A report on the meeting 'Functional Genomics and Systems Biology 2011', Wellcome Trust Conference Centre, Wellcome Trust Genome Campus, Hinxton, Cambridge, UK, 29 November to 1 December, 2011.

Inflammation represents the body's natural response to tissue damage; however, chronic inflammation may activate cell proliferation and induce deregulation of cell death in affected tissues. Chronic inflammation is an important factor in the development of hepatocellular carcinoma (HCC), although the precise underlying mechanism remains unknown. Epigenetic events, which are considered key mechanisms in the regulation of gene activity states, are also commonly deregulated in HCC. Here, we review the evidence that chronic inflammation might deregulate epigenetic processes, thus promoting oncogenic transformation, and we propose a working hypothesis that epigenetic deregulation is an underlying mechanism by which inflammation might promote HCC development. In this scenario, different components of the inflammatory response might directly and indirectly induce changes in epigenetic machineries ('epigenetic switch'), including those involved in setting and propagating normal patterns of DNA methylation, histone modifications and non-coding RNAs in hepatocytes. We discuss the possibility that self-reinforcing cross-talk between inflammation and epigenetic mechanisms might amplify inflammatory signals and maintain a chronic state of inflammation culminating in cancer development. The potential role of inflammation-epigenome interactions in the emergence and maintenance of cancer stem cells is also discussed.

A report on the 6th International Conference on Genomics (ICG-VI), Shenzhen, China, 12-15 November 2011.

Side-effects are the unintended consequence of therapeutic treatments, but they can also be seen as valuable read-outs of drug effects in humans; these effects are difficult to infer or predict from pre-clinical models. Indeed, some studies suggest that drugs with similar side-effect profiles may also share therapeutic properties through related mechanisms of action. A recent publication exploits this concept to systematically investigate new indications for already marketed drugs, and presents a strategy to get the most out of the tiny portion of chemicals that have proved to be effective and safe.

A report on the Euroepistem 2011 meeting 'Epigenomic Programming and Stem Cells for Drug Discovery', Paris, France, 21-22 November 2011.

Most of the recently identified autoimmunity loci are shared among multiple autoimmune diseases. The pattern of genetic association with autoimmune phenotypes varies, suggesting that certain subgroups of autoimmune diseases are likely to share etiological similarities and underlying mechanisms of disease. In this review, we summarize the major findings from recent studies that have sought to refine genotype-phenotype associations in autoimmune disease by identifying both shared and distinct autoimmunity loci. More specifically, we focus on information from recent genome-wide association studies of rheumatoid arthritis, ankylosing spondylitis, celiac disease, multiple sclerosis, systemic lupus erythematosus, type 1 diabetes and inflammatory bowel disease. Additional work in this area is warranted given both the opportunity it provides to elucidate pathogenic mechanisms in autoimmunity and its potential to inform the development of improved diagnostic and therapeutic tools for this group on complex human disorders.

Targeted therapy with tyrosine kinase inhibitors has led to a substantial improvement in the standard of care for patients with advanced or metastatic clear cell renal cell carcinoma. Because the mechanism of action, metabolism and transport of tyrosine kinase inhibitors can affect outcome and toxicity, several investigators have pursued the identification of single nucleotide polymorphisms (SNPs) in genes associated with these actions. We discuss SNPs associated with outcome and toxicity following sunitinib therapy and provide recommendations for future trials to facilitate the use of SNPs in personalized therapy for this disease.

The zebrafish model is rapidly gaining prominence in the study of development, hematopoiesis, and disease. The zebrafish provides distinct advantages over other vertebrate models during early embryonic development by producing transparent, externally fertilized embryos. Embryonic zebrafish are easily visualized and manipulated through microinjection, chemical treatment, and mutagenesis. These procedures have contributed to large-scale chemical, suppressor, and genetic screens to identify hematopoietic gene mutations. Genomic conservation and local synteny between the human and zebrafish genomes make genome-scale and epigenetic analysis of these mutations (by microarray, chromatin immunoprecipitation sequencing, and RNA sequencing procedures) powerful methods for translational research and medical discovery. In addition, large-scale screening techniques have resulted in the identification of several small molecules capable of rescuing hematopoietic defects and inhibiting disease. Here, we discuss the contributions of the zebrafish model to the understanding of hematopoiesis, hematopoietic stem cell development, and disease-related discovery. We also highlight the recent discovery of small molecules with clinical promise, such as dimethyl prostaglandin E2, 3F8, and thiazole-carboxamide 10A.

