ATPS-16IDENTIFICATION OF CLINICALLY RELEVANT DRUGS FOR GLIOBLASTOMA WITH SPECIFIC MOLECULAR SIGNATURE USING CHEMICAL BIOLOGY FINGERPRINTING

Abstract

Translating molecular subtyping of glioblastoma (GBM) into therapeutically actionable guidance remains an unfulfilled opportunity. A central challenge is to discover therapeutic targets matched to agents (drugs or tool compounds) that are predictably present in subclasses of GBM. Transcriptional patterns in GBM cases in TCGA can identify 12 distinct molecular contexts (mC), where, across a subset of genes and tumors, conditional dependencies of gene expression (signal & echo relationships) consistently emerge. We deployed a panel of 64 patient-derived xenografts from GBM patients whose gene expression profiles allow mapping against the same 12 molecular contexts identified from TCGA cases. These clinically-relevant, preclinical models afford bioinformatics mining for actionable targets as well as chemical library screening for activity against short-term cultures derived from the PDX models. Utilizing a technique we term Chemical Biology Fingerprinting (CBF), we interrogated a series of GBM PDX models using a small chemical library (650 compounds) of clinically relevant anti-cancer agents to uncover context-specific chemovulnerabilities. Preliminary data demonstrated that, mC14 GBM (GBM59, SF7300, GBM116), characterized by mt-P53 and transcriptional similarity to GBM proneural subtype, show distinct vulnerability to Arsenic Trioxide (ATO) as compared to mC4 GBM (GBM91, GBM102), characterized by CHEK2 and NF1 mutations and transcriptional patterns similar to GBM mesenchymal subtype. To clinically-validate an ATO vulnerability signature, we acquired 22 treatment naive archival patient samples, who were part of Phase I/II clinical trial to study efficacy of ATO and Temozolomide (TMZ) in combination with radiation in treatment of high grade gliomas (NCT00275067) and which exhibited varied survival with ATO treatment (91 days to >1000 days), and determined their molecular context classification using whole transcriptome sequencing (RNAseq). In summary, we demonstrate a subclassification of GBM into novel contexts and we also show that these contexts are differentially sensitive to clinically relevant drugs. Supported by NIH U01CA168397.