The term P4 medicine is used to denote an evolving field of medicine that uses systems biology approaches and information technologies to enhance wellness rather than just treat disease. Its four components include predictive, preventive, personalized, and participatory medicine. In the current paper, it is argued that in order to fulfill the promise of P4 medicine, a “fifth P” must be integrated--the population perspective--into each of the other four components. A population perspective integrates predictive medicine into the ecologic model of health; applies principles of population screening to preventive medicine; uses evidence-based practice to personalize medicine; and grounds participatory medicine on the three core functions of public health: assessment, policy development, and assurance. Population sciences--including epidemiology; behavioral, social, and communication sciences; and health economics, implementation science, and outcomes research--are needed to show the value of P4 medicine. Balanced strategies that implement both population- and individual-level interventions can best maximize health benefits, minimize harms, and avoid unnecessary healthcare costs.
In 2012, the National Cancer Institute (NCI) engaged the scientific community to provide a vision for cancer epidemiology in the 21st century. Eight overarching thematic recommendations, with proposed corresponding actions for consideration by funding agencies, professional societies, and the research community emerged from the collective intellectual discourse. The themes are (i) extending the reach of epidemiology beyond discovery and etiologic research to include multilevel analysis, intervention evaluation, implementation, and outcomes research; (ii) transforming the practice of epidemiology by moving towards more access and sharing of protocols, data, metadata, and specimens to foster collaboration, to ensure reproducibility and replication, and accelerate translation; (iii) expanding cohort studies to collect exposure, clinical and other information across the life course and examining multiple health-related endpoints; (iv) developing and validating reliable methods and technologies to quantify exposures and outcomes on a massive scale, and to assess concomitantly the role of multiple factors in complex diseases; (v) integrating “big data” science into the practice of epidemiology; (vi) expanding knowledge integration to drive research, policy and practice; (vii) transforming training of 21st century epidemiologists to address interdisciplinary and translational research; and (viii) optimizing the use of resources and infrastructure for epidemiologic studies. These recommendations can transform cancer epidemiology and the field of epidemiology in general, by enhancing transparency, interdisciplinary collaboration, and strategic applications of new technologies. They should lay a strong scientific foundation for accelerated translation of scientific discoveries into individual and population health benefits.
big data; clinical trials; cohort studies; epidemiology; genomics; medicine; public health; technologies; training; translational research
Recent emphasis on translational research (TR) is highlighting the role of epidemiology in translating scientific discoveries into population health impact. The authors present applications of epidemiology in TR through 4 phases designated T1–T4, illustrated by examples from human genomics. In T1, epidemiology explores the role of a basic scientific discovery (e.g., a disease risk factor or biomarker) in developing a “candidate application” for use in practice (e.g., a test used to guide interventions). In T2, epidemiology can help to evaluate the efficacy of a candidate application by using observational studies and randomized controlled trials. In T3, epidemiology can help to assess facilitators and barriers for uptake and implementation of candidate applications in practice. In T4, epidemiology can help to assess the impact of using candidate applications on population health outcomes. Epidemiology also has a leading role in knowledge synthesis, especially using quantitative methods (e.g., meta-analysis). To explore the emergence of TR in epidemiology, the authors compared articles published in selected issues of the Journal in 1999 and 2009. The proportion of articles identified as translational doubled from 16% (11/69) in 1999 to 33% (22/66) in 2009 (P = 0.02). Epidemiology is increasingly recognized as an important component of TR. By quantifying and integrating knowledge across disciplines, epidemiology provides crucial methods and tools for TR.
epidemiology; genomics; medicine; public health; translational research
The recent success of genome-wide association studies in finding susceptibility genes for many common diseases presents tremendous opportunities for epidemiologic studies of environmental risk factors. Analysis of gene-environment interactions, included in only a small fraction of epidemiologic studies until now, will begin to accelerate as investigators integrate analyses of genome-wide variation and environmental factors. Nevertheless, considerable methodological challenges are involved in the design and analysis of gene-environment interaction studies. The authors review these issues in the context of evolving methods for assessing interactions and discuss how the current agnostic approach to interrogating the human genome for genetic risk factors could be extended into a similar approach to gene-environment-wide interaction studies of disease occurrence in human populations.
environment; epidemiologic methods; genetics; genomics
Cancer epidemiology is at the cusp of a paradigm shift--propelled by an urgent need to accelerate the pace of translating scientific discoveries into healthcare 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 towards translation in the past few years and are most likely to drive the field further in the next decade. The drivers include: (1) collaboration and team science; (2) technology; (3) multi-level analyses and interventions; and (4) 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.
