Meta-analysis of the p53 Mutation Database for Mutant p53 Biological Activity Reveals a Methodologic Bias in Mutation Detection
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- Thierry Soussi
- 1Laboratoire de Génotoxicologie des tumeurs, UPMC, Dpt Pneumologie, Hôpital Tenon,
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- Bernard Asselain
- 2Service de Biostatistique, Section Médicale, Institut Curie, Paris,
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- Dalil Hamroun
- 3Laboratoire de Génétique Moléculaire et Chromosomique, Institut Universitaire de Recherche Clinique et CHU, CNRS UPR 1142, Montpellier Cedex, France, and
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- Shunsuke Kato
- 4Department of Clinical Oncology, Institute of Development, Aging, and Cancer, Tohoku University, Sendai, Japan
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- Chikashi Ishioka
- 4Department of Clinical Oncology, Institute of Development, Aging, and Cancer, Tohoku University, Sendai, Japan
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- Mireille Claustres
- 3Laboratoire de Génétique Moléculaire et Chromosomique, Institut Universitaire de Recherche Clinique et CHU, CNRS UPR 1142, Montpellier Cedex, France, and
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- Christophe Béroud
- 3Laboratoire de Génétique Moléculaire et Chromosomique, Institut Universitaire de Recherche Clinique et CHU, CNRS UPR 1142, Montpellier Cedex, France, and
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<jats:title>Abstract</jats:title> <jats:p>Purpose: Analyses of the pattern of p53 mutations have been essential for epidemiologic studies linking carcinogen exposure and cancer. We were concerned by the inclusion of dubious reports in the p53 databases that could lead to controversial analysis prejudicial to the scientific community.</jats:p> <jats:p>Experimental Design: We used the universal mutation database p53 database (21,717 mutations) combined with a new p53 mutant activity database (2,300 mutants) to perform functional analysis of 1,992 publications reporting p53 alterations. This analysis was done using a statistical approach similar to that of clinical meta-analyses.</jats:p> <jats:p>Results: This analysis reveals that some reports of infrequent mutations are associated with almost normal activities of p53 proteins. These particular mutations are frequently found in studies reporting multiple mutations in one tumor, silent mutations, or lacking mutation hotspots. These reports are often associated with particular methodologies, such as nested PCR, for which key controls are not satisfactory.</jats:p> <jats:p>Conclusions: We show the importance of accurate functional analysis before inferring any genetic variation. The quality of the p53 databases is essential in order to prevent erroneous analysis and/or conclusions. The availability of functional data from our new p53 web site (http://p53.free.fr and http://www.umd.be:2072/) will allow functional prescreening to identify potential artifactual data.</jats:p>
収録刊行物
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- Clinical Cancer Research
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Clinical Cancer Research 12 (1), 62-69, 2006-01-01
American Association for Cancer Research (AACR)