Integrative genomic and epigenomic analysis of cancer using long read sequencing

项目摘要 过去二十年来，癌症基因组测序经历了广泛的发展，导致了 大大增加了对癌症中遗传和表观遗传突变作用的理解。这在很大程度 通过高通量"第二代"测序技术和分析的发展， 用短片段来描述癌症基因组。最近，新一代高通量长读 主要来自Pacific Biosciences和Oxford Nanopore的测序仪器已经变得可用， 准备在许多应用中取代短读测序。我们和其他人用这些 技术，以发现每个癌症基因组的数万种变异，这些变异是使用 短读段，包括已知癌基因和癌症中的结构变体和差异甲基化区域 风险基因然而，这些技术有可能解决癌症生物学中许多悬而未决的问题， 长读序测序数据的分析在计算上要求很高，并且需要专门的算法， 要么效率太低，无法大规模使用，要么还不存在。在本提案中，我们将解决 长读技术在癌症基因组学基础研究和临床应用中的应用。一是 开发用于从长读段中发现结构变体和复杂重复扩增的改进方法， 两者都是疾病的主要诊断和预后指标，但使用 现有的方法。利用长读取的改进的定相能力，这项工作将包括 检测嵌合变体，揭示肿瘤异质性和癌前组织中的变体。接下来我们就 应用机器学习和系统级进步来加速和改进变体的比较 在大型患者队列中。重要的是，这将补偿单分子的易出错性质， 长读段测序，使这些比较在比较肿瘤正常样本时更准确， 相关患者的谱系，以便可以准确地识别复发性驱动突变。最后我们将 开发癌症基因组、转录组和表观遗传学分析联合分析的综合方法 基因组这些进展将改善融合基因的鉴定，并允许完全新形式的融合。 表观遗传学分析，例如跨转座因子的甲基化的等位基因特异性分析和其他遗传学分析。 重复的元素。综合我们将使用我们的方法检测的数千种新变体， 然后将开发算法，以识别和评估复发性遗传或表观遗传变异， 假定的驱动突变。所有方法都将开源发布，并将赋予我们，我们的ITCR 合作者和癌症基因组学社区，研究遗传和表观遗传变异， 完美的准确性，从而解锁了许多新的治疗和疾病的关联。

PROJECT SUMMARY The last twenty years have experienced extensive growth in the sequencing of cancer genomes, leading to a dramatically increased understanding of the role of genetic and epigenetic mutations in cancer. This has largely been enabled by developments in high-throughput “second-generation” sequencing technology and analysis that characterize cancer genomes using short-reads. Recently, a new generation of high-throughput long-read sequencing instruments, primarily from Pacific Biosciences and Oxford Nanopore, have become available that are poised to displace short-read sequencing for many applications. We and others have used these technologies to discover tens of thousands of variants per cancer genome that are not detectable using short-reads, including structural variants and differentially methylated regions in known oncogenes and cancer risk genes. These technologies carry the potential to address many open questions in cancer biology, however, the analysis of long-read sequencing data is computationally demanding and needs specialized algorithms that are either too inefficient to use at scale or do not yet exist. In this proposal, we will address several gaps in the application of long-read technology for basic research and clinical use in cancer genomics. First, we will develop improved methods for finding structural variants and complex repeat expansions from long-reads, both of which are major diagnostic and prognostic indicators of disease, yet are not accurately identified using existing methods. Leveraging the improved phasing capabilities of long reads, this work will include the detection of mosaic variants, revealing tumor heterogeneity and variants in precancerous tissues. Next, we will apply machine learning and systems level advances to accelerate and improve the comparison of variants across large patient cohorts. Critically, this will compensate for the error prone nature of single molecule long-read sequencing to make these comparisons more accurate when comparing tumor-normal samples or pedigrees of related patients so that recurrent driving mutations can be accurately identified. Finally, we will develop integrative methods for the joint analysis of genome, transcriptome, and epigenetic profiling of cancer genomes. These advances will improve the identification of fusion genes, and allow for entirely new forms of epigenetic analysis, such as the allele-specific analysis of methylation across transposable elements and other repetitive elements. Synthesizing the many thousands of novel variants we will detect using our methods, we will then develop algorithms that will identify and evaluate recurrent genetic or epigenetic variations as putative driving mutations. All methods will be released open-source and will empower us, our ITCR collaborators, and the cancer genomics community at large to study genetic and epigenetic variants with near perfect accuracy and thereby unlock many new associations to treatment and disease.