| 1. |
- Abel, Frida, 1974, et al.
(author)
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A 6-gene signature identifies four molecular subgroups of neuroblastoma
- 2011
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In: Cancer Cell International. - : Springer Science and Business Media LLC. - 1475-2867. ; 11:9
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Journal article (peer-reviewed)abstract
- Abstract Background There are currently three postulated genomic subtypes of the childhood tumour neuroblastoma (NB); Type 1, Type 2A, and Type 2B. The most aggressive forms of NB are characterized by amplification of the oncogene MYCN (MNA) and low expression of the favourable marker NTRK1. Recently, mutations or high expression of the familial predisposition gene Anaplastic Lymphoma Kinase (ALK) was associated to unfavourable biology of sporadic NB. Also, various other genes have been linked to NB pathogenesis. Results The present study explores subgroup discrimination by gene expression profiling using three published microarray studies on NB (47 samples). Four distinct clusters were identified by Principal Components Analysis (PCA) in two separate data sets, which could be verified by an unsupervised hierarchical clustering in a third independent data set (101 NB samples) using a set of 74 discriminative genes. The expression signature of six NB-associated genes ALK, BIRC5, CCND1, MYCN, NTRK1, and PHOX2B, significantly discriminated the four clusters (p
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| 2. |
- Abenius, Tobias, 1979, et al.
(author)
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System-scale network modeling of cancer using EPoC
- 2012
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In: Advances in Experimental Medicine and Biology. - New York, NY : Springer New York. - 0065-2598. - 9781441972095 ; 736:5, s. 617-643
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Journal article (peer-reviewed)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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| 3. |
- Alevronta, Eleftheria, et al.
(author)
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Dose-response relationships of intestinal organs and excessive mucus discharge after gynaecological radiotherapy
- 2021
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 16:4 April
-
Journal article (peer-reviewed)abstract
- Background The study aims to determine possible dose-volume response relationships between the rectum, sigmoid colon and small intestine and the ‘excessive mucus discharge’ syndrome after pelvic radiotherapy for gynaecological cancer. Methods and materials From a larger cohort, 98 gynaecological cancer survivors were included in this study. These survivors, who were followed for 2 to 14 years, received external beam radiation therapy but not brachytherapy and not did not have stoma. Thirteen of the 98 developed excessive mucus discharge syndrome. Three self-assessed symptoms were weighted together to produce a score interpreted as ‘excessive mucus discharge’ syndrome based on the factor loadings from factor analysis. The dose-volume histograms (DVHs) for rectum, sigmoid colon, small intestine for each survivor were exported from the treatment planning systems. The dose-volume response relationships for excessive mucus discharge and each organ at risk were estimated by fitting the data to the Probit, RS, LKB and gEUD models. Results The small intestine was found to have steep dose-response curves, having estimated dose-response parameters: γ : 1.28, 1.23, 1.32, D : 61.6, 63.1, 60.2 for Probit, RS and LKB respectively. The sigmoid colon (AUC: 0.68) and the small intestine (AUC: 0.65) had the highest AUC values. For the small intestine, the DVHs for survivors with and without excessive mucus discharge were well separated for low to intermediate doses; this was not true for the sigmoid colon. Based on all results, we interpret the results for the small intestine to reflect a relevant link. Conclusion An association was found between the mean dose to the small intestine and the occurrence of ‘excessive mucus discharge’. When trying to reduce and even eliminate the incidence of ‘excessive mucus discharge’, it would be useful and important to separately delineate the small intestine and implement the dose-response estimations reported in the study.
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| 4. |
- Alevronta, Eleftheria, et al.
(author)
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Dose-response relationships of the sigmoid for urgency syndrome after gynecological radiotherapy.
- 2018
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In: Acta oncologica (Stockholm, Sweden). - 1651-226X .- 0284-186X. ; 57:10, s. 1352-1358
-
Journal article (peer-reviewed)abstract
- To find out what organs and doses are most relevant for 'radiation-induced urgency syndrome' in order to derive the corresponding dose-response relationships as an aid for avoiding the syndrome in the future.From a larger group of gynecological cancer survivors followed-up 2-14years, we identified 98 whom had undergone external beam radiation therapy but not brachytherapy and not having a stoma. Of those survivors, 24 developed urgency syndrome. Based on the loading factor from a factor analysis, and symptom frequency, 15 symptoms were weighted together to a score interpreted as the intensity of radiation-induced urgency symptom. On reactivated dose plans, we contoured the small intestine, sigmoid colon and the rectum (separate from the anal-sphincter region) and we exported the dose-volume histograms for each survivor. Dose-response relationships from respective risk organ and urgency syndrome were estimated by fitting the data to the Probit, RS, LKB and gEUD models.The rectum and sigmoid colon have steep dose-response relationships for urgency syndrome for Probit, RS and LKB. The dose-response parameters for the rectum were D50: 51.3, 51.4, and 51.3Gy, γ50=1.19 for all models, s was 7.0e-09 for RS and n was 9.9×107 for LKB. For Sigmoid colon, D50 were 51.6, 51.6, and 51.5Gy, γ50 were 1.20, 1.25, and 1.27, s was 2.8 for RS and n was 0.079 for LKB.Primarily the dose to sigmoid colon as well as the rectum is related to urgency syndrome among gynecological cancer survivors. Separate delineation of the rectum and sigmoid colon in order to incorporate the dose-response results may aid in reduction of the incidence of the urgency syndrome.
