Tumoral expression of drug and xenobiotic metabolizing enzymes in breast cancer patients of different ethnicities with implications to personalized medicine

Aldo-keto reductases: Role in cancer development and theranostics

Aldo-keto reductases (AKRs) are a superfamily of enzymes that play crucial roles in various cellular processes, including the metabolism of xenobiotics, steroids, and carbohydrates. A growing body of evidence has unveiled the involvement of AKRs in the development and progression of various cancers. AKRs are aberrantly expressed in a wide range of malignant tumors. Dysregulated expression of AKRs enables the acquisition of hallmark traits of cancer by activating oncogenic signaling pathways and contributing to chemoresistance. AKRs have emerged as promising oncotherapeutic targets given their pivotal role in cancer development and progression. Inhibition of aldose reductase (AR), either alone or in combination with chemotherapeutic drugs, has evolved as a pragmatic therapeutic option for cancer. Several classes of synthetic aldo-keto reductase (AKR) inhibitors have been developed as potential anticancer agents, some of which have shown promise in clinical trials. Many AKR inhibitors from natural sources also exhibit anticancer effects. Small molecule inhibitors targeting specific AKR isoforms have shown promise in preclinical studies. These inhibitors disrupt the activation of oncogenic signaling by modulating transcription factors and kinases and sensitizing cancer cells to chemotherapy. In this review, we discuss the physiological functions of human AKRs, the aberrant expression of AKRs in malignancies, the involvement of AKRs in the acquisition of cancer hallmarks, and the role of AKRs in oncogenic signaling, and drug resistance. Finally, the potential of aldose reductase inhibitors (ARIs) as anticancer drugs is summarized.

Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era

Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors' immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.

Chemical sensors for imaging total cellular aliphatic aldehydes in live cells

Aliphatic aldehydes are reactive electrophilic carbonyls that cross-link with DNA and proteins leading to cellular toxicity and disease pathogenesis. This toxicity is due to the cooperative effect of multiple aldehydes via a common mechanism. Therefore, live-cell imaging of total aliphatic aldehydes, small-to-long chain (C1-C10), is highly desired to decipher their physiological and pathological functions. However, sensors for imaging total cellular aliphatic aldehydes are currently lacking despite their high concentrations (∼80 to >500 μM) inside cells. Herein, we report chemical sensors that generate a benzimidazole moiety upon reaction with aliphatic aldehydes of different chain lengths (C1-C10), resulting in turn-on fluorescence. These sensors exhibit high quantum yields, high dynamic range, and enable the quantification of changes in both the exogenous administration of aldehydes and endogenous real-time formation of aliphatic aldehydes in live mammalian cells. This tool has great potential to transform aldehyde research by illuminating cellular metabolites that have remained elusive in living systems.

Cellular resistance mechanisms in cancer and the new approaches to overcome resistance mechanisms chemotherapy

Despite major advancements in cancer healing approaches over the last few decades, chemotherapy remains the most popular malignancy treatment. Chemotherapeutic drugs are classified into many kinds based on their mechanism of action. Multidrug resistance (MDR) is responsible for approximately 90% of fatalities in malignancy cases treated with standard chemotherapeutics or innovative targeted medicines. Many innovative prospective anti-cancer medicines displayed high anti-cancer efficacy in a single application. However, combining them with other medications improves cancer treatment efficacy. This supports the belief that a combination of drugs is significantly more effective than a single medicine. Due to the intricacy of MDR processes and the diversity of tumor illnesses, there will rarely be a single medicine that can be utilized to treat all types of cancer. Finding new medications that can reverse MDR in malignancy cells will augment efficacy of chemotherapeutic agents and allow us to treat cancers that are now incurable.

The Complex Dynamic of Phase I Drug Metabolism in the Early Stages of Doxorubicin Resistance in Breast Cancer Cells

The altered activity of drug metabolism enzymes (DMEs) is a hallmark of chemotherapy resistance. Cytochrome P450s (CYPs), mainly CYP3A4, and several oxidoreductases are responsible for Phase I metabolism of doxorubicin (DOX), an anthracycline widely used in breast cancer (BC) treatment. This study aimed to investigate the role of Phase I DMEs involved in the first stages of acquisition of DOX-resistance in BC cells. For this purpose, the expression of 92 DME genes and specific CYP-complex enzymes activities were assessed in either sensitive (MCF-7 parental cells; MCF-7/DOX^S) or DOX-resistant (MCF-7/DOX^R) cells. The DMEs genes detected to be significantly differentially expressed in MCF-7/DOX^R cells (12 CYPs and eight oxidoreductases) were indicated previously to be involved in tumor progression and/or chemotherapy response. The analysis of CYP-mediated activities suggests a putative enhanced CYP3A4-dependent metabolism in MCF-7/DOX^R cells. A discrepancy was observed between CYP-enzyme activities and their corresponding levels of mRNA transcripts. This is indicative that the phenotype of DMEs is not linearly correlated with transcription induction responses, confirming the multifactorial complexity of this mechanism. Our results pinpoint the potential role of specific CYPs and oxidoreductases involved in the metabolism of drugs, retinoic and arachidonic acids, in the mechanisms of chemo-resistance to DOX and carcinogenesis of BC.

