| 1. |
- Almstedt, Elin, 1988-, et al.
(författare)
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Integrative discovery of treatments for high-risk neuroblastoma
- 2020
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 11:1
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Tidskriftsartikel (refereegranskat)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.
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| 2. |
- Almstedt, Elin, et al.
(författare)
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Real-time evaluation of glioblastoma growth in patient-specific zebrafish xenografts
- 2021
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Ingår i: Neuro-Oncology. - : Oxford University Press. - 1522-8517 .- 1523-5866. ; 24:5, s. 726-738
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Tidskriftsartikel (refereegranskat)abstract
- Background: 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.Methods: We 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.Results: Our 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.Conclusions: Zebrafish 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.
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| 3. |
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| 4. |
- Krona, Cecilia, et al.
(författare)
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GLIOBLASTOMA GROWTH IS SHAPED BY INVASION ROUTE-SPECIFIC FUNCTIONAL SIGNATURES
- 2023
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Ingår i: Neuro-Oncology. - 1523-5866 .- 1522-8517. ; 25:Supplement: 5, MODL-16
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- One of the defining features of glioblastomas (GBMs) is the capacity for invasive growth along multiple anatomical pathways in the brain. GBM is well-studied on a genetic and molecular level, but clinically relevant and experimentally tractable models of invasive growth are largely lacking. Here, we report an integrated study of patient-matched information, genomic- and molecular profiles with growth in mouse brains to expose treatments and biomarkers associated with glioblastoma invasion and recurrence. In total, 64 patient-derived cell lines (PDCLs) were injected into the striatum of n ≥ 4 mice each. The 45 tumor-forming PDCLs were each scored for 10 distinct growth characteristics (n = 182 mice). The repertoire of phenotypes was highly divergent, and our material included clear cases of perivascular route invasion, white matter route invasion, perineuronal satellitosis, and gliosarcoma. We explored if cellular pathways, monitored by RNA-sequencing, could account for these differences. GSEA highlighted a positive enrichment for highly proliferative proneural tumors characterized by Notch activation, neuronal signaling, and epigenetic gene regulatory programs in the tumor-initiating lines. Transcriptional signatures were also strongly predictive of route-specific invasion. Diffuse invasion was predominantly seen in classical-subtype PDCLs with astrocytic or outer radial glia-like signatures. Proneural PDCLs, in turn, grew as solid tumors with an invasive peripheral region around vasculature, and mesenchymal tumors were more demarcated. To explore the therapeutic implications of our findings, we used our data-driven method (TargetTranslator, Nat Comm 2020) to predict the drug vulnerabilities of different types of invasive glioblastoma. Defined GBM tumors with perivascular invasion are characterized by increased IGFR1, MAPK/ERK, PI3K/AKT/mTOR, and JAK2 signaling. Diffusively growing GBM tumors, on the other hand, depend more on Wnt/β-catenin signaling, neuronal signaling, and active inflammatory response. Using a sphere invasion assay, we confirm that targeting both PI3K- and Wnt signaling selectively reduces glioblastoma invasion, highlighting their therapeutic potential.
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| 6. |
- Rosén, Emil, et al.
(författare)
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Inference of glioblastoma migration and proliferation rates using single time-point images
- 2023
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Ingår i: Communications Biology. - : Springer Nature. - 2399-3642. ; 6:1
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Tidskriftsartikel (refereegranskat)abstract
- Cancer cell migration is a driving mechanism of invasion in solid malignant tumors. Anti-migratory treatments provide an alternative approach for managing disease progression. However, we currently lack scalable screening methods for identifying novel anti-migratory drugs. To this end, we develop a method that can estimate cell motility from single end-point images in vitro by estimating differences in the spatial distribution of cells and inferring proliferation and diffusion parameters using agent-based modeling and approximate Bayesian computation. To test the power of our method, we use it to investigate drug responses in a collection of 41 patient-derived glioblastoma cell cultures, identifying migration-associated pathways and drugs with potent anti-migratory effects. We validate our method and result in both in silico and in vitro using time-lapse imaging. Our proposed method applies to standard drug screen experiments, with no change needed, and emerges as a scalable approach to screen for anti-migratory drugs. The spatial positioning of cultured glioblastoma cells is used to estimate cell motility and drug effects from single end-point images in vitro.
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| 7. |
- Rosén, Emil
(författare)
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Modeling glioblastoma growth patterns and their mechanistic origins
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Glioblastoma (GBM) is the most common and aggressive primary brain cancer. GBM cells migrate away from the primary lesion and invade healthy brain tissue. The invading cells escape surgical resection, radiotherapy and develop resistance to chemotherapy. Consequently, despite treatment, recurrence is inevitable, and survival is only 14 months. For this purpose, we conducted four studies where we integrated experimental data from extensive patient material with image analysis and mathematical modeling.In study 1, we developed a tool, TargetTranslator, integrating different data modalities to identify new treatments. We implemented an image analysis pipeline to validate our results using a deep artificial neural network to quantify neuroblastoma cell differentiation.In study 2, we integrated the zebrafish and image analysis from study 1 to develop a high-throughput in vivo assay. Zebrafish were orthotopically injected with GBM cells, and each fish's tumor growth and vital status were automatically measured. We characterized the in vivo proliferation rate, survival, and treatment response to the drug marizomib for several patient-derived cell cultures. Light-sheet imaging also revealed two distinct growth types. The first set of cell cultures grew as bulk tumors, whereas the second set invaded vasculature as single cells.In study 3, we used the image analysis from study 1, coupled with an agent-based model to estimate in vitro cell migration and proliferation from single end-point images. The method was validated by a time series data set and applied to a large high-content drug screen of GBM cells. We identified three promising candidates for reducing GBM cell migration. The method can estimate migration on any end-point images of adherent cells without any additional experimental cost.Study 4 characterized the growth and invasive patterns of 45 patient-derived GBM cell cultures in orthogonal mouse xenografts. We found that up to four independent axes of variation could describe the phenotypes and were associated with distinct transcriptomic pathways. The transcriptomic pathways were in part associated with common genomic alterations and subtypes in GBM. We further identified a particularly aggressive GBM phenotype.In conclusion, this thesis was interdisciplinary and aimed to measure survival, invasion, and morphology from extensive patient material. The work had given us new insight into GBM invasion and growth and developed several scalable models suitable for evaluating new therapies.
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