Integrating bioinformatics into multiscale models for hepatocellular carcinoma

将生物信息学整合到肝细胞癌的多尺度模型中

• 批准号: 9490092
• 负责人: Andrew Josef Ewald
• 金额: $ 60.79万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-17 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9490092
• 关键词: Address; Algorithmic Analysis; Algorithms; Automobile Driving; Binding; Biochemical Reaction; Bioinformatics; Biological Assay; Cancer Model; Cell Death; Cell model; Cell physiology; Cell surface; Cells; Cessation of life; Clinical Trials; Collaborations; Communities; Complex; Computational Technique; Computational algorithm; Computer Simulation; Coupled; Data; Diagnosis; Differential Equation; Disease model; Drug Kinetics; Epithelial-Stromal Communication; Gene Expression; Gene Expression Profile; Gene Proteins; Gene Targeting; Genetic; Genomics; Geometry; Growth; Histologic; Human; Hybrids; Image; In Vitro; Invaded; Knowledge; Laboratories; Ligands; Liver; Liver neoplasms; Malignant Epithelial Cell; Malignant Neoplasms; Malignant neoplasm of liver; Mathematics; Measurement; Measures; Mitotic; Modality; Modeling; Molecular; Molecular Profiling; Mus; Optics; Organ; Organoids; Pharmaceutical Preparations; Pharmacodynamics; Phenotype; Prediction of Response to Therapy; Primary carcinoma of the liver cells; Proteomics; Reaction; Research Personnel; Signal Pathway; Signal Transduction; Source Code; Stromal Cells; Survival Rate; Techniques; The Cancer Genome Atlas; Therapeutic; Time; Translations; Transport Process; Transport Reaction; Tumor Cell Invasion; Tumor stage; Validation; base; biological systems; cancer cell; cell growth; cell type; cellular imaging; data modeling; design; experimental study; extracellular; genomic data; global health; human data; improved; in vivo; inhibitor/antagonist; innovation; mathematical model; model development; molecular imaging; molecular modeling; mortality; mouse model; multi-scale modeling; mutational status; network models; novel; open source; outcome prediction; personalized medicine; pharmacodynamic model; pharmacokinetic model; phosphoproteomics; predicting response; prediction algorithm; reconstruction; response; targeted treatment; treatment response; treatment strategy; tumor; tumor growth; tumor microenvironment; tumor progression

Project Summary Liver cancer is a major global health problem, responsible for the 3rd most cancer deaths worldwide. Diagnosis often occurs at late stages, at which point liver tumors have complex tumor/stroma interactions across multiple spatial and temporal scales. The resulting multiscale interactions drive tumor progression and therapeutic response. The proposed project will develop new mathematical/computational techniques to model molecular, cellular, tumor, and organ scales to elucidate the mechanisms driving liver cancer progression and to predict the response to targeted therapeutics. The investigator team is uniquely suited to develop the proposed multiscale models of hepatocellular carcinoma (HCC), the most common type of liver cancer. The expertise of the four PIs/PDs is synergistic, combining a state of the art multiscale computational models of cancer (Dr. Popel) with molecular and cellular features inferred from bioinformatics analysis (Dr. Fertig) using state of the art 3D in vitro organoid models (Dr. Ewald) and in vivo mouse models of HCC (Dr. Tran). The well-integrated experimental/computational design of the proposal will result in new algorithms for predictive computational modeling of therapeutic response in HCC. We include extensive experimental studies for model development, parameter tuning, and validation. Specific Aim 1 will infer bioinformatically the signaling pathways important in crosstalk between cancer and stromal cells, integrate models of intracellular signaling and 3D extracellular ligand transport and biochemical reactions and embed them into the cell fate decision rules of an agent-based model of cellular agents resulting in a multiscale hybrid model. The model will be parameterized with phospho- proteomic data under relevant ligand stimulations identified by the bioinformatics analysis and with growth, invasion, proteomic, and genomic data from co-cultured cancer and stromal cells and organoids; independent data will be used for model validation. We will use this model to predict outcomes in a 3D in vitro organoid model of HCC. Specific Aim 2 will extend and adapt this hybrid model to model the tumor microenvironment and to account for the drug pharmacokinetic and pharmacodynamic, the 3D geometry of the liver, molecular interactions in vivo and cellular composition inferred from bioinformatics analysis. Finally, Specific Aim 3 will develop new bioinformatics analysis algorithms to initialize the model with distribution of cellular agents and molecular states from The Cancer Genome Atlas (TCGA) genomic and proteomic data to predict the efficacy of targeted therapeutics in the diverse genetic backgrounds of human liver cancer. The project will develop innovative computational techniques to integrate features at both the molecular and cellular scales from genomics and proteomics analysis with multiscale computational models to predict therapeutic response. The resulting computational algorithms will address the IMAG cutting edge challenge of fusing data-rich and data- poor scales for predictive multiscale computational modeling of biological systems.

项目总结