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Knowledge Integration in Cancer: Current Landscape and Future Prospects

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Date 2012 Oct 25
PMID 23093546
Citations 7
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Abstract

Knowledge integration includes knowledge management, synthesis, and translation processes. It aims to maximize the use of collected scientific information and accelerate translation of discoveries into individual and population health benefits. Accumulated evidence in cancer epidemiology constitutes a large share of the 2.7 million articles on cancer in PubMed. We examine the landscape of knowledge integration in cancer epidemiology. Past approaches have mostly used retrospective efforts of knowledge management and traditional systematic reviews and meta-analyses. Systematic searches identify 2,332 meta-analyses, about half of which are on genetics and epigenetics. Meta-analyses represent 1:89-1:1162 of published articles in various cancer subfields. Recently, there are more collaborative meta-analyses with individual-level data, including those with prospective collection of measurements [e.g., genotypes in genome-wide association studies (GWAS)]; this may help increase the reliability of inferences in the field. However, most meta-analyses are still done retrospectively with published information. There is also a flurry of candidate gene meta-analyses with spuriously prevalent "positive" results. Prospective design of large research agendas, registration of datasets, and public availability of data and analyses may improve our ability to identify knowledge gaps, maximize and accelerate translational progress or-at a minimum-recognize dead ends in a more timely fashion.

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References
1.
Kyzas P, Denaxa-Kyza D, Ioannidis J . Almost all articles on cancer prognostic markers report statistically significant results. Eur J Cancer. 2007; 43(17):2559-79. DOI: 10.1016/j.ejca.2007.08.030. View

2.
Zhang L, Zhou J, Wang J, Liang G, Li J, Zhu Y . Absence of association between N-acetyltransferase 2 acetylator status and colorectal cancer susceptibility: based on evidence from 40 studies. PLoS One. 2012; 7(3):e32425. PMC: 3293792. DOI: 10.1371/journal.pone.0032425. View

3.
Ioannidis J . Why most published research findings are false. PLoS Med. 2005; 2(8):e124. PMC: 1182327. DOI: 10.1371/journal.pmed.0020124. View

4.
Gogele M, Minelli C, Thakkinstian A, Yurkiewich A, Pattaro C, Pramstaller P . Methods for meta-analyses of genome-wide association studies: critical assessment of empirical evidence. Am J Epidemiol. 2012; 175(8):739-49. DOI: 10.1093/aje/kwr385. View

5.
Andre F, McShane L, Michiels S, Ransohoff D, Altman D, Reis-Filho J . Biomarker studies: a call for a comprehensive biomarker study registry. Nat Rev Clin Oncol. 2011; 8(3):171-6. DOI: 10.1038/nrclinonc.2011.4. View