{"entity": "researcher", "timestamp": "2026-07-12T18:27:11.322Z", "family": "Almstedt", "given": "Elin", "initials": "E", "orcid": "0000-0002-1946-9138", "affiliations": [], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/c4414ac7f9284bf9bf0b52530ec19ee3.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/c4414ac7f9284bf9bf0b52530ec19ee3"}}, "publications": [{"entity": "publication", "iuid": "38139cec014548ceb9c9565608cf39c9", "links": {"self": {"href": "https://publications.scilifelab.se/publication/38139cec014548ceb9c9565608cf39c9.json"}, "display": {"href": "https://publications.scilifelab.se/publication/38139cec014548ceb9c9565608cf39c9"}}, "title": "Real-time evaluation of glioblastoma growth in patient-specific zebrafish xenografts.", "authors": [{"family": "Almstedt", "given": "Elin", "initials": "E", "orcid": "0000-0002-1946-9138", "researcher": {"href": "https://publications.scilifelab.se/researcher/c4414ac7f9284bf9bf0b52530ec19ee3.json"}}, {"family": "Ros\u00e9n", "given": "Emil", "initials": "E", "orcid": "0000-0002-1664-2257", "researcher": {"href": "https://publications.scilifelab.se/researcher/db0751a3a95e488bb849059c09c4fc7d.json"}}, {"family": "Gloger", "given": "Marleen", "initials": "M"}, {"family": "Stockgard", "given": "Rebecka", "initials": "R"}, {"family": "Hekmati", "given": "Neda", "initials": "N"}, {"family": "Koltowska", "given": "Katarzyna", "initials": "K", "orcid": "0000-0002-6841-8900", "researcher": {"href": "https://publications.scilifelab.se/researcher/06a8aeda504340c1af3ab893fd413a65.json"}}, {"family": "Krona", "given": "Cecilia", "initials": "C"}, {"family": "Nelander", "given": "Sven", "initials": "S"}], "type": "journal article", "published": "2022-05-04", "journal": {"issn": "1523-5866", "title": "Neuro-oncology", "volume": "24", "issue": "5", "pages": "726-738", "issn-l": "1522-8517"}, "abstract": "Patient-derived xenograft (PDX) models of glioblastoma (GBM) are a central tool for neuro-oncology research and drug development, enabling the detection of patient-specific differences in growth, and in vivo drug response. However, existing PDX models are not well suited for large-scale or automated studies. Thus, here, we investigate if a fast zebrafish-based PDX model, supported by longitudinal, AI-driven image analysis, can recapitulate key aspects of glioblastoma growth and enable case-comparative drug testing.\n\nWe engrafted 11 GFP-tagged patient-derived GBM IDH wild-type cell cultures (PDCs) into 1-day-old zebrafish embryos, and monitored fish with 96-well live microscopy and convolutional neural network analysis. Using light-sheet imaging of whole embryos, we analyzed further the invasive growth of tumor cells.\n\nOur pipeline enables automatic and robust longitudinal observation of tumor growth and survival of individual fish. The 11 PDCs expressed growth, invasion and survival heterogeneity, and tumor initiation correlated strongly with matched mouse PDX counterparts (Spearman R = 0.89, p < 0.001). Three PDCs showed a high degree of association between grafted tumor cells and host blood vessels, suggesting a perivascular invasion phenotype. In vivo evaluation of the drug marizomib, currently in clinical trials for GBM, showed an effect on fish survival corresponding to PDC in vitro and in vivo marizomib sensitivity.