Application of Boolean Networks to discover stem and progenitor cells

应用布尔网络发现干细胞和祖细胞

• 批准号: 8111574
• 负责人: Debashis Sahoo
• 金额: $ 15.21万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8111574
• 关键词: B cell differentiation; B-Lymphocytes; Cancer Model; Cell Separation; Cells; Chin; Clinical; Collaborations; Data; Data Set; Databases; Development; Developmental Gene; Developmental Process; Diagnostic; Epithelial; Future; Gene Expression; Genes; Goals; Health; Hematological Disease; Hematopoietic stem cells; Human; Immunocompromised Host; Knowledge; Laboratories; Lymphocyte; Malignant Neoplasms; Malignant neoplasm of urinary bladder; Methods; Mining; Mission; Modeling; Mus; Natural regeneration; Organ; Pathway interactions; Patients; Phase; Physicians; Play; Population; Publishing; Research; Research Infrastructure; Research Personnel; Resistance; Role; Scientist; Stem cells; Survival Analysis; Systems Biology; T-Cell Development; Testing; Time; Tissue Microarray; Tissues; Transitional Cell Carcinoma; United States; United States National Institutes of Health; Urothelium; Validation; Work; Xenograft procedure; base; cancer cell; cancer stem cell; cancer therapy; cancer type; combat; computerized tools; conventional therapy; human disease; human tissue; injured; molecular marker; neoplastic cell; novel; novel strategies; progenitor; prognostic; protein expression; regenerative; stem; tool; tumor; tumor xenograft

DESCRIPTION (provided by applicant): We have developed several novel approaches for analysis of massively parallel gene expression datasets. First, StepMiner, a tool that identifies step-wise transitions in the time course microarray datasets. Second, BooleanNet, a method of discovering Boolean implications between genes using these large numbers of gene expression datasets. Recently, we published a new method called MiDReG (Mining Developmentally Regulated Genes) that uses Boolean implications to successfully predict genes in developmental pathways. By initially applying this approach to lymphocyte differentiation, we discovered previously unrecognized markers for B cell differentiation, as well as a novel branchpoint of B cell and T cell development. The proposed project will build on our successful prediction of human B cell developmental genes using MiDReG to develop a general method for discovering cancer stem and progenitor cells. I am planning to validate this approach in human bladder cancer (Transitional Cell Carcinoma), first, because it is a simple model cancer to test, and, second, because our laboratory has the expertise to isolate and test cell populations for tumor-initiating potential. This method will be optimized for the discovery of stem and progenitor cells in bladder cancer and hopefully it will serve as a starting point for similar studies in other types of cancers. More than 90% of human bladder cancers arise from a simple epithelial tissue called urothelium, and are commonly called transitional cell carcinomas (TCC). Furthermore, human bladder cancer is fifth most common cancer in the United States. Our laboratory has established a working model for the xenotransplantation of human bladder cancer in mice and has recently discovered a tumor-initiating population in bladder cancer. Recent studies show that cancer is heterogeneous and forms a hierarchy of original tumor cell populations. However, a detailed bladder cancer developmental hierarchy remains unknown. My primary near-term goal in this proposal is to understand this hierarchy of bladder cancer cells using a systems biology approach to predict (diagnostic/prognostic) genes that mark specific populations. I will validate this approach using human cancer tissue microarrays in collaboration with Dr. Matt van de Rijn, and using xenotransplantation in collaboration with Drs. Robert Chin and Jens-Peter Volkmer. In the longer term, I will extend the method to identify stem and progenitor cells in other types of cancers during my independent investigator phase. PUBLIC HEALTH RELEVANCE: Identification of cancer stem and progenitor cells contributes strongly to the understanding of development and differentiation of cancer. Cancer stem cells may be at the core of the resistance of cancers to conventional therapies, and are thus likely the target of future cancer treatments. Also, identification of cancer stem cells may aid in understanding the normal developmental process. Tissue stem cells have long term regenerative potential that may be used to regenerate injured or diseased organs. The identification of hematopoietic stem cells, for example, has substantially changed both how we understand the normal developmental process and how we approach treatment of hematological disease. Knowledge about these cells will play a pivotal role in developing better ways to combat human diseases. Our findings will be relevant to the mission of the NIH because it has a direct impact on human health.

描述（由申请人提供）：我们已经开发了几种用于分析大规模并行基因表达数据集的新方法。首先，StepMiner，一种识别时间过程微阵列数据集中的逐步转换的工具。第二，布尔网络，一种使用这些大量基因表达数据集发现基因之间布尔含义的方法。最近，我们发表了一种名为MiDReG（Mining Developmentally Regulated Genes）的新方法，该方法使用布尔含义成功预测发育途径中的基因。通过首次将这种方法应用于淋巴细胞分化，我们发现了以前未被识别的B细胞分化的标记，以及B细胞和T细胞发育的新分支点。 该项目将建立在我们使用MiDReG成功预测人类B细胞发育基因的基础上，以开发发现癌症干细胞和祖细胞的通用方法。我计划在人类膀胱癌（移行细胞癌）中验证这种方法，首先，因为它是一种简单的癌症模型，其次，因为我们的实验室具有分离和测试细胞群的肿瘤启动潜力的专业知识。这种方法将被优化用于发现膀胱癌中的干细胞和祖细胞，并希望它将作为其他类型癌症中类似研究的起点。超过90%的人类膀胱癌起源于称为尿路上皮的简单上皮组织，通常称为移行细胞癌（TCC）。此外，人类膀胱癌是美国第五大常见癌症。我们的实验室已经建立了人膀胱癌小鼠异种移植的工作模型，最近发现了膀胱癌中的肿瘤起始群体。最近的研究表明，癌症是异质性的，并形成了原始肿瘤细胞群的层次结构。然而，详细的膀胱癌发展层次仍然未知。我在这个提案中的主要近期目标是使用系统生物学方法来预测（诊断/预后）标记特定人群的基因，以了解膀胱癌细胞的这种层次结构。我将与Matt货车de Rijn博士合作，使用人类癌症组织微阵列验证这种方法，并与Robert Chin博士和Jens-Peter Rummer博士合作使用异种移植。从长远来看，我将在我的独立研究者阶段扩展该方法，以识别其他类型癌症中的干细胞和祖细胞。 公共卫生相关性：癌症干细胞和祖细胞的鉴定有助于理解癌症的发展和分化。癌症干细胞可能是癌症对传统疗法的抵抗力的核心，因此可能是未来癌症治疗的目标。此外，癌症干细胞的鉴定可能有助于理解正常的发育过程。组织干细胞具有长期再生潜力，可用于再生受损或患病器官。例如，造血干细胞的鉴定已经大大改变了我们对正常发育过程的理解以及我们如何治疗血液病。关于这些细胞的知识将在开发更好的方法来对抗人类疾病方面发挥关键作用。我们的发现将与NIH的使命相关，因为它对人类健康有直接影响。