MIT/Mayo Physical Sciences Center for Drug Distribution and Efficacy in Brain Tumors

总体-摘要 选择具有最佳药代动力学和药效学特性的相关治疗药物， 在整个靶细胞群中充分抑制预期靶点将是成功的关键 基因组学引导的精准医疗策略。脑肿瘤的最佳药物治疗尤其是 由于脉管系统和肿瘤微环境内的多个物理屏障， 在高度不均匀的药物输送中。这导致相当一部分肿瘤细胞暴露于亚- 治疗药物水平限制了治疗效果，并可能导致代偿性细胞信号传导， 耐药性的出现。因此，这一建议的一个中心原则是，不了解限制， 新疗法在脑肿瘤中的物理递送和分配是集体研究的主要原因， 未能将外周恶性肿瘤中实现的令人兴奋的治疗进展和生存率提高扩展到 脑肿瘤的治疗在本PS-OC中，我们将重点了解影响 在患者和动物的高度集成分析中的异质药物分布和由此产生的生物学 使用三维MR成像、受激拉曼散射（SRS）显微镜、基质 辅助激光解吸/电离质谱成像（MALDI-MSI），免疫组织化学（IHC）， 磷酸蛋白质组学、邻位连接测定（PLA）和RNAseq。将这些数据集集成到 在多种肿瘤模型中评估的一系列药物将阐述调节肿瘤细胞分布的关键因素。 这些药物，并提供了一个多尺度模型，可用于选择一个平台的建设 基于个体肿瘤的MRI特征，使用最佳预测药物分布的靶向治疗。 在这种情况下，我们将直接满足肿瘤学物理科学计划的目标， 物理科学和癌症研究的观点和方法，以解决一个复杂的和具有挑战性的 癌症研究中的问题。特别是根据PAR-14-49的解释，我们将解决： 癌症的物理动力学：肿瘤、播散细胞和肿瘤细胞内的物理特性和力如何影响肿瘤的生长和转移？ 定殖和转移的位点有助于治疗递送和功效？这些因素是如何 影响癌症进展和治疗耐药性的演变？ 癌症的时空组织和信息传递： 在动态时空环境的背景下检查治疗抗性， 明确进展和耐药机制，合理设计治疗策略？

OVERALL – SUMMARY The selection of relevant therapeutic agents with optimal pharmacokinetic and pharmacodynamic properties to adequately suppress the intended target across the entire target cell population will be central to the success of genomics-guided precision medicine strategies. Optimal drug therapy for brain tumors is especially challenging due to multiple physical barriers within the vasculature and tumor microenvironment that can result in highly heterogeneous drug delivery. This results in a significant fraction of tumor cells being exposed to sub- therapeutic drug levels that limit the efficacy of therapy and may lead to compensatory cell signaling and emergence of drug resistance. Thus, a central tenet of this proposal is that failure to understand limitations in the physical delivery and distribution of novel therapeutics into brain tumors is a major reason for the collective failure to extend the exciting treatment advances and survival gains realized in peripheral malignancies to the treatment of brain tumors. In this PS-OC, we will focus on understanding physical factors that influence heterogeneous drug distribution and the resulting biology in a highly integrated analysis of patient and animal tumor models using 3-dimensional MR imaging, stimulated Raman scattering (SRS) microscopy, matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), immunohistochemistry (IHC), phosphoproteomics, proximity ligation assays (PLA), and RNAseq. Integration of these data sets across a series of drugs evaluated in multiple tumor models will elaborate critical factors that modulate distribution of these drugs and provide the platform for construction of a multi-scale model that could be used to select a targeted therapeutic with an optimal predicted drug distribution based on MRI features of an individual tumor. In this context, we will directly meet the goal of the Physical Sciences in Oncology Program to integrate physical sciences and cancer research perspectives and approaches to address a complex and challenging question in cancer research. Specifically paraphrased from PAR-14-49, we will address: Physical Dynamics of Cancer: How do physical properties and forces within tumors, disseminating cells, and sites of colonization and metastasis contribute to therapeutic delivery and efficacy? How do these factors affect cancer progression and evolution of therapeutic resistance? Spatio-Temporal Organization and Information Transfer in Cancer: Can the evolutionary dynamics of therapeutic resistance be examined in the context of dynamic spatio-temporal environments to better define mechanisms of progression and resistance and rationally design therapeutic strategies?