mutations and pathway aberrations, giving rise to specific molecular characteristics closely connected to clinical behavior, outcomes, and drug sensitivity. This review summarizes the knowledge regarding TNBC molecular subtypes and how it can be harnessed to devise tailored treatment strategies instead of blindly using targeted drugs. We provide an overview of novel targeted agents and key insights about new treatment modalities with an emphasis on the androgen receptor signaling pathway, cancer stem cell-associated pathways, phosphatidylinositol 3-kinase (PI3K)/AKT pathway, growth factor signaling, and immunotherapy.

Pharmacological activity of OST-01, a natural product from baccharis coridifolia, on breast cancer cells

Natural products have long been a viable source of therapeutic agents, providing unique structures and mechanisms that may be beneficial for cancer treatment. Herein we first report on the anticancer activity OST-01, a natural product from Baccharis Coridifolia, on breast cancer cells, including triple-negative breast cancer (TNBC). OST-01 significantly inhibited cell proliferation and oncogenic activities of TNBC cells in vitro. OST-01 also markedly inhibited TNBC tumor growth in vivo, with > 50% reduction in tumor size compared to vehicle control treatment in different in vivo models, i.e., cell line-derived (CDX), patient-derived (PDX), and mammary fat pad xenografts. Mechanistically, OST-01 induces ferroptosis by downregulating LRP8-regulated selenoproteins, i.e., GPX4. A shift from a basal-mesenchymal to a luminal-epithelial state of breast cancer stem cells (BCSCs) as supported by the downregulation of stemness (e.g., CD44) and mesenchymal (e.g., FN1 and vimentin) markers, along with the upregulation of differentiation markers (e.g., CD24) and luminal-epithelial markers (e.g., CK19), was also observed.

A review of six bioactive compounds from preclinical studies as potential breast cancer inhibitors

Breast cancer is one of the predominant causes of mortality in women worldwide. Although therapeutics such as surgery, chemotherapy, hormonal therapy, and radiotherapy have been used, they are associated with adverse effects or multidrug resistance. The use of natural compounds is a promising strategy, owing to their abundance and medicinal value. This review focuses on six natural compounds, namely cinnamaldehyde, diosmin, taxifolin, phloretin, arctigenin, and eugenol, and details their mechanisms of breast cancer inhibition based on in vitro and in vivo studies. These compounds generally promote apoptosis and cell cycle arrest, hinder metastasis and invasion, and decrease tumor growth. This review reinforces the use of natural compounds as therapeutics for breast cancer from their preclinical studies. These compounds might be promising for drug development due to their abundance, high reliability, and safety.

Advances in Ultrasound-Targeted Microbubble Destruction (UTMD) for Breast Cancer Therapy

Breast cancer is one of the most common types of cancer in women worldwide and is a leading cause of cancer deaths among women. As a result, various treatments have been developed to combat this disease. Breast cancer treatment varies based on its stage and type of pathology. Among the therapeutic options, ultrasound has been employed to assist in the treatment of breast cancer, including radiation therapy, chemotherapy, targeted immunotherapy, hormonal therapy, and, more recently, radiofrequency ablation for early-stage and inoperable patients. One notable advancement is ultrasound-targeted microbubble destruction (UTMD), which is gradually becoming a highly effective and non-invasive anti-tumor modality. This technique can enhance chemical, genetic, immune, and anti-vascular therapies through its physical and biological effects. Specifically, UTMD improves drug transfer efficiency and destroys tumor neovascularization while reducing toxic side effects on the body during tumor treatment. Given these developments, the application of ultrasound-assisted therapy to breast cancer has gained significant attention from research scholars. In this review, we will discuss the development of various therapeutic modalities for breast cancer and, importantly, highlight the application of ultrasound microbubble-targeted disruption techniques in breast cancer treatment.

