| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
Editorial |
Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NIH, Rockville, Maryland
Requests for reprints: Neil E. Caporaso, Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NIH, 6120 Executive Boulevard, EPS 7116, Rockville, MD 20892. E-mail: caporasn{at}exchange.nih.gov
The ascendancy of the molecular epidemiology approach (1, 2), simply defined as including biomarkers in population-based study designs, is clear by any survey of current population studies. Most scientists would agree that embedding advanced technologies in molecular epidemiology designs will be key to achieving breakthroughs. Given the increasingly central role of molecular epidemiology in unraveling major questions in cancer, there is a compelling argument to expand its scope to include two areas that are often absent, namely, behavior and outcome. Integrative epidemiology refers to population-based study designs incorporating information or biomarkers that add the behavior and outcome ‘wings’ to the traditional/familiar molecular epidemiology paradigm that extends from exposure to disease (see Fig. 1 , integrative epidemiology).
|
Second, with respect to outcome, molecular epidemiology emphasizes cancer incidence with the investigation of outcomes relegated to clinical trials, where unfortunately, an emphasis on exposure, genetics, and biomarkers has, until recently, often been absent. Heredity influences treatment response and toxicity, and at least a few genes influence current therapy (12) and specifically cancer therapy (12). It is widely anticipated that genomics research (integrated with proteomics and expression) will eventually enhance risk stratification by genetic or biomarker defined subsets, with the potential for targeted prevention and therapy (i.e., personalized medicine; ref. 13). Spitz has used the term integrative epidemiology (14) to advocate this approach, emphasizing that selected genes/markers may influence both risk and outcome, highlighting glutathione S-transferase family genes, cyclin D1, and matrix metalloproteinase 1, as examples. How common such associations are is unknown, but as we currently lack a comprehensive understanding of the common modifier genes for any major human cancer (15), it would seem clear that if both disease and outcome can be investigated using the same technology, biospecimens, and study platform, such an effort could be both informative and efficient.
There are two central advantages of integrative epidemiology. The first is that a given study platform can accommodate a greatly expanded group of investigations. Epidemiologists traditionally emphasize well-defined exposure and disease measures in their studies, along with the collection of biospecimens. Given this baseline commitment, adding the relevant behavior and outcome components is efficient, as often minor adjustments to existing designs (i.e., questionnaires, biospecimens, collection protocols, informed consents, etc.) may capture large new classes of information. Even when more substantial resources are required, the overall effort will be far less then fielding a de novo study. Epidemiologists traditional focus on data quality will enhance interpretation. The application of diverse high-technology approaches (genomics, proteomics, etc.) along with clinical and epidemiologic data, and tissue resources is expected to provide the molecular tools to both elucidate mechanism and refine risk and response (16, 17).
The second advantage is that integrative epidemiology allows investigation of characteristics (genes, biomarkers, etc.) that span more than one study feature (i.e., a gene with pleiotropic effects related to both disease and outcome as noted above) or behavior and exposure. Tobacco smoking is related to lung cancer and smokers who persist after diagnosis have increased second tumors and a poorer outcome (18). Isolated investigations will not reveal the actual complex roles of pleiotropic genes relevant to cancer without integrative epidemiology studies that include the relevant study domains (i.e., disease, outcome, etc.). The additional data will also permit more sophisticated analysis to sort out causal pathways in the presence of mediator variables (19) and interaction (20).
Arguments against such approaches generally cite cost and complexity, in part, because integrative studies require participation of diverse disciplines with resulting large study teams. Both meta-analyses and recent findings from large scans (21) indicate that relative risks of susceptibility genes for common cancers are modest, so substantial sample size is mandatory to detect weak genetic signals, to achieve power to detect gene-environment and gene-gene effects, and to investigate subgroups. Accordingly, consortia have formed or are planned for virtually every major disease (22) and it is clear that team science featuring interdisciplinary teams will be an increasingly prominent trend (23). It follows that the increased cost will mandate that available efficiencies be applied to maximize the science. If we consider each study domain (behavior, outcome, exposure, genetics, disease, etc.) to be a ‘node’, and connections between nodes reflect potential areas for scientific investigation, adding behavior and outcome clearly enhances scientific opportunities (i.e., increases the number of nodes) for a given study platform. Efficiency also improves with larger studies [i.e., the absolute cost of an integrative epidemiology study is greater but the marginal cost (per unit of information) is lower].
In conclusion, integrated designs that incorporate behavior and outcome data into population-based studies will help fully realize the benefits anticipated for molecular epidemiology. As we commit large resources to conducting high-technology studies, platforms that incorporate these elements will provide an enhanced scientific payoff.
 |
References |
|---|
 Top References |
|---|
This article has been cited by other articles:
|
|
 |
|
  S. Wacholder, J. K. Loukissas, and P. Hartge Studies of Genes and Cancer Survival: Pieces of the Puzzle J Natl Cancer Inst, June 20, 2007; 99(12): 908 - 909. [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Cancer Research | Clinical Cancer Research |
| Cancer Epidemiology Biomarkers & Prevention | Molecular Cancer Therapeutics |
| Molecular Cancer Research | Cancer Prevention Research |
| Cancer Prevention Journals Portal | Cancer Reviews Online |
| Annual Meeting Education Book | Meeting Abstracts Online |
