| 1. |
- Abenius, Tobias, 1979, et al.
(författare)
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System-scale network modeling of cancer using EPoC
- 2012
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Ingår i: Advances in Experimental Medicine and Biology. - New York, NY : Springer New York. - 0065-2598. - 9781441972095 ; 736:5, s. 617-643
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Tidskriftsartikel (refereegranskat)abstract
- One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.
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| 2. |
- Gerlee, Philip, 1980, et al.
(författare)
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Searching for Synergies: Matrix Algebraic Approaches for Efficient Pair Screening
- 2013
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:7
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Tidskriftsartikel (refereegranskat)abstract
- Functionally interacting perturbations, such as synergistic drugs pairs or synthetic lethal gene pairs, are of key interest in both pharmacology and functional genomics. However, to find such pairs by traditional screening methods is both time consuming and costly. We present a novel computational-experimental framework for efficient identification of synergistic target pairs, applicable for screening of systems with sizes on the order of current drug, small RNA or SGA (Synthetic Genetic Array) libraries (>1000 targets). This framework exploits the fact that the response of a drug pair in a given system, or a pair of genes' propensity to interact functionally, can be partly predicted by computational means from (i) a small set of experimentally determined target pairs, and (ii) pre-existing data (e.g. gene ontology, PPI) on the similarities between targets. Predictions are obtained by a novel matrix algebraic technique, based on cyclical projections onto convex sets. We demonstrate the efficiency of the proposed method using drug-drug interaction data from seven cancer cell lines and gene-gene interaction data from yeast SGA screens. Our protocol increases the rate of synergism discovery significantly over traditional screening, by up to 7-fold. Our method is easy to implement and could be applied to accelerate pair screening for both animal and microbial systems.
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| 3. |
- Jörnsten, Rebecka, 1971, et al.
(författare)
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Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
- 2011
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Ingår i: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 7
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Tidskriftsartikel (refereegranskat)abstract
- DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.
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| 4. |
- Danielsson, Anna, 1973, et al.
(författare)
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Accumulation of DNA methylation alterations in paediatric glioma stem cells following fractionated dose irradiation
- 2020
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Ingår i: Clinical Epigenetics. - : Springer Science and Business Media LLC. - 1868-7075 .- 1868-7083. ; 12:1
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Tidskriftsartikel (refereegranskat)abstract
- Background Radiation is an important therapeutic tool. However, radiotherapy has the potential to promote co-evolution of genetic and epigenetic changes that can drive tumour heterogeneity, formation of radioresistant cells and tumour relapse. There is a clinical need for a better understanding of DNA methylation alterations that may follow radiotherapy to be able to prevent the development of radiation-resistant cells. Methods We examined radiation-induced changes in DNA methylation profiles of paediatric glioma stem cells (GSCs) in vitro. Five GSC cultures were irradiated in vitro with repeated doses of 2 or 4 Gy. Radiation was given in 3 or 15 fractions. DNA methylation profiling using Illumina DNA methylation arrays was performed at 14 days post-radiation. The cellular characteristics were studied in parallel. Results Few fractions of radiation did not result in significant accumulation of DNA methylation alterations. However, extended dose fractionations changed DNA methylation profiles and induced thousands of differentially methylated positions, specifically in enhancer regions, sites involved in alternative splicing and in repetitive regions. Radiation induced dose-dependent morphological and proliferative alterations of the cells as a consequence of the radiation exposure. Conclusions DNA methylation alterations of sites with regulatory functions in proliferation and differentiation were identified, which may reflect cellular response to radiation stress through epigenetic reprogramming and differentiation cues.
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| 5. |
- Ferreyra Vega, Sandra, et al.
(författare)
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Longitudinal DNA methylation analysis of adult-type IDH-mutant gliomas.
- 2023
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Ingår i: Acta neuropathologica communications. - : Springer Science and Business Media LLC. - 2051-5960. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Diffuse gliomas are the most prevalent malignant primary brain tumors in adults and remain incurable despite standard therapy. Tumor recurrence is currently inevitable, which contributes to a persistent high morbidity and mortality in these patients. In this study, we examined the genome-wide DNA methylation profiles of primary and recurrent adult-type IDH-mutant gliomas to elucidate DNA methylation changes associated with tumor progression (with or without malignant transformation). We analyzed DNA methylation profiles of 37 primary IDH-mutant gliomas and 42 paired recurrences using the DNA methylation EPIC beadChip array. DNA methylation-based classification reflected the tumor progression over time. We observed a methylation subtype switch in a proportion of IDH-mutant astrocytomas; the primary tumors were subclassified as low-grade astrocytomas, which progressed to high-grade astrocytomas in the recurrent tumors. The CNS WHO grade 4 IDH-mutant astrocytomas did not always resemble methylation subclasses of higher grades. The number of differentially methylated CpG sites increased over time, and astrocytomas accumulated more differentially methylated CpG sites than oligodendrogliomas during tumor progression. Few differentially methylated CpG siteswere shared between patients. We demonstrated that DNA methylation profiles are mostly maintained during IDH-mutant glioma progression, but CpG site-specific methylation alterations can occur.
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| 6. |
- Ferreyra Vega, Sandra, et al.
