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: 10524181
• 负责人: Andrew Josef Ewald
• 金额: $ 6.83万
• 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 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10524181
• 关键词: 3-Dimensional; 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 Models; Coupled; Data; Diagnosis; Differential Equation; Disease model; Drug Kinetics; Epithelial-Stromal Communication; Gene Expression; Gene Expression Profile; Gene Proteins; 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; data-driven model; design; experimental study; extracellular; genomic data; global health; human data; improved; in silico; in vivo; inhibitor; innovation; liver cancer model; 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; pharmacokinetics and pharmacodynamics; phosphoproteomics; predicting response; prediction algorithm; real-time images; 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.

项目概要 肝癌是全球主要的健康问题，是全球第三大癌症死亡原因。诊断 通常发生在晚期，此时肝肿瘤在多个方面具有复杂的肿瘤/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推动肿瘤进展和治疗 回复。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。研究人员团队非常适合开发拟议的 肝细胞癌（HCC）（最常见的肝癌类型）的多尺度模型。的专业知识 四个 PI/PD 具有协同作用，结合了最先进的癌症多尺度计算模型（Dr. Popel）通过生物信息学分析（Fertig 博士）使用状态推断出分子和细胞特征 art 3D 体外类器官模型（Ewald 博士）和 HCC 体内小鼠模型（Tran 博士）。整合良好的 该提案的实验/计算设计将产生用于预测计算的新算法 HCC 治疗反应建模。我们为模型开发进行了广泛的实验研究， 参数调整和验证。具体目标 1 将从生物信息学角度推断重要的信号通路 癌症和基质细胞之间的串扰，整合细胞内信号传导和 3D 细胞外信号传导模型 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 细胞因子模型产生多尺度混合模型。该模型将用磷酸盐参数化 通过生物信息学分析确定的相关配体刺激下的蛋白质组数据和生长情况， 来自共培养的癌症、基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。我们将使用该模型来预测 3D 体外类器官的结果 HCC 模型。具体目标 2 将扩展和调整这种混合模型来模拟肿瘤微环境 并解释药物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，通过细胞因子的分布来初始化模型， 来自癌症基因组图谱 (TCGA) 基因组和蛋白质组数据的分子状态可预测疗效 人类肝癌不同遗传背景的靶向治疗。该项目将发展 创新的计算技术可以整合分子和细胞尺度的特征 使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 由此产生的计算算法将解决 IMAG 融合丰富数据和数据的前沿挑战。 生物系统预测多尺度计算模型的尺度较差。

项目成果

Genomic biomarkers to guide precision radiotherapy in prostate cancer.

• DOI: 10.1002/pros.24373
• 发表时间: 2022-08
• 期刊: PROSTATE
• 影响因子: 2.8
• 作者: Sutera, Philip;Deek, Matthew P.;Van Der Eecken, Kim;Wyatt, Alexander W.;Kishan, Amar U.;Molitoris, Jason K.;Ferris, Matthew J.;Siddiqui, M. Minhaj;Rana, Zaker;Mishra, Mark V.;Kwok, Young;Davicioni, Elai;Spratt, Daniel E.;Ost, Piet;Feng, Felix Y.;Tran, Phuoc T.
• 通讯作者: Tran, Phuoc T.

Histology Specific Molecular Biomarkers: Ushering in a New Era of Precision Radiation Oncology.

组织学特异性分子生物标志物：开创精准放射肿瘤学的新时代。

• DOI: 10.1016/j.semradonc.2023.03.001
• 发表时间: 2023
• 期刊: Seminars in radiation oncology
• 影响因子: 3.5
• 作者: Sutera,Philip;Skinner,Heath;Witek,Matthew;Mishra,Mark;Kwok,Young;Davicioni,Elai;Feng,Felix;Song,Daniel;Nichols,Elizabeth;Tran,PhuocT;Bergom,Carmen
• 通讯作者: Bergom,Carmen

TP53 structure-function relationships in metastatic castrate-sensitive prostate cancer and the impact of APR-246 treatment.

转移性去势敏感前列腺癌中的 TP53 结构-功能关系以及 APR-246 治疗的影响。

• DOI: 10.1002/pros.24629
• 发表时间: 2024
• 期刊: The Prostate
• 作者: Hoang,Tung;Sutera,Philip;Nguyen,Triet;Chang,Jinhee;Jagtap,Shreya;Song,Yang;Shetty,AmolC;Chowdhury,DipanwitaD;Chan,Aaron;Carrieri,FrancescaA;Hathout,Lara;Ennis,Ronald;Jabbour,SalmaK;Parikh,Rahul;Molitoris,Jason;Song,Danie
• 通讯作者: Song,Danie

Metastasis-directed Therapy Prolongs Efficacy of Systemic Therapy and Improves Clinical Outcomes in Oligoprogressive Castration-resistant Prostate Cancer.

• DOI: 10.1016/j.euo.2020.05.004
• 发表时间: 2021-06
• 期刊: European urology oncology
• 影响因子: 8.2
• 作者: Deek MP;Taparra K;Phillips R;Velho PI;Gao RW;Deville C;Song DY;Greco S;Carducci M;Eisenberger M;DeWeese TL;Denmeade S;Pienta K;Paller CJ;Antonarakis ES;Olivier KR;Park SS;Tran PT;Stish BJ
• 通讯作者: Stish BJ

STOMPing Out Hormone-Sensitive Metastases With Local Therapies in Prostate Cancer.

通过局部疗法消除前列腺癌的激素敏感性转移。

• DOI: 10.1200/jco.2017.76.5495
• 发表时间: 2018
• 期刊: Journal of clinical oncology : official journal of the American Society of Clinical Oncology
• 作者: Phillips,RyanM;Hayman,Jonathan;Tran,PhuocT
• 通讯作者: Tran,PhuocT