Heterogeneity in breast cancer

Navigating precision: the crucial role of next-generation sequencing recurrence risk assessment in tailoring adjuvant therapy for hormone receptor-positive, human epidermal growth factor Receptor2-negative early breast cancer

Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common subtype, representing over two-thirds of new diagnoses. Adjuvant therapy, which encompasses various medications and treatment durations, is the standard approach for managing early stage HR+ HER2- breast cancer. Optimizing treatment is essential to minimize unnecessary side effects while addressing the biological variability inherent in HR+/HER2- breast cancers. Incorporating biological biomarkers into treatment decisions, alongside traditional clinical factors, is vital. Gene expression assays can identify patients unlikely to benefit from adjuvant chemotherapy, thereby refining treatment strategies and improving risk assessment. This paper reviews evidence for several genomic tests, including Oncotype DX, MammaPrint, Breast Cancer Index, RucurIndex, and EndoPredict, which assist in tailoring adjuvant therapy. Additionally, we explore the role of liquid biopsies in personalizing treatment, emphasizing the importance of considering late relapse risks and potential benefits of extended systemic therapy for HR+/HER2- breast cancer patients.

A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin

Tumor heterogeneity contributes significantly to chemoresistance, a leading cause of treatment failure. To better personalize therapies, it is essential to develop tools capable of identifying and predicting intra- and inter-tumor heterogeneities. Biology-inspired mathematical models are capable of attacking this problem, but tumor heterogeneity is often overlooked in in-vivo modeling studies, while phenotypic considerations capturing spatial dynamics are not typically included in in-vitro modeling studies. We present a data assimilation-prediction pipeline with a two-phenotype model that includes a spatiotemporal component to characterize and predict the evolution of in-vitro breast cancer cells and their heterogeneous response to chemotherapy. Our model assumes that the cells can be divided into two subpopulations: surviving cells unaffected by the treatment, and irreversibly damaged cells undergoing treatment-induced death. MCF7 breast cancer cells were previously cultivated in wells for up to 1000 hours, treated with various concentrations of doxorubicin and imaged with time-resolved microscopy to record spatiotemporally-resolved cell count data. Images were used to generate cell density maps. Treatment response predictions were initialized by a training set and updated by weekly measurements. Our mathematical model successfully calibrated the spatiotemporal cell growth dynamics, achieving median [range] concordance correlation coefficients of > .99 [.88, >.99] and .73 [.58, .85] across the whole well and individual pixels, respectively. Our proposed data assimilation-prediction approach achieved values of .97 [.44, >.99] and .69 [.35, .79] for the whole well and individual pixels, respectively. Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting.

Spatially Informed Graph Structure Learning Extracts Insights from Spatial Transcriptomics

Embeddings derived from cell graphs hold significant potential for exploring spatial transcriptomics (ST) datasets. Nevertheless, existing methodologies rely on a graph structure defined by spatial proximity, which inadequately represents the diversity inherent in cell-cell interactions (CCIs). This study introduces STAGUE, an innovative framework that concurrently learns a cell graph structure and a low-dimensional embedding from ST data. STAGUE employs graph structure learning to parameterize and refine a cell graph adjacency matrix, enabling the generation of learnable graph views for effective contrastive learning. The derived embeddings and cell graph improve spatial clustering accuracy and facilitate the discovery of novel CCIs. Experimental benchmarks across 86 real and simulated ST datasets show that STAGUE outperforms 15 comparison methods in clustering performance. Additionally, STAGUE delineates the heterogeneity in human breast cancer tissues, revealing the activation of epithelial-to-mesenchymal transition and PI3K/AKT signaling in specific sub-regions. Furthermore, STAGUE identifies CCIs with greater alignment to established biological knowledge than those ascertained by existing graph autoencoder-based methods. STAGUE also reveals the regulatory genes that participate in these CCIs, including those enriched in neuropeptide signaling and receptor tyrosine kinase signaling pathways, thereby providing insights into the underlying biological processes.

Breast cancer epidemiology, diagnostic barriers, and contemporary trends in breast nanotheranostics and mechanisms of targeting

INTRODUCTION

Breast cancer is one of the main causes of mortality in women globally. Early and accurate diagnosis represents a milestone in cancer management. Several breast cancer diagnostic agents are available. Many chemotherapeutic agents in conventional dosage forms are approved; nevertheless, they lack cancer cell specificity, resulting in improper treatment and undesirable side effects. Recently, nanotheranostics has emerged as a new paradigm to achieve safe and effective cancer diagnosis and management.

AREA COVERED

This review provides insight into breast cancer epidemiology, barriers hindering the early diagnosis, and effective delivery of chemotherapeutics. Also, conventional diagnostic agents and recent nanotheranostic platforms have been used in breast cancer. In addition, mechanisms of cancer cell targeting and nano-carrier surface functionalization as an effective approach for chemotherapeutic targeting were reviewed along with future perspectives.

EXPERT OPINION

We proposed that modified nano-carriers may provide an efficacious approach for breast cancer drug targeting. These nanotheranostics need more clinical evaluations to confirm their efficacy in cancer management. In addition, we recommend the use of artificial intelligence (AI) as a promising approach for early and efficient assessment of breast lesions. AI allows better interpretation and analysis of nanotheranostic data, which minimizes misdiagnosis and avoids the belated intervention of health care providers.

The impact of previous therapy on overall-survival in registration clinical trials for 1st line metastatic breast cancer a systemic review

AIM

To explore the impact of previous treatment on the efficacy of investigational new drugs in registration trials for 1st line metastatic breast cancer (MBC).

METHODS

Thirteen US Food and Drug Administration (FDA) approved indications for 1st line MBC between 1/2000-12/2023 were identified and their supporting publications were searched in the ClinicalTrials.gov and Google Scholar. Where available, hazard ratios (HRs) and 95 % confidence intervals (CI) for overall-survival (OS) were pooled into meta-analysis and the difference in the magnitude of OS benefit between treatment naïve and previously treated patients was analyzed.

RESULTS

There was no difference in the magnitude of OS benefit between treatment-naïve and previously treated patients (HR=0.72 versus 0.80,p for difference=0.25). In indications for triple-negative BC, treatment-naïve patients had higher magnitude of OS benefit compared to previously treated patients (HR=0.53 versus 0.81,p=0.03). In indications for luminal disease, the magnitude of benefit was comparable between the subgroups.

CONCLUSIONS

In trials supporting 1st line therapy for TNBC the magnitude of benefit is significantly higher in treatment naïve compared to previously treated patients. Our findings may represent a previously unrecognized bias, potentially over-estimating the benefit of triple-negative BC new drugs.

Effectiveness of combined targeted and hormonal therapies for post-menopausal women with hormone receptor-positive and HER2-negative advanced breast cancer: A systematic review and meta-analysis of RCTs

OBJECTIVE

we aim to synthesize available evidence on the effectiveness of hormonal plus targeted therapies for post-menopausal women with hormone receptor-positive and HER2-negative advanced breast cancer.

DATA SOURCES AND METHODS

We searched the following databases: Medline, PubMed, Cochrane Library, CINAHL, Web of Science, Scopus, and African Journal. Only studies that investigated the effectiveness of hormonal therapy combined with targeted therapy for HR+/HER2- advanced breast cancer treatment were included. The outcomes were progression-free survival (PFS), overall survival (OS) and objective response rate (ORR). A random-effect meta-analysis model was employed. Statistical analysis was performed using Comprehensive Meta-analysis version 3.

RESULTS

24 studies were included in the meta-analysis with an overall sample size of 7635. Median PFS, OS and ORR were found to be significantly increased in the combination group compared to hormonal monotherapy [SMD = 6.072 (95% CI = 3.785-8.360), p < 0.001], [SMD = 1.614 (95% CI = 0.139-3.089), p = 0.032] and [OR = 1.584 (CI 1.134-2.213), p = 0.007] respectively. Subgroup analysis showed a significant difference in PFS and ORR between patients who received "hormonal therapy + CDK4/6 inhibitors" vs hormonal therapy only [SMD = 6.015 (CI 3.069-8.960), p < 0.001], (OR = 1.828 (CI 1.030-3.243), p = 0.039] respectively.

CONCLUSION

Compared with hormonal monotherapy, targeted plus hormonal therapy significantly improves PFS, OS and ORR in postmenopausal women with HR+/HER2- advanced breast cancer.

The prognostic potential of mammographic growth rate of invasive breast cancer in the Nijmegen breast cancer screening cohort

OBJECTIVES

Insight into the aggressiveness of potential breast cancers found in screening may optimize recall decisions. Specific growth rate (SGR), measured on mammograms, may provide valuable prognostic information. This study addresses the association of SGR with prognostic factors and overall survival in patients with invasive carcinoma of no special type (NST) from a screened population.

