The evolving discipline of molecular epidemiology of cancer

Relationship Between Urinary 4-(Methylnitrosamino)-1-(3-Pyridyl)-1-Butanol and Lung Cancer Risk in the General Population: A Community-Based Prospective Cohort Study

No published studies have prospectively evaluated the association between urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) levels and lung cancer risk in the general population. Here, we conducted a prospective community-based cohort study in the Republic of Korea to evaluate the relationship between urinary NNAL levels and lung cancer risk using prediagnostic urine samples. This prospective cohort study was based on the Korean National Cancer Center Community Cohort. During the follow-up period, 173 primary lung cancer cases were identified. Total urinary NNAL levels were measured by liquid chromatography-tandem mass spectrometry, and data were analyzed using multivariable Cox proportional hazards regression models. The risk of lung cancer was significantly increased per unit of natural log-transformed urinary NNAL (HR, 1.27; 95% CI, 1.09-1.48), after adjusting for age, region, entry year into the cohort, education achievement, alcohol consumption status, BMI, smoking status, and urinary cotinine levels. Cox proportional-hazards models with NNAL quartiles also showed positive dose-response relationships with risk of lung cancer. A significantly increased risk of lung cancer was found in the fourth quartile of urinary NNAL levels (HR, 3.27; 95% CI, 1.37-7.79, P for trend < 0.01). After stratification with sex, the significant association remained in only men. Urinary NNAL levels are associated with the risk of lung cancer in the general population, and this association is independent from the quantification of cigarette smoking and nicotine uptake.

Challenges of the heterogeneous nutrition response: interpreting the group mean

Extensive research demonstrates unequivocally that nutrition plays a fundamental role in maintaining health and preventing disease. In parallel nutrition research provides evidence that the risks and benefits of diet and lifestyle choices do not affect people equally, as people are inherently variable in their responses to nutrition and associated interventions to maintain health and prevent disease. To simplify the inherent complexity of human subjects and their nutrition, with the aim of managing expectations for dietary guidance required to ensure healthy populations and individuals, nutrition researchers often seek to group individuals based on commonly used criteria. This strategy relies on demonstrating meaningful conclusions based on comparison of group mean responses of assigned groups. Such studies are often confounded by the heterogeneous nutrition response. Commonly used criteria applied in grouping study populations and individuals to identify mechanisms and determinants of responses to nutrition often contribute to the problem of interpreting the results of group comparisons. Challenges of interpreting the group mean using diverse populations will be discussed with respect to studies in human subjects, in vivo and in vitro model systems. Future advances in nutrition research to tackle inter-individual variation require a coordinated approach from funders, learned societies, nutrition scientists, publishers and reviewers of the scientific literature. This will be essential to develop and implement improved study design, data recording, analysis and reporting to facilitate more insightful interpretation of the group mean with respect to population diversity and the heterogeneous nutrition response.

The use of genotoxicity biomarkers in molecular epidemiology: applications in environmental, occupational and dietary studies

Molecular epidemiology is an approach increasingly used in the establishment of associations between exposure to hazardous substances and development of disease, including the possible modulation by genetic susceptibility factors. Environmental chemicals and contaminants from anthropogenic pollution of air, water and soil, but also originating specifically in occupational contexts, are potential sources of risk of development of disease. Also, diet presents an important role in this process, with some well characterized associations existing between nutrition and some types of cancer. Genotoxicity biomarkers allow the detection of early effects that result from the interaction between the individual and the environment; they are therefore important tools in cancer epidemiology and are extensively used in human biomonitoring studies. This work intends to give an overview of the potential for genotoxic effects assessment, specifically with the cytokinesis blocked micronucleus assay and comet assay in environmental and occupational scenarios, including diet. The plasticity of these techniques allows their inclusion in human biomonitoring studies, adding important information with the ultimate aim of disease prevention, in particular cancer, and so it is important that they be included as genotoxicity assays in molecular epidemiology.

A new statistical method for curve group analysis of longitudinal gene expression data illustrated for breast cancer in the NOWAC postgenome cohort as a proof of principle

Background

The understanding of changes in temporal processes related to human carcinogenesis is limited. One approach for prospective functional genomic studies is to compile trajectories of differential expression of genes, based on measurements from many case-control pairs. We propose a new statistical method that does not assume any parametric shape for the gene trajectories.