Classifications of triple-negative breast cancer: insights and current therapeutic approaches

Triple-negative breast cancer (TNBC) is an aggressive and challenging type of cancer, characterized by the absence of specific receptors targeted by current therapies, which limits effective targeted treatment options. TNBC has a high risk of recurrence and distant metastasis, resulting in lower survival rates. Additionally, TNBC exhibits significant heterogeneity at histopathological, proteomic, transcriptomic, and genomic levels, further complicating the development of effective treatments. While some TNBC subtypes may initially respond to chemotherapy, resistance frequently develops, increasing the risk of aggressive recurrence. Therefore, precisely classifying and characterizing the distinct features of TNBC subtypes is crucial for identifying the most suitable molecular-based therapies for individual patients. In this review, we provide a comprehensive overview of these subtypes, highlighting their unique profiles as defined by various classification systems. We also address the limitations of conventional therapeutic approaches and explore innovative biological strategies, all aimed at advancing the development of targeted and effective therapeutic strategies for TNBC.

Evaluation of agreement between common clustering strategies for DNA methylation-based subtyping of breast tumours

AIMS

Clustering algorithms have been widely applied to tumor DNA methylation datasets to define methylation-based cancer subtypes. This study aimed to evaluate the agreement between subtypes obtained from common clustering strategies.

MATERIALS & METHODS

We used tumor DNA methylation data from 409 women with breast cancer from the Melbourne Collaborative Cohort Study (MCCS) and 781 breast tumors from The Cancer Genome Atlas (TCGA). Agreement was assessed using the adjusted Rand index for various combinations of number of CpGs, number of clusters and clustering algorithms (hierarchical, K-means, partitioning around medoids, and recursively partitioned mixture models).

RESULTS

Inconsistent agreement patterns were observed for between-algorithm and within-algorithm comparisons, with generally poor to moderate agreement (ARI <0.7). Results were qualitatively similar in the MCCS and TCGA, showing better agreement for moderate number of CpGs and fewer clusters (K = 2). Restricting the analysis to CpGs that were differentially-methylated between tumor and normal tissue did not result in higher agreement.

CONCLUSION

Our study highlights that common clustering strategies involving an arbitrary choice of algorithm, number of clusters and number of methylation sites are likely to identify different DNA methylation-based breast tumor subtypes.

Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling

BACKGROUND

Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates.

PURPOSE

To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2).

STUDY TYPE

Retrospective.

POPULATION

621 invasive ductal cancer, 245 HER2-zero, 191 HER2-low, and 185 HER2-positive. For Task-1, 488 cases for training and 133 for testing. For Task-2, 294 cases for training and 82 for testing.

FIELD STRENGTH/SEQUENCE

3.0 T; 3D T1-weighted DCE, short time inversion recovery T2, and single-shot EPI DWI.

ASSESSMENT

Pathological information and BI-RADS features were compared. Random Forest was used to select MRI features, and then four machine learning (ML) algorithms: decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and artificial neural nets (ANN), were applied to build models.

STATISTICAL TESTS

Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. For ML models, the generated probability was used to construct the ROC curves.

RESULTS

Peritumoral edema, the presence of multiple lesions and non-mass enhancement (NME) showed significant differences. For distinguishing HER2-zero from non-zero (low + positive), multiple lesions, edema, margin, and tumor size were selected, and the k-NN model achieved the highest AUC of 0.86 in the training set and 0.79 in the testing set. For differentiating HER2-low from HER2-positive, multiple lesions, edema, and margin were selected, and the DT model achieved the highest AUC of 0.79 in the training set and 0.69 in the testing set.

DATA CONCLUSION

BI-RADS features read by radiologists from preoperative MRI can be analyzed using more sophisticated feature selection and ML algorithms to build models for the classification of HER2 status and identify HER2-low.

LEVEL OF EVIDENCE: 4

TECHNICAL EFFICACY

Stage 2.

