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- Kanai, M, et al.
(författare)
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- 2023
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swepub:Mat__t
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| 2. |
- Menden, MP, et al.
(författare)
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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
- 2019
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10:1, s. 2674-
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Tidskriftsartikel (refereegranskat)abstract
- The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.
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- Gomez-Cabrero, D, et al.
(författare)
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STATegra, a comprehensive multi-omics dataset of B-cell differentiation in mouse
- 2019
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Ingår i: Scientific data. - : Springer Science and Business Media LLC. - 2052-4463. ; 6:1, s. 256-
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Tidskriftsartikel (refereegranskat)abstract
- Multi-omics approaches use a diversity of high-throughput technologies to profile the different molecular layers of living cells. Ideally, the integration of this information should result in comprehensive systems models of cellular physiology and regulation. However, most multi-omics projects still include a limited number of molecular assays and there have been very few multi-omic studies that evaluate dynamic processes such as cellular growth, development and adaptation. Hence, we lack formal analysis methods and comprehensive multi-omics datasets that can be leveraged to develop true multi-layered models for dynamic cellular systems. Here we present the STATegra multi-omics dataset that combines measurements from up to 10 different omics technologies applied to the same biological system, namely the well-studied mouse pre-B-cell differentiation. STATegra includes high-throughput measurements of chromatin structure, gene expression, proteomics and metabolomics, and it is complemented with single-cell data. To our knowledge, the STATegra collection is the most diverse multi-omics dataset describing a dynamic biological system.
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- Kular, L, et al.
(författare)
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Epigenetic clock indicates accelerated aging in glial cells of progressive multiple sclerosis patients
- 2022
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Ingår i: Frontiers in aging neuroscience. - : Frontiers Media SA. - 1663-4365. ; 14, s. 926468-
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Tidskriftsartikel (refereegranskat)abstract
- Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disease of the central nervous system (CNS) characterized by irreversible disability at later progressive stages. A growing body of evidence suggests that disease progression depends on age and inflammation within the CNS. We aimed to investigate epigenetic aging in bulk brain tissue and sorted nuclei from MS patients using DNA methylation-based epigenetic clocks.MethodsWe applied Horvath’s multi-tissue and Shireby’s brain-specific Cortical clock on bulk brain tissue (n = 46), sorted neuronal (n = 54), and glial nuclei (n = 66) from post-mortem brain tissue of progressive MS patients and controls.ResultsWe found a significant increase in age acceleration residuals, corresponding to 3.6 years, in glial cells of MS patients compared to controls (P = 0.0024) using the Cortical clock, which held after adjustment for covariates (Padj = 0.0263). The 4.8-year age acceleration found in MS neurons (P = 0.0054) did not withstand adjustment for covariates and no significant difference in age acceleration residuals was observed in bulk brain tissue between MS patients and controls.ConclusionWhile the findings warrant replication in larger cohorts, our study suggests that glial cells of progressive MS patients exhibit accelerated biological aging.
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