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

癌症是由于个体一生中发生的基因组改变的累积造成的，并导致细胞集合不受控制地生长成肿瘤。这些突变是进化过程的一部分，该过程可能在患者诊断前几十年就开始了。更好地了解肿瘤随时间的演变历史可能会对肿瘤如何和为何发展以及哪些突变驱动其生长产生重要的见解。 DNA 测序技术的最新进展彻底改变了人类基因组各个方面的测量方式，并有可能揭示癌症和许多其他人类疾病的分子基础。然而，要充分发挥这些技术进步的潜力，需要专门设计用于分析这些数据的新颖算法方法。例如，DNA测序数据仅捕获测序时肿瘤的信息，而不是它如何进化到当前状态的信息。尽管近年来开发了许多算法来从 DNA 序列数据推断肿瘤进化信息，但这一计算发展领域相对年轻，仍然存在许多未解决的挑战。该项目将重点开发计算方法，以改进癌症基因组进化历史的推断，同时扩大本科生在计算生物学领域的研究参与。肿瘤的进化史可以描述为一棵有根树，其顶点代表肿瘤历史期间存在的不同肿瘤群体。 虽然许多计算方法旨在从测序数据推断这一历史，但仍有很大的改进空间。 例如，事实证明，合并多个数据信号（例如，单核苷酸变异和拷贝数畸变）很困难，并且在同一数据集上运行时，不同的方法可能会产生不同的结果。该项目将开发直接解决这一限制和其他限制的计算方法。该项目不是提出另一种方法来直接推断肿瘤的进化历史，而是开发共识方法，在给定潜在肿瘤历史树的集合的情况下，推断出单个共识树。此外，该项目将研究推断肿瘤进化史的理论和实践局限性。 这将包括分析何时理论上可检测到全基因组重复等大规模事件，以及模拟研究以调查实际考虑因素（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。 该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 将协调一个跨机构的计算生物学本科生研讨会，为学士学位机构的学生和教师提供互动和合作的场所。

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.