Defining the relationship between tumor composition, spatial heterogeneity, drug delivery, and drug efficacy

Cancer genomic analysis enables the selection of therapeutic targets for precision medicine based on the detection of oncogenic events in a patient tumor. Despite significant advances in the field, there remains a daunting challenge in identifying optimal therapeutic agents that will be well distributed throughout the tumor and efficacious in their target inhibition. This challenge is exacerbated in brain tumors due to the blood-brain and blood-tumor barriers (collectively referred to subsequently as the BBB) that can limit distribution into the central nervous system of otherwise highly effective therapies and likely contribute to the failure of many therapies targeting brain pathologies. The spatially heterogeneous distribution of targeted therapy throughout a patient tumor significantly impacts the molecular and cellular response. Local concentrations above a critical threshold can lead to target inhibition and cell death, but may also generate an adaptive response and activation of compensatory resistance pathways. At sub-threshold concentrations, our preliminary data indicate a paradoxical signaling network activation resulting in increased proliferation. For any given therapeutic agent and any given tumor, the tumor physical parameters and physico-chemical properties of the therapeutic agent that regulate drug distribution and efficacy are currently poorly understood.

Determining the physical factors regulating therapeutic distribution and efficacy in vivo is a daunting challenge. Therapeutic distribution may be affected by its size, affinity, and molecular composition in combination with multiple physical factors of the tumor, such as vascular distribution and integrity along with spatial cellular and molecular heterogeneity.

In the first phase of this project, we will define the tumor tissue architecture and determine its effect on drug distribution.  Previous models, including those developed by the Wittrup lab, have focused on the effect of diffusion and distance from vasculature, to predict therapeutic delivery. While these models have worked well for many tumors, they are limited in predicting drug distribution into CNS tumors, due to the effect of the blood-brain barrier (intact in native vessels; aberrant in neovasculature), hypoxia, necrosis, edema, tumor growth kinetics, and the different dispersion behaviors of these tumors relative to non-CNS tumors. Using a variety of latest- generation bio-physical imaging and analytical techniques, combined with computational models provided by the Data Handling and Integration Core, we will generate improved computational models quantifying the physical properties of the tumor tissue architecture and their effect on drug distribution.

In the second phase of this project, we will quantify the adaptive molecular response to different therapeutic doses. To understand the overall tumor response to therapy it is necessary to quantify drug distribution and drug efficacy, including target inhibition and the systems-level signaling network-wide response to this inhibition. To quantify dynamic responses to different therapeutic concentrations for different durations, we propose to utilize cutting-edge systems biology analytical techniques, including next-gen RNA sequencing and mass spectrometry based proteomics and phosphoproteomics. To connect this data to cellular biology outcome, we will quantify tumor size, apoptosis, senescence, and proliferation under each condition. Multivariate data driven computational modeling in the Data Handling and Integration Core will identify the transcript and signaling nodes regulating cell response to therapy. Mechanistic pharmacodynamics modeling will then be used to predict the dynamic molecular and cellular response to spatially heterogeneous drug distribution. Models will be validated by IHC, PLA, and LCM-RNASeq to obtain spatially resolved response to therapy with approximately cellular resolution.