Integrative signaling models to decipher complex cancer phenotypes

解读复杂癌症表型的整合信号模型

基本信息

批准号：
8700343
负责人：
ANDREA Hope BILD
金额：
$ 55.13万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-08 至 2017-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8700343
关键词：
3-Dimensional Accounting Address Biochemical Biological Biological Assay Biological Markers Biology Breast Cancer Cell Breast Cancer Treatment Cancer Patient Cancer cell line Cell Line Clinical Clinical Trials Complex Comprehension Computing Methodologies Coupling Data Data Set Development Drug Targeting Drug resistance Event Factor Analysis Foundations Gene Expression Gene Expression Profile Gene Mutation Genes Genomics Goals Growth Growth Factor Receptors Human Image Analysis Individual Investigation Knowledge Malignant Neoplasms Methods Modeling Molecular Analysis Mutation Mutation Analysis Non-linear Models Oncogene Deregulation Outcome Pathway Analysis Pathway interactions Patients Pattern Pharmaceutical Preparations Phenotype Pleural effusion disorder Primary Neoplasm Proteomics Proto-Oncogene Proteins c-akt RNA Sequences Receptor Signaling Regimen Research Resistance Resources Sampling Series Signal Pathway Signal Transduction Solid Neoplasm Standardization Statistical Models Testing The Cancer Genome Atlas Therapeutic Translating Validation Work base computer based statistical methods computerized tools drug sensitivity inhibitor/antagonist knock-down malignant breast neoplasm neoplastic cell network models novel novel strategies portability pre-clinical research clinical testing response therapy resistant tool treatment strategy tumor tumor growth

项目摘要

DESCRIPTION (provided by applicant): The focus of our research is to investigate core signaling pathways that contribute to cancer growth, and to develop models to accurately determine optimal therapeutic regimens for cancer patients. Recent results from clinical trials using targeted therapies for solid tumors have shown that drug response is oftentimes not driven by one mutation or pathway alone. Instead, response is confounded by interactions between the target gene and deregulation of downstream and alternative pathways. Therefore, our studies aim to model how signaling pathways work in relation to others in human tumors, and to identify patterns that correlate to drug response. We hypothesize that integrated 'omic' pathway models composed of multiple components of the growth factor receptor pathways will define biologically distinct subtypes of breast cancer and will accurately predict drug response in patient tumors. Specifically, we will develop and use genomic signatures centered on multiple levels of the growth factor receptor networks (GFRNs) to investigate how these pathways signal in human tumors. Novel statistical modeling approaches, including probabilistic barcode data standardization and Bayesian factor analysis for prediction of pathways and pathway interactions in tumors will move beyond individual pathway predictions to instead profile multi-pathway models in human tumors. Further, these models will integrate 'omic' data types, including RNA-sequencing, mutation status, and proteomic data, enabling a more comprehensive analysis of GFRN deregulation. GFRN pathway activity predictions and sensitivity/resistance to drugs that target the respective pathways will be validated in both cell lines as well as in "fresh" human tumor cells grown in 3-dimensional culture. Importantly, clinical validation of the pathway profiles will be carried out with I-SPY 2 (Investigation of Seril Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2) clinical trial data, which uses targeted therapies directed at GFR pathway components in the treatment of breast cancer. Ultimately, our studies will generate a series of well-validated pathway based biomarkers for individualized assessment of drug responsiveness, as well as interrogation of the coordinate deregulation of specific GFRN components in human tumors.

描述（由申请人提供）：我们的研究重点是研究有助于癌症生长的核心信号传导途径，并开发模型以准确确定癌症患者的最佳治疗方案。使用靶向疗法对实体瘤的临床试验的最新结果表明，药物反应通常不受一个突变或途径的驱动。取而代之的是，目标基因与下游和替代途径的放松管制之间的相互作用使响应混淆。因此，我们的研究旨在模拟信号通路如何与人类肿瘤中的其他人相关，并确定与药物反应相关的模式。我们假设由生长因子受体途径的多个组成部分组成的综合“ OMIC”途径模型将定义乳腺癌的生物学上不同的亚型，并将准确预测药物反应患者肿瘤。具体而言，我们将开发和使用以多个级别的生长因子受体网络（GFRN）为中心的基因组特征，以研究这些途径在人肿瘤中的信号。新型的统计模型方法，包括概率条形码数据标准化和贝叶斯因子分析，用于预测肿瘤中途径和途径相互作用的预测，而将超越单个途径预测，而是介绍人类肿瘤中的多条纹模型。此外，这些模型将集成“ OMIC”数据类型，包括RNA测序，突变状态和蛋白质组学数据，从而更全面地分析GFRN放松管制。 GFRN途径的活性预测和对靶向各个途径的药物的敏感性/抗性将在两个细胞系以及在三维培养中生长的“新鲜”人类肿瘤细胞中进行验证。重要的是，将使用I-SPY 2进行途径谱图的临床验证（对Seril研究的研究以通过成像和分子分析来预测您的治疗反应2）临床试验数据，该数据使用针对GFR途径成分的靶向疗法在乳腺癌治疗中。最终，我们的研究将产生一系列基于验证的途径生物标志物，以对药物反应性进行个性化评估，并对人类肿瘤中特定GFRN成分的坐标放松管制进行询问。