Modeling the Role of Differentiation in Cancer Progression

斯坦福癌症系统生物学中心(CCSB)旨在发现分子机制 通过将癌症作为一个复杂的生物系统来研究癌症进展的基础，该系统在一定程度上是由 分化受损。越来越多的证据表明，许多癌症像正常组织一样，是由 处于不同分化阶段的细胞的层次结构，疾病是通过自我更新来维持的 亚群。我们的首要目标是提供对自我更新的更好理解 癌症的特性将使我们能够确定分子治疗靶点和根除策略 这种疾病，或将其保持在非致命性状态。我们的生物项目与新颖的 计算技术，旨在剖析潜在受损的过程和因果因素 在几种血液系统恶性肿瘤中，分化是癌症进展的驱动因素。为了辨认 癌症进展的机制基础，基于网络和多尺度的观点是强制性的。 越来越多的人认识到，癌症等疾病是由于大脑协调功能紊乱造成的 复杂生物系统的性能。这种系统生物学观点需要被纳入。 高吞吐量、高维数据和计算方法的发展，特别适合于 它的分析。斯坦福CCSB将开发三种基本的和相互关联的方法来全面 癌症的系统分析。首先，将开发强大的方法来推断分子调控网络 这驱动了表型过程，如分化。第二，计算方法将是 开发了一种可以识别和隔离癌症潜在进展模式的技术，然后可以 与潜在的监管网络相关。第三，将开发可执行模型，以便有可能 提出假设性的问题，例如，预测有针对性的干预可能会如何影响 随后的病程。这些计算方法将被应用于微分学的研究 AML、滤泡性淋巴瘤和T-ALL。在急性髓系白血病中，我们将确定驱动白血病干细胞的调控网络 细胞。在滤泡性淋巴瘤中，我们将分析bcr敏感和bcr不敏感的关系 亚群。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 教育和外展旨在为下一代癌症研究人员提供坚实的基础 科学研究的综合实验和计算方法。

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 selfrenewing 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 ntegrative experimental and computational methods of scientific research.