Prognostic implications of response to neoadjuvant chemotherapy in breast cancer subtypes

The current study was designed to assess the response to treatment, as well as clinical and survival outcomes, across different breast cancer subtypes in patients who underwent neoadjuvant chemotherapy (NAC). From 2014 to 2019, a total of 139 patients who were histologically confirmed to have breast cancer, underwent NAC, and subsequently received breast and axillary surgery, were retrospectively included in this study. The rates of pathological complete response (pCR) to NAC were significantly higher for HER2-positive and triple-negative subtypes than for luminal A and HER2-negative subtypes (p = 0.013). Multivariate analysis for disease-free survival (DFS) revealed that tumour grade and the presence of pCR were independent prognostic factors. The presence or absence of a pCR with NAC was an independent prognostic indicator in the multivariate analysis for overall survival (OS). Lastly, achieving a pCR was independently predicted by 18F-FDG PET/CT findings, the HER2-positive subtype, and the triple-negative subtype. Despite the inherent methodological limitations, our findings underscore the significance of identifying predictive markers to tailor NAC plans, with the aim of improving survival outcomes.

The prognostic and predictive value of the luminal-like subtype in hormone receptor-positive breast cancer: an analysis of the DATA trial

BACKGROUND

This study determines the prognostic value of the luminal-like subtype in patients with hormone receptor-positive breast cancer and explores whether the efficacy of extended anastrozole therapy differs between patients with luminal A-like versus luminal B-like tumours.

MATERIALS AND METHODS

The phase III DATA study (NCT00301457) examined the efficacy of 6 versus 3 years of anastrozole in postmenopausal women with early-stage hormone receptor-positive breast cancer who had received 2-3 years of tamoxifen. Patients with available formalin-fixed paraffin-embedded tissue blocks were identified and classified by immunohistochemical luminal-like subtype. Distant recurrence (DR) and breast cancer-specific mortality (BCSM) were compared by luminal-like subtype and treatment arm using competing risk methods.

RESULTS

This study included 788 patients: 491 had a luminal A-like tumour and 297 had a luminal B-like tumour. The median follow-up time was 13.1 years. Patients with luminal B-like tumours experienced a higher risk of DR [subdistribution hazard ratio (sHR) 1.44, 95% confidence interval (CI) 1.03-2.01, P = 0.03] and BCSM (sHR 1.68, 95% CI 1.15-2.45, P = 0.008) than patients with luminal A-like tumours. The efficacy of extended anastrozole therapy differed between patients with luminal A-like tumours (DR: sHR 0.51, 95% CI 0.30-0.88, P = 0.02; BCSM: sHR 0.39, 95% CI 0.19-0.82, P = 0.01) and patients with luminal B-like tumours (DR: sHR 2.09, 95% CI 0.96-4.53, P = 0.06; BCSM: sHR 2.36, 95% CI 0.80-7.00, P = 0.12) (P-interaction = 0.03 and P-interaction = 0.06, respectively).

CONCLUSION

In patients with hormone receptor-positive breast cancer, the luminal B-like subtype was associated with a significantly worse prognosis when compared with the luminal A-like subtype. Extended anastrozole therapy halved the risk of DR and BCSM in patients with luminal A-like tumours, whereas no effect was seen in patients with luminal B-like tumours.

Classification of Molecular Subtypes of Breast Cancer Using Radiomic Features of Preoperative Ultrasound Images