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| 5. |
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| 6. |
- Allerbo, Oskar, 1985, et al.
(author)
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Elastic Gradient Descent, an Iterative Optimization Method Approximating the Solution Paths of the Elastic Net
- 2023
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In: Journal of Machine Learning Research. - 1533-7928 .- 1532-4435. ; 24, s. 1-35
-
Journal article (peer-reviewed)abstract
- The elastic net combines lasso and ridge regression to fuse the sparsity property of lasso with the grouping property of ridge regression. The connections between ridge regression and gradient descent and between lasso and forward stagewise regression have previously been shown. Similar to how the elastic net generalizes lasso and ridge regression, we introduce elastic gradient descent, a generalization of gradient descent and forward stagewise regression. We theoretically analyze elastic gradient descent and compare it to the elastic net and forward stagewise regression. Parts of the analysis are based on elastic gradient flow, a piecewise analytical construction, obtained for elastic gradient descent with infinitesimal step size. We also compare elastic gradient descent to the elastic net on real and simulated data and show that it provides similar solution paths, but is several orders of magnitude faster. Compared to forward stagewise regression, elastic gradient descent selects a model that, although still sparse, provides considerably lower prediction and estimation errors.
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| 7. |
- Allerbo, Oskar, 1985, et al.
(author)
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Flexible, non-parametric modeling using regularized neural networks
- 2022
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In: Computational Statistics. - : Springer Science and Business Media LLC. - 0943-4062 .- 1613-9658. ; 37:4, s. 2029-2047
-
Journal article (peer-reviewed)abstract
- Non-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often requires non-trivial data exploration. Here, as an alternative, we propose PrAda-net, a one-hidden-layer neural network, trained with proximal gradient descent and adaptive lasso. PrAda-net automatically adjusts the size and architecture of the neural network to reflect the complexity and structure of the data. The compact network obtained by PrAda-net can be translated to additive model components, making it suitable for non-parametric statistical modelling with automatic model selection. We demonstrate PrAda-net on simulated data, where we compare the test error performance, variable importance and variable subset identification properties of PrAda-net to other lasso-based regularization approaches for neural networks. We also apply PrAda-net to the massive U.K. black smoke data set, to demonstrate how PrAda-net can be used to model complex and heterogeneous data with spatial and temporal components. In contrast to classical, statistical non-parametric approaches, PrAda-net requires no preliminary modeling to select the functional forms of the additive components, yet still results in an interpretable model representation. © 2021, The Author(s).
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| 8. |
- Allerbo, Oskar, 1985, et al.
(author)
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Non-linear, sparse dimensionality reduction via path lasso penalized autoencoders
- 2021
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In: Journal of Machine Learning Research. - : Microtome Publishing. - 1532-4435 .- 1533-7928. ; 22
-
Journal article (peer-reviewed)abstract
- High-dimensional data sets are often analyzed and explored via the construction of a latent low-dimensional space which enables convenient visualization and efficient predictive modeling or clustering. For complex data structures, linear dimensionality reduction techniques like PCA may not be sufficiently flexible to enable low-dimensional representation. Non-linear dimension reduction techniques, like kernel PCA and autoencoders, suffer from loss of interpretability since each latent variable is dependent of all input dimensions. To address this limitation, we here present path lasso penalized autoencoders. This structured regularization enhances interpretability by penalizing each path through the encoder from an input to a latent variable, thus restricting how many input variables are represented in each latent dimension. Our algorithm uses a group lasso penalty and non-negative matrix factorization to construct a sparse, non-linear latent representation. We compare the path lasso regularized autoencoder to PCA, sparse PCA, autoencoders and sparse autoencoders on real and simulated data sets. We show that the algorithm exhibits much lower reconstruction errors than sparse PCA and parameter-wise lasso regularized autoencoders for low-dimensional representations. Moreover, path lasso representations provide a more accurate reconstruction match, i.e. preserved relative distance between objects in the original and reconstructed spaces. ©2021 Oskar Allerbo and Rebecka Jörnsten.
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| 9. |
- Almstedt, Elin, 1988-, et al.
(author)
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Integrative discovery of treatments for high-risk neuroblastoma
- 2020
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723 .- 2041-1723. ; 11:1
-
Journal article (peer-reviewed)abstract
- Despite advances in the molecular exploration of paediatric cancers, approximately 50% of children with high-risk neuroblastoma lack effective treatment. To identify therapeutic options for this group of high-risk patients, we combine predictive data mining with experimental evaluation in patient-derived xenograft cells. Our proposed algorithm, TargetTranslator, integrates data from tumour biobanks, pharmacological databases, and cellular networks to predict how targeted interventions affect mRNA signatures associated with high patient risk or disease processes. We find more than 80 targets to be associated with neuroblastoma risk and differentiation signatures. Selected targets are evaluated in cell lines derived from high-risk patients to demonstrate reversal of risk signatures and malignant phenotypes. Using neuroblastoma xenograft models, we establish CNR2 and MAPK8 as promising candidates for the treatment of high-risk neuroblastoma. We expect that our method, available as a public tool (targettranslator.org), will enhance and expedite the discovery of risk-associated targets for paediatric and adult cancers.
