Center

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 分化受损越来越多的证据表明，许多癌症，如正常组织，是由 一个层次的细胞在不同阶段的分化，并认为这种疾病是维持自我更新， 亚群我们的首要目标是提供一个更好的了解自我更新的属性， 癌症，这将使我们能够确定分子治疗目标和战略，以消除这种疾病，或 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 几种恶性血液病的进展。这种方法将使我们能够确定差异 这些恶性肿瘤之间的联系，以及可能推广到其他癌症的共性。 为了确定癌症进展的机制基础， 观点是强制性的。越来越多的疾病，如癌症，被认为是由于中断， 一个复杂的生物系统的协调性能。这种系统生物学观点需要 高通量、高维数据的结合和计算方法的发展 专门针对其分析。一个全面的，有三个基本的和艰巨的要求， 癌症的系统分析首先，需要强有力的方法来推断分子调控网络， 驱动分化等表型过程。第二，需要计算方法， 识别和分离癌症进展的潜在模式，然后将其与潜在的 监管网络。第三，可执行模型是可取的，这样就有可能提出假设性的“如果” 例如，有针对性的干预措施可能会影响疾病的后续进程。 我们将开发的方法作为一个CCSB目标这三个特定的计算目标。它们是量身定做的 解决我们正在研究的生物系统在我们的整体CCSB目标，以了解的作用， 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，虽然在 如果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会验证 生物学结论，还有方法本身。此外，实验验证将 在反复精炼和改进我们的计算模型中发挥着至关重要的作用。 血液恶性肿瘤提供了一个独特的机会，研究自我更新和分化的作用， 在癌症中。免疫系统的细胞通过一个分级过程从造血干细胞（HSC）发育而来 分化成更特殊的细胞类型，这已经得到了很好的定义和研究。自我更新HSC 最初产生具有分化成多细胞潜能的多能祖细胞（MPPs 但缺乏自我更新能力。MPP在体内产生寡能共同骨髓祖细胞（CMP） 和共同类淋巴细胞祖细胞（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专门的细胞类型， 自我更新能力，最终导致主要的效应细胞，如T细胞，B细胞，巨噬细胞， 粒细胞我们将剖析导致去调节分化的过程，以及异常细胞的获得。 在骨髓和淋巴谱系中的自我更新能力。为此，我们将研究三个 互补系统：人急性髓性白血病（AML）、人滤泡性淋巴瘤（FL）和 人和小鼠T细胞急性淋巴母细胞性淋巴瘤（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 它集成了跨多个尺度（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。通过 结合实验和计算的方法，我们的目标是预测和验证的关键异常 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方法。会有 与实验项目密切互动，在迭代过程中，生物学验证计算 预测为改进的计算模型提供了基础。因此，计算方法 开发将在一个项目下进行，该项目与我们CCSB的所有实验组密切互动。 斯坦福大学CCSB代表了我们作为U 56 ICBP规划中心的现状的演变。在我们 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）成为一个关键的统一 癌症进展的主题。这项建议建立在我们的调查结果之上。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 控制这些过程的机制在癌症中变得不受控制。

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.