Recent Trends in Nanomedicine-Based Strategies to Overcome Multidrug Resistance in Tumors

Cancer is the leading cause of economic and health burden worldwide. The commonly used approaches for the treatment of cancer are chemotherapy, radiotherapy, and surgery. Chemotherapy frequently results in undesirable side effects, and cancer cells may develop resistance. Combating drug resistance is a challenging task in cancer treatment. Drug resistance may be intrinsic or acquired and can be due to genetic factors, growth factors, the increased efflux of drugs, DNA repair, and the metabolism of xenobiotics. The strategies used to combat drug resistance include the nanomedicine-based targeted delivery of drugs and genes using different nanocarriers such as gold nanoparticles, peptide-modified nanoparticles, as well as biomimetic and responsive nanoparticles that help to deliver payload at targeted tumor sites and overcome resistance. Gene therapy in combination with chemotherapy aids in this respect. siRNA and miRNA alone or in combination with chemotherapy improve therapeutic response in tumor cells. Some natural substances, such as curcumin, quercetin, tocotrienol, parthenolide, naringin, and cyclosporin-A are also helpful in combating the drug resistance of cancer cells. This manuscript summarizes the mechanism of drug resistance and nanoparticle-based strategies used to combat it.

Eligibility Criteria Perpetuate Disparities in Enrollment and Participation of Black Patients in Pancreatic Cancer Clinical Trials

PURPOSE

Clinical trials determine safety and efficacy of cancer therapeutics and establish standards of care. Minority patient participation in cancer clinical trials is dismal. We aimed to determine the impact of eligibility criteria on disparities in pancreatic ductal adenocarcinoma (PDAC) clinical trial candidacy.

METHODS

Traditional PDAC trial eligibility criteria were obtained from ClinicalTrials.gov. Patients with PDAC who sought care at Virginia Commonwealth University Health from 2010 to 2019 were included. Clinical data were obtained from billing codes and discrete values in the electronic medical record. Eligibility criteria differences between racial groups were determined using chi-squared tests and unconditional maximum likelihood-based odds ratios.

RESULTS

Among 676 patients, most identified as Black or White race (42.5% and 51.6%, respectively). Using traditional criteria, Black patients were more likely to be ineligible for participation compared with White patients (42.4% v 33.2%, P = .023) secondary to hypoalbuminemia (14.1% v 7.9%, P = .023), HIV (3.1% v 0.3%, P = .010), hepatitis B (1.7% v 0%, P = .043), and hepatitis C (9.1% v 3.4%, P = .005). Black patients were also numerically more likely to be ineligible because of renal dysfunction, recent coronary stenting, and uncontrolled diabetes mellitus. Prior cancer treatment excluded fewer Black than White patients (9.1% v 14.0%, P = .072), most attributable to lower rates of neoadjuvant chemotherapy received. Strategic eligibility criteria revisions could equalize ineligibility rates between Black and White patients (26.8% v 24.8%, P = .581).

CONCLUSION

Traditional eligibility criteria differentially exclude Black patients from participating in PDAC clinical trials. These criteria perpetuate disparities, limit generalizability, and are often not medically justifiable. Revised criteria may improve participant diversity, without compromising safety or study results.

ABC transporters in breast cancer: their roles in multidrug resistance and beyond

ATP-binding cassette (ABC) transporters are membrane-spanning proteins involved in cholesterol homeostasis, transport of various molecules in and out of cells and organelles, oxidative stress, immune recognition, and drug efflux. They are long implicated in the development of multidrug resistance in cancer chemotherapy. Existing clinical and molecular evidence has also linked ABC transporters with cancer pathogenesis, prognostics, and therapy. In this review, we aim to provide a comprehensive update on all ABC transporters and their roles in drug resistance in breast cancer (BC). For solid tumours such as BC, various ABC transporters are highly expressed in less differentiated subtypes and metastases. ABCA1, ABCB1 and ABCG2 are key players in BC chemoresistance. Restraining these transporters has evolved as a possible mechanism to reverse this phenomenon. Further, ABCB1 and ABCC1 are important in BC prognosis. Newer therapeutic approaches have been developed to target all these molecules to dysregulate their effect, reduce cell viability, induce apoptosis, and increase drug sensitivity. In the future, targeted therapy for specific genetic variations and upstream or downstream molecules can help improve patient prognosis.

Can Exposure to Environmental Pollutants Be Associated with Less Effective Chemotherapy in Cancer Patients?