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| 10. |
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| 11. |
- Andersson, Viktor, 1995, et al.
(author)
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Controlled Decent Training
- 2023
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Journal article (other academic/artistic)abstract
- In this work, a novel and model-based artificial neural network (ANN) training method is developed supported by optimal control theory. The method augments training labels in order to robustly guarantee training loss convergence and improve training convergence rate. Dynamic label augmentation is proposed within the framework of gradient descent training where the convergence of training loss is controlled. First, we capture the training behavior with the help of empirical Neural Tangent Kernels (NTK) and borrow tools from systems and control theory to analyze both the local and global training dynamics (e.g. stability, reachability). Second, we propose to dynamically alter the gradient descent training mechanism via fictitious labels as control inputs and an optimal state feedback policy. In this way, we enforce locally H2 optimal and convergent training behavior. The novel algorithm, Controlled Descent Training (CDT), guarantees local convergence. CDT unleashes new potentials in the analysis, interpretation, and design of ANN architectures. The applicability of the method is demonstrated on standard regression and classification problems.
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| 12. |
- Andersson, Viktor, 1995, et al.
(author)
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Controlled Descent Training
- 2024
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In: International Journal of Robust and Nonlinear Control. - 1099-1239 .- 1049-8923.
-
Journal article (peer-reviewed)abstract
- In this work, a novel and model-based artificial neural network (ANN) training method is developed supported by optimal control theory. The method augments training labels in order to robustly guarantee training loss convergence and improve training convergence rate. Dynamic label augmentation is proposed within the framework of gradient descent training where the convergence of training loss is controlled. First, we capture the training behavior with the help of empirical Neural Tangent Kernels (NTK) and borrow tools from systems and control theory to analyze both the local and global training dynamics (e.g. stability, reachability). Second, we propose to dynamically alter the gradient descent training mechanism via fictitious labels as control inputs and an optimal state feedback policy. In this way, we enforce locally H2 optimal and convergent training behavior. The novel algorithm, Controlled Descent Training (CDT), guarantees local convergence. CDT unleashes new potentials in the analysis, interpretation, and design of ANN architectures. The applicability of the method is demonstrated on standard regression and classification problems.
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| 13. |
- Barrenäs, Fredrik, et al.
(author)
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Disease-Associated MRNA Expression Differences in Genes with Low DNA Methylation
- 2012
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Other publication (other academic/artistic)abstract
- Although the importance of DNA methylation for mRNA expression has been shown for individualgenes in several complex diseases, such a relation has been difficult to show on a genome-wide scale.Here, we used microarrays to examine the relationship between DNA methylation and mRNAexpression in CD4+ T cells from patients with seasonal allergic rhinitis (SAR) and healthy controls.SAR is an optimal disease model because the disease process can be studied by comparing allergenchallengedCD4+ T cells obtained from patients and controls, and mimicked in Th2 polarised T cellsfrom healthy controls. The cells from patients can be analyzed to study relations between methylationand mRNA expression, while the Th2 cells can be used for functional studies. We found that DNAmethylation, but not mRNA expression clearly separated patients from controls. Similar to studies ofother complex diseases, we found no general relation between DNA methylation and mRNAexpression. However, when we took into account the absence or presence of CpG islands in thepromoters of disease associated genes an association was found: low methylation genes without CpGislands had significantly higher expression levels of disease-associated genes. This association wasconfirmed for genes whose expression levels were regulated by a transcription factor of knownrelevance for allergy, IRF4, using combined ChIP-chip and siRNA mediated silencing of IRF4expression. In summary, disease-associated increases of mRNA expression were found in lowmethylation genes without CpG islands in CD4+ T cells from patients with SAR. Further studies arewarranted to examine if a similar association is found in other complex diseases.
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| 14. |
- Barrenäs, Fredrik, et al.
(author)
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Highly interconnected genes in disease-specific networks are enriched for disease-associated polymorphisms
- 2012
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In: Genome Biology. - : BioMed Central. - 1465-6906 .- 1474-760X .- 1465-6914. ; 13:6, s. R46-
-
Journal article (peer-reviewed)abstract
- BACKGROUND: Complex diseases are associated with altered interactions between thousands of genes. We developed a novel method to identify and prioritize disease genes, which was generally applicable to complex diseases.RESULTS: We identified modules of highly interconnected genes in disease-specific networks derived from integrating gene-expression and protein interaction data. We examined if those modules were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies. First, we analyzed publicly available gene expression microarray and genome-wide association study (GWAS) data from 13, highly diverse, complex diseases. In each disease, highly interconnected genes formed modules, which were significantly enriched for genes harboring disease-associated SNPs. To test if such modules could be used to find novel genes for functional studies, we repeated the analyses using our own gene expression microarray and GWAS data from seasonal allergic rhinitis. We identified a novel gene, FGF2, whose relevance was supported by functional studies using combined small interfering RNA-mediated knock-down and gene expression microarrays. The modules in the 13 complex diseases analyzed here tended to overlap and were enriched for pathways related to oncological, metabolic and inflammatory diseases. This suggested that this union of the modules would be associated with a general increase in susceptibility for complex diseases. Indeed, we found that this union was enriched with GWAS genes for 145 other complex diseases.CONCLUSIONS: Modules of highly interconnected complex disease genes were enriched for disease-associated SNPs, and could be used to find novel genes for functional studies.
