Integrative signaling models to decipher complex cancer phenotypes

解读复杂癌症表型的整合信号模型

• 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
• 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

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）：我们研究的重点是研究有助于癌症生长的核心信号通路，并开发模型来准确确定癌症患者的最佳治疗方案。 使用实体瘤靶向治疗的临床试验的最新结果表明，药物反应通常不是由一种突变或途径单独驱动的。 相反，靶基因与下游和替代途径的失调之间的相互作用会混淆反应。 因此，我们的研究旨在模拟人类肿瘤中信号通路如何与其他信号通路相关，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成分组成的整合“组学”途径模型将定义生物学上不同的乳腺癌亚型，并准确预测乳腺癌的药物反应。 患者肿瘤。 具体来说，我们将开发和使用以多个水平的生长因子受体网络（GFRN）为中心的基因组特征来研究这些途径如何在人类肿瘤中发出信号。 新颖的统计建模方法，包括用于预测肿瘤通路和通路相互作用的概率条形码数据标准化和贝叶斯因子分析，将超越单个通路预测，而是描绘人类肿瘤中的多通路模型。 此外，这些模型将整合“组学”数据类型，包括 RNA 测序、突变状态和蛋白质组数据，从而能够对 GFRN 放松管制进行更全面的分析。 GFRN 通路活性预测以及对针对各个通路的药物的敏感性/耐药性将在两种细胞系以及在 3 维培养中生长的“新鲜”人类肿瘤细胞中得到验证。 重要的是，通路概况的临床验证将使用 I-SPY 2（通过成像和分子分析预测治疗反应的 Seril 研究调查 2）临床试验数据进行，该试验使用针对乳腺癌治疗中 GFR 通路成分的靶向疗法。 最终，我们的研究将产生一系列经过充分验证的基于通路的生物标志物，用于个体化评估药物反应性，以及询问人类肿瘤中特定 GFRN 成分的协调失调。