Since environmental pollutants are ubiquitous and many of them are resistant to degradation, we are exposed to many of them on a daily basis. Notably, these pollutants can have harmful effects on our health and be linked to the development of disease. Epidemiological evidence together with a better understanding of the mechanisms that link toxic substances with the development of diseases, suggest that exposure to some environmental pollutants can lead to an increased risk of developing cancer. Furthermore, several studies have raised the role of low-dose exposure to environmental pollutants in cancer progression. However, little is known about how these compounds influence the treatments given to cancer patients. In this work, we present a series of evidences suggesting that environmental pollutants such as bisphenol A (BPA), benzo[a]pyrene (BaP), persistent organic pollutants (POPs), aluminum chloride (AlCl₃), and airborne particulate matter may reduce the efficacy of some common chemotherapeutic drugs used in different types of cancer. We discuss the potential underlying molecular mechanisms that lead to the generation of this chemoresistance, such as apoptosis evasion, DNA damage repair, activation of pro-cancer signaling pathways, drug efflux and action of antioxidant enzymes, among others.

Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo

Bayesian networks (BNs) provide a probabilistic, graphical framework for modeling high-dimensional joint distributions with complex correlation structures. BNs have wide applications in many disciplines, including biology, social science, finance and biomedical science. Despite extensive studies in the past, network structure learning from data is still a challenging open question in BN research. In this study, we present a sequential Monte Carlo (SMC)-based three-stage approach, GRowth-based Approach with Staged Pruning (GRASP). A double filtering strategy was first used for discovering the overall skeleton of the target BN. To search for the optimal network structures we designed an adaptive SMC (adSMC) algorithm to increase the quality and diversity of sampled networks which were further improved by a third stage to reclaim edges missed in the skeleton discovery step. GRASP gave very satisfactory results when tested on benchmark networks. Finally, BN structure learning using multiple types of genomics data illustrates GRASP’s potential in discovering novel biological relationships in integrative genomic studies.

Cytochrome P450-2D6: A novel biomarker in liver cancer health disparity

Liver cancer morbidity and mortality rates differ among ethnic groups. In the United States, the burden of liver cancer in Asian Americans (AS) is higher compared to Caucasian Americans (CA). Research on liver cancer health disparities has mainly focused on environmental and socioeconomic factors yet has ignored the genotypic differences among various racial/ethnic groups. This lack of molecular level understanding has hindered the development of personalized medical approaches for liver cancer treatment. To understand the genetic heterogeneity of liver cancer between AS and CA, we performed a systematic analysis of RNA-seq data of AS and CA patients from The Cancer Genome Atlas (TCGA). We used four differential gene expression analysis packages; DESeq2, limma, edgeR, and Superdelta2, to identify the differentially expressed genes. Our analysis identified cytochrome P450-2D6 enzyme (CYP2D6) as the gene with the greatest differential expression with higher levels in AS compared to CA. To scrutinize the underlying mechanism of CYP2D6, Ingenuity Pathway Analysis (IPA) and Cytoscape were conducted and found hepatocyte nuclear factor-4α (HNF4A) and interleukin-6 (IL6) in direct association with CYP2D6. IL6 is downregulated in AS compared to CA, while HNF4A is not significantly different. Herein, we report that CYP2D6 may serve as a putative biomarker in liver cancer health disparities. Its negative association with IL6 proclaims an intricate relationship between CYP2D6 and inflammation in the ethnic differences seen in AS and CA liver cancer patients. The goal of the present study was to understand how genetic factors may contribute to the interethnic variability of liver cancer prevalence and outcomes in AS and CA patients. Identifying ethnic-specific genes may help ameliorate detection, diagnosis, surveillance, and treatments of liver cancer, as well as reduce disease-related incidence and mortality rates in the vulnerable population.

Cytochrome 4Z1 Expression Is Correlated with Poor Prognosis in Patients with Cervical Cancer

Background: cervical cancer is one of the most common malignancies in women worldwide and its management remains challenging and complex. As Cytochrome4Z1 (CYP4Z1) is overexpressed in many tumours, its expression in cervical cancer is unknown. Therefore, the present study aimed to evaluate CYP4Z1 expression in cervical cancers. Methods: CYP4Z1 expression was immunohistochemically assessed in 100 cases of cervical cancers along with ten normal cervix tissues, and the enzyme’s relationship to several clinicopathological features and survival was explored. Results: CYP4Z1 was strongly expressed in 55% of cervical cancer patients. Normal cervix samples were negative for CYP4Z1 expression. Importantly, this expression was significantly found in patients with the late stage of the disease, lymph node metastasis, and high tumour invasion (p < 0.05). Interestingly, CYP4Z1 expression was significantly correlated with shorter survival times of cervical cancer patients. Univariate analysis showed that CYP4Z1 expression, tumour stage, lymph node metastasis, and tumour invasion were significantly correlated with patient survival (p < 0.05). The multivariate analysis revealed that only CYP4Z1 expression and tumour stage were significantly correlated with patient survival (p < 0.05). Conclusions: CYP4Z1 expression is associated with cervical cancer patients’ survival and may serve as an independent predictor of poor prognosis in cervical cancer patients.