A report on the 9th Annual Cold Spring Harbor/Wellcome Trust meeting 'Pharmacogenomics and Personalized Medicine', Hinxton, Cambridge, UK, 29 September to 2 October 2011.

Marketing pressures, regulatory policies, clinical guidelines, and consumer demand all affect health care providers' knowledge and use of health-related genetic tests that are sold and/or advertised to consumers. In addition, clinical guidelines, regulatory policies, and educational efforts are needed to promote the informed use of genetic tests that are sold and advertised to consumers and health care providers. A shift in culture regarding the regulation of genetic tests that are sold directly to consumers is suggested: by recent actions taken by the US Food and Drug Administration (FDA), including letters sent to direct-to-consumer (DTC) genetic testing companies stating that their tests meet the definition of medical devices; by public meetings held by the FDA to discuss laboratory developed tests; and by the convening of the Molecular and Clinical Genetics Panel to gather input on scientific issues concerning DTC genetic tests that make medical claims. This review provides a brief overview of DTC advertising and the regulation of pharmaceuticals and genetic tests in the United States. It highlights recent changes in the regulatory culture regarding genetic tests that are sold to consumers, and discusses the impact on health care providers of selling and advertising genetic tests directly to consumers.

An original yeast-based phenomic model for DeltaF508 CFTR, by far the most prevalent allele of the CFTR gene responsible for cystic fibrosis, has been developed by the groups of Elizabeth A Miller and John L Hartman. This model allows potential modifier genes of the DeltaF508 CFTR allele to be uncovered. Hence, brewer's yeast Saccharomyces cerevisiae is not only needed at happy hour, it also represents an invaluable tool for human geneticists.

The human leukocyte antigen (HLA) is key to many aspects of human physiology and medicine. All current sequence-based HLA typing methodologies are targeted approaches requiring the amplification of specific HLA gene segments. Whole genome, exome and transcriptome shotgun sequencing can generate prodigious data but due to the complexity of HLA loci these data have not been immediately informative regarding HLA genotype. We describe HLAminer, a computational method for identifying HLA alleles directly from shotgun sequence datasets (http://www.bcgsc.ca/platform/bioinfo/software/hlaminer). This approach circumvents the additional time and cost of generating HLA-specific data and capitalizes on the increasing accessibility and affordability of massively parallel sequencing.

Embryonic stem (ES) cells are of great interest as a model system for studying early developmental processes and because of their potential therapeutic applications in regenerative medicine. Obtaining a systematic understanding of the mechanisms that control the 'stemness' - self-renewal and pluripotency - of ES cells relies on high-throughput tools to define gene expression and regulatory networks at the genome level. Such recently developed systems biology approaches have revealed highly interconnected networks in which multiple regulatory factors act in combination. Interestingly, stem cells and cancer cells share some properties, notably self-renewal and a block in differentiation. Recently, several groups reported that expression signatures that are specific to ES cells are also found in many human cancers and in mouse cancer models, suggesting that these shared features might inform new approaches for cancer therapy. Here, we briefly summarize the key transcriptional regulators that contribute to the pluripotency of ES cells, the factors that account for the common gene expression patterns of ES and cancer cells, and the implications of these observations for future clinical applications.

Pharmacogenetic research aims to study how genetic variation may influence drug efficacy and/or toxicity; pharmacogenomics expands this quest to the entire genome. Pharmacogenetic findings may help to uncover new drug targets, illuminate pathophysiology, clarify disease heterogeneity, aid in the fine-mapping of genetic associations, and contribute to personalized treatment. In diabetes, there is precedent for the successful application of pharmacogenetic concepts to monogenic forms of the disease, such as maturity onset diabetes of the young or neonatal diabetes. Whether similar insights will be produced for the common form of type 2 diabetes remains to be seen. With recent advances in genetic approaches, the successive application of candidate gene studies, large-scale genotyping studies and genome-wide association studies has begun to generate suggestive results that may lead to changes in clinical practice. However, many potential barriers to the translation of pharmacogenetic discoveries to the clinical management of diabetes still remain. Here, we offer a contemporary overview of the field in its current state, identify potential obstacles, and highlight future directions.

