Biological subtypes of breast cancer: Prognostic and therapeutic implications

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.

Subtyping breast lesions via collective intelligence based long-tailed recognition in ultrasound

Breast lesions display a wide spectrum of histological subtypes. Recognizing these subtypes is vital for optimizing patient care and facilitating tailored treatment strategies compared to a simplistic binary classification of malignancy. However, this task relies on invasive biopsy tests, which carry inherent risks and can lead to over-diagnosis, unnecessary expenses, and pain for patients. To avoid this, we propose to infer lesion subtypes from ultrasound images directly. Meanwhile, the incidence rates of different subtypes exhibit a skewed long-tailed distribution that presents substantial challenges for effective recognition. Inspired by collective intelligence in clinical diagnosis to handle complex or rare cases, we proposed a framework-CoDE-to amalgamate diverse expertise of different backbones to bolster robustness across varying scenarios for automated lesion subtyping. It utilizes dual-level balanced individual supervision to fully exploit prior knowledge while considering class imbalance. It is also equipped with a batch-based online competitive distillation module to stimulate dynamic knowledge exchange. Experimental results demonstrate that the model surpassed the state-of-the-art approaches by more than 7.22% in F1-score facing a challenging breast dataset with an imbalance ratio as high as 47.9:1.

Cyclin-dependent kinase 4 and 6 inhibitors in breast cancer treatment

Breast cancer is the second largest cancer in the world, and it has highest mortality rate in women worldwide. The aberrant activation of the cyclin-dependent kinase 4 and 6 (CDK4/6) pathway plays an important role in uncontrolled breast cancer cell proliferation. Therefore, targeting CDK4/6 to improve overall survival rates has been a strong interest in breast cancer therapeutics. Till date, four CDK4/6 inhibitors have been developed and approved for hormone receptor-positive and human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer therapies with great success. However, acquired resistance to CDK4/6 inhibitors has emerged and limits their effectiveness in breast cancer. In this review, we systematically discussed the mechanisms of resistance to CDK4/6 inhibitors including the cell cycle-specific and cell cycle-nonspecific mechanisms. Also, we analyzed combination strategies with other signaling inhibitors in clinical and preclinical settings that further expand the clinical application of CDK4/6 inhibitors in future breast cancer therapies.

Proteomic insights into lymph node metastasis in breast cancer subtypes: Key biomarkers and pathways

BACKGROUND

Breast cancer (BC) is a significant global cause of death in women, primarily due to its diversity and metastatic potential.

METHODS

BC, healthy lymph node (HL), and metastatic lymph node (ML) tissues were collected from 19 patients diagnosed with infiltrating ductal carcinoma. Protein isolation was performed, followed by two-dimensional gel electrophoresis (2DE) and mass spectrometry (MALDI-TOF/TOF) to identify differentially expressed proteins. Bioinformatic analyses, including protein-protein interaction networks and molecular pathways, were conducted using STRING. Kaplan-Meier analysis was performed with KM plotter to evaluate the prognostic significance of identified proteins. Receiver operating characteristic (ROC) curves were generated using TCGA and GTEx data from UCSC Xena and easyROC to assess diagnostic relevance.

RESULTS

Distinct pathways related to cytoskeletal regulation, immune modulation, and oxidative stress response were enriched in each subtype. Key proteins such as TUBA1C, CCT6A, and Vimentin (LNA), CAPZB and ENO1 (LNB), GSTO1 (HER2 OE), and CORO1A and LAP3 (TNBC) were identified as significant in driving metastatic behavior. KM survival analysis showed that CAPZB (LNB) and CORO1A (TNBC) were associated with patient outcomes, while GSTO1 was linked to improved distant metastasis-free survival in HER2 OE. ROC analysis highlighted CAPZB as a strong diagnostic marker.

CONCLUSIONS

These findings form a basis for comprehending the molecular mechanisms underlying metastasis in different subtypes of breast cancer. They may lead to the identification of new therapeutic targets for customized interventions against invasion and metastasis. Further validation is required to confirm their clinical utility in larger cohorts.

Design, synthesis, and biological evaluation of novel PROTACs compounds with good ERα degradation ability

A series of ER-PROTACs compounds were designed, synthesized and tested for their ability to degrade estrogen receptor proteins and exhibit Human breast cancer cells (MCF-7) inhibition activity. Molecular docking simulations were performed using Discovery studio. Among these compounds, QDE-003-W had the highest estrogen receptor protein degradation ability and cellular activity, with a DC50 value of 95 nM, for estrogen receptor protein degradation and an IC50 value of 30.2 nM for cellular activity. Furthermore, the molecular docking study revealed that the biological activity of QDE-003-W depended on its suitable linker length, which gave us some reference significance for the study of ER-PROTACs. And compared with fulvestrant, QDE-003-W exhibited more favorable pharmacokinetic (PK) characteristics. No significant adverse side effects were observed under the administration protocol, which indicates that the tested mice had excellent tolerance to both the administration method and the dosage. Such favorable PK characteristics and safety features further enhance the prospects of QDE-003-W as a viable candidate for subsequent preclinical and clinical development.

Hypoxia-Responsive Polymersomes for Stemness Reduction in Patient-Derived Solid Tumor Spheroids

Aggressive solid tumors are associated with rapid growth, early hypoxia, a lack of targeted therapies, and a poor prognosis. The hypoxic niches within the rapidly growing solid tumors give rise to a stem-cell-like phenotype with higher metastasis and drug resistance. To overcome the drug resistance of these regions, we used hypoxia-responsive polymersomes with an encapsulated anticancer drug (doxorubicin, Dox) and a stemness modulator (all-trans retinoic acid, ATRA). Reductase enzymes overexpressed in hypoxia reduce the azobenzene linker of the polymers, disrupt the bilayer structure of the polymersomes, and release the encapsulated drugs. We used triple-negative breast cancer (TNBC) as a representative of aggressive and hypoxic solid tumors. We observed that ATRA synergistically enhanced the efficacy of Dox in killing cancer cells. A synergistic combination of the two drug-encapsulated polymersomes reduced the volumes of patient-derived TNBC spheroids by 90%. In contrast, Dox alone decreased the spheroid volumes by 70% and encapsulated ATRA by 19%. Mechanistic studies revealed that ATRA inhibited efflux pumps, leading to a higher concentration of doxorubicin within TNBC cells. In addition, the combination of encapsulated Dox and ATRA significantly decreased stemness expression of the TNBC cells in hypoxia compared to that of Dox alone.

Machine learning predictions of tumor progression: How reliable are we?

BACKGROUND

Cancer continues to pose significant challenges in healthcare due to the complex nature of tumor progression. In this digital era, artificial intelligence has emerged as a powerful tool that can potentially transform multiple aspects of cancer care.

METHODS

In the current study, we conducted a comprehensive literature search across databases such as PubMed, Scopus, and IEEE Xplore. Studies published between 2014 and 2024 were considered. The selection process involved a systematic screening based on predefined inclusion and exclusion criteria. Studies were included if they focused on applying machine learning techniques for tumor progression modeling, diagnosis, or prognosis, were published in peer-reviewed journals or conference proceedings, were available in English, and presented experimental results, simulations, or real-world applications. In total, 87 papers were included in this review, ensuring a diverse and representative analysis of the field. A workflow is included to illustrate the procedure followed to achieve this aim.

RESULTS

This review delves into the cutting-edge applications of machine learning (ML), including supervised learning methods like Support Vector Machines and Random Forests, as well as advanced deep learning (DL). It focuses on the integration of ML into oncological research, particularly its application in tumor progression through the tumor microenvironment, genetic data, histopathological data, and radiological data. This work provides a critical analysis of the challenges associated with the reliability and accuracy of ML models, which limit their clinical integration.

CONCLUSION

This review offers expert insights and strategies to address these challenges in order to improve the robustness and applicability of ML in real-world oncology settings. By emphasizing the potential for personalized cancer treatment and bridging gaps between technology and clinical needs, this review serves as a comprehensive resource for advancing the integration of ML models into clinical oncology.

