Computational Modeling of Cancer Biology

癌症生物学的计算模型

• 批准号: 6887281
• 负责人: SYLVIA KATINA PLEVRITIS
• 金额: $ 45万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-30 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6887281
• 关键词: animal genetic material tag; computational biology; computer simulation; human genetic material tag; laboratory mouse; microarray technology; model design /development; molecular biology information system; neoplastic transformation; oncogenes

DESCRIPTION (provided by applicant): This proposal is a "Planning Grant" to develop a premier multi-disciplinary program in computational modeling of cancer biology at Stanford University. We will assemble a team of cancer biologists, oncologists, engineers, mathematicians, computer scientists and statisticians to work jointly toward identifying the molecular mechanisms that drive the transformation of low grade malignancy to high grade malignancy. Little is known about this neoplastic transformation process, yet whole genome expression data is available on disease pre- and post-transformation. The analysis of such vast amounts of data requires new computational tools of the type that we propose in this application. By making use of our sophisticated mathematical models and new computational methods, we aim to identify key regulatory networks and signaling pathways that underlie the neoplastic transformation process in humans and mice from whole genome expression data. Our study in humans will focus on the transformation of follicular lymphoma to high grade lymphoma using gene expression data derived from tissue that is correlated to clinical events of disease progression and patient outcomes. Our study in transgenic mice will focus on the transformation of tumors from dependence to independence of MYC, the initiating oncogene. The mouse model not only gives us a valuable experimental system for validation but also focuses on a specific oncogene that has been highly implicated in the transformation of human follicular lymphoma. By studying the human and mouse system in parallel (first separately and then in combination) with the use of new computational methods, we hope to gain robust insights on fundamental regulatory and signaling processes that underlie the neoplastic transformation. Discovering these mechanisms can eventually lead to the development of molecularly targeted therapies that may ultimately reduce cancer mortality. By the end of the "Planning Grant" we fully expect a major expansion of our computational cancer biology program.

描述(由申请者提供)：这项提案是一项“计划资助”，旨在发展斯坦福大学癌症生物学计算建模方面的主要多学科项目。我们将召集一支由癌症生物学家、肿瘤学家、工程师、数学家、计算机科学家和统计学家组成的团队，共同努力识别推动低度恶性向高度恶性转化的分子机制。人们对这种肿瘤转化过程知之甚少，但关于疾病转化前和转化后的全基因组表达数据是可用的。分析如此大量的数据需要我们在本应用程序中提出的类型的新计算工具。通过利用我们复杂的数学模型和新的计算方法，我们的目标是从全基因组表达数据中识别人类和小鼠肿瘤转化过程中的关键调控网络和信号通路。我们在人类身上的研究将重点放在滤泡性淋巴瘤向高级别淋巴瘤的转化上，使用来自组织的基因表达数据，这些数据与疾病进展的临床事件和患者预后相关。我们在转基因小鼠中的研究将集中在肿瘤从依赖到独立的转变，MYC是启动癌基因。小鼠模型不仅为我们提供了一个有价值的实验系统进行验证，而且还关注了与人类滤泡性淋巴瘤转化高度相关的特定癌基因。通过使用新的计算方法并行研究人类和小鼠系统(先是单独的，然后是组合的)，我们希望对肿瘤转化背后的基本调控和信号过程获得强有力的见解。发现这些机制最终可能导致分子靶向治疗的发展，最终可能会降低癌症死亡率。到“计划拨款”结束时，我们完全预计我们的计算癌症生物学计划将得到重大扩展。