Background

In an ongoing clinical trial, the genetic and environmental risk assessment (GERA) blood test offers subjects information about personal colorectal cancer risk through measurement of two novel low-to-moderate risk factors. We sought to examine predictors of uptake of the GERA blood test among participants randomized to the Intervention arm.

Methods

Primary care patients aged 50 to 74 years eligible for colorectal cancer screening are randomized to receive a mailed stool blood test kit to complete at home (Control) or to the control condition plus an in-office blood test called GERA that includes assessment of red blood cell folate and DNA-testing for two MTHFR (methylenetetrahydrofolate reductase) single nucleotide polymorphisms (SNPs) (Intervention). For the present study, baseline survey data are examined in participants randomized to the Intervention.

Results

The first 351 intervention participants (161 African American/190 white) were identified. Overall, 249 (70.9%) completed GERA testing. Predictors of GERA uptake included race (African American race, odds ratio (OR) 0.51 (0.29 to 0.87)), and being more knowledgeable about GERA and colorectal cancer screening (OR 1.09 (1.01 to 1.18)). Being married (OR 1.81 (1.09 to 3.00)) was also significant in the multivariable model.

Conclusions

Participant uptake of GERA testing was high. GERA uptake varied, however, according to socio-demographic background and knowledge.

Genomic variants with a key role in causing cancer or affecting the response to cancer therapeutics need to be identified so that they can be targeted for therapy. The transFIC tool aims to identify somatic point mutations that drive cancer in sequencing projects. This package is available as a web service, a stand-alone program and a website. It improves the functional prediction scores generated by popular established prediction tools and will be useful to cancer researchers.

See research article: http://genomemedicine.com/content/4/11/89

A report on the Cold Spring Harbor Laboratory meeting on 'Personal Genomes', Cold Spring Harbor, New York, USA, 30 September to 2 October, 2011.

We here present a comparative genome, transcriptome and functional network analysis of three human cancer cell lines (A431, U251MG and U2OS), and investigate their relation to protein expression. Gene copy numbers significantly influenced corresponding transcript levels; their effect on protein levels was less pronounced. We focused on genes with altered mRNA and/or protein levels to identify those active in tumor maintenance. We provide comprehensive information for the three genomes and demonstrate the advantage of integrative analysis for identifying tumor-related genes amidst numerous background mutations by relating genomic variation to expression/protein abundance data and use gene networks to reveal implicated pathways.

A report on the Wellcome Trust Scientific Conference 'Epigenomics of Common Diseases', Hinxton, Cambridge, UK, September 13-16, 2011.

Background

The advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).

Methods

We evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.

Results

Both biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.

Conclusions

Biomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness.

Bibliographic records in the PubMed database of biomedical literature are annotated with Medical Subject Headings (MeSH) by curators, which summarize the content of the articles. Two recent publications explain how to generate profiles of MeSH terms for a set of bibliographic records and to use them to define any given concept by its associated literature. These concepts can then be related by their keyword profiles, and this can be used, for example, to detect new associations between genes and inherited diseases.

See related research articles: http://www.biomedcentral.com/1471-2105/13/249/abstracthttp://genomemedicine.com/content/4/9/75/abstract

Epigenome-wide association studies (EWAS) can be used to investigate links between early life environment, epigenetics and disease. However, such studies raise the question of which came first: the mark or the malady? A recent study has demonstrated that EWAS can be performed on neonatal 'Guthrie' heel-prick blood spots. As Guthrie cards are collected from all newborn infants and stored indefinitely in many countries, they represent an important timepoint to compare with later disease-associated epigenetic marks.

A report on the Wellcome Trust Scientific Conference 'The Genomics of Common Diseases 2011', held at the Wellcome Trust Conference Centre, Hinxton, Cambridge, UK, 30 August to 2 September 2011.