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  • 1.
    Andersson-Evelönn, Emma
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Haider, Zahra
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Köhn, Linda
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Ljungberg, Börje
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Urology and Andrology.
    Roos, Göran
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Degerman, Sofie
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    DNA methylation associates with survival in non-metastatic clear cell renal cell carcinoma2019In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 19, article id 65Article in journal (Refereed)
    Abstract [en]

    Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer and is associated with poor prognosis if metastasized. Up to one third of patients with local disease at diagnosis will develop metastasis after nephrectomy, and there is a need for new molecular markers to identify patients with high risk of tumor progression. In the present study, we performed genome-wide promoter DNA methylation analysis at diagnosis to identify DNA methylation profiles associated with risk for progress.

    Method: Diagnostic tissue samples from 115 ccRCC patients were analysed by Illumina HumanMethylation450K arrays and methylation status of 155,931 promoter associated CpGs were related to genetic aberrations, gene expression and clinicopathological parameters.

    Results: The ccRCC samples separated into two clusters (cluster A/B) based on genome-wide promoter methylation status. The samples in these clusters differed in tumor diameter (p < 0.001), TNM stage (p < 0.001), morphological grade (p < 0.001), and patients outcome (5 year cancer specific survival (pCSS5yr) p < 0.001 and cumulative incidence of progress (pCIP5yr) p < 0.001. An integrated genomic and epigenomic analysis in the ccRCCs, revealed significant correlations between the total number of genetic aberrations and total number of hypermethylated CpGs (R = 0.435, p < 0.001), and predicted mitotic age (R = 0.407, p < 0.001). We identified a promoter methylation classifier (PMC) panel consisting of 172 differently methylated CpGs accompanying progress of disease. Classifying non-metastatic patients using the PMC panel showed that PMC high tumors had a worse prognosis compared with the PMC low tumors (pCIP5yr 38% vs. 8%, p = 0.001), which was confirmed in non-metastatic ccRCCs in the publically available TCGA-KIRC dataset (pCIP5yr 39% vs. 16%, p < 0.001).

    Conclusion: DNA methylation analysis at diagnosis in ccRCC has the potential to improve outcome-prediction in non-metastatic patients at diagnosis.

  • 2.
    Borssén, Magnus
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Haider, Zahra
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Norén-Nyström, Ulrika
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Paediatrics.
    Schmiegelow, Kjeld
    Åsberg, Ann E.
    Kanerva, Jukka
    Madsen, Hans O.
    Marquart, Hanne
    Heyman, Mats
    Hultdin, Magnus
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Roos, Göran
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Forestier, Erik
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Medical and Clinical Genetics.
    Degerman, Sofie
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology. Department of Paediatrics, University Hospital of Trondheim, Norway.
    DNA Methylation Adds Prognostic Value to Minimal Residual Disease Status in Pediatric T-Cell Acute Lymphoblastic Leukemia2016In: Pediatric Blood & Cancer, ISSN 1545-5009, E-ISSN 1545-5017, Vol. 63, no 7, p. 1185-1192Article in journal (Refereed)
    Abstract [en]

    Background. Despite increased knowledge about genetic aberrations in pediatric T-cell acute lymphoblastic leukemia (T-ALL), no clinically feasible treatment-stratifying marker exists at diagnosis. Instead patients are enrolled in intensive induction therapies with substantial side effects. In modern protocols, therapy response is monitored by minimal residual disease (MRD) analysis and used for postinduction risk group stratification. DNA methylation profiling is a candidate for subtype discrimination at diagnosis and we investigated its role as a prognostic marker in pediatric T-ALL. Procedure. Sixty-five diagnostic T-ALL samples from Nordic pediatric patients treated according to the Nordic Society of Pediatric Hematology and Oncology ALL 2008 (NOPHO ALL 2008) protocol were analyzed by HumMeth450K genome wide DNA methylation arrays. Methylation status was analyzed in relation to clinical data and early T-cell precursor (ETP) phenotype. Results. Two distinct CpG island methylator phenotype (CIMP) groups were identified. Patients with a CIMP-negative profile had an inferior response to treatment compared to CIMP-positive patients (3-year cumulative incidence of relapse (CIR3y) rate: 29% vs. 6%, P = 0.01). Most importantly, CIMP classification at diagnosis allowed subgrouping of high-risk T-ALL patients (MRD >= 0.1% at day 29) into two groups with significant differences in outcome (CIR3y rates: CIMP negative 50% vs. CIMP positive 12%; P = 0.02). These groups did not differ regarding ETP phenotype, but the CIMP-negative group was younger (P = 0.02) and had higher white blood cell count at diagnosis (P = 0.004) compared with the CIMP-positive group. Conclusions. CIMP classification at diagnosis in combination with MRD during induction therapy is a strong candidate for further risk classification and could confer important information in treatment decision making.