Methods

The trajectory of a gene is defined as the curve representing the changes in gene expression levels in the blood as a function of time to cancer diagnosis. In a nested case–control design it consists of differences in gene expression levels between cases and controls. Genes can be grouped into curve groups, each curve group corresponding to genes with a similar development over time. The proposed new statistical approach is based on a set of hypothesis testing that can determine whether or not there is development in gene expression levels over time, and whether this development varies among different strata. Curve group analysis may reveal significant differences in gene expression levels over time among the different strata considered. This new method was applied as a “proof of concept” to breast cancer in the Norwegian Women and Cancer (NOWAC) postgenome cohort, using blood samples collected prospectively that were specifically preserved for transcriptomic analyses (PAX tube). Cohort members diagnosed with invasive breast cancer through 2009 were identified through linkage to the Cancer Registry of Norway, and for each case a random control from the postgenome cohort was also selected, matched by birth year and time of blood sampling, to create a case-control pair. After exclusions, 441 case-control pairs were available for analyses, in which we considered strata of lymph node status at time of diagnosis and time of diagnosis with respect to breast cancer screening visits.

Results

The development of gene expression levels in the NOWAC postgenome cohort varied in the last years before breast cancer diagnosis, and this development differed by lymph node status and participation in the Norwegian Breast Cancer Screening Program. The differences among the investigated strata appeared larger in the year before breast cancer diagnosis compared to earlier years.

Conclusions

This approach shows good properties in term of statistical power and type 1 error under minimal assumptions. When applied to a real data set it was able to discriminate between groups of genes with non-linear similar patterns before diagnosis.

ASSOCIATION BETWEEN MURINE DOUBLE MINUTE 2 - T309G polymorphism and recurrence of hepatocellular carcinoma after surgical treatment

BACKGROUND

Discovery and incorporation of biomarker panels to cancer studies enabled the understanding of genetic variation and its interference in carcinogenesis at molecular level. The potential association between single nucleotide polymorphism (SNP) 309 and increased development of tumors, such as hepatocellular carcinoma, has been subject to several studies. This is the first study on this association conducted in Brazil.

METHODS

62 cases of cirrhotic patients with hepatocellular carcinoma surgically treated by partial hepatectomy (HPT) or by liver transplantation (LTX) from 2000 to 2009 at Santa Casa Hospital Complex, in the city of Porto Alegre, were retrospectively analyzed. Tumor samples from surgical specimen were collected and prepared for study in paraffin blocks.

RESULTS

Overall survival was 26.7 months in the HPT group and 62.4 months in the LTX group (P <0.01). Overall tumor recurrence was 66.7% in the HPT group (10/15) and 17% in the LTX group (8/47) (X²=13.602, P <0.01). Alpha-fetoprotein levels >200ng/mL, microvascular invasion and histological grade were associated with tumor recurrence (P <0.01). Recurrence rates in each surgical group and analysis of factors associated with tumor recurrence, when stratified for each genotypic pattern, were both not statistically significant.

CONCLUSION

G/G genotype was not associated with tumor recurrence after surgical treatment and it did not show any correlation with other prognostic factors.

Adding Mendelian randomization to a meta-analysis-a burgeoning opportunity

Current literature is teeming with tens of thousands of meta-analyses, but only a small fraction made seminal contributions to enriching our understanding of the mechanisms of carcinogenesis, possibly due to chance, bias, confounding, or reverse causality. The incorporation of Mendelian randomization (MR) with a meta-analysis has revolutionized traditional practice and is emerging as a viable technique to strengthen causal unconfounded inferences from observational data. We therefore highlight the importance of integrated MR meta-analysis in cancer epidemiology and provide an overview of three existing instrumental selection strategies in medical literature.

Molecular epidemiology, and possible real-world applications in breast cancer

Gene-environment interaction, a key idea in molecular epidemiology, has enabled the development of personalized medicine. This concept includes personalized prevention. While genome-wide association studies have identified a number of genetic susceptibility loci in breast cancer risk, however, the application of this knowledge to practical prevention is still underway. Here, we briefly review the history of molecular epidemiology and its progress in breast cancer epidemiology. We then introduce our experience with the trial combination of GWAS-identified loci and well-established lifestyle and reproductive risk factors in the risk prediction of breast cancer. Finally, we report our exploration of the cumulative risk of breast cancer based on this risk prediction model as a potential tool for individual risk communication, including genetic risk factors and gene-environment interaction with obesity.

