| 1. |
- Campbell, PJ, et al.
(författare)
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Pan-cancer analysis of whole genomes
- 2020
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 578:7793, s. 82-
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Tidskriftsartikel (refereegranskat)abstract
- Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1–3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10–18.
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| 2. |
- Carlevaro-Fita, J, et al.
(författare)
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Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis
- 2020
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Ingår i: Communications biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1, s. 56-
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Tidskriftsartikel (refereegranskat)abstract
- Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis.
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| 3. |
- Diamanti, Klev, 1987-, et al.
(författare)
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Integration of whole-body [18F]FDG PET/MRI with non-targeted metabolomics can provide new insights on tissue-specific insulin resistance in type 2 diabetes
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific phenotypes requires a multi-omics approach. In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body mass index, we calculated associations between parameters of whole-body positron emission tomography (PET)/magnetic resonance imaging (MRI) during hyperinsulinemic euglycemic clamp and non-targeted metabolomics profiling for subcutaneous adipose tissue (SAT) and plasma. Plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Visceral adipose tissue (VAT) and SAT insulin sensitivity (Ki), were positively associated with several lysophospholipids, while the opposite applied to branched-chain amino acids. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver Ki. Bile acids and carnitines in adipose tissue were inversely associated with VAT Ki. Furthermore, we detected several metabolites that were significantly higher in T2D than normal/prediabetes. In this study we present novel associations between several metabolites from SAT and plasma with the fat fraction, volume and insulin sensitivity of various tissues throughout the body, demonstrating the benefit of an integrative multi-omics approach.
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| 4. |
- Diamanti, Klev, 1987-, et al.
(författare)
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Organ-specific metabolic pathways distinguish prediabetes, type 2 diabetes, and normal tissues
- 2022
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Ingår i: Cell Reports Medicine. - : Elsevier BV. - 2666-3791. ; 3:10
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Tidskriftsartikel (refereegranskat)abstract
- Environmental and genetic factors cause defects in pancreatic islets driving type 2 diabetes (T2D) together with the progression of multi-tissue insulin resistance. Mass spectrometry proteomics on samples from five key metabolic tissues of a cross-sectional cohort of 43 multi-organ donors provides deep coverage of their proteomes. Enrichment analysis of Gene Ontology terms provides a tissue-specific map of altered biological processes across healthy, prediabetes (PD), and T2D subjects. We find widespread alterations in several relevant biological pathways, including increase in hemostasis in pancreatic islets of PD, increase in the complement cascade in liver and pancreatic islets of PD, and elevation in cholesterol biosynthesis in liver of T2D. Our findings point to inflammatory, immune, and vascular alterations in pancreatic islets in PD that are hypotheses to be tested for potential contributions to hormonal perturbations such as impaired insulin and increased glucagon production. This multi-tissue proteomic map suggests tissue-specific metabolic dysregulations in T2D. © 2022 The Author(s)
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| 5. |
- Barrenäs, Fredrik, et al.
