Computational Prediction of Genetic Drivers of Breast Cancer Metastases

项目摘要 虽然转移是癌症死亡的主要原因，但其许多分子要求仍然未知。 追踪从上游转移驱动到下游的生物网络的计算方法 效应物具有鉴定癌症治疗的新的干预点的潜力。发展这些类型的 方法需要具有统计网络模型专业知识和深入参与癌症研究的个人 实验系统该奖学金将培训PI预测转录和信号传导的流程 通过生物网络传递信息，将这些变化与表型和行为后果联系起来 用于来源于转移性和非转移性乳腺癌类器官的细胞和组织。贝德 （计算）和Ewald（实验）实验室由国家癌症研究所共同资助，作为癌症研究所的一部分。 靶向发现和开发（CTD 2）中心专注于乳腺癌转移。该中心提供 一个独特的强大的环境，导师，资源和基础设施，使PI能够使用他的 统计物理学的正式培训，作为开发和应用计算新方法的基础 肿瘤学研究将利用三维器官型细胞培养和实验方法， 通过群体遗传学鉴定基因工程小鼠模型中的转移驱动基因和效应基因 以及来自正在进行的IRB批准的人类受试者研究的原发性肿瘤标本。通过RNA识别的基因- Seq将用新的网络扰动理论来分析，以将上游驱动器连接到下游驱动器。 效应器这些推断出的网络将反过来与表型和行为后果相联系， 乳腺类器官和组织。虽然不在本建议书的范围内，但JHU CTD 2中心拥有 验证具有预防或治疗转移性乳腺癌临床潜力的发现的使命和资源 癌症以及具有类似分子机制的潜在的其它侵袭性或转移性癌症。PI的 生物和统计物理学背景，包括计算方法，使数学和 所提出的研究的计算方面。研究金将提供必要的培训， PI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。私家侦探会有 与霍普金斯和其他机构在未来的合作机会，将是一个有效的导师 在他自己的实验室里培训计算肿瘤学研究人员。

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.