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

三阴性乳腺癌患者 EGFR-MAPK 通路的预测模型

基本信息

批准号：
10612033
负责人：
Timothy C Elston
金额：
$ 55.79万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10612033
关键词：
Address Adopted Animal Model Biological Markers Biological Models Breast Cancer Patient Breast Epithelial Cells Cancer Patient Cell Line Characteristics Clinical Clinical Trials Computer Models DNA DNA Sequence Alteration DNA copy number Data Decision Making Development Differential Equation Disease Disparate Drug resistance ERBB2 gene Elasticity Epidermal Growth Factor Receptor Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor Erlotinib Evaluation Experimental Models Feedback Gene Expression Gene Expression Profiling Genetically Engineered Mouse Genomics Goals Growth Heterogeneity Human Inbred BALB C Mice Individual MAP Kinase Gene MAPK Signaling Pathway Pathway MEKs Machine Learning Malignant Neoplasms Mammary Neoplasms Mathematics Measurement Methods Modeling Molecular Mus Normal Cell Pathway interactions Patients Performance Pharmaceutical Preparations Pharmacotherapy Phase Phase II Clinical Trials Phosphotransferases Post-Translational Protein Processing Property Proteins Proteomics Regression Analysis Regulation Resources Sampling Signal Pathway Signal Transduction Specimen Statistical Models Structure System TP53 gene Taxonomy Technology Testing Therapeutic Time Tissues Tumor Subtype Validation Xenograft procedure cancer subtypes cell growth design disease heterogeneity disorder subtype drug sensitivity effective therapy exome sequencing human disease in vivo Model inhibitor learning network mRNA sequencing malignant breast neoplasm mathematical model model building molecular targeted therapies mouse model multi-scale modeling neoplastic cell network architecture novel therapeutics predictive modeling skills treatment strategy triple-negative invasive breast carcinoma tumor tumor growth tumor xenograft

项目摘要

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.

EGFR-MAPK通路是人类三阴性兽癌（TNBC）中的关键信号传导通路。我们建议利用丰富的动物模型系统和2项人体临床试验的基因组和蛋白质组数据，建立TNBC患者EGFR-MAPK信号通路活性的预测模型。异质性 TNBC的发展阻碍了先前基于预测路径的计算模型的发展，大多数方法基于来自单个细胞系或动物模型的实验数据，外推以适合多种肿瘤亚型。我们的方法是使用一个多样化的实验模型系统，反映了异质性疾病亚型，然后使用两种不同的和互补的方法来建立计算模型。我们将同时使用机械和统计建模方法，各种尺度，包括来自药物处理的肿瘤和细胞系的数据，测定基因表达， DNA拷贝数、DNA突变和蛋白质激酶组活性。最后，我们将测试这些计算人类肿瘤模型，以评估其预测性能。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Kinome inhibition states and multiomics data enable prediction of cell viability in diverse cancer types.

DOI：
10.1371/journal.pcbi.1010888
发表时间：
2023-02
期刊：
PLoS computational biology
影响因子：
4.3
作者：
通讯作者：

Translating transcriptomic findings from cancer model systems to humans through joint dimension reduction.

DOI：
10.1038/s42003-023-04529-3
发表时间：
2023-02-16
期刊：
Communications biology
影响因子：
5.9
作者：
通讯作者：

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors.

DOI：
10.7717/peerj.16342
发表时间：
2023
期刊：
PeerJ
影响因子：
2.7
作者：
Joisa CU;Chen KA;Berginski ME;Golitz BT;Jenner MR;Herrera Loeza G;Yeh JJ;Gomez SM
通讯作者：
Gomez SM