Collaborative Research: Rational Design of Anticancer Drug Combinations using Dynamic Multidimensional Theory

合作研究：利用动态多维理论合理设计抗癌药物组合

• 批准号: 1545838
• 负责人: Eric Siggia
• 金额: $ 4.68万
• 依托单位: Rockefeller University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-15 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545838&HistoricalAwards=false
• 关键词: Collaborative; Research; Rational; Design; Anticancer

This award is part of the NSF effort to promote significant advances in the fundamental understanding of cancer biology made possible through multidisciplinary research that involves experts in theoretical physics, applied mathematics, and computer science.Achieving durable control of metastatic solid tumors will require high-order targeted therapeutic combinations, because single-agent therapeutics eventually become thwarted by the development of tumor drug resistance. However, design of combinatorial regimens cannot be done by empirical trial and error in the clinical setting. The goal of the project is to blend a systems biology network-based theoretical framework with an integrated experimental and analytical program in order to address the combinatorial regimen challenge in oncology. Based on areas of exemplary clinical need, investigator expertise, and the availability of patient-derived tumor tissue, the project will focus on BRAF-mutant melanoma and PIK3CA-mutant, estrogen receptor positive (ER+) breast cancer as initial tumor types in which to pilot the approach. In addition the project will offer interdisciplinary training and research experience to postdoctoral and clinical fellows, graduate students, and indirectly to all members of the groups who participate. Professional development of all trainees will be enhanced by yearly meetings of the whole project team which will include tutorials on modeling and experimental methodologies. A symposium on the quantitative science of cancer will be organized at the Dana Farber Cancer Institute during the third year of this project. Team members are also committed to broadening the participation of women and under-represented minorities in STEM fields by pro-active recruitment and mentoring.The project will integrate dynamic modeling of signal transduction pathways relevant to cell proliferation and apoptosis, genomic and evolutionary analyses of tumor cells, and systematic cell death and therapeutic resistance studies. The dynamic models will be informed, tested, and iterated using experimental approaches applied to relevant cancer model systems. The experiments leverage emerging technologies such as pooled genome-wide open reading frame screens, dynamic BH3 profiling of cancer cells' closeness to the apoptotic threshold, whole exome sequencing and single cell RNA-seq analysis. The models will recapitulate steady state signaling network activation, acute adaptive effects of treatment (e.g., feedback dysregulation) and the range of drug-resistant states that may emerge following longer-term drug exposure. Tumor cell heterogeneity will be represented by the implementation of different initial configurations or state overrides of network components. Using newly developed systems control methodologies, the models will be used to prioritize drug combinations and dosing/scheduling principles for in vitro and in vivo testing. The final result will be a theoretical and experimentally validated approach that can be generalized across many other cancer types. This project develops a new framework to address cancer as a deregulated complex dynamical system and it will lead to an improved understanding of adaptive and acquired drug resistance mechanisms. The project will make a significant contribution toward a major goal of cancer precision medicine, namely the identification of optimal high-order combinations for individual cancer patients. The project will also establish new connections between evolutionary theory and dynamical systems theory. The theoretical and methodological advances will be applicable or adaptable to other cancers and diseases in general, leading to potentially transformative impacts on human health. This proposal is cofunded by the Physics of Living Systems Program in the Physics Division and the Systems and Synthetic Biology Program in the Molecular and Cellular Biosciences Division.

This award is part of the NSF effort to promote significant advances in the fundamental understanding of cancer biology made possible through multidisciplinary research that involves experts in theoretical physics, applied mathematics, and computer science.Achieving durable control of metastatic solid tumors will require high-order targeted therapeutic combinations, because single-agent therapeutics eventually become thwarted by the development of tumor drug resistance.但是，组合方案的设计不能通过临床环境中的经验试验和错误来完成。该项目的目的是将基于系统生物网络的理论框架与集成的实验和分析计划融合在一起，以应对肿瘤学中的组合方案挑战。基于示例性临床需求，研究者专业知识以及患者衍生的肿瘤组织的可用性，该项目将重点放在BRAF突变药物黑色素瘤和PIK3CA突变物，雌激素受体阳性（ER+）乳腺癌作为初始肿瘤类型中，以实现这种方法。此外，该项目将为博士后和临床研究员，研究生以及参与的所有团体的所有成员提供跨学科的培训和研究经验。整个项目团队的年度会议将提高所有学员的专业发展，其中包括建模和实验方法的教程。该项目的第三年，将在Dana Farber癌症研究所组织有关癌症定量科学的研讨会。团队成员还致力于通过积极的招聘和指导扩大妇女和人为少数群体在STEM领域的参与。该项目将积分与细胞增殖和凋亡，基因组和进化分析有关的信号转导途径的动态建模，对肿瘤细胞的基因组和进化分析，以及系统的细胞死亡死亡死亡和系统的耐药性和治疗性耐药性研究。将使用应用于相关的癌症模型系统的实验方法来了解，测试和迭代动态模型。该实验利用了新兴技术，例如全基因组开放式阅读框架筛选，癌细胞与凋亡阈值接近的动态BH3分析，整个外显子组测序和单细胞RNA-seq分析。这些模型将概括稳态信号网络激活，治疗的急性自适应作用（例如，反馈失调）以及在长期药物暴露后可能出现的抗药性状态范围。肿瘤细胞的异质性将通过实施不同的初始配置或网络组件的状态覆盖来表示。使用新开发的系统控制方法，这些模型将用于优先考虑用于体外和体内测试的药物组合和剂量/调度原理。最终结果将是一种理论和实验验证的方法，可以在许多其他癌症类型中推广。该项目开发了一个新的框架来解决癌症，作为一个放松管制的复杂动力学系统，这将导致人们对自适应和获得的耐药性机制的理解。该项目将为癌症精确医学的主要目标做出重大贡献，即鉴定单个癌症患者的最佳高阶组合。该项目还将在进化论与动态系统理论之间建立新的联系。一般而言，理论和方法论的进步将适用或适应其他癌症和疾病，从而导致对人类健康的潜在变革性影响。该建议是由物理学部门的物理学计划以及分子和细胞生物科学划分的系统和合成生物学计划的辅助。