Variation in Ara-C Pathway Genes and Treatment Outcomes in AML

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DESCRIPTION (provided by applicant): The ultimate goal of this research is to define the role of genetic variation in the genes involved in ara-C transport and biotransformation in determining the efficacy and toxicity of treatment of acute myelogenous leukemia (AML). The aims of this initial proposal are 1) to identify and characterize genetic variation in the human equilibrative nucleoside transporter (ENT1), deoxycytidine kinase (CDK), cytosolic 5'-nucleotidase II (NT5C2) and cytidine deaminase (CDA) genes and assess the impact of this variation on gene expression in vitro and in vivo; 2) to determine if specific haplotypes in patient ENT1, DCK, NT5C2 and CDA genes predict overall survival in children and adolescents undergoing chemotherapy for de novo AML and 3) to determine if specific haplotypes at these loci is associated with the risk of mucosal toxicity, myelosuppression and infection in these patients. The objective is to identify relatively common variation in the population that may be useful in predicting response in a significant fraction of the patient population. This exploratory R21 proposal (in response to PA-03-064, replaced by PA-05-062) will study genetic variation in four key enzymes in ara-C metabolic pathway and perform correlative analyses between selected haplotypes and genotypes of the individual gene versus treatment outcomes in a large cohort of young patients with AML treated on a single cooperative group phase III trial. This work should lead to a larger-scale R01 study to also examine other enzymes in ara-C pathway as well as test salient findings emerging from this study in replicate studies of pediatric and adult patients with AML. Lay summary: New drug treatment and bone marrow transplantation have greatly improved the survival of children with acute myelogenous leukemia. However, there is a great deal of variation among patients in terms of treatment outcomes and the toxic side effects of treatment. The ultimate goal is to use this information prospectively to optimize individual therapy to maximize response and minimize adverse drug reactions.