METHODS

In this historic cohort study, 293 women with NST were identified from all participants in the Nijmegen screening program (2003-2007). Information on clinicopathological factors was retrieved from patient files and follow-up on vital status through municipalities. On consecutive mammograms, tumor volumes were estimated. After comparing five growth functions, SGR was calculated using the best-fitting function. Regression and multivariable survival analyses described associations between SGR and prognostic factors as well as overall survival.

RESULTS

Each one standard deviation increase in SGR was associated with an increase in the Nottingham prognostic index by 0.34 [95% confidence interval (CI): 0.21-0.46]. Each one standard deviation increase in SGR increased the odds of a tumor with an unfavorable subtype (based on histologic grade and hormone receptors; odds ratio 2.14 [95% CI: 1.45-3.15]) and increased the odds of diagnosis as an interval cancer (versus screen-detected; odds ratio 1.57 [95% CI: 1.20-2.06]). After a median of 12.4 years of follow-up, 78 deaths occurred. SGR was not associated with overall survival (hazard ratio 1.12 [95% CI: 0.87-1.43]).

CONCLUSIONS

SGR may indicate prognostically relevant differences in tumor aggressiveness if serial mammograms are available. A potential association with cause-specific survival could not be determined and is of interest for future research.

RNA splicing factor RBFOX2 is a key factor in the progression of cancer and cardiomyopathy

BACKGROUND

Alternative splicing of pre-mRNA is a fundamental regulatory process in multicellular eukaryotes, significantly contributing to the diversification of the human proteome. RNA-binding fox-1 homologue 2 (RBFOX2), a member of the evolutionarily conserved RBFOX family, has emerged as a critical splicing regulator, playing a pivotal role in the alternative splicing of pre-mRNA. This review provides a comprehensive analysis of RBFOX2, elucidating its splicing activity through direct and indirect binding mechanisms. RBFOX2 exerts substantial influence over the alternative splicing of numerous transcripts, thereby shaping essential cellular processes such as differentiation and development.

MAIN BODY OF THE ABSTRACT

Dysregulation of RBFOX2-mediated alternative splicing has been closely linked to a spectrum of cardiovascular diseases and malignant tumours, underscoring its potential as a therapeutic target. Despite significant progress, current research faces notable challenges. The complete structural characterisation of RBFOX2 remains elusive, limiting in-depth exploration beyond its RNA-recognition motif. Furthermore, the scarcity of studies focusing on RBFOX2-targeting drugs poses a hindrance to translating research findings into clinical applications.

CONCLUSION

This review critically assesses the existing body of knowledge on RBFOX2, highlighting research gaps and limitations. By delineating these areas, this analysis not only serves as a foundational reference for future studies but also provides strategic insights for bridging these gaps. Addressing these challenges will be instrumental in unlocking the full therapeutic potential of RBFOX2, paving the way for innovative and effective treatments in various diseases.

Therapeutic Potential of PLK1, KIF4A, CDCA5, UBE2C, CDT1, SKA3, AURKB, and PTTG1 Genes in Triple-Negative Breast Cancer: Correlating Their Expression with Sensitivity to GSK 461364 and IKK 16 Drugs

The treatment of triple-negative breast cancer (TNBC) has been associated with challenges due to the lack of expression of ER, PR, and HER2 receptors in tumor cells. This study aimed to identify genes with potential therapeutic targets in TNBC. Data from the cancer genome atlas regarding breast cancer (BC) were downloaded. After initial preprocessing, cancer samples were categorized into four groups: TNBC, HER2-positive, luminal A, and luminal B. Gene expression differences between these groups were calculated, focusing on genes that showed differential expression in TNBC. A protein-protein interaction network was conducted to identify hub genes among the candidate genes related to TNBC. The protein expression of candidate genes was assessed using immunohistochemistry data from the human protein atlas. Drug resistance and sensitivity associated with hub genes were identified using data from PharmacoDB. TNBC samples and the RT-qPCR method were used to confirm the results. Our findings revealed that eight genes, namely PLK1, KIF4A, CDCA5, UBE2C, CDT1, SKA3, AURKB, and PTTG1, had significant upregulation at the RNA level in TNBC subgroup compared to other subgroups and could be considered hub genes in TNBC. Compared to other subgroups, their expression level in TNBC samples had high sensitivity and specificity. RT-qPCR results also demonstrated a significant increase in levels of SKA3 and PTTG1 in the TNBC compared to healthy tissue and other subgroups. The protein expression of these genes was notably high in some BC samples. PharmacoDB data showed that some candidate genes were closely linked to drug sensitivity of GSK 461364 and IKK 16. The results of this study showed a significant increase in the expression level of PLK1, KIF4A, CDCA5, UBE2C, CDT1, SKA3, AURKB, and PTTG1 in TNBC compared to other BC subgroups. These genes show considerable promise as therapeutic targets for the TNBC subgroup.

Preliminary study on DCE-MRI radiomics analysis for differentiation of HER2-low and HER2-zero breast cancer

Purpose

This study aims to evaluate the utility of radiomic features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in distinguishing HER2-low from HER2-zero breast cancer.

Patients and methods

We retrospectively analyzed 118 MRI cases, including 78 HER2-low and 40 HER2-zero patients confirmed by immunohistochemistry or fluorescence in situ hybridization. From each DCE-MRI case, 960 radiomic features were extracted. These features were screened and reduced using intraclass correlation coefficient, Mann-Whitney U test, and least absolute shrinkage to establish rad-scores. Logistic regression (LR) assessed the model's effectiveness in distinguishing HER2-low from HER2-zero. A clinicopathological MRI characteristic model was constructed using univariate and multivariate analysis, and a nomogram was developed combining rad-scores with significant MRI characteristics. Model performance was evaluated using the receiver operating characteristic (ROC) curve, and clinical benefit was assessed with decision curve analysis.

Results

The radiomics model, clinical model, and nomogram successfully distinguished between HER2-low and HER2-zero. The radiomics model showed excellent performance, with area under the curve (AUC) values of 0.875 in the training set and 0.845 in the test set, outperforming the clinical model (AUC = 0.691 and 0.672, respectively). HER2 status correlated with increased rad-score and Time Intensity Curve (TIC). The nomogram outperformed both models, with AUC, sensitivity, and specificity values of 0.892, 79.6%, and 82.8% in the training set, and 0.886, 83.3%, and 90.9% in the test set.

Conclusions

The DCE-MRI-based nomogram shows promising potential in differentiating HER2-low from HER2-zero status in breast cancer patients.

The role of the ALKBH5 RNA demethylase in invasive breast cancer

BACKGROUND

N6-methyladenosine (m6A) is the most common internal RNA modification and is involved in regulation of RNA and protein expression. AlkB family member 5 (ALKBH5) is a m6A demethylase. Given the important role of m6A in biological mechanisms, m6A and its regulators, have been implicated in many disease processes, including cancer. However, the contribution of ALKBH5 to invasive breast cancer (BC) remains poorly understood. The aim of this study was to evaluate the clinicopathological value of ALKBH5 in BC.

METHODS

Publicly available data were used to investigate ALKBH5 mRNA alterations, prognostic significance, and association with clinical parameters at the genomic and transcriptomic level. Differentially expressed genes (DEGs) and enriched pathways with low or high ALKBH5 expression were investigated. Immunohistochemistry (IHC) was used to assess ALKBH5 protein expression in a large well-characterised BC series (n = 1327) to determine the clinical significance and association of ALKBH5 expression.

RESULTS

Reduced ALKBH5 mRNA expression was significantly associated with poor prognosis and unfavourable clinical parameters. ALKBH5 gene harboured few mutations and/or copy number alternations, but low ALKBH5 mRNA expression was seen. Patients with low ALKBH5 mRNA expression had a number of differentially expressed genes and enriched pathways, including the cytokine-cytokine receptor interaction pathway. Low ALKBH5 protein expression was significantly associated with unfavourable clinical parameters associated with tumour progression including larger tumour size and worse Nottingham Prognostic Index group.

CONCLUSION

This study implicates ALKBH5 in BC and highlights the need for further functional studies to decipher the role of ALKBH5 and RNA m6A methylation in BC progression.

Comprehensive Transcriptome Analysis Expands lncRNA Functional Profiles in Breast Cancer

Breast cancer is a heterogeneous disease that arises as a multi-stage process involving multiple cell types. Patients diagnosed with the same clinical stage and pathological classification may have different prognoses and therapeutic responses due to alterations in molecular genetics. As an essential marker for the molecular subtyping of breast cancer, long non-coding RNAs (lncRNAs) play a crucial role in gene expression regulation, cell differentiation, and the maintenance of genomic stability. Here, we developed a modular framework for lncRNA identification and applied it to a breast cancer cohort to identify novel lncRNAs not previously annotated. To investigate the potential biological function, regulatory mechanisms, and clinical relevance of the novel lncRNAs, we elucidated the genomic and chromatin features of these lncRNAs, along with the associated protein-coding genes and putative enhancers involved in the breast cancer regulatory networks. Furthermore, we uncovered that the expression patterns of novel and annotated lncRNAs identified in breast cancer were related to the hormone response in the PAM50 subtyping criterion, as well as the immune response and progression states of breast cancer across different immune cells and immune checkpoint genes. Collectively, the comprehensive identification and functional analysis of lncRNAs revealed that these lncRNAs play an essential role in breast cancer by altering gene expression and participating in the regulatory networks, contributing to a better insight into breast cancer heterogeneity and potential avenues for therapeutic intervention.

