Ultrasensitive identification and precise quantitation of low frequency somatic m

低频体细胞的超灵敏识别和精确定量

• 批准号: 8334013
• 负责人: Jay Ashok Shendure
• 金额: $ 18.6万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-16 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8334013
• 关键词: Algorithms; Alleles; Biological Assay; Cell Line; Clinical; Code; Consensus; Consensus Sequence; DNA; DNA Sequence; Detection; Development; Diagnosis; Diagnostic; Early Diagnosis; Event; Frequencies; Gene Frequency; Genes; Genetic; Genetic Heterogeneity; Genotype; Goals; Haploidy; Malignant Neoplasms; Medicine; Methods; Molecular; Monitor; Monitoring for Recurrence; Mutate; Mutation; Mutation Detection; Nucleotides; Oncogenes; Patient Care; Patient Monitoring; Protocols documentation; Public Health; Reaction; Reading; Reagent; Reproducibility; Sampling; Screening for cancer; Secondary to; Somatic Mutation; Surveys; Technology; Translations; Tumor Suppressor Genes; analytical tool; base; cancer genetics; cancer genome; cancer genomics; cancer recurrence; clinical application; clinically relevant; cost effective; cost effectiveness; digital; genome sequencing; human disease; multiplex detection; mutant; outcome forecast; progenitor; research study; single molecule; tool; tumor

DESCRIPTION (provided by applicant): The ultrasensitive detection of clinically relevant somatic alterations in cancer genomes has great potential for impacting patient care, e.g. for early detection, establishing diagnoses, refining prognoses, guiding treatment, and monitoring recurrence. However, current technologies are poorly suited to the robust detection of somatic mutations present at very low frequencies. Massively parallel sequencing represents one path forward, but its sensitivity to detect very rare events is fundamentally constrained by the sequencing error rate. Our goal is to develop a new experimental paradigm that overcomes this limitation. In our approach, each copy of a target sequence that is present in a sample is molecularly tagged during the first cycle of a multiplex capture reaction with a unique barcode sequence. After amplification, target amplicons and their corresponding barcodes are subjected to massively parallel sequencing. During analysis, the barcodes are used to associate sequence reads sharing a common origin. Through oversampling, barcode-associated reads error-correct one another to yield an independent haploid consensus for each progenitor molecule, i.e. "molecular counting". Furthermore, the collapsing of commonly derived reads inherently corrects for any allele-specific bias during amplification, such that estimates of mutant allele frequency can be accompanied by precise confidence bounds. In our first aim, we will develop experimental methods and analytical tools that enable the robust detection of targeted somatic mutations via molecular counting to frequencies as low as 1 mutated copy in a background of 100,000 unmutated copies. In our second aim, we will develop three ultrasensitive, multiplex molecular counting assays that are specifically targeted at panels of clinically relevant cancer mutations or genes, and rigorously evaluate these for reproducibility. The availability of robust, cost-effective, generically applicable tools for the ultrasensitive, multiplex detection of rare somatic events will be a transformative step forward for the translation of discoveries in cancer genetics to a clinical setting.

描述（由申请人提供）：癌症基因组中临床相关体细胞改变的超灵敏检测具有影响患者护理的巨大潜力，例如用于早期检测、建立诊断、完善诊断、指导治疗和监测复发。然而，目前的技术不太适合于以非常低的频率存在的体细胞突变的稳健检测。大规模并行测序代表了一条前进的道路，但其检测非常罕见事件的灵敏度从根本上受到测序错误率的限制。我们的目标是开发一种新的实验范式，克服这一限制。在我们的方法中，在多重捕获反应的第一个循环期间，用独特的条形码序列对样品中存在的靶序列的每个拷贝进行分子标记。扩增后，对靶扩增子及其相应的条形码进行大规模平行测序。在分析期间，条形码用于关联共享共同来源的序列读段。通过过采样，条形码相关读数彼此纠错以产生每个祖分子的独立单倍体共有序列，即“分子计数”。此外，通常衍生的读数的崩溃固有地校正扩增期间的任何等位基因特异性偏差，使得突变等位基因频率的估计可以伴随有精确的置信界限。在我们的第一个目标中，我们将开发实验方法和分析工具，通过分子计数在100，000个未突变拷贝的背景中低至1个突变拷贝的频率，实现对靶向体细胞突变的稳健检测。在我们的第二个目标中，我们将开发三种超灵敏的多重分子计数检测方法，专门针对临床相关的癌症突变或基因组，并严格评估这些方法的可重复性。用于罕见体细胞事件的超灵敏、多重检测的稳健、具有成本效益、通用的工具的可用性将是将癌症遗传学发现转化为临床环境的变革性一步。