Improved Diagnosis in ALL

描述（由申请人提供）： 急性淋巴细胞白血病（ALL）是最常见的儿童恶性肿瘤， 是美国儿童癌症死亡的第二大原因 1.每年约3000例，占所有儿科病例的75% 在美国，儿童被诊断为白血病， 继续稳步增长2.尽管在治疗这种疾病方面取得了巨大的进展， 疾病，复发的患者的绝对数量（最初 被定义为“标准”风险）的儿童人数大于诊断为 AML、神经母细胞瘤或肾母细胞瘤2。这些病人似乎有一个 不同类别的疾病，无法使用现有的 当时的临床/病理学方法（如白色血细胞计数） 诊断。 该提案的目标是定义一个诊断系统，来自基因 表达谱，这将区分肿瘤最初 被归类为“标准风险”，可接受治疗（实现长期 无病生存期（LTDFS））和无病生存期（2 年）。目前对这一不良结局组的挽救治疗无效 （不到10%的病例中有LTDFS），但有证据表明， 在诊断这些病例时加强治疗， 非常有效。表达谱和分析，对临床数据不知情， 先前已经显示出容易区分不同类型的 白血病肿瘤（ALL vs. AML）3.我们假设类似的技术将 使我们能够识别肿瘤亚型， 在“标准”风险组内复发。我们的团队由主席 儿童肿瘤组（Reaman博士），CNMC（11个研究中心之一）， 一个国家将获得14，000，000美元的NHLBI PGA赠款，用于分析临床 样品）（斯蒂芬博士），以及该领域最有经验的几个小组 表达阵列统计和生物信息学在社区（博士。 Butte，Golub，Trent）. 在本提案的R21部分中，我们将首先开发一个 原理验证，一个基于cDNA阵列的系统，在一个步骤中， 提供目前获得的预后融合转录本信息 通过细胞遗传学我们有一个内部组织库， 骨髓抽吸物（组织同质性极高）， 快速冻结，将用作整个提案的资源。 其次，我们将表达谱50个肿瘤的“标准”风险-没有 复发和50个密切匹配的肿瘤的“标准”风险，迅速复发 在治疗后，使用两种方法来定义一组预测基因候选物， 监督和非监督技术。在R33阶段，这套 预测基因候选者将通过分析另外100个ALL来验证 对复发状态设盲的样本。这些样品将同时 使用cDNA阵列对已知的预后指标进行基因分型， 我们检测易位状态的能力（特异性和灵敏度 通过与COG数据库比较确定）。最后，两个寡核苷酸 它们能够检测肿瘤RNA的易位状态， 经验证的预测因子基因集将被并入定制的Affytrix中。 阵列用作快速、一步、廉价的分子诊断工具。的 该提案的未来目标是前瞻性地诊断复发， 可以在出现时加强治疗。

DESCRIPTION (provided by applicant): Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy and the second leading cause of deaths from childhood cancer in the United States: 1. Approximately 3000 cases a year, representing 75 percent of all pediatric leukemias, are diagnosed in children in the United States, and the incidence continues to increase steadily 2. Despite dramatic progress in treating this disease, the absolute number of patients who relapse (who are initially defined as 'standard' risk) is greater than the number of children diagnosed with AML, neuroblastoma, or Wilms tumor 2. These patients appear to have a different class of disease, which cannot be differentiated using available clinical/pathological approaches (such as white blood celI count) at the time of diagnosis. The goal of this proposal is to define a diagnostic system, derived from gene expression profiles, which would differentiate between tumors initially classified as 'standard risk' which are amenable to therapy (achieve long-term disease free survival (LTDFS)) and those which are not (relapse within 2 years). Salvage therapy for this poor outcome group is currently ineffectual (LTDFS in less than 10 percent of cases) but there is evidence that intensification of treatment at the time of diagnosis for these cases would be highly effective. Expression profiling and analysis, blinded to clinical data, has previously been shown to easily discriminate between different types of leukemia tumors (ALL vs. AML) 3. We hypothesize that similar techniques will enable us to identify tumor sub-types with increased propensity to rapidly relapse within the 'standard' risk group. Our team consists of the Chairman of the Children's Oncology Group (Dr. Reaman), CNMC (one of 11 sites in the country to receive a $14,000,000 NHLBI PGA grant for profiling clinical samples)(Dr. Stephan), and several of the most experienced groups in the field of expression array statistics and bioinformatics in the community (Drs. Butte, Golub, Trent). In the R21 portion of this proposal we will first develop, as a proof-of-principle, a cDNA array-based system that can, in a single step, provide prognostic fusion transcript information, which is currently obtained by cytogenetics. We have an in-house tissue bank of greater than 3000 leukemia bone marrow aspirates (extremely high tissue homogeneity) which have been flash frozen which will be used as the resource for the entire proposal. Secondly, we will expression profile 50 tumors of the 'standard' risk - no relapse and 50 closely matched tumors of 'standard' risk which relapse rapidly after therapy to define a set of predictor gene candidates using both supervised and unsupervised techniques. In the R33 phase, this set of predictor gene candidates will be validated by profiling an additional 100 ALL samples blinded to relapse status. These samples will simultaneously be genotyped for the known prognostic indicators using the cDNA array to validate our ability to detect translocation status (specificity and sensitivity determined by comparison with the COG database). Finally, both the oligos which are able to detect translocation status from tumor RNA, as well as the validated predictor gene set will be incorporated onto a custom Affymetrix array for use as a rapid, one-step, inexpensive molecular diagnostic tool. The future goal of this proposal is to prospectively diagnose relapse so that therapy can be intensified at the time of presentation.