Computational Prediction of Genetic Drivers of Breast Cancer Metastases

乳腺癌转移遗传驱动因素的计算预测

• 批准号: 9910689
• 负责人: Michael G. Lerner
• 金额: $ 6.28万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-27 至 2020-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9910689
• 关键词: 3-Dimensional; Advanced Malignant Neoplasm; Basic Cancer Research; Behavioral; Biological; Biological Models; Breast Cancer Model; Breast Cancer Prevention; Breast cancer metastasis; Cancer Biology; Cancer Etiology; Cancer Intervention; Cause of Death; Cell Culture Techniques; Cell Proliferation; Cells; Cessation of life; Clinical; Collaborations; Computational Technique; Computing Methodologies; Data; Databases; Development; Disseminated Malignant Neoplasm; Environment; FDA approved; Fellowship; Foundations; Funding; Future; Gene Expression; Genes; Genetic; Genetic Models; Genetic Transcription; Genetically Engineered Mouse; Genomics; Goals; Home environment; Human; Individual; Infrastructure; Institution; Institutional Review Boards; Invaded; Joints; Knowledge; Lead; Malignant Neoplasms; Mammary gland; Mathematics; Measurement; Mentors; Mentorship; Metastatic breast cancer; Methods; Mission; Modeling; Molecular; Morbidity - disease rate; National Cancer Institute; Neoplasm Metastasis; Nonmetastatic; Oncology; Organoids; Pathway Analysis; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Phosphotransferases; Physics; Population Genetics; Primary Neoplasm; Protein Databases; Protein Kinase; Proteins; Regulator Genes; Research; Research Personnel; Research Training; Resources; Sampling; Scientist; Signal Transduction; Specimen; Study Subject; System; Therapeutic; Therapeutic Intervention; Therapeutic Uses; Tissues; Training; Tumor Cell Invasion; United States; Validation; Woman; anticancer research; base; cancer therapy; collaborative environment; curriculum development; genetic regulatory protein; genomic data; human subject; improved; innovation; malignant breast neoplasm; mathematical methods; mortality; mouse genetics; neoplastic cell; network models; novel; novel therapeutics; perturbation theory; physical science; prevent; programs; research and development; targeted treatment; transcription factor; transcriptome sequencing; tumor; tumor growth; undergraduate student

Project Summary While metastasis is the main cause of cancer mortality, many of its molecular requirements remain unknown. Computational methods that trace the biological networks from upstream metastasis drivers to downstream effectors have the potential to identify new points of intervention for cancer therapies. Developing these types of methods requires individuals with expertise in statistical network models and deep engagement with cancer experimental systems. This fellowship will train the PI to predict the flow of both transcriptional and signaling information through biological networks, connecting these changes to phenotypic and behavioral consequences for cells and tissue derived from metastatic and non-metastatic breast cancer organoids. The Bader (computational) and Ewald (experimental) labs are jointly funded by the National Cancer Institute as a Cancer Target Discovery and Development (CTD2) Center focused on breast cancer metastasis. This center provides a uniquely powerful environment of mentorship, resources, and infrastructure that will enable the PI to use his formal training in statistical physics as the foundation for developing and applying new methods for computational oncology. Research will exploit three-dimensional organotypic cell culture and experimental methods motivated by population genetics to identify metastasis driver and effector genes in genetically engineered mouse models and in primary tumor specimens from an ongoing IRB-approved human subjects study. Genes identified by RNA- Seq will be analyzed with novel network perturbation theory to connect upstream drivers to downstream effectors. These inferred networks will in turn be connected to phenotypic and behavioral consequences for mammary organoids and tissues. Although outside the scope of this proposal, the JHU CTD2 Center has the mission and resources to validate findings with clinical potential for preventing or treating metastatic breast cancer, and potentially other invasive or metastatic cancers with similar molecular mechanisms. The PI’s background in biological and statistical physics, including computational methods, enables the mathematical and computational aspects of the proposed research. The fellowship will provide essential training that will permit the PI to lead independent research that combines physical sciences methods with experimental innovations and data-rich -omics measurements for cancer basic research and to aid therapeutic advances. The PI will have robust opportunities to collaborate with Hopkins and other institutions in future and will be an effective mentor for training computational oncology researchers in his own lab.

项目摘要 虽然转移是癌症死亡的主要原因，但它的许多分子要求仍然未知。 追踪从上游转移驱动因素到下游的生物网络的计算方法 效应器有可能为癌症治疗确定新的干预点。开发这些类型的 方法需要具备统计网络模型专业知识并深入研究癌症的人员 实验系统。这一奖学金将训练PI预测转录和信号的流动 通过生物网络，将这些变化与表型和行为后果联系起来 适用于来自转移性和非转移性乳腺癌有机化合物的细胞和组织。蝙蝠侠 (计算)和Ewald(实验)实验室由国家癌症研究所作为癌症联合资助 靶点发现和开发中心(CTD2)专注于乳腺癌转移。这个中心提供 一个独特的强大的导师、资源和基础设施环境，使PI能够使用HIS 正规的统计物理培训是开发和应用新的计算方法的基础 肿瘤学。研究将开发三维器官型细胞培养和实验方法 用群体遗传学确定基因工程小鼠模型中的转移驱动基因和效应基因 以及来自IRB批准的正在进行的人类受试者研究的原发肿瘤样本。由RNA鉴定的基因- SEQ将使用新的网络扰动理论进行分析，以连接上游驱动程序和下游驱动程序 效应器。这些推断的网络将反过来连接到表型和行为后果 乳腺器官和组织。虽然超出了本提案的范围，但JHU CTD2中心拥有 使命和资源，以验证具有预防或治疗转移性乳癌临床潜力的研究结果 癌症，以及可能具有类似分子机制的其他浸润性或转移性癌症。少年派的 生物和统计物理学的背景，包括计算方法，使数学和 拟议研究的计算方面。该奖学金将提供必要的培训，使 PI将领导将物理科学方法与实验创新相结合的独立研究 以及用于癌症基础研究和帮助治疗进展的数据丰富的组学测量。私家侦探将拥有 未来与霍普金斯大学和其他机构合作的良好机会，并将成为有效的导师 在自己的实验室里培训计算肿瘤学研究人员。