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| 15. |
- Björnsson, Bergthor, et al.
(author)
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Digital twins to personalize medicine
- 2020
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In: Genome Medicine. - : Springer Science and Business Media LLC. - 1756-994X. ; 12:1
-
Research review (peer-reviewed)abstract
- Personalized medicine requires the integration and processing of vast amounts of data. Here, we propose a solution to this challenge that is based on constructing Digital Twins. These are high-resolution models of individual patients that are computationally treated with thousands of drugs to find the drug that is optimal for the patient.
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| 16. |
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| 17. |
- Cook, Daniel John, 1986, et al.
(author)
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Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness
- 2020
-
In: Cancer Medicine. - : Wiley. - 2045-7634. ; 9:10, s. 3551-3562
-
Journal article (peer-reviewed)abstract
- Cancer Medicine published by John Wiley & Sons Ltd. Background: Characterizing breast cancer progression and aggressiveness relies on categorical descriptions of tumor stage and grade. Interpreting these categorical descriptions is challenging because stage convolutes the size and spread of the tumor and no consensus exists to define high/low grade tumors. Methods: We address this challenge of heterogeneity in patient-specific cancer samples by adapting and applying several tools originally created for understanding heterogeneity and phenotype development in single cells (specifically, single-cell topological data analysis and Wanderlust) to create a continuous metric describing breast cancer progression using bulk RNA-seq samples from individual patient tumors. We also created a linear regression-based method to predict tumor aggressiveness in vivo from bulk RNA-seq data. Results: We found that breast cancer proceeds along three convergent phenotype trajectories: luminal, HER2-enriched, and basal-like. Furthermore, 31 296 genes (for luminal cancers), 17 827 genes (for HER2-enriched), and 18 505 genes (for basal-like) are dynamically differentially expressed during breast cancer progression. Across progression trajectories, our results show that expression of genes related to ADP-ribosylation decreased as tumors progressed (while PARP1 and PARP2 increased or remained stable), suggesting the potential for a differential response to PARP inhibitors based on cancer progression. Additionally, we developed a 132-gene expression regression equation to predict mitotic index and a 23-gene expression regression equation to predict growth rate from a single breast cancer biopsy. Conclusion: Our results suggest that breast cancer dynamically changes during disease progression, and growth rate of the cancer cells is associated with distinct transcriptional profiles.
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| 18. |
- Fries, Niklas, 1991-
(author)
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Data-driven quality management using explainable machine learning and adaptive control limits
- 2023
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Doctoral thesis (other academic/artistic)abstract
- In industrial applications, the objective of statistical quality management is to achieve quality guarantees through the efficient and effective application of statistical methods. Historically, quality management has been characterized by a systematic monitoring of critical quality characteristics, accompanied by manual and experience-based root cause analysis in case of an observed decline in quality. Machine learning researchers have suggested that recent improvements in digitization, including sensor technology, computational power, and algorithmic developments, should enable more systematic approaches to root cause analysis.In this thesis, we explore the potential of data-driven approaches to quality management. This exploration is performed with consideration to an envisioned end product which consists of an automated data collection and curation system, a predictive and explanatory model trained on historical process and quality data, and an automated alarm system that predicts a decline in quality and suggests worthwhile interventions. The research questions investigated in this thesis relate to which statistical methods are relevant for the implementation of the product, how their reliability can be assessed, and whether there are knowledge gaps that prevent this implementation.This thesis consists of four papers: In Paper I, we simulated various types of process-like data in order to investigate how several dataset properties affect the choice of methods for quality prediction. These properties include the number of predictors, their distribution and correlation structure, and their relationships with the response. In Paper II, we reused the simulation method from Paper I to simulate multiple types of datasets, and used them to compare local explanation methods by evaluating them against a ground truth.In Paper III, we outlined a framework for an automated process adjustment system based on a predictive and explanatory model trained on historical data. Next, given a relative cost between reduced quality and process adjustments, we described a method for searching for a worthwhile adjustment policy. Several simulation experiments were performed to demonstrate how to evaluate such a policy.In Paper IV, we described three ways to evaluate local explanation methods on real-world data, where no ground truth is available for comparison. Additionally, we described four methods for decorrelation and dimension reduction, and describe the respective tradeoffs. These methods were evaluated on real-world process and quality data from the paint shop of the Volvo Trucks cab factory in Umeå, Sweden.During the work on this thesis, two significant knowledge gaps were identified: The first gap is a lack of best practices for data collection and quality control, preprocessing, and model selection. The other gap is that although there are many promising leads for how to explain the predictions of machine learning models, there is still an absence of generally accepted definitions for what constitutes an explanation, and a lack of methods for evaluating the reliability of such explanations.
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| 19. |
- Gerlee, Philip, 1980, et al.
(author)
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Searching for Synergies: Matrix Algebraic Approaches for Efficient Pair Screening
- 2013
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:7
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Journal article (peer-reviewed)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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| 20. |
- Gustafsson, Johan, 1976, et al.
