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Clinical Cancer Research Vol. 12, 62-69, January 2006
© 2006 American Association for Cancer Research


Human Cancer Biology

Meta-analysis of the p53 Mutation Database for Mutant p53 Biological Activity Reveals a Methodologic Bias in Mutation Detection

Thierry Soussi1, Bernard Asselain2, Dalil Hamroun3, Shunsuke Kato4, Chikashi Ishioka4, Mireille Claustres3 and Christophe Béroud3

Authors' Affiliations: 1 Laboratoire de Génotoxicologie des tumeurs, UPMC, Dpt Pneumologie, Hôpital Tenon, 2 Service de Biostatistique, Section Médicale, Institut Curie, Paris, 3 Laboratoire de Génétique Moléculaire et Chromosomique, Institut Universitaire de Recherche Clinique et CHU, CNRS UPR 1142, Montpellier Cedex, France, and 4 Department of Clinical Oncology, Institute of Development, Aging, and Cancer, Tohoku University, Sendai, Japan

Requests for reprints: Thierry Soussi, Laboratoire de Génotoxicologie des Tumeurs, Dpt Pneumologie, UPMC, Hôpital Tenon, 4 rue de la Chine, 75019 Paris, France. Phone: 33-1-5601-6515; Fax: 33-1-5601-7248; E-mail: thierry.soussi{at}free.fr.

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.

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.

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.

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.




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I. G. Campbell, W. Qiu, K. Polyak, I. Haviv, C. S. Zander, T. Soussi, G. Zalcman, E. Bergot, P. Hainaut, D. H. Roukos, et al.
Breast-Cancer Stromal Cells with TP53 Mutations
N. Engl. J. Med., April 10, 2008; 358(15): 1634 - 1636.
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Copyright © 2006 by the American Association for Cancer Research.