Radiomics has been used as a non-invasive medical image analysis technique for diagnosis and prognosis prediction of breast cancer. This study intended to use radiomics based on preoperative Doppler ultrasound images to classify four molecular subtypes of breast cancer. A total of 565 female breast cancer patients diagnosed by postoperative pathology in a hospital between 2014 and 2022 were included in this study. Radiomic features extracted from preoperative ultrasound images and clinical features were used to construct models for the classification of molecular subtypes of breast cancer. The least absolute shrinkage and selection operator (LASSO) regression was applied for the final screening of radiomic features and clinical features. Three classifiers including Logistic regression, support vector machine (SVM), and XGBoost were utilized to construct model. Model performance was assessed primarily by the area under the receiver operating characteristic curve (AUC) and 95% confidence interval (CI). The mean age of these patients was 54.58 (± 11.27) years. Of these 565 patients, 130 (23.01%) were Luminal A subtype, 329 (58.23%) were Luminal B subtype, 65 (11.50%) were human epidermal growth factor receptor-2 (HER-2) subtype, and 41 (7.26%) were triple negative (TN) subtype. A total of 12 clinical features and 8 radiomic features were selected for model construction. The AUC of the SVM model [0.826 (95%CI 0.808-0.845)] was higher than that of the Logistic regression model [0.776 (95%CI 0.756-0.796)] and the XGB model [0.800 (95%CI 0.779-0.821)] in the multiple classification of breast cancer. For the single classification of breast cancer, the AUC of the SVM model was 0.710 (95%CI 0.660-0.760) for Luminal A subtype, 0.639 (95%CI 0.592-0.685) for Luminal B subtype, 0.754 (95%CI 0.695-0.813) for HER-2 subtype, and 0.832 (95%CI 0.771-0.892) for TN subtype. The SVM model with radiomic features combined with clinical features shows good performance in classifying four molecular subtypes of breast cancer.

Hormone Receptor-Positive HER2-Negative/MammaPrint High-2 Breast Cancers Closely Resemble Triple-Negative Breast Cancers

PURPOSE

The MammaPrint (MP) prognostic assay categorizes breast cancers into high- and low-risk subgroups, and the high-risk group can be further subdivided into high-1 (MP-H1), and very high-risk high-2 (MP-H2). The aim of this analysis was to assess clinical and molecular differences between the hormone receptor-positive (HR+)/HER2-negative MP-H1, -H2, and triple-negative (TN) MP-H1 and -H2 cancers.

EXPERIMENTAL DESIGN

Pretreatment gene expression data from 742 HER2-negative breast cancers enrolled in the I-SPY2 neoadjuvant trial were used. Prognostic risk categories were assigned using the MP assay. Transcriptional similarities across the four receptor and prognostic groups were assessed using principal component analyses and by identifying differentially expressed genes. We also examined pathologic complete response rates and event-free survivals by risk group.

RESULTS

Principal component analysis showed that HR+/MP-H2 tumors clustered with TN/MP-H2 cancers. Only 125 genes showed differential expression between the HR+/MP-H2 and TN/MP-H2 cancers, whereas 1,465 genes were differentially expressed between HR+/MP-H2 and -H1. Gene set analysis revealed similarly high expression of cell cycle, DNA repair, and immune infiltration-related pathways in HR+/MP-H2 and TN/MP-H2 cancers. HR+/MP-H2 cancers also showed low estrogen receptor-related gene expression. Pathologic complete response rates were similarly high in TN/MP-H2 and HR+/MP-H2 cancers (42% vs. 30.5%; P = 0.11), and MP-H2 cancers with residual cancer had similarly poor event-free survival regardless of estrogen receptor status.

CONCLUSIONS

In conclusion, HR+/MP-H2 cancers closely resemble TN breast cancers in transcriptional and clinical features and benefit from similar treatment strategies.

Prognostic significance of total choline on in-vivo proton MR spectroscopy for prediction of late recurrence in patients with hormone receptor-positive, HER2-negative early breast cancer

PURPOSE

In-vivo proton magnetic resonance spectroscopy (MRS) is a non-invasive method of analyzing choline metabolism that has been used to predict breast cancer prognosis. A strong choline peak may be a surrogate for aggressive tumor biology but its clinical relevance is unclear. The present study assessed whether total choline (tCho), as measured by proton MRS, can predict late recurrence in patients with hormone receptor (HR)-positive, HER2-negative early breast cancer.

METHODS

The study cohort included 261 HR+/HER2- breast cancer patients who underwent diagnostic single-voxel proton MRS (3.0T scanner) prior to first-line surgery from March 2011 to July 2014. The relationships between tCho compound peak integral (tChoi) values and others prognostic factor were analyzed, as were the effects of tChoi on 10-year disease-free survival (DFS) and overall survival (OS). The clinical significance of tChoi was also analyzed using Harrell's C-index.

