The impact of next generation sequencing technologies on haematological research - A review

Next-generation sequencing (NGS) technologies have begun to revolutionize the field of haematological malignancies through the assessment of a patient's genetic makeup with a minimal cost. Significant discoveries have already provided a unique insight into disease initiation, risk stratification and therapeutic intervention. Sequencing analysis will likely form part of the routine diagnostic testing in the future. However, a number of important issues need to be addressed for that to become a reality with regard to result interpretation, laboratory workflow, data storage and ethical issues. In this review we summarize the contribution that NGS has already made to the field of haematological malignancies. Finally, we discuss the challenges that NGS technologies will bring in relation to data storage, ethical and legal issues and laboratory validation. Despite these challenges, we predict that high-throughput DNA sequencing will redefine haematological malignancies based on individualized genomic analysis.

Genomics and the impact of new technologies on the management of colorectal cancer

High-throughput genomic technologies have the potential to have a major impact on preclinical and clinical drug development and the selection and stratification of patients in clinical trials. These technologies, which are at varying stages of commercialization, include array-based comparative genomic hybridization, single-nucleotide polymorphism arrays, and (the most mature example) expression-based arrays. One of the rate-limiting steps in the routine clinical application of expression array-based technology is the need for suitable clinical samples. One of the major challenges moving forward, therefore, relates to the ability to use formalin-fixed, paraffin-embedded--derived tissue in expression profiling-based approaches.

The TGF-β-inducible miR-23a cluster attenuates IFN-γ levels and antigen-specific cytotoxicity in human CD8+ T cells

Cytokine secretion and degranulation represent key components of CD8(+) T-cell cytotoxicity. While transcriptional blockade of IFN-γ and inhibition of degranulation by TGF-β are well established, we wondered whether TGF-β could also induce immune-regulatory miRNAs in human CD8(+) T cells. We used miRNA microarrays and high-throughput sequencing in combination with qRT-PCR and found that TGF-β promotes expression of the miR-23a cluster in human CD8(+) T cells. Likewise, TGF-β up-regulated expression of the cluster in CD8(+) T cells from wild-type mice, but not in cells from mice with tissue-specific expression of a dominant-negative TGF-β type II receptor. Reporter gene assays including site mutations confirmed that miR-23a specifically targets the 3'UTR of CD107a/LAMP1 mRNA, whereas the further miRNAs expressed in this cluster-namely, miR-27a and -24-target the 3'UTR of IFN-γ mRNA. Upon modulation of the miR-23a cluster by the respective miRNA antagomirs and mimics, we observed significant changes in IFN-γ expression, but only slight effects on CD107a/LAMP1 expression. Still, overexpression of the cluster attenuated the cytotoxic activity of antigen-specific CD8(+) T cells. These functional data thus reveal that the miR-23a cluster not only is induced by TGF-β, but also exerts a suppressive effect on CD8(+) T-cell effector functions, even in the absence of TGF-β signaling.

Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapies

BACKGROUND: While the discovery of new drugs is a complex, lengthy and costly process, identifying new uses for existing drugs is a cost-effective approach to therapeutic discovery. Connectivity mapping integrates gene expression profiling with advanced algorithms to connect genes, diseases and small molecule compounds and has been applied in a large number of studies to identify potential drugs, particularly to facilitate drug repurposing. Colorectal cancer (CRC) is a commonly diagnosed cancer with high mortality rates, presenting a worldwide health problem. With the advancement of high throughput omics technologies, a number of large scale gene expression profiling studies have been conducted on CRCs, providing multiple datasets in gene expression data repositories. In this work, we systematically apply gene expression connectivity mapping to multiple CRC datasets to identify candidate therapeutics to this disease.

