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: 2212511
• 负责人: Ion Mandoiu
• 金额: $ 49.93万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212511&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.

癌症是一个动力学进化过程，在该过程中，肿瘤细胞的种群不断发展以争夺资源，转移和逃避免疫反应和治疗。因此，癌症进化动力学的定量对于理解癌症进展的机制至关重要。单细胞测序已使肿瘤组成以最好的分辨率来表征，从而为研究人员提供了可能允许这种定量的数据。但是，为了实现这种潜力，需要适当的算法和数据分析工具。通过整合系统发育，种群遗传学和统计学习，从基因组数据中提取进化参数的计算学科称为系统动力学。尽管几乎所有现有的系统动力学方法都是为病毒开发的，但越来越多地认识到，该方法也与癌症生物学高度相关。但是，癌症系统动力学算法的发展面临着与癌症基因组数据性质有关的许多挑战。该提案的总体目标是通过开发系统的系统动力学框架来解决这些挑战，从而从单细胞DNA测序（SCDNA-SEQ）数据中开发了癌症系统发育树的联合推断和进化参数。该框架将使癌症研究人员能够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，该项目将支持佐治亚州立大学（Title III of Title of Black Institution of Black Institation），康涅狄格大学和UConn Health的创新跨学科课程以及对各种本科生和研究生的生物信息学培训。该项目具有三个相互关系的技术目标。首先，研究人员将开发用于克隆频率和分阶段癌症克隆基因组谱（包括拷贝数变化曲线和单核苷酸变体）联合重建的算法。该项目将集中于低覆盖的SCDNA-seq，可以提供足够的克隆数据，以确保系统分析所需的癌症系统发育中分支事件的密度。其次，研究人员将设计一种新的肿瘤内系统动力学推断方法。这包括使用新型基于两人的中位树方法的合理克隆系统发育树的可扩展构建，以及最大的后验癌症和可突变性景观的后验推断。所提出方法的区别特征是使用凸优化技术而不是MCMC采样，这将确保开发的计算工具的可扩展性和准确性。最后，将进行一组全面的实验，以验证和评估开发方法的准确性。 这些将包括有关模拟和公开可用的SCDNA-SEQ数据的计算实验，以及使用由UConn Health在UConn Health进行的体外和体内实验产生的SCDNA-SEQ数据集。该奖项反映了NSF的法定任务，并认为通过基金会的知识优点和广泛的criperia criperia criperia criperia criperia criperia criperia criperia rection the Awsion the奖项。