Pharmacokinetics, metabolism and off-target effects in the rat of 8-[(1H- benzotriazol-1-yl)amino]octanoic acid, a selective inhibitor of human cytochrome P450 4Z1: β-oxidation as a potential augmenting pathway for inhibition

8-[(1H-1,2,3-benzotriazol-1-yl)amino]octanoic acid (8-BOA) was recently identified as a selective and potent mechanism-based inactivator (MBI) of breast cancer-associated CYP4Z1 and exhibited favourable inhibitory activity in vitro, thus meriting in vivo characterization.The pharmacokinetics and metabolism of 8-BOA in rats was examined after a single IV bolus dose of 10 mg/kg. A biphasic time-concentration profile resulted in relatively low clearance and a prolonged elimination half-life.The major circulating metabolites identified in plasma were products of β-oxidation; congeners losing two and four methylene groups accounted for >50% of metabolites by peak area. The -(CH₂)₂ product was characterized previously as a CYP4Z1 MBI and so represents an active metabolite that may contribute to the desired pharmacological effect.Ex vivo analysis of total CYP content in rat liver and kidney microsomes showed that off-target CYP inactivation was minimal; liver microsomal probe substrate metabolism also demonstrated low off-target inactivation. Standard clinical chemistries provided no indication of acute toxicity.In silico simulations using the free concentration of 8-BOA in plasma suggested that the in vivo dose used here may effectively inactivate CYP4Z1 in a xenografted tumour.

Identification of Proteins Deregulated by Platinum-Based Chemotherapy as Novel Biomarkers and Therapeutic Targets in Non-Small Cell Lung Cancer

Platinum-based chemotherapy remains the cornerstone of treatment for most people with non-small cell lung cancer (NSCLC), either as adjuvant therapy in combination with a second cytotoxic agent or in combination with immunotherapy. Resistance to therapy, either in the form of primary refractory disease or evolutionary resistance, remains a significant issue in the treatment of NSCLC. Hence, predictive biomarkers and novel combinational strategies are required to improve the effectiveness and durability of treatment response 6for people with NSCLC. The aim of this study was to identify novel biomarkers and/or druggable proteins from deregulated protein networks within non-oncogene driven disease that are involved in the cellular response to cisplatin. Following exposure of NSCLC cells to cisplatin, in vitro quantitative mass spectrometry was applied to identify altered protein response networks. A total of 65 proteins were significantly deregulated following cisplatin exposure. These proteins were assessed to determine if they are druggable targets using novel machine learning approaches and to identify whether these proteins might serve as prognosticators of platinum therapy. Our data demonstrate novel candidates and drug-like molecules warranting further investigation to improve response to platinum agents in NSCLC.

Identifying county-level factors for female breast cancer incidence rate through a large-scale population study

Female breast cancer (FBC) incidence rate (IR) varies greatly across counties in the United States (U.S.). Factors contributing to these geographic disparities have not been fully understood at the population level. In this study, we investigated the relationships between the county-level FBC IR and a diverse set of variables in demographics, socioeconomics, life style, health care accessibility, and environment. Our study included 1,277 counties in the U.S. where the female population was 10,000 or above for at least one race/ethnicity. After controlling for the racial/ethnic and other significant factors, percent of husband-wife family households (pHWFH) for a racial/ethnic group in a county is negatively associated with FBC IR (p < 0.001). A 10% increase in married family households may lower a county's IR by 5.2 cases per 100,000 females per year. We also found that PM2.5 (fine inhalable particles with a diameter of 2.5 micrometers or less) is positively associated with FBC IR (p < 0.001). Counties with the highest level of PM2.5 have approximately 4 additional FBC new cases per 100,000 females per year than counties with the lowest level of PM2.5. Furthermore, we found that the county-level factors contributing to FBC IR vary significantly for different racial groups using race-specific models. While confirming most of the previously known patient- and neighborhood-level risk factors (such as race/ethnicity, income, and health care accessibility), our study identified two significant county-level factors contributing to the spatial disparity of FBC IR across the U.S. The newly-identified beneficial factor (marriage) and risk factor (PM2.5), together with the verified known factors, may help provide insights to officials of health departments/organizations for them to make decisions on cancer intervention strategies.

Triage of documents containing protein interactions affected by mutations using an NLP based machine learning approach

BACKGROUND

Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation.

RESULTS

Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score.

CONCLUSIONS

The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods.

