权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Modeling MicroRNA-Target Functional Networks for Acute Myeloid Leukemia Prognosis

急性髓系白血病预后的 MicroRNA 靶点功能网络建模

基本信息

批准号：
8688175
负责人：
Xinan Holly Yang
金额：
$ 16.67万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-07-01 至 2016-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8688175
关键词：
Accounting Acute Lymphocytic Leukemia Acute Myelocytic Leukemia Adult Back Base Sequence Bioconductor Biologic Characteristic Biological Biology Blast Cell Cell model Cells ChIP-seq Characteristics Chromosome abnormality Clinic Clinical Collection Commit Complex Computational algorithm Data Data Analyses Data Set Databases Development Disease Environment Epigenetic Process Gene Expression Gene Expression Profile Gene Targeting Genes Genotype Goals Hematopoietic Neoplasms Human Individual Karyotype Knowledge Link Machine Learning Malignant Neoplasms Measurement Mediating Meta-Analysis Methodology Methods MicroRNAs Modeling Molecular Molecular Abnormality Molecular Profiling Molecular Target Monitor Oncogenes Oncogenic Outcome Pathway interactions Patients Phenotype Population Post-Transcriptional Regulation Pre-Clinical Model Process Property Protein Analysis Proteins Qualifying Reading Recurrent disease Regulator Genes Regulatory Pathway Relapse Research Sampling Signal Pathway Software Tools Statistical Algorithm Statistical Models Stem cells Structure Testing Therapeutic Training Transcription factor genes Translating Yang base chemotherapeutic agent cohort computer framework feeding gene interaction genome-wide innovation insight knowledge base leukemia leukemic stem cell mathematical methods network models novel novel strategies outcome forecast prognostic programs protein protein interaction public health relevance response self-renewal stemness tool transcription factor transcriptome sequencing

项目摘要

DESCRIPTION (provided by applicant): Acute myeloid leukemia (AML) is the most common type of adult hematopoietic malignancy. It has a high rate of disease relapse, a consequence of chemoresistance. Recent biological studies suggest that a major component of the relapse phenotype resides in a rare population of leukemic stem cells (LSC), characterized by extensive proliferative and self-renewal potential, and poor response to standard chemotherapeutic agents. We hypothesize that i) common molecular characteristics inherent to LSC-enriched populations reflect the biology of heterogeneous AML with unfavorable prognostic features; ii) these associations can be derived from systematic analysis of gene and microRNA expression profiles, and other biologic characteristics of bulk AML samples. The overarching goal of this project is to define LSC-characterized transcription factor (TF) and microRNA interactions in heterogeneous AMLs. We proposed to integrate both qualified data-driven and curated knowledge about LSC characteristics, and clinical outcomes from bulk AML. Specifically, we will perform meta-analyses on a pathway level to build LSC-specific biology, focusing on AML prognostic TF/microRNA deregulation. These approaches are supported by our two pioneering mathematical methodologies: the Functional Analysis of Individual Microarray Expression (FAIME) and the mechanism-anchored Phenotypes-Genotype Network (PGNet). FAIME provides a novel process for transforming extensive available gene expression profiles into individual pathway profiles, resulting in more reproducible pathway signatures (46% overlap among three cohorts, empirical p<0.001). The PGNet method reveals genes regulated by disease-critical regulators and can accurately predict patient outcomes - shifting the paradigm from single gene/microRNA analysis towards "mechanism anchored profiling". Using PGNet, we have successfully predicted that the epigenetic regulator HDAC9 is associated with survival in acute lymphoblastic leukemia. Innovatively, this project will interrogate microRNAs/genes that regulate LSC-specific biological pathways and AML prognostication, integrating regulator-regulator interactions and regulator-gene interactions. In Aim 1, we will build LSC-specific gene pathways and identify regulator-gene interactions. In Aim 2, we will correlate LSC-specific regulators and their target genes corresponding to AML outcomes. In Aim 3, we will develop novel approaches to computationally model the crucial interactions among the LSC-specific regulators and prognostic gene targets. Both the abundance of available AML patient profiles and the proven ability of our proposed methods suggest that we will achieve our aim to build an LSC-driven prognostic model.

描述(申请人提供)：急性髓系白血病(AML)是成人最常见的血液系统恶性肿瘤。它具有很高的疾病复发率，这是化疗耐药的结果。最近的生物学研究表明，复发表型的一个主要组成部分存在于罕见的白血病干细胞(LSC)群体中，其特征是具有广泛的增殖和自我更新能力，并且对标准化疗药物的反应较差。我们假设，i)LSC富集群固有的共同分子特征反映了具有不良预后特征的异质性AML的生物学特征；ii)这些关联可以从对大量AML样本的基因和microRNA表达谱以及其他生物学特征的系统分析中得出。该项目的总体目标是定义LSC特征的转录因子(TF)和异质性AML中的microRNA相互作用。我们建议整合有关LSC特征和散发性AML的临床结果的合格数据驱动和精心策划的知识。具体地说，我们将在一个途径水平上进行荟萃分析，以建立LSC特异的生物学，重点是AML预后的TF/microRNA解除调控。这些方法得到了我们两种开创性的数学方法的支持：个体微阵列表达的功能分析(FAIME)和机制锚定的表型-基因网络(PGNet)。FAIME提供了一种新的方法，可以将广泛可用的基因表达谱转换为单独的路径谱，从而产生更具重复性的路径签名(三个队列中有46%的重叠，经验p&lt；0.001)。PGNet方法揭示了受疾病关键调节器调控的基因，并可以准确预测患者的结果-将范式从单基因/microRNA分析转变为“机制锚定的剖析”。利用PGNet，我们成功地预测了表观遗传调控基因HDAC9与急性淋巴细胞白血病患者的生存相关。创新地，该项目将询问调控LSC特定生物通路和AML预测的microRNAs/基因，整合调节因子-调节因子相互作用和调节因子-基因相互作用。在目标1中，我们将构建LSC特异的基因通路，并确定调控基因之间的相互作用。在目标2中，我们将关联LSC特异性调控因子及其与AML结果相对应的靶基因。在目标3中，我们将开发新的方法来计算模拟LSC特异性调控因子和预测基因靶标之间的关键相互作用。大量可用的AML患者资料和我们建议的方法被证明的能力都表明，我们将实现我们的目标，即建立一个LSC驱动的预后模型。