Maximum efficiency sequencing using nuclease-based mutation enrichment and digital barcodes

使用基于核酸酶的突变富集和数字条形码进行最高效率测序

• 批准号: 9355330
• 负责人: G. Mike Makrigiorgos
• 金额: $ 45.67万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9355330
• 关键词: Affect; Alleles; Biological Markers; Cancer Etiology; Cancer Patient; Clinical; Computational Technique; DNA; DNA Sequence Alteration; Detection; Development; Exons; Fingerprint; Future; Genes; Genomic DNA; Genomics; Heterogeneity; Length; Liquid substance; Low Prevalence; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Methodology; Methods; Molecular; Molecular Profiling; Mutate; Mutation; Names; Neoplasm Metastasis; Noise; Oligonucleotides; Oncogenes; Operative Surgical Procedures; Patients; Pharmacotherapy; Plasma; Preparation; Public Health; Recurrence; Role; Sampling; Screening for cancer; Sensitivity and Specificity; Site; Somatic Mutation; Speed; Steam; Technology; Testing; Time; Translating; Tumor Suppressor Genes; base; cancer diagnosis; cancer therapy; chemoradiation; circulating DNA; clinical application; clinical practice; cohort; cost; cost effective; deep sequencing; digital; field study; follow-up; individual patient; innovation; liquid biopsy; mutant; mutation screening; next generation sequencing; novel; nuclease; outcome forecast; personalized decision; personalized medicine; platform-independent; prevent; response; screening; sequencing platform; single molecule; treatment choice; tumor; tumor heterogeneity

Project Summary Low-level tumor somatic DNA mutations can have profound implications for development of metastasis, prognosis, choice of treatment, follow-up or early cancer detection strategies. Unless they are effectively detected, these low-level mutations can misinform patient management decisions or become missed opportunities for personalized medicine. Next generation sequencing (NGS) technologies can effectively reveal prevalent somatic mutations, yet they 'lose steam' when it comes to detecting low-level DNA mutations in tumors with clonal heterogeneity, or in bodily fluids, and their integration with clinical practice is problematic. For mutations at allelic ratio of ~2-5% or less, NGS generates excessive false positives (‘noise’) independent of sequencing depth and hinders personalized clinical decisions based on mutational profiling. Recent adaptations of NGS to detect rare mutations using random barcoding strategies may overcome the noise but invariably diminish its high throughput capability and increase costs. We recently developed NaMe-PrO, a simple and powerful technology to eliminate wild-type sequences from large numbers of targets in genomic DNA. NaME-PrO utilizes a nuclease guided by probes to thousands of DNA targets, to render WT sequences non-amplifiable thereby allowing mutation–containing sequences to amplify and be sequenced as if they were clonal mutations. This R33 proposes to develop quantitative NaME- PrO (qNaME-PrO), which combines NaME-PrO with a novel use of molecular barcoding, to provide strict enumeration of original mutation abundance for all mutant sequences following their enrichment. Thereby converting rare mutations to high abundance mutations, boosting confidence in their detection and circumventing the need for repeated and wasteful sequence reads during NGS. The method creates the potential for massively parallel mutation enrichment prior to sequencing and engenders a new paradigm whereby rare mutations do not require deep sequencing for their detection. The R33 (Aims 1&2) will optimize and develop qNaME-PrO panels to cover all known mutation hotspots and full length exons in tumor suppressor genes and oncogenes relevant to lung cancer. In Aim 3 the method will be field-tested in a compilation of longitudinally collected plasma samples from patients undergoing radio-chemo-therapy. Being able to extract ‘the mutated portion of a large genomic target’ from a mixed clinical sample prior to downstream analysis will translate to a major boost in the speed, accuracy and cost of sequencing low- prevalence mutations in heterogeneous tumors and bodily fluids and will accelerate clinical application of NGS for cancer diagnosis, prognosis and management. Therefore relevance to Public Health is high.

项目摘要 低水平的肿瘤体细胞DNA突变可能对转移的发展具有深远的影响， 预后、治疗选择、随访或癌症早期检测策略。除非他们能有效地 检测到这些低水平的突变可能会误导患者的管理决策或被遗漏 个性化医疗的机会。下一代测序(NGS)技术可以有效地揭示 普遍存在的体细胞突变，然而当涉及到检测低水平的DNA突变时，它们却失去了动力 克隆异质性的肿瘤，或体液中的肿瘤，以及它们与临床实践的结合是有问题的。 对于等位基因比例在~2-5%或更低的突变，NGS会产生过多的不依赖于噪声的假阳性 影响测序深度，阻碍基于突变图谱的个性化临床决策。近期 改编NGS以使用随机条码策略检测罕见的突变可能会克服噪声，但 不可避免地会削弱其高吞吐能力并增加成本。 我们最近开发了NAME-PRO，这是一种简单而强大的技术来消除野生型序列 来自基因组DNA中的大量靶点。NAME-PRO利用一种由探针引导的核酸酶 使WT序列不可扩增，从而允许包含突变的序列 进行扩增和测序，就像它们是克隆突变一样。本R33建议开发量化名称- PRO(qNaME-PRO)，它结合了NAME-PRO和分子条形码的新用途，以提供严格的 所有突变序列在浓缩后的原始突变丰度的计数。从而 将罕见突变转化为高丰度突变，增强了对它们的检测和 避免了在NGS期间重复和浪费序列读取的需要。该方法创建 在测序之前有可能进行大规模平行突变浓缩，并产生新的范例 因此，罕见的突变不需要为它们的检测进行深度测序。R33(目标1和2)将进行优化 并开发qNaME-PRO面板，覆盖所有已知的突变热点和肿瘤的全长外显子 与肺癌相关的抑癌基因和癌基因。在目标3中，该方法将在一个 从接受放化疗的患者纵向收集的血浆样本的汇编。 能够从混合的临床样本中提取“大基因组靶标的突变部分” 到下游分析将转化为大大提高测序的速度、准确性和成本。 异质肿瘤和体液中的流行突变将加速NGS的临床应用 用于癌症诊断、预后和管理。因此，与公共卫生的相关性很高。