(author)
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DSAVE: Detection of misclassified cells in single-cell RNA-Seq data
- 2020
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203 .- 1932-6203. ; 15:12 December
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Journal article (peer-reviewed)abstract
- Single-cell RNA sequencing has become a valuable tool for investigating cell types in complex tissues, where clustering of cells enables the identification and comparison of cell populations. Although many studies have sought to develop and compare different clustering approaches, a deeper investigation into the properties of the resulting populations is lacking. Specifically, the presence of misclassified cells can influence downstream analyses, highlighting the need to assess subpopulation purity and to detect such cells. We developed DSAVE (Down-SAmpling based Variation Estimation), a method to evaluate the purity of single-cell transcriptome clusters and to identify misclassified cells. The method utilizes down-sampling to eliminate differences in sampling noise and uses a log-likelihood based metric to help identify misclassified cells. In addition, DSAVE estimates the number of cells needed in a population to achieve a stable average gene expression profile within a certain gene expression range. We show that DSAVE can be used to find potentially misclassified cells that are not detectable by similar tools and reveal the cause of their divergence from the other cells, such as differing cell state or cell type. With the growing use of single-cell RNA-seq, we foresee that DSAVE will be an increasingly useful tool for comparing and purifying subpopulations in single-cell RNA-Seq datasets.
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| 21. |
- Gustafsson, Johan, 1976, et al.
(author)
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Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
- 2023
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 120:6
-
Journal article (peer-reviewed)abstract
- Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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| 22. |
- Gustafsson, Johan, 1976, et al.
(author)
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Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data
- 2022
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Journal article (other academic/artistic)abstract
- Single-cell RNA sequencing has the potential to unravel the differences in metabolism across cell types and cell states in both the healthy and diseased human body. The use of existing knowledge in the form of genome-scale metabolic models (GEMs) holds promise to strengthen such analyses, but the combined use of these two methods requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the tINIT algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile, emphasizing the need to study them separately rather than to build models from bulk RNA-Seq data. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.
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| 23. |
- Gustafsson, Johan, 1976, et al.
(author)
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Sources of variation in cell-type RNA-Seq profiles
- 2020
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In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:9
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Journal article (peer-reviewed)abstract
- Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.
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| 24. |
- Jauhiainen, Alexandra, 1981, et al.
(author)
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Transcriptional and metabolic data integration and modeling for identification of active pathways
- 2012
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In: Biostatistics. - : Oxford University Press (OUP). - 1465-4644 .- 1468-4357. ; 13:4, s. 748-761
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Journal article (peer-reviewed)abstract
- With the growing availability of omics data generated to describe different cells and tissues, the modeling and interpretation of such data has become increasingly important. Pathways are sets of reactions involving genes, metabolites, and proteins highlighting functional modules in the cell. Therefore, to discover activated or perturbed pathways when comparing two conditions, for example two different tissues, it is beneficial to use several types of omics data. We present a model that integrates transcriptomic and metabolomic data in order to make an informed pathway-level decision. Since metabolites can be seen as end-points of perturbations happening at the gene level, the gene expression data constitute the explanatory variables in a sparse regression model for the metabolite data. Sophisticated model selection procedures are developed to determine an appropriate model. We demonstrate that the transcript profiles can be used to informatively explain the metabolite data from cancer cell lines. Simulation studies further show that the proposed model offers a better performance in identifying active pathways than, for example, enrichment methods performed separately on the transcript and metabolite data.
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| 25. |
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| 26. |
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| 27. |
- Johansson, Patrik
(author)
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Large scale integration and interactive exploration of cancer data – with applications to glioblastoma
- 2018
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Doctoral thesis (other academic/artistic)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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| 28. |
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| 29. |
- Jörnsten, Rebecka, 1971, et al.
(author)
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Network modeling of the transcriptional effects of copy number aberrations in glioblastoma
- 2011
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In: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 7
-
Journal article (peer-reviewed)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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| 30. |
- Kellgren, Therese, 1983-
(author)
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Hidden patterns that matter : statistical methods for analysis of DNA and RNA data
- 2020
-
Doctoral thesis (other academic/artistic)abstract
- Understanding how the genetic variations can affect characteristics and function of organisms can help researchers and medical doctors to detect genetic alterations that cause disease and reveal genes that causes antibiotic resistance. The opportunities and progress associated with such data come however with challenges related to statistical analysis. It is only by using properly designed and employed tools, that we can extract the information about hidden patterns. In this thesis we present three types of such analysis. First, the genetic variant in the gene COL17A1 that causes corneal dystrophy with recurrent erosions is reveled. By studying Next-generation sequencing data, the order of the nucleotides in the DNAsequence was be obtained, which enabled us to detect interesting variants in the genome. Further, we present results of an experimental design study with the aim to make the best selection from a family that is affected by an inherited disease. In second part of the work, we analyzed a novel antibiotic resistance Staphylococcus epidermidis clone that is only found in northern Europe. By investigating its genetic data, we revealed similarities to a world known antibiotic resistance clone. As a result, the antibiotic resistance profile is established from the DNA sequences. Finally, we also focus on the challenges related to the abundance of genetic data from different sources. The increasing number of public gene expression datasets gives us opportunity to increase our understanding by using information from multiple sources simultaneously. Naturally, this requires merging independent datasets together. However, when doing so, the technical and biological variation in the joined data increases. We present a pre-processing method to construct gene co-expression networks from a large diverse gene-expression dataset.
