Kooperativer Bibliotheksverbund

Berlin Brandenburg

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  • 1
    In: Neuro-Oncology, 2018, Vol. 20(suppl6), pp.vi77-vi78
    Description: ONC201 is the first selective antagonist of dopamine receptor D2 (DRD2) and D3 (DRD3) for clinical oncology that has exhibited preliminary clinical activity in high grade gliomas. We investigated DRD2 dysregulation in glioma and its role in ONC201 efficacy. Investigating CRISPR screens across a spectrum of cancer revealed that glioma cell lines had the highest DRD2 gene essentiality scores, indicating that glioma is a tumor type with the most vulnerability to DRD2 antagonism. Investigation of TCGA revealed that DRD2 is highly expressed in glioblastoma relative to other dopamine receptor family members and is associated with a relatively poor clinical prognosis. Tissue microarray analysis confirmed DRD2 overexpression in glioblastoma relative to normal brain. A linear correlation between DRD2 mRNA and ONC201 GI50 was observed among NCI60 glioblastoma cell lines. Similarly, we found a significant concordance between a cell line’s sensitivity to ONC201 within the Genomics of Drug Sensitivity in Cancer (GDSC) panel and its DRD2 gene essentiality score. Next, we ranked the relative contribution of each dopamine receptor to ONC201 efficacy using a bioinformatics approach based on a generalized linear model. We found that the strongest negative contributor was DRD2 – where a negative contribution denotes a decreased IC50 value as expression increases. Interestingly, DRD5, a D1-like dopamine receptor that counteracts DRD2 signaling, was measured as having the highest positive score – indicating that low expression of DRD5 was correlated with ONC201 efficacy. DRD5 expression was significantly inversely correlated with ONC201 potency in the NCI60 and GDSC datasets. Furthermore, a missense DRD5 mutation was identified in tumor cells with acquired resistance to ONC201. Resistance could be recapitulated with overexpression of the mutant or wild-type DRD5 gene. In conclusion, DRD2 dysregulation and DRD5 expression predict preclinical ONC201 glioma sensitivity that may be used to identify additional settings for clinical evaluation.
    Keywords: Medicine;
    ISSN: 1522-8517
    E-ISSN: 1523-5866
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  • 2
    Language: English
    In: Cancer Research, 07/01/2018, Vol.78(13 Supplement), pp.3896-3896
    ISSN: 0008-5472
    E-ISSN: 1538-7445
    Source: CrossRef
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  • 3
    Language: English
    In: Cancer Research, 07/01/2017, Vol.77(13 Supplement), pp.5039-5039
    ISSN: 0008-5472
    E-ISSN: 1538-7445
    Source: CrossRef
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  • 4
    Language: English
    In: Cancer Research, 07/01/2017, Vol.77(13 Supplement), pp.1563-1563
    ISSN: 0008-5472
    E-ISSN: 1538-7445
    Source: CrossRef
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  • 5
    Language: English
    In: Clinical cancer research : an official journal of the American Association for Cancer Research, 17 December 2018
    Description: Dopamine receptor D2 (DRD2) is a G protein-coupled receptor antagonized by ONC201, an anti-cancer small molecule in clinical trials for high grade gliomas and other malignancies. DRD5 is a dopamine receptor family member that opposes DRD2 signaling. We investigated the expression of these dopamine receptors in cancer and their influence on tumor cell sensitivity to ONC201. The Cancer Genome Atlas was used to determine DRD2/DRD5 expression broadly across human cancers. Cell viability assays were performed with ONC201 in 〉1,000 Genomic of Drug Sensitivity in Cancer and NCI60 cell lines. Immunohistochemistry staining of DRD2/DRD5 was performed in tissue microarrays and archival tumor tissues of glioblastoma patients treated with ONC201. Whole exome sequencing was performed in RKO cells with and without acquired ONC201 resistance. Wild-type and mutant DRD5 constructs were generated for overexpression studies. DRD2 overexpression broadly occurs across tumor types and is associated with a poor prognosis. Whole exome sequencing of cancer cells with acquired resistance to ONC201 revealed a de novo Q366R mutation in the DRD5 gene. Expression of Q366R DRD5 was sufficient to induce tumor cell apoptosis, consistent with a gain-of-function. DRD5 overexpression in glioblastoma cells enhanced DRD2/DRD5 heterodimers and DRD5 expression was inversely correlated with innate tumor cell sensitivity to ONC201. Investigation of archival tumor samples from recurrent glioblastoma patients treated with ONC201 revealed that low DRD5 expression was associated with relatively superior clinical outcomes. These results implicate DRD5 as a negative regulator of DRD2 signaling and tumor sensitivity to ONC201 DRD2 antagonism.
    ISSN: 1078-0432
    E-ISSN: 15573265
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  • 6
    Language: English
    In: Trends in Pharmacological Sciences, August 2019, Vol.40(8), pp.555-564
    Description: Stakeholders across the entire healthcare chain are looking to incorporate artificial intelligence (AI) into their decision-making process. From early-stage drug discovery to clinical decision support systems, we have seen examples of how AI can improve efficiency and decrease costs. In this Opinion, we discuss some of the key factors that should be prioritized to enable the successful integration of AI across the healthcare value chain. In particular, we believe a focus on model interpretability is crucial to obtain a deeper understanding of the underlying biological mechanisms and guide further investigations. Additionally, we discuss the importance of integrating diverse types of data within any AI framework to limit bias, increase accuracy, and model the interdisciplinary nature of medicine. We believe that widespread adoption of these practices will help accelerate the continued integration of AI into our current healthcare framework.
    Keywords: Machine Learning ; Artificial Intelligence ; Drug Development ; Model Interpretability ; Pharmacy, Therapeutics, & Pharmacology
    ISSN: 0165-6147
    E-ISSN: 1873-3735
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  • 7
    Language: English
    In: Briefings in bioinformatics, 03 June 2019
    Description: Despite the fact that deep learning has achieved remarkable success in various domains over the past decade, its application in molecular informatics and drug discovery is still limited. Recent advances in adapting deep architectures to structured data have opened a new paradigm for pharmaceutical research. In this survey, we provide a systematic review on the emerging field of graph convolutional networks and their applications in drug discovery and molecular informatics. Typically we are interested in why and how graph convolution networks can help in drug-related tasks. We elaborate the existing applications through four perspectives: molecular property and activity prediction, interaction prediction, synthesis prediction and de novo drug design. We briefly introduce the theoretical foundations behind graph convolutional networks and illustrate various architectures based on different formulations. Then we summarize the representative applications in drug-related problems. We also discuss the current challenges and future possibilities of applying graph convolutional networks to drug discovery.
    Keywords: Computational Drug Development ; Graph Convolution Network
    ISSN: 14675463
    E-ISSN: 1477-4054
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