nCounter Prep Station and the Digital Analyzer

描述(由申请人提供)： 基于细胞的免疫疗法在治疗恶性肿瘤方面前景广阔，但它们是现代医学发展和实施的最大挑战之一。通常没有或很少的标志物，如独特的临床特征，通过成像的特定形态变化，或血清学标志物，可以预测这些生物疗法的疗效，甚至治疗潜力。尽管存在这些障碍，以及在非营利性学术机构内产生基于细胞的免疫疗法的挑战，基于免疫的疗法正在开发和在人类身上进行测试，前提和承诺是它们的特异性和使用与传统疗法不重叠的效应机制根除肿瘤的能力。德克萨斯大学(UT)癌症免疫研究中心(CCIR)安德森癌症中心(MDACC)在设计和实施基于增强被动和主动免疫的临床级免疫疗法方面开展了一项积极的计划。目前，输注的细胞和从患者(和动物)中回收的细胞的表型特征是通过流式细胞术进行蛋白质表达的特征和对表达基因的有限分析。然而，重要的是能够对免疫治疗前后差异表达的免疫基因进行分类，以评估治疗的治疗潜力，并进行具有预期改善生物效果的下一代免疫治疗的合理设计。目前用于分析免疫基因表达的两种方法，微量分析和Q-PCR，由于所需的大量材料，在增强免疫治疗的潜力方面的作用有限，所产生的偏差发生在mRNA有限且不覆盖阵列的动态浓度范围时，或者由于酶扩增而发生的偏差。简单地说，由于回收的组织数量很少，因此不可能对输注后恢复的免疫细胞进行全面的mRNA表达谱分析来评估激活状态、归巢潜力和免疫表型。幸运的是，现在可以使用NanoString Technologies，Inc.(西雅图，华盛顿州)的nCounter分析系统获得新技术。该系统不使用酶或扩增步骤，但即使在低表达水平下，也可以在一次分析中高精度地分析数百个基因，即使使用直接从细胞培养、组织或全血裂解产物中获得的样本，而不需要纯化样本，并且可以显著地分析来自福尔马林固定石蜡包埋(FFPE)组织样本的总纯化RNA。该系统采用了一种新的数字技术，使基因表达的直接多路测量成为可能，同时提供高水平的灵敏度和精确度，包括检测分数折叠变化差异。这项技术使用分子条形码和单分子成像技术，在一次反应中检测和计数数百个独特的转录本。全自动系统易于使用，非常适合于希望验证基因表达签名、使用少量起始材料并研究已定义基因集的研究人员。

DESCRIPTION (provided by applicant): Cell-based immunotherapies hold great promise for treatment of malignancies, yet they are among the greatest challenges to develop and implement in modern medicine. There are typically no or few markers such as distinct clinical features, specific morphological changes by imaging, or serological identifiers, which can predict efficacy or even therapeutic potential of these biologic therapies. Despite these hurdles, and the challenge of generating cell-based immunotherapies from within not-for-profit academic institutions, immune- based therapies are being developed and tested in humans based on the premise and promise of their specificity and ability to eradicate tumor using effector mechanisms that are non-overlapping with conventional therapies. The Center for Cancer Immunology Research (CCIR) at University of Texas (UT) M.D. Anderson Cancer Center (MDACC) has an active program in designing and implementing clinical-grade immunotherapies based on enhancing passive and active immunity. Currently, the phenotype of cells infused and cells recovered from patients (and animals) are characterized by flow cytometry for protein expression and a limited analysis of expressed genes. However, it is vital to be able to classify immune genes that are differentially expressed before and after immunotherapy to gauge the therapeutic potential of the treatment and to undertake the rational design of next generation immunotherapeutics with predicted improved biologic effect. The two current approaches to profiling immune gene expression, microanalysis and Q-PCR, have a limited role in enhancing the potential of immunotherapy due to the large amount of material needed, with the resultant bias that occurs when mRNA is limiting and does not cover the array's dynamic concentration range, or the bias occurring with enzymatic amplification. Simply put, comprehensive mRNA expression profiling to assess activation status, homing potential, and immunophenotype, is not possible on the immune cells recovered after infusion due to the small amount of recovered tissue. Fortunately, new technology is now available using the nCounter Analysis System from NanoString Technologies, Inc. (Seattle, WA). This system uses no enzymes or amplification steps, yet can analyze hundreds of genes in a single assay with high precision, even at low expression levels using samples directly obtained from cell culture, tissue, or whole blood lysates without sample purification and significantly can assay total purified RNA derived from formalin- fixed paraffin-embedded (FFPE) tissue samples. This system employs a novel digital technology to enable direct multiplexed measurement of gene expression while offering a high level of sensitivity and precision, including detection of fractional fold-change differences. The technology uses molecular barcodes and single molecule imaging to detect and count hundreds of unique transcripts in a single reaction. The fully automated system is easy to use and is ideally suited for researchers \on this grant application who wish to validate gene expression signatures, working with small amounts of starting material and studying defined gene sets.