(författare)
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Interleukin-15 response signature predicts RhCMV/SIV vaccine efficacy
- 2021
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Ingår i: PLoS Pathogens. - : Public Library of Science (PLoS). - 1553-7366 .- 1553-7374. ; 17:7
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Tidskriftsartikel (refereegranskat)abstract
- Simian immunodeficiency virus (SIV) challenge of rhesus macaques (RMs) vaccinated with strain 68-1 Rhesus Cytomegalovirus (RhCMV) vectors expressing SIV proteins (RhCMV/SIV) results in a binary outcome: stringent control and subsequent clearance of highly pathogenic SIV in similar to 55% of vaccinated RMs with no protection in the remaining 45%. Although previous work indicates that unconventionally restricted, SIV-specific, effector-memory (EM)-biased CD8(+) T cell responses are necessary for efficacy, the magnitude of these responses does not predict efficacy, and the basis of protection vs. non-protection in 68-1 RhCMV/SIV vector-vaccinated RMs has not been elucidated. Here, we report that 68-1 RhCMV/SIV vector administration strikingly alters the whole blood transcriptome of vaccinated RMs, with the sustained induction of specific immune-related pathways, including immune cell, toll-like receptor (TLR), inflammasome/cell death, and interleukin-15 (IL-15) signaling, significantly correlating with subsequent vaccine efficacy. Treatment of a separate RM cohort with IL-15 confirmed the central involvement of this cytokine in the protection signature, linking the major innate and adaptive immune gene expression networks that correlate with RhCMV/SIV vaccine efficacy. This change-from-baseline IL-15 response signature was also demonstrated to significantly correlate with vaccine efficacy in an independent validation cohort of vaccinated and challenged RMs. The differential IL-15 gene set response to vaccination strongly correlated with the pre-vaccination activity of this pathway, with reduced baseline expression of IL-15 response genes significantly correlating with higher vaccine-induced induction of IL-15 signaling and subsequent vaccine protection, suggesting that a robust de novo vaccine-induced IL-15 signaling response is needed to program vaccine efficacy. Thus, the RhCMV/SIV vaccine imparts a coordinated and persistent induction of innate and adaptive immune pathways featuring IL-15, a known regulator of CD8(+) T cell function, that support the ability of vaccine-elicited unconventionally restricted CD8(+) T cells to mediate protection against SIV challenge.
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| 6. |
- Cavalli, Marco, et al.
(författare)
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A Multi-Omics Approach to Liver Diseases : Integration of Single Nuclei Transcriptomics with Proteomics and HiCap Bulk Data in Human Liver
- 2020
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Ingår i: Omics. - : Mary Ann Liebert Inc. - 1536-2310 .- 1557-8100. ; 24:4, s. 180-194
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Tidskriftsartikel (refereegranskat)abstract
- The liver is the largest solid organ and a primary metabolic hub. In recent years, intact cell nuclei were used to perform single-nuclei RNA-seq (snRNA-seq) for tissues difficult to dissociate and for flash-frozen archived tissue samples to discover unknown and rare cell subpopulations. In this study, we performed snRNA-seq of a liver sample to identify subpopulations of cells based on nuclear transcriptomics. In 4282 single nuclei, we detected, on average, 1377 active genes and we identified seven major cell types. We integrated data from 94,286 distal interactions (p < 0.05) for 7682 promoters from a targeted chromosome conformation capture technique (HiCap) and mass spectrometry proteomics for the same liver sample. We observed a reasonable correlation between proteomics and in silico bulk snRNA-seq (r = 0.47) using tissue-independent gene-specific protein abundancy estimation factors. We specifically looked at genes of medical importance. The DPYD gene is involved in the pharmacogenetics of fluoropyrimidine toxicity and some of its variants are analyzed for clinical purposes. We identified a new putative polymorphic regulatory element, which may contribute to variation in toxicity. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and we investigated all known risk genes. We identified a complex regulatory landscape for the SLC2A2 gene with 16 candidate enhancers. Three of them harbor somatic motif breaking and other mutations in HCC in the Pan Cancer Analysis of Whole Genomes dataset and are candidates to contribute to malignancy. Our results highlight the potential of a multi-omics approach in the study of human diseases.
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| 7. |
- Garbulowski, Mateusz, et al.
(författare)
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Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder
- 2021
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Ingår i: Frontiers in Genetics. - : Frontiers Media S.A.. - 1664-8021. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.
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| 8. |
- Garbulowski, Mateusz, et al.
(författare)
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Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment
- 2022
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 14:4
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Tidskriftsartikel (refereegranskat)abstract
- Simple Summary Gliomas are heterogenous types of cancer, therefore the therapy should be personalized and targeted toward specific pathways. We developed a methodology that corrected strong batch effects from The Cancer Genome Atlas datasets and estimated glioma grade-specific co-enrichment mechanisms using machine learning. Our findings created hypotheses for annotations, e.g., pathways, that should be considered as therapeutic targets. Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.
