Advances in breast cancer: pathways to personalized medicine

Characterization of intrinsic subtypes of breast cancer and their relationship with staging: an observational study

Background

Breast cancer is one of the main causes of morbidity and mortality among women around the world. In Peru, it has recently surpassed cervical cancer as the most commonly reported cancer. Studying the relationship between intrinsic breast cancer subtypes and disease staging can optimize diagnosis, prognosis, and treatment. Therefore, there is a need for better risk stratification, selection of personalized treatment, and improved early detection strategies. We conducted this study to address the lack of data on underrepresented populations such as the Peruvian population. The objective of the study was to analyze the distribution of intrinsic subtypes of breast cancer and their correlation with prognostic factors and demographic characteristics among women in Peru.

Methods

A descriptive, retrospective observational study was conducted, analyzing 67 cases of breast cancer of various intrinsic subtypes diagnosed at a referral hospital in Peru. Clinical, demographic, and pathological data were collected, including histological type, intrinsic subtype, tumor stage, and geographic origin of the patients. Intrinsic subtypes were classified through immunohistochemistry, and the data were processed to determine their distribution and correlation with prognostic factors such as disease stage.

Results

The mean age of the 67 patients included in the study was 54.2 years. The majority of cases originated from the city of Cajamarca (56.7%, n = 38). Invasive breast carcinoma of no special type was the most common histological type (62.7%, n = 42). Among the intrinsic subtypes, luminal B was the most common (31.3%, n = 21), followed by luminal A and triple-negative (22.4%, n = 15), both with the same frequency. Furthermore, 16.4% (n = 11) of patients presented with metastasis at the time of evaluation. A high frequency of tumors was observed in Tumor, Nodes, Metastasis (TNM) stages 3 and 4, accounting for 49.2% (n = 33).

Conclusion

This study describes the heterogeneity of breast cancer based on the identification of intrinsic subtypes within the analyzed population. The high frequency of luminal B, luminal A, and triple-negative subtypes is notable. The highest frequency of identified cases was in the advanced stages, highlighting the need for personalized treatments and improved early detection strategies.

Green Synthesis of Pyrazole Linked Thiazole Derivatives With Enhanced Anti-Breast Cancer Activity: Water Extract of Banana Peel-Mediated Protocol, Anticancer Screening, Epidermal Growth Factor Receptor Inhibition, In Silico Absorption-Distribution-Metabolism-Excretion and Density Functional Theory Calculations

The current research investigates the green and efficient water extract of banana peel (WEB) mediated synthesis of thiazole derivatives and their anticancer activity against the MCF-7 breast cancer cell line. A total of 10 novel thiazole derivatives were synthesized by green protocol and characterized by Fourier-transform infrared, proton nuclear magnetic resonance (1H NMR), carbon-13 (13C) NMR and mass spectral methods confirming the successful synthesis of the desired thiazole derivatives. A 3:1 ethanol-to-WEB solvent mixture was identified as optimal, achieving an 83% yield within 1 h. The synthesis protocol accommodates a range of aryl and halogen substitutions, allowing for flexibility in compound design and resulting in high yields (75-88%) across diverse derivatives. Compound 4e, bearing a methoxy group, emerged as the most potent candidate with a minimum inhibitory concentration (IC50) of 11.03 ± 1.047 µM, while compound 4c, featuring a chlorine substituent, also showed strong activity with an IC50 of 13.36 ± 1.21 µM. The structure-activity relationship analysis indicated that both electron-donating and electron-withdrawing groups significantly influence anticancer potency. Molecular docking studies with epidermal growth factor receptors revealed that compound 4e has the highest binding affinity, with a docking score of -6.98 kcal/mol, surpassing the standard drug Gefitinib (-6.45 kcal/mol). Density functional theory study revealed electronic, chemical and biological characteristics of 4e through frontier molecular orbital, molecular electrostatic potential and global descriptors. Absorption-distribution-metabolism-excretion analysis suggested favourable drug-like properties for most compounds. These findings highlight the WEB-mediated synthesis as a valuable approach for developing bioactive thiazole derivatives with potential as anticancer agents.

Identification of gene signatures associated with lactation for predicting prognosis and treatment response in breast cancer patients through machine learning

As a newly discovered histone modification, abnormal lactation has been found to be present in and contribute to the development of various cancers. The aim of this study was to investigate the potential role between lactylation and the prognosis of breast cancer patients. Lactylation-associated subtypes were obtained by unsupervised consensus clustering analysis. Lactylation-related gene signature (LRS) was constructed by 15 machine learning algorithms, and the relationship between LRS and tumor microenvironment (TME) as well as drug sensitivity was analyzed. In addition, the expression of genes in the LRS in different cells was explored by single-cell analysis and spatial transcriptome. The expression levels of genes in LRS in clinical tissues were verified by RT-PCR. Finally, the potential small-molecule compounds were analyzed by CMap, and the molecular docking model of proteins and small-molecule compounds was constructed. LRS was composed of 6 key genes (SHCBP1, SIM2, VGF, GABRQ, SUSD3, and CLIC6). BC patients in the high LRS group had a poorer prognosis and had a TME that promoted tumor progression. Single-cell analysis and spatial transcriptome revealed differential expression of the key genes in different cells. The results of PCR showed that SHCBP1, SIM2, VGF, GABRQ, and SUSD3 were up-regulated in the cancer tissues, whereas CLIC6 was down-regulated in the cancer tissues. Arachidonyltrifluoromethane, AH-6809, W-13, and clofibrate can be used as potential target drugs for SHCBP1, VGF, GABRQ, and SUSD3, respectively. The gene signature we constructed can well predict the prognosis as well as the treatment response of BC patients. In addition, our predicted small-molecule complexes provide an important reference for personalized treatment of breast cancer patients.

Multi-omics approaches for understanding gene-environment interactions in noncommunicable diseases: techniques, translation, and equity issues

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

Advances in predicting breast cancer driver mutations: Tools for precision oncology (Review)

In the modern era of medicine, prognosis and treatment, options for a number of cancer types including breast cancer have been improved by the identification of cancer‑specific biomarkers. The availability of high‑throughput sequencing and analysis platforms, the growth of publicly available cancer databases and molecular and histological profiling facilitate the development of new drugs through a precision medicine approach. However, only a fraction of patients with breast cancer with few actionable mutations typically benefit from the precision medicine approach. In the present review, the current development in breast cancer driver gene identification, actionable breast cancer mutations, as well as the available therapeutic options, challenges and applications of breast precision oncology are systematically described. Breast cancer driver mutation‑based precision oncology helps to screen key drivers involved in disease development and progression, drug sensitivity and the genes responsible for drug resistance. Advances in precision oncology will provide more targeted therapeutic options for patients with breast cancer, improving disease‑free survival and potentially leading to significant successes in breast cancer treatment in the near future. Identification of driver mutations has allowed new targeted therapeutic approaches in combination with standard chemo‑ and immunotherapies in breast cancer. Developing new driver mutation identification strategies will help to define new therapeutic targets and improve the overall and disease‑free survival of patients with breast cancer through efficient medicine.

Quality of Life Outcomes in Breast Cancer Patients Receiving Chemotherapy With or Without Radiation Therapy

PURPOSE

Understanding quality of life (QOL) implications of individual components of breast cancer treatment is important as systemic therapies continue to improve oncologic outcomes. We hypothesized that adjuvant radiation therapy does not significantly impact QOL domains in breast cancer patients undergoing chemotherapy.

