Integrative signaling models to decipher complex cancer phenotypes

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，并开发模型以准确确定癌症患者的最佳治疗方案。 最近使用靶向治疗实体瘤的临床试验结果表明，药物反应通常不是由一个突变或途径单独驱动的。 相反，反应被靶基因与下游和替代途径的失调之间的相互作用混淆。 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组分组成的整合“组学”途径模型将定义乳腺癌的生物学不同亚型，并将准确预测药物反应。 肿瘤患者。 具体来说，我们将开发和使用以生长因子受体网络（GFRN）的多个水平为中心的基因组签名，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，用于预测肿瘤中的通路和通路相互作用，将超越单个通路预测，而不是描述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学数据，从而能够更全面地分析GFRN失调。 将在两种细胞系以及在三维培养物中生长的“新鲜”人肿瘤细胞中验证GFRN途径活性预测和对靶向相应途径的药物的敏感性/耐药性。 重要的是，将使用I-SPY 2（通过成像和分子分析预测治疗反应的系列研究调查2）临床试验数据进行途径特征的临床验证，该临床试验数据使用针对GFR途径组分的靶向治疗来治疗乳腺癌。 最终，我们的研究将产生一系列经过充分验证的基于途径的生物标志物，用于药物反应性的个体化评估，以及对人类肿瘤中特定GFRN组分的协调失调的询问。

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.