Somatic mutations in the p53 tumour suppressor gene are found in many human cancers. In addition, germline p53 mutations have been identified in individuals from cancer-prone families and in isolated cancer patients affected at a young age or suffering from multiple tumours. A large fraction of the cancer-prone families with germline p53 mutation conform to the criteria of Li-Fraumeni syndrome (LFS), a rare familial autosomal dominant cancer syndrome characterised by early-onset sarcomas, brain tumours, premenopausal breast cancer, leukaemias and adrenocortical tumours. Individuals carrying germline p53 mutations have a very broad spectrum of clinical manifestations in terms of penetrance, tumour type, tumour location and age of onset. The collection of a large set of data is thus necessary to establish possible correlations between the type and location of a germline p53 mutation and its phenotypic consequences. Such genotype-phenotype correlations may in turn improve the counselling and preventive approaches in the affected families. Reports of germline p53 mutations have accumulated rapidly since 1990, when their association with LFS and increased cancer susceptibility was made. Because the currently available databases of p53 gene mutations either exclude germline mutations or contain incomplete data, we created a comprehensive database of those cases of germline p53 mutations for which sufficient detail is given in the literature. In addition to listing all mutations, the database includes detailed information about the families, affected individuals and their tumours. It therefore provides a powerful means for drawing correlations between various aspects of germline p53 mutations. The database describes each p53 mutation (type of the mutation, exon and codon affected by the mutation, nucleotide and amino acid change), each family (family history of cancer, diagnosis of LFS), each affected individual (sex, generation, p53 status, from which parent the mutation was inherited) and each tumour (type, age of onset, p53 status (loss of heterozygosity and immunostaining)). Each entry contains the original reference(s). Individuals affected by cancer who were experimentally shown not to carry a germline p53 mutation are not listed. Affected individuals belonging to a branch of the pedigree where a germline p53 mutation was excluded (phenocopies) are also not included. The current (August 1999) version of the database lists 697 tumours from 542 individuals belonging to 141 independent pedigrees with germline p53 mutations. The database is updated every four months. It is freely available and can be accessed via the World Wide Web at http://www.lf2.cuni.cz/homepage.htm. It is in Excel 97 format and can be loaded as Excel file or tab delimited text file. The legend to the database can be loaded as Word 97 file or plain text file.
Sedlacek Z., Kodet R., Poustka A., Goetz P. (1998) A database of germline p53 mutations in cancer-prone families. Nucleic Acids Res. 26: 214-215
Somatic mutations in the tumor suppressor gene TP53 are frequent in most human cancers and germline TP53 mutations are associated with a rare cancer-prone syndrome, the Li-Fraumeni syndrome. Over the past ten years, several databases of TP53 mutations have been developed. The most extensive of these databases is maintained and developed at the International Agency for Research on Cancer. The IARC TP53 Database (http://www.iarc.fr/p53) compiles data on human somatic and germline TP53 genetic variations that are reported in the peer-reviewed literature. With over 18,500 somatic and 225 germline mutations and 1,000 citations in the world literature, this database is now recognized as a major source of information on TP53 mutation patterns in human cancer. It can be searched and analyzed online and is useful to draw hypotheses on the nature of the molecular events involved in TP53 mutagenesis and on the natural history of cancer. The database is available at http://www.iarc.fr/P53/ .
Recent develoments :
Recent developments include:
- restructuring of the database which is now patient-centered, with more detailed annotations on the patient (carcinogen exposure, virus infection, genetic background).
- data on mutation prevalence (R6 update) and on clinical outcome (next update).
- an online search system that allows the online analysis of somatic mutation data (available through the 'database search' option). This ASP (Active Server Pages) application allows the identification and selection of specific sets of data according to user's queries and produces graphical outputs (histograms and pie charts) of mutation patterns, codon distribution and tumor spectrum. A search of reference criteria (author name, PubMed entry, title, etc...) allows the analysis of mutation data by individual publication to generate graphs and figures.
- the entire dataset, or sets of data selected according to the user's queries, can be downloaded as tab-delimited text files, in a compressed format.
- a comprehensive user guide is available online as well as a slide-show on TP53 mutations, database structure/content and examples of mutation analysis.
The project is funded by IARC and supported by the European Community (contract: QLG1-1999-00273).
1. Olivier M, Eeles R, Hollstein M, Khan MA, Harris C.C, Hainaut P. The IARC TP53 Database: new online mutation analysis and recommendations to users. Hum Mutat 2002 Jun;19(6):607-14
2. Hernandez-Boussard T, Montesano R, Hainaut P. Sources of bias in the detection and reporting of p53 mutations in human cancer: analysis of the IARC p53 mutation database. Genet Anal. 1999;14(5-6):229-33.
Databases and software applications for the analysis of DNA mutations at the human p53 gene, the human hprt gene and both the rodent transgenic lacI and lacZ locus have been created. The number of mutations in each database is as follows: p53 - 5,900; hprt - 2,500; lacI - approximately 1,500 transgenic and 8,000 bacterial; lacZ - approximately 200. The databases themselves are stand-alone dBASE files and the software for analysis of the databases runs on IBM-compatible computers running Microsoft Windows. Each database has a separate software analysis program. The software created for these databases permit the filtering, ordering, report generation and display of information in the database. In addition, a significant number of routines have been developed for the analysis of single base substitutions. The databases and software can be obtained at: http://metalab.unc.edu/dnam/mainpage.html Each database is in the dBASE format and is present as a stand-alone file. Information common to all databases includes (i) base position, (ii) the nature of the mutation, (iii) amino acid position, (iv) wt and mutant amino acid, (v) the local sequence around a mutation and (vi) literature citation. Information specific to the p53 database includes (i) cancer type, (ii) cell origin (tumor, cell line, etc.) and (iii) loss of heterozygosity. Data particular to the hprt database includes (i) mutagen, (ii) dose, (iii) background and induced mutation frequencies, (iv) information whether the mutant was generated in vivo or in vitro, (v) mRNA splicing information for mutants affecting splicing and (vi) cell type. Data contained in the lacZ transgenic database includes (i) dose, (ii) time from last treatment to animal sacrifice, (iii) supplier, species, strain, sex and age of animal, (iv) the organs selected for mutation analysis, (v) the mutant fraction in each organ, (vi) the total PFU analyzed and (vii) the plaque color. Data in the lacI database are very similar to the lacZ database. A significant number of routines have been developed for the analysis of single base substitutions, including programs to (i) determine if two mutational spectra are different, (ii) display mutable amino acids in the protein, (iii) determine if mutations show a DNA strand bias, (iv) determine the frequency of transitions and transversions, (v) display the number and kind of mutations observed at each base in the coding region and (vi) perform nearest neighbor analysis. For genes with exons, a routine will display the number of mutations and mutable sites in each exon. Graphics displays are available for mutated amino acids and for mutational spectra representation.
The website of the Ohio State University Human Cancer Genetics Bioinformatics group. This site has many resources, including databases of promoters and transcription factors, software tools to predict potential P53 consensus binding sites and to predict first exon and promoter regions and a software toolkit for developing web-based applications to view genomic data.