\n\nZebrafish xenografts of GBM, monitored by AI methods in an automated process, present a scalable alternative to mouse xenograft models for the study of glioblastoma tumor initiation, growth, and invasion, applicable to patient-specific drug evaluation.", "doi": "10.1093/neuonc/noab264", "pmid": "34919147", "labels": {"Genome Engineering Zebrafish": "Service"}, "xrefs": [{"db": "pii", "key": "6432157"}, {"db": "pmc", "key": "PMC9071311"}], "notes": [], "created": "2021-12-20T12:13:15.480Z", "modified": "2022-08-24T11:18:59.919Z"}, {"entity": "publication", "iuid": "9b592e44470a4ba18b57b727b2011b71", "links": {"self": {"href": "https://publications.scilifelab.se/publication/9b592e44470a4ba18b57b727b2011b71.json"}, "display": {"href": "https://publications.scilifelab.se/publication/9b592e44470a4ba18b57b727b2011b71"}}, "title": "Integrative discovery of treatments for high-risk neuroblastoma.", "authors": [{"family": "Almstedt", "given": "Elin", "initials": "E", "orcid": "0000-0002-1946-9138", "researcher": {"href": "https://publications.scilifelab.se/researcher/c4414ac7f9284bf9bf0b52530ec19ee3.json"}}, {"family": "Elgendy", "given": "Ramy", "initials": "R", "orcid": "0000-0002-2592-3448", "researcher": {"href": "https://publications.scilifelab.se/researcher/0a5ce4db4317446bb1ef113f7c6e8eb5.json"}}, {"family": "Hekmati", "given": "Neda", "initials": "N"}, {"family": "Ros\u00e9n", "given": "Emil", "initials": "E", "orcid": "0000-0002-1664-2257", "researcher": {"href": "https://publications.scilifelab.se/researcher/db0751a3a95e488bb849059c09c4fc7d.json"}}, {"family": "W\u00e4rn", "given": "Caroline", "initials": "C"}, {"family": "Olsen", "given": "Thale Kristin", "initials": "TK"}, {"family": "Dyberg", "given": "Cecilia", "initials": "C"}, {"family": "Doroszko", "given": "Milena", "initials": "M"}, {"family": "Larsson", "given": "Ida", "initials": "I", "orcid": "0000-0001-5422-4243", "researcher": {"href": "https://publications.scilifelab.se/researcher/1ac604ca793048e9b6ddaa3459b4e97a.json"}}, {"family": "Sundstr\u00f6m", "given": "Anders", "initials": "A"}, {"family": "Arsenian Henriksson", "given": "Marie", "initials": "M"}, {"family": "P\u00e5hlman", "given": "Sven", "initials": "S"}, {"family": "Bexell", "given": "Daniel", "initials": "D", "orcid": "0000-0001-9426-9550", "researcher": {"href": "https://publications.scilifelab.se/researcher/dda650768a264d93a80f40da6cb8d7e1.json"}}, {"family": "Vanlandewijck", "given": "Michael", "initials": "M", "orcid": "0000-0002-0709-7808", "researcher": {"href": "https://publications.scilifelab.se/researcher/aa2148fbafb44d59bd110e36bd77769c.json"}}, {"family": "Kogner", "given": "Per", "initials": "P", "orcid": "0000-0002-2202-9694", "researcher": {"href": "https://publications.scilifelab.se/researcher/e963274b921a4a2c8263f509334d4e22.json"}}, {"family": "J\u00f6rnsten", "given": "Rebecka", "initials": "R"}, {"family": "Krona", "given": "Cecilia", "initials": "C"}, {"family": "Nelander", "given": "Sven", "initials": "S"}], "type": "journal article", "published": "2020-01-03", "journal": {"title": "Nat Commun", "issn": "2041-1723", "issn-l": "2041-1723", "volume": "11", "issue": "1", "pages": "71"}, "abstract": "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.", "doi": "10.1038/s41467-019-13817-8", "pmid": "31900415", "labels": {"Genome Engineering Zebrafish": "Service"}, "xrefs": [{"db": "pii", "key": "10.1038/s41467-019-13817-8"}, {"db": "pmc", "key": "PMC6941971"}], "notes": [], "created": "2020-01-08T11:24:16.666Z", "modified": "2021-12-09T14:05:49.139Z"}]}