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De la farmacogenética a la farmacogenómica. Javier Benitez Programa Genética del Cáncer Humano Centro Nacional Investigaciones Oncológicas Madrid Junio 06. Antitumoral treatments. They are agressives, inspecifics and with a limited therapeutic margin.
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De la farmacogenética a la farmacogenómica Javier Benitez Programa Genética del Cáncer Humano Centro Nacional Investigaciones Oncológicas Madrid Junio 06
Antitumoral treatments • They are agressives, inspecifics and with a limited therapeutic margin. • Risk of toxicity , treatment failure or even death • - Wide interpatient variability in effects
Antitumoral treatments II • Children with ALL. 75% get total remision (cure) • 25% with treatment failure and/or severe toxicity • Sarcomas: 50-75% long term survival (cure) • Cases with severe toxicity CNS and GUS • - Breat cancer: Tamoxifen for ER and PR positive tumors (50%) • Secondary effects in some patients: Uterine cancer, tromboembolims
Genetic bases of farmacological response • - Genetic variability might explain many of these situations. • Their study could lead to individualised treatment and new drug developments. • Pharmacogenetics: It studies candidate genes • Pharmacogenomics: It describes a broader strategy to identify many genes that are relevant to the pharmacological effects of a given medication. It is based in a targeted (candidate pathways) or whole genome analysis.
Enzimatic activity of TPMT-6MP according to genotype Cheok et al, Nat Review 2006
Correlation between TPMT genotype and 6MP toxicity Cheok et al, Nat Review 2006
Allelic frequencies in Spanish Population (www.bioinfo.cnio.es) 40 SNPs from 14 genes No differences with other populations More genes under study 100 patients with ALL MTFR and MTX G238C G460A A719C TPMT and 6MP (http://bioinfo.cnio.es/cgi-bin/cegen/frequencies.cgi)
Strategy II. MTX pathway (folate analogue) 1 1- Entry 2- Degradation 3- target 4- metabolyze 5-…………….. …………. They study 32 genes from this pathway and identify some of them associated to MTX resistence. They found differences among ALL subtypes. 2 3 4 Kager et al. J Clin Invest 2005
Strategy III. Genome Wide Approach Global gene expression profiling using DNA microarrays can identify: - genes with levels of expression that are related to drug response. - New drug targets It is a complementary strategy to the identification of SNPs in genes that alter protein function and drug response.
Median OS = 10 months >10 m <10 m P=0.0001 LB PTCL Expression Profiling of T-Cell Lymphomas Differentiates Peripheral and Lymphoblastic Lymphomas and Defines Survival Related Genes Treatment response/ survival Genetic signature: 6 genes NFkB 165 genes differenciate both groups Martinez Delgado et al.Clin. Cancer Res, 2004.
CYP3A4 CYP3A7 CYP51 CYP8B1 A cluster of CYP3As genes is associated with evolution Martinez Delgado et al. Leukemia 2005
Log Rank p=0.001 Expression of CYP3A4 is associated to survival of PTCLs Normalized CYP3A4 expression PTCLs • CYP3A4 is an important drug metabolizing enzyme, CYP3A4 expression in tumors could then be mediating the response to chemotherapy. • Detection of CYP3A4 expression could have clinical interest by identifying tumors more resistant to chemotherapy at the time of diagnosis. An alternative treatment? Martinez Delgado et al. (in preparation)
Periferal T-cell lymphomas: HSP90 as drug target HSP90 is a chaperone HSP family inhibits apoptotic pathways Overexpression of HSP90 - bad prognosis 1 2 3 4 Genes correlated to proliferation not specifically related to cell cycle regulation: HSP90 5 PROLIFERATION 6 • Inhibitors of HSP90 (17AAG) under study • No effect in normal lymphocytes • Good responsein peripheral T cell lines • Marta Cuadros et al. In preparation 7 8 9 10
Conclusions • Pharmacogenetics is starting to be introduced and applied in the clinical practice (6MP; MTX.....) • The study of the response based on multiple genes (polygenic model) is now underway • Pharmacogenomics permits the identification of new therapeutic targets and groups of genes that modulate the pharmacological response. • It is still necessary to validate data. Problems with population variability, techniques, platforms etc....
Acknowledgements Genotyping Lab Human Genetics Lab: Lara P. Fernandez Eva Barroso Goria Ribas Beatriz Martinez Marta Cuadros CeGen Madrid Genotyping Lab: Emilio Gonzalez Roger Milner Ana Gonzalez Charo Jesus Mari Endocrine Group: Mercedes Robledo Fátima Mercadillo Cristina Rodriguez