DIPG-18. AI HUNTING FOR NEW DRUGS IN PEDIATRIC GLIOMA

Abstract

Abstract Diffuse intrinsic pontine glioma (DIPG) is one of the most devastating pediatric gliomas, with nearly all patients succumbing to progressive tumor growth within two years of diagnosis. The identification of effective therapeutics based on the genetic features is of high importance for improving treatment outcomes for DIPG patients. We have developed machine learning algorithms that allow profiling thousands of genetic features, which can all contribute towards drug discovery. This study aimed to identify potential therapeutic agents using a computational pipeline to perform an in-silico screen for novel drugs. We then tested the identified drugs against a panel of patient-derived DIPG cell lines. Using a systematic computational approach with publicly available databases of gene signature in DIPG patients and cancer cell lines treated with a library of clinically available drugs, we identified drug hits with the ability to reverse a DIPG gene signature to one that matches normal tissue background. The biological and molecular effects of drug treatment was analyzed by cell viability assay and RNA sequence. In vivo DIPG mouse model survival studies were also conducted. As a result, two of three identified drugs showed potency against the DIPG cell lines. Triptolide and mycophenolate mofetil (MMF) demonstrated significant inhibition of cell viability in DIPG cell lines. Guanosine rescued reduced cell viability induced by MMF. In vivo, MMF treatment significantly inhibited tumor growth in orthotopic DIPG xenograft models. In conclusion, we identified clinically available drugs with the ability to reverse DIPG gene signatures and anti-DIPG activity in vitro and in vivo. This novel approach can repurpose drugs and significantly decrease the cost and time normally required in drug discovery.