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

基本信息

批准号：
8919263
负责人：
Debashis Sahoo
金额：
$ 24.15万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-07-01 至 2017-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8919263
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

项目摘要

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.

项目概要我们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。 StepMiner，一种识别时间过程微阵列数据集中逐步转变的工具第二，BooleanNet，一种使用大量基因表达来发现基因之间布尔含义的方法最近，我们发布了一种名为 MiDReG（挖掘发育调控基因）的新方法。通过最初的应用，使用布尔含义成功预测发育途径中的基因。淋巴细胞分化的方法，我们发现了以前未被识别的 B 细胞分化标记，以及 B 细胞和 T 细胞发育的新分支点。拟议的项目将建立在我们对人类 B 细胞发育基因的成功预测的基础上 MiDReG 开发了一种发现癌症干细胞和祖细胞的通用方法，我计划进行验证。这种方法适用于人类膀胱癌（移行细胞癌），因为它是一种简单的癌症模型测试，其次，因为我们的实验室拥有分离和测试肿瘤起始细胞群的专业知识该方法将针对膀胱癌和祖细胞的发现进行优化。希望它能够作为针对其他类型超过 90% 的人类癌症的类似研究的起点。膀胱癌起源于一种称为尿路上皮的简单上皮组织，通常称为移行性癌此外，人类膀胱癌是美国第五大常见癌症。我们实验室建立了人类膀胱癌小鼠异种移植工作模型并且最近发现了膀胱癌的肿瘤起始人群。然而，膀胱癌具有异质性并形成原始肿瘤细胞群的层次结构。我在本提案中的近期主要目标是了解这一点。使用系统生物学方法预测膀胱癌细胞的（诊断/预后）基因我将合作使用人类癌症组织微阵列来标记特定人群。与 Matt van de Rijn 博士合作，并与 Robert Chin 和 Jens-Peter 博士合作使用异种移植。从长远来看，我将扩展该方法来识别其他类型癌症中的干细胞和祖细胞。在我的独立调查员阶段。