RESULTS: We developed a robust method to compile a combined gene signature for colorectal cancer across multiple datasets. Connectivity mapping analysis with this signature of 148 genes identified 10 candidate compounds, including irinotecan and etoposide, which are chemotherapy drugs currently used to treat CRCs. These results indicate that we have discovered high quality connections between the CRC disease state and the candidate compounds, and that the gene signature we created may be used as a potential therapeutic target in treating the disease. The method we proposed is highly effective in generating quality gene signature through multiple datasets; the publication of the combined CRC gene signature and the list of candidate compounds from this work will benefit both cancer and systems biology research communities for further development and investigations.

Mapping protein-DNA interactions using ChIP-sequencing

Chromatin immunoprecipitation (ChIP) allows enrichment of genomic regions which are associated with specific transcription factors, histone modifications, and indeed any other epitopes which are present on chromatin. The original ChIP methods used site-specific PCR and Southern blotting to confirm which regions of the genome were enriched, on a candidate basis. The combination of ChIP with genomic tiling arrays (ChIP-chip) allowed a more unbiased approach to map ChIP-enriched sites. However, limitations of microarray probe design and probe number have a detrimental impact on the coverage, resolution, sensitivity, and cost of whole-genome tiling microarray sets for higher eukaryotes with large genomes. The combination of ChIP with high-throughput sequencing technology has allowed more comprehensive surveys of genome occupancy, greater resolution, and lower cost for whole genome coverage. Herein, we provide a comparison of high-throughput sequencing platforms and a survey of ChIP-seq analysis tools, discuss experimental design, and describe a detailed ChIP-seq method.Chromatin immunoprecipitation (ChIP) allows enrichment of genomic regions which are associated with specific transcription factors, histone modifications, and indeed any other epitopes which are present on chromatin. The original ChIP methods used site-specific PCR and Southern blotting to confirm which regions of the genome were enriched, on a candidate basis. The combination of ChIP with genomic tiling arrays (ChIP-chip) allowed a more unbiased approach to map ChIP-enriched sites. However, limitations of microarray probe design and probe number have a detrimental impact on the coverage, resolution, sensitivity, and cost of whole-genome tiling microarray sets for higher eukaryotes with large genomes. The combination of ChIP with high-throughput sequencing technology has allowed more comprehensive surveys of genome occupancy, greater resolution, and lower cost for whole genome coverage. Herein, we provide a comparison of high-throughput sequencing platforms and a survey of ChIP-seq analysis tools, discuss experimental design, and describe a detailed ChIP-seq method.

Taking risks with translational research

The recent bankruptcy filing by deCODE, a company with an exceptional pedigree in associating genetic variance with disease onset, highlights the commercial risks of translational research. Indeed, deCODE's approach was similar to that adapted by academic researchers who seek to connect genetics and disease. We argue here that neither a purely corporate nor purely academic model is entirely appropriate for such research. Instead, we suggest that the private sector undertake the high-throughput elements of translational research, while the public sector and governments assume the role of providing long-term funding to develop gifted scientists with the confidence to attempt to use genetic data as a stepping stone to a truly mechanistic understanding of complex disease.

Chromatin immunoprecipitation (ChIP) methodology and readouts

The identification of direct nuclear hormone receptor gene targets provides clues to their contribution to both development and cancer progression. Until recently, the identification of such direct target genes has relied on a combination of expression analysis and in silico searches for consensus binding motifs in gene promoters. Consensus binding motifs for transcription factors are often defined using in vitro DNA binding strategies. Such in vitro strategies fail to account for the many factors that contribute significantly to target selection by transcription factors in cells beyond the recognition of these short consensus DNA sequences. These factors include DNA methylation, chromatin structure, posttranslational modifications of transcription factors, and the cooperative recruitment of transcription factor complexes. Chromatin immunoprecipitation (ChIP) provides a means of isolating transcription factor complexes in the context of endogenous chromatin, allowing the identification of direct transcription factor targets. ChIP can be combined with site-specific PCR for candidate binding sites or alternatively with cloning, genomic microarrays or more recently direct high throughput sequencing to identify novel genomic targets. The application of ChIP-based approaches has redefined consensus binding motifs for transcription factors and provided important insights into transcription factor biology.

