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

该奖项是美国国家科学基金会（NSF）努力的一部分，旨在通过涉及理论物理学、应用数学和计算机科学专家的多学科研究，促进对癌症生物学基础理解的重大进展。实现转移性实体瘤的持久控制将需要高阶靶向治疗组合，因为单药治疗最终会因肿瘤耐药性的发展而受阻。然而，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。该项目的目标是将基于系统生物学网络的理论框架与综合实验和分析程序相结合，以解决肿瘤学中组合方案的挑战。基于典型的临床需求、研究者的专业知识和患者来源的肿瘤组织的可用性，该项目将重点关注braf突变的黑色素瘤和pik3ca突变、雌激素受体阳性（ER+）乳腺癌，作为该方法的初始肿瘤类型进行试点。此外，该项目将为博士后和临床研究员、研究生以及间接参与的所有小组成员提供跨学科培训和研究经验。所有受训者的专业发展将通过整个项目团队的年度会议来加强，会议将包括建模和实验方法的教程。在该项目的第三年期间，将在达纳法伯癌症研究所组织一次关于癌症定量科学的研讨会。团队成员还致力于通过积极招聘和指导，扩大女性和代表性不足的少数群体在STEM领域的参与。该项目将整合与细胞增殖和凋亡相关的信号转导途径的动态建模，肿瘤细胞的基因组和进化分析，以及系统的细胞死亡和治疗耐药性研究。动态模型将被告知，测试，并使用应用于相关癌症模型系统的实验方法进行迭代。实验利用了新兴技术，如全基因组开放阅读框屏幕，癌细胞接近凋亡阈值的动态BH3分析，全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、治疗的急性适应性效应（例如，反馈失调）以及长期药物暴露后可能出现的耐药状态范围。肿瘤细胞的异质性将通过实现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，这些模型将用于体外和体内试验的药物组合和给药/调度原则的优先排序。最终的结果将是一个理论和实验验证的方法，可以推广到许多其他类型的癌症。该项目开发了一个新的框架来解决癌症作为一个不受管制的复杂动力系统，它将导致对适应性和获得性耐药机制的更好理解。该项目将为癌症精准医疗的主要目标做出重大贡献，即为个体癌症患者确定最佳的高阶组合。该项目还将在进化理论和动力系统理论之间建立新的联系。理论和方法上的进步将适用于或适用于其他癌症和一般疾病，从而对人类健康产生潜在的变革性影响。本提案由物理部的生命系统物理学项目和分子与细胞生物科学部的系统与合成生物学项目共同资助。

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.