Alteration of HER2 Status During Breast Cancer Progression: A Clinicopathological Analysis Focusing on HER2-Low Status

Recent studies have shown that novel antibody-drug conjugates (ADCs) can improve clinical outcomes in patients with HER2-low breast cancers. This study aimed to investigate alteration of HER2 status during breast cancer progression with an emphasis on HER2-low status. Using 386 paired samples of primary and recurrent breast cancers, HER2 discordance rate between primary and matched recurrent samples, the relationships between HER2 discordance and clinicopathological characteristics and clinical outcomes of the patients were analyzed. HER2 discordance rate between primary breast cancer and first recurrence was 25.9% (κ = 0.586) with mostly zero-to-low (10.6%) or low-to-zero (9.3%) conversion. There was no significant difference in the discordant rates according to type or location of the recurrence. Of 70 cases with a second recurrence, HER2 discordance rate between the primary tumor and the second recurrence was 27.1% (κ = 0.554). HER2 discordance was associated with lower HER2 level, lymphovascular invasion, and progesterone receptor positivity of the primary tumor. In further analyses, HER2-zero-to-low conversion was associated with lymph node metastasis and hormone receptor (HR) positivity, whereas HER2-low-to-zero conversion was associated with HR negativity and triple-negative subtype. In survival analyses, HER2 discordance was associated with decreased overall survival of patients in the HR-positive group but not in the HR-negative group. Furthermore, patients with HER2-low-to-zero converted tumors showed worse overall survival compared with those with HER2-low concordant tumors. In conclusion, HER2 status changes during breast cancer progression in significant proportions, mostly between zero and low status. As HER2 instability increases during progression and affects clinical outcome, HER2 status needs to be reevaluated in recurrent settings.

Opportunities and Challenges in the Development of Antibody-Drug Conjugate for Triple-Negative Breast Cancer: The Diverse Choices and Changing Needs

Triple-negative breast cancer (TNBC) is a highly heterogeneous breast cancer subtype, which is also characterized by the aggressive phenotype, high recurrence rate, and poor prognosis. Antibody-drug conjugate (ADC) is a monoclonal antibody with a cytotoxic payload connected by a linker. ADC is gaining more and more attention as a targeted anti-cancer agent. Clinical studies of emerging ADC drugs such as sacituzumab govitecan and trastuzumab deruxtecan in patients with metastatic breast cancer (including TNBC) are progressing rapidly. In view of its excellent clinical efficacy and good tolerability, Sacituzumab govitecan gained accelerated approval by the FDA for the treatment of advanced metastatic TNBC in 2020. This review discusses the treatment status and challenges in TNBC, with an emphasis on the current status of ADC development and clinical trials in TNBC and metastatic breast cancer. We also summarize the clinical experience and future exploration directions of ADC development for TNBC patients.

Heterogeneous Evolution of Breast Cancer Cells-An Endogenous Molecular-Cellular Network Study

Breast cancer heterogeneity presents a significant challenge in clinical therapy, such as over-treatment and drug resistance. These challenges are largely due to its obscure normal epithelial origins, evolutionary stability, and transitions on the cancer subtypes. This study aims to elucidate the cellular emergence and maintenance of heterogeneous breast cancer via quantitative bio-process modeling, with potential benefit to therapeutic strategies for the disease. An endogenous molecular-cellular hypothesis posits that both pathological and physiological states are phenotypes evolved from and shaped by interactions among a number of conserved modules and cellular factors within a biological network. We hereby developed a model of core endogenous network for breast cancer in accordance with the theory, quantifying its intrinsic dynamic properties with dynamic modeling. The model spontaneously generates cell states that align with molecular classifications at both the molecular and modular level, replicating four widely recognized molecular subtypes of the cancer and validating against data extracted from the TCGA database. Further analysis shows that topologically, a singular progression gateway from normal breast cells to cancerous states is identified as the Luminal A-type breast cancer. Activated positive feedback loops are found to stabilize cellular states, while negative feedback loops facilitate state transitions. Overall, more routes are revealed on the cellular transition between stable states, and a traceable count explains the origin of breast cancer heterogeneity. Ultimately, the research intended to strength the search for therapeutic targets.

MiR-223-3p in Cancer Development and Cancer Drug Resistance: Same Coin, Different Faces

MicroRNAs (miRNAs) are mighty post-transcriptional regulators in cell physiology and pathophysiology. In this review, we focus on the role of miR-223-3p (henceforth miR-223) in various cancer types. MiR-223 has established roles in hematopoiesis, inflammation, and most cancers, where it can act as either an oncogenic or oncosuppressive miRNA, depending on specific molecular landscapes. MiR-223 has also been linked to either the sensitivity or resistance of cancer cells to treatments in a context-dependent way. Through this detailed review, we highlight that for some cancers (i.e., breast, non-small cell lung carcinoma, and glioblastoma), the oncosuppressive role of miR-223 is consistently reported in the literature, while for others (i.e., colorectal, ovarian, and pancreatic cancers, and acute lymphocytic leukemia), an oncogenic role prevails. In prostate cancer and other hematological malignancies, although an oncosuppressive role is frequently described, there is less of a consensus. Intriguingly, NLRP3 and FBXW7 are consistently identified as miR-223 targets when the miRNA acts as an oncosuppressor or an oncogene, respectively, in different cancers. Our review also describes that miR-223 was increased in biological fluids or their extracellular vesicles in most of the cancers analyzed, as compared to healthy or lower-risk conditions, confirming the potential application of this miRNA as a diagnostic and prognostic biomarker in the clinic.

UBR7 in concert with EZH2 inhibits the TGF-β signaling leading to extracellular matrix remodeling

The intricate interplay between resident cells and the extracellular matrix (ECM) profoundly influences cancer progression. In triple-negative breast cancer (TNBC), ECM architecture evolves due to the enrichment of lysyl oxidase, fibronectin, and collagen, promoting distant metastasis. Here we uncover a pivotal transcription regulatory mechanism involving the epigenetic regulator UBR7 and histone methyltransferase EZH2 in regulating transforming growth factor (TGF)-β/Smad signaling, affecting the expression of ECM genes. UBR7 loss leads to a dramatic reduction in facultative heterochromatin mark H3K27me3, activating ECM genes. UBR7 plays a crucial role in matrix deposition in adherent cancer cells and spheroids, altering collagen content and lysyl oxidase activity, directly affecting matrix stiffness and invasiveness. These findings are further validated in vivo in mice models and TNBC patients, where reduced UBR7 levels are accompanied by increased ECM component expression and activity, leading to fibrosis-mediated matrix stiffness. Thus, UBR7 is a master regulator of matrix stiffening, influencing the metastatic potential of TNBC.

TWEAK/Fn14 signalling driven super-enhancer reprogramming promotes pro-metastatic metabolic rewiring in triple-negative breast cancer

Triple Negative Breast Cancer (TNBC) is the most aggressive breast cancer subtype suffering from limited targeted treatment options. Following recent reports correlating Fibroblast growth factor-inducible 14 (Fn14) receptor overexpression in Estrogen Receptor (ER)-negative breast cancers with metastatic events, we show that Fn14 is specifically overexpressed in TNBC patients and associated with poor survival. We demonstrate that constitutive Fn14 signalling rewires the transcriptomic and epigenomic landscape of TNBC, leading to enhanced tumour growth and metastasis. We further illustrate that such mechanisms activate TNBC-specific super enhancers (SE) to drive the transcriptional activation of cancer dependency genes via chromatin looping. In particular, we uncover the SE-driven upregulation of Nicotinamide phosphoribosyltransferase (NAMPT), which promotes NAD+ and ATP metabolic reprogramming critical for filopodia formation and metastasis. Collectively, our study details the complex mechanistic link between TWEAK/Fn14 signalling and TNBC metastasis, which reveals several vulnerabilities which could be pursued for the targeted treatment of TNBC patients.

