Integrative genomic and epigenomic analysis of cancer using long read sequencing

使用长读长测序对癌症进行综合基因组和表观基因组分析

• 批准号: 10187808
• 负责人: MICHAEL SCHATZ
• 金额: $ 38.35万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10187808
• 关键词: Accounting; Address; Algorithmic Analysis; Algorithms; Alleles; Automobile Driving; Basic Science; Bioinformatics; Biological Sciences; Cancer Biology; Cancerous; Cataloging; Characteristics; Clinical; Communities; Complex; Computing Methodologies; Copy Number Polymorphism; Cytosine; DNA Sequence Rearrangement; DNA Transposable Elements; Data; Data Set; Detection; Development; Diagnostic; Disease; Ensure; Epigenetic Process; Gene Expression Profiling; Generations; Genes; Genetic; Genetic Variation; Genetic study; Genome; Genomics; Genotype; Goals; Graph; Growth; Health; Individual; Joints; Karyotype; Machine Learning; Malignant Neoplasms; Mediating; Methods; Methylation; Minisatellite Repeats; Modeling; Monitor; Mosaicism; Mutation; Nature; Normal tissue morphology; Oncogenes; Outcome; Pathogenicity; Patients; Phase; Population; Prognostic Marker; Protein Isoforms; Recurrence; Repetitive Sequence; Research; Research Personnel; Resolution; Resources; Role; Sample Size; Sampling; Signal Transduction; Somatic Mutation; Statistical Methods; Structure; System; Tandem Repeat Sequences; Technology; Tissues; Tumor Suppressor Proteins; Variant; Work; base; cancer genome; cancer genomics; cancer initiation; cancer risk; cancer therapy; cancer type; cohort; disorder risk; driver mutation; epigenetic profiling; epigenetic variation; epigenome; epigenomics; experience; fusion gene; genetic pedigree; genetic variant; genome analysis; genome sequencing; improved; indexing; insight; instrument; methylome; nanopore; novel; novel strategies; open source; power analysis; premalignant; risk variant; sequencing platform; single molecule; transcriptome; transcriptomics; tumor; tumor heterogeneity; tumor progression

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.

项目概要 过去二十年，癌症基因组测序取得了长足发展，导致了 这在很大程度上提高了人们对遗传和表观遗传突变在癌症中的作用的认识。 高通量“第二代”测序技术和分析的发展已成为可能 最近，新一代高通量长读长技术用于表征癌症基因组。 主要来自 Pacific Biosciences 和 Oxford Nanopore 的测序仪器已经上市， 我们和其他人已经准备好在许多应用中取代短读长测序。 技术可发现数以万计的癌症基因组，而这些基因组是使用无法检测到的 短读，包括已知癌基因和癌症中的结构变异和差异甲基化区域 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。 长读长测序数据的分析对计算的要求很高，并且需要专门的算法 要么效率太低而无法大规模使用，要么尚不存在。在本提案中，我们将解决其中的几个差距。 首先，我们将长读技术应用于癌症基因组学的基础研究和临床。 开发改进的方法来从长读中查找结构变异和复杂的重复扩展， 两者都是疾病的主要诊断和预后指标，但尚未通过使用准确识别 利用现有方法改进的长读取定相能力，这项工作将包括 检测镶嵌变异，揭示肿瘤异质性和癌前组织的变异。 应用机器学习和系统级进步来加速和改进变体的比较 至关重要的是，这将弥补单分子容易出错的性质。 长读长测序使这些比较在比较肿瘤与正常样本或 最后，我们将分析相关患者的家谱，以便准确识别反复发生的驱动突变。 开发联合分析癌症基因组、转录组和表观遗传图谱的综合方法 这些进步将改善融合基因的识别，并允许全新形式的融合。 表观遗传分析，例如跨转座元件的甲基化的等位基因特异性分析和其他 合成我们将使用我们的方法检测到的数千种新变体。 然后将开发算法来识别和评估反复出现的遗传或表观遗传变异 假定的驱动突变。所有方法都将开源，并将赋予我们 ITCR 权力。 合作者和整个癌症基因组学界，以近乎接近的方式研究遗传和表观遗传变异 完美的准确性，从而解锁许多治疗和疾病的新关联。