An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer

Host kinase regulation of Plasmodium vivax dormant and replicating liver stages

Upon transmission to the liver, Plasmodium vivax parasites form replicating schizonts, which continue to initiate blood-stage infection, or dormant hypnozoites that reactivate weeks to months after initial infection. P. vivax phenotypes in the field vary significantly, including the ratio of schizonts to hypnozoites formed and the frequency and timing of relapse. Evidence suggests that both parasite genetics and environmental factors underly this heterogeneity. We previously demonstrated that data on the effect of a panel of kinase inhibitors with overlapping targets on Plasmodium liver stage infection, in combination with a computational approach called kinase regression (KiR), can be used to uncover novel host regulators of infection. Here, we applied KiR to evaluate the extent to which P. vivax liver-stage parasites are susceptible to changes in host kinase activity. We identified a role for a subset of host kinases in regulating schizont and hypnozoite infection and schizont size and characterized overlap as well as variability in host phosphosignaling dependencies between parasite forms and across multiple patient isolates. Striking, our data point to variability in host dependencies across P. vivax isolates, suggesting one possible origin of the heterogeneity observed across P. vivax in the field.

Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is characterized by excessive liver fat deposition, and progresses to liver cirrhosis, and even hepatocellular carcinoma. However, the invasive diagnosis of NAFLD with histopathological evaluation remains risky. This study investigated potential genes correlated with NAFLD, which may serve as diagnostic biomarkers and even potential treatment targets.

METHODS

The weighted gene co-expression network analysis (WGCNA) was constructed based on dataset E-MEXP-3291. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to evaluate the function of genes.

RESULTS

Blue module was positively correlated, and turquoise module negatively correlated with the severity of NAFLD. Furthermore, 8 driving genes (ANXA9, FBXO2, ORAI3, NAGS, C/EBPα, CRYAA, GOLM1, TRIM14) were identified from the overlap of genes in blue module and GSE89632. And another 8 driving genes were identified from the overlap of turquoise module and GSE89632. Among these driving genes, C/EBPα (CCAAT/enhancer binding protein α) was the most notable. By validating the expression of C/EBPα in the liver of NAFLD mice using immunohistochemistry, we discovered a significant upregulation of C/EBPα protein in NAFLD.

CONCLUSION

we identified two modules and 16 driving genes associated with the progression of NAFLD, and confirmed the protein expression of C/EBPα, which had been paid little attention to in the context of NAFLD, in the present study. Our study will advance the understanding of NAFLD. Moreover, these driving genes may serve as biomarkers and therapeutic targets of NAFLD.

Gene expression profile analysis to discover molecular signatures for early diagnosis and therapies of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is one of the most lethal subtypes of breast cancer (BC), and it accounts for approximately 10%-20% of all invasive BCs diagnosed worldwide. The survival rate of TNBC in stages III and IV is very low, and a large number of patients are diagnosed in these stages. Therefore, the purpose of this study was to identify TNBC-causing molecular signatures and anti-TNBC drug agents for early diagnosis and therapies. Five microarray datasets that contained 304 TNBC and 109 control samples were collected from the Gene Expression Omnibus (GEO) database, and RNA-Seq data with 116 tumor and 124 normal samples were collected from TCGA database to identify differentially expressed genes (DEGs) between TNBC and control samples. A total of 64 DEGs were identified, of which 29 were upregulated and 35 were downregulated, by using the statistical limma R-package. Among them, seven key genes (KGs) were commonly selected from microarray and RNA-Seq data based on the high degree of connectivity through PPI (protein-protein interaction) and module analysis. Out of these seven KGs, six KGs (TOP2A, BIRC5, AURKB, ACTB, ASPM, and BUB1B) were upregulated and one (EGFR) was downregulated. We also investigated their differential expression patterns with different subtypes and progression stages of BC by the independent datasets of RNA-seq profiles from UALCAN database, which indicated that they may be potential biomarkers for early diagnosis. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses with the proposed DEGs were performed using the online Enrichr database to investigate the pathogenetic processes of TNBC highlighting KGs. Then, we performed gene regulatory network analysis and identified three transcriptional (SOX2, E2F4, and KDM5B) and three post-transcriptional (hsa-mir-1-3p, hsa-mir-124-3p, and hsa-mir-34a-5p) regulators of KGs. Finally, we proposed five KG-guided repurposable drug molecules (imatinib, regorafenib, pazopanib, teniposide, and dexrazoxane) for TNBC through network pharmacology and molecular docking analyses. These drug molecules also showed significant binding performance with some cancer-related PTM-sites (phosphorylation, succinylation, and ubiquitination) of top-ranked four key proteins (EGFR, AURKB, BIRC5, and TOP2A). Therefore, the findings of this computational study may play a vital role in early diagnosis and therapies against TNBC by wet-lab validation.

Bioinformatics Approach for Data Capturing: The Case of Breast Cancer

Background:

With the rapid evolution in advanced computer systems and various statistical algorithms, it is now a days possible to analyze complex biological data. Bioinformatics is an interface between computational and biological assemblies. It is applied in various fields of biological as well as medical sciences.

Aim:

The manuscript aims to summarize the developments in the field of breast cancer research through the applications of bioinformatics.

Methods:

Various search engines like google, science direct, Scopus, PubMed, etc., were used for the literature survey.

Results:

It describes the bioinformatics analysis tools and models, which include mainly artificial neural network models.

Conclusion:

Bioinformatics is the evolutionary approach that is used for the capturing of data from the various case studies related to breast cancer.

