CRII: AF: Towards an Accurate and Complete Characterization of the Solution Space in Phylogeny Estimation from Mixed Samples

CRII：AF：在混合样本的系统发育估计中实现解决方案空间的准确和完整的表征

基本信息

批准号：
1850502
负责人：
Mohammed El-Kebir
金额：
$ 17.5万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-15 至 2022-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850502&HistoricalAwards=false
关键词：
CRII AF Towards Accurate Complete

项目摘要

Cancers result from an evolutionary process during which mutations accumulate in a population of cells, leading to the presence of distinct cellular populations within the same tumor with varying complements of mutations. Thus, to understand and treat cancer, researchers must view the disease through the lens of evolution. Phylogenetic trees, or phylogenies, are mathematical models to describe the evolutionary history and relationships of entities observed at the present time. They have been traditionally applied to study biological species and languages. In the context of cancer, tumor phylogenies are essential to improve our understanding of basic mechanisms of cancer progression, and to develop personalized cancer treatment plans tailored to the unique evolutionary history of a patient's tumor. This project addresses a challenge that is unique to cancer phylogenetics, i.e. phylogeny inference from mixed tumor samples, which form the majority of current cancer sequencing studies. While a biological sample in traditional phylogenetics contains sequences from cells with identical genomes, a mixed tumor sample is composed of sequences from cells with distinct genomes. Consequently, multiple phylogenetic trees may be inferred from the same mixed input samples, potentially leading to diverging conclusions in downstream clinical and basic science analyses of cancers. To address this challenge, this project seeks new algorithms, theory and practical implementations for characterizing the solution space in phylogeny estimation from mixed tumor samples. In addition, this award will support the advancement, training and education of students at all levels through course and outreach module design. The underlying combinatorial problem of current cancer phylogenetics methods is the Perfect Phylogeny Mixture (PPM) problem, where, given an m-by-n mutation frequency matrix F, the task is to infer a two-state perfect phylogeny tree T that explains the composition of the m mixed samples and the evolutionary history of the n mutations. This problem is not only nondeterministic polynomial time (NP) complete, but it also exhibits non-uniqueness of solutions, i.e. multiple perfect phylogeny trees T may explain a single input mutation frequency matrix F. Multiple solutions may lead to alternate conclusions in downstream analyses in cancer genomics. Thus, it is important to accurately and completely characterize the solution space by, for instance, generating solutions uniformly at random. However, current methods are unable to do so. This project will address these shortcomings through the following three research activities. First, this project will characterize conditions for statistical identifiability for the PPM model, which is a fundamental question in phylogenetics. Second, this project will develop almost uniform sampling and approximate counting algorithms that incorporate a probabilistic data error model. Third, the team of researchers will apply the resulting algorithms in a variety of downstream analyses in cancer to assess robustness of conclusions in the light of uncertainty due to non-uniqueness. Importantly, the new mathematical and computational techniques developed as part of this project will be applicable to other settings where multiple optima are encountered.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.

癌症是由于进化过程而导致的，在此过程中，突变在细胞群中积累，导致在同一肿瘤中存在不同的细胞种群，并具有不同的突变补体。因此，要了解和治疗癌症，研究人员必须通过进化的角度看待疾病。系统发育树或系统发育是数学模型，用于描述目前观察到的实体的进化历史和关系。传统上，它们被应用于研究生物物种和语言。在癌症的背景下，肿瘤系统发育对于提高我们对癌症进展的基本机制的理解至关重要，并制定了针对患者肿瘤独特进化史的个性化癌症治疗计划。该项目解决了癌症系统发育学特征的挑战，即来自混合肿瘤样品的系统发育，这是当前癌症测序研究的大多数。尽管传统系统发育学中的生物样品包含来自具有相同基因组细胞的序列，但混合肿瘤样品由来自具有不同基因组细胞的序列组成。因此，可以从相同的混合输入样品中推断出多个系统发育树，这有可能导致癌症下游临床和基础科学分析的分歧。为了应对这一挑战，该项目寻求新的算法，理论和实际实现，以表征解决混合肿瘤样品的系统发育估计中的溶液空间。此外，该奖项将通过课程和外展模块设计在各个级别上的学生进行进步，培训和教育。当前癌症系统发育方法方法的潜在组合问题是完美的系统发育混合物（PPM）问题，在给定M-BY-N突变频率矩阵F中，任务是推断两态完美的系统发育树T，解释了M混合样品的组成和N突变的进化史。这个问题不仅是不确定的多项式时间（NP）完整，而且还表现出非唯一性，即多个完美的系统发育树T可能解释了单个输入突变频率矩阵F。多个溶液可能会导致癌症下游分析中的其他结论。因此，重要的是要通过例如随机生成溶液来准确并完全表征解决方案空间。但是，当前的方法无法做到。该项目将通过以下三项研究活动解决这些缺点。首先，该项目将表征PPM模型的统计可识别性条件，PPM模型是系统发育学的一个基本问题。其次，该项目将开发几乎均匀的采样和近似计数算法，以结合概率数据误差模型。第三，研究人员将在癌症的各种下游分析中应用所得算法，以根据不确定性而评估结论的稳健性。重要的是，作为该项目的一部分开发的新数学和计算技术将适用于遇到多个Optima的其他环境。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛影响的评估标准通过评估来获得支持的。