METHODS

Data was drawn from three prospective studies in women with localized breast cancer being treated with chemotherapy from March 2014 to December 2019. Patient-reported measures were collected at baseline (pretreatment) and post-treatment using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) measure, which consists of 5 subscales. Changes in mean QOL scores in patients who received radiotherapy were compared to those who did not using a one-sided noninferiority method. Statistical significance was determined below 0.05 to meet noninferiority.

RESULTS

In a sample of 175 patients, 131 were treated with radiation and 44 had no radiation. The sample consisted mostly of stage I-II breast cancer (78%) with hormone receptor positive (59%) disease, receiving either neoadjuvant (36%) or adjuvant chemotherapy (64%). Mean change in QOL for the group treated with radiation compared to no radiation was noninferior with respect to Physical Well-Being (P = .0027), Social/Family Well-Being (P = .0002), Emotional Well-Being (P = .0203), FACIT-Fatigue Subscale (P = .0072), and the Total FACIT-F score (P = .0005); however, mean change in QOL did not meet noninferiority for Functional Well-Being (P = .0594).

CONCLUSION

Patient-reported QOL from baseline to post-treatment, using the Total FACIT-F score, was noninferior in patients treated with versus without radiation therapy. This finding, in addition to individualized QOL subscales, provides important information in the informed decision-making process when discussing the effects of locoregional radiation on QOL in localized breast cancer patients treated with chemotherapy.

Precision medicine in breast cancer (Review)

Precision medicine in breast cancer is a revolutionary approach that customizes diagnosis and treatment based on individual and tumor characteristics, departing from the traditional one-size-fits-all approach. Breast cancer is diverse, with various subtypes driven by distinct genetic mutations. Understanding this diversity is crucial for tailored treatment strategies that target specific vulnerabilities in each tumor. Genetic testing, particularly for mutations in breast cancer gene (BRCA) DNA repair-associated genes, helps assess hereditary risks and influences treatment decisions. Molecular subtyping guides personalized treatments, such as hormonal therapies for receptor-positive tumors and human epidermal growth factor receptor 2 (HER2)-targeted treatments. Targeted therapies, including those for HER2-positive and hormone receptor-positive breast cancers, offer more effective and precise interventions. Immunotherapy, especially checkpoint inhibitors, shows promise, particularly in certain subtypes such as triple-negative breast cancer, with ongoing research aiming to broaden its effectiveness. Integration of big data and artificial intelligence enhances personalized treatment strategies, while liquid biopsies provide real-time insights into tumor dynamics, aiding in treatment monitoring and modification. Challenges persist, including accessibility and tumor complexity, but emerging technologies and precision prevention offer hope for improved outcomes. Ultimately, precision medicine aims to optimize treatment efficacy, minimize adverse effects and enhance the quality of life for patients with breast cancer.

Predicting gene signature in breast cancer patients with multiple machine learning models

AIMS

The aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models.

METHODS

In this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO).

RESULTS

We identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K-Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples.

CONCLUSIONS

Our findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

Nutrition Intervention and Microbiome Modulation in the Management of Breast Cancer

Breast cancer (BC) is one of the most common cancers worldwide and a leading cause of cancer-related deaths among women. The escalating incidence of BC underscores the necessity of multi-level treatment. BC is a complex and heterogeneous disease involving many genetic, lifestyle, and environmental factors. Growing evidence suggests that nutrition intervention is an evolving effective prevention and treatment strategy for BC. In addition, the human microbiota, particularly the gut microbiota, is now widely recognized as a significant player contributing to health or disease status. It is also associated with the risk and development of BC. This review will focus on nutrition intervention in BC, including dietary patterns, bioactive compounds, and nutrients that affect BC prevention and therapeutic responses in both animal and human studies. Additionally, this paper examines the impacts of these nutrition interventions on modulating the composition and functionality of the gut microbiome, highlighting the microbiome-mediated mechanisms in BC. The combination treatment of nutrition factors and microbes is also discussed. Insights from this review paper emphasize the necessity of comprehensive BC management that focuses on the nutrition-microbiome axis.

Menin signaling and therapeutic targeting in breast cancer

To date, mounting evidence have shown that patients with multiple endocrine neoplasia type 1 (MEN1) may face an increased risk for breast carcinogenesis. The product of the MEN1 gene, menin, was also indicated to be an important regulator in breast cancer signaling network. Menin directly interacts with MLL, EZH2, JunD, NF-κB, PPARγ, VDR, Smad3, β-catenin and ERα to modulate gene transcriptions leading to cell proliferation inhibition. Moreover, interaction of menin-FANCD2 contributes to the enhancement of BRCA1-mediated DNA repair mechanism. Ectopic expression of menin causes Bax-, Bak- and Caspase-8-dependent apoptosis. However, despite numbers of menin inhibitors were exploited in other cancers, data on the usage of menin inhibitors in breast cancer treatment remain limited. In this review, we focused on the menin associated signaling pathways and gene transcription regulations, with the aim of elucidating its molecular mechanisms and of guiding the development of novel menin targeted drugs in breast cancer therapy.

Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study

BACKGROUND

Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients.

METHOD

A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline.

RESULTS

Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models.

CONCLUSION

Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.

Gene-environment interactions within a precision environmental health framework

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Underpinnings of the Halachic Approach to BRCA Screening and Intervention: Facilitating Provider Counseling for Observant Jewish Populations

BACKGROUND

Halacha is the corpus of Jewish law which serves as a life blueprint for observant Jewish individuals. Health professionals counseling halachically observant populations at risk for breast cancer gene (BRCA) mutations should be well informed of the halachic approach to screening for BRCA mutations and subsequent interventions.

AIM

To address the intersection of halacha with ethical norms and current medical evidence-based data as they relate to potential and identified BRCA mutation carriers at their various stages of decision-making.

RESULTS

Halacha, ethics, and medicine have much in common, but there are specific principles which guide halacha; decision-making in light of halacha is complex and varies with respect to the multi-faceted aspects of screening and intervention. Halacha encourages the exercise of autonomy regarding situations in which beneficence is not clear-cut and dependent on subjective perceptions.

CONCLUSIONS

Health professionals knowledgeable of halacha are better equipped to counsel the observant Jewish population at risk of BRCA mutations or identified as mutation carriers, enabling them to present targeted questions to halachic authorities and thus achieve optimal decision-making.

Chlordiazepoxide against signalling, receptor and regulatory proteins of breast cancer: a structure-based in-silico approach

Among the most prevalent forms of cancer are breast, lung, colon-rectum, and prostate cancers, and breast cancer is a major global health challenge, contributing to 2.26 million cases with approximately 685,000 deaths worldwide in 2020 alone, typically beginning in the milk ducts or lobules that produce and transport milk during lactation and it is becoming challenging to treat as the tissues are developing resistance, which makes urgent calls for new multitargeted drugs. The multitargeted drug design provides a better solution, simultaneously targeting multiple pathways, even when the drug resists one, it remains effective for others. In this study, we included four crucial proteins that perform signalling, receptor, and regulatory action, namely- NUDIX Hydrolases, Dihydrofolate Reductase, HER2/neu Kinase and EGFR and performed multitargeted molecular docking studies against human-approved drugs using HTVS, SP and extra precise algorithms and filtered the poses with MM\GBSA, suggested a benzodiazepine derivative chlordiazepoxide, used as an anxiolytic agent, can be a multitargeted inhibitor with docking and MM\GBSA score ranging from - 4.628 to - 7.877 and - 18.59 to - 135.86 kcal/mol, respectively, and the most interacted residues were 6ARG, 6GLU, 3TRP, and 3VAL. The QikProp-based ADMET and DFT computations showed the suitability and stability of the drug candidate followed by 100 ns MD simulation in water and MMGBSA on trajectories, resulting in stable performance and many intermolecular interactions to make the complexes stable, which favours that chlordiazepoxide can be a multitargeted breast cancer inhibitor. However, experimental validation is needed before its use.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