Maximizing the reusability of gene expression data by predicting missing metadata

Reusability is part of the FAIR data principle, which aims to make data Findable, Accessible, Interoperable, and Reusable. One of the current efforts to increase the reusability of public genomics data has been to focus on the inclusion of quality metadata associated with the data. When necessary metadata are missing, most researchers will consider the data useless. In this study, we developed a framework to predict the missing metadata of gene expression datasets to maximize their reusability. We found that when using predicted data to conduct other analyses, it is not optimal to use all the predicted data. Instead, one should only use the subset of data, which can be predicted accurately. We proposed a new metric called Proportion of Cases Accurately Predicted (PCAP), which is optimized in our specifically-designed machine learning pipeline. The new approach performed better than pipelines using commonly used metrics such as F1-score in terms of maximizing the reusability of data with missing values. We also found that different variables might need to be predicted using different machine learning methods and/or different data processing protocols. Using differential gene expression analysis as an example, we showed that when missing variables are accurately predicted, the corresponding gene expression data can be reliably used in downstream analyses.

Genetic factors associated with cancer racial disparity - an integrative study across twenty-one cancer types

It is well known that different racial groups have significantly different incidence and mortality rates for certain cancers. It has been suggested that biological factors play a major role in these cancer racial disparities. Previous studies on the biological factors contributing to cancer racial disparity have generated a very large number of candidate factors, although there is modest agreement among the results of the different studies. Here, we performed an integrative analysis using genomic data of 21 cancer types from TCGA, GTEx, and the 1000 Genomes Project to identify biological factors contributing to racial disparity in cancer. We also built a companion website with additional results for cancer researchers to freely mine. Our study identified genes, gene families, and pathways displaying similar differential expression patterns between different racial groups across multiple cancer types. Among them, XKR9 gene expression was found to be significantly associated with overall survival for all cancers combined as well as for several individual cancers. Our results point to the interesting hypothesis that XKR9 could be a novel drug target for cancer immunotherapy. Bayesian network modeling showed that XKR9 is linked to important cancer-related genes, including FOXM1, cyclin B1, and RB1CC1 (RB1 regulator). In addition, metabolic pathways, neural signaling pathways, and several cancer-related gene families were found to be significantly associated with cancer racial disparities for multiple cancer types. Single nucleotide polymorphisms (SNPs) discovered through integrating data from the TCGA, GTEx, and 1000 Genomes databases provide biologists the opportunity to test highly promising, targeted hypotheses to gain a deeper understanding of the genetic drivers of cancer racial disparity and cancer biology in general.

An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein-protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA-mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan-Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan-Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA-lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

miR-452 Reverses Abnormal Glycosylation Modification of ERα and Estrogen Resistance in TNBC (Triple-Negative Breast Cancer) Through Targeting UGT1A1

Background: The breast epithelial cells in patients with triple-negative breast cancer (TNBC) actually have specific estrogen receptor (ER) expression, and the abnormal glycosylation of UGT1A1 in TNBC cells resulted in abnormal expression and function of ERα through regulating the modification of ERα. Therefore, our study targets the role of UGT1A1 expression, then glycosylation modification of ERα (estrogen receptor α) and estrogen resistance in development of TNBC.

Methods: The differential expression of mRNA and miRNA in TNBC tissues was tested. Luciferase activity was analyzed in TNBC cells treated with miR-452. Moreover, the human mammary gland and TNBC cell lines were dealt with estrogen and miR-452 or its inhibitors, then proliferation ability was further determined. Moreover, the role of interaction between UGT1A1 and ERα in the glycosylation modification of ERα and UGT activity, and metabolism of estrogen were assessed. The effects of miR-452 on TNBC by improving abnormal glycosylation modification of ERα by targeting UGT1A1 and estrogen resistance were studied in vitro and in vivo.

Results: The expression level of UGT1A1 in TNBC tumor tissues was higher than its matched para-tumorous tissues, but the miR-452 expression was opposite. The glycosylation modification site of ERα expressed in TNBC cells was different from that of normal mammary epithelial cells. The estrogen 17β-estradiol (E2) significantly promoted mitotic entry of TNBC cells. The interaction between UGT1A1 and ERα affected the expression level of each other, as well as the UGT enzyme activity and proliferation of TNBC cells. UGT1A1 induced production of intracellular estrogens and TNBC proliferation, but it could be reversed by overexpression of ERα. Upregulation of ERα caused the downregulation of UGT1A1 and marked decrease of intracellular estrogen products, and then suppressed TNBC proliferation. Moreover, UGT1A1 was the target gene of miR-452; miR-452 antagomir restrained TNBC xenograft.

Conclusion: Our results demonstrated that estrogen was a positive factor in the proliferation of TNBC cells at onset of mitosis through accentuating the expression and enzyme activity of UGT1A1. However, miR-452 targeted to UGT1A1, then regulated glycosylation modification of ERα, estrogen metabolism, and TNBC development associated with estrogen resistance.