HER2-Low Breast Cancer: Now and in the Future

Breast cancer is a heterogeneous disease, and its subtypes are characterized by hormone receptor and human epidermal growth factor receptor 2 (HER2) expression status. "HER2-low" tumors, which exhibit a low level of HER2 expression (immunohistochemistry 1+ or 2+ without gene amplification), were conventionally considered not amenable to anti-HER2 targeting agents based on the results of a phase III trial of trastuzumab. However, this perspective is being challenged by the emergence of novel anti-HER2 antibody-drug conjugates, such as trastuzumab-deruxtecan. These innovative therapies have demonstrated remarkable efficacy against HER2-low breast cancer, shedding new light on a previously overlooked category of breast cancer. Such promising results highlight the need for in-depth investigations of the biology and prognostic implications of HER2-low tumors. In this review, we comprehensively summarize the current evidence surrounding this topic and highlight areas that warrant further exploration and research in the future.

ACSL3 regulates breast cancer progression via lipid metabolism reprogramming and the YES1/YAP axis

OBJECTIVE

Mitochondrial fatty acid oxidation is a metabolic pathway whose dysregulation is recognized as a critical factor in various cancers, because it sustains cancer cell survival, proliferation, and metastasis. The acyl-CoA synthetase long-chain (ACSL) family is known to activate long-chain fatty acids, yet the specific role of ACSL3 in breast cancer has not been determined.

METHODS

We assessed the prognostic value of ACSL3 in breast cancer by using data from tumor samples. Gain-of-function and loss-of-function assays were also conducted to determine the roles and downstream regulatory mechanisms of ACSL3 in vitro and in vivo.

RESULTS

ACSL3 expression was notably downregulated in breast cancer tissues compared with normal tissues, and this phenotype correlated with improved survival outcomes. Functional experiments revealed that ACSL3 knockdown in breast cancer cells promoted cell proliferation, migration, and epithelial-mesenchymal transition. Mechanistically, ACSL3 was found to inhibit β-oxidation and the formation of associated byproducts, thereby suppressing malignant behavior in breast cancer. Importantly, ACSL3 was found to interact with YES proto-oncogene 1, a member of the Src family of tyrosine kinases, and to suppress its activation through phosphorylation at Tyr419. The decrease in activated YES1 consequently inhibited YAP1 nuclear colocalization and transcriptional complex formation, and the expression of its downstream genes in breast cancer cell nuclei.

CONCLUSIONS

ACSL3 suppresses breast cancer progression by impeding lipid metabolism reprogramming, and inhibiting malignant behaviors through phospho-YES1 mediated inhibition of YAP1 and its downstream pathways. These findings suggest that ACSL3 may serve as a potential biomarker and target for comprehensive therapeutic strategies for breast cancer.

Independent Validation of the BRENDA-Score Breast Cancer Prognosis Prediction Tool In Chinese Patients

BACKGROUND

The BRENDA-Score was developed and used to predict the prognosis of patients with breast cancer (BC). This study was performed to validate the use of this tool in Chinese patients with primary invasive BC patients.

METHODS

Patients underwent surgery for BC from January 2009 to December 2016. Discrimination was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Calibrations were assessed by comparing predicted and observed 5-year and 10-year metastasis-free survival (MFS) in the overall cohort and patient subgroups.

RESULTS

A total of 2029 BC patients were enrolled. Kaplan-Meier analysis revealed significant differences in MFS risk groups (log-rank test P < .01). ROC analysis showed good accuracy for 5-year MFS (AUC 0.779) and fair accuracy for 10-year MFS (AUC 0.728). The BRENDA-Score accurately predicted 5-year and 10-year MFS in the entire cohort and in all other predefined subgroups, except for the 5-year MFS in the subgroup aged<40 years, which was overestimated (differences between the predicted and observed MFS were 6.7%, P < .05). The 5-year MFS rates of ER- positive and ER-negative patients were 90.9% and 80.6%, respectively (P < .05). The 10-year MFS rates of ER-positive and ER-negative patients were 78.0% and 73.7%, respectively (P = .25).

CONCLUSIONS

The BRENDA-Score accurately predicted 5-year and 10-year MFS. The results showed good validity, transportability, and potential clinical value. However, the results for 5-years MFS should be interpreted carefully in patients aged <40 years. After 10 years the value of the ER as a prognostic factor was less important.

Rat Models of Hormone Receptor-Positive Breast Cancer

Hormone receptor-positive (HR+) breast cancer (BC) is the most common type of breast cancer among women worldwide, accounting for 70-80% of all invasive cases. Patients with HR+ BC are commonly treated with endocrine therapy, but intrinsic or acquired resistance is a frequent problem, making HR+ BC a focal point of intense research. Despite this, the malignancy still lacks adequate in vitro and in vivo models for the study of its initiation and progression as well as response and resistance to endocrine therapy. No mouse models that fully mimic the human disease are available, however rat mammary tumor models pose a promising alternative to overcome this limitation. Compared to mice, rats are more similar to humans in terms of mammary gland architecture, ductal origin of neoplastic lesions and hormone dependency status. Moreover, rats can develop spontaneous or induced mammary tumors that resemble human HR+ BC. To date, six different types of rat models of HR+ BC have been established. These include the spontaneous, carcinogen-induced, transplantation, hormone-induced, radiation-induced and genetically engineered rat mammary tumor models. Each model has distinct advantages, disadvantages and utility for studying HR+ BC. This review provides a comprehensive overview of all published models to date.

MiRNAs as potential biomarkers in early breast cancer detection: a systematic review

Breast cancer remains a significant global health challenge, with high incidence and mortality rates. While mammography has contributed to declining mortality, its limitations in sensitivity and specificity for early detection, particularly in distinguishing between pure atypical ductal hyperplasia (ADH), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC), highlight the need for more precise tools. Even with core needle biopsy (CNB), conclusive diagnoses often require surgical excision. This underscores the urgency for non-invasive biomarkers to improve early detection and differentiation, potentially reducing invasive procedures. Recent research has shifted focus from mRNA to microRNAs (miRNAs) as promising biomarkers for breast cancer screening. These small non-coding RNAs, which exhibit abnormal expression patterns in breast cancer patients' tissue and serum/plasma, play crucial roles in early breast cancer development by modulating proto-oncogenes or tumor suppressor genes at the post-transcriptional level. Notably, miRNAs such as miR-21, miR-155, and miR-200c are key regulators of cell proliferation and apoptosis, with the potential to distinguish between normal tissue and various stages of breast lesions, including ADH, DCIS, and IDC. Additionally, miRNAs in serum and plasma offer a non-invasive method to differentiate breast cancer stages. This review aims to consolidate current knowledge on early breast lesions and explore the potential of miRNAs as biomarkers for early breast cancer detection, which could enhance risk prediction and reduce reliance on invasive diagnostic procedures.

Network pharmacology-based screening of active constituents of Avicennia marina and their clinical biochemistry related mechanism against breast cancer

Breast cancer is the second major cause of cancer death in women globally. Avicennia marina is a medicinal plant that belongs to the family Acanthaceae and is known as grey or white mangrove. It has antioxidant, antiviral, anticancer, anti-inflammatory, and antibacterial activity in the treatment of various diseases including cancer. The goal of the study is to use a network pharmacology method to identify the potential phenomena of bioactive compounds of A. marina in the treatment of breast cancer and explore clinical biochemistry related aspects. A total of 74 active compounds of A. marina were retrieved from various databases as well as a literature review and collectively 429 targets of these compounds were identified by STITCH and Swiss Target Prediction databases. Breast cancer related 15606 potential targets were retrieved from the GeneCards database. A Venn diagram was drawn to find common key targets. To check the biological functions, the GO enrichment and KEGG pathways analysis of 171 key targets were performed through the DAVID database. To understand the interactions among key targets, Protein-protein interaction (PPI) studies were completed using the STRING database, and the Protein-Protein Interaction (PPI) network, as well as the compound-target-pathway network, was constructed using Cytoscape 3.9.0. Finally, molecular docking analysis of 5 hub genes named tumor protein 53 (TP53), catenin beta 1 (CTNNB1), interleukin 6 (IL6), tumor necrosis factor (TNF), and RAC-alpha serine/threonine protein kinases 1 (AKT1) with the active constituent of A. marina against breast cancer were performed. Additionally, a molecular docking study demonstrates that active drugs have a higher affinity for the target that may be used to decrease breast cancer. The molecular dynamic simulation analysis predicted the very stable behavior of docked complexes with no global structure deviations seen. The MMGBSA further supported strong intermolecular interactions with net energy values as; AKT1_Betulinic_acid (-20.97 kcal/mol), AKT1_Stigmasterol (-44.56 kcal/mol), TNF_Betulinic_acid (-28.68 kcal/mol) and TNF_Stigmastero (-29.47 kcal/mol).Communicated by Ramaswamy H. Sarma.

