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Integrative discovery of treatments for high-risk neuroblastoma.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
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2020 (English)In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 11, no 1, article id 71Article in journal (Refereed) Published
Abstract [en]

Despite advances in the molecular exploration of paediatric cancers, approximately 50% of children with high-risk neuroblastoma lack effective treatment. To identify therapeutic options for this group of high-risk patients, we combine predictive data mining with experimental evaluation in patient-derived xenograft cells. Our proposed algorithm, TargetTranslator, integrates data from tumour biobanks, pharmacological databases, and cellular networks to predict how targeted interventions affect mRNA signatures associated with high patient risk or disease processes. We find more than 80 targets to be associated with neuroblastoma risk and differentiation signatures. Selected targets are evaluated in cell lines derived from high-risk patients to demonstrate reversal of risk signatures and malignant phenotypes. Using neuroblastoma xenograft models, we establish CNR2 and MAPK8 as promising candidates for the treatment of high-risk neuroblastoma. We expect that our method, available as a public tool (targettranslator.org), will enhance and expedite the discovery of risk-associated targets for paediatric and adult cancers.

Place, publisher, year, edition, pages
2020. Vol. 11, no 1, article id 71
National Category
Cancer and Oncology Cell and Molecular Biology Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:uu:diva-402363DOI: 10.1038/s41467-019-13817-8PubMedID: 31900415OAI: oai:DiVA.org:uu-402363DiVA, id: diva2:1386112
Available from: 2020-01-16 Created: 2020-01-16 Last updated: 2020-02-04Bibliographically approved
In thesis
1. New targeted therapies for malignant neural tumors: From systematic discovery to zebrafish models
Open this publication in new window or tab >>New targeted therapies for malignant neural tumors: From systematic discovery to zebrafish models
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Cancers in the neural system presents a major health challenge. The most aggressive brain tumor in adults, glioblastoma, has a median survival of 15 months and few therapeutic options. High-risk neuroblastoma, a childhood tumor originating in the sympathetic nervous system, has a 5-year survival under 50%, despite extensive therapy. Molecular characterization of these tumors has had some, but so far limited, clinical impact. In neuroblastoma, patients with ALK mutated tumors can benefit from treatment with ALK inhibitors. In glioblastoma, molecular subgroups have not yet revealed any subgroup-specific gene dependencies due to tumor heterogeneity and plasticity. In this thesis, we identify novel treatment candidates for neuroblastoma and glioblastoma. 

In paper I, we discover novel drug targets for high-risk neuroblastoma by integrating patient data, large-scale pharmacogenomic profiles, and drug-protein interaction maps. Using a novel algorithm, TargetTranslator, we identify more than 80 targets for this patient group. Activation of cannabinoid receptor 2 (CNR2) or inhibition of mitogen-activated protein kinase 8 (MAPK8) reduces tumor growth in zebrafish and mice models of neuroblastoma, establishing TargetTranslator as a useful tool for target discovery in cancer. 

In paper II, we screen approximately 1500 compounds across 100 molecularly characterized cell lines from patients to uncover heterogeneous responses to drugs in glioblastoma. We identify several connections between pathway activities and drug response. Sensitivity to proteasome inhibition is linked to oxidative stress response and p53 activity in cells, and can be predicted using a gene signature. We also discover sigma receptors as novel drug targets for glioblastoma and find a synergistic vulnerability in targeting cholesterol homeostasis.

In paper III, we systematically explore novel targets for glioblastoma using an siRNA screen. Downregulation of ZBTB16 decreases cell cycle-related proteins and transcripts in patient-derived glioblastoma cells. Using a zebrafish assay, we find that ZBTB16 promotes glioblastoma invasion in vivo

In paper IV, we characterized the growth of seven patient-derived glioblastoma cell lines in orthotopic zebrafish xenografts. Using automated longitudinal imaging, we find that tumor engraftment strongly correlates with tumor initiation capacity in mice xenografts and that the heterogeneous response to proteasome inhibitors is maintained in vivo

In summary, this thesis identifies novel targets for glioblastoma and neuroblastoma using systematic approaches. Treatment candidates are evaluated in novel zebrafish xenograft models that are developed for high-throughput glioblastoma and neuroblastoma drug evaluation. Together, this thesis provides promising evidence of new therapeutic options for malignant neural tumors.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2020. p. 61
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1632
Keywords
neuroblastoma, glioblastoma, data integration, zebrafish models, precision medicine
National Category
Cancer and Oncology Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy) Basic Medicine
Research subject
Oncology; Medical Cell Biology; Medical Informatics; Pharmacokinetics and Drug Therapy; Molecular Medicine
Identifiers
urn:nbn:se:uu:diva-402542 (URN)978-91-513-0857-9 (ISBN)
Public defence
2020-03-06, Rudbecksalen, Rudbecklaboratoriet, Dag Hammarskjölds väg 20, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2020-02-14 Created: 2020-01-18 Last updated: 2020-02-14

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