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.

项目摘要 肝癌是一个主要的全球健康问题，是全球第三大癌症死亡原因。诊断 通常发生在晚期，此时肝脏肿瘤在多个肿瘤细胞中具有复杂的肿瘤/间质相互作用。 空间和时间尺度。由此产生的多尺度相互作用驱动肿瘤进展和治疗 反应拟议的项目将开发新的数学/计算技术， 细胞，肿瘤和器官尺度，以阐明驱动肝癌进展的机制，并预测 对靶向治疗的反应。研究人员团队是唯一适合开发拟议的 肝细胞癌（HCC）的多尺度模型，HCC是最常见的肝癌类型。的专门知识 这四个PI/PD是协同作用的，结合了最先进的癌症多尺度计算模型（Dr. Popel）与从生物信息学分析（Fertig博士）推断的分子和细胞特征，使用 art 3D体外类器官模型（Ewald博士）和HCC的体内小鼠模型（Tran博士）。良好的整合 该提案的实验/计算设计将导致预测计算的新算法 HCC治疗反应的建模。我们包括广泛的实验研究模型的发展， 参数调整和验证。特异性目标1将从生物信息学上推断出在以下方面重要的信号通路： 癌症和基质细胞之间的串扰，细胞内信号传导和3D细胞外 配体运输和生化反应，并将它们嵌入到基于代理的细胞命运决策规则中 模型的细胞代理导致多尺度混合模型。该模型将用磷酸化参数化， 通过生物信息学分析鉴定的相关配体刺激下的蛋白质组数据和生长， 来自共培养的癌症和基质细胞和类器官的侵袭、蛋白质组学和基因组数据;独立 数据将用于模型验证。我们将使用该模型来预测3D体外类器官的结果 HCC模型Specific Aim 2将扩展和调整这种混合模型，以模拟肿瘤微环境 并解释药物的药代动力学和药效学，肝脏的3D几何形状，分子 从生物信息学分析推断的体内相互作用和细胞组成。第3章具体目标 开发新的生物信息学分析算法，用细胞因子的分布来初始化模型， 癌症基因组图谱（TCGA）基因组和蛋白质组数据的分子状态，以预测疗效 在人类肝癌的不同遗传背景中的靶向治疗。该项目将开发 创新的计算技术，以整合在分子和细胞尺度的功能， 利用多尺度计算模型进行基因组学和蛋白质组学分析，以预测治疗反应。的 由此产生的计算算法将解决IMAG融合数据丰富和数据- 用于生物系统的预测性多尺度计算建模的差尺度。