  • 3.
    Borssén, Magnus
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Nordlund, Jessica
    Haider, Zahra
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Larsson, Pär
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Kanerva, Jukka
    Schmiegelow, Kjeld
    Flaegstad, Trond
    Jónsson, Ólafur Gísli
    Frost, Britt-Marie
    Palle, Josefine
    Forestier, Erik
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Medical and Clinical Genetics.
    Heyman, Mats
    Hultdin, Magnus
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Lönnerholm, Gudmar
    Degerman, Sofie
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    DNA methylation holds prognostic information in relapsed precursor B-cell acute lymphoblastic leukemia2018In: Clinical Epigenetics, E-ISSN 1868-7083, Vol. 10, article id 31Article in journal (Refereed)
    Abstract [en]

    Background: Few biological markers are associated with survival after relapse of B-cell precursor acute lymphoblastic leukemia (BCP-ALL). In pediatric T-cell ALL, we have identified promoter-associated methylation alterations that correlate with prognosis. Here, the prognostic relevance of CpG island methylation phenotype (CIMP) classification was investigated in pediatric BCP-ALL patients.

    Methods: Six hundred and one BCP-ALL samples from Nordic pediatric patients (age 1-18) were CIMP classified at initial diagnosis and analyzed in relation to clinical data.

    Results: Among the 137 patients that later relapsed, patients with a CIMP-profile (n = 42) at initial diagnosis had an inferior overall survival (pOS(5years) 33%) compared to CIMP+ patients (n = 95, pOS(5years) 65%) (p = 0.001), which remained significant in a Cox proportional hazards model including previously defined risk factors.

    Conclusion: CIMP classification is a strong candidate for improved risk stratification of relapsed BCP-ALL.

  • 4.
    Bovinder Ylitalo, Erik
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Thysell, Elin
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Jernberg, Emma
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Crnalic, Sead
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Orthopaedics.
    Widmark, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Bergh, Anders
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Degerman, Sofie
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Wikström, Pernilla
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Integrated DNA methylation and gene expression analysis of molecular heterogeneity in prostate cancer bone metastasisManuscript (preprint) (Other academic)
  • 5.
    Degerman, Sofie
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Domellöf, Magdalena
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Linder, Jan
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Haraldsson, Susann
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Elgh, Eva
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Roos, Göran
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Forsgren, Lars
    Umeå University, Faculty of Medicine, Department of Pharmacology and Clinical Neuroscience, Clinical Neuroscience.
    Long Leukocyte Telomere Length at Diagnosis Is a Risk Factor for Dementia Progression in Idiopathic Parkinsonism2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 12, article id e113387Article in journal (Refereed)
    Abstract [en]

    Telomere length (TL) is regarded as a marker of cellular aging due to the gradual shortening by each cell division, but is influenced by a number of factors including oxidative stress and inflammation. Parkinson's disease and atypical forms of parkinsonism occur mainly in the elderly, with oxidative stress and inflammation in afflicted cells. In this study the relationship between blood TL and prognosis of 168 patients with idiopathic parkinsonism (136 Parkinson's disease [PD], 17 Progressive Supranuclear Palsy [PSP], and 15 Multiple System Atrophy [MSA]) and 30 controls was investigated. TL and motor and cognitive performance were assessed at baseline (diagnosis) and repeatedly up to three to five years follow up. No difference in TL between controls and patients was shown at baseline, nor any significant difference in TL stability or attrition during follow up. Interestingly, a significant relationship between TL at diagnosis and cognitive phenotype at follow up in PD and PSP patients was found, with longer mean TL at diagnosis in patients that developed dementia within three years.