Snail1 as a key prognostic biomarker of cancer-associated fibroblasts in breast tumors

Accurate cancer diagnosis is crucial for selecting optimal treatments, yet current classification systems often include non-responders who receive ineffective therapies. Cancer-associated fibroblasts (CAFs) play a central role in tumor progression, and CAF biomarkers are increasingly recognized for their prognostic value. Recent studies have revealed significant heterogeneity within CAF populations, with distinct subtypes linked to different tumors and stages of disease. In this review, we summarize recent findings from patient samples and mouse models of breast cancer, focusing on gene signatures identified by single-cell RNA sequencing that define CAF subtypes and predict cancer prognosis. Additionally, we explore the genes and pathways regulated by Snail1, a transcription factor whose expression in breast and colon CAFs is associated with malignancy. Altogether these data emphasize the fibrotic and immunosuppressive roles of Snail1-expressing fibroblasts and unveil an undescribed streamlined Snail1-related gene signature in CAFs with prognostic potential in breast cancer and other solid tumors.

Circular RNAs: Emerging regulators of signaling pathways in epithelial-mesenchymal transition and angiogenesis during breast cancer progression

Circular RNAs (circRNAs) have emerged as important regulators of gene expression and cellular activities, and abnormalities in circRNAs in breast cancer have been linked to important biological processes like epithelial-mesenchymal transition (EMT) and angiogenesis, both essential for tumor metastasis. EMT facilitates the transition of epithelial cancer cells into a mesenchymal phenotype, enhancing their invasive and migratory capabilities, while angiogenesis promotes tumor progression by forming new blood vessels. CircRNAs also interact with microRNAs to regulate signaling pathways such as TGF-β, Wnt/-catenin, and VEGF. Besides EMT and angiogenesis, studies have identified that circRNAs affect metabolic reprogramming, chemoresistance, tumor microenvironment remodeling, and immunological evasion. Thus, circRNAs play a multifaceted role in the development of breast cancer. They hold potential as non-invasive biomarkers and therapeutic targets due to their high stability, resistance to exonuclease degradation, abundance in body fluids, and diverse expression patterns across different tissues. This review summarizes and critically assesses existing understanding of the functional roles and molecular processes of circRNAs in controlling EMT and angiogenesis during breast cancer progression.

The estrobolome: Estrogen-metabolizing pathways of the gut microbiome and their relation to breast cancer

Increasing evidence links the gut microbiome to carcinogenesis. Disruptions in estrogen regulation by the estrobolome-gut microbiota with estrogen-related functions-may promote breast cancer. However, precise information on estrobolome targets and their underlying mechanisms is limited. This review identifies relevant targets for measuring the estrobolome, focusing on enzymes and microbial taxa involved in processing estrogens, precursors, metabolites, and phytoestrogens, to facilitate the exploration of potential links to breast cancer. Evidence from breast cancer case-control studies is synthesized to assess alignment with these targets, highlight gaps in the evidence, and suggest new paths forward. Findings from case-control studies were heterogeneous and showed limited alignment with estrobolome targets, with only Escherichia coli and Roseburia inulinivorans identified as differentially abundant and functionally relevant between cases and controls. The lack of compelling evidence for estrobolome-specific mechanisms may reflect measurement challenges or may suggest that broader ecological changes in the microbiome, which influence a network of interacting mechanisms, are more influential for carcinogenesis. To clarify the estrobolome's role in breast cancer, future research should use advanced sequencing techniques and methods such as metabolomics and transcriptomics, while considering clinical and behavioral factors that may modify estrobolome mechanisms.

Constructing shared genetic architecture between bioavailable testosterone and luminal A breast cancer in female

BACKGROUND

Observational studies have showed a strong association between bioavailable testosterone (BT) and breast cancer. However, the role of genetic factors in their comorbidity remains unknown.

METHODS

Using large genome-wide association study (GWAS) data, we employed linkage disequilibrium score regression (LDSC) to identify the breast cancer subtype most genetically correlated with BT. We then constructed the shared genetic architecture between BT and this subtype by: (1) applied Heritability Estimation from Summary Statistics for local genetic correlations and stratified-LDSC for partitioned heritability; (2) performed a cross-trait GWAS meta-analysis to find novel single-nucleotide polymorphism (SNP) and validated through colocalization; (3) conducted both cross-tissue and single-tissue transcriptome-wide association studies (TWAS) and validated the candidate genes through Mendelian randomization (MR); (4) investigated SNP-heritability enrichment at the gene set, tissue, and cell levels using Multi-marker Analysis of GenoMic Annotation.

RESULTS

Luminal A breast cancer (Luminal ABC) was selected as it is a common subtype of breast cancer and demonstrates a superior genetic correlation with BT. We identified strong local correlations in 132 distinct genomic regions and confirmed shared SNPs including rs1432679 and rs7175852. TWAS highlighted two pleiotropic genes, MICALL1 and TRIOBP, with TRIOBP validated by MR. We also found six shared pathways and luminal cells in mammary gland pregnancy shared between BT and Luminal ABC. For tissue-specific enrichment, BT was mainly found in the liver and adrenal gland, whereas Luminal ABC was found in the minor salivary gland.

CONCLUSIONS

This study sheds light on the genetic architecture of BT and Luminal ABC and suggests new avenues for research and therapy.

Fully automatic HER2 tissue segmentation for interpretable HER2 scoring

Breast cancer is the most common cancer in women, with HER2 (human epidermal growth factor receptor 2) overexpression playing a critical role in regulating cell growth and division. HER2 status, assessed according to established scoring guidelines, offers important information for treatment selection. However, the complexity of the task leads to variability in human rater assessments. In this work, we propose a fully automated, interpretable HER2 scoring pipeline based on pixel-level semantic segmentations, designed to align with clinical guidelines. Using polygon annotations, our method balances annotation effort with the ability to capture fine-grained details and larger structures, such as non-invasive tumor tissue. To enhance HER2 segmentation, we propose the use of a Wasserstein Dice loss to model class relationships, ensuring robust segmentation and HER2 scoring performance. Additionally, based on observations of pathologists' behavior in clinical practice, we propose a calibration step to the scoring rules, which positively impacts the accuracy and consistency of automated HER2 scoring. Our approach achieves an F1 score of 0.832 on HER2 scoring, demonstrating its effectiveness. This work establishes a potent segmentation pipeline that can be further leveraged to analyze HER2 expression in breast cancer tissue.

FDA approves Datroway: a novel therapy for HR-positive, HER2-negative metastatic breast cancer

The Food and Drug Administration (FDA) has approved datopotamab deruxtecan (Datroway) for the treatment of unresectable or metastatic HR-positive, HER2-negative breast cancer. This antibody-drug conjugate comprises a monoclonal antibody (mAb) that targets TROP2, a cytotoxic agent DXd, and a linker. The mAb binds to TROP2 on cancer cells, facilitating internalization, after which DXd is released, inducing cell death. In the TROPION-Breast01 trial, datopotamab deruxtecan demonstrated superiority over conventional chemotherapy. It provides a promising alternative for patients who have failed prior endocrine or chemotherapy, offering enhanced efficacy than traditional treatments. However, additional studies are required to thoroughly assess the drug's safety and efficacy, as well as to establish the most current information regarding its optimal dosage and administration guidelines.

Non-coding RNAs, a double-edged sword in breast cancer prognosis

Cancer is a rising issue worldwide, and numerous studies have focused on understanding the underlying reasons for its occurrence and finding proper ways to defeat it. By applying technological advances, researchers are continuously uncovering and updating treatments in cancer therapy. Their vast functions in the regulation of cell growth and proliferation and their significant role in the progression of diseases, including cancer. This review provides a comprehensive analysis of ncRNAs in breast cancer, focusing on long non-coding RNAs such as HOTAIR, MALAT1, and NEAT1, as well as microRNAs such as miR-21, miR-221/222, and miR-155. These ncRNAs are pivotal in regulating cell proliferation, metastasis, drug resistance, and apoptosis. Additionally, we discuss experimental approaches that are useful for studying them and highlight the advantages and challenges of each method. We then explain the results of these clinical trials and offer insights for future studies by discussing major existing gaps. On the basis of an extensive number of studies, this review provides valuable insights into the potential of ncRNAs in cancer therapy. Key findings show that even though the functions of ncRNAs are vast and undeniable in cancer, there are still complications associated with their therapeutic use. Moreover, there is an absence of sufficient experiments regarding their application in mouse models, which is an area to work on. By emphasizing the crucial role of ncRNAs, this review underscores the need for innovative approaches and further studies to explore their potential in cancer therapy.

Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics

The tumor microenvironment (TME) of triple-negative breast cancer (TNBC) is a highly heterogeneous and very aggressive form of the disease that has few suitable treatment options; however, spatial transcriptomics (ST) is a powerful tool for elucidation of the TME in TNBC. Because of its spatial context preservation, ST has a unique capability to map tumor-stroma interactions, immune infiltration, and therapy resistance mechanisms (which are key to understanding TNBC progression), compared with conventional transcriptomics. This review shows the use of ST in TNBC, its utilization in spatial biomarker identification, intratumoral heterogeneity definition, molecular subtyping refinement, and prediction of immunotherapy responses. Recent insight from ST-driven insights has explained the key spatial patterns on immune evasion, chemotherapy resistance, racial disparities of TNBC, and aspects for patient stratification and therapeutic decision. With the integration of ST with the subjects of proteomics and imaging mass cytometry, this approach has been enlarged and is now applied in precision medicine and biomarker discovery. Recently, advancements in AI-based spatial analysis for tumor classification, identification of biomarkers, and creation of therapy prediction models have occurred. However, continued developments in ST technologies, computational tools, and partnerships amongst multiple centers to facilitate the integration of ST into clinical routine practice are needed to unlock novel therapeutic targets.

Targeting MDM2-p53 interaction for breast cancer therapy

Breast cancer is a significant global concern, with limited effective treatment options. Therefore, therapies with high efficacy and low complications, unlike the existing chemotherapies, are urgently required. To address this issue, advances have been made in therapies targeting molecular pathways related to the murine double minute 2 proto-oncogene (MDM2)-tumor proteinp53 (TP53) interaction. This review aims to investigate the efficacy of MDM2 inhibition in restoring TP53 activity in breast cancer cells, as evidenced by clinical studies, reviews, and trials. TP53 is a tumor suppressor and MDM2 facilitates proteasomal degradation of TP53. MDM2 and TP53 activity is tightly regulated. However, cancerous breast cells overexpress MDM2 through five hypothesized mechanisms. Consequently, TP53 levels decrease with increased tumor cell proliferation. Three strategies have been identified for controlling MDM2 upregulation in cells with wild-type or mutated TP53. MDM2 inhibitors (MDM2i) are administered in combination with existing chemotherapies to reduce their effects on healthy cells. Few clinical and preclinical studies have been conducted using MDM2i, which necessitates high-quality clinical trials to support their therapeutic potential in breast cancer therapy.

A novel method for subgroup discovery in precision medicine based on topological data analysis

BACKGROUND

The Mapper algorithm is a data mining topological tool that can help us to obtain higher level understanding of disease by visualising the structure of patient data as a similarity graph. It has been successfully applied for exploratory analysis of cancer data in the past, delivering several significant subgroup discoveries. Using the Mapper algorithm in practice requires setting up multiple parameters. The graph then needs to be manually analysed according to a research question at hand. It has been highlighted in the literature that Mapper's parameters have significant impact on the output graph shape and there is no established way to select their optimal values. Hence while using the Mapper algorithm, different parameter values and consequently different output graphs need to be studied. This prevents routine application of the Mapper algorithm in real world settings.

METHODS

We propose a new algorithm for subgroup discovery within the Mapper graph. We refer to the task as hotspot detection as it is designed to identify homogenous and geometrically compact subsets of patients, which are distinct with respect to their clinical or molecular profiles (e.g. survival). Furthermore, we propose to include the existence of a hotspot as a criterion while searching the parameter space, addressing one of the key limitations of the Mapper algorithm (i.e. parameter selection).

RESULTS

Two experiments were performed to demonstrate the efficacy of the algorithm, including an artificial hotspot in the Two Circles dataset and a real world case study of subgroup discovery in oestrogen receptor-positive breast cancer. Our hotspot detection algorithm successfully identified graphs containing homogenous communities of nodes within the Two Circles dataset. When applied to gene expression data of ER+ breast cancer patients, appropriate parameters were identified to generate a Mapper graph revealing a hotspot of ER+ patients with poor prognosis and characteristic patterns of gene expression. This was subsequently confirmed in an independent breast cancer dataset.

CONCLUSIONS

Our proposed method can be effectively applied for subgroup discovery with pathology data. It allows us to find optimal parameters of the Mapper algorithm, bridging the gap between its potential and the translational research.

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.

Awareness of genomic testing among patients with breast cancer in Europe

PURPOSE

Genomic testing, involving expression profiling of tumour tissue, is a powerful tool for determining appropriate treatments for certain cancer patients. This study aimed to evaluate awareness of genomic testing in breast cancer patients in five European countries.

METHODS

The survey was initiated by Cancer Patients Europe and developed with patient associations, oncologists, and a psycho-oncologist. Participants were recruited via email and social media and completed a 42-question internet survey.

RESULTS

Of 1383 participants in eligible countries completing the survey, 566 women with current or previous HR+/HER2- breast cancer, potentially eligible for genomic testing, were analysed. 245 (43.3 %) were aged 50-59 years and 381 (67.3 %) had received higher education. 238 participants (42.1 %) had heard about genomic testing; 122 (21.6 %) were informed of their eligibility for testing, and 104 (18.4 %) were given reasons for the test. The majority (N = 479; 84.6 %) felt they lacked sufficient information to decide, and only 139 (24.6 %) opted for testing. Overall, 246 (43.5 %) wanted more information on additional testing and 234 (41.3 %) wanted more information on treatment options. The main information sources were medical professionals (N = 363; 64.1 %) and the internet (N = 351; 62.0 %). However, 398 participants (70.3 %) indicated that their healthcare professionals did not advise them on where to find more information.

CONCLUSIONS

This study highlights insufficient awareness of, and access to, genomic testing in breast cancer. Healthcare professionals need to improve communication with patients regarding genomic testing and involve them in shared decision-making. Likewise, patient associations have a role in providing clear information to patients.

Metabolomic Profiling of Disease Progression Following Radiotherapy for Breast Cancer

BACKGROUND

This study aims to explore metabolic biomarkers and pathways in breast cancer prognosis.

METHODS

We performed a global post-radiotherapy (RT) urinary metabolomic analysis of 120 breast cancer patients: 60 progression-free (PF) patients as the reference and 60 with progressive disease (PD: recurrence, second primary, metastasis, or death). UPLC-MS/MS (Metabolon Inc.) identified 1742 biochemicals (1258 known and 484 unknown structures). Following normalization to osmolality, log transformation, and imputation of missing values, a Welch's two-sample t-test was used to identify biochemicals and metabolic pathways that differed between PF and PD groups. Data analysis and visualization were performed with MetaboAnalyst.

RESULTS

Metabolic biomarkers and pathways that significantly differed between the PD and PF groups were the following: amino acid metabolism, including phenylalanine, tyrosine, and tryptophan biosynthesis (impact value (IV) = 1.00; p = 0.0007); histidine metabolism (IV = 0.60; p < 0.0001); and arginine and proline metabolism (IV = 0.70; p = 0.0035). Metabolites of carbohydrate metabolism, including glucose (p = 0.0197), sedoheptulose (p = 0.0115), and carboxymethyl lysine (p = 0.0098), were elevated in patients with PD. Gamma-glutamyl amino acids, myo-inositol, and oxidative stress biomarkers, including 7-Hydroxyindole Sulfate and sulfate, were elevated in patients who died (p ≤ 0.05).

CONCLUSIONS

Amino acid metabolism emerged as a key pathway in breast cancer progression, while carbohydrate and oxidative stress metabolites also showed potential utility as biomarkers for breast cancer progression. These findings demonstrate applications of metabolomics in identifying metabolic biomarkers and pathways as potential targets for predicting breast cancer progression.

Baseline 18F-FDG PET/CT for predicting pathological response to neoadjuvant chemotherapy and prognosis in locally advanced breast cancer patients: analysis of tumor and lymphoid organs metabolic parameters

PURPOSE

To investigate metabolic parameters from baseline 18F-FDG PET/CT as predictors of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and disease recurrence in locally advanced breast cancer (LABC) patients.

