Project: Research project

Project Details


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.
Effective start/end date8/1/197/31/21


  • National Cancer Institute: $102,364.00


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