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• 批准号: 8448715
• 负责人: SYLVIA KATINA PLEVRITIS
• 金额: $ 126.14万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8448715
• 关键词: Biological; Cells; Computational Technique; Computing Methodologies; Data; Development; Disease; Goals; Hematologic Neoplasms; Malignant Neoplasms; Methods; Modeling; Molecular; Network-based; Normal tissue morphology; Pattern; Performance; Process; Property; Published Comment; Staging; Systems Analysis; Systems Biology; complex biological systems; design; novel; role model; self-renewal; therapeutic target; tumor progression

The Stanford Center for Systems Biology of Cancer (CCSB) aims to discover molecular mechanisms underiying cancer progression by studying cancer as a complex biological system that is driven, in part, by impaired differentiation. Increasing evidence indicates that many cancers, like normal tissue, are composed of a hierarchy of cells at different stages of differentiation, and that the disease is maintained hy a self-renewing subpopulation. Our overarching goal is to provide a better understanding of the self-renewing properties of cancer that will enable us to identify molecular therapeutic targets and strategies to eradicate this disease, or to maintain it in a nonlethal state. Our biological projects are integrated with novel computational techniques, designed to dissect processes and causal factors underlying impaired differentiation as a driver of cancer progression in several hematologic malignancies. This approach will enable us to ascertain differences between these malignancies, and commonalities which may generalize to other cancers. In order to identify mechanistic underpinnings of cancer progression, a network-based and multiscale viewpoint is mandatory. Increasingly, diseases such as cancer are recognized as resulting from disruption in the coordinated performance of a complex biological system. This systems biology viewpoint necessitates the incorporation of high throughput, high dimensional data, and development of computational methods specifically geared to its analysis. There are three essential and interiocking requirements for a comprehensive systems analysis of cancer. First, powerful methods are required to infer molecular regulatory networks that drive phenotypic processes such as differentiation. Second, computational approaches are needed that can identify and isolate underlying patterns of progression in cancer, which can then be related to underlying regulatory networks. Third, executable models are desirable so that it is possible to pose hypothetical "what if' questions to predict how, for example, a targeted intervention might affect the subsequent course of disease. The approaches we will develop as a CCSB target these three specific computational aims. They are tailored to address the biological systems we are studying in our overall CCSB goal to understand the role of differentiation and self-renewal cancer. However, they will have much wider applicability. Thus, although here we apply them to particular biological systems, experimental testing of model predictions will validate not only the biological conclusions, but also the methodologies themselves. Furthermore, experimental validation will play a crucial role in iteratively refining and improving our computational models. Hematologic malignancies provide a unique opportunity to study the role of self-renewal and differentiation in cancer. Cells ofthe immune system develop from hematopoietic stem cells (HSCs) by a hierarchical process of differentiation to more specialized cell types, that has been well defined and studied. Self-renewing HSCs give rise initially to multipotent progenitors (MPPs) that have the potential to differentiate into multiple cell types, but lack self-renewal capacity. MPPs in tum give rise to oligopotent Common Myeloid Progenitor (CMP) and Common Lymphoid Progenitor (CLP), generating the major myeloid and lymphoid lineages that comprise the immune system. Subsequent differentiation produces progressively more specialized cell types that lack self-renewal ability, ultimately resulting in the major effector cells such as T-cells, B-cells, macrophages, and granulocytes. We will dissect the processes leading to deregulated differentiation, and acquisition of aberrant self-renewal ability in both myeloid and lymphoid lineages. For this purpose we will investigate three complementary systems: human Acute Myeloid Leukemia (AML), human Follicular Lymphoma (FL), and human and mouse T-cell Acute Lymphoblastic Lymphoma (T-ALL). Our computational methods produce network-level representations of molecular and cellular interactions that integrate diverse data types across multiple scales (molecular, cellular phenotypes, tumor phenotype, clinical outcomes) and filter the results through the viewpoint of differentiation and self-renewal pathways. By combining experimental and computational methods, we aim to predict and validate the critical aberrant molecular events that establish and maintain the self-renewal capacity of cancer, and how they relate to differentiation in normal cellular hierarchies. Our approaches are based on machine learning, executable models, multiscale modeling, and methods from the mathematics of geometry and topology. There will be a close interaction with experimental projects, in an iterative process where biological validation of computational predictions provides the basis for improved computational models. For this reason, computational methods development will occur under one project that interacts closely with all the experimental groups in our CCSB. The Stanford CCSB represents an evolution from our current status as a U56 ICBP Planning Center. In our cross-species systems biology analysis FL transformation and transgenic mouse models, the role of differentiation (and particulariy the aberrant activation of self-renewal programs) emerged as a key unifying theme in cancer progression. This proposal builds on our findings. We will extend our integrated systems studies into the role of differentiation and self-renewal in cancer, and how normal regulatory networks governing these processes become deregulated in cancer.

