| 1. |
- 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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| 2. |
- Baskaran, Sathishkumar, 1988-
(författare)
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New Molecular Approaches to Glioblastoma Therapy
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Glioblastoma (GBM) is the most common high-grade brain tumor diagnosed in patients who are more than 50 years of age. The standard of care treatment is surgery, followed by radiotherapy and chemotherapy. The median life expectancy of patients is only between 12 to 15 months after receiving current treatment regimes. Hence, identification of new therapeutic compounds and gene targets are highly warranted. This thesis describes four interlinked studies to attain this goal. In study 1, we explored drug combination effects in a material of 41 patient-derived GBM cell (GC) cultures. Synergies between three compounds, pterostilbene, gefitinib, and sertraline, resulted in effective killing of GC and can be predicted by biomarkers. In study 2, we performed a large-scale screening of FDA approved compounds (n=1544) in a larger panel of GCs (n=106). By combining the large-scale drug response data with GCs genomics data, we built a novel computational model to predict the sensitivity of each compound for a given GC. A notable finding was that GCs respond very differently to proteasome inhibitors in both in-vitro and in-vivo. In study 3, we explored new gene targets by RNAi (n=1112) in a panel of GC cells. We found that loss of transcription factor ZBTB16/PLZF inhibits GC cell viability, proliferation, migration, and invasion. These effects were due to downregulation of c-MYC and Cyclin B1 after the treatment. In study 4, we tested the genomic stability of three GCs upon multiple passaging. Using molecular and mathematical analyses, we showed that the GCs undergo both systematic adaptations and sequential clonal takeovers. Such changes tend to affect a broad spectrum of pathways. Therefore, a systematic analysis of cell culture stability will be essential to make use of primary cells for translational oncology.Taken together, these studies deepen our knowledge of the weak points of GBM and provide several targets and biomarkers for further investigation. The work in this thesis can potentially facilitate the development of targeted therapies and result in more accurate tools for patient diagnostics and stratification.
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| 3. |
- Johansson, Patrik
(författare)
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Large scale integration and interactive exploration of cancer data – with applications to glioblastoma
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Glioblastoma is the most common malignant brain tumor, with a median survival of approximately 15 months. The standard of care treatment consists of surgical resection followed by radiotherapy and chemotherapy, where chemotherapy only prolongs survival by approximately 3 months. There is therefore an urgent need for new approaches to better understand the molecular vulnerabilities of glioblastoma. To this end, we have conducted four interdisciplinary studies.In study 1 we develop a method for efficiently constructing and exploring large integrative network models that include multiple cohorts and multiple types of molecular data. We apply this method to 8 cancers from The Cancer Genome Atlas (TCGA) and make the integrative network available for exploration and visualization through a custom web interface.In study 2 we establish a biobank of 48 patient derived glioblastoma cell cultures called the Human Glioma Cell Culture (HGCC) resource. We show that the HGCC cell cultures represent all transcriptional subtypes, carry genomic aberrations typical of glioblastoma, and initiate tumors in vivo. The HGCC is an open resource for translational glioblastoma research, made available through hgcc.se.In study 3 we extend the analysis of HGCC cell cultures both in terms of number (to over 100) and in terms of data types (adding mutation, methylation and drug response data). Large-scale drug profiling starting from over 1500 compounds identified two distinct groups of cell cultures defined by vulnerability to proteasome inhibition, p53/p21 activity, stemness and protein turnover. By applying machine learning methods to the combined drug profiling and matched genomics data we construct a first network of predictive biomarkers.In study 4 we use the methods developed in study 1 applied to the data generated in studies 2 and 3 to construct an integrative network model of HGCC and glioblastoma data from TCGA. We present an interactive method for exploring this network based on searching for network patterns representing specific hypotheses defined by the user.In conclusion, this thesis combines the development of integrative models with applications to novel data relevant for translational glioblastoma research. This work highlights several potentially therapeutically relevant aspects, and paves a path towards more comprehensive and informative models of glioblastoma.
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| 4. |
- Almstedt, Elin, 1988-
(författare)
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New targeted therapies for malignant neural tumors : From systematic discovery to zebrafish models
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 5. |
- Larsson, Ida, 1993-
(författare)
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Integrative modeling of intratumoral heterogeneity, plasticity and regulation in nervous system cancers
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The adult brain tumor glioblastoma (GBM) is characterized by short survival and a lack of efficient treatments. Median survival is 15 months from time of diagnosis and the 5-year survival rate is only 7 %. There is an urgent need for more efficient treatment against GBM, but there are many challenges, including the high extent of heterogeneity of GBM. The tumoral heterogeneity of GBM ranges from interpatient to intratumoral. The aim of this thesis has been to address unanswered questions relating to the intratumoral heterogeneity of GBM, with three specific focuses; (1) the organization of GBM cell state transitions (paper I and III), (2) the regulation of cell states and cell state transitions (paper II), and (3) targeted interventions against cell states (paper II and IV).In paper I, we develop an experimental-computational method to measure and quantify cell state transitions. We find that GBM cell states organize hierarchically, with a clear “source state” feeding cells downwards in the hierarchy towards a “sink state” with negative growth rate, but with multi-directional transitions between intermediate states. In paper II, we address the lack of computational methods to identify regulators of intratumoral heterogeneity by developing an algorithm called scRegClust that uses scRNA-seq data to estimate regulatory programs. Through an integrative study of the regulatory landscape of neuro-oncology we find two potential regulators of the macrophage-induced mesenchymal transition in GBM.In paper III, we explore the energy-concept as a way of measuring differentiation potential of single cells, instead of relying on gene markers or gene signatures of stemness. We fit a model called the Ising model from statistical mechanics to scRNA-seq data and show both on synthetic and real data that the estimated Ising energy is a good measure of a cell’s differentiation potential, where high Ising energy indicate a high degree of stemness.Finally, in paper IV, another experimental-computational method is developed to investigate drug-induced effects on both inter- and intratumoral heterogeneity. In summary, the high extent of intratumoral heterogeneity in nervous system cancer is a major caveat for the development of more efficient treatments. In this thesis we have taken a systems biology approach to understand how this heterogeneity is structured and how we can exploit that knowledge for therapeutic purposes.
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| 6. |
- Matuszewski, Damian J., et al.
(författare)
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Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma
- 2018
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Ingår i: SLAS Discovery. - : Elsevier BV. - 2472-5560 .- 2472-5552. ; 23:10, s. 1030-1039
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Tidskriftsartikel (refereegranskat)abstract
- Image-based analysis is an increasingly important tool to characterize the effect of drugs in large-scale chemical screens. Herein, we present image and data analysis methods to investigate population cell-cycle dynamics in patient-derived brain tumor cells. Images of glioblastoma cells grown in multiwell plates were used to extract per-cell descriptors, including nuclear DNA content. We reduced the DNA content data from per-cell descriptors to per-well frequency distributions, which were used to identify compounds affecting cell-cycle phase distribution. We analyzed cells from 15 patient cases representing multiple subtypes of glioblastoma and searched for clusters of cell-cycle phase distributions characterizing similarities in response to 249 compounds at 11 doses. We show that this approach applied in a blind analysis with unlabeled substances identified drugs that are commonly used for treating solid tumors as well as other compounds that are well known for inducing cell-cycle arrest. Redistribution of nuclear DNA content signals is thus a robust metric of cell-cycle arrest in patient-derived glioblastoma cells.
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