RESULTS

Mean tChoi in HR+/HER2- study group was 15.47 and we set the cut-off for tChoi at 15 for survival analysis. 10-year DFS differed significantly between tChoi <15 and ≥15 (p = 0.017), with differences differing significantly for late (5-10 years; p = 0.02) but not early (0-5 years; p = 0.323) recurrence. Cox regression analysis showed that tChoi was significantly predictive of 10-year DFS (p = 0.046, OR 2.69) and tended to be predictive of late recurrence (HR 4.36, p = 0.066). Harrell's C-index showed that the Ki-67 index (AUC = 0.597) and lymphovascular invasion (AUC = 0.545) were also predictive of survival, with the addition of normalized tChoi improving the AUC to 0.622 (p = 0.014), indicating better predictive power.

CONCLUSION

tChoi determined by in vivo MRS was predictive of prognosis in patients with HR+/HER2- early breast cancer. This parameter may serve as a valuable, non-invasive tool to predict prognosis when combined with other known prognostic factors.

Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images

SIGNIFICANCE

A deep learning model, trained using transcriptomic data, inexpensively quantifies and fine-maps ITH due to subtype admixture in routine images of LumA breast cancer, the most favorable subtype. This new approach could facilitate exploration of the mechanisms behind such heterogeneity and its impact on selection of therapy for individual patients.

Leveraging nanostructured lipid carriers to enhance targeted delivery and efficacy in breast cancer therapy: a comprehensive review

Cancer, characterized by uncontrolled cell growth and proliferation, continues to be a major global health concern. Breast cancer, the most commonly diagnosed cancer among women, remains a leading cause of cancer-related deaths worldwide. Conventional treatment modalities such as surgery, radiation, and chemotherapy have made significant strides in improving patient outcomes. However, these approaches often face challenges such as limited efficacy, systemic toxicity, and multidrug resistance. Nanotechnology has emerged as a promising avenue for revolutionizing cancer therapy, offering targeted drug delivery, enhanced efficacy, and reduced side effects. Among the various nanocarrier systems, nanostructured lipid carriers (NLCs) have gained considerable attention for their unique advantages. Comprising a blend of solid and liquid lipids, NLCs offer improved drug loading capacity, enhanced stability, sustained release, and biocompatibility. This manuscript provides a comprehensive overview of the role of NLCs in breast cancer management, covering their formulation, methods of preparation, advantages, and disadvantages. Additionally, several studies are presented to illustrate the efficacy of NLCs in delivering anticancer drugs to breast tumors. These studies demonstrate the ability of NLCs to enhance drug cytotoxicity, improve tumor suppression, and minimize systemic toxicity. This manuscript aims to contribute to the existing literature by consolidating current knowledge and providing insights into the future directions of NLC-based therapeutics in breast cancer management.

Evaluating the Prognostic Role of the PAM50 Signature and Selected Immune-Related Signatures for Recurrence in Patients With T1abN0 Breast Cancer

BACKGROUND

De-escalation of adjuvant treatment in patients with T1abN0 breast cancer is discussed internationally. Identification of new prognostic factors in these patients may assist this de-escalation. The PAM50 signature and tumor inflammation signature (TIS), Programmed Cell Death Protein 1 (PD-1) and Programmed Cell Death Ligand 1 (PD-L1) signatures are possible prognostic factors for recurrence.

MATERIALS AND METHODS

Danish patients with T1abN0 breast cancer diagnosed between 2007-2016 were identified, the NanoString Breast Cancer 360 Panel was performed on tissue samples from cases with recurrence matched 1:1 with controls without recurrence (n = 234). The association between gene signatures and recurrence was analyzed with conditional logistic regression.