MATERIALS AND METHODS

From 142 LABC in 137 patients (bilateral-synchronous BC: 5/137), the following parameters from baseline (pre-treatment) 18F-FDG PET/CT were retrospectively analyzed, along with clinic-histological data: primary tumor activity (SUVmax, SUVmean, SUVpeak, tumor-to-liver ratio-TLR-, MTV, TLG); lymphoid organs activity (spleen and bone marrow SUVmax and SUVmean, spleen-to-liver ratio-SLR-, bone marrow-to-liver ratio-BLR); and PET-positive lymph-nodes' number. Predictors of pCR and recurrence-free survival (RFS) were assessed by univariable logistic regression and Cox regression (significant or suggestive association: p < 0.05; p < 0.10).

RESULTS

74/142 tumors were "Luminal A/B HER2-", 44/142 "Luminal B HER2+/HER2+", 24/142 TNBC; pCR after NAC occurred in 26/142 tumors (18.3%) and disease recurrence at follow-up (45 ± 18.1 months) in 25/127 assessable patients (19.7%). Significant or suggestive predictors of NAC response, in Luminal A/B HER2-: lower spleen SUVmax and patients' age (OR 0.06; 0.93) for pCR; lower TLRmax, TLRmean and BLRmax (OR 1.33; 1.22; and 26.42) for residual disease. Significant negative RFS predictors: higher SUVmax, SUVmean, SUVpeak (HR 1.10; 1.15; 1.11), TLRmax and TLRmean (HR 1.02; 1.00), MTV and TLG (HR 1.32; 1.26) in Luminal A/B HER2-; higher spleen SUVmax, PET-positive nodes' number and patients' age (HR 6.24; 1.20; 1.08) in Luminal B HER2+/HER2+.

CONCLUSION

Primary tumor and lymphoid organs parameters at baseline 18F-FDG PET/CT resulted as predictors of NAC response and prognosis in LABC patients, respectively, reflecting the BC cells' proliferative activity and metabolic burden, and the role of tumor-induced immune-system activation on tumors' behavior and treatment responsiveness. In LABC candidates to NAC, baseline PET information could improve treatment planning and prognostic stratification.

Tanshinone IIA destabilizes SLC7A11 by regulating PIAS4-mediated SUMOylation of SLC7A11 through KDM1A, and promotes ferroptosis in breast cancer

INTRODUCTION

Breast cancer (BC) is the most common malignancy in women with unfavorite prognosis.

OBJECTIVES

Tanshinone IIA (Tan IIA) inhibits BC progression, however, the underlying mechanism remains largely undefined.

METHODS

The cytotoxicity of Tan IIA was assessed by CCK-8 and LDH assays. Ferroptosis was monitored by the level of MDA, Fe2+, lipid ROS and GSH. IHC and western blot were employed to detect the localization and expression of SLC7A11, PIAS4, KDM1A and other key molecules. The SUMOylation of SLC7A11 was detected by Ni-beads pull-down assay and Co-IP. Luciferase and ChIP assays were employed to detect the direct association between KDM1A and PIAS4 promoter. The proliferative and metastatic properties of BC cells were assessed by colony formation, CCK-8 and Transwell assays, respectively. The in vitro findings were verified in xenograft and lung metastasis models.

RESULTS

Tan IIA promoted ferroptosis by suppressing SLC7A11 in BC cells. Silencing of PIAS4 or KDM1A inhibited cell growth and metastasis in BC. Mechanistically, PIAS4 facilitated the SUMOylation of SLC7A11 via direct binding to SLC7A11, and KDM1A acted as a transcriptional activator of PIAS4. Functional studies further revealed that Tan IIA decreased KDM1A expression, thus suppressing PIAS4 expression transcriptionally. The inhibition of PIAS4-dependent SUMOylation of SLC7A11 further induced ferroptosis, thereby inhibiting proliferation and metastasis in BC.

CONCLUSION

Tan IIA promoted ferroptosis and inhibited tumor growth and metastasis via suppressing KDM1A/PIAS4/SLC7A11 axis.

Quality of care in Colombian women with early-onset breast cancer in two time periods: findings from a nationwide administrative registry cohort

Background

Early-onset breast cancer (EOBC) refers to breast cancer diagnosed in women aged 18-45 years, being in many cases associated with hereditary breast cancer syndromes, diagnosed at more advanced stages and worse prognosis. In this paper, we sought to describe the main characteristics of EOBC and quality of care within the framework of the national health system in Colombia.

Methods

Cross-sectional study. We used a national administrative cancer registry, including women diagnosed with EOBC between 2017 and 2022. Demographic and clinical characteristics, as well as quality healthcare indicators, were compared (numbers and percentages) over two periods (2017-2019, 2020-2022), stratified by health insurance scheme.

Findings

7621 women with incident EOBC were included, constituting 19.4% (7621/39,238) of all breast cancers reported in the study period. The mean age was 39.2 (SD 5.2). Most of the cases (23% [1753/7621]) were diagnosed at stage IIA. Systemic therapy was the most frequent first treatment. When comparing both periods, the main areas of improvement were related to breast-conserving surgery for early stages (from 60.3% [459/761] to 68.3% [699/1024]), access to palliative care for metastatic cancer (from 29.5% [59/199] to 54.9% [101/184]), and reduction of waiting times. The time from collecting biopsy samples to receiving results showed the biggest improvement between periods (from a mean of 24.5 to 5.0 days). However, delays in initiating treatment persist, with an average of over two months.

Interpretation

While the quality of breast cancer care in women with EOBC has improved in recent years in Colombia, mainly due to better access to specific technologies and treatments, there are important challenges regarding early detection and health services delays that require corrective measures.

Funding

Work at the IARC/WHO was supported by regular budget funding.

Navigating heme pathways: the breach of heme oxygenase and hemin in breast cancer

Breast cancer remains a significant global health challenge, with diverse subtypes and complex molecular mechanisms underlying its development and progression. This review comprehensively examines recent advances in breast cancer research, with a focus on classification, molecular pathways, and the role of heme oxygenases (HO), heme metabolism implications, and therapeutic innovations. The classification of breast cancer subtypes based on molecular profiling has significantly improved diagnosis and treatment strategies, allowing for tailored approaches to patient care. Molecular studies have elucidated key signaling pathways and biomarkers implicated in breast cancer pathogenesis, shedding light on potential targets for therapeutic intervention. Notably, emerging evidence suggests a critical role for heme oxygenases, particularly HO-1, in breast cancer progression and therapeutic resistance, highlighting the importance of understanding heme metabolism in cancer biology. Furthermore, this review highlights recent advances in breast cancer therapy, including targeted therapies, immunotherapy, and novel drug delivery systems. Understanding the complex interplay between breast cancer subtypes, molecular pathways, and innovative therapeutic approaches is essential for improving patient outcomes and developing more effective treatment strategies in the fight against breast cancer.

Nuclear α-actinin-4 regulates breast cancer invasiveness and EMT

Epithelial-to-mesenchymal transition (EMT) is a key process where cells lose their adhesion properties and augment their invasive properties. α-Actinin4 (ACTN4) is an actin crosslinking protein that responds to mechanical stimuli and is found to be elevated in breast cancer patients. While ACTN4 has been implicated in regulating cancer invasiveness by modulating cytoskeletal organization, its nuclear functions remain much less explored. Here we address this question by first establishing a correlation between nuclear localization and invasiveness in breast cancer cells. Using cancer databases, we then establish a correlation between ACTN4 expression and EMT in breast cancer. Interestingly, TGFβ-induced EMT induction in MCF10A normal mammary epithelial cells leads to increased ACTN4 expression and nuclear enrichment. We then show that ACTN4 knockdown in MDA-MB-231 breast cancer cells, which harbor sizeable fraction of nuclear ACTN4, leads to reduced invasiveness and loss of mesenchymal traits. Similar behavior was observed in knockdown cells expressing K255E ACTN4, which is primarily localized to the cytosol. Together, our findings establish a role for nuclear ACTN4 in regulating invasiveness via modulation of EMT.

PRODE recovers essential and context-essential genes through neighborhood-informed scores

Gene context-essentiality assessment supports precision oncology opportunities. The variability of gene effects inference from loss-of-function screenings across models and technologies limits identifying robust hits. We propose a computational framework named PRODE that integrates gene effects with protein-protein interactions to generate neighborhood-informed essential (NIE) and neighborhood-informed context essential (NICE) scores. It outperforms the canonical gene effect approach in recovering missed essential genes in shRNA screens and prioritizing context-essential hits from CRISPR-KO screens, as supported by in vitro validations. Applied to Her2 + breast cancer tumor samples, PRODE identifies oxidative phosphorylation genes as vulnerabilities with prognostic value, highlighting new therapeutic opportunities.