cancer; epidemiology; medicine; public health; translational research
Remarkable progress has been made in the last decade in new methods for biological measurements using sophisticated technologies that go beyond the established genome, proteome, and gene expression platforms. These methods and technologies create opportunities to enhance cancer epidemiologic studies. In this article, we describe several emerging technologies and evaluate their potential in epidemiologic studies. We review the background, assays, methods, and challenges, and offer examples of the use of mitochondrial DNA and copy number assessments, epigenomic profiling (including methylation, histone modification, microRNAs (miRNAs), and chromatin condensation), metabolite profiling (metabolomics), and telomere measurements. We map the volume of literature referring to each one of these measurement tools and the extent to which efforts have been made at knowledge integration (e.g. systematic reviews and meta-analyses). We also clarify strengths and weaknesses of the existing platforms and the range of type of samples that can be tested with each of them. These measurement tools can be used in identifying at-risk populations and providing novel markers of survival and treatment response. Rigorous analytical and validation standards, transparent availability of massive data, and integration in large-scale evidence are essential in fulfilling the potential of these technologies.
Epigenetics; methylation; mitochondria; risk assessment; telomerase
To evaluate perceived risk, control, worry, and severity about diabetes, coronary heart disease (CHD) and stroke among individuals at increased familial risk of diabetes.
Data analyses were based on the Family Healthware™ Impact Trial. Baseline health beliefs were compared across three groups: (1) no family history of diabetes, CHD or stroke (n = 836), (2) family history of diabetes alone (n = 267), and (3) family history of diabetes and CHD and/or stroke (n = 978).
After adjusting for age, gender, race, education and BMI, scores for perceived risk for diabetes (p < 0.0001), CHD (p < 0.0001) and stroke (p < 0.0001) were lowest in Group 1 and highest in Group 3. Similar results were observed about worry for diabetes (p < 0.0001), CHD (p < 0.0001) and stroke (p < 0.0001). Perceptions of control or severity for diabetes, CHD or stroke did not vary across the three groups.
Among individuals at increased familial risk for diabetes, having family members affected with CHD and/or stroke significantly influenced perceived risk and worry. Tailored lifestyle interventions for this group that assess health beliefs and emphasize approaches for preventing diabetes, as well as its vascular complications, may be an effective strategy for reducing the global burden of these serious but related chronic disorders.
Family history; Health beliefs; Diabetes; Coronary heart disease; Stroke
We examined hospital use of the epidermal growth factor receptor (EGFR) assay for lung cancer patients. Our goal was to inform the development of a model to predict T3 translation of guideline-directed, molecular diagnostic tests.
This was a retrospective observational study. Using logistic regression, we analyzed the association between likelihood to order the EGFR assay and hospital’s institutional and regional characteristics.
Significant institutional predictors included: Affiliation with an academic medical center (odds ratio [OR], 1.48; 95% CI, 1.20–1.83), Participation in an NCI clinical research cooperative group (OR, 2.06, 1.66–2.55), PET scan (OR, 1.44, 1.07–1.94) and cardio thoracic surgery (OR, 1.90, 1.52–2.37) services. Significant regional predictors included: Metropolitan county (OR, 2.08, 1.48 to 2.91), Above average education (OR, 1.46, 1.09 to 1.96), Above average income (OR, 1.46, 1.04–2.05). Distance from an NCI cancer center was a negative predictor (OR, 0.996, 0.995–0.998), a 34% decrease in likelihood for every 100 miles.
In 2010, 12% of US acute care hospitals ordered the EGFR assay, suggesting most lung cancer patients did not have access to this test. This case study illustrated the need for: 1) Increased dissemination and implementation research. 2) Interventions to improve adoption of guideline-directed, molecular diagnostic tests by community hospitals.
equity; access; lung; cancer; genomics
Genome-wide association studies have identified multiple genetic susceptibility variants to several complex human diseases. However, risk-genotype frequency at loci showing robust associations might differ substantially among different populations. In this paper, we present methods to assess the contribution of genetic variants to the difference in the incidence of disease between different population groups for different scenarios. We derive expressions for the contribution of a single genetic variant, multiple genetic variants, and the contribution of the joint effect of a genetic variant and an environmental factor to the difference in the incidence of disease. The contribution of genetic variants to the difference in incidence increases with increasing difference in risk-genotype frequency, but declines with increasing difference in incidence between the two populations. The contribution of genetic variants also increases with increasing relative risk and the contribution of joint effect of genetic and environmental factors increases with increasing relative risk of the gene–environmental interaction. The contribution of genetic variants to the difference in incidence between two populations can be expressed as a function of the population attributable risks of the genetic variants in the two populations. The contribution of a group of genetic variants to the disparity in incidence of disease could change considerably by adding one more genetic variant to the group. Any estimate of genetic contribution to the disparity in incidence of disease between two populations at this stage seems to be an elusive goal.
difference in incidence; genetic variants; gene–environmental interaction; population attributable risks
In recent decades, extensive resources have been invested to develop cellular, molecular and genomic technologies with clinical applications that span the continuum of cancer care.
