Validating and Translating Array Signatures

描述(由申请人提供) 在癌症领域，微阵列分析被用来识别正常组织和疾病组织之间的具体差异。这些努力正在导致识别可以为癌症类型分类提供关键信息的小基因集或签名。我们在这里提出了一种机制，用于将这些发现转化为基于高度多重、通用引物驱动的RTPCR(UP-RTPCR)的具有成本效益的定量分析。在这项提案中，我们将演示和验证人工神经网络分析用于识别小基因签名和UP-RTPCR技术的组合使用，以将这些签名转换为高通量分析，用于研究和临床环境。成功与否将通过这种方法区分4种小的、圆形的、蓝色细胞肿瘤的能力来衡量。这项工作将与贾维德·汗博士和加里·福格尔博士合作进行。

DESCRIPTION (provided by applicant) In the area of cancer, microarray analysis is being used to identify specific differences between normal and disease tissues. Such efforts are leading to the identification of small gene sets, or signatures, that can provide key information for classifying cancer types. We propose here a mechanism for translating these discoveries into cost-effective, quantitative assays based on the use of highly multiplexed, universal-primer-driven rtPCR (UP-rtPCR). In this proposal we will demonstrate and validate the combined use of artificial neural network analysis for identification of small gene signatures and UP-rtPCR technology for translating these signatures into high throughput assays for use in research and clinical settings. Success will be measured by the ability of this approach to differentiate 4 types of small, round, blue-cell tumors. This work will be performed in collaboration with Drs. Javed Khan and Gary Fogel.