The Impact of Vitamin D and Its Dietary Supplementation in Breast Cancer Prevention: An Integrative Review

Vitamin D deficiency is currently a significant public health issue closely linked to numerous diseases, such as breast cancer. This study aims to determine the estimated optimal serum levels of vitamin D to have a protective effect against breast cancer, in addition to exploring the biological mechanisms and risk factors involved. A literature search of articles published in the last 5 years was conducted, and simple statistical analyses using mean and standard deviation were performed to calculate the average concentration of vitamin D from different available studies. It has been observed that serum levels of vitamin D ≥ 40.26 ng/mL ± 14.19 ng/mL could exert a protective effect against breast cancer. Additionally, various biological mechanisms, such as those related to the immune system, and risk factors like diet implicated in this relationship were elucidated. Consequently, it can be concluded that proper serum levels of vitamin D may have a protective effect against breast cancer, and dietary supplementation may be an appropriate procedure to achieve these optimal vitamin D concentrations.

Structure-Based In Silico Approaches Reveal IRESSA as a Multitargeted Breast Cancer Regulatory, Signalling, and Receptor Protein Inhibitor

Breast cancer begins in the breast cells, mainly impacting women. It starts in the cells that line the milk ducts or lobules responsible for producing milk and can spread to nearby tissues and other body parts. In 2020, around 2.3 million women across the globe received a diagnosis, with an estimated 685,000 deaths. Additionally, 7.8 million women were living with breast cancer, making it the fifth leading cause of cancer-related deaths among women. The mutational changes, overexpression of drug efflux pumps, activation of alternative signalling pathways, tumour microenvironment, and cancer stem cells are causing higher levels of drug resistance, and one of the major solutions is to identify multitargeted drugs. In our research, we conducted a comprehensive screening using HTVS, SP, and XP, followed by an MM/GBSA computation of human-approved drugs targeting HER2/neu, BRCA1, PIK3CA, and ESR1. Our analysis pinpointed IRESSA (Gefitinib-DB00317) as a multitargeted inhibitor for these proteins, revealing docking scores ranging from -4.527 to -8.809 Kcal/mol and MM/GBSA scores between -49.09 and -61.74 Kcal/mol. We selected interacting residues as fingerprints, pinpointing 8LEU, 6VAL, 6LYS, 6ASN, 5ILE, and 5GLU as the most prevalent in interactions. Subsequently, we analysed the ADMET properties and compared them with the standard values of QikProp. We extended our study for DFT computations with Jaguar and plotted the electrostatic potential, HOMO and LUMO regions, and electron density, followed by a molecular dynamics simulation for 100 ns in water, showing an utterly stable performance, making it a suitable drug candidate. IRESSA is FDA-approved for lung cancer, which shares some pathways with breast cancers, clearing the hurdles of multitargeted drugs against breast and lung cancer. This has the potential to be groundbreaking; however, more studies are needed to concreate IRESSA's role.

Dual-loaded liposomal carriers to combat chemotherapeutic resistance in breast cancer

INTRODUCTION

The resistance to chemotherapy is a significant hurdle in breast cancer treatment, prompting the exploration of innovative strategies. This review discusses the potential of dual-loaded liposomal carriers to combat chemoresistance and improve outcomes for breast cancer patients.

AREAS COVERED

This review discusses breast cancer chemotherapy resistance and dual-loaded liposomal carriers. Drug efflux pumps, DNA repair pathways, and signaling alterations are discussed as chemoresistance mechanisms. Liposomes can encapsulate several medicines and cargo kinds, according to the review. It examines how these carriers improve medication delivery, cancer cell targeting, and tumor microenvironment regulation. Also examined are dual-loaded liposomal carrier improvement challenges and techniques.

EXPERT OPINION

The use of dual-loaded liposomal carriers represents a promising and innovative strategy in the battle against chemotherapy resistance in breast cancer. This article has explored the various mechanisms of chemoresistance in breast cancer, emphasizing the potential of dual-loaded liposomal carriers to overcome these challenges. These carriers offer versatility, enabling the encapsulation and precise targeting of multiple drugs with different modes of action, a crucial advantage when dealing with the complexity of breast cancer treatment.

Beyond genetics: Exploring the role of epigenetic alterations in breast cancer

Breast cancer remains a major global health challenge. Its rising incidence is attributed to factors such as delayed diagnosis, the complexity of its subtypes, and increasing drug resistance, all contributing to less-than-ideal patient outcomes. Central to the progression of breast cancer are epigenetic aberrations, which significantly contribute to drug resistance and the emergence of cancer stem cell traits. These include alterations in DNA methylation, histone modifications, and the expression of non-coding RNAs. Understanding these epigenetic changes is crucial for developing advanced breast cancer management strategies despite their complexity. Investigating these epigenetic modifications offers the potential for novel diagnostic markers, more accurate prognostic indicators, and the identification of reliable predictors of treatment response. This could lead to the development of new targeted therapies. However, this requires sustained, focused research efforts to navigate the challenges of understanding breast cancer carcinogenesis and its epigenetic underpinnings. A deeper understanding of epigenetic mechanisms in breast cancer can revolutionize personalized medicine. This could lead to significant improvements in patient care, including early detection, precise disease stratification, and more effective treatment options.

Unveiling breast cancer risk profiles: a survival clustering analysis empowered by an online web application

Aim: To develop a shiny app for doctors to investigate breast cancer treatments through a new approach by incorporating unsupervised clustering and survival information. Materials & methods: Analysis is based on the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset, which contains 1726 subjects and 22 variables. Cox regression was used to identify survival risk factors for K-means clustering. Logrank tests and C-statistics were compared across different cluster numbers and Kaplan-Meier plots were presented. Results & conclusion: Our study fills an existing void by introducing a unique combination of unsupervised learning techniques and survival information on the clinician side, demonstrating the potential of survival clustering as a valuable tool in uncovering hidden structures based on distinct risk profiles.

Radiomics to Differentiate Malignant and Benign Breast Lesions: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis

Breast cancer is a prevalent global health concern, necessitating accurate diagnostic tools for effective management. Diagnostic imaging plays a pivotal role in breast cancer diagnosis, staging, treatment planning, and outcome evaluation. Radiomics is an emerging field of study in medical imaging that contains a broad set of computational methods to extract quantitative features from radiographic images. This can be utilized to guide diagnosis, treatment response, and prognosis in clinical settings.  A systematic review was performed in concordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Quality was assessed using the radiomics quality score. Diagnostic sensitivity and specificity of radiomics analysis, with 95% confidence intervals (CIs), were included for meta-analysis. The area under the curve analysis was recorded. An extensive statistical analysis was performed following the Cochrane guidelines. Statistical significance was determined if p-values were less than 0.05. Statistical analyses were conducted using Review Manager (RevMan), Version 5.4.1. A total of 31 manuscripts involving 8,773 patients were included, with 17 contributing to the meta-analysis. The cohort comprised 56.2% malignant breast cancers and 43.8% benign breast lesions. MRI demonstrated a sensitivity of 0.91 (95% CI: 0.89-0.92) and a specificity of 0.84 (95% CI: 0.82-0.86) in differentiating between benign and malignant breast cancers. Mammography-based radiomic features predicted breast cancer subtype with a sensitivity of 0.79 (95% CI: 0.76-0.82) and a specificity of 0.81 (95% CI: 0.79-0.84). Ultrasound-based analysis yielded a sensitivity of 0.92 (95% CI: 0.90-0.94) and a specificity of 0.85 (95% CI: 0.83-0.88). Only one study reported the results of radiomic evaluation from CT, which had a sensitivity of 0.95 (95% CI: 0.88-0.99) and a specificity of 0.56 (95% CI: 0.45-0.67).  Across different imaging modalities, radiomics exhibited robust diagnostic accuracy in differentiating benign and malignant breast lesions. The results underscore the potential of radiomic assessment as a minimally invasive alternative or adjunctive diagnostic tool for breast cancer. This is pioneering data that reports on a novel diagnostic approach that is understudied and underreported. However, due to study limitations, the complexity of this technology, and the need for future development, biopsy still remains the current gold standard method of determining breast cancer type.

Roles of long noncoding RNA in triple-negative breast cancer

INTRODUCTION

Long noncoding RNAs (lncRNAs) play crucial roles in regulating various hallmarks in cancers. Triple-negative (Estrogen receptor, ER; Human epidermal growth factor receptor 2, HER2; Progesterone receptor, PR) breast cancer (TNBC) is the most aggressive form of breast cancers with a poor prognosis and no available molecular targeted therapy.

METHODS

We reviewed the current literature on the roles of lncRNAs in the pathogenesis, therapy resistance, and prognosis of patients with TBNC.

RESULTS

LncRNAs are associated with TNBC pathogenesis, therapy resistance, and prognosis. For example, lncRNAs such as small nucleolar RNA host gene 12 (SNHG12), highly upregulated in liver cancer (HULC) HOX transcript antisense intergenic RNA (HOTAIR), lincRNA-regulator of reprogramming (LincRNA-ROR), etc., are aberrantly expressed in TNBC and are involved in the pathogenesis of the disease. LncRNAs act as a decoy, scaffold, or sponge to regulate the expression of genes, miRNAs, and transcription factors associated with pathogenesis and progression of TNBC. Moreover, lncRNAs such as ferritin heavy chain 1 pseudogene 3 (FTH1P3), BMP/OP-responsive gene (BORG) contributes to the therapy resistance property of TNBC through activating ABCB1 (ATP-binding cassette subfamily B member 1) drug efflux pumps by increasing DNA repair capacity or by inducing signaling pathway involved in therapeutic resistance.

CONCLUSION

In this review, we outline the functions of various lncRNAs along with their molecular mechanisms involved in the pathogenesis, therapeutic resistance of TBNC. Also, the prognostic implications of lncRNAs in patients with TNBC is illustrated. Moreover, potential strategies targeting lncRNAs against highly aggressive TNBC is discussed in this review.

DNA repair pathways in breast cancer: from mechanisms to clinical applications

BACKGROUND

Breast cancer (BC) is a complex disease with various subtypes and genetic alterations that impact DNA repair pathways. Understanding these pathways is essential for developing effective treatments and improving patient outcomes.

AREA COVERED

This study investigates the significance of DNA repair pathways in breast cancer, specifically focusing on various pathways such as nucleotide excision repair, base excision repair, mismatch repair, homologous recombination repair, non-homologous end joining, fanconi anemia pathway, translesion synthesis, direct repair, and DNA damage tolerance. The study also examines the role of these pathways in breast cancer resistance and explores their potential as targets for cancer treatment.

CONCLUSION

Recent advances in targeted therapies have shown promise in exploiting DNA repair pathways for BC treatment. However, much research is needed to improve the efficacy of these therapies and identify new targets. Additionally, personalized treatments that target specific DNA repair pathways based on tumor subtype or genetic profile are being developed. Advances in genomics and imaging technologies can potentially improve patient stratification and identify biomarkers of treatment response. However, many challenges remain, including toxicity, resistance, and the need for more personalized treatments. Continued research and development in this field could significantly improve BC treatment.

Subjective valuation of Iranian women for screening for gene-related diseases: a case of breast cancer

BACKGROUND

About 5-10% of breast cancer cases are attributed to a gene mutation. To perform preventive interventions for women with a gene mutation, genetic screening BRCA tests have recently been implemented in Iran. The present study aimed to determine Iranian women's subjective valuation for screening BRCA tests for early detection of breast cancer to help policymakers to make decisions about genetic screening tests for breast cancer and to know the applicants.

METHODS

An online survey was completed by women older than 30 years old in Tehran, the capital of Iran in 2021. A hypothetical scenario about genetic screening tests for breast cancer was defined. The subjective valuation for the tests was assessed by a willingness to pay (WTP) using the contingent valuation method (CVM) by payment card. Demographics, history of breast cancers, knowledge, and physiological variables were considered as independent variables, and a logistic regression model assessed the relationship between WTP and the variables.

RESULTS

660 women were included. 88% of participants intended to participate in BRCA genetic screening for breast cancer if it were free. The mean WTP for the tests was about $ 20. Based on the logistic regression, income, family history of breast or ovarian cancer, and positive attitude were associated with WTP.

CONCLUSIONS

Iranian women were willing to intend for genetic screening BRCA tests and pay for them as well. The result of the present study is of great importance for policy makers when it comes to funding and determining co-payments for BRCA genetic screening tests. To achieve a high participation rate of women in breast cancer screening plans, a positive attitude should be promoted as a psychological factor. Educational and informative programs can help.

Personalized Injury Reduction Strategies in Sports Medicine: Lessons Learned from Advances in Breast Cancer Treatment: A Clinical Commentary

Background

Injury rates across sport have risen over the past twenty years, despite increased efforts in training and injury prevention. The rise in injury rates suggest that current approaches to estimating injury risk and risk management are not effective. One factor limiting progress is the inconsistency in screening, risk assessment, and risk management strategies to guide injury mitigation approaches.

Clinical Question

How can sports physical therapists identify and apply lessons learned from other healthcare fields to improve athlete injury risk and risk management strategies?

Key Results

Breast cancer mortality has consistently decreased over the last 30 years, largely attributed to advances in personalizing the prevention and treatment strategies which include modifiable and non-modifiable factors when assessing risk, the transition to personalized medicine, and the systematic approach used to investigate individual risk factors. Three critical phases have facilitated the identification and importance of individual risk factors and developing targeted, personalized strategies for breast cancer risk including: 1) Establishing the potential relationship between factors and outcomes; 2) Prospectively investigate the strength and direction of the relationship; 3) Investigating if intervening on identified factors alters prognosis.

Clinical Application

Applying lessons learned from other healthcare fields could improve shared decision making between the clinician and athlete concerning risk assessment and management. Examples include calculating only non-modifiable risk, creating individualized screening schedules based on risk assessment, or calculating the influence of each intervention on the athlete's injury risk. A systematic approach to identify and intervene on risk is needed to improve athlete outcomes.