In December 2006, the National Cancer Institute sponsored the first workshop to uniquely examine the state of health services research on cancer-related cellular, molecular and genomic technologies and identify challenges and priorities for expanding the evidence base on their effectiveness in routine care.
This article summarizes the workshop outcomes, which included development of a comprehensive research agenda that incorporates health and safety endpoints, utilization patterns, patient and provider preferences, quality of care and access, disparities, economics and decision modeling, trends in cancer outcomes, and health-related quality of life among target populations.
Ultimately, the successful adoption of useful technologies will depend on understanding and influencing the patient, provider, health care system and societal factors that contribute to their uptake and effectiveness in ‘real-world’ settings.
Genomics; Health services research; Emerging technologies; Translational research
The Epidemiology and Genomics Research Program (EGRP) at the National Cancer Institute (NCI) is develop scientific priorities for cancer epidemiology research in the next decade. We would like to engage the research community and other stakeholders in a planning effort that will include a workshop, in December, 2012, to help shape new foci for cancer epidemiology research. To facilitate the process of defining the future of cancer epidemiology, we invite the research community to join in an ongoing Web-based conversation at http://blog-epi.grants.cancer.gov/ to develop priorities and the next generation of high-impact studies.
We propose guidelines to evaluate the cumulative evidence of gene–environment (G × E) interactions in the causation of human cancer. Our approach has its roots in the HuGENet and IARC Monographs evaluation processes for genetic and environmental risk factors, respectively, and can be applied to common chronic diseases other than cancer. We first review issues of definitions of G × E interactions, discovery and modelling methods for G × E interactions, and issues in systematic reviews of evidence for G × E interactions, since these form the foundation for appraising the credibility of evidence in this contentious field. We then propose guidelines that include four steps: (i) score the strength of the evidence for main effects of the (a) environmental exposure and (b) genetic variant; (ii) establish a prior score category and decide on the pattern of interaction to be expected; (iii) score the strength of the evidence for interaction between the environmental exposure and the genetic variant; and (iv) examine the overall plausibility of interaction by combining the prior score and the strength of the evidence and interpret results. We finally apply the scheme to the interaction between NAT2 polymorphism and tobacco smoking in determining bladder cancer risk.
Genetics; environment; interactions; evaluations
Advances in genomics and related fields promise a new era of personalized medicine in the cancer care continuum. Nevertheless, there are fundamental challenges in integrating genomic medicine into cancer practice. We explore how multilevel research can contribute to implementation of genomic medicine. We first review the rapidly developing scientific discoveries in this field and the paucity of current applications that are ready for implementation in clinical and public health programs. We then define a multidisciplinary translational research agenda for successful integration of genomic medicine into policy and practice and consider challenges for successful implementation. We illustrate the agenda using the example of Lynch syndrome testing in newly diagnosed cases of colorectal cancer and cascade testing in relatives. We synthesize existing information in a framework for future multilevel research for integrating genomic medicine into the cancer care continuum.
The clinical utility is uncertain for many cancer genomic applications. Comparative effectiveness research (CER) can provide evidence to clarify this uncertainty.
To identify approaches to help stakeholders make evidence-based decisions, and to describe potential challenges and opportunities using CER to produce evidence-based guidance.
We identified general CER approaches for genomic applications through literature review, the authors’ experiences, and lessons learned from a recent, seven-site CER initiative in cancer genomic medicine. Case studies illustrate the use of CER approaches.
Evidence generation and synthesis approaches include comparative observational and randomized trials, patient reported outcomes, decision modeling, and economic analysis. We identified significant challenges to conducting CER in cancer genomics: the rapid pace of innovation, the lack of regulation, the limited evidence for clinical utility, and the beliefs that genomic tests could have personal utility without having clinical utility. Opportunities to capitalize on CER methods in cancer genomics include improvements in the conduct of evidence synthesis, stakeholder engagement, increasing the number of comparative studies, and developing approaches to inform clinical guidelines and research prioritization.