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| 31. |
- Kling, Teresia, 1985, et al.
(author)
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Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content
- 2015
-
In: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 43:15
-
Journal article (peer-reviewed)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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| 32. |
- Kling, Teresia, 1985, et al.
(author)
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Integrative Modeling Reveals Annexin A2-mediated Epigenetic Control of Mesenchymal Glioblastoma
- 2016
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In: Ebiomedicine. - : Elsevier BV. - 2352-3964. ; 12, s. 72-85
-
Journal article (peer-reviewed)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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| 33. |
- Krona, Cecilia, et al.
(author)
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GLIOBLASTOMA GROWTH IS SHAPED BY INVASION ROUTE-SPECIFIC FUNCTIONAL SIGNATURES
- 2023
-
In: Neuro-Oncology. - 1522-8517. ; 25:Supplement: 5, MODL-16
-
Conference paper (other academic/artistic)abstract
- One of the defining features of glioblastomas (GBMs) is the capacity for invasive growth along multiple anatomical pathways in the brain. GBM is well-studied on a genetic and molecular level, but clinically relevant and experimentally tractable models of invasive growth are largely lacking. Here, we report an integrated study of patient-matched information, genomic- and molecular profiles with growth in mouse brains to expose treatments and biomarkers associated with glioblastoma invasion and recurrence. In total, 64 patient-derived cell lines (PDCLs) were injected into the striatum of n ≥ 4 mice each. The 45 tumor-forming PDCLs were each scored for 10 distinct growth characteristics (n = 182 mice). The repertoire of phenotypes was highly divergent, and our material included clear cases of perivascular route invasion, white matter route invasion, perineuronal satellitosis, and gliosarcoma. We explored if cellular pathways, monitored by RNA-sequencing, could account for these differences. GSEA highlighted a positive enrichment for highly proliferative proneural tumors characterized by Notch activation, neuronal signaling, and epigenetic gene regulatory programs in the tumor-initiating lines. Transcriptional signatures were also strongly predictive of route-specific invasion. Diffuse invasion was predominantly seen in classical-subtype PDCLs with astrocytic or outer radial glia-like signatures. Proneural PDCLs, in turn, grew as solid tumors with an invasive peripheral region around vasculature, and mesenchymal tumors were more demarcated. To explore the therapeutic implications of our findings, we used our data-driven method (TargetTranslator, Nat Comm 2020) to predict the drug vulnerabilities of different types of invasive glioblastoma. Defined GBM tumors with perivascular invasion are characterized by increased IGFR1, MAPK/ERK, PI3K/AKT/mTOR, and JAK2 signaling. Diffusively growing GBM tumors, on the other hand, depend more on Wnt/β-catenin signaling, neuronal signaling, and active inflammatory response. Using a sphere invasion assay, we confirm that targeting both PI3K- and Wnt signaling selectively reduces glioblastoma invasion, highlighting their therapeutic potential.
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| 34. |
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| 35. |
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| 36. |
- Landfors, Mattias, 1979-
(author)
-
Normalization and analysis of high-dimensional genomics data
- 2012
-
Doctoral thesis (other academic/artistic)abstract
- In the middle of the 1990’s the microarray technology was introduced. The technology allowed for genome wide analysis of gene expression in one experiment. Since its introduction similar high through-put methods have been developed in other fields of molecular biology. These high through-put methods provide measurements for hundred up to millions of variables in a single experiment and a rigorous data analysis is necessary in order to answer the underlying biological questions. Further complications arise in data analysis as technological variation is introduced in the data, due to the complexity of the experimental procedures in these experiments. This technological variation needs to be removed in order to draw relevant biological conclusions from the data. The process of removing the technical variation is referred to as normalization or pre-processing. During the last decade a large number of normalization and data analysis methods have been proposed. In this thesis, data from two types of high through-put methods are used to evaluate the effect pre-processing methods have on further analyzes. In areas where problems in current methods are identified, novel normalization methods are proposed. The evaluations of known and novel methods are performed on simulated data, real data and data from an in-house produced spike-in experiment.
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| 37. |
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| 38. |
- Larsson, Ida, et al.
(author)
-
Modeling glioblastoma heterogeneity as a dynamic network of cell states
- 2021
-
In: Molecular Systems Biology. - : EMBO. - 1744-4292. ; 17:9
-
Journal article (peer-reviewed)abstract
- Tumor cell heterogeneity is a crucial characteristic of malignant brain tumors and underpins phenomena such as therapy resistance and tumor recurrence. Advances in single-cell analysis have enabled the delineation of distinct cellular states of brain tumor cells, but the time-dependent changes in such states remain poorly understood. Here, we construct quantitative models of the time-dependent transcriptional variation of patient-derived glioblastoma (GBM) cells. We build the models by sampling and profiling barcoded GBM cells and their progeny over the course of 3 weeks and by fitting a mathematical model to estimate changes in GBM cell states and their growth rates. Our model suggests a hierarchical yet plastic organization of GBM, where the rates and patterns of cell state switching are partly patient-specific. Therapeutic interventions produce complex dynamic effects, including inhibition of specific states and altered differentiation. Our method provides a general strategy to uncover time-dependent changes in cancer cells and offers a way to evaluate and predict how therapy affects cell state composition.