(författare)
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Spatial heterogeneity in DNA methylation and chromosomal alterations in diffuse gliomas and meningiomas
- 2022
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Ingår i: Modern Pathology. - : Elsevier BV. - 0893-3952. ; 35:11, s. 1551-1561
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Tidskriftsartikel (refereegranskat)abstract
- Adult-type diffuse gliomas and meningiomas are the most common primary intracranial tumors of the central nervous system. DNA methylation profiling is a novel diagnostic technique increasingly used also in the clinic. Although molecular heterogeneity is well described in these tumors, DNA methylation heterogeneity is less studied. We therefore investigated the intratumor genetic and epigenetic heterogeneity in diffuse gliomas and meningiomas, with focus on potential clinical implications. We further investigated tumor purity as a source for heterogeneity in the tumors. We analyzed genome-wide DNA methylation profiles generated from 126 spatially separated tumor biopsies from 39 diffuse gliomas and meningiomas. Moreover, we evaluated five methods for measurement of tumor purity and investigated intratumor heterogeneity by assessing DNA methylation-based classification, chromosomal copy number alterations and molecular markers. Our results demonstrated homogeneous methylation-based classification of IDH-mutant gliomas and further corroborates subtype heterogeneity in glioblastoma IDH-wildtype and high-grade meningioma patients after excluding samples with low tumor purity. We detected a large number of differentially methylated CpG sites within diffuse gliomas and meningiomas, particularly in tumors of higher grades. The presence of CDKN2A/B homozygous deletion differed in one out of two patients with IDH-mutant astrocytomas, CNS WHO grade 4. We conclude that diffuse gliomas and high-grade meningiomas are characterized by intratumor heterogeneity, which should be considered in clinical diagnostics and in the assessment of methylation-based and molecular markers.
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| 7. |
- Kling, Teresia, 1985, et al.
(författare)
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DNA methylation-based age estimation in pediatric healthy tissues and brain tumors.
- 2020
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Ingår i: Aging. - : Impact Journals, LLC. - 1945-4589. ; 12:21, s. 21037-21056
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Tidskriftsartikel (refereegranskat)abstract
- Several DNA methylation clocks have been developed to reflect chronological age of human tissues, but most clocks have been trained on adult samples. The rapid methylome changes in children and the role of epigenetics in pediatric tumors calls for tools accurately estimating methylation age in children. We aimed to evaluate seven methylation clocks in multiple tissues from healthy children to inform future studies on the optimal clock for pediatric cohorts, and analyzed the methylation age in brain tumors. We found that clocks trained on pediatric samples were the best in all tested tissues, highlighting the need for dedicated clocks. For blood samples, the Skin and blood clock had the best correlation with chronological age, while PedBE was the most accurate for saliva and buccal samples, and Horvath for brain tissue. Horvath methylation age was accelerated in pediatric brain tumors and the acceleration was subtype-specific for atypical teratoid rhabdoid tumor (ATRT), ependymoma, medulloblastoma and glioma. The subtypes with the highest acceleration corresponded to the worst prognostic categories in ATRT, ependymoma and glioma, whereas the relationship was reversed in medulloblastoma. This suggests that methylation age has potential as a prognostic biomarker in pediatric brain tumors and should be further explored.
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| 8. |
- Kling, Teresia, 1985, et al.
(författare)
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Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content
- 2015
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 43:15
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Tidskriftsartikel (refereegranskat)abstract
- Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets. © 2015 The Author(s).
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| 9. |
- Kling, Teresia, 1985, et al.
(författare)
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Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma
- 2016
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Ingår i: Ebiomedicine. - : Elsevier BV. - 2352-3964. ; 12, s. 72-85
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Tidskriftsartikel (refereegranskat)abstract
- Glioblastomas are characterized by transcriptionally distinct subtypes, but despite possible clinical relevance, their regulation remains poorly understood. The commonly used molecular classification systems for GBM all identify a subtype with high expression of mesenchymal marker transcripts, strongly associated with invasive growth. We used a comprehensive data-driven network modeling technique (augmented sparse inverse covariance selection, aSICS) to define separate genomic, epigenetic, and transcriptional regulators of glioblastoma subtypes. Our model identified Annexin A2 (ANXA2) as a novel methylation-controlled positive regulator of the mesenchymal subtype. Subsequent evaluation in two independent cohorts established ANXA2 expression as a prognostic factor that is dependent on ANXA2 promoter methylation. ANXA2 knockdown in primary glioblastoma stem cell-like cultures suppressed known mesenchymal master regulators, and abrogated cell proliferation and invasion. Our results place ANXA2 at the apex of a regulatory cascade that determines glioblastoma mesenchymal transformation and validate aSICS as a general methodology to uncover regulators of cancer subtypes. (C) 2016 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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| 10. |
- Kling, Teresia, 1985, et al.
(författare)
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Methylation Analysis Using Microarrays: Analysis and Interpretation.
- 2019
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Ingår i: Methods in molecular biology (Clifton, N.J.), Tumor Profiling. - New York, NY : Humana Press. - 1940-6029. ; , s. 205-217
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Bokkapitel (refereegranskat)abstract
- This chapter discusses analysis and interpretation of large-scale Illumina DNA methylation microarray data, used in the context of cancer studies. We outline commonly used normalization procedures and list issues to consider regarding data preprocessing. Focusing on software packages for R, we describe methods for finding features in the methylation data that are of importance for generating and testing hypotheses in cancer research, like differentially methylated positions or regions and global methylation trends.
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