Advanced development of Lancet, an emerging tool for complex variant calling in cancer genomics

柳叶刀的高级开发，一种用于癌症基因组学中复杂变异调用的新兴工具

• 批准号: 10491232
• 负责人: Giuseppe Narzisi
• 金额: $ 49.08万
• 依托单位: NEW YORK GENOME CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10491232
• 关键词: Address; Adoption; Advanced Development; Algorithms; Bandage; Behavior; Blood; Chromosomes; Color; Communities; Complex; Computer software; DNA sequencing; Data; Detection; Development; Disease; Disseminated Malignant Neoplasm; Documentation; Engineering; Event; Evolution; Genome; Genomic Segment; Goals; Graph; Haplotypes; Joints; Knowledge; Libraries; Link; Longitudinal cohort; Malignant Neoplasms; Manuals; Methods; Modeling; Modernization; Mutation; Patients; Performance; Phase; Play; Primary Neoplasm; Publications; Relapse; Research; Research Personnel; Resolution; Role; Sample Size; Sampling; Single Nucleotide Polymorphism; Somatic Mutation; Source Code; Structure; Testing; The Cancer Genome Atlas; Time; Update; Variant; Visualization; anticancer research; base; cancer cell; cancer genomics; cancer therapy; cohort; cost; data visualization; design; exome sequencing; genome analysis; genome sequencing; genome-wide; high throughput analysis; improved; improved functioning; insertion/deletion mutation; light weight; longitudinal analysis; novel strategies; performance tests; programs; prototype; tool; tumor; tumor progression; tumorigenesis; user-friendly; variant detection; web site; whole genome

ABSTRACT One of the central challenges in cancer genomics is the ability to accurately detect somatic mutations in heterogeneous tumors, and precisely determine their clonal origin and evolution. This fundamental knowledge is central to the discovery of new cancer therapies. In recent years, reductions in the cost of whole-genome and whole-exome sequencing have enabled researchers to address these questions in unprecedented detail. However, a major limitation in the field has been a paucity of methods for variant calling that extend beyond identifying simple single-nucleotide variants (SNVs) and small indels to allow the characterization of complex structural changes that also play a significant role in tumorigenesis and cancer progression. Indels of more than a few bases are challenging to discover with typically used alignment-based methods. In addition, most variant callers analyze tumor and normal data separately, which can introduce false positives such as when a mutation shows partial support in the normal sample. Towards addressing these shortcomings, we recently introduced Lancet, a new somatic variant caller developed under the auspices of the ITCR R21. Lancet leverages local assembly and joint analysis of tumor-normal paired data using region-focused colored de Bruijn graphs, with on- the-fly repeat composition analysis and a self-tuning k-mer strategy. This results in relatively reduced reference bias; an improved ability to detect variations that significantly diverge from the reference chromosome representations; a reduction in the scale of the analysis, leading to increased power and sensitivity to detect variants through localized, comprehensive graph exploration; and dynamic adjustment of calling behavior according to the sequence conditions of each genomic region. In testing, Lancet shows superior performance to all major alignment-based methods in terms of accuracy, particularly in the detection of ‘twilight zone’ indels (30- 250 bp). Given its continued adoption and successful application in over a dozen high-impact publications, Lancet is poised for more advanced development to enable continued improvements in its variant calling power, precision, and analytical capabilities. Specifically, Lancet is currently limited by longer runtimes than alignment- based methods, reduced sensitivity for longer insertions, lack of interactive visualization of the colored de Bruijn graph, and the inability to jointly analyze longitudinal data. To address these limitations, we propose the following Specific Aims: 1) Increase computational performance and facilitate user adoption and third-party development; 2) Add new features and enhancements to improve variant detection, phasing, and data visualization; and 3) Enable joint assembly and analysis of longitudinal data. Impact: With additional development, the next iteration of Lancet will feature advanced algorithms for fast, efficient, accurate, localized, user-friendly, and application- modifiable variant analysis of phased genome-wide timeseries data, establishing it as one of the leading methods for variant calling in cancer research.

抽象的 癌症基因组学的核心挑战之一是准确检测体细胞突变的能力 异质肿瘤，并精确确定其克隆起源和进化。这些基础知识 对于发现新的癌症疗法至关重要。近年来，随着全基因组研究成本的降低， 全外显子组测序使研究人员能够以前所未有的细节解决这些问题。 然而，该领域的一个主要限制是缺乏超出范围的变异调用方法 识别简单的单核苷酸变异 (SNV) 和小插入缺失，以表征复杂的 结构变化在肿瘤发生和癌症进展中也发挥着重要作用。插入缺失超过 使用常用的基于比对的方法很难发现一些碱基。此外，大多数变体 呼叫者分别分析肿瘤和正常数据，这可能会引入误报，例如当突变时 在正常样本中显示出部分支持。为了解决这些缺点，我们最近推出了 Lancet 是在 ITCR R21 的支持下开发的一种新的体细胞变异识别器。 《柳叶刀》利用本地 使用以区域为中心的彩色 de Bruijn 图对肿瘤-正常配对数据进行组装和联合分析， 即时重复组成分析和自调整 k-mer 策略。这导致参考相对减少 偏见;检测与参考染色体显着不同的变异的能力得到提高 陈述；分析规模的缩小，导致检测能力和灵敏度的提高 通过本地化、全面的图形探索进行变体；以及调用行为的动态调整 根据每个基因组区域的序列条件。在测试中，Lancet 表现出优于 所有主要的基于比对的方法在准确性方面，特别是在检测“暮光区”插入缺失方面（30- 250bp）。鉴于其在十多个高影响力出版物中的持续采用和成功应用， 《柳叶刀》准备进行更先进的开发，以持续改进其变异识别能力， 精度和分析能力。具体来说，Lancet 目前受到比对齐更长的运行时间的限制 - 基于方法，较长插入的灵敏度降低，缺乏彩色 de Bruijn 的交互式可视化 图，并且无法联合分析纵向数据。为了解决这些限制，我们提出以下建议 具体目标： 1）提高计算性能并促进用户采用和第三方开发； 2) 添加新功能和增强功能，以改进变异检测、定相和数据可视化；和 3) 实现纵向数据的联合组装和分析。影响：随着额外的开发，下一个迭代 《柳叶刀》将采用先进的算法，实现快速、高效、准确、本地化、用户友好和应用。 分阶段全基因组时间序列数据的可修改变异分析，使其成为领先方法之一 用于癌症研究中的变异调用。