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| 39. |
- Larsson, Ida, et al.
(author)
-
Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers
- 2024
-
Other publication (other academic/artistic)abstract
- Nervous system cancers contain a large spectrum of transcriptional cell states, reflecting processes active during normal development, injury response and growth. However, we lack a good understanding of these states' regulation and pharmacological importance. Here, we describe the integrated reconstruction of such cellular regulatory programs and their therapeutic targets from extensive collections of single-cell RNA sequencing data (scRNA-seq) from both tumors and developing tissues. Our method, termed single-cell Regulatory-driven Clustering (scRegClust), predicts essential kinases and transcription factors in little computational time thanks to a new efficient optimization strategy. Using this method, we analyze scRNA-seq data from both adult and childhood brain cancers to identify transcription factors and kinases that regulate distinct tumor cell states. In adult glioblastoma, our model predicts that blocking the activity of PDGFRA, DDR1, ERBB3 or SOX6, or increasing YBX1-activity, would potentiate temozolomide treatment. We further perform an integrative study of scRNA-seq data from both cancer and the developing brain to uncover the regulation of emerging meta-modules. We find a meta-module regulated by the transcription factors SPI1 and IRF8 and link it to an immune-mediated mesenchymal-like state. Our algorithm is available as an easy-to-use R package and companion visualization tool that help uncover the regulatory programs underlying cell plasticity in cancer and other diseases.
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| 40. |
- Larsson, Ida, et al.
(author)
-
Using drug-induced cell states to build therapeutic combinations against nervous system cancers
-
Other publication (other academic/artistic)abstract
- Evidence is amounting that nervous system cancers are heterogeneous at the single cell level, yet data are currently scarce on how therapeutic agents affect this heterogeneity. Here, we describe a new, data-driven strategy to identify drugs that modulate the intratumoral heterogeneity of nervous system cancers. First, we demonstrate that drugs elicit structured changes in pathway activation in patient-derived cells from glioblastomas, neuroblastomas and medulloblastomas. Second, we present a mathematical model to estimate how drugs induce changes in tumor heterogeneity, as defined by single cell RNA sequencing atlases of each disease. Finally, as an evaluation of our method we use it to identify candidate synergistic drug pairs based on the drugs' effects on intratumoral heterogeneity.
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| 41. |
- Lång, Adam, et al.
(author)
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Estimating the differentiation potential and plasticity of cancer cells using statistical mechanics
-
Other publication (other academic/artistic)abstract
- Cell differentiation is a crucial property of both normal and cancerous cells, that is driven by complex underlying processes. A number of computational methods can score the differentiation potential of individual cells based on their RNA expression. However, we lack a unifying model to explain how differentiation arises from underlying gene regulation and external perturbations. Here, we show that an adaptation of the Ising model, commonly used in statistical mechanics, can bridge this gap, thereby offering a way to identify normal and cancer stem cells. Our new model states that every cell updates its gene expression pattern according to a Boltzmann distribution, influenced by the gene-gene network and an external perturbation field. We first show that this model can be fitted to scRNAseq data sets. We apply the model to a range of data sets to demonstrate its efficacy in separating cells with varying differentiation potential and creating a pseudo-temporal ordering of cells in a GBM data set. Additionally, we explore other aspects of the model to identify known chromosomal aberrations of GBM from single cells and predict therapeutic interventions. This framework has potential applications in many cancer types and can be used to identify CSCs and measure differentiation potential without relying on stemness signatures or marker genes.
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| 42. |
- Magnusson, Rasmus, 1992-, et al.
(author)
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LASSIM-A network inference toolbox for genome-wide mechanistic modeling
- 2017
-
In: PLoS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 13:6, s. Article no. e1005608 -
-
Journal article (peer-reviewed)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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| 43. |
- Martinez, David, et al.
(author)
-
NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures
- 2023
-
In: Briefings in Bioinformatics. - : OXFORD UNIV PRESS. - 1467-5463 .- 1477-4054. ; 24:5
-
Journal article (peer-reviewed)abstract
- Precision medicine relies on the identification of robust disease and risk factor signatures from omics data. However, current knowledge-driven approaches may overlook novel or unexpected phenomena due to the inherent biases in biological knowledge. In this study, we present a data-driven signature discovery workflow for DNA methylation analysis utilizing network-coherent autoencoders (NCAEs) with biologically relevant latent embeddings. First, we explored the architecture space of autoencoders trained on a large-scale pan-tissue compendium (n = 75 272) of human epigenome-wide association studies. We observed the emergence of co-localized patterns in the deep autoencoder latent space representations that corresponded to biological network modules. We determined the NCAE configuration with the strongest co-localization and centrality signals in the human protein interactome. Leveraging the NCAE embeddings, we then trained interpretable deep neural networks for risk factor (aging, smoking) and disease (systemic lupus erythematosus) prediction and classification tasks. Remarkably, our NCAE embedding-based models outperformed existing predictors, revealing novel DNA methylation signatures enriched in gene sets and pathways associated with the studied condition in each case. Our data-driven biomarker discovery workflow provides a generally applicable pipeline to capture relevant risk factor and disease information. By surpassing the limitations of knowledge-driven methods, our approach enhances the understanding of complex epigenetic processes, facilitating the development of more effective diagnostic and therapeutic strategies.
