Collaborative Research: III: Medium: Algorithms for scalable inference and phylodynamic analysis of tumor haplotypes using low-coverage single cell sequencing data

合作研究：III：中：使用低覆盖率单细胞测序数据对肿瘤单倍型进行可扩展推理和系统动力学分析的算法

• 批准号: 2415562
• 负责人: Pavel Skums
• 金额: $ 49.84万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2415562&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Medium; Algorithms

Cancer is a dynamical evolutionary process, where populations of tumor cells are continuously evolving to compete for resources, to metastasize, and to escape immune responses and therapy. Quantification of cancer evolutionary dynamics is therefore essential to understanding the mechanisms of cancer progression. Single-cell sequencing has enabled characterization of tumor composition at the finest possible resolution, thus providing researchers with the data needed to potentially allow for such quantification. However, to realize this potential, appropriate algorithms and data analysis tools are needed. The computational discipline that extracts evolutionary parameters from genomic data by integrating phylogenetics, population genetics and statistical learning is called phylodynamics. While almost all existing phylodynamics methods are developed for viruses, there is a growing realization that this methodology is also highly relevant to cancer biology. However, the development of cancer phylodynamics algorithms faces many challenges associated with the nature of cancer genomics data. The overarching goal of this proposal is to address these challenges by developing a phylodynamic framework for joint inference of cancer phylogenetic trees and evolutionary parameters from single-cell DNA sequencing (scDNA-Seq) data. This framework will allow cancer researchers to carry out a statistically and computationally sound evaluation of the effects of particular genome alterations or their combinations. In addition, this project will support development of innovative cross-disciplinary curricula, and bioinformatics training for diverse cohorts of undergraduate and graduate students at Georgia State University (Title III designation of Predominantly Black Institution), University of Connecticut, and UConn Health.The project has three interrelated technical aims. First, investigators will develop algorithms for joint reconstruction of clonal frequencies and phased cancer clone genomic profiles (including copy number variation profiles and single nucleotide variants). The project will concentrate on low-coverage scDNA-seq that can provide enough clonal data to guarantee the density of branching events in the cancer phylogenies necessary for phylodynamics analysis. Second, the researchers will design a novel methodology for intra-tumor phylodynamics inference. This includes scalable construction of plausible clone phylogenetic trees using a novel bipartition-based median-tree approach, together with maximum a posteriori inference of cancer fitness and mutability landscapes. The distinguishing feature of the proposed approach is the use of convex optimization techniques rather than MCMC sampling, which will guarantee scalability and accuracy of developed computational tools. Finally, a comprehensive set of experiments will be conducted to validate and assess the accuracy of developed methods. These will include computational experiments on simulated and publicly available scDNA-Seq data, as well as using scDNA-Seq datasets generated by in vitro and in vivo experiments conducted at UConn Health.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.

癌症是一个动态的进化过程，肿瘤细胞群不断进化，争夺资源，转移，逃避免疫反应和治疗。因此，癌症进化动力学的量化对于理解癌症进展的机制至关重要。单细胞测序能够以尽可能高的分辨率表征肿瘤成分，从而为研究人员提供可能允许这种量化所需的数据。然而，要实现这一潜力，需要适当的算法和数据分析工具。通过整合系统遗传学、群体遗传学和统计学习，从基因组数据中提取进化参数的计算学科被称为系统动力学。虽然几乎所有现有的系统动力学方法都是针对病毒开发的，但人们越来越意识到这种方法也与癌症生物学高度相关。然而，癌症系统动力学算法的发展面临着许多与癌症基因组学数据性质相关的挑战。本提案的总体目标是通过开发一个系统动力学框架，从单细胞DNA测序（scDNA-Seq）数据中联合推断癌症系统发生树和进化参数，来解决这些挑战。这个框架将允许癌症研究人员对特定基因组改变或其组合的影响进行统计和计算上的合理评估。此外，该项目将支持开发创新的跨学科课程，并为佐治亚州立大学（标题III指定以黑人为主的机构）、康涅狄格大学和康涅狄格大学健康中心的本科生和研究生的不同群体提供生物信息学培训。该项目有三个相互关联的技术目标。首先，研究人员将开发用于克隆频率和阶段性癌症克隆基因组图谱（包括拷贝数变异图谱和单核苷酸变异）联合重建的算法。该项目将专注于低覆盖率的scDNA-seq，可以提供足够的克隆数据，以保证系统动力学分析所需的癌症系统发育分支事件的密度。其次，研究人员将设计一种新的方法来推断肿瘤内的系统动力学。这包括使用一种新的基于双分区的中位树方法可扩展地构建合理的克隆系统发育树，以及对癌症适应度和易变性景观的最大后验推断。该方法的显著特点是使用凸优化技术而不是MCMC采样，这将保证开发的计算工具的可扩展性和准确性。最后，将进行一套全面的实验来验证和评估所开发方法的准确性。这些将包括模拟和公开可用的scDNA-Seq数据的计算实验，以及使用康涅狄格大学健康中心进行的体外和体内实验产生的scDNA-Seq数据集。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。