AURKA overexpression accompanies dysregulation of DNA-damage response genes in invasive urothelial cell carcinoma

Invasive urothelial cell carcinoma (UCC) is characterized by increased chromosomal instability and follows an aggressive clinical course in contrast to non-invasive disease. To identify molecular processes that confer and maintain an aggressive malignant phenotype, we used a high-throughput genome-wide approach to interrogate a cohort of high and low clinical risk UCC tumors. Differential expression analyses highlighted cohesive dysregulation of critical genes involved in the G(2)/M checkpoint in aggressive UCC. Hierarchical clustering based on DNA Damage Response (DDR) genes separated tumors according to a pre-defined clinical risk phenotype. Using array-comparative genomic hybridization, we confirmed that the DDR was disrupted in tumors displaying high genomic instability. We identified DNA copy number gains at 20q13.2-q13.3 (AURKA locus) and determined that overexpression of AURKA accompanied dysregulation of DDR genes in high risk tumors. We postulated that DDR-deficient UCC tumors are advantaged by a selective pressure for AURKA associated override of M phase barriers and confirmed this in an independent tissue microarray series. This mechanism that enables cancer cells to maintain an aggressive phenotype forms a rationale for targeting AURKA as a therapeutic strategy in advanced stage UCC.

Do persistent organic pollutants interact with the stress response? Individual compounds, and their mixtures, interaction with the glucocorticoid receptor

Persistent organic pollutants (POPs) are toxic substances, highly resistant to environmental degradation, which can bio-accumulate and have long-range atmospheric transport potential (UNEP 2001). The majority of studies on endocrine disruption have focused on interferences on the sexual steroid hormones and so have overlooked disruption to glucocorticoid hormones. Here the endocrine disrupting potential of individual POPs and their mixtures has been investigated in vitro to identify any disruption to glucocorticoid nuclear receptor transcriptional activity. POP mixtures were screened for glucocorticoid receptor (GR) translocation using a GR redistribution assay (RA) on a CellInsight(TM) NXT High Content Screening (HCS) platform. A mammalian reporter gene assay (RGA) was then used to assess the individual POPs, and their mixtures, for effects on glucocorticoid nuclear receptor transactivation. POP mixtures did not induce GR translocation in the GR RA or produce an agonist response in the GR RGA. However, in the antagonist test, in the presence of cortisol, an individual POP, p,p'-dichlorodiphenyldichloroethylene (DDE), was found to decrease glucocorticoid nuclear receptor transcriptional activity to 72.5% (in comparison to the positive cortisol control). Enhanced nuclear transcriptional activity, in the presence of cortisol, was evident for the two lowest concentrations of perfluorodecanoic acid (PFOS) potassium salt (0.0147mg/ml and 0.0294mg/ml), the two highest concentrations of perfluorodecanoic acid (PFDA) (0.0025mg/ml and 0.005mg/ml) and the highest concentration of 2,2',4,4'-tetrabromodiphenyl ether (BDE 47) (0.0000858mg/ml). It is important to gain a better understanding of how POPs can interact with GRs as the disruption of glucocorticoid action is thought to contribute to complex diseases.

SNP discovery using Next Generation Transcriptomic Sequencing in Atlantic herring (Clupea harengus)