Leukemia inhibitory factor in peripheral blood as a prognostic marker in breast cancer

PURPOSE

Analysis of gene expression profiles in peripheral blood cells has been explored as an approach for early detection of breast cancer. Thus, we aimed to evaluate the diagnostic and prognostic potential of LIF expression in the peripheral blood of women with breast cancer.

METHODS

A total of 121 women over 18 years of age with breast cancer and 80 healthy women were included. Peripheral blood samples were collected from patients at diagnosis and during chemotherapy treatment, as well as samples from healthy women for comparison. LIF expression was evaluated by qPCR.

RESULTS

LIF expression does not differ between patients and healthy women, ruling out its use as a liquid biopsy diagnostic tool for this disease. However, LIF expression increases during treatment in patients with positive progression and luminal tumor subtype, suggesting its potential as a prognostic marker. To reinforce this result, there was a negative correlation between LIF and HIF-1α and LIF and heparanase expression in blood, and LIF blood expression and a prognostic tumor score, all known markers associated with an unfavorable prognosis.

CONCLUSION

An increase in LIF expression in patients with negative disease progression suggests its potential as a therapeutic response indicator, particularly for those with the luminal tumor subtype. The negative correlation between LIF, HIF-1α, and heparanase provides new insights into LIF's role in breast cancer progression, with potential clinical implications for treatment monitoring. Elevated LIF expression in blood samples may reflect its interaction in tumorigenesis, immune regulation, and the tumor microenvironment.

Canine, Feline, and Murine Mammary Tumors as a Model for Translational Research in Breast Cancer

In veterinary medicine, mammary tumors are the most common neoplasms in female dogs and the third most frequent in cats, representing a significant challenge. Efforts have been directed toward adopting standardized diagnostic criteria to better understand tumor behavior and progression in these species. Meanwhile, the use of animal models has substantially advanced the understanding of comparative mammary carcinogenesis. These models provide critical insights into factors responsible for the disease in humans, with the expectation that such factors can be identified and controlled. In this context, this review presents a work based mainly on articles published by a research group specializing in mammary pathology (Laboratory of Comparative Pathology-Department of General Pathology-ICB/UFMG) and its collaborators, complementing their results with literature findings. The publications were categorized into animal research, experimental research, and human research. These studies addressed topics such as diagnosis, prognostic and predictive factors, tumor microenvironment, inflammation associated with tumors, treatment approaches, and factors influencing tumor growth. The conceptual network analysis underscores the importance of in vivo breast cancer models, both experimental and spontaneous, for understanding tumor progression mechanisms and therapeutic responses, offering valuable contributions to veterinary and human oncology.

A preclinical design approach for translation of biohybrid photosensitive nanoplatform for photodynamic therapy of breast cancer

Translating photodynamic therapy research into practice with high efficacy should follow characterization and optimization steps in an integrated process. In this way, integration of molecular-based simulations with in vitro and in vivo studies to produce more accurate models with higher prediction precision is inevitable. This study reports on the development a two-phase approach to design and synthesize, and characterize novel hybrid nano-photosensitizers to target breast cancer using molecular docking, microfluidic-based testing and animal studies. In the first phase, using pkCSM artificial intelligence tool and molecular docking, pharmacokinetic weaknesses of photoprotoporphyrin and its retention potential within albumin protein were identified. Biohybrid photosensitive nanoplatform were then synthesized using opto-microfluidics and characterized. In addition, an in-vivo method was used to qualitatively evaluate the opto-biological stability of the final optimized biohybrid nanoplatform. At the second phase, variables of photodynamic treatment were firstly optimized using design of experiment method for the optimized biohybrid photosensitive nanoplatform. Then, on-chip photodynamic studies were carried out in static and dynamic conditions. Results revealed that optimized biohybrid photosensitive nanoplatform as a nano-photosensitizer induced significant death of triple negative human cancer cells in static and dynamic cell cultures under optimized irradiation conditions. Consequently, the presented multiphase study that combined in-silico simulations with microfluidic-based synthesis and characterizations of nano-photosensitizers provided a more convergent model for development of efficacious cancer treatment for photodynamic therapy applications.

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.

The analysis of transcriptomic signature of TNBC-searching for the potential RNA-based predictive biomarkers to determine the chemotherapy sensitivity

Neoadjuvant chemotherapy is the foundation treatment for triple-negative breast cancer (TNBC) and frequently results in pathological complete response (pCR). However, there are large differences in clinical response and survival after neoadjuvant chemotherapy of TNBC patients. The aim was to identify genes whose expression significantly associates with the efficacy of neoadjuvant chemotherapy in patients with TNBC. Transcriptomes of 46 formalin-fixed paraffin-embedded (FFPE) tumor samples from TNBC patients were analyzed by RNA-seq by comparing 26 TNBCs with pCR versus 20 TNBCs with pathological partial remission (pPR). Subsequently, we narrowed down the list of genes to those that strongly correlated with drug sensitivity of 63 breast cancer cell lines based on Dependency Map Consortium data re-analysis. Furthermore, the list of genes was limited to those presenting specific expression in breast tumor cells as revealed in three large published single-cell RNA-seq breast cancer datasets. Finally, we analyzed which of the selected genes were significantly associated with overall survival (OS) in TNBC TCGA dataset. A total of 105 genes were significantly differentially expressed in comparison between pPR versus pCR. As revealed by PLSR analysis in breast cancer cell lines, out of 105 deregulated genes, 42 were associated with sensitivity to docetaxel, doxorubicin, paclitaxel, and/or cyclophosphamide. We found that 24 out of 42 sensitivity-associated genes displayed intermediate or strong expression in breast malignant cells using single-cell RNAseq re-analysis. Finally, 10 out of 24 genes were significantly associated with overall survival in TNBC TCGA dataset. Our RNA-seq-based findings suggest that there might be transcriptomic signature consisted of 24 genes specifically expressed in tumor malignant cells for predicting neoadjuvant response in FFPE samples from TNBC patients prior to treatment initiation. Additionally, nine out of 24 genes were potential survival predictors in TNBC. This group of 24 genes should be further investigated for its potential to be translated into a predictive test(s).

Triple-Negative, HER2/Neu, and Ki67 Markers in Breast Cancer Patients Undergoing Standard of Care Treatment in India: Real-World Evidence on Tumor Aggressiveness and Survival Outcomes

INTRODUCTION

Breast cancer is among the most prevalent cancers in women globally, with patients' survival adversely impacted by Ki67 expression and triple-negative phenotypes. In this study, we examined the relationship between HER2/neu, triple-negative, and Ki67 phenotypes and tumor aggressiveness along with the survival of breast cancer patients from India.

MATERIALS AND METHODS

A retrospective cohort study was performed using hospital-based data from a tertiary care center spanning January 2013 to August 2023. The study included breast cancer patients who received neoadjuvant chemotherapy based on preoperative assessment and/or adjuvant chemotherapy following postoperative evaluation. Patients with other primary cancers or those treated with investigational drugs were excluded. Data on variables such as age, parity, menopause, cancer stage and grade, estrogen receptor (ER), progesterone receptor (PR), HER2/neu, Ki67 score, and the use of biologicals, hormones, and chemoradiotherapy were analyzed using correlation and regression tests to identify factors associated with aggressive tumor behavior. Kaplan-Meier survival model and Cox-proportional hazard test were applied.

RESULTS

A total of 389 breast cancer patients with a mean age of 54.3 years met the eligibility criteria and were included in the analysis. A higher prevalence of hormone receptor positivity was observed among Indian patients than in Western countries. Younger, premenopausal women were more likely to present with high-grade tumors and high Ki67 scores, poorer overall survival, and the need for chemoradiotherapy. On the other hand, nulliparous patients mostly had triple-negative tumors with high Ki67 scores. Aggressive tumor behavior was linked to HER2/neu positivity, ER and PR negativity, and the requirement for both neoadjuvant and adjuvant chemotherapy. At the same time, premenopausal patients more frequently were candidates for radiation therapy. HER2/neu expression demonstrated a moderate negative correlation with co-expression of hormone receptors (ER and PR). Cancer grade was associated with Ki67 levels, nulliparity, triple-negative status, and hormone receptor expressions (ER and PR). Median survival was not reached for the study cohort.