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| 44. |
- Moreau, M., et al.
(author)
-
Chronological Changes in MicroRNA Expression in the Developing Human Brain
- 2013
-
In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:4
-
Journal article (peer-reviewed)abstract
- Objective MicroRNAs (miRNAs) are endogenously expressed noncoding RNA molecules that are believed to regulate multiple neurobiological processes. Expression studies have revealed distinct temporal expression patterns in the developing rodent and porcine brain, but comprehensive profiling in the developing human brain has not been previously reported. Methods We performed microarray and TaqMan-based expression analysis of all annotated mature miRNAs (miRBase 10.0) as well as 373 novel, predicted miRNAs. Expression levels were measured in 48 post-mortem brain tissue samples, representing gestational ages 14–24 weeks, as well as early postnatal and adult time points. Results Expression levels of 312 miRNAs changed significantly between at least two of the broad age categories, defined as fetal, young, and adult. Conclusions We have constructed a miRNA expression atlas of the developing human brain, and we propose a classification scheme to guide future studies of neurobiological function.
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| 45. |
- Nestor, Colm, et al.
(author)
-
DNA Methylation Changes Separate Allergic Patients from Healthy Controls and May Reflect Altered CD4⁺ T-Cell Population Structure
- 2014
-
In: PLoS Genetics. - : Public Library of Science (PLoS). - 1553-7390 .- 1553-7404. ; 10:1
-
Journal article (peer-reviewed)abstract
- Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients = 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina's HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols = 12), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells.
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| 46. |
- Oh, J. H., et al.
(author)
-
A Factor Analysis Approach for Clustering Patient Reported Outcomes
- 2016
-
In: Methods of Information in Medicine. - : Georg Thieme Verlag KG. - 0026-1270 .- 2511-705X. ; 55:5, s. 431-439
-
Journal article (peer-reviewed)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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| 47. |
- Rodriguez-Saona, C.R., et al.
(author)
-
Color preference, seasonality, spatial distribution and species composition of thrips (Thysanoptera: Thripidae) in northern highbush blueberries
- 2010
-
In: Crop Protection. - : Elsevier BV. - 0261-2194. ; 29:11, s. 1331-1340
-
Journal article (peer-reviewed)abstract
- We investigated color preference, seasonal abundance, spatial distribution and species composition of thrips in northern highbush blueberries, Vaccinium corymbosum L, in New jersey (USA). White sticky traps were more attractive to thrips compared with yellow or blue traps. Thrips captures using white sticky traps showed that their flight activity begins 20-30 d after the onset of flowering, with 10, 50 and 90% of trap captures observed at 383, 647 and 1231 degree-day accumulations, respectively (10 degrees C base temperature). Two methods were used to study thrips distribution within a blueberry bush. First, white sticky traps were placed within the bush canopy at three different heights. The highest numbers of thrips were caught on traps in the middle and top one-third of the canopy while the lowest numbers were caught in the bottom one-third. A second method determined the distribution of thrips on the blueberry plant at different heights and phenological stages. The highest numbers of thrips were found on young leaves at lower parts of the canopy, whereas flowers and fruit had fewer thrips and none were found on buds; these thrips were identified as, Scirtothrips ruthveni (88% of adults) and Frankliniella tritici (12%). The distribution of thrips within a blueberry planting was investigated using an evenly-spaced grid of white sticky traps in combination with on bush beating-tray samples. Thrips counts from traps correlated with direct counts on the bush across the entire blueberry field (macro-scale level); however, within the field (micro-scale level), there was no correlation between the number of thrips on traps and on individual bushes near traps. Early in the season, trap counts were higher on bushes closer to the forest, indicative of movement of thrips from wild hosts into blueberry fields. However, this was not the case for direct on bush counts or trap counts for the later part of the season, where there was no clear forest "edge" effect. Percent fruit injury due to thrips feeding was low, and it correlated with thrips counts on bushes but not from counts on traps. Overall, our data show that thrips counts on sticky traps need to be interpreted with care because these numbers weakly correlated with the numbers of thrips on bushes at the micro-scale level and percent fruit injury; however, they can be useful predictors of thrips activity across entire blueberry fields (macro-scale).
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| 48. |
- Rosén, Emil
(author)
-
Modeling glioblastoma growth patterns and their mechanistic origins
- 2021
-
Doctoral thesis (other academic/artistic)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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| 49. |
- Steineck, Gunnar, 1952, et al.
(author)
-
Identifying radiation-induced survivorship syndromes affecting bowel health in a cohort of gynecological cancer survivors
- 2017
-
In: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 12:2
-
Journal article (peer-reviewed)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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| 50. |
- Steineck, Gunnar, 1952, et al.
(author)
-
Late radiation-induced bowel syndromes, tobacco smoking, age at treatment and time since treatment - gynecological cancer survivors
- 2017
-
In: Acta Oncologica. - : Informa UK Limited. - 0284-186X .- 1651-226X. ; 56:5, s. 682-691
-
Journal article (peer-reviewed)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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