Mechanisms of Multidrug Resistance in Cancer Chemotherapy

Cancer is one of the main causes of death worldwide. Despite the significant development of methods of cancer healing during the past decades, chemotherapy still remains the main method for cancer treatment. Depending on the mechanism of action, commonly used chemotherapeutic agents can be divided into several classes (antimetabolites, alkylating agents, mitotic spindle inhibitors, topoisomerase inhibitors, and others). Multidrug resistance (MDR) is responsible for over 90% of deaths in cancer patients receiving traditional chemotherapeutics or novel targeted drugs. The mechanisms of MDR include elevated metabolism of xenobiotics, enhanced efflux of drugs, growth factors, increased DNA repair capacity, and genetic factors (gene mutations, amplifications, and epigenetic alterations). Rapidly increasing numbers of biomedical studies are focused on designing chemotherapeutics that are able to evade or reverse MDR. The aim of this review is not only to demonstrate the latest data on the mechanisms of cellular resistance to anticancer agents currently used in clinical treatment but also to present the mechanisms of action of novel potential antitumor drugs which have been designed to overcome these resistance mechanisms. Better understanding of the mechanisms of MDR and targets of novel chemotherapy agents should provide guidance for future research concerning new effective strategies in cancer treatment.

High FUT3 expression is a marker of lower overall survival of breast cancer patients

The complex enzyme network responsible for glycan synthesis suffers significant changes during the first steps of tumor development, leading to the early formation of tumor-associated glycan signatures. Among the glycosylation pathways, changes in fucosylation emerged as one of most important features in cancer. Αlpha-1,3/4-fucosyltransferase (FUT3) has been linked to pro-tumor and anti-tumor pathways depending on the cancer type. The present study aimed to understand the gene and protein expression profiles of FUT3 in three different and independent cohorts composed by invasive breast cancer patients: Local Brazilian population, METABRIC and TCGA. FUT3 transcripts and protein were measured in the Brazilian population by real-time PCR and Western blotting, respectively. Clinical records and FUT3 levels from public METABRIC and TCGA cohorts were accessed through CBioPortal database. FUT3 expression was analyzed in each cohort using the appropriated statistic tools. Survival meta-analysis in triple negative patients was performed using five independent cohorts including GSE41119, GSE47994 and GSE86945, data obtained from GEO repository available at NCBI database, and METABRIC and TCGA. Our analysis showed that high FUT3 levels were consistently associated to reduced invasive breast cancer patients overall survival. This finding is particularly significant in triple negative patients. These results together with the previously knowledge regarding the involvement of FUT3 in pro-tumor and anti-tumor mechanisms led us to purpose a model for FUT3 expression regulation throughout breast cancer establishment and progression.

Rationally Designed Ruthenium Complexes for Breast Cancer Therapy

Since the discovery of the anticancer potential of ruthenium-based complexes, several species were reported as promising candidates for the treatment of breast cancer, which accounts for the greatest number of new cases in women every year worldwide. Among these ruthenium complexes, species containing bioactive ligand(s) have attracted increasing attention due to their potential multitargeting properties, leading to anticancer drug candidates with a broader range of cellular targets/modes of action. This review of the literature aims at providing an overview of the rationally designed ruthenium-based complexes that have been reported to date for which ligands were carefully selected for the treatment of hormone receptor positive breast cancers (estrogen receptor (ER+) or progesterone receptor (PR+)). In addition, this brief survey highlights some of the most successful examples of ruthenium complexes reported for the treatment of triple negative breast cancer (TNBC), a highly aggressive type of cancer, regardless of if their ligands are known to have the ability to achieve a specific biological function.

Subgroup analysis of Japanese patients in a Phase 3 study of atezolizumab in advanced triple-negative breast cancer (IMpassion130)

BACKGROUND

In the randomised Phase 3 IMpassion130 trial, atezolizumab combined with nab-paclitaxel (atezo + nab-P) in 902 patients with triple-negative breast cancer (TNBC) showed prolonged progression-free survival (PFS) in both the intention-to-treat (ITT) population and programmed death-ligand 1 (PD-L1)-positive subgroup compared with placebo plus nab-P (plac + nab-P). This study assessed the efficacy and safety of atezo + nab-P in the IMpassion130 Japanese subpopulation.

METHODS

Eligible patients had unresectable locally advanced or metastatic TNBC previously untreated with chemotherapy for metastatic disease. Patients were randomised 1:1 to receive either atezo + nab-P or plac + nab-P. Co-primary endpoints were investigator-assessed PFS and overall survival (ITT population and PD-L1-positive subgroup). These were also assessed in the Japanese subpopulation.

RESULTS

There were 65 Japanese patients (34 atezo + nab-P; 31 plac + nab-P). The PD-L1-positive subgroup included 25 patients (12 atezo + nab-P; 13 plac + nab-P). Median PFS was 7.4 months (atezo + nab-P) versus 4.6 months (plac + nab-P; hazard ratio [HR], 0.47; 95% CI, 0.25-0.90). In the PD-L1-positive subgroup, median PFS was 10.8 months (atezo + nab-P) versus 3.8 months (plac + nab-P; HR, 0.04; 95% CI, <0.01-0.35). Safety results in the Japanese subgroup were consistent with those in the overall population. The Japanese subgroup had a lower incidence of adverse events leading to treatment withdrawal than the overall population. More patients in the atezo + nab-P arm had neutrophil count decreases and stomatitis than patients in the plac + nab-P arm.

