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 肝癌是一个重大的全球健康问题，是全球第三大癌症死亡原因。诊断学 通常发生在晚期，此时肝肿瘤有复杂的肿瘤/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推动了肿瘤的进展和治疗 回应。拟议的项目将开发新的数学/计算技术来模拟分子， 细胞、肿瘤和器官标度，以阐明推动肝癌进展的机制和预测 对靶向治疗的反应。调查员团队非常适合开发拟议的 最常见的肝癌类型--肝细胞癌的多尺度模型。的专业知识 四个PI/PD是协同的，结合了最先进的癌症多尺度计算模型(Dr。 从生物信息学分析中推断出的分子和细胞特征(Fertig博士)，使用状态 ART 3D体外器官模型(Ewald博士)和活体小鼠肝癌模型(Tran博士)。完美整合的 该方案的实验/计算设计将产生用于预测计算的新算法 肝细胞癌治疗反应的模型化。我们包括广泛的模型开发实验研究， 参数调整和验证。特殊目标1将从生物信息学的角度推断重要的信号通路 肿瘤和间质细胞之间的串扰，细胞内信号和3D细胞外信号的集成模型 配体运输和生化反应，并将它们嵌入到基于智能体的细胞命运决策规则中 导致多尺度混合模型的细胞代理模型。模型将使用磷酸盐进行参数化处理。 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， 来自共培养的癌细胞、间质细胞和类器官的侵袭、蛋白质组和基因组数据；独立的 数据将用于模型验证。我们将使用这个模型来预测3D体外有机化合物的结果 肝细胞癌模型。《特定目标2》将扩展和调整这一混合模型以模拟肿瘤微环境 为了解释药物的药代动力学和药效学，肝脏的3D几何结构，分子 从生物信息学分析推断体内相互作用和细胞组成。最后，具体目标3将 开发新的生物信息学分析算法，以利用细胞制剂的分布和 来自癌症基因组图谱(TCGA)的分子状态基因组和蛋白质组数据用于预测疗效 在人类肝癌的不同遗传背景下的靶向治疗的研究。该项目将得到发展 创新的计算技术，将分子和细胞尺度上的特征从 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。这个 由此产生的计算算法将解决IMAG融合数据丰富和数据- 生物系统的预测性多尺度计算建模的尺度不佳。