  • 6.
    Degerman, Sofie
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Josefsson, Maria
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Nordin Adolfsson, Annelie
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Wennstedt, Sigrid
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Haider, Zahra
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Pudas, Sara
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).
    Hultdin, Magnus
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Adolfsson, Rolf
    Umeå University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry.
    Maintained memory in aging is associated with young epigenetic age2017In: Neurobiology of Aging, ISSN 0197-4580, E-ISSN 1558-1497, Vol. 55, p. 167-171Article in journal (Refereed)
    Abstract [en]

    Epigenetic alterations during aging have been proposed to contribute to decline in physical and cognitive functions, and accelerated epigenetic aging has been associated with disease and all-cause mortality later in life. In this study, we estimated epigenetic age dynamics in groups with different memory trajectories (maintained high performance, average decline, and accelerated decline) over a 15-year period. Epigenetic (DNA-methylation [DNAm]) age was assessed, and delta age (DNAm age - chronological age) was calculated in blood samples at baseline (age: 55-65 years) and 15 years later in 52 age- and gender-matched individuals from the Betula study in Sweden. A lower delta DNAm age was observed for those with maintained memory functions compared with those with average (p = 0.035) or accelerated decline (p = 0.037). Moreover, separate analyses revealed that DNAm age at follow-up, but not chronologic age, was a significant predictor of dementia (p = 0.019). Our findings suggest that young epigenetic age contributes to maintained memory in aging.

  • 7.
    Degerman, Sofie
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Landfors, Mattias
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Siwicki, Jan Konrad
    Revie, John
    Borssen, Magnus
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Evelönn, Emma
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Forestier, Erik
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Medical and Clinical Genetics.
    Chrzanowska, Krystyna H.
    Ryden, Patrik
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Keith, W. Nicol
    Roos, Göran
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Immortalization of T-Cells Is Accompanied by Gradual Changes in CpG Methylation Resulting in a Profile Resembling a Subset of T-Cell Leukemias2014In: Neoplasia, ISSN 1522-8002, E-ISSN 1476-5586, Vol. 16, no 7, p. 606-615Article in journal (Refereed)
    Abstract [en]

    We have previously described gene expression changes during spontaneous immortalization of T-cells, thereby identifying cellular processes important for cell growth crisis escape and unlimited proliferation. Here, we analyze the same model to investigate the role of genome-wide methylation in the immortalization process at different time points pre-crisis and post-crisis using high-resolution arrays. We show that over time in culture there is an overall accumulation of methylation alterations, with preferential increased methylation close to transcription start sites (TSSs), islands, and shore regions. Methylation and gene expression alterations did not correlate for the majority of genes, but for the fraction that correlated, gain of methylation close to TSS was associated with decreased gene expression. Interestingly, the pattern of CpG site methylation observed in immortal T-cell cultures was similar to clinical T-cell acute lymphoblastic leukemia (T-ALL) samples classified as CpG island methylator phenotype positive. These sites were highly overrepresented by polycomb target genes and involved in developmental, cell adhesion, and cell signaling processes. The presence of non-random methylation events in in vitro immortalized T-cell cultures and diagnostic T-ALL samples indicates altered methylation of CpG sites with a possible role in malignant hematopoiesis.