BRAF Expression and Copy Number Alterations Predict Unfavorable Tumor Features and Adverse Outcomes in Patients With Breast Cancer

Background: The role of BRAF in breast cancer pathogenesis is still unclear. To address this knowledge gap, this study is aimed at evaluating the impact of BRAF gene expression and copy number alterations (CNAs) on clinicopathologic characteristics and survival in patients with breast cancer. Methods: The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset was obtained from the cBioPortal public domain. Tumoral BRAF mRNA expression and CNAs along with demographic and tumor data for patients with breast cancer were retrieved. The association of BRAF expression and CNAs with breast cancer clinicopathologic characteristics was analyzed. The impact of BRAF mRNA expression on the overall survival of patients was assessed using Kaplan-Meier survival analysis. Results: BRAF gene mRNA log intensity expression was positively correlated with tumor size and the Nottingham Prognostic Index (NPI) (p < 0.001). Alternatively, BRAF gene expression was negatively correlated with the age at diagnosis (p = 0.003). The average BRAF mRNA expression was significantly higher in premenopausal patients, patients with high tumor grade, hormone receptor-negative status, and non-luminal tumors compared to postmenopausal patients, patients with low-grade, hormone receptor-positive, and luminal disease. BRAF gain and high-level amplification copy numbers were significantly associated with higher NPI scores and larger tumor sizes compared to neutral copy number status. Survival analysis revealed no discernible differences in overall survival for patients with low and high BRAF mRNA expression. Conclusion: High BRAF mRNA expression as well as the gain and high-level amplification copy numbers were associated with advanced tumor characteristics and unfavorable prognostic factors in breast cancer. BRAF could be an appealing target for the treatment of premenopausal patients with hormone receptor-negative breast cancer.

A comprehensive single-cell breast tumor atlas defines epithelial and immune heterogeneity and interactions predicting anti-PD-1 therapy response

We present an integrated single-cell RNA sequencing atlas of the primary breast tumor microenvironment (TME) containing 236,363 cells from 119 biopsy samples across eight datasets. In this study, we leverage this resource for multiple analyses of immune and cancer epithelial cell heterogeneity. We define natural killer (NK) cell heterogeneity through six subsets in the breast TME. Because NK cell heterogeneity correlates with epithelial cell heterogeneity, we characterize epithelial cells at the level of single-gene expression, molecular subtype, and 10 categories reflecting intratumoral transcriptional heterogeneity. We develop InteractPrint, which considers how cancer epithelial cell heterogeneity influences cancer-immune interactions. We use T cell InteractPrint to predict response to immune checkpoint inhibition (ICI) in two breast cancer clinical trials testing neoadjuvant anti-PD-1 therapy. T cell InteractPrint was predictive of response in both trials versus PD-L1 (AUC = 0.82, 0.83 vs. 0.50, 0.72). This resource enables additional high-resolution investigations of the breast TME.

Cancer-associated fibroblast-derived gene signature discriminates distinct prognoses by integrated single-cell and bulk RNA-seq analyses in breast cancer

BACKGROUND

Cancer-associated fibroblasts (CAFs) are one of the most predominant cellular subpopulations in the tumor stroma and play an integral role in cancer occurrence and progression. However, the prognostic role of CAFs in breast cancer remains poorly understood.

METHODS

We identified a number of CAF-related biomarkers in breast cancer by combining single-cell and bulk RNA-seq analyses. Based on univariate Cox regression as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a novel CAF-associated prognostic model was developed. Breast cancer patients were grouped according to the median risk score and further analyzed for outcome, clinical characteristic, pathway activity, genomic feature, immune landscape, and drug sensitivity.

RESULTS

A total of 341 CAF-related biomarkers were identified from single-cell and bulk RNA-seq analyses. We eventually screened eight candidate prognostic genes, including CERCAM, EMP1, SDC1, PRKG1, XG, TNN, WLS, and PDLIM4, and constructed the novel CAF-related prognostic model. Grouped by the median risk score, high-risk patients showed a significantly worse prognosis and exhibited distinct pathway activities such as uncontrolled cell cycle progression, angiogenesis, and activation of glycolysis. In addition, the combined risk score and tumor mutation burden significantly improved the ability to predict patient prognosis. Importantly, patients in the high-risk group had a higher infiltration of M2 macrophages and a lower infiltration of CD8+ T cells and activated NK cells. Finally, we calculated the IC50 for a range of anticancer drugs and personalized the treatment regimen for each patient.

CONCLUSION

Integrating single-cell and bulk RNA-seq analyses, we identified a list of compositive CAF-associated biomarkers and developed a novel CAF-related prognostic model for breast cancer. This robust CAF-derived gene signature acts as an excellent predictor of patient outcomes and treatment responses in breast cancer.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Overexpression of WT1 in all molecular subtypes of breast cancer and its impact on survival: exploring oncogenic and tumor suppressor roles of distinct WT1 isoforms

BACKGROUND

Breast cancer is a highly heterogeneous solid tumor, posing challenges in developing targeted therapies effective for all mammary carcinoma subtypes. WT1 emerges as a promising target for breast cancer therapy due to its potential oncogenic role in various cancer types. Previous works have yielded inconsistent results. Therefore, further studies are needed to clarify the behavior of this complex gene in breast cancer.

METHODS AND RESULTS

In this study, we examined WT1 expression in both Formalin Fixed Paraffin Embedded breast tumors (n = 41) and healthy adjacent tissues (n = 41) samples from newly diagnosed cases of ductal invasive breast cancer. The fold change in gene expression between the tumor and healthy tissue was determined by calculating 2-∆∆Ct. Disease-free survival analysis was computed using the Kaplan-Meier method. To identify the expression levels of different WT1 isoforms, we explored the ISOexpresso database. Relative quantification of the WT1 gene revealed an overexpression of WT1 in most cases. The percentage of patients surviving free of disease at 8 years of follow-up was lower in the group overexpressing WT1 compared to the group with down-regulated WT1.

CONCLUSIONS

Interestingly, this overexpression was observed in all molecular subtypes of invasive breast cancer, underscoring the significance of WT1 as a potential target in all these subtypes. The observed WT1 down-expression in a few cases of invasive breast cancer, associated with better survival outcomes, may correspond to the down-regulation of a particular WT1-KTS (-) isoform: the WT1 A isoform (EX5-/KTS-). The co-expression of this WT1 oncogenic isoform with a regulated WT1- tumor suppressor isoform, such as the major WT1 F isoform (EX5-/KTS +), could also explain such survival outcomes. Due to its capacity to adopt dual roles, it becomes imperative to conduct individual molecular expression profiling of the WT1 gene. Such an approach holds great promise in the development of personalized treatment strategies for breast cancer.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Optical imaging reveals chemotherapy-induced metabolic reprogramming of residual disease and recurrence

Fewer than 20% of triple-negative breast cancer patients experience long-term responses to mainstay chemotherapy. Resistant tumor subpopulations use alternative metabolic pathways to escape therapy, survive, and eventually recur. Here, we show in vivo, longitudinal metabolic reprogramming in residual disease and recurrence of triple-negative breast cancer xenografts with varying sensitivities to the chemotherapeutic drug paclitaxel. Optical imaging coupled with metabolomics reported an increase in non-glucose-driven mitochondrial metabolism and an increase in intratumoral metabolic heterogeneity during regression and residual disease in resistant MDA-MB-231 tumors. Conversely, sensitive HCC-1806 tumors were primarily reliant on glucose uptake and minimal changes in metabolism or heterogeneity were observed over the tumors' therapeutic life cycles. Further, day-matched resistant HCC-1806 tumors revealed a higher reliance on mitochondrial metabolism and elevated metabolic heterogeneity compared to sensitive HCC-1806 tumors. Together, metabolic flexibility, increased reliance on mitochondrial metabolism, and increased metabolic heterogeneity are defining characteristics of persistent residual disease, features that will inform the appropriate type and timing of therapies.

FBXO8 is a novel prognostic biomarker in different molecular subtypes of breast cancer and suppresses breast cancer progression by targeting c-MYC

F-box only protein 8 (FBXO8) is a recently identified member of the F-box proteins, showcasing its novelty in this protein family. Extensive research has established FBXO8's role as a tumor suppressor in various cancers, including hepatocellular carcinoma, and colorectal cancer, Nevertheless, its functional, mechanistic, and prognostic roles in primary and metastatic breast cancer, particularly in different molecular subtypes of breast cancer, various stages, as well as its potential implications in immunotherapy, tumor microenvironment, and prognostic survival among breast cancer patients, remain unexplored. In this article, we employed a multi-dimensional investigation leveraging TCGA, TIMER, TISIDB, STRING, MEXPRESS, UALCAN, and cBioPortal databases to explore the underlying suppression mechanism of FBXO8 in breast cancer. FBXO8 negatively correlates with MYC, NOTCH, WNT and inflammatory signaling pathways in breast tumor microenvironment. Furthermore we conducted RT-PCR, western blot, cell proliferation, cell migration, and mRNA target gene RT-PCR analyses to elucidate the role of FBXO8 in breast cancer progression. Mechanistically, PTEN and FBXW7 expression were down-regulated and MYC, IL10, IL6, NOTCH1, WNT6 mRNA expressions were up-regulated in FBXO8 knockdown cell lines. c-MYC silenced cells showed an increase in FBXO8 protein level, which suggests a negative feedback loop between FBXO8 and c-MYC to control breast cancer metastasis. These findings illuminate the novel role of FBXO8 as a prognostic and therapeutic target across different molecular subtypes of breast cancer. Finally, through the utilization of virtual screening and Molecular Dynamics simulations, we successfully identified two FDA-approved medications, Ledipasvir and Paritaprevir, that demonstrated robust binding capabilities and interactions with FBXO8.

