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 肝癌是全球主要的健康问题，导致全球第三大癌症死亡。诊断 通常发生在晚期，此时肝肿瘤在多个中具有复杂的肿瘤/基质相互作用 空间和时间尺度。由此产生的多尺度相互作用驱动肿瘤的进展和治疗性 回复。拟议的项目将开发新的数学/计算技术来建模分子， 细胞，肿瘤和器官尺度，以阐明驱动肝癌进展的机制并预测 对靶向治疗药的反应。调查员团队非常适合开发拟议的 肝细胞癌（HCC）的多尺度模型，这是最常见的肝癌类型。专业知识 这四个PIS/PD是协同作用，结合了癌症的最先进的多尺度计算模型（博士 popel）具有使用生物信息学分析（Fortig博士）推断出的分子和细胞特征 ART 3D体外器官模型（Ewald博士）和HCC的体内小鼠模型（Tran博士）。整合的 提案的实验/计算设计将导致预测计算的新算法 HCC中治疗反应的建模。我们包括用于模型开发的广泛实验研究， 参数调整和验证。特定目标1将在生物信息上推断出重要的信号通路 癌症和基质细胞之间的串扰，整合细胞内信号传导和3D细胞外模型 配体运输和生化反应，并将其嵌入基于代理的细胞命运决策规则 细胞剂的模型导致多尺度混合模型。该模型将通过磷酸化进行参数化 通过生物信息学分析鉴定的相关配体刺激下的蛋白质组学数据，并随着生长的增长 来自共培养的癌症，基质细胞和类器官的侵袭，蛋白质组学和基因组数据；独立的 数据将用于模型验证。我们将使用此模型预测3D体外器官中的结果 HCC的模型。特定的目标2将扩展并适应该混合模型以建模肿瘤微环境 为了说明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出体内和细胞组成的相互作用。最后，特定的目标3将 开发新的生物信息学分析算法，以通过细胞剂的分布和 来自癌症基因组图集（TCGA）基因组和蛋白质组学数据的分子状态以预测功效 在人肝癌的多种遗传背景下有针对性的治疗剂。该项目将开发 创新的计算技术以整合来自分子和细胞量表的特征 基因组学和蛋白质组学分析具有多尺度计算模型，以预测治疗反应。这 由此产生的计算算法将解决融合数据丰富和数据融合的成像尖端挑战 生物系统的预测多尺度计算建模的尺度较差。