  • 8.
    Del Peso-Santos, Teresa
    et al.
    Umeå University, Faculty of Science and Technology, Department of Molecular Biology (Faculty of Science and Technology).
    Landfors, Mattias
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Skärfstad, Eleonore
    Umeå University, Faculty of Science and Technology, Department of Molecular Biology (Faculty of Science and Technology).
    Ryden, Patrik
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Shingler, Victoria
    Umeå University, Faculty of Science and Technology, Department of Molecular Biology (Faculty of Science and Technology).
    Pr is a member of a restricted class of σ70-dependent promoters that lack a recognizable -10 element2012In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 40, no 22, p. 11308-11320Article in journal (Refereed)
    Abstract [en]

    The Pr promoter is the first verified member of a class of bacterial σ(70)-promoters that only possess a single match to consensus within its -10 element. In its native context, the activity of this promoter determines the ability of Pseudomonas putida CF600 to degrade phenolic compounds, which provides proof-of-principle for the significance of such promoters. Lack of identity within the -10 element leads to non-detection of Pr-like promoters by current search engines, because of their bias for detection of the -10 motif. Here, we report a mutagenesis analysis of Pr that reveals strict sequence requirements for its activity that includes an essential -15 element and preservation of non-consensus bases within its -35 and -10 elements. We found that highly similar promoters control plasmid- and chromosomally- encoded phenol degradative systems in various Pseudomonads. However, using a purpose-designed promoter-search algorithm and activity analysis of potential candidate promoters, no bona fide Pr-like promoter could be found in the entire genome of P. putida KT2440. Hence, Pr-like σ(70)-promoters, which have the potential to be a widely distributed class of previously unrecognized promoters, are in fact highly restricted and remain in a class of their own.

  • 9.
    Evelönn, Emma Andersson
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Degerman, Sofie
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Köhn, Linda
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology. Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Ljungberg, Börje
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Urology and Andrology.
    Roos, Göran
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    DNA methylation status defines clinicopathological parameters including survival for patients with clear cell renal cell carcinoma (ccRCC)2016In: Tumor Biology, ISSN 1010-4283, E-ISSN 1423-0380, Vol. 37, no 8, p. 10219-10228Article in journal (Refereed)
    Abstract [en]

    Epigenetic alterations in the methylome have been associated with tumor development and progression in renal cell carcinoma (RCC). In this study, 45 tumor samples, 12 tumor-free kidney cortex tissues, and 24 peripheral blood samples from patients with clear cell RCC (ccRCC) were analyzed by genome-wide promoter-directed methylation arrays and related to clinicopathological parameters. Unsupervised hierarchical clustering separated the tumors into two distinct methylation groups (clusters A and B), where cluster B had higher average methylation and increased number of hypermethylated CpG sites (CpGs). Furthermore, tumors in cluster B had, compared with cluster A, a larger tumor diameter (p = 0.033), a higher morphologic grade (p < 0.001), a higher tumor-node-metastasis (TNM) stage (p < 0.001), and a worse prognosis (p = 0.005). Higher TNM stage was correlated to an increase in average methylation level (p = 0.003) and number of hypermethylated CpGs (p = 0.003), whereas a number of hypomethylated CpGs were mainly unchanged. However, the predicted age of the tumors based on methylation profile did not correlate with TNM stage, morphological grade, or methylation cluster. Differently methylated (DM) genes (n = 840) in ccRCC samples compared with tumor-free kidney cortex samples were predominantly hypermethylated and a high proportion were identified as polycomb target genes. The DM genes were overrepresented by transcription factors, ligands, and receptors, indicating functional alterations of significance for ccRCC progression. To conclude, increased number of hypermethylated genes was associated with increased TNM stage of the tumors. DNA methylation classification of ccRCC tumor samples at diagnosis can serve as a clinically applicable prognostic marker in ccRCC.

  • 10.
    Fahlén, Jessica
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Landfors, Mattias
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Freyhult, Eva
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Medicine, Molecular Infection Medicine Sweden (MIMS).
    Bylesjö, Max
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Hvidsten, Torgeir
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Rydén, Patrik
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Bioinformatics strategies for cDNA-microarray data processing2009In: Batch effects and noise in microarray experiments: sources and solutions / [ed] Scherer, Andreas, Wiley and Sons , 2009, 1, , p. 272p. 61-74Chapter in book (Other academic)
    Abstract [en]

    