Translational cancer research: balancing prevention and treatment to combat cancer globally

Cancer research is drawing on the human genome project to develop new molecular-targeted treatments. This is an exciting but insufficient response to the growing, global burden of cancer, particularly as the projected increase in new cases in the coming decades is increasingly falling on developing countries. The world is not able to treat its way out of the cancer problem. However, the mechanistic insights from basic science can be harnessed to better understand cancer causes and prevention, thus underpinning a complementary public health approach to cancer control. This manuscript focuses on how new knowledge about the molecular and cellular basis of cancer, and the associated high-throughput laboratory technologies for studying those pathways, can be applied to population-based epidemiological studies, particularly in the context of large prospective cohorts with associated biobanks to provide an evidence base for cancer prevention. This integrated approach should allow a more rapid and informed translation of the research into educational and policy interventions aimed at risk reduction across a population.

Cancer and the goals of integration

Cancer is not one, but many diseases, and each is a product of a variety of causes acting (and interacting) at distinct temporal and spatial scales, or "levels" in the biological hierarchy. In part because of this diversity of cancer types and causes, there has been a diversity of models, hypotheses, and explanations of carcinogenesis. However, there is one model of carcinogenesis that seems to have survived the diversification of cancer types: the multi-stage model of carcinogenesis. This paper examines the history of the multistage theory, and uses the theory as a case study in the limits and goals of unification as a theoretical virtue, comparing and contrasting it with "integrative" research.

Towards defining biomarkers indicating resistances to targeted therapies

An impressive, but often short objective response was obtained in many tumor patients treated with different targeted therapies, but most of the patients develop resistances against these drugs. So far, a number of distinct mechanisms leading to intrinsic as well as acquired resistances have been identified in tumors of distinct origin. These can arise from genetic alterations, like mutations, truncations, and amplifications or due to deregulated expression of various proteins and signal transduction pathways, but also from cellular heterogeneity within tumors after an initial response. Therefore, biomarkers are urgently needed for cancer prognosis and personalized cancer medicine. The application of "ome"-based technologies including cancer (epi)genomics, next generation sequencing, cDNA microarrays and proteomics might led to the predictive or prognostic stratification of patients to categorize resistance mechanisms and to postulate combinations of treatment strategies. This review discusses the implementation of proteome-based analysis to identify markers of pathway (in)activation in tumors and the resistance mechanisms, which represent major clinical problems as a tool to optimize individually tailored therapies based on targeted drugs. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Integrating genetic and genomic information into effective cancer care in diverse populations

This paper provides an overview of issues in the integration of genetic (related to hereditary DNA) and genomic (related to genes and their functions) information in cancer care for individuals and families who are part of health care systems worldwide, from low to high resourced. National and regional cancer plans have the potential to integrate genetic and genomic information with a goal of identifying and helping individuals and families with and at risk of cancer. Healthcare professionals and the public have the opportunity to increase their genetic literacy and communication about cancer family history to enhance cancer control, prevention, and tailored therapies.

Omics in population studies: a molecular epidemiology perspective

The convergence of striking developments in (bio)-technology, increasing availability of biobanked samples, and advances in biostatistics and bio-informatics allow an optimistic outlook for epidemiological research. In this special issue on Omics in Population Studies: A Molecular Epidemiology Perspective we explore and reflect on the potential of these new developments in both exposure science and clinical research since they provide the essential link between exposure and disease and may enable scientists to improve their understanding of disease origin and progression. As noted in this special issue, this is an exciting time for epidemiology. While cancer and other noncommunicable diseases rise in number worldwide, various new tools can be applied effectively to increase understanding of the underlying causes and potential for progression to improve their prevention and treatment.

Research ethics in the post-genomic era

New high-throughput 'omics techniques are providing exciting opportunities in clinical medicine and toxicology, especially in the development of biomarkers. In health science research there are traditional ethical considerations that are reasonably obvious, like balancing health benefits and health risks, autonomy mainly pursued by informed consent, and protecting privacy. Epidemiological studies applying new large-scale approaches (e.g., high-throughput or high-content methods and global studies that utilize biobanking of samples and produce large-scale datasets) present new challenges that call for re-evaluation of standard ethical considerations. In this context, assessment of the ethics underlying study designs, bioinformatics, and statistics applied in the generation and clinical translation of research results should also be considered. Indeed, there are ethical considerations in the research process itself, in research objectives and how research is pursued (e.g., which methodologies are selected and how they are carried out). Maintaining research integrity is critical, as demonstrated by the relatively frequent retraction of scientific papers following violations of good scientific practice. Abiding by the laws is necessary but not sufficient for good research ethics, which is and remains in the hands of the scientific community at the level of both individual scientists and organizations. Senior scientists are responsible for the transfer of research tradition to the next generation of scientists through education, mentorship, and setting an example by their own behavior, as well as by creating systems in institutions that support good research ethics.