Advances of Epigenetic Biomarkers and Epigenome Editing for Early Diagnosis in Breast Cancer

Epigenetic modifications are known to regulate cell phenotype during cancer progression, including breast cancer. Unlike genetic alterations, changes in the epigenome are reversible, thus potentially reversed by epi-drugs. Breast cancer, the most common cause of cancer death worldwide in women, encompasses multiple histopathological and molecular subtypes. Several lines of evidence demonstrated distortion of the epigenetic landscape in breast cancer. Interestingly, mammary cells isolated from breast cancer patients and cultured ex vivo maintained the tumorigenic phenotype and exhibited aberrant epigenetic modifications. Recent studies indicated that the therapeutic efficiency for breast cancer regimens has increased over time, resulting in reduced mortality. Future medical treatment for breast cancer patients, however, will likely depend upon a better understanding of epigenetic modifications. The present review aims to outline different epigenetic mechanisms including DNA methylation, histone modifications, and ncRNAs with their impact on breast cancer, as well as to discuss studies highlighting the central role of epigenetic mechanisms in breast cancer pathogenesis. We propose new research areas that may facilitate locus-specific epigenome editing as breast cancer therapeutics.

Integrated analysis of pivotal biomarker of LSM1, immune cell infiltration and therapeutic drugs in breast cancer

The discovery of early diagnosis and prognostic markers for breast cancer can significantly improve survival and reduce mortality. LSM1 is known to be involved in the general process of mRNA degradation in complexes containing LSm subunits, but the molecular and biological functions in breast cancer remain unclear. Here, the expression of LSM1 mRNA in breast cancer was estimated using The Cancer Genome Atlas (TCGA), Oncomine, TIMER and bc-GenExMiner databases. We found that functional LSM1 inactivation caused by mutations and profound deletions predicted poor prognosis in breast cancer (BRCA) patients. LSM1 was highly expressed in both BRCA tissues and cells compared to normal breast tissues/cells. High LSM1 expression is associated with poorer overall survival and disease-free survival. The association between LSM1 and immune infiltration of breast cancer was assessed by TIMER and CIBERSORT algorithms. LSM1 showed a strong correlation with various immune marker sets. Most importantly, pharmacogenetic analysis of BRCA cell lines revealed that LSM1 inactivation was associated with increased sensitivity to refametinib and trametinib. However, both drugs could mimic the effects of LSM1 inhibition and their drug sensitivity was associated with MEK molecules. Therefore, we investigated the clinical application of LSM1 to provide a basis for sensitive diagnosis, prognosis and targeted treatment of breast cancer.

siRNA Targeting Mcl-1 Potentiates the Anticancer Activity of Andrographolide Nanosuspensions via Apoptosis in Breast Cancer Cells

Breast cancer is the second leading cause of cancer-related death in the US. However, recurrence is frequently found despite adjuvant therapy being available. Combination therapy with cytotoxic drugs and gene therapy is being developed to be a new promising cancer treatment strategy. Introducing substituted dithiocarbamate moieties at the C12 position of andrographolide (3nAG) could improve its anticancer selectivity in the MCF-7 breast cancer cell line. However, its hydrophobicity is one of its main drawbacks. This work successfully prepared 3nAG nanosuspension stabilized with the chitosan derivative NSC (3nAGN-NSC) to increase solubility and pharmacological effectiveness. siRNAs have emerged as a promising therapeutic alternative for interfering with particular mRNA. The 3nAGN-NSC had also induced Mcl-1 mRNA expression in MCF-7 human breast cancer cells at 8, 12, and 24 h. This indicates that, in addition to Mcl-1 silencing by siRNA (siMcl-1) in MCF-7 with substantial Mcl-1 reliance, rationally devised combination treatment may cause the death of cancer cells in breast cancer. The Fa-CI analysis showed that the combination of 3nAGN-NSC and siMcl-1 had a synergistic effect with a combination index (CI) value of 0.75 (CI < 1 indicating synergistic effects) at the fractional inhibition of Fa 0.7. The synergistic effect was validated by flow cytometry, with the induction of apoptosis as the mechanism of reduced cell viability. Our findings suggested the rational use of 3nAGN-NSC in combination with siMcl-1 to kill breast cancer cells.

Feasibility of genetic testing for cancer risk assessment programme in Nigeria

Background

A high frequency of BRCA mutations has been established in Nigerian breast cancer (BC) patients. Recently, patients’ and first-degree relatives’ interest have been raised on cancer genetic risk assessment through our awareness activities in Nigeria. This led to the emergence of nurse-led cancer genetic counselling (CGC) and testing aimed at providing standard-of-care for individuals at increased risk of hereditary breast and ovarian cancers.

Methods

In June 2018, CGC and testing of patients with BC and ovarian cancer (OC) commenced in collaboration with Color Genomics Inc. for a 30-panel gene testing. Previously trained nurses in CGC at the University College Hospital, Ibadan offered genetic counselling (GC) to willing patients with BC and gynaecological cancer in four out-patient oncology clinics and departments for the pilot study. Consultation consisted of CGC, patient’s history, pedigree and sample collection for genetic testing (GT).

Results

Forty-seven patients – 40 with BC, five with OC and two with endometrial cancer received GC, and all chose to undergo GT. The average age at testing was 48.2 ± 12.1 years. Eight women reported a known family cancer history and there were more perceived benefits than barriers to GT with the patients experiencing the desire for none of their relative to have cancer. Results revealed no mutations in 27 (57.4%), 16 (4.0%) variants of unknown significance and 4 (8.5%) pathogenic mutations.

Conclusion

Personalised cancer care utilises GC and testing for cancer risk assessment towards prevention and early detection in high risk women. The study indicates the necessity of expanded cancer genetic services for integration into patient care and cancer prevention.

Identifying women with increased risk of breast cancer and implementing risk-reducing strategies and supplemental imaging

Breast cancer (BC) is the second most common cancer in women, affecting 1 in 8 women in the United States (12.5%) in their lifetime. However, some women have a higher lifetime risk of BC because of genetic and lifestyle factors, mammographic breast density, and reproductive and hormonal factors. Because BC risk is variable, screening and prevention strategies should be individualized after considering patient-specific risk factors. Thus, health care professionals need to be able to assess risk profiles, identify high-risk women, and individualize screening and prevention strategies through a shared decision-making process. In this article, we review the risk factors for BC, risk-assessment models that identify high-risk patients, and preventive medications and lifestyle modifications that may decrease risk. We also discuss the benefits and limitations of various supplemental screening methods.

Receipt of Guideline-Concordant Care Does Not Explain Breast Cancer Mortality Disparities by Race in Metropolitan Atlanta

BACKGROUND

Racial disparities in breast cancer mortality in the United States are well documented. Non-Hispanic Black (NHB) women are more likely to die of their disease than their non-Hispanic White (NHW) counterparts. The disparity is most pronounced among women diagnosed with prognostically favorable tumors, which may result in part from variations in their receipt of guideline care. In this study, we sought to estimate the effect of guideline-concordant care (GCC) on prognosis, and to evaluate whether receipt of GCC modified racial disparities in breast cancer mortality.

PATIENTS AND METHODS

Using the Georgia Cancer Registry, we identified 2,784 NHB and 4,262 NHW women diagnosed with a stage I-III first primary breast cancer in the metropolitan Atlanta area, Georgia, between 2010 and 2014. Women were included if they received surgery and information on their breast tumor characteristics was available; all others were excluded. Receipt of recommended therapies (chemotherapy, radiotherapy, endocrine therapy, and anti-HER2 therapy) as indicated was considered GCC. We used Cox proportional hazards models to estimate the impact of receiving GCC on breast cancer mortality overall and by race, with multivariable adjusted hazard ratios (HRs).

