Standardized NanoArray PCR for Gene Expression Profiling of Lung Cancer

描述（由申请人提供）：癌症的分子表征，特别是通过基因转录谱分析，在改善预后、治疗选择和临床结果方面具有很大的潜力。然而，由于缺乏易于部署的、具有常规临床使用所需的准确性、低RNA要求和位点间一致性的检测试剂盒，使用表达特征进行癌症预后和治疗选择的潜力受到阻碍。我们提出了一种基于两种经过验证的PCR技术的创新解决方案，其组合独特地解决了诊断分析可重复性的问题。我们的计划是在BioTrove公司开发的新型纳米流体PCR平台上实施标准化RT (StaRT)-PCR，这是托莱多大学开发的一种经过验证的竞争性PCR方法，以简化流体工作流程，提高测量吞吐量，同时降低测试成本并保持低RNA输入。与现有的杂交或实时qPCR方法相比，标准化纳米阵列PCR （SNAP）将提供与RT-PCR相同的动态范围和质量，但需要更少的RNA输入，并且更易于临床部署。这一发展将需要逐步整合成熟的技术。首先，将开发16种肺肿瘤预后基因的实时qPCR TaqMan检测。通过创建竞争模板和竞争对手特异性染料标记探针，这些检测将转换为StaRT-PCR。在StaRT-PCR中添加扩增前步骤将减少RNA输入要求，从而使每个样品能够进行数千次检测。最后，将检测转移到OpenArray纳米pcr板将简化流体处理。使用从肺临床肿瘤切除中分离的RNA，将为集成平台展示动态范围和相当于实时qPCR的精度。在初始开发阶段完成后，我们将在两个关键的基因表达谱实验中比较SNAP和real - time qPCR。首先，我们将通过监测作为减少RNA样品输入的函数的精度损失来比较每种方法所需的最小RNA量。其次，我们将通过测量三个实验室中七个肺肿瘤切除样本的基因表达谱来证明较低的位点间变异性，这是部署能力的关键因素。满足这些特定目标将导致寻求更多资金用于涉及福尔马林固定石蜡包埋（FFPE）具有广泛临床病史的肺标本的多地点预后验证研究。

DESCRIPTION (provided by applicant): Molecular characterization of cancer, in particular by gene transcription profiling, has great potential to improve prognosis, therapeutic selection and clinical outcomes. However, the potential for using expression signatures for cancer prognosis and treatment selection is hampered by lack of readily deployable test kits with the accuracy, low RNA requirement and inter-site concordance required for routine clinical use. We propose an innovative solution based on two well-validated PCR technologies whose combination uniquely addresses the problem of diagnostic assay reproducibility. Our plan is to implement Standardized RT (StaRT)-PCR, a proven competitive PCR method developed at the University of Toledo, in a novel nanofluidic PCR platform developed by BioTrove Inc. in order to streamline the fluidic workflow, improve measurement throughput, and at the same time reduce test cost and maintain low RNA input. As compared to existing hybridization or real-time qPCR approaches, Standardized NanoArray PCR (SNAP) will provide the same dynamic range and quality as RT-PCR, yet require less RNA input and be more readily clinically deployable. The development will entail step-wise integration of proven technologies. First, real-time qPCR TaqMan assays will be developed for 16 lung tumor prognostic genes. These assays will be converted to StaRT-PCR by creation of competitive template and a competitor specific dye-labeled probe. Adding a pre-amplification step to StaRT-PCR will reduce the RNA input requirements to enable thousands of tests per sample. Finally, moving the assays into the OpenArray nano-PCR plate will streamline fluid handling. Using RNA isolated from lung clinical tumor resections, dynamic range and precision equivalent to real-time qPCR will be demonstrated for the integrated platform. After the initial development phase is complete, we will compare SNAP and real time qPCR in two critical gene expression profiling experiments. First we will compare the minimum amount of RNA required for each method by monitoring loss of precision as a function of decreasing RNA sample input. Second we will demonstrate lower inter-site variability, a critical factor for deploy-ability, by measuring gene expression profiles of seven lung tumor resection samples in three laboratories. Meeting these Specific Aims will lead to seeking further funding for multi-site prognostic validation studies involving formalin-fixed, paraffin-embedded (FFPE) lung specimens with extensive clinical history.