Genomics and proteomics: how long do we need to reach clinical results?

BACKGROUND

Discovery of the ideal biomarker for clinical care remains a major challenge. Recent progress in genomic and proteomic technologies has allowed the identification of thousands of potential markers, although the benefits of these findings in clinical routine use are not completely evident yet.

METHODS

Major genomics and proteomics approaches are outlined and their clinical applications are described. Future developments in clinical nephrology are discussed.

CONCLUSION

Genomics and proteomics technologies, used to measure gene expression at the transcript and at the protein levels, provide complementary information, which paves the way for systems biology. The fields of genomics and proteomics continue to develop rapidly, and it is evident that there is great potential for their ability to predict diseases and outcomes. However, there are several tasks that must be accomplished to convert all these '-omics' approaches into clinical practice. Collaboration between clinicians, scientists and healthcare funding organizations together with specific guideline development and high-throughput analytical automation will be crucial to reach the final potential of these technologies.

"Drivers" of translational cancer epidemiology in the 21st century: needs and opportunities

Cancer epidemiology is at the cusp of a paradigm shift--propelled by an urgent need to accelerate the pace of translating scientific discoveries into health care and population health benefits. As part of a strategic planning process for cancer epidemiologic research, the Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI) is leading a "longitudinal" meeting with members of the research community to engage in an on-going dialogue to help shape and invigorate the field. Here, we review a translational framework influenced by "drivers" that we believe have begun guiding cancer epidemiology toward translation in the past few years and are most likely to drive the field further in the next decade. The drivers include: (i) collaboration and team science, (ii) technology, (iii) multilevel analyses and interventions, and (iv) knowledge integration from basic, clinical, and population sciences. Using the global prevention of cervical cancer as an example of a public health endeavor to anchor the conversation, we discuss how these drivers can guide epidemiology from discovery to population health impact, along the translational research continuum.

Evaluation of excess statistical significance in meta-analyses of 98 biomarker associations with cancer risk

BACKGROUND

Numerous biomarkers have been associated with cancer risk. We assessed whether there is evidence for excess statistical significance in results of cancer biomarker studies, suggesting biases.

METHODS

We systematically searched PubMed for meta-analyses of nongenetic biomarkers and cancer risk. The number of observed studies with statistically significant results was compared with the expected number, based on the statistical power of each study under different assumptions for the plausible effect size. We also evaluated small-study effects using asymmetry tests. All statistical tests were two-sided.

RESULTS

We included 98 meta-analyses with 847 studies. Forty-three meta-analyses (44%) found nominally statistically significant summary effects (random effects). The proportion of meta-analyses with statistically significant effects was highest for infectious agents (86%), inflammatory (67%), and insulin-like growth factor (IGF)/insulin system (52%) biomarkers. Overall, 269 (32%) individual studies observed nominally statistically significant results. A statistically significant excess of the observed over the expected number of studies with statistically significant results was seen in 20 meta-analyses. An excess of observed vs expected was observed in studies of IGF/insulin (P ≤ .04) and inflammation systems (P ≤ .02). Only 12 meta-analyses (12%) had a statistically significant summary effect size, more than 1000 case patients, and no hints of small-study effects or excess statistical significance; only four of them had large effect sizes, three of which pertained to infectious agents (Helicobacter pylori, hepatitis and human papilloma viruses).

CONCLUSIONS

Most well-documented biomarkers of cancer risk without evidence of bias pertain to infectious agents. Conversely, an excess of statistically significant findings was observed in studies of IGF/insulin and inflammation systems, suggesting reporting biases.

Clinical proteomics and OMICS clues useful in translational medicine research

Since the advent of the new proteomics era more than a decade ago, large-scale studies of protein profiling have been used to identify distinctive molecular signatures in a wide array of biological systems, spanning areas of basic biological research, clinical diagnostics, and biomarker discovery directed toward therapeutic applications. Recent advances in protein separation and identification techniques have significantly improved proteomic approaches, leading to enhancement of the depth and breadth of proteome coverage.

Proteomic signatures, specific for multiple diseases, including cancer and pre-invasive lesions, are emerging. This article combines, in a simple manner, relevant proteomic and OMICS clues used in the discovery and development of diagnostic and prognostic biomarkers that are applicable to all clinical fields, thus helping to improve applications of clinical proteomic strategies for translational medicine research.

