GENETIC PREDICTORS OF LEUKEMIA THERAPY RESPONSE

描述（由申请人提供）：儿科急性淋巴细胞白血病（ALL）是最常见的儿科癌症。虽然许多儿童通过风险分层治疗治愈，但很大一部分复发或经历治疗相关的毒性。我们假设ALL治疗反应是一种复杂的性状，可能部分由常见的基因型变异解释。本项目将在两项标准风险ALL的国家随机临床试验（CCG-1891和CCG-1952）中评估基因型变异与治疗结果之间的关联。这项研究有四个目标。第一个目的是测试多态性的影响，涉及甲氨蝶呤（MTX）的影响，在CCG-1891样本集的治疗结果。第二个目的是验证CCG-1952样本集中CCG-1891样本集中的关联。第三个目标是将分析基因-基因相互作用的新方法扩展并应用于CCG-1891和CCG-1952的组合样本集。第四个目标是开发一个包括基因型数据的ALL复发风险预测模型。我们先前的工作已经在CCG-1891样本集中证明，MTHFR C677 T变异纯合子患者的复发率增加。我们推测，其他基因多态性介导的MTX效应将修改复发和毒性风险。其次，我们假设在CCG-1891中观察到的显著关联将在CCG-1952中复制。第三，我们假设递归分区（PRP）模式将允许识别预测复发和毒性的多态性组。第四，我们假设基因型数据将提高复发风险预测模型的临床实用性。我们建议对120例复发患者和360例CCG-1891持续缓解（CR）患者以及200例复发患者和600例CCG-1952 CR患者进行巢式病例对照研究来验证这些假设。该应用程序将识别和验证改变ALL治疗结果的多态性，并将严格评估基因型数据中捕获的其他预测信息。

DESCRIPTION (provided by applicant): Pediatric acute lymphoblastic leukemia (ALL) is the most common pediatric cancer. Although many children are cured by risk stratified therapy, a significant portion either relapse or experience therapy related toxicity. We hypothesize that ALL treatment response is a complex trait which may be partially explained by common genotypic variants. This project will evaluate the association between genotypic variants and therapy outcome on two national randomized clinical trials (CCG-1891 and CCG-1952) of standard risk ALL. This study has four aims. The first aim is to test the impact of polymorphisms, involved in methotrexate (MTX) effect, on treatment outcome in the CCG-1891 sample set. The second aim is to validate associations seen in the CCG-1891 sample set in the CCG-1952 sample set. The third aim is to extend and apply new methods for the analysis of gene-gene interactions to a combined sample set of CCG-1891 and CCG-1952. The fourth aim is to develop a predictive model of ALL relapse risk that includes genotype data. Our prior work has demonstrated in the CCG-1891 sample set that patients homozygous for the MTHFR C677T variant have an increased rate of relapse. We hypothesize that other polymorphisms in the genes mediating MTX effect will modify relapse and toxicity risk. Second, we hypothesize that significant associations seen in CCG-1891 will replicate in CCG-1952. Third, we hypothesize that patterning with recursive partitioning (PRP) will allow identification of polymorphism groups that predict relapse and toxicity. Fourth, we hypothesize that genotype data will improve the clinical utility of predictive models of relapse risk. We propose to test these hypotheses with a nested case control study of 120 relapse patients and 360 patients in continuous remission (CR) on CCG-1891, and of 200 relapse patients and 600 patients in CR on CCG-1952. This application will identify and validate polymorphisms that modify ALL therapy outcome and will rigorously evaluate the additional predictive information captured in genotype data.