Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer

Bioinformatics characterization of variants of uncertain significance in pediatric sensorineural hearing loss

Introduction

Rapid advancements in Next Generation Sequencing (NGS) and bioinformatics tools have allowed physicians to obtain genetic testing results in a more rapid, cost-effective, and comprehensive manner than ever before. Around 50% of pediatric sensorineural hearing loss (SNHL) cases are due to a genetic etiology, thus physicians regularly utilize targeted sequencing panels that identify variants in genes related to SNHL. These panels allow for early detection of pathogenic variants which allows physicians to provide anticipatory guidance to families. Molecular testing does not always reveal a clear etiology due to the presence of multigenic variants with varying classifications, including the presence of Variants of Uncertain Significance (VUS). This study aims to perform a preliminary bioinformatics characterization of patients with variants associated with Type II Usher Syndrome in the presence of other multigenic variants. We also provide an interpretation algorithm for physicians reviewing molecular results with medical geneticists.

Methods

Review of records for multigenic and/or VUS results identified several potential subjects of interest. For the purposes of this study, two ADGRV1 compound heterozygotes met inclusion criteria. Sequencing, data processing, and variant calling (the process by which variants are identified from sequence data) was performed at Invitae (San Francisco CA). The preliminary analysis followed the recommendations outlined by the American College of Medical Genetics and Association for Molecular Pathology (ACMG-AMP) in 2015 and 2019. The present study utilizes computational analysis, predictive data, and population data as well as clinical information from chart review and publicly available information in the ClinVar database.

Results

Two subjects were identified as compound heterozygotes for variants in the gene ADGRV1. Subject 1's variants were predicted as deleterious, while Subject 2's variants were predicted as non-deleterious. These results were based on known information of the variants from ClinVar, multiple lines of computational data, population databases, as well as the clinical presentation.

Discussion

Early molecular diagnosis through NGS is ideal, as families are then able to access a wide range of resources that will ultimately support the child as their condition progresses. We recommend that physicians build strong relationships with medical geneticists and carefully review their interpretation before making recommendations to families, particularly when addressing the VUS. Reclassification efforts of VUS are supported by studies like ours that provide evidence of pathogenic or benign effects of variants.

Efficacy of metformin and electrical pulses in breast cancer MDA-MB-231 cells

Aim

Triple-negative breast cancer (TNBC) is a very aggressive subset of breast cancer, with limited treatment options, due to the lack of three commonly targeted receptors, which merits the need for novel treatments for TNBC. Towards this need, the use of metformin (Met), the most widely used type-2 diabetes drug worldwide, was explored as a repurposed anticancer agent. Cancer being a metabolic disease, the modulation of two crucial metabolites, glucose, and reactive oxygen species (ROS), is studied in MDA-MB-231 TNBC cells, using Met in the presence of electrical pulses (EP) to enhance the drug efficacy.

Methods

MDA-MB-231, human TNBC cells were treated with Met in the presence of EP, with various concentrations Met of 1 mmol/L, 2.5 mmol/L, 5 mmol/L, and 10 mmol/L. EP of 500 V/cm, 800 V/cm, and 1,000 V/cm (with a pulse width of 100 µs at 1 s intervals) were applied to TNBC and the impact of these two treatments was studied. Various assays, including cell viability, microscopic inspection, glucose, ROS, and wound healing assay, were performed to characterize the response of the cells to the combination treatment.

Results

Combining 1,000 V/cm with 5 mmol/L Met yielded cell viability as low as 42.6% at 24 h. The glucose level was reduced by 5.60-fold and the ROS levels were increased by 9.56-fold compared to the control, leading to apoptotic cell death.

Conclusions

The results indicate the enhanced anticancer effect of Met in the presence of electric pulses. The cell growth is inhibited by suppressing glucose levels and elevated ROS. This shows a synergistic interplay between electroporation, Met, glucose, and ROS metabolic alterations. The results show promises for combinational therapy in TNBC patients.

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.

Mining Potential Drug Targets for Osteoporosis Based on CeRNA Network

OBJECTIVE

To identify key pathological hub genes, micro RNAs (miRNAs), and circular RNAs (circRNAs) of osteoporosis (OP) and construct their ceRNA network in an effort to explore the potential biomarkers and drug targets for OP therapy.