    Pre-processing plays a vital role in cDNA-microarray data analysis. Without proper pre-processing it is likely that the biological conclusions will be misleading. However, there are many alternatives and in order to choose a proper pre-processing procedure it is necessary to understand the effect of different methods. This chapter discusses several pre-processing steps, including image analysis, background correction, normalization, and filtering. Spike-in data are used to illustrate how different procedures affect the analytical ability to detect differentially expressed genes and estimate their regulation. The result shows that pre-processing has a major impact on both the experiment’s sensitivity andits bias. However, general recommendations are hard to give, since pre-processing consists of several actions that are highly dependent on each other. Furthermore, it is likely that pre-processing have a major impact on downstream analysis, such as clustering and classification, and pre-processing methods should be developed and evaluated with this in mind.

  • 11.
    Freyhult, Eva
    et al.
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Önskog, Jenny
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Hvidsten, Torgeir R.
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Rydén, Patrik
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Umeå University, Faculty of Social Sciences, Department of Statistics.
    Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering2010In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 11, article id 503Article in journal (Refereed)
    Abstract [en]

    Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization.

    Result: We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions.

    Conclusions: The choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data.

  • 12.
    Haider, Zahra
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Golovleva, Irina
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Medical and Clinical Genetics.
    Erlanson, Martin
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Noren-Nyström, Ulrika
    Hultdin, Magnus
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Degerman, Sofie
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Epigenetic and genetic distinctions between T-cell acute lymphoblastic leukemia and lymphomaManuscript (preprint) (Other academic)
  • 13.
    Haider, Zahra
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Larsson, Pär
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Köhn, Linda
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology. Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Schmiegelow, Kjeld
    Flaegstad, Trond
    Kanerva, Jukka
    Heyman, Mats
    Hultdin, Magnus
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Degerman, Sofie
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    An integrated transcriptome analysis in T-cell acute lymphoblastic leukemia links DNA methylation subgroups to dysregulated TAL1 and ANTP homeobox gene expression2019In: Cancer Medicine, ISSN 2045-7634, E-ISSN 2045-7634, Vol. 8, no 1, p. 311-324Article in journal (Refereed)
    Abstract [en]

    Classification of pediatric T‐cell acute lymphoblastic leukemia (T‐ALL) patients into CIMP (CpG Island Methylator Phenotype) subgroups has the potential to improve current risk stratification. To investigate the biology behind these CIMP subgroups, diagnostic samples from Nordic pediatric T‐ALL patients were characterized by genome‐wide methylation arrays, followed by targeted exome sequencing, telomere length measurement, and RNA sequencing. The CIMP subgroups did not correlate significantly with variations in epigenetic regulators. However, the CIMP+ subgroup, associated with better prognosis, showed indicators of longer replicative history, including shorter telomere length (P = 0.015) and older epigenetic (P < 0.001) and mitotic age (P < 0.001). Moreover, the CIMP+ subgroup had significantly higher expression of ANTP homeobox oncogenes, namely TLX3, HOXA9, HOXA10, and NKX2‐1, and novel genes in T‐ALL biology including PLCB4, PLXND1, and MYO18B. The CIMP− subgroup, with worse prognosis, was associated with higher expression of TAL1 along with frequent STIL‐TAL1 fusions (2/40 in CIMP+ vs 11/24 in CIMP−), as well as stronger expression of BEX1. Altogether, our findings suggest different routes for leukemogenic transformation in the T‐ALL CIMP subgroups, indicated by different replicative histories and distinct methylomic and transcriptomic profiles. These novel findings can lead to new therapeutic strategies.

