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DDDR-33. TRANSCRIPTOMICS-BASED NOVEL DRUG DISCOVERY IN DIFFUSE MIDLINE GLIOMA

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Abstract Introduction. Pediatric high-grade gliomas are the most common cause of cancer-related death in children, of which the most malignant and devastating tumors include diffuse midline glioma (DMG). Recent availability of tumor samples and advances in next-generation sequencing enable the profiling of thousands of molecular features in DMG. We employ a system-based approach that uses gene expression as a representation of the molecular state, providing an avenue for therapeutic discovery. Previously, we used this approach to identify repurposed candidates for DMG; however, the final candidate had poor brain penetration, prompting us to discover novel compounds with better brain penetration. To apply it to novel compound discovery, we need the expression profiles of all library compounds, which is impractical for millions of compounds. To overcome this, we have developed a platform, Gene expression profiles Predictor on chemical Structures (GPS), that incorporates machine learning algorithms which leverage existing drug-induced gene expression profiles to predict gene expression based on compound structure. methods. DMG RNA-Seq samples were acquired from St. Jude Children’s Research Hospital and Children’s Brain Tumor Network (CBTN). We developed deep learning autoencoder to search reference normal tissues from Genotype-Tissue Expression (GTEx). A DMG meta-signature was created and fed into GPS to predict drug candidates from the Enamine CNS library, seven of which were experimentally tested in vitro and the most promising one subsequently validated in vivo. Results. Cell proliferation analyzed by MTS assay showed three compounds with IC50< 50 µM in K27M-mutant DMG cell lines (SF8628, DIPG007, SU-DIPG36). Toxicity study showed no adverse effects in the mice treated with 100-200 mg/kg of the most promising compound (IC50 = 1.9 µM in DMG cells). Conclusion. Our novel computational framework leverages deep learning to fill the gap in drug discovery in DMG. Work is underway to determine antitumor activity, brain penetration (via HPLC) and survival benefit in the mice bearing DMG PDX treated with the lead compound.
Title: DDDR-33. TRANSCRIPTOMICS-BASED NOVEL DRUG DISCOVERY IN DIFFUSE MIDLINE GLIOMA
Description:
Abstract Introduction.
Pediatric high-grade gliomas are the most common cause of cancer-related death in children, of which the most malignant and devastating tumors include diffuse midline glioma (DMG).
Recent availability of tumor samples and advances in next-generation sequencing enable the profiling of thousands of molecular features in DMG.
We employ a system-based approach that uses gene expression as a representation of the molecular state, providing an avenue for therapeutic discovery.
Previously, we used this approach to identify repurposed candidates for DMG; however, the final candidate had poor brain penetration, prompting us to discover novel compounds with better brain penetration.
To apply it to novel compound discovery, we need the expression profiles of all library compounds, which is impractical for millions of compounds.
To overcome this, we have developed a platform, Gene expression profiles Predictor on chemical Structures (GPS), that incorporates machine learning algorithms which leverage existing drug-induced gene expression profiles to predict gene expression based on compound structure.
methods.
DMG RNA-Seq samples were acquired from St.
Jude Children’s Research Hospital and Children’s Brain Tumor Network (CBTN).
We developed deep learning autoencoder to search reference normal tissues from Genotype-Tissue Expression (GTEx).
A DMG meta-signature was created and fed into GPS to predict drug candidates from the Enamine CNS library, seven of which were experimentally tested in vitro and the most promising one subsequently validated in vivo.
Results.
Cell proliferation analyzed by MTS assay showed three compounds with IC50< 50 µM in K27M-mutant DMG cell lines (SF8628, DIPG007, SU-DIPG36).
Toxicity study showed no adverse effects in the mice treated with 100-200 mg/kg of the most promising compound (IC50 = 1.
9 µM in DMG cells).
Conclusion.
Our novel computational framework leverages deep learning to fill the gap in drug discovery in DMG.
Work is underway to determine antitumor activity, brain penetration (via HPLC) and survival benefit in the mice bearing DMG PDX treated with the lead compound.

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