Integrative signaling models to decipher complex cancer phenotypes

描述(申请人提供)：我们的研究重点是调查促进癌症生长的核心信号通路，并开发模型以准确地确定癌症患者的最佳治疗方案。最近对实体肿瘤进行靶向治疗的临床试验结果表明，药物反应往往不是由一个突变或途径单独驱动的。相反，靶基因与下游和替代途径的放松调控之间的相互作用使反应变得混乱。因此，我们的研究旨在模拟人类肿瘤中信号通路与其他通路的关系，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整合的“组学”途径模型将定义生物学上不同的乳腺癌亚型，并将准确地预测药物反应。 病人肿瘤。具体地说，我们将开发和使用以多个水平的生长因子受体网络(GFRN)为中心的基因组信号，以研究这些途径如何在人类肿瘤中发出信号。新的统计建模方法，包括概率条形码数据标准化和贝叶斯因子分析，用于预测肿瘤中的路径和路径相互作用，将超越单个路径预测，转而描述人类肿瘤的多路径模型。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途径的活性预测和针对各自途径的药物的敏感性/耐药性将在这两个细胞系以及在三维培养中生长的“新鲜”人类肿瘤细胞中得到验证。重要的是，将使用I-SPY 2(使用成像和分子分析预测您的治疗反应的SERIL研究调查2)临床试验数据对途径特征进行临床验证，该临床试验数据使用针对乳腺癌治疗中GFR途径成分的靶向疗法。最终，我们的研究将产生一系列经过验证的基于通路的生物标记物，用于药物反应的个体化评估，以及对人类肿瘤中特定GFRN成分的协调解除调控的询问。

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.