  • 14. Kostjukovits, Svetlana
    et al.
    Degerman, Sofie
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Pekkinen, Minna
    Klemetti, Paula
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Roos, Göran
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Taskinen, Mervi
    Makitie, Outi
    Decreased telomere length in children with cartilage-hair hypoplasia2017In: Journal of Medical Genetics, ISSN 0022-2593, E-ISSN 1468-6244, Vol. 54, no 5, p. 365-370Article in journal (Refereed)
    Abstract [en]

    Background Cartilage-hair hypoplasia (CHH) is an autosomal recessive chondrodysplasia caused by RMRP (RNA component of mitochondrial RNA processing endoribonuclease) gene mutations. Manifestations include short stature, variable immunodeficiency, anaemia and increased risk of malignancies, all of which have been described also in telomere biology disorders. RMRP interacts with the telomerase RT (TERT) subunit, but the influence of RMRP mutations on telomere length is unknown. We measured relative telomere length (RTL) in patients with CHH, their first-degree relatives and healthy controls and correlated RTL with clinical and laboratory features. Methods The study cohort included 48 patients with CHH with homozygous (n=36) or compound heterozygous RMRP mutations (median age 38.2 years, range 6.0-70.8 years), 86 relatives (74 with a heterozygous RMRP mutation) and 94 unrelated healthy controls. We extracted DNA from peripheral blood, sequenced the RMRP gene and measured RTL by qPCR. Results Compared with age-matched and sex-matched healthy controls, median RTL was significantly shorter in patients with CHH (n=40 pairs, 1.05 vs 1.21, p=0.017), but not in mutation carriers (n=48 pairs, 1.16 vs 1.10, p=0.224). RTL correlated significantly with age in RMRP mutation carriers (r=-0.482, p < 0.001) and non-carriers (r=-0.498, p<0.001), but not in patients (r=-0.236, p=0.107). In particular children (< 18 years) with CHH had shorter telomeres than controls (median RTL 1.12 vs 1.26, p=0.008). In patients with CHH, RTL showed no correlation with genotype, clinical or laboratory characteristics. Conclusions Telomere length was decreased in children with CHH. We found no correlation between RTL and clinical or laboratory parameters.

  • 15.
    Landfors, Mattias
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Normalization and analysis of high-dimensional genomics data2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    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.

  • 16.
    Landfors, Mattias
    et al.
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Fahlén, Jessica
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Rydén, Patrik
    Umeå University, Faculty of Social Sciences, Department of Statistics. Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    MC-normalization: a novel method for dye-normalization of two-channel microarray data2009In: Statistical Applications in Genetics and Molecular Biology, ISSN 1544-6115, E-ISSN 1544-6115, Vol. 8, no 1, p. 42-Article in journal (Refereed)
    Abstract [en]

    Motivation: Pre-processing plays a vital role in two-color microarray data analysis. An analysis is characterized by its ability to identify differentially expressed genes (its sensitivity) and its ability to provide unbiased estimators of the true regulation (its bias). It has been shown that microarray experiments regularly underestimate the true regulation of differentially expressed genes. We introduce the MC-normalization, where C stands for channel-wise normalization, with considerably lower bias than the commonly used standard methods.

    Methods: The idea behind the MC-normalization is that the channels’ individual intensities determine the correction, rather than the average intensity which is the case for the widely used MA-normalization. The two methods were evaluated using spike-in data from an in-house produced cDNA-experiment and a public available Agilent-experiment. The methods were applied on background corrected and non-background corrected data. For the cDNA-experiment the methods were either applied separately on data from each of the print-tips or applied on the complete array data. Altogether 24 analyses were evaluated. For each analysis the sensitivity, the bias and two variance measures were estimated.

    Results: We prove that the MC-normalization has lower bias than the MA-normalization. The spike-in data confirmed the theoretical result and suggest that the difference is significant. Furthermore, the empirical data suggest that the MC-and MA-normalization have similar sensitivity. A striking result is that print-tip normalizations did have considerably higher sensitivity than analyses using the complete array data.

  • 17.
    Landfors, Mattias
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology.
    Philip, Philge
    Umeå University, Faculty of Science and Technology, Department of Molecular Biology (Faculty of Science and Technology).
    Rydén, Patrik
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Stenberg, Per
    Umeå University, Faculty of Science and Technology, Department of Molecular Biology (Faculty of Science and Technology).
    Normalization of high dimensional genomics data where the distribution of the altered variables is skewed2011In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 6, no 11, p. e27942-Article in journal (Refereed)
    Abstract [en]

    Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.

