Center for Systematic Modeling of Cancer Development

癌症发展系统建模中心

• 批准号: 9103432
• 负责人: STEPHEN TC WONG
• 金额: $ 15.81万
• 依托单位: METHODIST HOSPITAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2016-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9103432
• 关键词: Apoptosis; Bioinformatics; Biological; Birth; Breast; Cancer Biology; Cancer Model; Cells; Clinical; Computer Simulation; Data Analyses; Development; Distant; Evolution; Experimental Designs; Feedback; Future; Gene Expression; Generations; Germ Cells; Goals; Growth; Image; Investigation; Knowledge; Malignant Neoplasms; Methodology; Modeling; Movement; Outcome; Pharmaceutical Preparations; Procedures; Process; Public Health; Role; Signal Transduction; Systems Biology; Therapeutic; Tumor Biology; bioimaging; cancer cell; cancer stem cell; cell behavior; cell motility; data mining; design; functional genomics; in vivo; killings; malignant breast neoplasm; mathematical model; multi-scale modeling; novel; research study; response; screening; tool; treatment response; tumor

The overall goal ofthis proposal is to build a multi-scale modeling platform for investigation ofthe breast cancer, with special emphasis on the roles ofthe tumor-initiating cells (TIC). This modeling platform will mainly consist of two closely related components: biological experiments and mathematical computational modeling. For the experiment component, we seek to use newly developed experimental and imaging methodologies to identify, localize, purify and characterize TIC. Further experiments will be designed to discover the spatial localization and movement, and specific changes in gene expression and cellular signaling of breast cancer TIC. Combined functional genomics and data mining strategies will allow us to characterize novel growth regulators. Further, our combined experimental and systems biology approach will allow us to evaluate responses to experimental therapeutics that may inhibit or kill TIC specifically in a manner not possible before. For the mathematical modeling component, we will develop bioinformatics and bio-imaging models to integrate and analyze the data generated from biological experiments, and make use ofthe information obtained from data analysis, biological knowledge to build in silico models to model TIC behavior, cancer cell apoptosis, cell migration, cycle and drug treatment response. Besides providing a basic framework for understanding the mechanism underlying breast cancer stem cell evolution, the models can also give birth to hypotheses or experimental design. More important, these models will allow one to predict the biological state under investigation and predict how the natural process will behave in various circumstances. Iterative feedback between these two components will refine our proposed platform further. The ultimate goal is an integrated modeling platform of breast cancer biology that can mimic in vivo processes faithfully enough to serve as a h5TDOthesis-generation and screening tool, and in the distant future, as a tool for evaluating clinical procedures and their expected outcomes.

这项提议的总体目标是建立一个多尺度建模平台，以调查乳腺癌， 特别强调了肿瘤发射细胞（TIC）的作用。这个建模平台将主要组成 两个密切相关的组件：生物实验和数学计算建模。为了 实验组件，我们试图使用新开发的实验和成像方法来识别， 本地化，纯化和表征抽动。将设计进一步的实验以发现空间本地化 和运动，以及乳腺癌的基因表达和细胞信号的特异性变化。合并 功能基因组学和数据挖掘策略将使我们能够表征新的生长调节剂。更远， 我们组合的实验和系统生物学方法将使我们能够评估对实验的反应 可以以某种方式专门抑制或杀死TIC的治疗剂。对于数学 建模组件，我们将开发生物信息学和生物成像模型，以整合和分析数据 由生物学实验产生，并利用从数据分析中获得的信息 知识以在计算机模型中建立以建模抽动行为，癌细胞凋亡，细胞迁移，循环和药物 治疗反应。除了提供理解乳房基础机制的基本框架 癌症干细胞的演化，模型还可以孕育假设或实验设计。更重要的是， 这些模型将允许人们预测正在研究的生物状态，并预测自然 流程将在各种情况下行事。这两个组件之间的迭代反馈将完善我们的 提议的平台进一步。最终目标是乳腺癌生物学的综合建模平台 可以忠实地模仿体内的过程，以作为H5Tdothesens-Generation和筛查工具，以及 遥远的未来，是评估临床程序及其预期结果的工具。

项目成果

Cancer stem cell, niche and EGFR decide tumor development and treatment response: A bio-computational simulation study.

• DOI: 10.1016/j.jtbi.2010.10.016
• 发表时间: 2011-01-21
• 期刊: JOURNAL OF THEORETICAL BIOLOGY
• 影响因子: 2
• 作者: Zhu, Xiuwei;Zhou, Xiaobo;Lewis, Michael T.;Xia, Ling;Wong, Stephen
• 通讯作者: Wong, Stephen

Epithelial derived CTGF promotes breast tumor progression via inducing EMT and collagen I fibers deposition.

• DOI: 10.18632/oncotarget.4659
• 发表时间: 2015-09-22
• 期刊: Oncotarget
• 作者: Zhu X;Zhong J;Zhao Z;Sheng J;Wang J;Liu J;Cui K;Chang J;Zhao H;Wong S
• 通讯作者: Wong S

DrugComboRanker: drug combination discovery based on target network analysis.

• DOI: 10.1093/bioinformatics/btu278
• 发表时间: 2014-06-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Huang L;Li F;Sheng J;Xia X;Ma J;Zhan M;Wong ST
• 通讯作者: Wong ST

A novel method of transcriptional response analysis to facilitate drug repositioning for cancer therapy.

• DOI: 10.1158/0008-5472.can-11-2333
• 发表时间: 2012-01-01
• 期刊: Cancer research
• 影响因子: 11.2
• 作者: Jin G;Fu C;Zhao H;Cui K;Chang J;Wong ST
• 通讯作者: Wong ST

Systems biology-based drug repositioning identifies digoxin as a potential therapy for groups 3 and 4 medulloblastoma.

• DOI: 10.1126/scitranslmed.aat0150
• 发表时间: 2018-10-24
• 期刊: Science translational medicine
• 影响因子: 17.1
• 作者: Huang L;Garrett Injac S;Cui K;Braun F;Lin Q;Du Y;Zhang H;Kogiso M;Lindsay H;Zhao S;Baxter P;Adekunle A;Man TK;Zhao H;Li XN;Lau CC;Wong STC
• 通讯作者: Wong STC