GENETIC PREDICTORS OF LEUKEMIA THERAPY RESPONSE

描述（申请人提供）：小儿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特征，可能部分解释了常见的基因型变异。本项目将评估两项标准风险ALL的国家随机临床试验（CCG-1891和CCG-1952）的基因型变异与治疗结果之间的关系。这项研究有四个目的。第一个目的是在CCG-1891样本集中测试涉及甲氨蝶呤（MTX）效应的多态性对治疗结果的影响。第二个目的是验证CCG-1952样本集中CCG-1891样本集中所见的关联。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。第四个目标是建立一个包括基因型数据的ALL复发风险预测模型。我们之前的工作已经在CCG-1891样本集中证明，纯合MTHFR C677T变体的患者复发率增加。我们假设介导MTX效应的基因中的其他多态性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，我们假设递归划分（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。我们建议通过一项巢式病例对照研究来检验这些假设，该研究包括120名复发患者和360名持续缓解（CR）的CCG-1891患者，以及200名复发患者和600名持续缓解（CR）的CCG-1952患者。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.