The introduction of Next Generation Sequencing (NGS) has revolutionised population genetics, providing studies of non-model species with unprecedented genomic coverage, allowing evolutionary biologists to address questions previously far beyond the reach of available resources. Furthermore, the simple mutation model of Single Nucleotide Polymorphisms (SNPs) permits cost-effective high-throughput genotyping in thousands of individuals simultaneously. Genomic resources are scarce for the Atlantic herring (Clupea harengus), a small pelagic species that sustains high revenue fisheries. This paper details the development of 578 SNPs using a combined NGS and high-throughput genotyping approach. Eight individuals covering the species distribution in the eastern Atlantic were bar-coded and multiplexed into a single cDNA library and sequenced using the 454 GS FLX platform. SNP discovery was performed by de novo sequence clustering and contig assembly, followed by the mapping of reads against consensus contig sequences. Selection of candidate SNPs for genotyping was conducted using an in silico approach. SNP validation and genotyping were performed simultaneously using an Illumina 1,536 GoldenGate assay. Although the conversion rate of candidate SNPs in the genotyping assay cannot be predicted in advance, this approach has the potential to maximise cost and time efficiencies by avoiding expensive and time-consuming laboratory stages of SNP validation. Additionally, the in silico approach leads to lower ascertainment bias in the resulting SNP panel as marker selection is based only on the ability to design primers and the predicted presence of intron-exon boundaries. Consequently SNPs with a wider spectrum of minor allele frequencies (MAFs) will be genotyped in the final panel. The genomic resources presented here represent a valuable multi-purpose resource for developing informative marker panels for population discrimination, microarray development and for population genomic studies in the wild.

Comprehensive molecular pathology analysis of small bowel adenocarcinoma reveals novel targets with potential for clinical utility

Small bowel accounts for only 0.5% of cancer cases in the US but incidence rates have been rising at 2.4% per year over the past decade. One-third of these are adenocarcinomas but little is known about their molecular pathology and no molecular markers are available for clinical use. Using a retrospective 28 patient matched normal-tumor cohort, next-generation sequencing, gene expression arrays and CpG methylation arrays were used for molecular profiling. Next-generation sequencing identified novel mutations in IDH1, CDH1, KIT, FGFR2, FLT3, NPM1, PTEN, MET, AKT1, RET, NOTCH1 and ERBB4. Array data revealed 17% of CpGs and 5% of RNA transcripts assayed to be differentially methylated and expressed respectively (p < 0.01). Merging gene expression and DNA methylation data revealed CHN2 as consistently hypermethylated and downregulated in this disease (Spearman -0.71, p < 0.001). Mutations in TP53 which were found in more than half of the cohort (15/28) and Kazald1 hypomethylation were both were indicative of poor survival (p = 0.03, HR = 3.2 and p = 0.01, HR = 4.9 respectively). By integrating high-throughput mutational, gene expression and DNA methylation data, this study reveals for the first time the distinct molecular profile of small bowel adenocarcinoma and highlights potential clinically exploitable markers.

An interview with Manuel Salto-Tellez on diagnostic pathology: the future is morphomolecular

Professor Manuel Salto-Tellez of Queen’s University, Belfast, Northern Ireland is an expert histopathologist and molecular diagnostician. Professor Salto-Tellez is a lead investigator at the Northern Ireland Molecular Pathology Laboratory and also serves as a member of the Editorial Advisory Board for Expert Review of Molecular Diagnostics. In this interview, he proposes directions for the future of molecular pathology and molecular diagnostics, integrating all aspects of pathology toward a common goal.

Gene regulatory mechanisms underpinning prostate cancer susceptibility

Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating ‘big data’ across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

NanoStreams: Codesigned Microservers for Edge Analytics in Real Time

NanoStreams explores the design, implementation,and system software stack of micro-servers aimed at processingdata in-situ and in real time. These micro-servers can serve theemerging Edge computing ecosystem, namely the provisioningof advanced computational, storage, and networking capabilitynear data sources to achieve both low latency event processingand high throughput analytical processing, before consideringoff-loading some of this processing to high-capacity datacentres.NanoStreams explores a scale-out micro-server architecture thatcan achieve equivalent QoS to that of conventional rack-mountedservers for high-capacity datacentres, but with dramaticallyreduced form factors and power consumption. To this end,NanoStreams introduces novel solutions in programmable & con-figurable hardware accelerators, as well as the system softwarestack used to access, share, and program those accelerators.Our NanoStreams micro-server prototype has demonstrated 5.5×higher energy-efficiency than a standard Xeon Server. Simulationsof the microserver’s memory system extended to leveragehybrid DDR/NVM main memory indicated 5× higher energyefficiencythan a conventional DDR-based system.