RESULTS

We found that NHB and NHW women were almost equally likely to receive GCC (65% vs 63%, respectively). Failure to receive GCC was associated with an increase in the hazard of breast cancer mortality (HR, 1.74; 95% CI, 1.37-2.20). However, racial disparities in breast cancer mortality persisted despite whether GCC was received (HRGCC: 2.17 [95% CI, 1.61-2.92]; HRnon-GCC: 1.81 [95% CI, 1.28-2.91] ).

CONCLUSIONS

Although receipt of GCC is important for breast cancer outcomes, racial disparities in breast cancer mortality did not diminish with receipt of GCC; differences in mortality between Black and White patients persisted across the strata of GCC.

Prognostic and diagnostic roles of prolyl 4-hydroxylase subunit α members in breast cancer

Aim: We aimed to evaluate the diagnostic and prognostic values of P4HAs in breast cancer (BC) patients. Materials & methods: Kaplan-Meier plotter was used to evaluate the prognostic values of P4HAs and correlations between their expression and clinical characteristics were assessed based on The Cancer Genome Atlas and the Human Protein Atlas. Results: The current study showed that P4HAs were highly expressed in BC patients with clinical stage I compared with nontumor control and elevated P4HAs were correlated with poor survival outcomes. Subtypes analysis revealed that P4HA1 and P4HA2 were most expressed in HER2+ subtypes patients. Univariate analysis displayed that elevated P4HA1 and P4HA3 correlated with unfavorable recurrence-free survival in mutated TP53 patients. Conclusion: This study indicated the diagnostic and prognostic roles of P4HAs members and broadened the biomarker fields of early diagnosis and prognostic monitoring of BC patients.

Sequencing for germline mutations in Swedish breast cancer families reveals novel breast cancer risk genes

Identifying genetic cancer risk factors will lead to improved genetic counseling, cancer prevention and cancer care. Analyzing families with a strong history of breast cancer (BC) has been a successful method to identify genes that contribute to the disease. This has led to discoveries of high-risk genes like the BRCA-genes. Nevertheless, many BC incidences are of unknown causes. In this study, exome sequencing on 59 BC patients from 24 Swedish families with a strong history of BC was performed to identify variants in known and novel BC predisposing genes. First, we screened known BC genes and identified two pathogenic variants in the BRIP1 and PALB2 genes. Secondly, to identify novel BC genes, rare and high impact variants and segregating in families were analyzed to identify 544 variants in novel BC candidate genes. Of those, 22 variants were defined as high-risk variants. Several interesting genes, either previously linked with BC or in pathways that when flawed could contribute to BC, were among the detected genes. The strongest candidates identified are the FANCM gene, involved in DNA double-strand break repair, and the RAD54L gene, involved in DNA recombination. Our study shows identifying pathogenic variants is challenging despite a strong family history of BC. Several interesting candidates were observed here that need to be further studied.

Association between the genetic polymorphisms of the pharmacokinetics of anthracycline drug and myelosuppression in a patient with breast cancer with anthracycline-based chemotherapy

AIMS

Exploring the genetic polymorphisms involved in the metabolism of anthracyclines can explain the causes of individual differences in myelosuppression during anthracycline-based chemotherapy.

MAIN METHODS

By PCR and Sanger sequencing, SNP of candidate genes participating into the pharmacokinetics of anthracycline, including chemotherapeutic drug intake (SLC22A16 rs6907567), metabolism (AKR1A1 rs2088102, CBR1 rs20572) and transfer (ABCG2 rs2231142) are detected in 194 breast cancer patients undergoing anthracycline-based postoperative adjuvant chemotherapy.

KEY FINDINGS

The CBR1 rs20572 (C>T) polymorphic allele, the ABCG2 rs2231142 (G>T) polymorphic allele, or the two polymorphic allele in combination significantly reduced the risk of leukopenia (OR 0.412, 95% CI 0.187-0.905, p = 0.025) and neutropenia (OR 0.354, 95% CI 0.148-0.846, p = 0.018). Either polymorphic allele T of CBR1 rs20572, or polymorphic allele C of AKR1A1 rs2088102 combined with the presence of both ABCG2 rs2231142(G>T) and SLC22A16 rs6907567(A>G) mutations were at extremely low risk of severe anemia of grades 3 and 4 (OR 0.058, 95% CI 0.006-0.554, p = 0.008, OR 0.065, 95% CI 0.006-0.689, p = 0.022, OR 0.037, 95% CI 0.004-0.36, p = 0.015, respectively).

SIGNIFICANCE

These results suggested CBR1 rs20572, ABCG2 rs2231142, SLC22A16 rs6907567 and AKR1A1 rs2088102 might be potential protective factors for the reduction of hematologic toxicity incidence during anthracycline-based chemotherapy in breast cancer patients.

Receptor Discordance of Metastatic Breast Cancer Depending on the Molecular Subtype

Introduction

Almost 30% of all women with early-stage breast cancer develop metastases. Treatment of metastatic disease is often based on the immunohistochemical information of the primary tumor, despite possible discordance of the hormone and Her2 receptor status.

Objectives

The aim of this study was to compare the receptor status of the primary tumor with the metastasis, and to evaluate for receptor discordance with regard to the molecular subtype, receptor status, and the localization of the metastases.

Methods

We retrospectively analyzed the data of all consecutive women with metastatic breast cancer, who underwent treatment at the Medical University Vienna between 2009 and 2016. Associations were calculated using the χ²or Fisher's exact test; years from primary diagnosis to metastatic disease were calculated using the Kaplan-Meier method.

Results

We identified 213 metastatic breast cancer patients, of whom 67 (31.5%) showed a discordant receptor status. Out of 32 patients with luminal A subtype, 14 (43.8%) had a switch of at least one receptor; 27 of 53 patients (50.9%) with luminal B subtype and 21 of 32 patients (65.6%) with Her2+ subtype showed receptor discordance; for triple-negative disease, 5 of 19 patients (36.3%) had a switch of at least one receptor. In 63 samples of bone metastases, 13 (20.6%) had discordant estrogen receptor status (p = 0.04). In 55 samples of bone metastases, 35 (63.3%) had discordant Her2 status (p = 0.002).

Conclusions

Our data show high rates of receptor discordance in metastatic breast cancer. Apart from the primary tumor, the immunohistochemical receptor status of the metastasis needs to be verified. This can lead to a change in treatment and prognosis.

Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data

The ability to accurately predict response and then rigorously optimize a therapeutic regimen on a patient-specific basis, would transform oncology. Toward this end, we have developed an experimental-mathematical framework that integrates quantitative magnetic resonance imaging (MRI) data into a biophysical model to predict patient-specific treatment response of locally advanced breast cancer to neoadjuvant therapy. Diffusion-weighted and dynamic contrast-enhanced MRI data is collected prior to therapy, after 1 cycle of therapy, and at the completion of the first therapeutic regimen. The model is initialized and calibrated with the first 2 patient-specific MRI data sets to predict response at the third, which is then compared to patient outcomes (N = 18). The model's predictions for total cellularity, total volume, and the longest axis at the completion of the regimen are significant within expected measurement precision (P< 0.05) and strongly correlated with measured response (P < 0.01). Further, we use the model to investigate, in silico, a range of (practical) alternative treatment plans to achieve the greatest possible tumor control for each individual in a subgroup of patients (N = 13). The model identifies alternative dosing strategies predicted to achieve greater tumor control compared to the standard of care for 12 of 13 patients (P < 0.01). In summary, a predictive, mechanism-based mathematical model has demonstrated the ability to identify alternative treatment regimens that are forecasted to outperform the therapeutic regimens the patients clinically. This has important implications for clinical trial design with the opportunity to alter oncology care in the future.