METHODS

GSE7158, GSE201543, and GSE161361 microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by comparing OP patients with healthy controls and hub genes were screened by machine learning algorithms. Target miRNAs and circRNAs were predicted by FunRich and circbank, then ceRNA network were constructed by Cytoscape. Pathways affecting OP were identified by functional enrichment analysis. The hub genes were verified by receiver operating characteristic (ROC) curve and real time quantitative PCR (RT-qPCR). Potential drug molecules related to OP were predicted by DSigDB database and molecular docking was analyzed by autodock vina software.

RESULTS

A total of 179 DEGs were identified. By combining three machine learning algorithms, BAG2, MME, SLC14A1, and TRIM44 were identified as hub genes. Three OP-associated target miRNAs and 362 target circRNAs were predicted to establish ceRNA network. The ROC curves showed that these four hub genes had good diagnostic performance and their differential expression was statistically significant in OP animal model. Benzo[a]pyrene was predicted which could successfully bind to protein receptors related to the hub genes and it was served as the potential drug molecules.

CONCLUSION

An mRNA-miRNA-circRNA network is reported, which provides new ideas for exploring the pathogenesis of OP. Benzo[a]pyrene, as potential drug molecules for OP, may provide guidance for the clinical treatment.

Integrating Genomic Information with Tumor-Immune Microenvironment in Triple-Negative Breast Cancer

BACKGROUND

the development and progression of triple-negative breast cancer (TNBC) is driven by somatic driver mutations and the tumor-immune microenvironment. To date, data on somatic mutations has not been leveraged and integrated with information on the immune microenvironment to elucidate the possible oncogenic interactions and their potential effects on clinical outcomes. Here, we investigated possible oncogenic interactions between somatic mutations and the tumor-immune microenvironment, and their correlation with patient survival in TNBC.

METHODS

We performed analysis combining data on 7,875 somatic mutated genes with information on 1,751 immune-modulated genes, using gene-expression data as the intermediate phenotype, and correlated the resulting information with survival. We conducted functional analysis to identify immune-modulated molecular networks and signaling pathways enriched for somatic mutations likely to drive clinical outcomes.

RESULTS

We discovered differences in somatic mutation profiles between patients who died and those who survived, and a signature of somatic mutated immune-modulated genes transcriptionally associated with TNBC, predictive of survival. In addition, we discovered immune-modulated molecular networks and signaling pathways enriched for somatic mutations.

CONCLUSIONS

The investigation revealed possible oncogenic interactions between somatic mutations and the tumor-immune microenvironment in TNBC, likely to affect clinical outcomes.

PARP Inhibitors: A Major Therapeutic Option in Endocrine-Receptor Positive Breast Cancers

Simple Summary

OlympiAD and EMBRACA trials demonstrated the efficacy of PARPi, compared to chemotherapy, in patients with HER2-negative metastatic breast cancers (mBC) carrying a germline BRCA mutation. Patients with ER+/HER2-BRCA-mutated mBC seemed to have a higher risk of early disease progression while on CDK4/6 inhibitors and benefit from PARPi, especially when prescribed before chemotherapy. Importantly, the frequency of BRCA pathogenic variant (PV) carriers among ER+/HER2- breast cancer patients has been underestimated, and 50% of all BRCA1/2 mutated breast cancers are actually of ER+/HER2- subtype. Recent studies also highlight the benefit of PARPi in BRCA wild type mBC with HRD representing up to 20% of ER+/HER2- breast cancers. The OLYMPIA trial also demonstrated PARPi utility in patients with ER+/HER2- early breast cancers with BRCA PV at high risk of relapse. Consequently, implementation of early genotyping and new strategies for identifying patients with high-risk ER+/HER2- HRD breast cancers likely to benefit from PARPi is of high importance.

