GENETIC PREDICTORS OF LEUKEMIA THERAPY RESPONSE

描述(申请人提供)：儿童急性淋巴细胞性白血病(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，但相当一部分儿童要么复发，要么经历与治疗相关的毒性。我们假设所有的治疗反应都是一个复杂的性状，这可能部分地可以用常见的基因变异来解释。该项目将评估两个国家级随机临床试验(CCG-1891和CCG-1952)中标准风险ALL的基因变异和治疗结果之间的关系。本研究有四个目的。第一个目的是在CCG-1891样本集中测试与甲氨蝶呤(MTX)效应有关的基因多态对治疗结果的影响。第二个目标是验证CCG-1952样本集中CCG-1891样本集中看到的关联。第三个目标是将基因-基因相互作用分析的新方法扩展和应用于CCG-1891和CCG-1952的组合样本集。第四个目标是开发包括基因数据在内的所有复发风险的预测模型。我们之前的工作已经在CCG-1891样本集中证明，MTHFR C677T变异纯合子患者的复发率增加。我们假设，介导MTX效应的基因中的其他多态将改变复发和毒性风险。其次，我们假设在CCG-1891中看到的显著关联将在CCG-1952中复制。第三，我们假设模式与递归分割(PRP)将允许识别预测复发和毒性的多态群体。第四，我们假设基因数据将改善复发风险预测模型的临床实用性。我们建议用CCG-1891的120名复发患者和360名持续缓解(CR)患者以及CCG-1952的200名复发患者和600名CR患者的嵌套病例对照研究来验证这些假设。这项应用将识别和验证改变所有治疗结果的多态，并将严格评估在基因数据中捕获的额外预测信息。

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.