Predictive Modeling of the EGFR-MAPK pathway for Triple Negative Breast Cancer Patients

EGFR-MAPK通路是人类三阴性野兽癌(TnBC)中的关键信号通路。我们 建议利用来自丰富的动物模型系统的基因组和蛋白质组数据，以及两项人类临床试验， 建立TNBC患者EGFR-MAPK信号通路活性的预测模型。异质性 已经阻碍了先前基于预测通路的计算模型的发展，因为 大多数方法都是基于单个细胞系或动物模型的实验数据，然后 外推以适应多种肿瘤亚型。我们的方法是使用一个多样化的实验模型系统 反映疾病亚型的异质性，然后用两种截然不同、相辅相成的方法构建 计算模型。我们将同时使用机械论和统计建模方法，在 各种尺度，包括来自药物治疗的肿瘤和细胞系的数据，分析基因表达， DNA拷贝数、DNA突变和蛋白质激动组活性。最后，我们将测试这些计算 用于评估其预测性能的人体肿瘤模型。

The EGFR-MAPK pathway is a key signaling pathway in human Triple Negative Beast Cancers (TNBC). We propose to leverage genomic and proteomic data from a rich animal model system, and 2 human clinical trials, to build predictive models of the EGFR-MAPK signaling pathway activity for TNBC patients. The heterogeneity of TNBC has hindered previous development of predictive pathway-based computational models because most approaches are based on experimental data from a single cell line or animal model that is then extrapolated to fit multiple tumor subtypes. Our approach is to use a diverse experimental model system that reflects the heterogeneous disease subtypes, and then use two distinct and complementary methods to build the computational model. We will simultaneously use mechanistic and statistical modeling approaches, at a variety of scales, that incorporate data from drug treated tumors and cell lines, assayed for gene expression, DNA copy number, DNA mutations, and protein kinome activity. Lastly, we will test these computational models on human tumors to evaluate their predictive performance.