Admin-Core-001

基因组学指导的精准医学有望确定个体的关键治疗靶点(S) 使患者能够选择最有效的治疗策略。对这一战略的成功至关重要 需要选择具有最佳药代动力学和药效学的相关治疗剂 属性，以便在整个目标单元格群体中充分抑制预期目标。虽然相关 对于所有癌症来说，选择合适的药物治疗方法对脑肿瘤来说尤其具有挑战性， 正常和病变区域的血脑屏障可以显著限制药物的分布和 对这些肿瘤的疗效。事实上，超过95%的FDA批准的药物在大脑中的积累有限，并且 当前预测药物进入大脑的算法，基于药物的物理化学特征 治疗剂，预测性很差。在麻省理工学院/梅奥PS-OC中，我们将开发一个药物建模平台 脑肿瘤的分布从生物和组织到亚细胞分布和 信号和转录网络效应，以支持基于磁共振成像(MRI)的建模 启用临床翻译。与基因组学指导的治疗脆弱性描述相结合， 所提出的药物分布和疗效的多尺度模型可用于选择具有靶向治疗的 基于单个肿瘤的MRI特征的最优预测药物分布。 肿瘤学的一个关键原则是，只有当潜在的根治疗法有效地靶向治疗时，才有可能治愈。 100%的肿瘤细胞群。许多脑肿瘤的侵袭性，孤立的肿瘤细胞侵袭 进入正常大脑的区域，使得这些肿瘤的治疗特别具有挑战性，尽管令人兴奋 神经外科、放射治疗、癌症基因组学(靶点识别)和癌症药理学的进展 (靶向治疗)，对于大多数原发或转移性脑肿瘤患者的预后 在几十年的过程中发生了巨大的变化。这项提议的核心原则之一是 未能理解脑瘤中新疗法的物理传递和分布的局限性是一种 这是缺乏进展的主要原因。在大多数脑肿瘤中，血管系统的完整性和相关 血脑屏障是异质性的，严重限制了药物输送到肿瘤的至少某些部分。超越 血管系统和血脑屏障，其他调节肿瘤治疗输送的物理特征很差 了解，所有这些因素最终导致治疗药物的空间异质性范围 在肿瘤细胞群体中暴露。此外，动态的分子和细胞反应在一个 异质性肿瘤对时间调控和空间异质性分子靶向的作用 人们对治疗学知之甚少。同样未知的是，优化大小、亲和力和/或 治疗剂的化学性质可以克服这些物理限制。因此，有一个巨大的 未得到满足的医疗需求，以提高我们对这些影响药物分配的物理因素的了解，并 利用这些知识为这些毁灭性的肿瘤开发更有效的治疗策略。在这个PSOC中， 我们将重点了解影响异质药物分布的物理因素和 使用三维磁共振对患者和动物肿瘤模型进行高度集成的生物学分析 成像、受激拉曼散射(SRS)显微镜、基质辅助激光解吸/电离质量 光谱成像(MALDI-MSI)、免疫组织化学(IHC)、磷酸蛋白质组学、邻近结扎 和RNAseq.在多个肿瘤中评估一系列药物的这些数据集的整合 模型将详细阐述调节药物分配的关键因素，并为构建 计划中的多比例模型。

Genomics-guided precision medicine promises to identify the key therapeutic target(s) in an individual patient to enable selection of the most efficacious therapeutic strategy. Central to the success of this strategy needs to be the selection of relevant therapeutic agents with optimal pharmacokinetic and pharmacodynamic properties to adequately suppress the intended target across the entire target cell population. While relevant for all cancers, the selection of appropriate pharmacotherapies is especially challenging in brain tumors, in which the blood-brain barrier in normal and diseased regions can significantly limit drug distribution and efficacy for these tumors. In fact, over 95% of FDA-approved drugs have limited accumulation in the brain, and current predictive algorithms for drug distribution into the brain, based on physico-chemical features of the therapeutic agent, are poorly predictive. In the MIT/Mayo PS-OC, we will develop a platform for modeling drug distribution in brain tumors across scales from organism and tissue down to sub-cellular distribution and signaling and transcript network effect to support magnetic resonance imaging (MRI)-based modeling to enable clinical translation. Integrated with a genomics-guided delineation of therapeutic vulnerabilities, the proposed multi-scale model of drug distribution and efficacy could be used to select a targeted therapeutic with an optimal predicted drug distribution based on MRI features of an individual tumor. A key principle in oncology is that cure is only possible if a potentially curative treatment effectively targets 100% of the tumor cell population. The invasive nature of many brain tumors, with isolated tumor cells invading into regions of normal brain, has made these tumors especially challenging to treat, and despite exciting advances in neurosurgery, radiation therapy, cancer genomics (target identification), and cancer pharmacology (targeted therapeutics), the prognosis for most patients with primary or metastatic brain tumors has not significantly changed over the course of several decades. One of the central tenets of this proposal is that failure to understand limitations in physical delivery and distribution of novel therapeutics into brain tumors is a major reason for this lack of progress. In most brain tumors, the integrity of the vasculature and associated BBB is heterogeneous and critically limits drug delivery to at least some parts of the tumor. Beyond vasculature and the BBB, other physical features regulating therapeutic delivery into tumors are poorly understood, and all of these factors ultimately result in a spatially heterogeneous range of therapeutic drug exposure across a tumor cell population. Further, the dynamic molecular and cellular responses in a heterogeneous tumor to temporally regulated and spatially heterogeneous molecularly targeted therapeutics are poorly understood. Also unknown is the extent that optimizing size, affinity, and/or chemical properties of the therapeutic agent may overcome these physical limitations. Thus, there is a huge unmet medical need to improve our understanding of these physical factors influencing drug distribution and to use this knowledge to develop more effective therapeutic strategies for these devastating tumors. In this PSOC, 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 drug distribution and provide a platform for construction of the planned multi-scale model.