Statistical Models and Analysis of Complex Genomic Variation in Clonal Mixtures

克隆混合物中复杂基因组变异的统计模型和分析

• 批准号: 9097734
• 负责人: Hanlee P Ji
• 金额: $ 24.8万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-06 至 2017-09-13
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9097734
• 关键词: Address; Admixture; Algorithms; Alleles; Allelic Imbalance; Biomedical Research; Cancer Patient; Cell Line; Clinical; Clonal Expansion; Communities; Complex; Computer Simulation; Computer software; DNA; DNA Sequence; DNA Sequence Analysis; DNA Sequence Rearrangement; Data; Data Set; Detection; Diagnosis; Diploidy; Disease; Disease Progression; Drug resistance; Evolution; Genetic; Genetic Variation; Genome; Genome Scan; Genomic DNA; Goals; Health; Individual; Infection; Joints; Malignant Neoplasms; Maps; Measures; Methods; Modeling; Monitor; Mutation; Neoplasm Metastasis; Nucleotides; Partner in relationship; Pathogenesis; Phenotype; Population; Procedures; Process; Reading; Recording of previous events; Sampling; Sensitivity and Specificity; Signal Transduction; Site; Source; Staging; Statistical Data Interpretation; Statistical Models; Targeted Resequencing; Technology; Testing; Time; Variant; Viral; Virus; Virus Diseases; analytical tool; cancer cell; design; direct application; genetic evolution; genetic variant; genome sequencing; genomic variation; human disease; human subject; improved; innovation; interest; neoplastic cell; next generation; open source; population based; targeted sequencing; therapy resistant; tumor; whole genome

DESCRIPTION (provided by applicant): Next generation DNA sequencing (NGS) approaches are widely used in studying human diseases and identifying causative genetic variants. Increasingly, NGS methods are being used to define biologically relevant clonal mixtures, a frequently observed phenomenon in human disease. Examples of clonal mixtures in human disease include tumor cell subpopulations that are a part of cancer. Within a single tumor and clearly evident in metastatic tumor sites, cancer cell clonal populations exist, are genetically distinct and carry their own unique set of somatic variants. A similar phenomenon occurs in viral infection where multiple viral quasispecies are harbored within an infected individual; each quasispecies has their own unique set of genetic variants. One can quantitatively measure expansions or shrinkage in clonal populations as seen in changes in allelic representation of clonal variants. Specific cellular phenotypes are attributable to the unique clonal variants and changes in their representation can be indicators of evolutionary processes. This is frequently the case for drug resistance in cancer and viral infections. Thus, clonal genetic variation has major implications for the pathogenesis of human disease and is increasingly being tested as a longitudinal indicator of disease progression and treatment resistance. The general availability of whole genome and deep targeted resequencing provides an opportunity to conduct systematic analysis of heterogeneous DNA mixtures that have different clonal components. However, in many cases the genetic variant of interest is present at very small proportions (< 5%) and this makes the delineation of these clonal variants exceeding difficult. Many of the widely employed NGS analysis methods are optimized for detecting normal diploid genome variation. These approaches are not optimal for delineating genomic variants from complex clonal mixtures. Some genomic DNA variant classes such as genomic rearrangements are extremely difficult to detect in the context of clonal mixtures. To improve the assessment of clonal variation and evolution of specific clonal populations, we will develop innovative models and robust, sensitive statistical procedures. These methods will enable one to deconvolute genomic variation in clonal mixtures and consider clonal alterations through time and space. We will focus on improving the delineation of complex variations such as genomic rearrangements and other structural variations in genetic mixtures. To develop our methods, we will use heterogeneous DNA sequence data sets with in silico spike in variants and consider the lowest threshold of detection that we can achieve with the best sensitivity and specificity. Subsequently, we will test these methods on NGS data sets from clinical samples, delineate clonal populations based on unique variants and consider quantitative changes in allelic representation as seen in clonal expansion. These samples will be subject to whole genome and targeted resequencing. Cancer relevant samples will include tumors with matched normal, primary and metastatic DNA. We will consider viral quasispecies for a set of clinical samples where we have matched viral nucleic samples obtained longitudinally over the course of infection from a single individual. As a final milestone, we will release our methods as open source software for the biomedical research community.

描述（由申请人提供）： 下一代DNA测序（NGS）方法被广泛用于研究人类疾病和鉴定病因遗传变异。 NGS方法越来越多地用于定义与生物学相关的克隆混合物，这是人类疾病中经常观察到的现象。人类疾病中克隆混合物的例子包括肿瘤细胞亚群，这是癌症的一部分。在单个肿瘤中，在转移性肿瘤部位明显明显，存在癌细胞克隆种群，在遗传上是不同的，并且具有自己独特的体细胞变异。在病毒感染中也发生了类似的现象，其中多个病毒准植物在感染者中含有。每个准特性都有自己独特的遗传变异。可以在克隆人群中可以定量测量克隆种群中的膨胀或收缩。特定的细胞表型归因于独特的克隆变体，其表示的变化可能是进化过程的指标。癌症和病毒感染中的耐药性通常是这种情况。因此，克隆遗传变异对人类疾病的发病机理具有重大影响，并且越来越多地作为疾病进展和抗药性的纵向指标进行检验。整个基因组和深度靶向重新方程的一般可用性为有不同克隆成分的异质DNA混合物进行系统分析提供了机会。但是，在许多情况下，感兴趣的遗传变异的比例很小（<5％），这使得这些克隆变体的描述超出了困难。优化了许多广泛使用的NGS分析方法，用于检测正常的二倍体基因组变异。这些方法对于从复杂的克隆混合物中描述基因组变体并不是最佳的。在克隆混合物的背景下，一些基因组DNA变异类别（例如基因组重排）非常困难。为了改善特定克隆人群的克隆变异和演变的评估，我们将开发创新的模型和强大的敏感统计程序。这些方法将使人们能够在克隆混合物中脱糖基因组变异，并考虑通过时间和空间进行克隆改变。我们将专注于改善复杂变化的描述，例如基因组重排和遗传混合物中的其他结构变化。为了开发我们的方法，我们将在变体中使用硅尖峰中使用异质的DNA序列数据集，并考虑我们可以通过最佳灵敏度和特异性实现的最低检测阈值。随后，我们将对来自临床样本的NGS数据集测试这些方法，基于独特的变体来描述克隆人群，并考虑在克隆扩张中看到的等位基因表示的定量变化。这些样品将受到整个基因组和靶向重新配置的约束。癌症相关样品将包括具有匹配正常，原发性和转移性DNA的肿瘤。我们将考虑一组临床样本的病毒准植物，在这些临床样本中，我们匹配了从单个个体的感染过程中纵向获得的病毒核样品。作为最后的里程碑，我们将发布我们的方法作为生物医学研究社区的开源软件。