CONCLUSION

Tumor aggressiveness was associated with a high Ki67 score, HER2/neu positivity, and the absence of hormone receptor expression in patients.

Metabolic Profiling of Breast Cancer Cell Lines: Unique and Shared Metabolites

Breast Cancer (BrCa) exhibits a high phenotypic heterogeneity, leading to the emergence of aggressive clones and the development of drug resistance. Considering the BrCa heterogeneity and that metabolic reprogramming is a cancer hallmark, we selected seven BrCa cell lines with diverse subtypes to provide their comprehensive metabolome characterization: five lines commonly used (SK-Br-3, T-47D, MCF-7, MDA-MB-436, and MDA-MB-231), and two patient-derived xenografts (Hbcx39 and Hbcx9). We characterized their endometabolomes using 1H-NMR spectroscopy. We found distinct metabolite profiles, with certain metabolites being common but differentially accumulated across the selected BrCa cell lines. High levels of glycine, lactate, glutamate, and formate, metabolites known to promote invasion and metastasis, were detected in all BrCa cells. In our experiment setting were identified unique metabolites to specific cell lines: xanthine and 2-oxoglutarate in SK-Br-3, 2-oxobutyrate in T-47D, cystathionine and glucose-1-phosphate in MCF-7, NAD+ in MDA-MB-436, isocitrate in MDA-MB-231, and NADP+ in Hbcx9. The unique and enriched metabolites enabled us to identify the metabolic pathways modulated in a cell-line-specific manner, which may represent potential candidate targets for therapeutic intervention. We believe this study may contribute to the functional characterization of BrCa cells and assist in selecting appropriate cell lines for drug-response studies.

Interaction of GPER-1 with the endocrine signaling axis in breast cancer

G Protein-Coupled Estrogen Receptor 1 (GPER-1) is a membrane estrogen receptor that has emerged as a key player in breast cancer development and progression. In addition to its direct influence on estrogen signaling, a crucial interaction between GPER-1 and the hypothalamic-pituitary-gonadal (HPG) axis has been evidenced. The novel and complex relationship between GPER-1 and HPG implies a hormonal regulation with important homeostatic effects on general organ development and reproductive tissues, but also on the pathophysiology of cancer, especially breast cancer. Recent research points to a great versatility of GPER-1, interacting with classical estrogen receptors and with signaling pathways related to inflammation. Importantly, through its activation by environmental and synthetic estrogens, GPER-1 is associated with hormone therapy resistance in breast cancer. These findings open new perspectives in the understanding of breast tumor development and raise the possibility of future applications in the design of more personalized and effective therapeutic approaches.

Unveiling the role of SYNGR4 in breast cancer development: a novel target for immunotherapy

Introduction

SYNGR4 is considered to be one of the causative genes for amyotrophic lateral sclerosis, but its role in breast cancer development has not been revealed.

Methods

The expression of SYNGR4 in a variety of malignancies including breast cancer was analyzed using Genotype Tissue Expression (GTEx) and the Cancer Genome Atlas (TCGA) databases and verified by specimens collected from our center. The effect of SYNGR4 on breast cancer prognosis was analyzed using bioinformatics and possible pathways by which this molecule affects breast cancer prognosis were explored. The effect of SYNGR4 on immune infiltration of breast cancer was analyzed using GSVA, and the effects of SYNGR4 on breast cancer proliferation, migration, and tumor-associated macrophage polarization in cancer foci were verified by cellular and animal experiments, respectively.

Results

SYNGR4 is highly expressed in a variety of malignant tumors, including breast cancer, and affects the prognosis of breast cancer patients. This may be a volatile effect through Organelle fission, chromosome segregation, nuclear division, etc. SYNGR4 overexpression affects breast cancer proliferation, migration, and tumor immune infiltration, and promotes breast cancer tumor-associated macrophage polarization toward M2.

Discussion

SYNGR4 overexpression can affect the prognosis of breast cancer patients by promoting M2 polarization of tumor-associated macrophages in breast cancer, and this molecule may be a novel target for breast cancer immunotherapy.

Targeting ROR2 homooligomerization disrupts ROR2-dependent signaling and suppresses stem-like cell properties of human breast adenocarcinoma

Breast cancer stem-like cells (CSCs) are enriched following treatment with chemotherapy, and posited as having a high level of plasticity and enhanced tumor-initiation capacity, which can enable cancer relapse. Here, we show that such features are shared by breast cancer (BCA) cells that express receptor tyrosine kinase-like orphan receptor (ROR2), which is expressed primarily during embryogenesis and by various cancers. We find that Wnt5a can induce ROR2 homooligomerization to activate noncanonical Wnt signaling and enhance tumor-initiation capacity of BCA cells. Molecular analysis reveals that the cysteine-rich domain and transmembrane domain are required for ROR2 homooligomerization to activate ROR2. Treatment with a newly generated monoclonal antibody (mAb) specific for ROR2 can block Wnt5a-induced ROR2 homooligomerization, ROR2-dependent noncanonical Wnt signaling, and impair the capacity of BCA patient-derived xenografts to initiate tumor in immune-deficient mice. Collectively, these results indicate that targeting ROR2 (e.g., using mAb) suppresses BCA stemness and, thereby, may prevent BCA relapse.

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.

Nanoparticulate drug combination inhibits DNA damage repair and PD-L1 expression in BRCA-mutant and wild type triple-negative breast cancer

The high mortality rate associated with metastatic breast cancer presents a significant global challenge. Inherent and chemotherapy-induced DNA damage repair, alongside immunosuppression, drastically contribute to triple-negative breast cancer (TNBC) relapse and metastasis. While poly (ADP-ribose) polymerase (PARP) inhibitors such as olaparib show effectiveness against BRCA1-mutant TNBC, they may lead to drug resistance and reduced efficacy due to increased programmed death-ligand 1 (PD-L1) expression. Our study explored the use of polymer-lipid nanoparticles (PLN) loaded with doxorubicin (DOX) and oligomeric hyaluronic acid (oHA), functionalized iRGD-peptide for integrins targeting (iRGD-DOX-oHA-PLN), to prevent TNBC immunosuppression, DNA repair, and metastasis. The results demonstrate that the iRGD-DOX-oHA-PLNs efficiently downregulated single and double-strand DNA repair proteins and enhanced DNA damage while decreasing PD-L1 expression compared to olaparib. Accordingly, iRGD-DOX-oHA-PLN treatment showed significantly higher efficiency in reducing levels of primary tumor growth and numbers of metastases to the lung and liver compared to olaparib in vitro and in vivo in both BRCA1-mutant and wild type TNBC orthotopic xenograft models.

A Comprehensive Analysis of Neoadjuvant Chemotherapy in Breast Cancer: Adverse Events, Clinical Response Rates, and Surgical and Pathological Outcomes-Bozyaka Experience

Objectives: To evaluate the neoadjuvant chemotherapy (NACTx) process in breast cancer (BC), its significant treatment-related adverse events (trAEs), tumor clinical response rates, and surgical and pathological outcomes, and to analyze factors influencing cavity shaving and axillary lymph node dissection (ALND) following sentinel lymph node biopsy (SLNB). Methods: A comprehensive retrospective study was conducted at a single center on patients who received NACTx for BC between 2015 and 2021. Results: Medical records of 242 patients were reviewed. Approximately one-fifth encountered grade ≥ 3 trAEs (21.5%), leading 3.3% to discontinue chemotherapy. Anthracycline cardiotoxicity (2.2%) caused one death (mortality rate = 0.4%). For clinical response and surgical and pathological outcomes, 229 patients were eligible. Clinical progression occurred in 3.9% of the patients (14% in triple-negative BC, p = 0.004). Breast-conserving surgery (BCS) was performed in 55% of the patients. There was no significant difference between the type of breast surgery (BCS vs. mastectomy) and molecular subtype, histology, tumor size, or tumor's pathological response degree. Cavity shaving was required in one-fifth of the patients who underwent BCS (n = 134) due to an invasive tumor at the surgical margin (SM). Tumor histology (invasive ductal vs. invasive lobular carcinoma; OR: 4.962, 95% CI 1.007-24.441, p = 0.049) and tumor SUVMax value (OR: 0.866, 95% CI 0.755-0.993, p = 0.039) had significant independent efficacy on SM positivity. Initially, 75% underwent SLNB, but nearly half of them needed ALND. ALND rates were significantly higher in the luminal A and LB-HER2(-) groups (87% vs. 69%) than in the HER2(+) and TN groups (43% to 50%) (p = 0.001). All luminal A patients and those with lobular histology required ALND after SLNB, but no patients in the HER2-enriched group required ALND. ER positivity and higher PR expression levels were associated with an increased need for ALND after SLNB, whereas HER2 positivity and higher SUVMax values of LN(s) were associated with a significantly reduced need for ALND. About 27% of the patients achieved overall pCR. No pCR was achieved in the LA group. Conclusions: The BC NACTx process requires close monitoring due to severe AEs and disease progression. NACTx decisions must be made on experienced multidisciplinary tumor boards, considering tumor characteristics and expected targets.

