This laboratory study is looking at DNA in tumor tissue samples from young patients with acute lymphoblastic leukemia. DNA analysis of tumor tissue may help doctors predict how well patients will respond to treatment
PRIMARY OBJECTIVE: I. To validate significant associations between SNPs and treatment outcome and toxicity on Children's Cancer Group (CCG)-1891 on an independent sample set from a successor CCG study for standard risk acute lymphoblastic leukemia (ALL), CCG-1952. II. To evaluate the role of SNPs in drug metabolizing enzymes and the development of veno-occlusive disease in patients on CCG-1952. III. To evaluate interactions among genotypes and other risk factors for treatment response in a combined data set of CCG-1891 and CCG-1952 with recently developed analytic tools for high dimensional data. IV. To develop predictive models utilizing genetic information obtained in Aim 1.1 and clinical data to predict treatment response and toxicity. OUTLINE: Tumor tissue samples undergo genotype assessment on the Pyrosequencing platform. Contingency tables and X\^2 test performs a univariate analysis of the risk of relapse and genotype, and multivariable analyses using logistic regression. Cox proportional hazards evaluate the risk of relapse given genotype and other confounders. Genotype patterning, classification and regression trees, and multifactor dimensionality reduction evaluates for patterns of single nucleotide polymorphisms associated with toxicity and relapse risk.
Study Type
OBSERVATIONAL
Enrollment
520
Correlative studies
Childrens Oncology Group
Philadelphia, Pennsylvania, United States
Leukemia Relapse
Contingency tables will be used to tabulate the relationship between relapse and genotype, race, leukemia cytogenetics, day 7 bone marrow status, and treatment arm
Time frame: Day 7
Development of veno-occlusive disease in patients on CCG-1952
Classification and Regression Trees (CART), genotype patterning, Multifactor Dimensionality Reduction (MDR) techniques will be used to identify SNP combinations associated with risk of relapse and VOD
Time frame: Day 28
Development of a predictive model of leukemia relapse
Predictive models will be developed utilizing genetic information obtained in Aim 1.1 and clinical data to predict treatment response
Time frame: Day 28
Development of a predictive model of leukemia toxicity
Predictive models will be developed utilizing genetic information obtained in Aim 1.1 and clinical data to predict treatment toxicity.
Time frame: Day 28
Development of grade III/IV toxicity as defined by the CCG toxicity criteria
Contingency tables will be used to tabulate categorical toxicities and toxicity severity grade.
Time frame: Day 28
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