斯坦福癌症系统生物学中心 (CCSB) 旨在发现分子机制 通过研究癌症作为一个复杂的生物系统来了解癌症的进展，该系统部分是由 分化受损。越来越多的证据表明，许多癌症，像正常组织一样，由 处于不同分化阶段的细胞层次结构，并且疾病通过自我更新来维持 亚人群。我们的首要目标是更好地理解自我更新的特性 癌症将使我们能够确定分子治疗靶点和根除这种疾病的策略，或者 使其保持在非致命状态。我们的生物项目与新颖的计算技术相结合， 旨在剖析作为癌症驱动因素的分化受损的过程和因果因素 几种血液系统恶性肿瘤的进展。这种方法将使我们能够确定差异 这些恶性肿瘤之间的共同点，以及可能推广到其他癌症的共同点。 为了确定癌症进展的机制基础，基于网络的多尺度研究 观点是强制性的。越来越多的人认识到，癌症等疾病是由于生命系统受到破坏而导致的。 复杂生物系统的协调性能。这种系统生物学观点需要 高通量、高维数据的结合以及计算方法的开发 专门针对其分析。综合性的要求有三个基本且相互关联的要求 癌症的系统分析。首先，需要强大的方法来推断分子调控网络 驱动表型过程，例如分化。其次，需要能够进行计算的方法 识别并分离癌症进展的潜在模式，然后将其与潜在的相关性联系起来 监管网络。第三，可执行模型是可取的，这样就可以提出假设的“假设” 例如，预测有针对性的干预措施如何影响随后的病程的问题。 我们将作为 CCSB 开发的方法针对这三个特定的计算目标。他们是量身定制的 解决我们正在研究的生物系统，CCSB 的总体目标是了解 分化和自我更新的癌症。然而，它们将具有更广泛的适用性。因此，虽然这里 我们将它们应用于特定的生物系统，模型预测的实验测试不仅将验证 生物学结论，还有方法本身。此外，实验验证将 在迭代完善和改进我们的计算模型中发挥着至关重要的作用。 血液恶性肿瘤为研究自我更新和分化的作用提供了独特的机会 在癌症中。免疫系统细胞通过分层过程从造血干细胞 (HSC) 发展而来 分化为更特化的细胞类型，这一点已经得到了很好的定义和研究。自我更新的 HSC 最初产生具有分化为多种细胞潜力的多能祖细胞（MPP） 类型，但缺乏自我更新能力。 MPP 反过来产生寡能共同骨髓祖细胞 (CMP) 和共同淋巴祖细胞 (CLP)，产生主要的骨髓和淋巴谱系，包括 免疫系统。随后的分化逐渐产生更特化的细胞类型，这些细胞类型缺乏 自我更新能力，最终产生主要效应细胞，如 T 细胞、B 细胞、巨噬细胞和 粒细胞。我们将剖析导致解除管制的分化和获得异常的过程 髓系和淋巴系的自我更新能力。为此，我们将调查三项 互补系统：人类急性髓系白血病 (AML)、人类滤泡性淋巴瘤 (FL) 和 人和小鼠 T 细胞急性淋巴细胞淋巴瘤 (T-ALL)。 我们的计算方法产生分子和细胞相互作用的网络级表示 跨多个尺度整合不同的数据类型（分子、细胞表型、肿瘤表型、 临床结果）并通过分化和自我更新途径的观点过滤结果。经过 结合实验和计算方法，我们的目标是预测和验证关键异常 建立和维持癌症自我更新能力的分子事件，以及它们如何与 正常细胞层次结构的分化。我们的方法基于机器学习、可执行 模型、多尺度建模以及几何和拓扑数学的方法。将会有一个 在迭代过程中与实验项目密切互动，其中计算的生物验证 预测为改进计算模型提供了基础。为此，计算方法 开发将在一个与我们 CCSB 的所有实验组密切互动的项目下进行。 斯坦福 CCSB 代表了我们目前作为 U56 ICBP 规划中心的演变。在我们的 跨物种系统生物学分析FL转化和转基因小鼠模型的作用 分化（特别是自我更新程序的异常激活）成为关键的统一 癌症进展的主题。该提案建立在我们的发现之上。我们将扩展我们的集成系统 研究分化和自我更新在癌症中的作用，以及正常的调节网络如何 在癌症中，对这些过程的控制变得放松。