Rational Identification of Novel Antibody-Drug Conjugate with High Bystander Killing Effect against Heterogeneous Tumors

Bystander-killing payloads can significantly overcome the tumor heterogeneity issue and enhance the clinical potential of antibody-drug conjugates (ADC), but the rational design and identification of effective bystander warheads constrain the broader implementation of this strategy. Here, graph attention networks (GAT) are constructed for a rational bystander killing scoring model and ADC construction workflow for the first time. To generate efficient bystander-killing payloads, this model is utilized for score-directed exatecan derivatives design. Among them, Ed9, the most potent payload with satisfactory permeability and bioactivity, is further used to construct ADC. Through linker optimization and conjugation, novel ADCs are constructed that perform excellent anti-tumor efficacy and bystander-killing effect in vivo and in vitro. The optimal conjugate T-VEd9 exhibited therapeutic efficacy superior to DS-8201 against heterogeneous tumors. These results demonstrate that the effective scoring approach can pave the way for the discovery of novel ADC with promising bystander payloads to combat tumor heterogeneity.

Clinical significance of low expression of CADM3 in breast cancer and preliminary exploration of related mechanisms

BACKGROUND

Cell adhesion molecule 3 (CADM3), a transmembrane glycoprotein on cell membranes, plays a role in the way of ligand and receptor interaction. However, there are few studies on CADM3 in tumors, and how it works in breast cancer (BC) remains unclear.

METHODS

The Cancer Genome Atlas (TCGA) database and clinical samples were used to analyze CADM3 expression and its correlation with clinicopathological factors and prognosis. Its correlation with immune infiltration was analyzed by TCGA. The effects of CADM3 on proliferation and migration were investigated by cell clonal formation, CCK-8, cell scratch and transwell assay. Protein interaction network was prepared and the function prediction of related genes was conducted. The correlation between CADM3 and MAPK pathway was further explored by western blot experiment.

RESULTS

The expression of CADM3 in BC tissues were significantly lower than that in adjacent normal tissues. High level of CADM3 was related to better prognosis of BC patients. CADM3 was an independent prognostic factor for BC. Expression of CADM3 was significantly associated with the status of ER and PR, age and PAM50 subtypes. CADM3 positively related to many immune infiltrating cells. Overexpression of CADM3 can notably reduce cell proliferation and migration. CADM3 was related to MAPK pathway and the phosphorylation of ERK1/2 and JNK1 was inhibited in BC cells with high CADM3.

CONCLUSIONS

Our research reveals the clinical significance of CADM3 in BC and indicates the critical roles of CADM3 in immune infiltration and MAPK pathway.

Combination Chemotherapy Optimization with Discrete Dosing

Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens; one way to alleviate this burden and better inform future trials is to build reliable models for drug administration. This paper presents a mixed-integer program for combination chemotherapy (utilization of multiple drugs) optimization that incorporates various important operational constraints and, besides dose and concentration limits, controls treatment toxicity based on its effect on the count of white blood cells. To address the uncertainty of tumor heterogeneity, we also propose chance constraints that guarantee reaching an operable tumor size with a high probability in a neoadjuvant setting. We present analytical results pertinent to the accuracy of the model in representing biological processes of chemotherapy and establish its potential for clinical applications through a numerical study of breast cancer.

18F-FDG Dedicated Breast PET Complementary to Breast MRI for Evaluating Early Response to Neoadjuvant Chemotherapy

Purpose To compare quantitative measures of tumor metabolism and perfusion using fluorine 18 (18F) fluorodeoxyglucose (FDG) dedicated breast PET (dbPET) and breast dynamic contrast-enhanced (DCE) MRI during early treatment with neoadjuvant chemotherapy (NAC). Materials and Methods Prospectively collected DCE MRI and 18F-FDG dbPET examinations were analyzed at baseline (T0) and after 3 weeks (T1) of NAC in 20 participants with 22 invasive breast cancers. FDG dbPET-derived standardized uptake value (SUV), metabolic tumor volume, and total lesion glycolysis (TLG) and MRI-derived percent enhancement (PE), signal enhancement ratio (SER), and functional tumor volume (FTV) were calculated at both time points. Differences between FDG dbPET and MRI parameters were evaluated after stratifying by receptor status, Ki-67 index, and residual cancer burden. Parameters were compared using Wilcoxon signed rank and Mann-Whitney U tests. Results High Ki-67 tumors had higher baseline SUVmean (difference, 5.1; P = .01) and SUVpeak (difference, 5.5; P = .04). At T1, decreases were observed in FDG dbPET measures (pseudo-median difference T0 minus T1 value [95% CI]) of SUVmax (-6.2 [-10.2, -2.6]; P < .001), SUVmean (-2.6 [-4.9, -1.3]; P < .001), SUVpeak (-4.2 [-6.9, -2.3]; P < .001), and TLG (-29.1 mL3 [-71.4, -6.8]; P = .005) and MRI measures of SERpeak (-1.0 [-1.3, -0.2]; P = .02) and FTV (-11.6 mL3 [-22.2, -1.7]; P = .009). Relative to nonresponsive tumors, responsive tumors showed a difference (95% CI) in percent change in SUVmax of -34.3% (-55.9%, 1.5%; P = .06) and in PEpeak of -42.4% (95% CI: -110.5%, 8.5%; P = .08). Conclusion 18F-FDG dbPET was sensitive to early changes during NAC and provided complementary information to DCE MRI that may be useful for treatment response evaluation. Keywords: Breast, PET, Dynamic Contrast-enhanced MRI Clinical trial registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2024.

Theranostic applications of peptide-based nanoformulations for growth factor defective cancers

Growth factors play a pivotal role in orchestrating cellular growth and division by binding to specific cell surface receptors. Dysregulation of growth factor production or activity can contribute to the uncontrolled cell proliferation observed in cancer. Peptide-based nanoformulations (PNFs) have emerged as promising therapeutic strategies for growth factor-deficient cancers. PNFs offer multifaceted capabilities including targeted delivery, imaging modalities, combination therapies, resistance modulation, and personalized medicine approaches. Nevertheless, several challenges remain, including limited specificity, stability, pharmacokinetics, tissue penetration, toxicity, and immunogenicity. To address these challenges and optimize PNFs for clinical translation, in-depth investigations are warranted. Future research should focus on elucidating the intricate interplay between peptides and nanoparticles, developing robust spectroscopic and computational methodologies, and establishing a comprehensive understanding of the structure-activity relationship governing peptide-nanoparticle interactions. Bridging these knowledge gaps will propel the translation of peptide-nanoparticle therapies from bench to bedside. While a few peptide-nanoparticle drugs have obtained FDA approval for cancer treatment, the integration of nanostructured platforms with peptide-based medications holds tremendous potential to expedite the implementation of innovative anticancer interventions. Therefore, growth factor-deficient cancers present both challenges and opportunities for targeted therapeutic interventions, with peptide-based nanoformulations positioned as a promising avenue. Nonetheless, concerted research and development endeavors are essential to optimize the specificity, stability, and safety profiles of PNFs, thereby advancing the field of peptide-based nanotherapeutics in the realm of oncology research.

Phytocompounds-based therapeutic approach: Investigating curcumin and green tea extracts on MCF-7 breast cancer cell line

BACKGROUND

Breast cancer (BC) has transcended lung cancer as the most common cancer in the world. Due to the disease's aggressiveness, rapid growth, and heterogeneity, it is crucial to investigate different therapeutic approaches for treatment. According to the World Health Organization (WHO), Plant-based therapeutics continue to be utilized as safe/non-toxic complementary or alternative treatments for cancer, even in developed countries, regardless of how cutting-edge conventional therapies are. Despite their low bioavailability, curcumin (CUR) and green tea (GT) represent safer therapeutic options. Due to their potent molecular-modulating properties on various cancer-related molecules and signaling pathways, they are considered gold-standard therapeutic agents and have been incorporated into the development of one or more therapeutic strategies of BC treatment.

