Advanced Computational Approaches to Delineating Dynamic Cancer Progression Processes by Using Massive Static Sample Data

使用大量静态样本数据描绘动态癌症进展过程的高级计算方法

• 批准号: 10546466
• 负责人: Steve Goodison
• 金额: $ 34.95万
• 依托单位: STATE UNIVERSITY OF NEW YORK AT BUFFALO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-05 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10546466
• 关键词: Acceleration; Address; Advisory Committees; Automobile Driving; Bioinformatics; Biological Process; Breast Cancer Model; Cancer Diagnostics; Catalogs; Cause of Death; Clinical; Clinical Management; Communities; Computer software; DNA Sequence Alteration; Data; Data Set; Development; Disease; Disease Progression; Disease-Free Survival; Economics; Epigenetic Process; Ethics; Event; Evolution; Experimental Designs; Foundations; Genes; Genetic; Genetic Variation; Genomic Instability; Genomics; Human; Interdisciplinary Study; Investigation; Investments; Learning; Life; Malignant - descriptor; Malignant Neoplasms; Methods; Modeling; Molecular; Molecular Profiling; Mutation; Natural Selections; Neoplasm Metastasis; Pathway Analysis; Pathway interactions; Pattern; Predisposition; Primary Neoplasm; Probability; Process; Progressive Disease; Research; Resolution; Resources; Role; Sampling; Series; Structure; Technology; Testing; The Cancer Genome Atlas; Therapeutic Intervention; Time; Tissue Sample; Tumor Biology; Tumor Tissue; United States National Institutes of Health; Visualization; Work; advanced disease; anticancer research; bioinformatics pipeline; breast cancer progression; cancer genome; cohort; dynamic system; genetic association; genetic information; genomic data; improved; in silico; indexing; insight; malignant breast neoplasm; novel; open source; prognostic; targeted treatment; theories; tissue archive; tumor; tumor progression

Abstract Human cancer is a dynamic disease that develops over an extended time period through the accumulation of a series of genetic alterations. Delineating the system dynamics of disease progression can significantly advance our understanding of tumor biology, and lay a critical foundation for the development of improved cancer diagnostics, prognostics and targeted therapeutics. Traditionally, system dynamics is approached through time-course studies achieved by repeated sampling of the same cohort of subjects across an entire biological process. However, due to ethical and economic constraints, it is not feasible to collect time-series data to study human cancer, and typically we can only obtain profile data from excised tumor tissues. Consequently, while major efforts continue to reveal the genomic events associated with human cancer, to date, it has been difficult to put the identified changes in the context of the dynamic disease process. With the rapid development of sequencing technology, many thousands of static tumor samples are being collected in large-scale cancer studies. This provides us with a unique opportunity to develop a novel analytical strategy to use static data, instead of time-course data, to study disease dynamics. Built logically on our previous work, we propose a large-scale interdisciplinary research plan to develop a series of novel methods that enable the construction of high-resolution cancer progression models by using massive static data, the identification of pivotal molecular events that drive stepwise disease progression, and the visualization of identified changes in a cancer development roadmap. If successfully implemented, this work can effectively overcome the existing sampling limitations, and open a new avenue of research to study cancer dynamics by using vast tissue archive, instead of performing resource-intensive or impractical time-course studies. The developed methods will be intensively tested on 27 breast cancer datasets comprised of ~9,000 samples. To our knowledge, no prior work has been performed on this scale to study breast cancer dynamics. The analysis will result in the first working model of breast cancer progression constructed by incorporating all genetic information. The constructed model can provide a foundation for the visualization of key progressive molecular events and facilitate the identification of pivotal driver genes and pathways and potential points of susceptibility for therapeutic intervention. Moreover, interrogation of the constructed model will enable us to test novel hypotheses in silico and to prioritize resources for more focused and detailed investigations experimentally. We expect that our work will have a broad impact. Although in this study we focus mainly on breast cancer, the developed methods can also be used to study other cancers and other human progressive diseases, where the lack of time-series data to study system dynamics is a ubiquitous problem.

抽象的 人类癌症是一种动态疾病，通过长期积累而发展。 一系列的基因改变。描绘疾病进展的系统动力学可以显着推进 我们对肿瘤生物学的理解，为改善癌症的发展奠定重要基础 诊断、预后和靶向治疗。传统上，系统动力学是通过 通过在整个生物过程中对同一组受试者重复采样来实现时间过程研究 过程。然而，由于伦理和经济的限制，收集时间序列数据来研究是不可行的。 人类癌症，通常我们只能从切除的肿瘤组织中获得轮廓数据。因此，虽然 重大努力不断揭示与人类癌症相关的基因组事件，迄今为止，这一直很困难 将已识别的变化置于动态疾病过程的背景下。随着我国的快速发展 测序技术，正在大规模癌症中采集数千个静态肿瘤样本 研究。这为我们提供了一个独特的机会来开发一种使用静态数据的新颖的分析策略， 而不是时间过程数据来研究疾病动态。在我们之前的工作的基础上，我们提出了一个逻辑 大规模跨学科研究计划，开发一系列新方法，使构建 通过使用大量静态数据建立高分辨率癌症进展模型，识别关键分子 推动疾病逐步进展的事件，以及癌症中已识别变化的可视化 发展路线图。如果成功实施，这项工作可以有效克服现有的采样问题 局限性，并开辟一条新的研究途径，通过使用大量的组织档案来研究癌症动力学，而不是 进行资源密集型或不切实际的时间过程研究。所开发的方法将集中 在包含约 9,000 个样本的 27 个乳腺癌数据集上进行了测试。据我们所知，之前没有任何工作 以这种规模进行研究乳腺癌动力学。分析将产生第一个工作模型 通过整合所有遗传信息构建乳腺癌进展。构建的模型可以 为关键进展分子事件的可视化提供基础，并促进识别 关键驱动基因和途径以及治疗干预的潜在易感点。而且， 对构建的模型的询问将使我们能够在计算机中测试新的假设并确定优先级 进行更集中和更详细的实验研究的资源。我们希望我们的工作能够有一个 影响广泛。虽然在这项研究中我们主要关注乳腺癌，但所开发的方法也可以用于 用于研究其他癌症和其他人类进行性疾病，但缺乏时间序列数据来研究 系统动力学是一个普遍存在的问题。