Center

中心

• 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.

斯坦福大学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，部分是由癌症进展的不足 差异障碍。越来越多的证据表明，许多癌症（如正常组织）由 分化的不同阶段的细胞层次结构，并保持这种疾病是自我更新的 亚种群。我们的总体目标是更好地理解对自我更新特性的理解 癌症将使我们能够确定分子治疗靶标和消除这种疾病的策略，或 将其保持在非致命状态。我们的生物项目与新颖的计算技术融合在一起， 旨在剖析作为癌症驱动的分化障碍的过程和因果因素 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 在这些恶性肿瘤和可能推广到其他癌症的共性之间。 为了识别癌症进展的机械基础，这是一个基于网络和多尺度的机械基础 观点是强制性的。越来越多的疾病被认为是由于中断而导致的 复杂的生物系统的协调性能。该系统生物学观点需要 合并高吞吐量，高维数据和计算方法的发展 专门针对其分析。全面有三个必需的互助要求 癌症的系统分析。首先，需要强大的方法来推断分子调节网络 驱动表型过程，例如分化。第二，需要计算方法 识别和隔离癌症中基本进展的潜在模式，然后可能与基础有关 监管网络。第三，可执行模型是可取的，因此可以假设“如果'怎么办” 预测例如，有针对性的干预措施可能会影响随后的疾病病程的问题。 作为CCSB，我们将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 为了解决我们正在总体CCSB目标中研究的生物系统，以了解 分化和自我更新癌症。但是，它们将具有更大的适用性。因此，尽管在这里 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可以验证 生物学结论，也是方法论本身。此外，实验验证将 在迭代精炼和改进我们的计算模型中起着至关重要的作用。 血液学恶性肿瘤为研究自我更新和差异化的作用提供了独特的机会 在癌症中。免疫系统的细胞通过分层过程从造血干细胞（HSC）发展 与更专业的细胞类型的分化，已得到很好的定义和研究。自我更新HSC 最初引起的多能祖细胞（MPP）有可能分化为多个细胞 类型，但缺乏自我更新能力。 TUM中的MPP引起寡头普通髓样祖细胞（CMP） 和常见的淋巴样祖细胞（CLP），产生构成的主要髓样和淋巴样谱系 免疫系统。随后的分化会逐渐缺乏专业的细胞类型 自我更新能力，最终导致主要效应细胞，例如T细胞，B细胞，巨噬细胞和 粒细胞。我们将剖析导致放松管制的过程，并获得异常 髓样和淋巴谱系中的自我更新能力。为此，我们将调查三个 互补系统：人类急性髓样白血病（AML），人卵泡淋巴瘤（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（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，我们旨在预测和验证临界异常 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。会有一个 在迭代过程中与实验项目密切相互作用，计算生物学验证 预测为改进的计算模型提供了基础。因此，计算方法 开发将在一个与CCSB中所有实验组紧密相互作用的项目下发生。 Stanford CCSB代表了我们目前作为U56 ICBP计划中心的状况的发展。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， 分化（尤其是自我更新程序的异常激活）作为关键统一 癌症发展的主题。该提案以我们的发现为基础。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，以及如何正常调节网络 管理这些过程在癌症中受到失调。