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Harnessing novel technologies for precision medicine in pediatric diffuse midline glioma

Lay summary

            Das unterstütze Projekt zielt darauf ab, i) eine tiefgreifende molekulare Charakterisierung von DMG-Modellen sowie umfangreiche Medikamentenscreens durchzuführen, ii) modernste Bioinformatik- und maschinelle Lernplattformen zu nutzen, um molekulare Signaturen des Ansprechens oder der Resistenz auf Therapeutika zu identifizieren, und iii) diese Beobachtungen mit CRISPR-Gen-Editierungstechnologien zu validieren.  Und da eine der wichtigsten Fragen bei der Behandlung von Kleinkindern mit Krebsmedikamenten darin besteht, zu beurteilen, wie die Medikamente die normale Hirnfunktion beeinflussen können, werden wir iv) Signaturen von gesunden und intakten Hirnnetzwerken erstellen und anschliessend die neurologischen Folgen der Verwendung von Medikamentenkandidaten kartieren. 

Das Ergebnis der Studie wird die Zusammenführung neuartiger Technologien zur schnellen Identifizierung und Validierung von Therapien sein, die in der Lage sind, das Überleben von Kindern, bei denen diffuse Mittelliniengliome diagnostiziert wurden, zu verlängern.

Abstract

Diffuse midline glioma (DMG) is a deadly cancer affecting children most commonly under the age of 10 years. DMG has a median survival of only 9-11 months and over 90% of children diagnosed with DMG will die within two years of diagnosis. Technological advancements, surgical/upfront biopsies, and cost-effective data generation platforms have resulted in the discovery of some of the molecular disease drivers. We and others have shown that highly recurrent heterozygous somatic mutations in genes encoding histone H3 variants, H3.1 (HIST1H3B, HIST1H3C) and H3.3 (H3F3A), occur in 74-93% of DIPG tumours1. These mutually exclusive mutations in histone H3 genes similarly converge on a critical lysine residue, resulting in the substitution with a methionine residue (K27M). Whilst the precise mechanisms of H3K27M are poorly understood, studies show that it results in dysfunction of EZH2, a core component of the polycomb repressive complex 2, global reduction in the repressive histone modification H3K27me3, and dysregulated gene expression. To date, however, there has been no advancements in therapy for DMG patients despite significant progress in understanding disease biology. We hypothesize that a synergistic approach based on distinct fields such as neurotechnology (Yanik lab), computational oncology (Waszak lab), and preclinical DMG research (Nazarian lab) will identify personalized therapeutic vulnerabilities and drug combination strategies for effective treatment of children with DMG. In this proposal, we will use resources from national and international experts to explore the following specific aims:Aim 1. Molecular characterization and high-throughput drug screening of preclinical DMG in vitro models (Nazarian lab). We will perform deep molecular characterization of 30 human DMG and 15 non-DMG brain tumor lines and perform PRISM-based drug screening using 15,000 FDA-approved and novel compounds.Aim 2. Genomics of drug response and prediction of drug combinations (Waszak lab). We will derive DMG-specific drug candidates and utilize our clinical multi-omic DMG resource to identify predictive models of drug response. We will further use artificial intelligence-based approaches to predict drug combinations for personalized therapeutic interventions.Aim 3. Validation of top candidate drugs and drug combinations using preclinical DMG in vivo models (Nazarian/Yanik lab). Single- and combinatorial drug screening will be performed to assess (1) general- and neuro-toxicity using our novel zebrafish model and (2) to assess survival using our orthotopic murine DMG models. Drugs with known molecular targets will be further validated using CRISPR/Cas9-mediated knockout of the gene encoding the putative target. Drugs with poor CNS penetration yet with high in vitro efficacy and low (neuro-)toxicity profiles will be selected to enhance CNS penetration using our existing FUS system that is coupled with a microdialysis system.Aim 4. Prediction of the impact of regional inactivation/ablation of top drug candidates on brain networks and behavior. (Yanik lab). One of the major questions in treating young children with anti-cancer drugs is to assess whether these procedures will interfere/destroy normal brain function. In this aim, we will first establish signatures of healthy and intact brain networks and behavior. Subsequently, the neurological consequence of using candidate drugs will be assessed. Drug effects on brain network and behavior will be characterized using rat models. This approach has significant future potential for both basic discovery/assessment of drug and its consequence on developing neuronal networks.

Last updated:1655553822

  Prof.Mehmet Fatih Yanik
Stephan Neuhauss
Javad Nazarian
Adriano Aguzzi