Abstract

Recently, OlympiAD and EMBRACA trials demonstrated the favorable efficacy/toxicity ratio of PARPi, compared to chemotherapy, in patients with HER2-negative metastatic breast cancers (mBC) carrying a germline BRCA mutation. PARPi have been largely adopted in triple-negative metastatic breast cancer, but their place has been less clearly defined in endocrine-receptor positive, HER2 negative (ER+/ HER2-) mBC. The present narrative review aims at addressing this question by identifying the patients that are more likely benefit from PARPi. Frequencies of BRCA pathogenic variant (PV) carriers among ER+/HER2- breast cancer patients have been underestimated, and many experts assume than 50% of all BRCA1/2 mutated breast cancers are of ER+/HER2- subtype. Patients with ER+/HER2- BRCA-mutated mBC seemed to have a higher risk of early disease progression while on CDK4/6 inhibitors and PARPi are effective especially when prescribed before exposure to chemotherapy. The OLYMPIA trial also highlighted the utility of PARPi in patients with early breast cancers at high risk of relapse and carrying PV of BRCA. PARPi might also be effective in patients with HRD diseases, representing up to 20% of ER+/HER2- breast cancers. Consequently, the future implementation of early genotyping strategies for identifying the patients with high-risk ER+/HER2- HRD breast cancers likely to benefit from PARPi is of high importance.

Necroptotic virotherapy of oncolytic alphavirus M1 cooperated with Doxorubicin displays promising therapeutic efficacy in TNBC

Triple-negative breast cancer (TNBC) is the most aggressive molecular subtype among breast tumors and remains a challenge even for the most current therapeutic regimes. Here, we demonstrate that oncolytic alphavirus M1 effectively kills both TNBC and non-TNBC. ER-stress and apoptosis pathways are responsible for the cell death in non-TNBC as reported in other cancer types, yet the cell death in TNBC does not depend on these pathways. Transcriptomic analysis reveals that the M1 virus activates necroptosis in TNBC, which can be pharmacologically blocked by necroptosis inhibitors. By screening a library of clinically available compounds commonly used for breast cancer treatment, we find that Doxorubicin enhances the oncolytic effect of the M1 virus by up to 100-fold specifically in TNBC in vitro, and significantly stalls the tumor growth of TNBC in vivo, through promoting intratumoral virus replication and further triggering apoptosis in addition to necroptosis. These findings reveal a novel antitumor mechanism and a new combination regimen of the M1 oncolytic virus in TNBC, and highlight a need to bridge molecular diagnosis with virotherapy.

Current Triple-Negative Breast Cancer Subtypes: Dissecting the Most Aggressive Form of Breast Cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease defined by the absence of estrogen receptor (ER) and progesterone receptor (PR) expression, and human epidermal growth factor receptor 2 (HER2) overexpression that lacks targeted treatments, leading to dismal clinical outcomes. Thus, better stratification systems that reflect intrinsic and clinically useful differences between TNBC tumors will sharpen the treatment approaches and improve clinical outcomes. The lack of a rational classification system for TNBC also impacts current and emerging therapeutic alternatives. In the past years, several new methodologies to stratify TNBC have arisen thanks to the implementation of microarray technology, high-throughput sequencing, and bioinformatic methods, exponentially increasing the amount of genomic, epigenomic, transcriptomic, and proteomic information available. Thus, new TNBC subtypes are being characterized with the promise to advance the treatment of this challenging disease. However, the diverse nature of the molecular data, the poor integration between the various methods, and the lack of cost-effective methods for systematic classification have hampered the widespread implementation of these promising developments. However, the advent of artificial intelligence applied to translational oncology promises to bring light into definitive TNBC subtypes. This review provides a comprehensive summary of the available classification strategies. It includes evaluating the overlap between the molecular, immunohistochemical, and clinical characteristics between these approaches and a perspective about the increasing applications of artificial intelligence to identify definitive and clinically relevant TNBC subtypes.

A Hybrid Supervised Machine Learning Classifier System for Breast Cancer Prognosis Using Feature Selection and Data Imbalance Handling Approaches

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier’s performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.

Small-Molecule Drug Discovery in Triple Negative Breast Cancer: Current Situation and Future Directions

Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, but an effective targeted therapy has not been well-established so far. Considering the lack of effective targets, where do we go next in the current TNBC drug development? A promising intervention for TNBC might lie in de novo small-molecule drugs that precisely target different molecular characteristics of TNBC. However, an ideal single-target drug discovery still faces a huge challenge. Alternatively, other new emerging strategies, such as dual-target drug, drug repurposing, and combination strategies, may provide new insight into the improvement of TNBC therapeutics. In this review, we focus on summarizing the current situation of a series of candidate small-molecule drugs in TNBC therapy, including single-target drugs, dual-target drugs, as well as drug repurposing and combination strategies that will together shed new light on the future directions targeting TNBC vulnerabilities with small-molecule drugs for future therapeutic purposes.