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?
OVERALL ? NARRATIVE Genomics-guided precision medicine promises to identify key therapeutic target(s) for an individual patient to enable selection of the most efficacious drug treatment. There are now several drugs designed to inhibit most therapeutic targets, creating the new challenge of selecting the drug with the optimal properties that will ensure adequate suppression of the intended target(s) throughout the tumor. While relevant for all cancers, the selection of appropriate drugs is especially challenging in brain tumors. Both normal and diseased regions of the brain have unique barriers to drug delivery, and the impact of these barriers on achievable drug levels is highly variable both within an individual patient tumor and across a population of patient tumors. The most common types of malignant brain tumors, brain metastases from cancers outside of the brain, and glioblastoma, have regions that are protected from most drugs, and low-level drug exposure in these regions can promote development of drug resistance. In fact, our data suggests that regions of sub-optimal drug exposure may be a critical reason why there have been no new effective drug treatments for brain tumors in over a decade. The goal of the MIT/Mayo PS-OC is to understand the physical parameters that limit drug delivery into brain tumors and use this information to build predictive models of drug distribution into brain tumors. Ultimately, these models could be combined with genomics-guided precision medicine to ensure that the best drug is selected for treatment of an individual brain tumor.
