Bioinformatics Data "Cleaning" for Immune Repertoire Sequencing

用于免疫组库测序的生物信息学数据“清理”

• 批准号: 8453266
• 负责人: David Scott Johnson
• 金额: $ 20.57万
• 依托单位: GIGAGEN, INC.
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-24 至 2013-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8453266
• 关键词: Algorithms; Area; Bioinformatics; Biological Assay; Capital; Clinic; Clinical; Data; Data Set; Detection; Device Designs; Diagnostic; Diagnostic Procedure; Disease; Doctor of Philosophy; FDA approved; Foundations; Grant; Human; Immune; Immune response; In Vitro; Informatics; Laboratories; Libraries; Licensing; Linear Models; Marketing; Measurement; Methods; Metric; Modeling; Molecular; Patients; Phase; Plague; Plasmids; Price; Process; Protocols documentation; Publishing; Receiver Operator Characteristics; Regression Analysis; Research; Research Personnel; Residual Neoplasm; Sampling; Scientist; Sequoia; Series; Services; Shipping; Ships; Small Business Innovation Research Grant; Specialist; System; T-Lymphocyte; Technology; Testing; Time; Training; Transplantation; Variant; Work; adaptive immunity; cost; innovation; leukemia; next generation; operation; product development; protocol development; tool; validation studies

DESCRIPTION (provided by applicant): The Specific Aim of this Phase I proposal is to test the feasibility of a bioinformatics technology for correction of amplification bias in T cell recepor ¿ (TCR ¿) repertoire sequencing (REP-SEQ), thus providing the foundation for this technology in clinical diagnostics. The T cell repertoire is the foundation of human adaptive immunity, and deep T cell repertoire sequencing is now commonly used in a research setting to quantify immune responses (Robins et al., 2009; Wang et al., 2010; Robins et al., 2012). Clinical immune repertoire sequencing has a large awaiting market because multiplexing all possible V(D)J combinations into a single assay significantly decreases material and labor costs compared with current diagnostic methods. For example, conventional leukemia minimal residual disease (MRD) work-ups demand laborious customization, cost up to ~$5000 per patient, and have a turnaround time of several weeks. We estimate that for the MRD market alone, our technology would save ~$140 million in annual costs for diagnostics labs, and would produce more sensitive data in less than half the time of standard MRD work-ups. The technical innovation of the product is to use bioinformatics to "clean" the no representative amplification that plagues multiplexed repertoire amplification (Robins et al., 2012). First, we will build a lare control library of TCR ¿ plasmid clones. Next, we will build REP-SEQ libraries using the control clones as templates and generate a large training set of data from these libraries using next-generation sequencing (NGS). Finally, we will build a linear model for correction of raw data using this training set, and test the feasibility of the linear model using a second set of TCR ¿ clones. We will require that the bioinformatics method consistently clean biased REP-SEQ measurements such that regression analysis between observed clone counts versus expected clone counts achieves an average R2 of >0.95 and an average slope of >0.9 (power=0.8, ¿=0.05) and such that clonotypes present as low as 0.01% have an average coefficient of variation (CV) of <10% across hundreds of measurements (power=0.8, ¿ =0.05). Additionally, the technology must be sufficiently sensitive for reliable detection of clonotypes that are present as low as 1 copy in 1 million, such that the area under the receiver operator characteristic curve (AUC) is greater than 0.8 across hundreds of measurements (¿ =0.05). The methods that we develop in Phase I will enable us to perform a large 510(k) validation study for FDA approval of a molecular kit for clinical REP-SEQ in Phase II. The final product will be priced at <$1000 per sample and will enable diagnostics labs throughout the US to streamline their operations without having to ship samples to a reference lab. PUBLIC HEALTH RELEVANCE: Diagnostics laboratories often analyze T cells to help characterize disease. We are building a streamlined, cheaper, and more comprehensive system for T cell analysis in clinical laboratories.

描述（由申请人提供）：本I期提案的具体目的是测试生物信息学技术用于纠正T细胞受体（TCR）库测序（REP-SEQ）扩增偏倚的可行性，从而为该技术在临床诊断中的应用提供基础。T细胞库是人类适应性免疫的基础，深度T细胞库测序现在通常用于研究环境中定量免疫反应（Robins et al., 2009; Wang et al., 2010; Robins et al., 2012）。临床免疫库测序有一个巨大的等待市场，因为与目前的诊断方法相比，将所有可能的V(D)J组合合并到一个单一的检测中显著降低了材料和人工成本。例如，传统的白血病微小残留病（MRD）检查需要繁琐的定制，每位患者的成本高达5000美元，并且需要几周的周转时间。我们估计，仅就MRD市场而言，我们的技术每年将为诊断实验室节省约1.4亿美元的成本，并且可以在不到标准MRD检查一半的时间内产生更敏感的数据。该产品的技术创新是利用生物信息学来“清理”困扰多路曲目扩增的无代表性扩增（Robins et al., 2012）。首先，我们将建立一个大型的TCR¿质粒克隆对照库。接下来，我们将使用对照克隆作为模板构建REP-SEQ库，并使用下一代测序（NGS）从这些库中生成大型训练数据集。最后，我们将使用该训练集构建用于校正原始数据的线性模型，并使用第二组TCR¿克隆测试线性模型的可行性。我们将要求生物信息学方法一致地清除有偏差的REP-SEQ测量，使观察到的克隆计数与预期克隆计数之间的回归分析平均R2为>.95，平均斜率为>0.9（功率=0.8，¿=0.05），并且使存在低至0.01%的克隆型在数百次测量中的平均变异系数(CV) <10%（功率=0.8，¿=0.05）。此外，该技术必须足够灵敏，能够可靠地检测出存在的克隆型