  • 18.
    Li, Xingru
    et al.
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Andersson-Evelönn, Emma
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Wang, Sihan
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Raviprakash, Tumkur Sitaram
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Urology and Andrology.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Ottosson, Sofia
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Andersson, Charlotta
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Nilsson, Sofie
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Ljungberg, Börje
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Urology and Andrology.
    Li, Aihong
    Umeå University, Faculty of Medicine, Department of Medical Biosciences.
    Prognostic Significance of Hypermethylation in the Promoter Region of the Wilms’ Tumour Gene 1 in Clear Cell Renal Cell CarcinomaManuscript (preprint) (Other academic)
  • 19.
    Rydén, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Andersson, Henrik
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology.
    Landfors, Mattias
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology.
    Näslund, Linda
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology.
    Hartmanová, Blanka
    Noppa, Laila
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology.
    Sjöstedt, Anders
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology.
    Evaluation of microarray data normalization procedures using spike-in experiments2006In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 7, no 300, p. 17-Article in journal (Refereed)
    Abstract [en]

    Background: Recently, a large number of methods for the analysis of microarray data have been proposed but there are few comparisons of their relative performances. By using so-called spike-in experiments, it is possible to characterize the analyzed data and thereby enable comparisons of different analysis methods.

    Results: A spike-in experiment using eight in-house produced arrays was used to evaluate established and novel methods for filtration, background adjustment, scanning, channel adjustment, and censoring. The S-plus package EDMA, a stand-alone tool providing characterization of analyzed cDNA-microarray data obtained from spike-in experiments, was developed and used to evaluate 252 normalization methods. For all analyses, the sensitivities at low false positive rates were observed together with estimates of the overall bias and the standard deviation. In general, there was a trade-off between the ability of the analyses to identify differentially expressed genes (i.e. the analyses' sensitivities) and their ability to provide unbiased estimators of the desired ratios. Virtually all analysis underestimated the magnitude of the regulations; often less than 50% of the true regulations were observed. Moreover, the bias depended on the underlying mRNA-concentration; low concentration resulted in high bias. Many of the analyses had relatively low sensitivities, but analyses that used either the constrained model (i.e. a procedure that combines data from several scans) or partial filtration (a novel method for treating data from so-called not-found spots) had with few exceptions high sensitivities. These methods gave considerable higher sensitivities than some commonly used analysis methods.

    Conclusion: The use of spike-in experiments is a powerful approach for evaluating microarray preprocessing procedures. Analyzed data are characterized by properties of the observed log-ratios and the analysis' ability to detect differentially expressed genes. If bias is not a major problem; we recommend the use of either the CM-procedure or partial filtration.

     

  • 20.
    Önskog, Jenny
    et al.
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology.
    Freyhult, Eva
    Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology.
    Landfors, Mattias
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology.
    Rydén, Patrik
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Hvidsten, Torgeir R
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology.
    Classification of microarrays: synergistic effects between normalization, gene selection and machine learning2011In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 12, no 1, article id 390Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Machine learning is a powerful approach for describing and predicting classes in microarray data. Although several comparative studies have investigated the relative performance of various machine learning methods, these often do not account for the fact that performance (e.g. error rate) is a result of a series of analysis steps of which the most important are data normalization, gene selection and machine learning.

    RESULTS: In this study, we used seven previously published cancer-related microarray data sets to compare the effects on classification performance of five normalization methods, three gene selection methods with 21 different numbers of selected genes and eight machine learning methods. Performance in term of error rate was rigorously estimated by repeatedly employing a double cross validation approach. Since performance varies greatly between data sets, we devised an analysis method that first compares methods within individual data sets and then visualizes the comparisons across data sets. We discovered both well performing individual methods and synergies between different methods.

    CONCLUSION: Support Vector Machines with a radial basis kernel, linear kernel or polynomial kernel of degree 2 all performed consistently well across data sets. We show that there is a synergistic relationship between these methods and gene selection based on the T-test and the selection of a relatively high number of genes. Also, we find that these methods benefit significantly from using normalized data, although it is hard to draw general conclusions about the relative performance of different normalization procedures.

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