CONCLUSIONS

Atezo + nab-P efficacy in Japanese patients was consistent with the overall IMpassion130 population. No new safety signals were observed, and tolerability was consistent with that of the overall population.

A network-based pathway-extending approach using DNA methylation and gene expression data to identify altered pathways

Pathway analysis allows us to gain insights into a comprehensive understanding of the molecular mechanisms underlying cancers. Currently, high-throughput multi-omics data and various types of large-scale biological networks enable us to identify cancer-related pathways by comprehensively analyzing these data. Combining information from multidimensional data, pathway databases and interaction networks is a promising strategy to identify cancer-related pathways. Here we present a novel network-based approach for integrative analysis of DNA methylation and gene expression data to extend original pathways. The results show that the extension of original pathways can provide a basis for discovering new components of the original pathway and understanding the crosstalk between pathways in a large-scale biological network. By inputting the gene lists of the extended pathways into the classical gene set analysis (ORA and FCS), we effectively identified the altered pathways which are correlated well with the corresponding cancer. The method is evaluated on three datasets retrieved from TCGA (BRCA, LUAD and COAD). The results show that the integration of DNA methylation and gene expression data through a network of known gene interactions is effective in identifying altered pathways.

Predicting drug activity against cancer cells by random forest models based on minimal genomic information and chemical properties

A key goal of precision medicine is predicting the best drug therapy for a specific patient from genomic information. In oncology, cancers that appear similar pathologically can vary greatly in how they respond to the same drug. Fortunately, data from high-throughput screening programs often reveal important relationships between genomic variability of cancer cells and their response to drugs. Nevertheless, many current computational methods to predict compound activity against cancer cells require large quantities of genomic, epigenomic, and additional cellular data to develop and to apply. Here we integrate recent screening data and machine learning to train classification models that predict the activity/inactivity of compounds against cancer cells based on the mutational status of only 145 oncogenes and a set of compound structural descriptors. Using IC50 values of 1 μM as activity cutoffs, our predictive models have sensitivities of 87%, specificities of 87%, and yield an area under the receiver operating characteristic curve equal to 0.94. We also develop regression models to predict log(IC50) values of compounds for cancer cells; the models achieve a Pearson correlation coefficient of 0.86 for cross-validation and up to 0.65-0.73 against blind test sets. Predictive performance remains strong when as few as 50 oncogenes are included. Finally, even when 40% of experimental IC50 values are missing from screening data, they can be imputed with sufficient reliability that classification accuracy is not diminished. The presented models are fast to generate and may serve as easily implemented screening tools for personalized oncology medicine, drug repurposing, and drug discovery.

African Pharmacogenomics Consortium: Consolidating pharmacogenomics knowledge, capacity development and translation in Africa: Consolidating pharmacogenomics knowledge, capacity development and translation in Africa

The African Pharmacogenomics Consortium (APC) was formally launched on the 6th September 2018. This white paper outlines its vision, and objectives towards addressing challenges of conducting and applying pharmacogenomics in Africa and identifies opportunities for advancement of individualized drugs use on the continent.  Africa, especially south of the Sahara, is beset with a huge burden of infectious diseases with much co-morbidity whose multiplicity and intersection are major challenges in achieving the sustainable development goals (SDG), SDG3, on health and wellness. The profile of drugs commonly used in African populations lead to a different spectrum of adverse drug reactions (ADRs) when compared to other parts of the world. Coupled with the genetic diversity among Africans, the APC is established to promote pharmacogenomics research and its clinical implementation for safe and effective use of medicine in the continent.  Variation in the way patients respond to treatment is mainly due to differences in activity of enzymes and transporters involved in pathways associated with each drug’s disposition.  Knowledge of pharmacogenomics, therefore, helps in identifying genetic variants in these proteins and their functional effects. Africa needs to consolidate its pharmacogenomics expertise and technological platforms to bring pharmacogenomics to use.

