CAREER: Algorithmic Approaches for Phylogenetic Analysis of Tumor Evolution

为了改善癌症患者的临床诊断和治疗，有必要更好地了解肿瘤是如何随着时间的推移而生长和发展的。这项工作的目的是了解基因改变发生的顺序，以及哪些改变发生在一起或在不同的细胞系中。这些知识对于减轻这种疾病对受影响者的影响至关重要。例如，针对不同细胞系中发生的遗传改变设计的治疗计划可能更有效地预防疾病复发。DNA测序技术的最新进展和用于分析这些技术产生的数据的方法，使人们得以一窥癌症背后的突变过程。研究人员现在有了更好的装备来推断最近诊断出的肿瘤的历史信息，包括在诊断前几个月甚至几年的时间里，肿瘤发展过程中事件发生的顺序。然而，使用这些方法推断的肿瘤病史仍有许多不确定性和差异。该项目将通过开发急需的方法来解决这些问题，这些方法可以从测序数据中推断出肿瘤历史的比较、总结和可视化。将为本科生和刚毕业的学生提供大量的机会，特别是那些来自代表性不足的群体的学生，以获得实践研究经验。这将与一些教育活动相结合，使具有最低计算机科学背景的本科生能够学习如何将他们的技能应用于重要的生物学问题的实际应用-这一领域在未来几年可能需要越来越多的劳动力。肿瘤发展的系统发育历史通常用一种被称为克隆树的标记的、有根的树来描述。该项目将导致比较、组织和交流克隆树信息的方法的发展。具体而言，该项目的智力目标是：(1)开发用于比较克隆树的新距离度量和评估此类度量的方法；(2)设计算法，既可以对小克隆树集合进行汇总，也可以对所有克隆树的大空间进行汇总；(3)设计一个可视化工具，供专家探索和比较克隆树。这些目标与一个教育计划相结合，该计划的重点是扩大和扩大本科生对计算生物学的参与：通过研讨会，创新的课堂体验，以及本科生直接参与研究过程的大量机会。该奖项反映了美国国家科学基金会的法定使命，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。

In order to improve clinical diagnosis and treatment of patients afflicted with cancer, there is a need for an improved understanding of how tumors grow and develop over time. This work seeks specifically to understand what order genetic alterations occur, and what alterations occur together or in separate cell lineages. Such knowledge will be fundamental towards lessening the impact of this disease on those affected. For example, treatment plans designed to target genetic alterations occurring in separate cell lineages may be more effective in preventing disease relapse. Recent advances in DNA sequencing technologies and methods designed to analyze the data produced by these technologies has enabled glimpses into the mutational processes underlying cancers. Researchers are now better equipped to infer information about the history of a recently diagnosed tumor, including the order that events took place during its development over months or even years prior to diagnosis. However, there is still much uncertainty and variation in the tumor histories inferred using these methods. This project will address these issues through the development of critically needed approaches that enable comparison, summarization, and visualization of tumor histories inferred from sequencing data. Numerous opportunities will be provided for undergraduate students and recent graduates, especially those from underrepresented groups, to gain hands-on research experience. This will be combined with several educational activities that enable undergraduate students with a minimal computer science background to learn about real world applications of how their skills can be applied to important biological problems - an area that is likely to need a growing workforce in the coming years.The phylogenetic history of how a tumor developed is typically described using a labeled, rooted tree called a clonal tree. This project will result in the development of methods that compare, organize, and communicate information about clonal trees. Specifically, the intellectual aims of the project are: (1) Develop new distance measures for comparing clonal trees and means for assessing such measures; (2) Design algorithms that allow for the summarization of both small sets of clonal trees and the larger space of all such trees; and (3) Design a visualization tool for experts to explore and compare clonal trees. These goals are integrated with an educational plan that focuses on expanding and broadening undergraduate involvement in computational biology: through workshops, innovative classroom experiences, and numerous opportunities for undergraduates to be directly involved in the research process.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.