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: 10328873
• 负责人: Steve Goodison
• 金额: $ 34.96万
• 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/10328873
• 关键词: 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; Life; Malignant - descriptor; Malignant Neoplasms; Methods; Modeling; Molecular; Molecular Profiling; Mutation; Natural Selections; Neoplasm Metastasis; Pathway Analysis; Pathway interactions; Pattern; Predisposition; Primary Neoplasm; Process; Progressive Disease; Research; Resolution; Resources; Role; Sampling; Series; Structure; System; 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; base; bioinformatics pipeline; breast cancer progression; cancer genome; cohort; genetic association; genetic information; genomic data; improved; in silico; indexing; insight; learning progression; 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.

摘要 人类癌症是一种动态疾病，其通过肿瘤细胞的积累在延长的时间段内发展。 一系列的基因突变。描述疾病进展的系统动力学可以显著提高 我们对肿瘤生物学的理解，并为改善癌症的发展奠定了重要基础 诊断学、免疫学和靶向治疗学。传统上，系统动力学是通过 通过在整个生物学研究中对同一队列受试者进行重复采样实现的时程研究 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 在某些情况下，我们可以从切除的肿瘤组织中获得轮廓数据，而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 尽管人们继续努力揭示与人类癌症相关的基因组事件，但迄今为止， 把这些变化放在疾病的动态过程中。的快速发展 测序技术，成千上万的静态肿瘤样本正在收集大规模的癌症 问题研究这为我们提供了一个独特的机会来开发一种新的分析策略来使用静态数据， 而不是时间进程数据，来研究疾病动态。建立在我们以前的工作逻辑上，我们提出了一个 大规模的跨学科研究计划开发一系列新的方法，使建设 通过使用大量静态数据，识别关键分子， 推动逐步疾病进展的事件，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抽样 限制，并开辟了一条新的研究途径，通过使用大量的组织档案来研究癌症动力学， 进行资源密集型或不切实际的时间过程研究。开发的方法将被密集地 在27个乳腺癌数据集上进行了测试，这些数据集包含约9，000个样本。据我们所知，没有任何先前的工作 来研究乳腺癌的动力学。分析将产生第一个工作模型， 乳腺癌的进展是通过整合所有遗传信息构建的。所构建的模型可以 为关键进展性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。此外，委员会认为， 对构建模型的询问将使我们能够在计算机上测试新的假设，并确定优先级。 资源进行更集中和详细的实验研究。我们希望我们的工作能有一个 广泛的影响。虽然在这项研究中，我们主要关注乳腺癌，但开发的方法也可以用于治疗乳腺癌。 用于研究其他癌症和其他人类进行性疾病，其中缺乏时间序列数据进行研究 系统动力学是一个普遍存在的问题。