Fibroblast growth factor receptor 4 as a prognostic indicator in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) constitutes up to 15% of all breast cancers. It is one of the most aggressive breast cancers and is more prone to metastasize compared with other subtypes. Breast cancer patients with this subtype usually have a poor prognosis. Fibroblast growth factor receptor 4 (FGFR4) belongs to the receptor tyrosine kinase (RTK) family, and early analyses identified that FGFR4 was involved in breast cancer. However, the prognostic effect of FGFR4 on TNBC is unknown. In the present study, we investigated the association between FGFR4 and TNBC prognosis.

Methods

A total of 282 TNBC patients were enrolled. FGFR4 protein expression was detected in these 282 TNBC patients using immunohistochemistry (IHC).

Results

In the present study, FGFR4 was highly expressed in TNBC patients. Lymph node metastasis (LNM) (P=0.033) and p53 status (P=0.019) were associated with high FGFR4 expression. Univariate analysis identified high FGFR4 expression (P=0.016) as a prognostic predictor, and multivariate analysis found that high FGFR4 expression (P=0.016) was an independent prognostic factor. The Kaplan-Meier survival curve showed that high FGFR4 protein expression was correlated with poorer overall survival (OS).

Conclusions

The results of our present study show that FGFR4 protein expression is correlated with a worse prognosis in TNBC.

Clinical outcome and toxicity from taxanes in breast cancer patients with BRCA1 and BRCA2 pathogenic germline mutations

Germline variations in genes coding for proteins involved in the oxidative stress and DNA repair greatly influence drug response and toxicity. Because BRCA1 and BRCA2 proteins play a role in DNA damage repair, we postulated that taxane-related toxicity is potentially higher and clinical outcome in different in patients with BRCA pathogenic variants (PV). Seven hundred nineteen women who underwent BRCA genetic testing and were treated with taxane-containing chemotherapy for early-stage breast cancer between 1997 and 2018 were included in the study. Patients with BRCA variants of uncertain significance were excluded. The Kaplan-Meier product-limit method was used to estimate recurrence-free survival (RFS) and overall survival (OS) rates. Logistic regression models were used to assess the association between chemotherapy toxicity and factors of interest. Cox regression models were used to assess the association between RFS and OS and factors of interest. Ninety-four (13%) and 54 (7%) patients had BRCA1 and BRCA2-PVs, respectively. While anemia (P = .0025) and leukopenia (P = .001) were more frequently seen in BRCA noncarriers, there was no difference in regards to peripheral neuropathy or other toxicities between the groups. Increasing doses of taxane were associated with increased risk of neutropenia, stomatitis, nausea, vomiting, acne/rash, and peripheral neuropathy across all groups. In a multivariate logistic regression model, BRCA2 status remained as an independent significant predictor for decreased hematologic toxicity (HR: 0.36; 95% CI: 0.20-0.67; P = .001) and increased gastrointestinal toxicity (HR: 1.93; 95% CI: 1.02-3.67; P = .04). Being overweight, obese and African-American race were significant predictors for peripheral neuropathy (P = .04; P = .03; P = .06, respectively). Total taxane dose received did not have any impact on survival outcomes. Our study demonstrates that taxane-containing chemotherapy regimens do not increase risk of peripheral neuropathy or hematologic toxicity in patients with BRCA PVs. The mechanisms for this finding need to be further investigated as it may provide an opportunity to combine taxanes with other agents, such as platinum salts or PARP inhibitors, with less anticipated toxicity.

Long noncoding RNA SOX21-AS1 regulates the progression of triple-negative breast cancer through regulation of miR-520a-5p/ORMDL3 axis

Recently, long noncoding RNAs (lncRNAs) have been reported as a new kind of controllers about cancer processes in biology. In spite of the dysregulation of lncRNAs in various kinds of cancers, only a little of the information was effective on the expression configuration and inner effects of lncRNAs in triple-negative breast cancer (TNBC). This study valued the expression of lncRNA SOX21-AS1 and the biological role it played in TNBC. In our research, SOX21-AS1 had a high expression in TNBC cell lines. The functional experiments showed that knockdown of SOX21-AS1 obviously restrained cell proliferation, migration, invasion, and epithelial-mesenchymal transition process and promoted cell apoptosis. Mechanistically, SOX21-AS1 was found to bind with miR-520a-5p. Besides, ORMDL3 was identified as a downstream target of miR-520a-5p, and the suppressed ORMDL3 expression induced by silenced SOX21-AS1 could be restored by miR-520a-5p inhibition. Further, data from rescue assays revealed that SOX21-AS1 could regulate the malignancy of TNBC via miR-520a-5p/ORMDL3 axis. All in all, we identified that SOX21-AS1 regulated the cellular process of TNBC cells via antagonizing miR-520a-5p availability to upregulate ORMDL3 expression.

How and why to build a mathematical model: A case study using prion aggregation

Biological systems are inherently complex, and the increasing level of detail with which we are able to experimentally probe such systems continually reveals new complexity. Fortunately, mathematical models are uniquely positioned to provide a tool suitable for rigorous analysis, hypothesis generation, and connecting results from isolated in vitro experiments with results from in vivo and whole-organism studies. However, developing useful mathematical models is challenging because of the often different domains of knowledge required in both math and biology. In this work, we endeavor to provide a useful guide for researchers interested in incorporating mathematical modeling into their scientific process. We advocate for the use of conceptual diagrams as a starting place to anchor researchers from both domains. These diagrams are useful for simplifying the biological process in question and distinguishing the essential components. Not only do they serve as the basis for developing a variety of mathematical models, but they ensure that any mathematical formulation of the biological system is led primarily by scientific questions. We provide a specific example of this process from our own work in studying prion aggregation to show the power of mathematical models to synergistically interact with experiments and push forward biological understanding. Choosing the most suitable model also depends on many different factors, and we consider how to make these choices based on different scales of biological organization and available data. We close by discussing the many opportunities that abound for both experimentalists and modelers to take advantage of collaborative work in this field.

Research participants' experiences with return of genetic research results and preferences for web-based alternatives

BACKGROUND

While there is increasing interest in sharing genetic research results with participants, how best to communicate the risks, benefits and limitations of research results remains unclear.

METHODS

Participants who received genetic research results answered open and closed-ended questions about their experiences receiving results and interest in and advantages and disadvantages of a web-based alternative to genetic counseling.

RESULTS

107 BRCA1/2 negative women with a personal or family history of breast cancer consented to receive genetic research results and 82% completed survey items about their experience. Most participants reported there was nothing they disliked (74%) or would change (85%) about their predisclosure or disclosure session (78% and 89%). They most frequently reported liking the genetic counselor and learning new information. Only 24% and 26% would not be willing to complete predisclosure counseling or disclosure of results by a web-based alternative, respectively. The most frequently reported advantages included convenience and reduced time. Disadvantages included not being able to ask questions, the risk of misunderstanding and the impersonal nature of the encounter.

CONCLUSION

Most participants receiving genetic research results report high satisfaction with telephone genetic counseling, but some may be willing to consider self-directed web alternatives for both predisclosure genetic education and return of results.