METHODS

We investigated the modulatory role of CUR and GT extracts on significant multi molecular targets in MCF-7 BC cell line to assess their potential as BC multi-targeting agents. We analyzed the phytocompounds in GT leaves using High-performance liquid chromatography (HPLC) and Gas chromatography-mass spectrometry (GC-MS) techniques. The mRNA expression levels of Raf-1, Telomerase, Tumor necrosis factor alpha (TNF-α) and Interleukin-8 (IL-8) genes in MCF-7 cells were quantified using quantitative real-time PCR (qRT-PCR). The cytotoxicity of the extracts was assessed by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and the released Lactate dehydrogenase (LDH), a valuable marker for identifying the programmed necrosis (necroptosis). Additionally, the concentrations of the necroptosis-related proinflammatory cytokines (TNF-α and IL-8) were measured using enzyme-linked immunosorbent assay (ELISA).

RESULTS

In contrast to the GT, the results showed the anticancer and cytotoxic properties of CUR against MCF-7 cells, with a relatively higher level of released LDH. The CUR extract downregulated the oncogenic Raf-1, suppressed the Telomerase and upregulated the TNF-α and IL-8 genes. Results from the ELISA showed a notable increase in IL-8 and TNF-α cytokines levels after CUR treatment, which culminated after 72 h.

CONCLUSIONS

Among both extracts, only CUR effectively modulated the understudy molecular targets, achieving multi-targeting anticancer activity against MCF-7 cells. Moreover, the applied dosage significantly increased levels of the proinflammatory cytokines, which represent a component of the cytokines-targeting-based therapeutic strategy. However, further investigations are recommended to validate this therapeutic approach.

Cuproptosis-related genes predict prognosis and trastuzumab therapeutic response in HER2-positive breast cancer

Breast cancer is the most common diagnosed cancer, the HER2-positive subtype account for 15% of all breast cancer. HER2-targeted therapy is the mainstay treatment for HER2-positive breast cancer. Cuproptosis is a novel form of programmed cell death, and is caused by mitochondrial lipoylation and destabilization of iron-sulfur proteins triggered by copper, which was considered as a key player in various biological processes. However, the roles of cuproptosis-related genes in HER2-positive breast cancer remain largely unknown. In the present study, we constructed a prognostic prediction model of HER2-positive breast cancer patients using TCGA database. Dysregulated genes for cells resistant to HER2-targeted therapy were analyzed in the GEO dataset. KEGG pathway, GO enrichment and GSEA was performed respectively. The immune landscape of DLAT was analyzed by CIBERSORT algorithm and TIDE algorithm. HER2-positive breast cancer patients with high CRGs risk score showed shorter OS. DLAT was downregulated and correlated with better survival of HER2-positive breast cancer patients (HR = 3.30, p = 0.022). High expressed DLAT was associated with resistant to HER2-targeted therapy. Knocking down DLAT with siRNA increased sensitivity of breast cancer to trastuzumab. KEGG pathway and GO enrichment of DEGs indicated that DLAT participates in various pathways correlated with organelle fission, chromosome segregation, nuclear division, hormone-mediated signaling pathway, regulation of intracellular estrogen receptor signaling pathway, condensed chromosome and PPAR signaling pathway. There was a negative correlation between TIDE and DLAT expression (r = - 0.292, p < 0.001), which means high DLAT expression is an indicator of sensitivity to immunotherapy. In conclusion, our study constructed a four CRGs signature prognostic prediction model and identified DLAT as an independent prognostic factor and associated with resistant to HER2-targeted therapy for HER2-positive breast cancer patients.

Biomarkers predictive of a response to extended endocrine therapy in breast cancer: a systematic review and meta-analysis

PURPOSE

Extension of adjuvant endocrine therapy beyond five years confers only modest survival benefit in breast cancer patients and carries risk of toxicities. This systematic review investigates the role of biomarker tests in predicting the clinical response to an extension of endocrine therapy.

METHODS

We searched Ovid MEDLINE, Ovid Embase, Global Index Medicus, and the Cochrane Central Register of Controlled Trials using an iterative approach to identify full-text articles related to breast cancer, endocrine therapy, and biomarkers.

RESULTS

Of the 1,217 unique reports identified, five studies were deemed eligible. Four investigated the Breast Cancer Index (BCI) assay in three distinct study populations. These studies consistently showed that BCI score was predictive of response to extended endocrine therapy among 1,946 combined patients, who were predominately non-Hispanic white and postmenopausal.

CONCLUSIONS

Evidence in the setting of predictive tests for extended endocrine therapy is sparse. Most relevant studies investigated the use of BCI, but these study populations were largely restricted to a single age, race, and ethnicity group. Future studies should evaluate a variety of biomarkers in diverse populations. Without sufficient evidence, physicians and patients face a difficult decision in balancing the benefits and risks of endocrine therapy extension.

Current views on in vivo models for breast cancer research and related drug development

INTRODUCTION

Animal models play a crucial role in breast cancer research, in particular mice and rats, who develop mammary tumors that closely resemble their human counterparts. These models allow the study of mechanisms behind breast carcinogenesis, as well as the efficacy and safety of new, and potentially more effective and advantageous therapeutic approaches. Understanding the advantages and disadvantages of each model is crucial to select the most appropriate one for the research purpose.

AREA COVERED

This review provides a concise overview of the animal models available for breast cancer research, discussing the advantages and disadvantages of each one for searching new and more effective approaches to treatments for this type of cancer.

EXPERT OPINION

Rodent models provide valuable information on the genetic alterations of the disease, the tumor microenvironment, and allow the evaluation of the efficacy of chemotherapeutic agents. However, in vivo models have limitations, and one of them is the fact that they do not fully mimic human diseases. Choosing the most suitable model for the study purpose is crucial for the development of new therapeutic agents that provide better care for breast cancer patients.

Clinical decision support tool for breast cancer recurrence prediction using SHAP value in cooperative game theory

Background

Recurrence remains the primary cause of death in patients with breast cancer. Although machine learning can efficiently predict the prognosis of breast cancer patients, the black-box nature of the model may result in a lack of evidence for clinicians when making critical decisions.

Methods

In this study, our main objective was twofold: (1) to develop a clinical decision support tool for predicting the prognosis of breast cancer and (2) to identify and explore the key factors that influence breast cancer recurrence. To achieve this, we employed an explainable ensemble learning method called Shapley additive explanation (SHAP), which leverages cooperative game theory. Using real-world data from 1629 breast cancer patients, we analyzed and uncovered the key factors associated with breast cancer recurrence. Subsequently, we used these identified factors to create a recurrence prediction model and establish a decision mechanism for the tool. The proposed method not only provides accurate recurrence predictions but also offers transparent explanations for these predictions.

Results

By utilizing four key factors, namely, tumor size, clinical stage III, number of lymph node metastases, and age, our decision support tool for predicting breast cancer recurrence achieved significant improvements. The extra-tree model exhibited an increased area under the receiver operating characteristic curve (AUC) of 0.97, while the Random Forest model demonstrated an improved AUC of 0.96. We also offer a decision mechanism for a recurrence prediction model based on the identified key factors. This transparent and interpretable decision-making process facilitated by our explainable ensemble learning model enhances trust and promotes its applicability in clinical settings.

Conclusions

The proposed explainable ensemble learning method shows promising results in predicting breast cancer recurrence, outperforming existing methods with high accuracy and transparency. This advancement has the potential to significantly improve clinical decision-making and patient outcomes in breast cancer treatment.

SRT-Server: powering the analysis of spatial transcriptomic data

BACKGROUND

Spatial resolved transcriptomics (SRT) encompasses a rapidly developing set of technologies that enable the measurement of gene expression in tissue while retaining spatial localization information. SRT technologies and the enabled SRT studies have provided unprecedent insights into the structural and functional underpinnings of complex tissues. As SRT technologies have advanced and an increasing number of SRT studies have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the analysis and interpretation of SRT data. However, despite the growing popularity of SRT studies and the widespread availability of SRT analysis methods, analysis of large-scale and complex SRT datasets remains challenging and not easily accessible to researchers with limited statistical and computational backgrounds.

RESULTS

Here, we present SRT-Server, the first webserver designed to carry out comprehensive SRT analyses for a wide variety of SRT technologies while requiring minimal prior computational knowledge. Implemented with cutting-edge web development technologies, SRT-Server is user-friendly and features multiple analytic modules that can perform a range of SRT analyses. With a flowchart-style interface, these different analytic modules on the SRT-Server can be dragged into the main panel and connected to each other to create custom analytic pipelines. SRT-Server then automatically executes the desired analyses, generates corresponding figures, and outputs results-all without requiring prior programming knowledge. We demonstrate the advantages of SRT-Server through three case studies utilizing SRT data collected from two common platforms, highlighting its versatility and values to researchers with varying analytic expertise.