Transcriptional output in a prospective design conditionally on follow-up and exposure: the multistage model of cancer

Transcriptomics as the analysis of mRNA and microRNA could be implemented in prospective studies both in peripheral blood and tissues. Its application in cancer epidemiology could provide a new understanding of the functional changes underlying the multistage model of carcinogenesis, as well as the relationship between these changes and exposure to carcinogens. Transcriptomics is not merely another -omics technology for risk assessment in traditional prospective studies. Instead, this novel approach has the potential to estimate the distribution of gene expression conditionally on different exposures, and to study the length of the different stages of carcinogenesis. If it proves to be a valid approach, transcriptomics could be an opportunity to make meaningful advances in our understanding of the carcinogenic process.

Population sciences, translational research, and the opportunities and challenges for genomics to reduce the burden of cancer in the 21st century

Advances in genomics and related fields are promising tools for risk assessment, early detection, and targeted therapies across the entire cancer care continuum. In this commentary, we submit that this promise cannot be fulfilled without an enhanced translational genomics research agenda firmly rooted in the population sciences. Population sciences include multiple disciplines that are needed throughout the translational research continuum. For example, epidemiologic studies are needed not only to accelerate genomic discoveries and new biological insights into cancer etiology and pathogenesis, but to characterize and critically evaluate these discoveries in well-defined populations for their potential for cancer prediction, prevention and response to treatment. Behavioral, social, and communication sciences are needed to explore genomic-modulated responses to old and new behavioral interventions, adherence to therapies, decision making across the continuum, and effective use in health care. Implementation science, health services, outcomes research, comparative effectiveness research, and regulatory science are needed for moving validated genomic applications into practice and for measuring their effectiveness, cost-effectiveness, and unintended consequences. Knowledge synthesis, evidence reviews, and economic modeling of the effects of promising genomic applications will facilitate policy decisions and evidence-based recommendations. Several independent and multidisciplinary panels have recently made specific recommendations for enhanced research and policy infrastructure to inform clinical and population research for moving genomic innovations into the cancer care continuum. An enhanced translational genomics and population sciences agenda is urgently needed to fulfill the promise of genomics in reducing the burden of cancer.

On the selectivity and efficacy of defense peptides with respect to cancer cells

Here, we review potential determinants of the anticancer efficacy of innate immune peptides (ACPs) for cancer cells. These determinants include membrane-based factors, such as receptors, phosphatidylserine, sialic acid residues, and sulfated glycans, and peptide-based factors, such as residue composition, sequence length, net charge, hydrophobic arc size, hydrophobicity, and amphiphilicity. Each of these factors may contribute to the anticancer action of ACPs, but no single factor(s) makes an overriding contribution to their overall selectivity and toxicity. Differences between the anticancer actions of ACPs seem to relate to different levels of interplay between these peptide and membrane-based factors.

DNA damage and nucleotide excision repair capacity in healthy individuals

Interindividual differences in DNA repair capacity (DRC) represent an important source of variability in genome integrity and thus influence health risk. In the last decade, DRC measurement has attracted attention as a potential biomarker in cancer prediction. Aim of the present exploratory study was to characterize the variability in DNA damage and DRC on 100 healthy individuals and to identify biological, lifestyle, or genetic factors modulating these parameters. The ultimate goal was to obtain reference data from cancer-free population, which may constitute background for further investigations on cancer patients. The endogenous DNA damage was measured as a level of DNA single-strand breaks and DRC, specific for nucleotide excision repair (NER), was evaluated using modified comet assay, following the challenge of peripheral blood mononuclear cells with benzo[a]pyrene diolepoxide. Additionally, genetic polymorphisms in NER genes (XPA, XPC, XPD, and XPG) were assessed. We have observed a substantial interindividual variability for both examined parameters. DNA damage was significantly affected by gender and alcohol consumption (P = 0.003 and P = 0.012, respectively), whereas DRC was associated with family history of cancer (P = 0.012). The stratification according to common variants in NER genes showed that DNA damage was significantly modulated by the presence of the variant T allele of XPC Ala499Val polymorphism (P = 0.01), while DRC was modulated by the presence of the A allele of XPA G23A polymorphism (P = 0.048). Our results indicate the range of endogenous DNA single-strand breaks and capacity of NER in healthy volunteers as well as the role of potentially relevant confounders. Environ. Mol. Mutagen. 2011. © 2011 Wiley-Liss, Inc.