The spatially informed mFISHseq assay resolves biomarker discordance and predicts treatment response in breast cancer

Current assays fail to address breast cancer's complex biology and accurately predict treatment response. On a retrospective cohort of 1082 female breast tissues, we develop and validate mFISHseq, which integrates multiplexed RNA fluorescent in situ hybridization with RNA-sequencing, guided by laser capture microdissection. This technique ensures tumor purity, unbiased whole transcriptome profiling, and explicitly quantifies intratumoral heterogeneity. Here we show mFISHseq has 93% accuracy compared to immunohistochemistry. Our consensus subtyping and risk groups mitigate single sample discordance, provide early and late prognostic information, and identify high risk patients with enriched immune signatures, which predict response to neoadjuvant immunotherapy in the multicenter, phase II, prospective I-SPY2 trial. We identify putative antibody-drug conjugate (ADC)-responsive patients, as evidenced by a 19-feature T-DM1 classifier, validated on I-SPY2. Deploying mFISHseq as a research-use only test on 48 patients demonstrates clinical feasibility, revealing insights into the efficacy of targeted therapies, like CDK4/6 inhibitors, immunotherapies, and ADCs.

Untangling Breast Cancer: Trailing Towards Nanoformulations-based Drug Development

All over the world, cancer death and prevalence are increasing. Breast cancer (BC) is the major cause of cancer mortality (15%) which makes it the most common cancer in women. BC is defined as the furious progression and quick division of breast cells. Novel nanotechnology-based approaches helped in improving survival rate, metastatic BC is still facing obstacles to treat with an expected overall 23% survival rate. This paper represents epidemiology, classification (non-invasive, invasive and metastatic), risk factors (genetic and non-genetic) and treatment challenges of breast cancer in brief. This review paper focus on the importance of nanotechnology-based nanoformulations for treatment of BC. This review aims to deliver elementary insight and understanding of the novel nanoformulations in BC treatment and to explain to the readers for enduring designing novel nanomedicine. Later, we elaborate on several types of nanoformulations used in tumor therapeutics such as liposomes, dendrimers, polymeric nanomaterials and many others. Potential research opportunities for clinical application and current challenges related to nanoformulations utility for the treatment of BC are also highlighted in this review. The role of artificial intelligence is elaborated in detail. We also confer the existing challenges and perspectives of nanoformulations in effective tumor management, with emphasis on the various patented nanoformulations approved or progression of clinical trials retrieved from various search engines.

Molecular Classification of Breast Cancer Using Weakly Supervised Learning

PURPOSE

The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer.

MATERIALS AND METHODS

Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches.

RESULTS

The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers.

CONCLUSION

The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Loss of ERα involved-HER2 induction mediated by the FOXO3a signaling pathway in fulvestrant-resistant breast cancer

Patients with estrogen receptor alpha (ERα)-positive breast cancer are commonly treated with anti-estrogen drugs as an initial treatment strategy. Fulvestrant, an estrogen receptor antagonist, effectively blocks ERα signaling; however, long-term fulvestrant treatment induces drug resistance in the absence of ERα. In this study, we investigated the molecular mechanism underlying the loss of ERα, FOXO3a, and induction of HER2 in fulvestrant-resistant breast cancer. Short-term fulvestrant treatment degraded ERα proteins via the ubiquitin-proteasome degradation pathway in MCF7 cells. MCF7 cells turn into highly proliferative cells (fulvestrant-resistant cells: Ful-R) after long-term fulvestrant treatment. These cells exhibit markedly suppressed estrogen and progesterone receptor levels. The phosphorylation of EGFR, HER2, and ERK was induced in Ful-R, and these phosphorylation inhibitors suppressed cell proliferation in Ful-R. FOXO3a, a transcriptional regulator of ERα was decreased in Ful-R, and a ubiquitin-proteasome inhibitor restored the expression of FOXO3a. These results suggest that the suppression of FOXO3a and ERα led to the increased expression of TGF-α, EGFR, and HER2 and subsequent cell proliferation in Ful-R. This study highlights the potential development of therapeutic drugs targeting FOXO3a for the treatment of HER2-positive, estrogen, and progesterone receptor-negative her2-type proliferative breast cancers.

Clinical Implications of Breast Cancer Intrinsic Subtypes

Estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancers have different genomic architecture and show large-scale gene expression differences consistent with different cellular origins, which is reflected in the luminal (i.e., ER+) versus basal-like (i.e., ER-) molecular class nomenclature. These two major molecular subtypes have distinct epidemiological risk factors and different clinical behaviors. Luminal cancers can be subdivided further based on proliferative activity and ER signaling. Those with a high expression of proliferation-related genes and a low expression of ER-associated genes, called luminal B, have a high risk of early recurrence (i.e., within 5 years), derive significant benefit from adjuvant chemotherapy, and may benefit from adding immunotherapy to chemotherapy. This subset of luminal cancers is identified as the genomic high-risk ER+ cancers by the MammaPrint, Oncotype DX Recurrence Score, EndoPredict, Prosigna, and several other molecular prognostic assays. Luminal A cancers are characterized by low proliferation and high ER-related gene expression. They tend to have excellent prognosis with adjuvant endocrine therapy. Adjuvant chemotherapy may not improve their outcome further. These cancers correspond to the genomic low-risk categories. However, these cancers remain at risk for distant recurrence for extended periods of time, and over 50% of distant recurrences occur after 5 years. Basal-like cancers are uniformly highly proliferative and tend to recur within 3-5 years of diagnosis. In the absence of therapy, basal-like breast cancers have the worst survival, but these also include many highly chemotherapy-sensitive cancers. Basal-like cancers are often treated with preoperative chemotherapy combined with an immune checkpoint inhibitor which results in 60-65% pathologic complete response rates that herald excellent long-term survival. Patients with residual cancer after neoadjuvant therapy can receive additional postoperative chemotherapy that improves their survival. Currently, there is no clinically actionable molecular subclassification for basal-like cancers, although cancers with high androgen receptor (AR)-related gene expression and those with high levels of immune infiltration have better prognosis, but currently their treatment is not different from basal-like cancers in general. A clinically important, minor subset of breast cancers are characterized by frequent HER2 gene amplification and high expression of a few dozen genes, many residing on the HER2 amplicon. This is an important subset because of the highly effective HER2 targeted therapies which are synergistic with endocrine therapy and chemotherapy. The clinical behavior of HER2-enriched cancers is dominated by the underlying ER subtype. ER+/HER2-enriched cancers tend to have more indolent course and lesser chemotherapy sensitivity than their ER counterparts.

The splicing machinery is dysregulated and represents a therapeutic vulnerability in breast cancer

Breast cancer (BCa) is a highly prevalent pathological condition (̴30% in women) with limited and subtype-dependent prognosis and therapeutic options. Therefore, BCa management might benefit from the identification of novel molecular elements with clinical potential. Since splicing process is gaining a great relevance in cancer, this work analysed the expression of multiple Spliceosome Components (SCs = 17) and Splicing Factors (SFs = 26) and found a drastic dysregulation in BCa (n = 69) vs. control (negative biopsies; n = 50) samples. Among all the components analysed, we highlight the upregulation of ESRP1 and down-regulation of PRPF8 and NOVA1 in BCa vs. control samples. Indeed, ESRP1 was specially overexpressed in triple-negative BCa (TNBCa) and associated with worse prognosis (i.e., higher BCa grade and lower overall survival), suggesting an association of ESRP1 with BCa aggressiveness. On the other hand, PRPF8 expression was generally downregulated in BCa with no associations to clinical characteristics, while NOVA1 expression was lower in TNBCa patients and highly aggressive tumours. Consistently, NOVA1 overexpression in vitro reduced functional parameters of aggressiveness in ER-/PR- cell lines (MDA-MB-231 and BT-549) but not in ER+/PR+ cells (MCF7), suggesting a critical role of NOVA1 in subtype-specific BCa. Finally, the in vitro pharmacological inhibition of splicing machinery using pladienolide B decreased aggressiveness features in all the BCa cell lines, showing a subtype-independent inhibitory potential, but being relatively innocuous in normal-like breast cells. These results demonstrate the profound dysregulation of the splicing machinery in BCa and their potential as source of promising diagnosis/prognosis markers, as well as valuable therapeutic targets for BCa.