CER offers a variety of methodological approaches to address stakeholders’ needs. Innovative approaches are needed to ensure an effective translation of genomic discoveries.
evidence synthesis; evidence generation; stakeholder; clinical utility
To test the association of family history of diabetes with the adoption of diabetes risk–reducing behaviors and whether this association is strengthened by physician advice or commonly known factors associated with diabetes risk.
RESEARCH DESIGN AND METHODS
We used cross-sectional data from the 2005–2008 National Health and Nutrition Examination Survey (NHANES) to examine the effects of family history of diabetes on the adoption of selected risk-reducing behaviors in 8,598 adults (aged ≥20 years) without diabetes. We used multiple logistic regression to model three risk reduction behaviors (controlling or losing weight, increasing physical activity, and reducing the amount of dietary fat or calories) with family history of diabetes.
Overall, 36.2% of U.S. adults without diabetes had a family history of diabetes. Among them, ~39.8% reported receiving advice from a physician during the past year regarding any of the three selected behaviors compared with 29.2% of participants with no family history (P < 0.01). In univariate analysis, adults with a family history of diabetes were more likely to perform these risk-reducing behaviors compared with adults without a family history. Physician advice was strongly associated with each of the behavioral changes (P < 0.01), and this did not differ by family history of diabetes.
Familial risk for diabetes and physician advice both independently influence the adoption of diabetes risk–reducing behaviors. However, fewer than half of participants with familial risk reported receiving physician advice for adopting these behaviors.
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 treatments. 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.
cancer; genetics; genomics; medicine; population sciences; public health; translation
Genome-wide association studies (GWAS) have successfully identified numerous genetic loci that are associated with phenotypic traits and diseases. GWAS Integrator is a bioinformatics tool that integrates information on these associations from the National Human Genome Research institute (NHGRI) Catalog, SNAP (SNP Annotation and Proxy Search), and the Human Genome Epidemiology (HuGE) Navigator literature database. This tool includes robust search and data mining functionalities that can be used to quickly identify relevant associations from GWAS, as well as proxy single-nucleotide polymorphisms (SNPs) and potential candidate genes. Query-based University of California Santa Cruz (UCSC) Genome Browser custom tracks are generated dynamically on the basis of users' selected GWAS hits or candidate genes from HuGE Navigator literature database (http://www.hugenavigator.net/HuGENavigator/gWAHitStartPage.do). The GWAS Integrator may help enhance inference on potential genetic associations identified from GWAS studies.
genome-wide association studies; database; bioinformatics
In the field of cancer, genetic association studies are among the most active and well-funded research areas, and have produced hundreds of genetic associations, especially in the genome-wide association studies (GWAS) era. Knowledge synthesis of these discoveries is the first critical step in translating the rapidly emerging data from cancer genetic association research into potential applications for clinical practice. To facilitate the effort of translational research on cancer genetics, we have developed a continually updated database named Cancer Genome-wide Association and Meta Analyses database that contains key descriptive characteristics of each genetic association extracted from published GWAS and meta-analyses relevant to cancer risk. Here we describe the design and development of this tool with the aim of aiding the cancer research community to quickly obtain the current updated status in cancer genetic association studies.
cancer; meta-analyses; pooled analyses; GWAS
Genetic prediction of common diseases is based on testing multiple genetic variants with weak effect sizes. Standard logistic regression and Cox Proportional Hazard models that assess the combined effect of multiple variants on disease risk assume multiplicative joint effects of the variants, but this assumption may not be correct. The risk model chosen may affect the predictive accuracy of genomic profiling. We investigated the discriminative accuracy of genomic profiling by comparing additive and multiplicative risk models. We examined genomic profiles of 40 variants with genotype frequencies varying from 0.1 to 0.4 and relative risks varying from 1.1 to 1.5 in separate scenarios assuming a disease risk of 10%. The discriminative accuracy was evaluated by the area under the receiver operating characteristic curve. Predicted risks were more extreme at the lower and higher risks for the multiplicative risk model compared with the additive model. The discriminative accuracy was consistently higher for multiplicative risk models than for additive risk models. The differences in discriminative accuracy were negligible when the effect sizes were small (<1.2), but were substantial when risk genotypes were common or when they had stronger effects. Unraveling the exact mode of biological interaction is important when effect sizes of genetic variants are moderate at the least, to prevent the incorrect estimation of risks.
discriminative accuracy; genomic profiles; sensitivity; specificity; additive models