The path to clinical proteomics research: integration of proteomics, genomics, clinical laboratory and regulatory science

Better biomarkers are urgently needed to cancer detection, diagnosis, and prognosis. While the genomics community is making significant advances in understanding the molecular basis of disease, proteomics will delineate the functional units of a cell, proteins and their intricate interaction network and signaling pathways for the underlying disease. Great progress has been made to characterize thousands of proteins qualitatively and quantitatively in complex biological systems by utilizing multi-dimensional sample fractionation strategies, mass spectrometry and protein microarrays. Comparative/quantitative analysis of high-quality clinical biospecimen (e.g., tissue and biofluids) of human cancer proteome landscape has the potential to reveal protein/peptide biomarkers responsible for this disease by means of their altered levels of expression, post-translational modifications as well as different forms of protein variants. Despite technological advances in proteomics, major hurdles still exist in every step of the biomarker development pipeline. The National Cancer Institute's Clinical Proteomic Technologies for Cancer initiative (NCI-CPTC) has taken a critical step to close the gap between biomarker discovery and qualification by introducing a pre-clinical "verification" stage in the pipeline, partnering with clinical laboratory organizations to develop and implement common standards, and developing regulatory science documents with the US Food and Drug Administration to educate the proteomics community on analytical evaluation requirements for multiplex assays in order to ensure the safety and effectiveness of these tests for their intended use.

Molecularization in nutritional science: a view from philosophy of science

SCOPE

Over the past decade, a trend toward molecularization, which could be observed in almost all bioscientific disciplines, now appears to have also developed in nutritional science. However, molecular nutrition research gives birth to a series of questions. Therefore, we take a look at the epistemological foundation of (molecular) nutritional science.

METHODS AND RESULTS

We (i) analyze the scientific status of (molecular) nutritional science and its position in the canon of other scientific disciplines, (ii) focus on the cognitive aims of nutritional science in general and (iii) on the chances and limits of molecular nutrition research in particular. By taking up the thoughts of an earlier work, we are analyzing (molecular) nutritional science from a strictly realist and emergentist-naturalist perspective.

CONCLUSION

Methodologically, molecular nutrition research is bound to a microreductive research approach. We emphasize, however, that it need not be a radical microreductionism whose scientific reputation is not the best. Instead we favor moderate microreductionism, which combines reduction with integration. As mechanismic explanations are one of the primary aims of factual sciences, we consider it as the task of molecular nutrition research to find profound, i.e. molecular-mechanismic, explanations for the conditions, characteristics and changes of organisms related to the organism-nutrition environment interaction.

Integrating mechanistic and polymorphism data to characterize human genetic susceptibility for environmental chemical risk assessment in the 21st century

Response to environmental chemicals can vary widely among individuals and between population groups. In human health risk assessment, data on susceptibility can be utilized by deriving risk levels based on a study of a susceptible population and/or an uncertainty factor may be applied to account for the lack of information about susceptibility. Defining genetic susceptibility in response to environmental chemicals across human populations is an area of interest in the NAS' new paradigm of toxicity pathway-based risk assessment. Data from high-throughput/high content (HT/HC), including -omics (e.g., genomics, transcriptomics, proteomics, metabolomics) technologies, have been integral to the identification and characterization of drug target and disease loci, and have been successfully utilized to inform the mechanism of action for numerous environmental chemicals. Large-scale population genotyping studies may help to characterize levels of variability across human populations at identified target loci implicated in response to environmental chemicals. By combining mechanistic data for a given environmental chemical with next generation sequencing data that provides human population variation information, one can begin to characterize differential susceptibility due to genetic variability to environmental chemicals within and across genetically heterogeneous human populations. The integration of such data sources will be informative to human health risk assessment.

Combined PET/MRI: one step further in multimodality imaging

Given the need for sophisticated in vivo detection techniques to better characterize the cellular and subcellular processes in animals and humans, molecular imaging has become an important discipline. Techniques in molecular imaging have developed from stand alone modalities to multimodality methods. Among these, the combination of positron emission tomography (PET) and computed tomography (CT) is a successful imaging method and has become an important tool in clinical practice. Technological approaches that combine magnetic resonance imaging (MRI) with diffuse optical tomography (DOT), fluorescence tomography (FT) and PET have now been introduced. PET/MRI and the resulting combination of molecular, morphological and functional information will pave the way for a better understanding of physiological and disease mechanisms in preclinical and clinical settings.