CRII: III: RUI: Computational Approaches for Inferring the Evolutionary Histories of Cancer Genomes

CRII：III：RUI：推断癌症基因组进化史的计算方法

• 批准号: 1657380
• 负责人: Layla Oesper
• 金额: $ 14.28万
• 依托单位: Carleton College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657380&HistoricalAwards=false
• 关键词: CRII; III; RUI; Computational; Approaches

Cancer results from the accumulation of genomic alterations that occur during the individual's lifetime and cause the uncontrolled growth of a collection of cells into a tumor. These mutations occur as part of an evolutionary process that may have begun decades before a patient?s diagnosis. Better understanding of the history of a tumor's evolution over time may yield important insight into how and why tumors develop, as well as which mutations drive their growth. Recent advances in DNA sequencing technologies have revolutionized how all aspects of the human genome are measured and have the potential to shed light on the molecular underpinnings of cancer and many other human diseases. However, realizing the full potential of these technological advances will require novel algorithmic methods specifically designed to analyze this data. For example, DNA sequencing data only captures information about the tumor at the time of sequencing, rather than how it evolved to its current state. While many algorithms have been developed in recent years to infer information about tumor evolution from DNA sequence data, this area of computational development is relatively young and many unsolved challenges remain. This project will focus on the development of computational approaches that enable improved inference of the evolutionary histories of cancer genomes, while simultaneously expanding undergraduate research participation in the field of computational biology. The evolutionary history of a tumor can be described as a rooted tree whose vertices represent different tumor populations that existed during the history of the tumor. While many computational methods aim to infer this history from sequencing data, there is much room for improvement. For instance, the incorporation of multiple data signals (e.g., single nucleotide variants and copy number aberrations) has proven difficult and different methods may produce different results when run on the same dataset. This project will develop computational approaches that directly address this and other limitations. Rather than proposing the advent of another method to directly infer the evolutionary history of a tumor, this project will develop consensus methods that, given a collection of potential tumor history trees, infer a single consensus tree. Furthermore, this project will investigate both theoretical and practical limitations to inferring the evolutionary history of tumors. This will include analysis of when large-scale events such as whole-genome duplications are theoretically detectable as well as simulation studies to investigate how practical considerations, such as: (i) sequencing coverage, (ii) number and distribution of sequenced samples, and (iii) noise in the sequenced data, limit or alter the ability to infer the evolutionary history of a tumor. This project will be completed with the help of undergraduate student researchers from a wide array of backgrounds, thus broadening student participation in computational biology and computer science. Additionally, the PI will coordinate a cross-institutional undergraduate workshop on computational biology that will provide a venue for both students and faculty at baccalaureate institutions to interact and collaborate.

癌症是由个体一生中发生的基因组改变的积累造成的，并导致一组细胞不受控制地生长成肿瘤。这些突变是进化过程的一部分，这一进化过程可能在患者S确诊前几十年就开始了。更好地了解肿瘤随时间演变的历史可能会对肿瘤如何和为什么发展以及哪些突变驱动它们的生长有重要的洞察力。DNA测序技术的最新进展彻底改变了人类基因组的所有方面的测量方式，并有可能揭示癌症和许多其他人类疾病的分子基础。然而，要实现这些技术进步的全部潜力，将需要专门设计的新算法方法来分析这些数据。例如，DNA测序数据只捕获测序时关于肿瘤的信息，而不是它如何进化到目前的状态。虽然近年来已经开发了许多算法来从DNA序列数据中推断关于肿瘤进化的信息，但这一计算开发领域还相对年轻，仍然存在许多未解决的挑战。该项目将专注于开发能够改进癌症基因组进化史推断的计算方法，同时扩大本科生在计算生物学领域的研究参与。肿瘤的进化史可以被描述为一棵有根的树，其顶点代表了肿瘤历史上存在的不同肿瘤种群。虽然许多计算方法的目标是从测序数据推断这一历史，但仍有很大的改进空间。例如，多个数据信号(例如单核苷酸变体和拷贝数偏差)的结合已被证明是困难的，当在相同的数据集上运行时，不同的方法可能产生不同的结果。该项目将开发直接解决这一限制和其他限制的计算方法。这个项目不是提出另一种方法来直接推断肿瘤的进化史，而是开发共识方法，在给定一组潜在的肿瘤历史树的情况下，推断出单一的共识树。此外，该项目将调查理论和实践的局限性，以推断肿瘤的进化史。这将包括对理论上何时可以检测到大规模事件(如全基因组复制)的分析，以及对实际考虑因素(例如：(I)测序覆盖范围、(Ii)测序样本的数量和分布以及(Iii)测序数据中的噪声)如何限制或改变推断肿瘤进化历史的能力的模拟研究。这个项目将在来自不同背景的本科生研究人员的帮助下完成，从而扩大学生对计算生物学和计算机科学的参与。此外，PI将协调一个关于计算生物学的跨机构本科生研讨会，该研讨会将为学士学位院校的学生和教职员工提供互动和合作的场所。

项目成果

Examining Tumor Phylogeny Inference in Noisy Sequencing Data

• DOI: 10.1109/bibm.2018.8621437
• 发表时间: 2018-12
• 期刊: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
• 作者: K. Tomlinson;Layla Oesper

Parameter, noise, and tree topology effects in tumor phylogeny inference

• DOI: 10.1186/s12920-019-0626-0
• 发表时间: 2019-12
• 期刊: BMC Medical Genomics
• 影响因子: 2.7
• 作者: K. Tomlinson;Layla Oesper

Emerging Topics in Cancer Evolution

癌症进化的新兴话题

• DOI: 10.1142/9789811250477_0036
• 发表时间: 2021
• 期刊: Proceedings of the Pacific Symposium on Biocomputing 2022
• 作者: El-Kebir, Mohammed;Morris, Quaid;Oesper, Layla;Sahinalp, S. Cenk
• 通讯作者: Sahinalp, S. Cenk

GraPhyC: Using Consensus to Infer Tumor Evolution

• DOI: 10.1109/tcbb.2020.3029689
• 发表时间: 2020-10
• 期刊: IEEE/ACM Transactions on Computational Biology and Bioinformatics
• 作者: Kiya W. Govek;Camden Sikes;Yangqiaoyu Zhou;Layla Oesper

Distance measures for tumor evolutionary trees

• DOI: 10.1093/bioinformatics/btz869
• 发表时间: 2020-04-01
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: DiNardo, Zach;Tomlinson, Kiran;Oesper, Layla
• 通讯作者: Oesper, Layla