Application of Boolean Networks to discover stem and progenitor cells

项目摘要 我们已经开发了几种新的方法来分析大规模并行的基因表达数据集。第一, StepMiner是一种工具，它可以识别时间进程微阵列数据集中的逐步过渡(fi)。第二，BoolanNet， 一种利用这些大量基因表达来发现基因间布尔蕴含的方法 数据集。最近，我们发表了一种名为MiDReG(挖掘发育调节基因)的新方法 使用布尔暗示成功地预测了发育途径中的基因。通过最初将此应用于 淋巴细胞分化的方法，我们发现了以前未知的B细胞分化标志物， 以及B细胞和T细胞发育的一个新的分支点。 拟议的项目将建立在我们成功预测人类B细胞发育基因的基础上 MiDReG开发一种发现癌症干细胞和祖细胞的通用方法。我正计划验证 这种方法在人类膀胱癌(移行细胞癌)中，fiRST，因为它是一种简单的癌症模型 第二，因为我们的实验室拥有分离和测试启动肿瘤的细胞群体的专业知识 潜力。这种方法将被优化用于在膀胱癌和膀胱癌中发现干细胞和祖细胞 希望这将成为其他类型癌症类似研究的起点。90%以上的人类 膀胱癌起源于一种叫做尿路上皮的简单上皮组织，通常被称为移行细胞癌。 细胞癌(TCC)。此外，人类膀胱癌是美国fi第12位最常见的癌症。 本实验室建立了小鼠人膀胱癌异种移植的工作模型 最近在膀胱癌中发现了一个启动肿瘤的人群。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体的层次结构。然而，一份详细的膀胱癌 发育等级仍不清楚。我在这项提案中的主要近期目标是理解这一点 使用系统生物学方法预测(诊断/预后)基因的膀胱癌细胞层级 标记种fic种群。我将合作使用人类癌症组织微阵列来验证这种方法 与Matt van de Rijn博士合作，并与Robert Chin博士和Jens-Peter博士合作使用异种移植 沃尔克默。从长远来看，我将把这种方法扩展到在其他类型的癌症中识别干细胞和祖细胞 在我的独立调查员阶段。

Project Summary 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 differentitaion, 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.