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Clinical, genomic and family data integration through ontologies to knowledge discovery on Li-Fraumeni syndrome

Grant number: 12/20390-5
Support type:Regular Research Grants
Duration: February 01, 2013 - January 31, 2015
Field of knowledge:Biological Sciences - Genetics - Human and Medical Genetics
Principal Investigator:Maria Isabel Alves de Souza Waddington Achatz
Grantee:Maria Isabel Alves de Souza Waddington Achatz
Home Institution: A C Camargo Cancer Center. Fundação Antonio Prudente (FAP). São Paulo , SP, Brazil
Assoc. researchers:Erika Maria Monteiro Santos ; Renata Wassermann

Abstract

Li-Fraumeni Syndrome (LFS) is a autosomal dominant hereditary disease, characterized by the high risk of multiple occurrence of cancer with early onset (mainly sarcoma, adrenocortical carcinoma, breast cancer and central nervous system tumors). It is associated to germline mutations on the TP53 tumor suppressor gene that codes a transcription factor responsible for regulating cell proliferation, genomic stability and apoptosis. In order to find individuals, based on clinical data, likely to have carcinogenic mutations on this gene, several criteria have been created since LFS was first described in 1969 (Li & Fraumeni, 1969). In a study (Gonzalez et al., 2009) in which these criteria performance predicting TP53 mutations were evaluated discovered that these criteria have high specificity and low sensibility.Hospital A.C. Camargo, since its foundation in 1953, have attended more than 800,000 patients. The Oncogenetics department started in 2000, and until now more than 600 families have attended consultations in this group. LFS was clinnically diagnosed in 130 gamilies, 33 with pathogenic germline mutations on TP53. All consultations are recorded in a computer database developed by the Medical Informatics group, including detailed pedigree and medical history of proband and family members.We aim to organize and provide data for researchers in an ontology-based database integration system, using open ontologies, in a way it will allow information to be updated and new sources to be added. A data navigation and extraction tool will be developed in order to grant easy access for researchers. In order to further evaluate this tool, we plan to extract all data available and perform statistic analysis in order to find, at least, the variables that compose the classical LFS. (AU)