Gene 36. Integrating Transcriptomics and Kinomics Identifies Synergistic Drug Combinations for Glioblastoma Treatment – Frost Institute for Data Science & Computing

Gene 36. Integrating Transcriptomics and Kinomics Identifies Synergistic Drug…

Glioblastoma (GBM) is the most common and malignant adult brain tumor. Despite years of research, few advancements have been made in its management. One promising avenue of research has been treatment with BRD4 inhibitors, which decrease oncogene expression in GBM cells. However, resistance to these inhibitors is rapidly acquired. Kinome reprogramming is thought to underlie this resistance, suggesting a need for combination therapy with kinase inhibitors. The goal of this study is to determine whether transcriptomic and kinomic profiling of GBM tumors will identify synergistic drug pairs for GBM treatment. We profiled the active kinome on a set of three newly-diagnosed GBM patient-derived xenograft (PDX) tumors and three recurrent tumors using quantitative SILAC mass spectrometry. Additionally, kinome reprogramming following BET inhibition was profiled in vitro using the BET inhibitor JQ1. Kinome activity was uploaded into our novel computational platform, SynergySeq, to assess the synergistic potential of kinase inhibitors with JQ1. Additionally, single-cell RNA-sequencing of a GBM tumor was used to determine the cell populations affected by each inhibitor. To quantify synergy in response to combination therapy, cells were treated with a combination matrix of JQ1 and a kinase inhibitor in variable concentrations, and cell death was quantified via ATP levels. Our results showed that newly-diagnosed tumors were predicted to be sensitive to combined BET inhibition with Bcr/Abl, EGFR, and FGFR kinase inhibitors. Recurrent tumors were sensitive to combined Bcr/Abl and FGFR inhibitors but were not sensitive to EGFR inhibitors. We screened inhibitors in vitro and found a synergistic effect for the combination of JQ1 and TAS120, a pan-FGFR inhibitor. These data suggest that clinically a brain penetrant BET inhibitor should be effective in combination with a brain penetrant FGFR inhibitor. Importantly, our computational platform is a novel informatics-based approach for targeted therapy in a patient-specific and disease-specific manner