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| 9. |
- Garbulowski, Mateusz
(författare)
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Patterns in big data bioinformatics : Understanding complex diseases with interpretable machine learning
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Alterations in the flow of genetic information may lead to complex diseases. Such changes are measured with various omics techniques that usually produce the so-called “big data”. Using interpretable machine learning (ML), we retrieved patterns from transcriptomics data sets. Specifically, we employed a rule-based ML to identify associations among features and a decision in a combinatorial manner, i.e. a co-prediction. We developed tools and methods that can be applied by a large community of bioinformaticians and proved their usability through a variety of studies.In paper I, we developed an R.ROSETTA package that provides an environment for rule-based ML relying on the rough sets. Basically, R.ROSETTA is an R wrapper of the ROSETTA toolkit; however, it extends its functions with various analytical solutions. The package was tested on a microarray gene expression case-control study of autism. Estimated models were highly accurate and provided lists of possible interactions among genes. Moreover, benchmarking revealed that R.ROSETTA was among the best performing rule- and decision tree-based methods.In paper II, we applied the R.ROSETTA together with a VisuNet package. We used both tools to perform a rule-based network analysis of autism spectrum disorder (ASD) subtypes. Here, we used microarray-based gene expression measures of ASD patients and controls from three data sets. We demonstrated that rule-based modelling is an efficient approach to merge multiple cohorts. Furthermore, we estimated centrality distances among produced subnetworks that revealed dissimilarities of ASD subtypes and controls. Finally, we discovered a highly probable interaction between EMC4 and TMEM30A genes.In paper III, we investigated our tools to perform an RNA-seq-based gene expression analysis of Acute Myeloid Leukemia (AML). We aimed at discovering gene expression patterns between the AML diagnosis and relapse. Specifically, we applied a rule-based network analysis to validate independent cohorts. Our study revealed that overexpressed CD6 and underexpressed INSR are highly co-predictive genes associated to the AML relapse. Finally, we demonstrated arc diagrams as a novel way of visualizing co-predictors.In paper IV, we analyzed glioma grading by performing a comprehensive ML analysis for RNA-seq data sets. We broadly preprocessed data sets and removed a strong batch effect that occurred between glioma grades. Afterwards, we performed ML evaluation on single-sample gene set enrichment scores that revealed topmost accurate collections and annotations that distinguish glioma grades. Among others, we found cell cycle, Fanconi anemia and cholesterol-related pathways associated to glioma progression. Finally, we discovered several co-enrichment mechanisms among annotations.
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| 10. |
- Garbulowski, Mateusz, et al.
(författare)
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R.ROSETTA : an interpretable machine learning framework
- 2021
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Ingår i: BMC Bioinformatics. - : BioMed Central (BMC). - 1471-2105. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundMachine learning involves strategies and algorithms that may assist bioinformatics analyses in terms of data mining and knowledge discovery. In several applications, viz. in Life Sciences, it is often more important to understand how a prediction was obtained rather than knowing what prediction was made. To this end so-called interpretable machine learning has been recently advocated. In this study, we implemented an interpretable machine learning package based on the rough set theory. An important aim of our work was provision of statistical properties of the models and their components.ResultsWe present the R.ROSETTA package, which is an R wrapper of ROSETTA framework. The original ROSETTA functions have been improved and adapted to the R programming environment. The package allows for building and analyzing non-linear interpretable machine learning models. R.ROSETTA gathers combinatorial statistics via rule-based modelling for accessible and transparent results, well-suited for adoption within the greater scientific community. The package also provides statistics and visualization tools that facilitate minimization of analysis bias and noise. The R.ROSETTA package is freely available at https://github.com/komorowskilab/R.ROSETTA. To illustrate the usage of the package, we applied it to a transcriptome dataset from an autism case–control study. Our tool provided hypotheses for potential co-predictive mechanisms among features that discerned phenotype classes. These co-predictors represented neurodevelopmental and autism-related genes.ConclusionsR.ROSETTA provides new insights for interpretable machine learning analyses and knowledge-based systems. We demonstrated that our package facilitated detection of dependencies for autism-related genes. Although the sample application of R.ROSETTA illustrates transcriptome data analysis, the package can be used to analyze any data organized in decision tables.
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