CAREER: Algorithms for Comprehensive and Cost-effective Cancer Phylogeny Inference from Multi-omics Single-cell Sequencing Data

癌症是美国人过早死亡的首要原因。为了减轻癌症的负担并改善临床实践，研究人员使用算法根据DNA测序数据重建肿瘤的进化史或系统发育。具体地说，肿瘤发展史描述了患者肿瘤的精确突变成分，从而使精确医学能够改善临床结果。此外，对来自患者队列的肿瘤系统发生的比较分析能够识别肿瘤进化和转移的关键机制，这反过来可能导致新的治疗途径。该领域的下一个技术前沿是单细胞测序(SCS)，它具有在单细胞分辨率下精确重建肿瘤进化历史的潜力。虽然SCS正在成为癌症基因组学事实上的标准，但这种新型数据的算法仍处于初级阶段，尚未全面模拟癌症的演变。该项目将通过开发新的工具来填补这一空白，这些工具将使从业者能够经济高效地使用SCS来全面研究肿瘤的演变，从而促进关于癌症进展的知识状态，这反过来可能导致更有针对性的癌症治疗。该项目的成果将通过开放源码软件传播。综合研究和教育活动包括跨学科的生物信息学课程开发，向高中生推广，以及为代表不足的群体的学生提供研究机会。该项目寻求新的模型、算法和实际实施，以准确和经济高效地从SCS数据推断全面的癌症发生，并将拟议的研究整合到教育和推广中。该项目将一种新的进化模型与高效的系统发育推断算法结合在一起，该模型结合了不同基因组规模的体细胞突变，能够处理使用多种实验技术获得的混合SCS数据。这项研究的目的是：(1)从混合单细胞测序数据推断全面的癌症系统发生；(2)设计经济高效的单细胞测序实验，使其能够进行准确的系统发生推断；以及(3)将开发的方法应用于从集成的、可扩展的、可扩展的和用户友好的分析管道中的临床合作者获得的数据。结果、软件和更多信息将在http://el-kebir.net/scp.This颁奖典礼上提供，该奖项反映了国家科学基金会的法定使命，并已通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Cancer is the leading cause of premature death in the United States. To lessen the burden of cancer and improve clinical practice, researchers use algorithms to reconstruct the evolutionary history, or phylogeny, of a tumor from DNA sequencing data. Specifically, a tumor phylogeny describes the precise mutational composition of a patient’s tumor, thus enabling precision medicine with improved clinical outcomes. Moreover, comparative analysis of tumor phylogenies from patient cohorts enables the identification of key mechanisms underlying tumor evolution and metastasis, which in turn may lead to new treatment avenues. The next technological frontier in the field is single-cell sequencing (SCS), which holds the potential to precisely reconstruct a tumor's evolutionary history at single cell resolution. While SCS is becoming the de facto standard in cancer genomics, algorithms for this new type of data are still in their infancy and do not yet comprehensively model cancer evolution. This project will address this gap by developing new tools that will enable practitioners to cost-efficiently use SCS to comprehensively study a tumor's evolution, thereby advancing the state of knowledge regarding cancer progression, which in turn may lead to better targeted cancer therapies. Results from this project will be disseminated through open-source software. The integrated research and educational activities include interdisciplinary bioinformatics curriculum development, outreach to high school students and research opportunities for students in underrepresented groups.This project seeks new models, algorithms and practical implementations to accurately and cost-efficiently infer comprehensive cancer phylogenies from SCS data and integrate the proposed research into education and outreach. The project couples a new evolutionary model that incorporates somatic mutations of varying genomic scales with efficient phylogeny inference algorithms that are able to deal with hybrid SCS data obtained using multiple experimental techniques. The aims of this research are: (1) inference of comprehensive cancer phylogenies from hybrid single-cell sequencing data, (2) design of cost-efficient single-cell sequencing experiments that are sufficiently powered to enable accurate phylogeny inference, and (3) application of the developed methods to data obtained from clinical collaborators within integrated, scalable, extensible and user-friendly analysis pipelines. Results, software and additional information will be available at http://el-kebir.net/scp.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.