Analysis of the pathogenic variants of BRCA1 and BRCA2 using next-generation sequencing in women with familial breast cancer: a case-control study

Background

Pathogenic variants (PVs) of BRCA genes entail a lifetime risk of developing breast cancer in 50–85% of carriers. Their prevalence in different populations has been previously reported. However, there is scarce information regarding the most common PVs of these genes in Latin-Americans. This study identified BRCA1 and BRCA2 PV frequency in a high-risk female population from Northeastern Mexico and determined the association of these mutations with the patients’ clinical and pathological characteristics.

Methods

Women were divided into three groups: aged ≤ 40 years at diagnosis and/or risk factors for hereditary breast cancer (n = 101), aged > 50 years with sporadic breast cancer (n = 22), and healthy women (n = 72). Their DNA was obtained from peripheral blood samples and the variants were examined by next-generation sequencing with Ion AmpliSeq BRCA1 and BRCA2 Panel using next-generation sequencing.

Results

PVs were detected in 13.8% group 1 patients (BRCA1, 12 patients; BRCA2, 2 patients). Only two patients in group 2 and none in group 3 exhibited BRCA1 PVs. Variants of uncertain significance were reported in 15.8% patients (n = 16). In group 1, patients with the triple-negative subtype, PV frequency was 40% (12/30). Breast cancer prevalence in young women examined in this study was higher than that reported by the National Cancer Institute Surveillance, Epidemiology (15.5% vs. 5.5%, respectively).

Conclusions

The detected BRCA1 and BRCA2 PV frequency was similar to that reported in other populations. Our results indicate that clinical data should be evaluated before genetic testing and highly recommend genetic testing in patients with the triple-negative subtype and other clinical aspects.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5950-4) contains supplementary material, which is available to authorized users.

Performance of survivin mRNA as a biomarker for breast cancer among Vietnamese women

Objective

This study aimed to perform the reverse-transcription polymerase chain reaction (RT-PCR) to express the survivin mRNA among patients with breast cancer in Vietnam and identify some potential associated clinical and pathological factors.

Methods

Peripheral blood (PB) samples and tissues on 43 patients with breast cancer and 21 patients with fibroids were obtained. The Real-time RT-PCR and gene sequencing techniques were employed to detect survivin gene in breast cancer cell lines and cancer tissues.

Results

Survivin mRNA transcription was detected in 32/43 (74,4%) of breast cancer tissues and 19/43 (44,2%) of PB samples of breast cancer patients, while it was detected in only 14,3 % fibrosis tissues and 0% in the blood of fibrosis patients. Survivin mRNA on the peripheral blood of breast cancer patients increased with tumor size, and stage of cancer (p < 0.05). In terms of breast cancer tissue, no difference was found in the rate of survivin mRNA expression in according to age, distant metastasis, lymph node, stages of cancer, and histopathology (p > 0.05).

Conclusions

Results provide the initial evidence of the expression of survivin mRNA in breast cancer patients in Vietnam, suggesting the role of survivin mRNA in breast cancer molecular pathology.

WITHDRAWN: Performance of survivin mRNA as a biomarker for breast cancer among Vietnamese women

Ahead of Print article withdrawn by publisher.

The Multitarget Activity of Natural Extracts on Cancer: Synergy and Xenohormesis

It is estimated that over 60% of the approved drugs and new drug developments for cancer and infectious diseases are from natural origin. The use of natural compounds as a potential source of antitumor agents has been deeply studied in many cancer models, both in vitro and in vivo. Most of the Western medicine studies are based on the use of highly selective pure compounds with strong specificity for their targets such as colchicine or taxol. Nevertheless, approximately 60% of fairly specific drugs in their initial research fail because of toxicity or ineffectiveness in late-stage preclinical studies. Moreover, cancer is a multifaceted disease that in most cases deserves a polypharmacological therapeutic approach. Complex plant-derived mixtures such as natural extracts are difficult to characterize and hardly exhibit high pharmacological potency. However, in some cases, these may provide an advantage due to their multitargeted mode of action and potential synergistic behavior. The polypharmacology approach appears to be a plausible explanation for the multigargeted mechanism of complex natural extracts on different proteins within the same signalling pathway and in several biochemical pathways at once. This review focuses on the different aspects of natural extracts in the context of anticancer activity drug development, with special attention to synergy studies and xenohormesis.

Long non-coding RNA (LncRNA) RMST in triple-negative breast cancer (TNBC): Expression analysis and biological roles research

Microarray showed that lncRNA RMST was differentially expressed in cervical cancer. Further experiments were conducted to detect the expression and biological function of RMST in triple-negative breast cancer (TNBC). Microarray was used to screen the differentially expressed lncRNAs in TNBC. QRT-PCR was applied to uncover the expression of RMST in TNBC tissues. The cell viability of RMST-transfected TNBC cells were probed by CKK-8 assay and colony formation assay. TUNEL assay was conducted to test the cell apoptosis and FCM assay was exerted to detect the cell cycle. The invasion and migration ability of transfected cells were examined by transwell assay. RMST played its biological function through regulating the mRNA or protein expression in cytoplasm. CCK-8 and colony formation assay unveiled that RMST could slow down the proliferation of TNBC cells to influence the tumor progression. TUNEL results revealed that RMST could enhance cell apoptosis in TNBC. The cell cycle detected by FCM assay indicated that RMST might induce the block of G0/G1 phase thus inhibiting TNBC cell proliferation. RMST overexpression could also restrain the invasion and migration abilities of TNBC cells. RMST played a role of tumor suppressor in TNBC through inhibiting cell proliferation, invasion and migration, enhancing cell apoptosis, and regulating cell cycle.

Oestrogen receptor-regulated glutathione S-transferase mu 3 expression attenuates hydrogen peroxide-induced cytotoxicity, which confers tamoxifen resistance on breast cancer cells

PURPOSE

Glutathione S-transferase mu 3 (GSTM3) is an enzyme involving in the detoxification of electrophilic compounds by conjugation with glutathione. Higher GSTM3 mRNA levels were reported in patients with ERα-positive breast cancer who received only tamoxifen therapy after surgery. Thus, this study aimed to clarify the oncogenic characteristics of GSTM3 in breast cancer and the mechanism of tamoxifen resistance.

METHODS

GSTM3 expression in human breast tumour tissues (n = 227) was analysed by RT-PCR and quantitative PCR. Western blot, promoter activity assays, and chromatin immunoprecipitation (ChIP) assays were used to investigate the mechanism of GSTM3 gene regulation. Hydrogen peroxide (H₂O₂)-induced cytotoxicity in breast cancer cells was detected by MTT assays and flow cytometry. The oncogenic characteristics of GSTM3 in MCF-7 cells were examined by siRNA knockdown in soft agar assays and a xenograft animal model.

RESULTS

GSTM3 mRNA was highly expressed in ER- and HER2-positive breast cancers. Moreover, patients who received adjuvant Herceptin had increased GSTM3 mRNA levels in tumour tissue. Oestrogen-activated GSTM3 gene expression through ERα-mediated recruitment of SP1, EP300, and AP-1 complexes. GSTM3-silenced MCF-7 cells were more sensitive to H₂O₂, with significantly inhibited proliferation and colony formation abilities. Tamoxifen-resistant (Tam-R) cells lacking GSTM3 showed enhanced sensitivity to H₂O₂, but this result was contrary to that obtained after short-term tamoxifen exposure. The animal model suggested that GSTM3 silencing might suppress the tumourigenic ability of MCF-7 cells and increase tumour cell apoptosis.

CONCLUSIONS

ROS production is one mechanism by which cancer drugs kill tumour cells, and according to our evidence, GSTM3 may play an important role in preventing breast cancer treatment-induced cellular cytotoxicity.