RUI: Using computational homology to detect DNA Copy Number Aberrations in Breast Cancer

RUI：利用计算同源性检测乳腺癌中的 DNA 拷贝数畸变

• 批准号: 1217324
• 负责人: David Ellis
• 金额: $ 24万
• 依托单位: San Francisco State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217324&HistoricalAwards=false
• 关键词: RUI; Using; computational; homology; detect

The analysis of large complex data sets poses a major challenge for computational mathematics. Topological Data Analysis (TDA) applies concepts from algebraic topology to address this challenge. Topological techniques have been used successfully in engineering and biology but are seldom applied to the analysis of genomic data. In cancer genomics, the identification of copy number aberrations (CNAs) such as gains and losses of DNA segments is an important problem because CNAs are known to contain cancer genes and therefore to be involved in misregulation of key signaling pathways. CNAs may occur independently or may co-occur. The latter are believed to act synergistically and therefore result in unforeseen consequences. For example, the finding that CNAs in 8p12 and 11q13.3 are co-amplified in some breast cancers led to the discovery of functional interactions between the MYC and the TP53 pathways. Such interactions offer an important paradigm for cancer research because many tumors are believed to use similar mechanisms for their progression. Identification of co-occurring CNAs has traditionally been hindered both by the large sample sizes required to find co-occurrences and by the lack of mathematical methods to identify them efficiently. Recently, large microarray-based data sets have become available for breast cancer. The proposed research aims to develop a new TDA-based method to detect independent and co-occurring CNAs in breast cancer. In the proposed approach each CNA profile is characterized by a set of biologically-meaningful topological spaces. Topological invariants of these spaces (i.e., Betti numbers) will be used to de-noise the data and identify CNAs. This project will yield broadly-applicable methods for: (1) representing complex data sets in forms that are amenable to topological analysis; (2) determining the statistical significance of TDA results; (3) computing topological invariants from large data sets.Rapid advances in the sciences have generated large, complex data sets of unprecedented proportions. New mathematical methods are urgently needed in order to solve fundamental problems in the analysis of such high-dimensional data. In the field of genomics, thousands of measurements have been obtained with the goal of unveiling molecular signatures that characterize essential biological processes. This field has significantly influenced the direction of breast cancer research because of its potential for differentiating various subtypes, pathways and prognoses of the disease. Currently, the major approach to detecting genomic signatures is focused on the identification of single independent events. However, there is increasing evidence that copy number aberrations (CNAs)-- such as amplifications and deletions of the genome--are not always independent of one another; rather, they may co-occur with synergistic and unforeseen consequences. For example, co-occurring CNAs detected in breast cancer have led to the identification of cross-talk between different signaling pathways. The systematic search for co-occurring CNAs has been hampered by a lack of mathematical methods adequate to identify them. The PI proposes to develop new methods in Topological Data Analysis to identify co-occurring CNAs in breast cancer. Further, because copy number changes are associated with other diseases and with evolutionary processes, this project will have important impacts across the sciences, both basic (e.g. evolution and development) and applied (e.g. diseases with a genetic component such as cancer, autism and multiple sclerosis). The proposed research will advance the field of mathematical genetics/genomics with new tools for analyzing complex interactions among genetic elements in genetic/genomic data. The methods developed also have the potential for extension to identify co-occurring events in large, complex longitudinal data sets. Furthering its broader impacts, the project will implement a series of public lectures on real-life applications of computational mathematics with special outreach to local school teachers, students and professionals.

对大型复杂数据集的分析对计算数学提出了重大挑战。拓扑数据分析(TDA)应用代数拓扑学中的概念来解决这一挑战。拓扑技术已经成功地应用于工程和生物学中，但很少被应用于基因组数据的分析。在癌症基因组学中，DNA片段的得失等拷贝数变异(CNA)的识别是一个重要的问题，因为CNA已知含有癌症基因，因此参与了关键信号通路的错误调控。CNAS可以独立发生，也可以同时发生。后者被认为是协同行动，因此会导致不可预见的后果。例如，在一些乳腺癌中，8p12和11q13.3中的CNA被共扩增，这一发现导致了MYC和TP53通路之间功能相互作用的发现。这种相互作用为癌症研究提供了一个重要的范例，因为许多肿瘤被认为在其进展过程中使用类似的机制。传统上，由于寻找共生化合物所需的大样本量，以及缺乏有效识别它们的数学方法，共生CNA的识别一直受到阻碍。最近，基于微阵列的大型数据集已经可以用于乳腺癌。这项拟议的研究旨在开发一种新的基于TDA的方法来检测乳腺癌中独立和共生的CNA。在所提出的方法中，每个CNA轮廓由一组具有生物意义的拓扑空间来表征。这些空间的拓扑不变量(即Betti数)将被用于数据去噪和识别CNA。这个项目将产生广泛适用的方法：(1)以适合于拓扑分析的形式表示复杂的数据集；(2)确定TDA结果的统计意义；(3)从大数据集中计算拓扑不变量。科学的快速发展产生了前所未有的大型复杂数据集。迫切需要新的数学方法来解决这些高维数据分析中的基本问题。在基因组学领域，已经获得了数以千计的测量结果，目的是揭示表征基本生物学过程的分子签名。这一领域显著影响了乳腺癌的研究方向，因为它具有区分疾病的不同亚型、途径和预后的潜力。目前，检测基因组特征的主要方法是识别单个独立事件。然而，越来越多的证据表明，拷贝数偏差(CNA)--例如基因组的扩增和缺失--并不总是彼此独立的；相反，它们可能会同时发生，并产生协同和不可预见的后果。例如，在乳腺癌中检测到的共生CNA导致了不同信号通路之间的串扰的识别。由于缺乏足够的数学方法来识别共生的CNA，系统地寻找共生CNA的工作一直受到阻碍。PI建议在拓扑数据分析中开发新的方法来识别乳腺癌中共存的CNA。此外，由于拷贝数变化与其他疾病和进化过程有关，该项目将对基础科学(如进化和发育)和应用科学(如带有遗传成分的疾病，如癌症、自闭症和多发性硬化症)产生重要影响。拟议的研究将推动数学遗传学/基因组学领域的发展，为分析遗传/基因组数据中遗传要素之间的复杂相互作用提供新的工具。开发的方法还有可能扩展到在大型、复杂的纵向数据集中识别共同发生的事件。为了进一步扩大其影响，该项目将举办一系列关于计算数学在现实生活中的应用的公开讲座，特别是面向当地学校的教师、学生和专业人员。