MOLECULAR SIGNATURES FOR OUTCOME PREDICTION AND THERAPEUTIC TARGETING IN ALL

描述（由申请人提供）：虽然大多数被诊断患有急性淋巴细胞白血病（ALL）的儿童现在通过现代治疗方案实现了长期生存，但超过20%的复发或死于耐药疾病。因此，ALL仍然是儿童癌症死亡的主要原因。这对于被诊断为“高危”ALL的儿童尤其如此，占所有病例的30%;青少年和成人ALL患者的结局也很差。通过对>900例高风险ALL病例的全面分子分析，我们已经推导并验证了预测ALL无复发生存期（RFS）的基因表达特征（“分类器”），我们发现了新的潜在遗传异常（IKAR 0 S/IKZF 1、JAK和CRLF 2突变或基因组重排），可作为该疾病的新诊断和治疗靶点。我们现在建议将这些基于RNA和DNA的分子特征转化为强大的临床诊断工具;在一个新的>500名高风险ALL患者队列中完成以这种方式测量的特征的验证，并开发新的算法以改进hsk分类，结果预测和治疗靶向;并在下一代COG试验中前瞻性地测试这些特征。我们的目标是：1）将我们的基于微阵列的基因表达分类器改进为最终的一组预测基因，并将该多分析物特征的定量测量转化为稳健的临床诊断平台（ABI TaqMan（r）自动定量RT-PCR卡）; 2）在500例高风险ALL病例的独立队列中验证该分类器相对于新定义的预后分子异常的预测能力（涉及IKAROS、JAK、CRLF 2）和已知风险因素3）在2011年开始的下一代COG临床试验中前瞻性地测试新的分子特征和风险算法的预测能力，该临床试验将累积>3000名患有高风险ALL的儿童;和4）确定这些分类器、分子特征和风险算法在青少年和年轻成人ALL患者队列以及成人NCI合作小组进行的临床试验中累积的ALL老年人中的预测效用。

DESCRIPTION (provided by applicant): While the majority of children diagnosed with acute lymphoblastic leukemia (ALL) now achieve long term survival on contemporary treatment regimens, more than 20% relapse or die of resistant disease. ALL thus remains the leading cause of cancer death in children. This is particularly true for children diagnosed with 'high-risk" ALL, accounting for 30% of all cases; outcomes also remain poor in adolescents and adults with ALL. From comprehensive molecular analyses of >900 high-risk ALL cases, we have derived and validated gene expression signatures ("classifiers") predictive of relapse-free survival (RFS) in ALL and we have discovered novel underlying genetic abnormalities (IKAR0S/IKZF1, JAK, and CRLF2 mutations or genomic rearrangements) that serve as new diagnostic and therapeutic targets for this disease. We now propose to translate these RNA and DNA-based molecular signatures into robust clinical diagnostic tools; complete the validation of the signatures measured in this fashion in a new cohort >500 high-risk ALL patients and develop new algorithms for improved hsk classification, outcome prediction, and therapeutic targeting; and, prospectively test these signatures in the next generation of COG trials. Our aims are to: 1) refine our microarray-based gene expression classifiers to a final set of predictive genes and translate the quantitative measurement of this multi-analyte signature to a robust clinical diagnostic platform (ABI TaqMan(r) automated, quantitative RT-PCR cards); 2) validate the predictive power of this classifier in an independent cohort of 500 high-risk ALL cases relative to the newly defined prognostic molecular abnormalities (involving IKAROS, JAK, CRLF2), and known risk factors (such as minimal residual disease) to develop a new molecular algorithm for risk classification and therapeutic targeting; 3) prospectively test the predictive power of the new molecular signatures and risk algorithms in the next generation of COG clinical trials opening in 2011 which will accrue >3000 children with high-risk ALL; and 4) determine the predictive utility of these classifiers, molecular signatures, and risk algorithms in adolescent and young adult ALL patient cohorts as well as older adults with ALL accrued to clinical trials conducted by the adult NCI Cooperative Groups.