A Real-World Comparison Between Adjuvant Docetaxel with Cyclophosphamide (TC) and Anthracycline-Taxane Chemotherapy in Early HER-2 Negative Breast Cancer

BACKGROUND

Anthracycline-taxane chemotherapy is the gold standard in high-risk breast cancer (BC), despite the potential risk of congestive heart failure (CHF). A suitable alternative for anthracycline-sparing chemotherapy is through the combination of docetaxel and cyclophosphamide (TC).

METHODS

Through a retrospective study of stage I-III HER2-negative BC, using administrative databases, we analyzed a total of 10,634 women treated with adjuvant chemotherapy in Ontario, Canada, between 2009 and 2017. We compared TC versus standardized anthracycline-taxane chemotherapies (ACT and FEC-D). We investigated the overall survival (OS), and explored the incidence of CHF, emergency department (ED) visits and febrile neutropenia.

RESULTS

With a median follow-up of 5.5 years, the 5-year analysis showed an increased OS in patients treated with TC, versus those treated with ACT, HR 0.77 (0.63-0.95, p = 0.015). Among ER+ BC, there was an increased OS in patients treated with ACT and FEC-D, versus those treated with TC, HR 0.70 (0.52-0.95, p = 0.021) and HR 0.71 (0.56-0.91, p = 0.007), respectively. There were no substantial differences in CHF, between TC and anthracycline-based treatments. Patients treated with TC and FEC-D had more ED visits, compared to those treated with ACT.

CONCLUSION

Our study shows that anthracycline-taxane regimens were the most commonly prescribed adjuvant chemotherapy options in HER2-negative BC. Women who received ACT had the lowest OS, likely due to their unfavorable pathology.

Dysregulated miRNA Expression and Androgen Receptor Loss in Racially Distinct Triple-Negative Breast Cancer

Androgen receptor (AR)-negative triple-negative breast cancer (TNBC), often termed quadruple-negative breast cancer (QNBC), disproportionately impacts women of African descent, leading to poorer overall survival (OS). MiRNAs regulate the expression of gene drivers involved in critical signaling pathways in TNBC, such as the AR gene, and their expression varies across races and breast cancer subtypes. This study investigates whether differentially expressed miRNAs influence AR transcription, potentially contributing to the observed disparities between African American (AA) and European American (EA) QNBC patients. Race-annotated TNBC samples (n = 129) were analyzed for AR expression status and revealed the prevalence of QNBC in AA patients compared to EA (76.6% vs. 57.7%) and a significant association of AR loss with poor survival among AAs. The Cancer Genome Atlas (TCGA) RNA-seq data showed that AAs with TNBC (n = 32) had lower AR mRNA levels than EAs (n = 67). Among TCGA patients in the AR-low group, AAs had significantly poorer OS than EAs. In our cohort, 46 miRNAs exhibited differential expression between AAs and EAs with QNBC. Ten of these miRNAs (miR-1185-5p, miR-1305, miR-3161, miR-3690, miR-494-3p, miR-509-3-5p, miR-619-3p, miR-628-3p, miR-873-5p, and miR-877-5p) were predicted to target the AR gene/signaling. The loss of AR expression is linked to poorer prognoses in AA women. The understanding of the specific miRNAs involved and their regulatory mechanisms on AR expression could provide valuable insights into why AA women are more prone to QNBC.

Modeling intratumor heterogeneity in breast cancer

Reduced therapy response in breast cancer has been correlated with heterogeneity in biomarker composition, expression level, and spatial distribution of cancer cells within a patient tumor. Thus, there is a need for models to replicate cell-cell, cell-stromal, and cell-microenvironment interactions during cancer progression. Traditional two-dimensional (2D) cell culture models are convenient but cannot adequately represent tumor microenvironment histological organization,in vivo3D spatial/cellular context, and physiological relevance. Recently, three-dimensional (3D)in vitrotumor models have been shown to provide an improved platform for incorporating compositional and spatial heterogeneity and to better mimic the biological characteristics of patient tumors to assess drug response. Advances in 3D bioprinting have allowed the creation of more complex models with improved physiologic representation while controlling for reproducibility and accuracy. This review aims to summarize the advantages and challenges of current 3Din vitromodels for evaluating therapy response in breast cancer, with a particular emphasis on 3D bioprinting, and addresses several key issues for future model development as well as their application to other cancers.

An Azomethine Derivative, BCS3, Targets XIAP and cIAP1/2 to Arrest Breast Cancer Progression Through MDM2-p53 and Bcl-2-Caspase Signaling Modulation

Background: Breast cancer influences more than 2 million women worldwide annually. Since apoptotic dysregulation is a cancer hallmark, targeting apoptotic regulators encompasses strategic drug development for cancer therapy. One such class of apoptotic regulators is inhibitors of apoptosis proteins (IAP) which are a class of E3 ubiquitin ligases that actively function to support cancer growth and survival. Methods: The current study reports design, synthesis, docking analysis (based on binding to IAP-BIR3 domains), anti-proliferative and anti-tumor potential of the azomethine derivative, 1-(4-chlorophenyl)-N-(4-ethoxyphenyl)methanimine (BCS3) on breast cancer (in vitro and in vivo) and its possible mechanisms of action. Results: Strong selective cytotoxic activity was observed in MDA-MB-231, MCF-7, and MDA-MB-468 breast cancer cell lines that exhibited IC50 values, 1.554 µM, 5.979 µM, and 6.462 µM, respectively, without affecting normal breast cells, MCF-10A. For the evaluation of the cytotoxic potential of BCS3, immunofluorescence, immunoblotting, and FACS (apoptosis and cell cycle) analyses were conducted. BCS3 antagonized IAPs, thereby causing MDM2-p53 and Bcl-2-Caspase-mediated intrinsic and extrinsic apoptosis. It also modulated p53 expression causing p21-CDK1/cyclin B1-mediated cell cycle arrest at S and G2/M phases. The in vitro findings were consistent with in vivo findings as observed by reduced tumor volume and apoptosis initiation (TUNEL assay) by IAP downregulation. BCS3 also produced potent synergistic effects with doxorubicin on tumor inhibition. Conclusions: Having witnessed the profound anti-proliferative potential of BCS3, the possible adverse effects related to anti-cancer therapy were examined following OECD 407 guidelines which confirmed its systemic safety profile and well tolerability. The results indicate the promising effect of BCS3 as an IAP antagonist for breast cancer therapy with fewer adverse effects.

Drug discovery of N-methyl-pyrazole derivatives as potent selective estrogen receptor degrader (SERD) for the treatment of breast cancer

Nowadays, ERα is considered to be a primary target for the treatment of breast cancer, and selective estrogen receptor degraders (SERDs) are emerging as promising antitumor agents. By analysing ERα-SERDs complexes, the pharmacophore features of SERDs and the crucial protein-ligand interactions were identified. Then, by utilizing the scaffold-hopping and bioisosteres strategy, 23 novel derivatives were designed, synthesized and biologically evaluated. Among these derivatives, A20 exhibited potent ERα binding affinity (IC50 = 24.0 nM), degradation ability (EC50 = 5.3 nM), excellent ER selectivity, and outstanding anti-proliferative effects on MCF-7 cells (IC50 = 0.28 nM). Further biological studies revealed that A20 could degrade ERα through proteasome-mediated pathway, suppress signal transduction of MCF-7 cells, and arrest the cell cycle in G1 phase. Moreover, A20 showed excellent antitumor effect (TGI = 92.98 %, 30 mg kg-1 day-1) in the MCF-7 xenograft model in vivo with good safety and favorable pharmacokinetics (F = 39.6 %), making it a promising candidate for the treatment of breast cancer.