Standardized NanoArray PCR for Gene Expression Profiling of Lung Cancer

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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.