RESULTS

Patients with the basal-like subtype had higher values of TIS, PD-1 and PD-L1 scores compared with other subtypes. Patients with higher PD-L1 score had significantly lower odds of recurrence (odds ratio [OR] 0.61, P = .01). Likewise, an increased TIS score was associated to lower, but nonsignificant odds of recurrence (OR 0.76, P = .07). Patients with human epidermal growth factor receptor 2 (HER2)-enriched subtype had significantly higher odds of recurrence compared with patients with luminal A subtype (OR 4.8, P = .03).

DISCUSSION

PAM50 and immune-related signatures provide important prognostic information in patients with T1abN0 breast cancer, which may refine the risk assessment in these patients.

Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images

Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting model. The pipeline was tested on 221 hold-out WSIs, achieving an F1 score of 0.95 for tumor vs non-tumor classification and a macro F1 score of 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development.

Role of miRNAs in breast cancer development and progression: Current research

Breast cancer, a complex and heterogeneous ailment impacting numerous women worldwide, persists as a prominent cause of cancer-related fatalities. MicroRNAs (miRNAs), small non-coding RNAs, have garnered significant attention for their involvement in breast cancer's progression. These molecules post-transcriptionally regulate gene expression, influencing crucial cellular processes including proliferation, differentiation, and apoptosis. This review provides an overview of the current research on the role of miRNAs in breast cancer. It discusses the role of miRNAs in breast cancer, including the different subtypes of breast cancer, their molecular characteristics, and the mechanisms by which miRNAs regulate gene expression in breast cancer cells. Additionally, the review highlights recent studies identifying specific miRNAs that are dysregulated in breast cancer and their potential use as diagnostic and prognostic biomarkers. Furthermore, the review explores the therapeutic potential of miRNAs in breast cancer treatment. Preclinical studies have shown the effectiveness of miRNA-based therapies, such as antagomir and miRNA mimic therapies, in inhibiting tumor growth and metastasis. Emerging areas, including the application of artificial intelligence (AI) to advance miRNA research and the "One Health" approach that integrates human and animal cancer insights, are also discussed. However, challenges remain before these therapies can be fully translated into clinical practice. In conclusion, this review emphasizes the significance of miRNAs in breast cancer research and their potential as innovative diagnostic and therapeutic tools. A deeper understanding of miRNA dysregulation in breast cancer is essential for their successful application in clinical settings. With continued research, miRNA-based approaches hold promise for improving patient outcomes in this devastating disease.

Serum α1-AT Levels and SERPINA1 Molecular Analysis in Breast Cancer: An Experimental and Computational Study

BACKGROUND/OBJECTIVES

Breast cancer (BC) is a heterogeneous disease with multifactorial origins, including environmental, genetic, and immunological factors. Inflammatory cytokines, such as alpha 1 antitrypsin (α1-AT), are increased in BC and affect physiological and pathological conditions. This study aimed to evaluate the serum levels of α1-AT and perform a computational analysis of SERPINA1 in BC, as well as their association with molecular subtypes and clinical features.

METHODS

For the experimental analysis, we evaluated 255 women with BC and 53 healthy women (HW) in a cross-sectional study. Molecular subtypes were identified by immunohistochemistry and TNM was used for clinical staging. Soluble levels of α1-AT were quantified by ELISA. Computational analysis of SERPINA1 expression was performed using GEPIA and cBioPortal.

RESULTS

α1-AT was increased in BC women versus HW (75.8 ng/mL vs. 532.2 ng/mL). Luminal A had higher concentration (547.5 ng/mL) than Triple Negative (TN) (484.1 ng/mL), but the levels were not associated with clinical stage. The computational analysis showed that SERPINA1 is overexpressed in BC with differential expression among subtypes; its overexpression is associated with a better prognosis, longer disease-free survival, and overall survival.

CONCLUSIONS

α1-AT levels are increased in women with BC women compared to HW. The Luminal A subtype shows higher soluble protein levels than the TN one. Furthermore, SERPINA1 mRNA overexpression in BC is linked to a protective effect.