III: Small: Models and Methods for Simultaneous Genotyping and Phylogeny Inference from Single-Cell DNA Data

III：小型：根据单细胞 DNA 数据同时进行基因分型和系统发育推断的模型和方法

• 批准号: 1812822
• 负责人: Luay Nakhleh
• 金额: $ 49.98万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812822&HistoricalAwards=false
• 关键词: III; Small; Models; Methods; Simultaneous

Cancer is a disease that emerges from a single cell in the somatic tissue and is driven by a complex interplay of somatic mutations, copy number aberrations and chromosomal rearrangements. As a tumor progresses, diverse genomic aberrations give rise to genetically heterogeneous subpopulations (clones) of cells that interact with each other in a Darwinian framework of mutations, fitness and selection. Intra-tumor heterogeneity (ITH) complicates the diagnosis and treatment of cancer patients and causes relapse and drug resistance. The emergence of next-generation sequencing (NGS) technologies has enabled a thorough analysis of tumor heterogeneity through the generation of large-scale quantitative genomic datasets. However, despite these advances, a comprehensive understanding of intra-tumor heterogeneity has proved elusive thus far.The project entails devising new mathematical formulations and developing new algorithmic techniques for inferring evolutionary histories of tumor cells in order to understand tumor evolution and ITH. In particular, all the models and methods target single-cell sequencing (SCS) data and account simultaneously for variant calling and genotyping, as well as evolutionary history inference. This simultaneous inference accounts in a principled manner for error in SCS data. Models based on both finite- and infinite-sites assumptions will be derived. The research is highly interdisciplinary, using combinatorial optimization and graph theory, as well as statistical estimation, to provide practical solutions to problems that arise in cancer genomics. While a major impact of the project will be on cancer biology, the project will also stimulate research at the intersection of mathematics, computer science and statistics. The project will provide training opportunities for students in a truly interdisciplinary setting. The project will produce freely available open-source software. The research results will be integrated into courses and disseminated through publications. All models and algorithms will be implemented in open-source software, and disseminated via Github and Bitbucket.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

癌症是从体细胞组织中的单个细胞出现的疾病，并且由体细胞突变、拷贝数畸变和染色体重排的复杂相互作用驱动。随着肿瘤的进展，不同的基因组畸变产生遗传异质性细胞亚群（克隆），这些亚群在突变、适应性和选择的达尔文框架中相互作用。肿瘤内异质性（ITH）使癌症患者的诊断和治疗复杂化，并导致复发和耐药性。下一代测序（NGS）技术的出现使得能够通过生成大规模定量基因组数据集来彻底分析肿瘤异质性。然而，尽管取得了这些进展，肿瘤内异质性的全面理解已被证明是难以捉摸的迄今为止。该项目需要设计新的数学公式和开发新的算法技术来推断肿瘤细胞的进化历史，以了解肿瘤的演变和ITH。特别是，所有的模型和方法都以单细胞测序（SCS）数据为目标，同时考虑变异识别和基因分型，以及进化历史推断。这种同时推断原则上解释了SCS数据中的错误。模型的基础上，有限和无限的网站的假设将得出。该研究是高度跨学科的，使用组合优化和图论以及统计估计，为癌症基因组学中出现的问题提供实用的解决方案。虽然该项目将对癌症生物学产生重大影响，但该项目也将促进数学，计算机科学和统计学交叉领域的研究。该项目将在真正的跨学科环境中为学生提供培训机会。该项目将制作免费提供的开放源码软件。研究结果将纳入课程，并通过出版物传播。所有模型和算法都将在开源软件中实现，并通过Github和Bitbucket传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data

• DOI: 10.1101/gr.243121.118
• 发表时间: 2019-11-01
• 期刊: GENOME RESEARCH
• 影响因子: 7
• 作者: Zafar, Hamim;Navin, Nicholas;Nakhleh, Luay
• 通讯作者: Nakhleh, Luay

Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data

• DOI: 10.1371/journal.pcbi.1008012
• 发表时间: 2020-07-01
• 期刊: PLOS COMPUTATIONAL BIOLOGY
• 影响因子: 4.3
• 作者: Mallory, Xian F.;Edrisi, Mohammadamin;Nakhleh, Luay
• 通讯作者: Nakhleh, Luay