Exploiting vulnerabilities in cancer signalling networks to combat targeted therapy resistance

Drug resistance remains one of the greatest challenges facing precision oncology today. Despite the vast array of resistance mechanisms that cancer cells employ to subvert the effects of targeted therapy, a deep understanding of cancer signalling networks has led to the development of novel strategies to tackle resistance both in the first-line and salvage therapy settings. In this review, we provide a brief overview of the major classes of resistance mechanisms to targeted therapy, including signalling reprogramming and tumour evolution; our discussion also focuses on the use of different forms of polytherapies (such as inhibitor combinations, multi-target kinase inhibitors and HSP90 inhibitors) as a means of combating resistance. The promise and challenges facing each of these polytherapies are elaborated with a perspective on how to effectively deploy such therapies in patients. We highlight efforts to harness computational approaches to predict effective polytherapies and the emerging view that exceptional responders may hold the key to better understanding drug resistance. This review underscores the importance of polytherapies as an effective means of targeting resistance signalling networks and achieving durable clinical responses in the era of personalised cancer medicine.

Differential prioritization of therapies to subtypes of triple negative breast cancer using a systems medicine method

Triple negative breast cancer (TNBC) is a group of cancers whose heterogeneity and shortage of effective drug therapies has prompted efforts to divide these cancers into molecular subtypes. Our computational platform, entitled GenEx-TNBC, applies concepts in systems biology and polypharmacology to prioritize thousands of approved and experimental drugs for therapeutic potential against each molecular subtype of TNBC. Using patient-based and cell line-based gene expression data, we constructed networks to describe the biological perturbation associated with each TNBC subtype at multiple levels of biological action. These networks were analyzed for statistical coincidence with drug action networks stemming from known drug-protein targets, while accounting for the direction of disease modulation for coinciding entities. GenEx-TNBC successfully designated drugs, and drug classes, that were previously shown to be broadly effective or subtype-specific against TNBC, as well as novel agents. We further performed biological validation of the platform by testing the relative sensitivities of three cell lines, representing three distinct TNBC subtypes, to several small molecules according to the degree of predicted biological coincidence with each subtype. GenEx-TNBC is the first computational platform to associate drugs to diseases based on inverse relationships with multi-scale disease mechanisms mapped from global gene expression of a disease. This method may be useful for directing current efforts in preclinical drug development surrounding TNBC, and may offer insights into the targetable mechanisms of each TNBC subtype.

Identifying host regulators and inhibitors of liver stage malaria infection using kinase activity profiles

Plasmodium parasites have extensive needs from their host hepatocytes during the obligate liver stage of infection, yet there remains sparse knowledge of specific host regulators. Here we assess 34 host-targeted kinase inhibitors for their capacity to eliminate Plasmodium yoelii-infected hepatocytes. Using pre-existing activity profiles of each inhibitor, we generate a predictive computational model that identifies host kinases, which facilitate Plasmodium yoelii liver stage infection. We predict 47 kinases, including novel and previously described kinases that impact infection. The impact of a subset of kinases is experimentally validated, including Receptor Tyrosine Kinases, members of the MAP Kinase cascade, and WEE1. Our approach also predicts host-targeted kinase inhibitors of infection, including compounds already used in humans. Three of these compounds, VX-680, Roscovitine and Sunitinib, each eliminate >85% of infection. Our approach is well-suited to uncover key host determinants of infection in difficult model systems, including field-isolated parasites and/or emerging pathogens.

Host kinases facilitate Plasmodium liver stage (LS) infection, but systematic accounting of important players is lacking. Here, the authors use a computational approach and kinase activity profiles to identify host kinase regulators of LS infection and drugs that could eliminate parasite burden.

Transforming growth factor β-activated kinase 1 inhibitor suppresses the proliferation in triple-negative breast cancer through TGF-β/TGFR pathway

Breast cancer is one of the most invasive cancer types in female population. The functional activity of Transforming growth factor β-activated kinase 1 (TAK1) in breast cancer progression increasingly attracts attention as it provides a potential target for antibreast cancer drug development. However, the fundamental role of TAK1 for triple-negative breast cancer (TNBC) progression and the effect of potential anti-TAK1 drug candidate needs to be further evaluated. Herein, we focused on the role of TAK1 in human breast cancer cells, and we hypothesized that the inhibition of TAK1 activation can repress the growth of human TNBC cells. We found that the TAK1 is robustly activated within cancer cell population of clinic-derived TNBC samples and the human breast cancer cell lines in culture. Furthermore, we determined the effect of 5Z-7-oxozeaenol (5Z-O), a TAK1-specific small molecule inhibitor, on proliferation of human TNBC cell line. 5Z-O treatment significantly suppressed the proliferation of human TNBC cells. Collectively, these demonstrate the role of TAK1 in human breast cancer and the antiproliferate effect of TAK1 inhibitor. Our study sets the stage for further research on TAK1 as a promising target for development of anti-TNBC drugs and therapeutic strategies.

GIW and InCoB, two premier bioinformatics conferences in Asia with a combined 40 years of history

Knowledge discovery in bioinformatics thrives on joint and inclusive efforts of stakeholders. Similarly, knowledge dissemination is expected to be more effective and scalable through joint efforts. Therefore, the International Conference on Bioinformatics (InCoB) and the International Conference on Genome Informatics (GIW) were organized as a joint conference for the first time in 13 years of coexistence. The Asia-Pacific Bioinformatics Network (APBioNet) and the Japanese Society for Bioinformatics (JSBi) collaborated to host GIW/InCoB2015 in Tokyo, September 9-11, 2015. The joint endeavour yielded 51 research articles published in seven journals, 78 poster and 89 oral presentations, showcasing bioinformatics research in the Asia-Pacific region. Encouraged by the results and reduced organizational overheads, APBioNet will collaborate with other bioinformatics societies in organizing co-located bioinformatics research and training meetings in the future. InCoB2016 will be hosted in Singapore, September 21-23, 2016.