A5061784298- Neuroblastoma Research and Treatments
- Glioma Diagnosis and Treatment
- Cancer, Hypoxia, and Metabolism
- Protein Degradation and Inhibitors
- Mathematical Biology Tumor Growth
- Ubiquitin and proteasome pathways
- Zebrafish Biomedical Research Applications
- Bioinformatics and Genomic Networks
- Cell Image Analysis Techniques
- 3D Printing in Biomedical Research
- Brain Metastases and Treatment
- Cancer, Lipids, and Metabolism
- Pharmacogenetics and Drug Metabolism
- Cancer-related Molecular Pathways
- Computational Drug Discovery Methods
- Angiogenesis and VEGF in Cancer
- Cancer Genomics and Diagnostics
- Kruppel-like factors research
Uppsala University
2017-2022
Science for Life Laboratory
2020
Abstract Despite advances in the molecular exploration of paediatric cancers, approximately 50% children with high-risk neuroblastoma lack effective treatment. To identify therapeutic options for this group patients, we combine predictive data mining experimental evaluation patient-derived xenograft cells. Our proposed algorithm, TargetTranslator, integrates from tumour biobanks, pharmacological databases, and cellular networks to predict how targeted interventions affect mRNA signatures...
Glioblastoma (GBM) is a malignant brain tumor with few therapeutic options. The disease presents complex spectrum of genomic aberrations, but the pharmacological consequences these aberrations are partly unknown. Here, we report an integrated pharmacogenomic analysis 100 patient-derived GBM cell cultures from human glioma culture (HGCC) cohort. Exploring 1,544 drugs, find that has two main subgroups, marked by differential response to proteasome inhibitors and mutually exclusive in TP53...
Abstract Background Patient-derived xenograft (PDX) models of glioblastoma (GBM) are a central tool for neuro-oncology research and drug development, enabling the detection patient-specific differences in growth, vivo response. However, existing PDX not well suited large-scale or automated studies. Thus, here, we investigate if fast zebrafish-based model, supported by longitudinal, AI-driven image analysis, can recapitulate key aspects growth enable case-comparative testing. Methods We...
Abstract One of the main bottlenecks anticancer drug development is assessment in vivo relevance emerging therapies. Previously, drugs that suppress progression neuroblastoma have been hard to identify. To address this problem, we propose a new computational technique, onTARGET, enables researchers select, with good accuracy, compounds are likely induce changes cellular pathways consistent specific clinical outcomes and subgroups. onTARGET prediction based on an integration massive...
Neuroblastoma (NB) cells exhibit a complex spectrum of pathway changes associated with oncogene activation, chromosome events, tumor micro-environment and super-enhancer states. So far, elucidating which pharmaceutical compounds could modulate the activation level each known in NB has not been feasible. To solve this problem, we have combined transcriptome profiling drug-perturbed mathematical modeling public data to create first map drug- NB-specific transcriptional signatures for more than...
Despite the many advances in molecular characterization of neuroblastoma, effective treatments for high-risk patients are currently lacking. Using publically available data, integrated computational analysis offers new opportunities to uncover druggable subgroups. In TargetTranslator project, we have combined state-of-the-art Big Data techniques identify targets subgroups neuroblastoma. Starting with clinical or genomic risk factors, builds a consensus signature across cohorts. This is...
Abstract Despite major advances in the molecular exploration of pediatric cancers, approximately 50 % children with high-risk neuroblastoma lack effective treatment. To identify new therapeutic options for this group patients, we have combined integrative data analysis experimental evaluation patient-derived xenograft cells. We propose a algorithm, TargetTranslator, which combines from tumor biobanks, pharmacological databases, and cellular networks, to predict how particular targeted...
Abstract Glioblastoma (GBM) is a malignant brain tumor with few therapeutic options. Because GBMs are genetically and functionally diverse, there little systematic understanding of how hundreds approved drugs may be active against GBM subpopulations. To identify new pharmacological subgroups GBM, we studied comprehensively the variation in drug response across 100 well-characterised patient-derived cell cultures used statistical models to explain patient specific cellular terms molecular...
Abstract Despite the many advances in molecular characterization of neuroblastoma, effective treatments for high-risk patients are currently lacking. Using publically available data, integrated computational analysis offers new opportunities to uncover druggable subgroups. In TargetTranslator project, we have combined state-of-the-art Big Data techniques identify targets subgroups neuroblastoma. Starting with clinical or genomic risk factors, builds a consensus signature across cohorts. This...
Abstract Neuroblastoma (NB) cells exhibit a complex spectrum of pathway changes associated with oncogene activation, chromosome events, tumor micro-environment and super-enhancer states. So far, elucidating which pharmaceutical compounds could modulate the activation level each known in NB has not been feasible. To solve this problem, we have combined transcriptome profiling drug-perturbed mathematical modeling public data to create first map drug- NB-specific transcriptional signatures for...