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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促进癌症生长的核心信号通路，并建立模型以准确确定癌症患者的最佳治疗方案。使用靶向治疗实体瘤的临床试验的最新结果表明，药物反应通常不是由一个突变或单独的途径驱动的。相反，反应被靶基因与下游和替代途径放松管制之间的相互作用所混淆。因此，我们的研究旨在模拟信号通路在人类肿瘤中的作用，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分组成的综合“组学”途径模型将定义生物学上不同的乳腺癌亚型，并将准确预测乳腺癌的药物反应