Integrative genomic and epigenomic analysis of cancer using long read sequencing

项目总结 在过去的二十年里，癌症基因组测序经历了广泛的增长，导致了 极大地提高了对基因和表观遗传突变在癌症中的作用的理解。这在很大程度上 高通量第二代测序技术和分析的发展使之成为可能 用短读法来刻画癌症基因组。最近，新一代高吞吐量长读 测序仪器，主要来自太平洋生物科学公司和牛津纳米孔公司，已经可以获得 在许多应用中将取代短读测序。我们和其他人已经使用了这些 在每个癌症基因组中发现数以万计的无法使用 短阅读，包括已知癌基因和癌症中的结构变异和差异甲基化区域 风险基因。然而，这些技术有可能解决癌症生物学中的许多悬而未决的问题， 分析长时间读取的测序数据对计算要求很高，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。在这项提案中，我们将解决以下几个差距 长阅读技术在癌症基因组学基础研究和临床应用中的应用。首先，我们将 开发改进的方法，从长阅读中寻找结构变体和复杂的重复扩展， 这两者都是疾病的主要诊断和预后指标，但不能用 现有的方法。利用改进的长读取分阶段功能，这项工作将包括 检测马赛克变异，揭示肿瘤的异质性和癌前组织中的变异。接下来，我们将 应用机器学习和系统水平的进步来加速和改进变种的比较 在大量的患者队列中。关键的是，这将弥补单分子容易出错的性质 长读测序，使这些比较更准确时，比较肿瘤正常样本或 相关患者的家系，以便能够准确地识别反复发生的驱动突变。最后，我们会 为癌症的基因组、转录组和表观遗传图谱的联合分析开发综合方法 基因组。这些进展将改进融合基因的鉴定，并允许全新的形式 表观遗传分析，如跨转座元件和其他的甲基化的等位基因特异性分析 重复的元素。我们用我们的方法合成了数千种我们将检测到的新变种，我们 然后将开发算法来识别和评估重复的遗传或表观遗传变异 推定的驱动突变。所有方法都将以开源方式发布，并将增强我们的ITCR 合作者，以及整个癌症基因组学社区，与NEAR一起研究遗传和表观遗传变异 完美的准确性，从而揭开了许多与治疗和疾病有关的新联系。

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.