Bioanalytical and Mass Spectrometric Methods for Aldehyde Profiling in Biological Fluids

Human exposure to aldehydes is implicated in multiple diseases including diabetes, cardiovascular diseases, neurodegenerative disorders (i.e., Alzheimer’s and Parkinson’s Diseases), and cancer. Because these compounds are strong electrophiles, they can react with nucleophilic sites in DNA and proteins to form reversible and irreversible modifications. These modifications, if not eliminated or repaired, can lead to alteration in cellular homeostasis, cell death and ultimately contribute to disease pathogenesis. This review provides an overview of the current knowledge of the methods and applications of aldehyde exposure measurements, with a particular focus on bioanalytical and mass spectrometric techniques, including recent advances in mass spectrometry (MS)-based profiling methods for identifying potential biomarkers of aldehyde exposure. We discuss the various derivatization reagents used to capture small polar aldehydes and methods to quantify these compounds in biological matrices. In addition, we present emerging mass spectrometry-based methods, which use high-resolution accurate mass (HR/AM) analysis for characterizing carbonyl compounds and their potential applications in molecular epidemiology studies. With the availability of diverse bioanalytical methods presented here including simple and rapid techniques allowing remote monitoring of aldehydes, real-time imaging of aldehydic load in cells, advances in MS instrumentation, high performance chromatographic separation, and improved bioinformatics tools, the data acquired enable increased sensitivity for identifying specific aldehydes and new biomarkers of aldehyde exposure. Finally, the combination of these techniques with exciting new methods for single cell analysis provides the potential for detection and profiling of aldehydes at a cellular level, opening up the opportunity to minutely dissect their roles and biological consequences in cellular metabolism and diseases pathogenesis.

Extracting chemical-protein interactions from literature using sentence structure analysis and feature engineering

Information about the interactions between chemical compounds and proteins is indispensable for understanding the regulation of biological processes and the development of therapeutic drugs. Manually extracting such information from biomedical literature is very time and resource consuming. In this study, we propose a computational method to automatically extract chemical-protein interactions (CPIs) from a given text. Our method extracts CPI pairs and CPI triplets from sentences, where a CPI pair consists of a chemical compound and a protein name, and a CPI triplet consists of a CPI pair along with an interaction word describing their relationship. We extracted a diverse set of features from sentences that were used to build multiple machine learning models. Our models contain both simple features, which can be directly computed from sentences, and more sophisticated features derived using sentence structure analysis techniques. For example, one set of features was extracted based on the shortest paths between the CPI pairs or among the CPI triplets in the dependency graphs obtained from sentence parsing. We designed a three-stage approach to predict the multiple categories of CPIs. Our method performed the best among systems that use non-deep learning methods and outperformed several deep-learning-based systems in the track 5 of the BioCreative VI challenge. The features we designed in this study are informative and can be applied to other machine learning methods including deep learning.

Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer

Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I-III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2.

Drug metabolizing enzymes and their inhibitors' role in cancer resistance

Despite continuous research on chemotherapeutic agents, different mechanisms of resistance have become a major pitfall in cancer chemotherapy. Although, exhaustive efforts are being made by several researchers to target resistance against chemotherapeutic agents, there is another class of resistance mechanism which is almost carrying on unattended. This class of resistance includes pharmacokinetics resistance such as efflux by ABC transporters and drug metabolizing enzymes. ABC transporters are the membrane bound proteins which are responsible for the movement of substrates through the cell membrane. Drug metabolizing enzymes are an integral part of phase-II metabolism that helps in the detoxification of exogenous, endogenous and xenobiotics substrates. These include uridine diphospho-glucuronosyltransferases (UGTs), glutathione-S-transferases (GSTs), dihydropyrimidine dehydrogenases (DPDs) and thiopurine methyltransferases (TPMTs). These enzymes may affect the role of drugs in both positive as well negative manner, depending upon the type of tissue and cells present and when present in tumors, can result in drug resistance. However, the underlying mechanism of resistance by drug metabolizing enzymes is still not clear. Here, we have tried to cover various aspects of these enzymes in relation to anticancer drugs.

Exploring the potential of needle trap microextraction combined with chromatographic and statistical data to discriminate different types of cancer based on urinary volatomic biosignature

The worldwide high cancer incidence and mortality demands for more effective and specific diagnostic strategies. In this study, we evaluated the efficiency of an innovative methodology, Needle Trap Microextraction (NTME), combined with gas chromatography-mass spectrometry (GC-MS), for the establishment of the urinary volatomic biosignature from breast (BC), and colon (CC) cancer patients as well as healthy individuals (CTL). To achieve this, 40 mL of the headspace of acidified urine (4 mL, 20% NaCl, pH = 2), equilibrated at 50 °C during 40 min, were loaded through the DVB/Car1000/CarX sorbent inside the NTD, and subjected to a GC-MS analysis. This allowed the identification of 130 VOMs from different chemical families that were further processed using discriminant analysis through the partial least squares method (PLS-DA). Several pathways are over activated in cancer patients, being phenylalanine pathway in BC and limonene and pinene degradation pathway in CC the most relevant. Butanoate metabolism is also highly activated in both cancers, as well as tyrosine metabolism in a lesser extension. In BC the xenobiotics metabolism by cytochrome P450 and fatty acid biosynthesis are also differentially activated. Different clusters corresponding to the groups recruited allowed to define sets of volatile organic metabolites (VOMs fingerprints) that exhibit high classification rates, sensitivity and specificity in the discrimination of the selected cancers. As far as we are aware, this is the first time that NTME is used for isolation urinary volatile metabolites, being the obtained results very promising.