CONCLUSIONS

Overall, SRT-Server presents a user-friendly, efficient, effective, secure, and expandable solution for SRT data analysis, opening new doors for researchers in the field. SRT-Server is freely available at https://spatialtranscriptomicsanalysis.com/ .

Collagen 1A1 (COL1A1) and Collagen11A1(COL11A1) as diagnostic biomarkers in Breast, colorectal and gastric cancers

PURPOSE

Collagen family genes (CFGs) play a significant role in the pathogenesis of cancers. This study aimed to evaluate changes in the expression levels (Els) of CFGs related to epithelial-mesenchymal transition (EMT) and metastasis in gastric (GC), breast (BC), and colorectal (CRC) cancers to introduce these genes as potential diagnostic biomarkers for these three types of cancer.

METHODS

The Cancer Genome Atlas (TCGA) examined ELS changes in CFGs associated with EMT and metastasis to determine their diagnostic value for GC, BC, and CRC. InteractiVenn was used to find genes shared by these three cancers. The biomarker role of CFGs was determined using the receiver operating characteristic (ROC) analysis. GC, BC, and CRC samples were analyzed using the RT-qPCR method to verify the bioinformatics results and evaluate the EL of the selected genes as biomarkers for these cancers.

RESULTS

The in-silico results showed a significant increase in the EL of several CFGs involved in EMT and metastasis in GC, BC, and CRC samples compared to healthy samples. Six common genes (COL11A1, COL12A1, COL1A1, COL1A2, COL5A1, and COL5A2) showed significantly increased in these three cancers, therebysupporting their oncogenic role. Furthermore, the biomarker-related analyses indicated that COL11A1 and COL1A1 were common diagnostic biomarkers for the three cancers. The RT-qPCR method confirmed that the ELs of COL11A1 and COL1A1 in the GC, BC, and CRC samples increased significantly compared to the adjacent normal samples.

CONCLUSION

CFGs in EMT and metastasis of GC, BC, and CRC are strong common diagnostic biomarkers for these cancers.

Unsupervised spatially embedded deep representation of spatial transcriptomics

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR's ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).

The application of radionuclide therapy for breast cancer

Radionuclide-mediated diagnosis and therapy have emerged as effective and low-risk approaches to treating breast cancer. Compared to traditional anatomic imaging techniques, diagnostic radionuclide-based molecular imaging systems exhibit much greater sensitivity and ability to precisely illustrate the biodistribution and metabolic processes from a functional perspective in breast cancer; this transitions diagnosis from an invasive visualization to a noninvasive visualization, potentially ensuring earlier diagnosis and on-time treatment. Radionuclide therapy is a newly developed modality for the treatment of breast cancer in which radionuclides are delivered to tumors and/or tumor-associated targets either directly or using delivery vehicles. Radionuclide therapy has been proven to be eminently effective and to exhibit low toxicity when eliminating both primary tumors and metastases and even undetected tumors. In addition, the specific interaction between the surface modules of the delivery vehicles and the targets on the surface of tumor cells enables radionuclide targeting therapy, and this represents an exceptional potential for this treatment in breast cancer. This article reviews the development of radionuclide molecular imaging techniques that are currently employed for early breast cancer diagnosis and both the progress and challenges of radionuclide therapy employed in breast cancer treatment.

Current Insights in Murine Models for Breast Cancer: Present, Past and Future

Breast cancer is an intricate disease that is increasing at a fast pace, and numerous heterogeneities within it further make it difficult to investigate. We have always used animal models to understand cancer pathology and create an in vivo microenvironment that closely resembles human cancer. They are considered an indispensable part of any clinical investigation regarding cancer. Animal models have a high potency in identifying the relevant biomarkers and genetic pathways involved in the course of disease prognosis. Researchers have previously explored a variety of organisms, including Drosophila melanogaster, zebrafish, and guinea pigs, to analyse breast cancer, but murine models have proven the most comprehensive due to their homologous nature with human chromosomes, easy availability, simple gene editing, and high adaptability. The available models have their pros and cons, and it depends on the researcher to select the one most relevant to their research question. Chemically induced models are cost-effective and simple to create. Transplantation models such as allografts and xenografts can mimic the human breast cancer environment reliably. Genetically engineered mouse models (GEMMs) help to underpin the genetic alterations involved and test novel immunotherapies. Virus-mediated models and gene knockout models have also provided new findings regarding breast cancer progression and metastasis. These mouse models have also enabled the visualization of breast cancer metastases. It is also imperative to consider the cost-effectiveness of these models. Despite loopholes, mouse models have evolved and are required for disease analysis.

β-eudesmol inhibits cell proliferation and induces ferroptosis via regulating MAPK signaling pathway in breast cancer

The aim of this study was to explore the influences and underlying mechanisms of β-eudesmol on breast cancer (BC). Different concentrations of β-eudesmol (0, 10, 20, and 40 μM) were taken to treat BC cells. Cell Counting Kit-8, colony formation assay, and flow cytometry were performed to evaluate the influences of β-eudesmol on cell viability, proliferation, and apoptosis. To assess the influences of β-eudesmol on cell ferroptosis, the change of ROS, SOD, MDA, and intracellular iron and Fe2+ were determined. The protein changes of apoptosis, ferroptosis, and MAPK pathway (Bcl-2, Bax, cleaved caspase-3, SLC7A11, GPX4, SLC40A1, Transferrin, MEK1, and ERK1/2) were checked utilizing Western blot. In a concentration-dependent manner, β-eudesmol restrained cell viability and proliferation. β-eudesmol promoted cell apoptosis, as evidenced by the decline level of Bcl-2 and the raised level of Bax and cleaved caspase-3. β-eudesmol enhanced the level of ROS, MDA, iron, Fe2+, and Transferrin, and lessened SOD activity and the protein expression of SLC7A11, GPX4, SLC40A1, MEK1, and ERK1/2. Moreover, ferroptosis inhibitor Fer-1 and MEK1 overexpression both reversed the changes on cell proliferation, apoptosis, and ferroptosis induced by β-eudesmol. β-eudesmol inhibited cell proliferation and promoted cell apoptosis and ferroptosis via regulating MAPK pathway in BC.

Chemical conjugation of aptamer-sphingomyelin nanosystems and their potential as inhibitors of tumour cell proliferation in breast cancer cells

Breast cancer is a complex and heterogeneous disease with a high mortality rate due to non-specific cytotoxicity, low intratumoral accumulation and drug resistance associated with the ineffectiveness of chemotherapy. In recent years, all efforts have been focused on finding new markers and therapeutic targets, protein kinase MNK1b being a promising candidate. Recently, an aptamer known as apMNK2F showed a highly specific interaction with this protein kinase, leading to a significant reduction in tumour cell proliferation, migration and colony formation. However, as aptamers are unable to penetrate the cell membrane and reach the target, these small biomolecules need to be conjugated to suitable vectors that can transport and protect them inside the cells. In this work, covalent conjugation between biocompatible and non-harmful nanoemulsions of vitamin E and sphingomyelin and the aptamer was performed to facilitate intracellular delivery of the therapeutic aptamer apMNK2F. All strategies employed were based on 2-step bioconjugation and optimized to get the simplest and most reproducible vehicle with the highest association efficiency (about 70% in all cases). The ability of the nanosystems to successfully deliver the conjugated therapeutic aptamer was demonstrated and compared to other commercial transfection agents such as Lipofectamine 2000, leading to an effective decrease of breast cancer cell proliferation in the MDA-MB-231 cell line. The proliferation inhibition of the aptamer nanoconjugates compared to the non-conjugated aptamer provides evidence that the antitumoral capacity derived from kinase interaction is improved in a dose-dependent manner. Furthermore, various experiments including cell migration and colony formation assays, along with apoptosis induction experiments, emphasize the significant antitumoral potential. Overall, the obtained results indicate that the developed formulation could be a promising therapy for the treatment of breast cancer.

Prognostic value of glucose transporter proteins-1 (GLUT1) in breast carcinoma

Several studies have reported increased glucose transporters (GLUT) expression in different cancer types, including breast cancer. The primary purpose of this study is to examine GLUT1 immunoexpression in breast cancer patients in Saudi Arabia and to determine its significance. The study examined the association between GLUT1 immunophenotype and the clinicopathological characteristics in breast cancer. GLUT1 expression was analyzed in retrospectively collected tissue samples (n = 578) from breast cancer patients using immunohistochemistry. A total of 311 (54%) of the cases expressed GLUT1 cytoplasmic immunohistochemical staining. In univariate analysis, we found a significant association between GLUT1 expression and high-grade tumors (p < 0.0001). Positive estrogen and progesterone receptor results predicted lower GLUT1 immunoexpression (p < 0.0001 for both). Vascular invasion showed a significant association with GLUT1 immunoexpression (p = 0.045). Our findings support that GLUT1 immunohistochemistry can be used as a marker to determine the grade and hormonal receptor status in breast cancer.