Modeling the Role of Differentiation in Cancer Progression

斯坦福大学癌症系统生物学中心（CCSB）旨在通过研究癌症作为一个复杂的生物系统来发现癌症进展的分子机制，该生物系统在一定程度上是由分化受损驱动的。越来越多的证据表明，许多癌症，像正常组织一样，是由处于不同分化阶段的细胞等级组成的，并且这种疾病是由自我更新的亚群维持的。我们的首要目标是更好地了解癌症的自我更新特性，这将使我们能够确定分子治疗靶点和策略，以根除这种疾病，或将其维持在非致命状态。我们的生物项目与新颖的计算技术相结合，旨在剖析作为几种血液恶性肿瘤进展驱动因素的受损分化的过程和因果因素。为了确定癌症进展的机制基础，基于网络和多尺度的观点是必要的。越来越多的疾病，如癌症，被认为是由于复杂的生物系统的协调性能的破坏。这种系统生物学的观点需要结合高通量、高维数据，并开发专门针对其分析的计算方法。斯坦福CCSB将为癌症的综合系统分析开发三种基本和相互关联的方法。首先，将开发强大的方法来推断驱动表型过程（如分化）的分子调控网络。其次，计算方法将被开发出来，可以识别和分离癌症进展的潜在模式，然后可以将其与潜在的调节网络联系起来。第三，将开发可执行的模型，以便可以提出假设的“如果”问题来预测如何，例如，有针对性的干预可能会影响疾病的后续进程。这些计算方法将应用于AML、滤泡性淋巴瘤和T-ALL的分化研究。在AML中，我们将确定驱动白血病干细胞的调控网络。在滤泡性淋巴瘤中，我们将分析bcr敏感和bcr不敏感亚群之间的关系。在T-ALL中，我们将在转基因小鼠模型中研究MYC的自我更新特性。我们的综合方法将使我们能够确定这些血液恶性肿瘤之间的差异，以及可能推广到其他癌症的共性。此外，斯坦福CCSB在教育和推广方面的努力旨在为下一代癌症研究人员提供综合实验和计算科学研究方法的坚实基础。

DESCRIPTION (provided by applicant): The Stanford Center for Systems Biology of Cancer (CCSB) aims to discover molecular mechanisms underlying 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 by 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. 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. The Stanford CCSB will develop three essential and interlocking methods for a comprehensive systems analysis of cancer. First, powerful methods will be developed to infer molecular regulatory networks that drive phenotypic processes such as differentiation. Second, computational approaches will be developed that can identify and isolate underlying patterns of progression in cancer, which can then be related to underlying regulatory networks. Third, executable models will be developed 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. These computational approaches will be applied to the study of differentiation in AML, Follicular Lymphoma and T-ALL. In AML, we will identify regulatory networks driving leukemic stem cells. In Follicular Lymphoma, we will analyze the relationship between BCR-sensitive and BCR-insensitive subpopulations. In T-ALL, we will study the self-renewing properties of MYC in a transgenic mouse model. Our integrative approach will enable us to ascertain differences between these hematologic malignancies, and commonalities which may generalize to other cancers. In addition, the Stanford CCSB's efforts in education and outreach aim to provide the next generation of cancer researchers with a solid foundation in integrative experimental and computational methods of scientific research.