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| 2. |
- 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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| 3. |
- 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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| 4. |
- Magnusson, Rasmus, 1992-, et al.
(författare)
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LASSIM-A network inference toolbox for genome-wide mechanistic modeling
- 2017
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Ingår i: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 13:6, s. Article no. e1005608 -
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Tidskriftsartikel (refereegranskat)abstract
- Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naive Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.
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- Oh, J. H., et al.
(författare)
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A Factor Analysis Approach for Clustering Patient Reported Outcomes
- 2016
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Ingår i: Methods of Information in Medicine. - : Georg Thieme Verlag KG. - 0026-1270 .- 2511-705X. ; 55:5, s. 431-439
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Tidskriftsartikel (refereegranskat)abstract
- Background: In the field of radiation oncology, the use of extensive patient reported outcomes is increasingly common to measure adverse side effects after radiotherapy in cancer patients. Factor analysis has the potential to identify an optimal number of latent factors (i.e., symptom groups). However, the ultimate goal of treatment response modeling is to understand the relationship between treatment variables such as radiation dose and symptom groups resulting from FA. Hence, it is crucial to identify clinically more relevant symptom groups and improved response variables from those symptom groups for a quantitative analysis. Objectives: The goal of this study is to design a computational method for finding clinically relevant symptom groups from PROs and to test associations between symptom groups and radiation dose. Methods: We propose a novel approach where exploratory factor analysis is followed by confirmatory factor analysis to determine the relevant number of symptom groups. We also propose to use a combination of symptoms in a symptom group identified as a new response variable in linear regression analysis to investigate the relationship between the symptom group and dose-volume variables. Results: We analyzed patient-reported gastrointestinal symptom profiles from 3 datasets in prostate cancer patients treated with radiotherapy. The final structural model of each dataset was validated using the other two datasets and compared to four other existing FA methods. Our systematic EFA-CFA approach provided clinically more relevant solutions than other methods, resulting in new clinically relevant outcome variables that enabled a quantitative analysis. As a result, statistically significant correlations were found between some dose volume variables to relevant anatomic structures and symptom groups identified by FA. Conclusions: Our proposed method can aid in the process of understanding PROs and provide a basis for improving our understanding of radiation-induced side effects.
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| 6. |
- Steineck, Gunnar, 1952, et al.
(författare)
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Identifying radiation-induced survivorship syndromes affecting bowel health in a cohort of gynecological cancer survivors
- 2017
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 12:2
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Tidskriftsartikel (refereegranskat)abstract
- © 2017 Steineck et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: During radiotherapy unwanted radiation to normal tissue surrounding the tumor triggers survivorship diseases; we lack a nosology for radiation-induced survivorship diseases that decrease bowel health and we do not know which symptoms are related to which diseases. Methods: Gynecological-cancer survivors were followed-up two to 15 years after having undergone radiotherapy; they reported in a postal questionnaire the frequency of 28 different symptoms related to bowel health. Population-based controls gave the same information. With a modified factor analysis, we determined the optimal number of factors, factor loadings for each symptom, factor-specific factor-loading cutoffs and factor scores. Results: Altogether data from 623 survivors and 344 population-based controls were analyzed. Six factors best explain the correlation structure of the symptoms; for five of these a statistically significant difference (P< 0.001, Mann-Whitney U test) was found between survivors and controls concerning factor score quantiles. Taken together these five factors explain 42 percent of the variance of the symptoms. We interpreted these five factors as radiation-induced syndromes that may reflect distinct survivorship diseases. We obtained the following frequencies, defined as survivors having a factor loading above the 95 percent percentile of the controls, urgency syndrome (190 of 623, 30 percent), leakage syndrome (164 of 623, 26 percent), excessive gas discharge (93 of 623, 15 percent), excessive mucus discharge (102 of 623, 16 percent) and blood discharge (63 of 623, 10 percent). Conclusion: Late effects of radiotherapy include five syndromes affecting bowel health; studying them and identifying the underlying survivorship diseases, instead of the approximately 30 long-term symptoms they produce, will simplify the search for prevention, alleviation and elimination.
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| 7. |
- Steineck, Gunnar, 1952, et al.
(författare)
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Late radiation-induced bowel syndromes, tobacco smoking, age at treatment and time since treatment - gynecological cancer survivors
- 2017
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Ingår i: Acta Oncologica. - : Informa UK Limited. - 0284-186X .- 1651-226X. ; 56:5, s. 682-691
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Tidskriftsartikel (refereegranskat)abstract
- Background: It is unknown whether smoking; age at time of radiotherapy or time since radiotherapy influence the intensity of late radiation-induced bowel syndromes.Material and methods: We have previously identified 28 symptoms decreasing bowel health among 623 gynecological-cancer survivors (three to twelve years after radiotherapy) and 344 matched population-based controls. The 28 symptoms were grouped into five separate late bowel syndromes through factor analysis. Here, we related possible predictors of bowel health to syndrome intensity, by combining factor analysis weights and symptom frequency on a person-incidence scale.Results: A strong (p<.001) association between smoking and radiation-induced urgency syndrome was found with a syndrome intensity (normalized factor score) of 0.4 (never smoker), 1.2 (former smoker) and 2.5 (current smoker). Excessive gas discharge was also related to smoking (p=.001). Younger age at treatment resulted in a higher intensity, except for the leakage syndrome. For the urgency syndrome, intensity decreased with time since treatment.Conclusions: Smoking aggravates the radiation-induced urgency syndrome and excessive gas discharge syndrome. Smoking cessation may promote bowel health among gynecological-cancer survivors. Furthermore, by understanding the mechanism for the decline in urgency-syndrome intensity over time, we may identify new strategies for prevention and alleviation.
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| 8. |
- Steineck, Gunnar, et al.
(författare)
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Late radiation-induced bowel syndromes, tobacco smoking, age at treatment and time since treatment–gynecological cancer survivors
- 2017
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Ingår i: Acta Oncologica. - 1651-226X .- 0284-186X. ; 56:5, s. 682-691
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Tidskriftsartikel (refereegranskat)abstract
- Background: It is unknown whether smoking; age at time of radiotherapy or time since radiotherapy influence the intensity of late radiation-induced bowel syndromes. Material and methods: We have previously identified 28 symptoms decreasing bowel health among 623 gynecological-cancer survivors (three to twelve years after radiotherapy) and 344 matched population-based controls. The 28 symptoms were grouped into five separate late bowel syndromes through factor analysis. Here, we related possible predictors of bowel health to syndrome intensity, by combining factor analysis weights and symptom frequency on a person-incidence scale. Results: A strong (p
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