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Search Results: 1 - 10 of 1173 matches for " Ola Myklebost "
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Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data
Junbai Wang, Trond B?, Inge Jonassen, Ola Myklebost, Eivind Hovig
BMC Bioinformatics , 2003, DOI: 10.1186/1471-2105-4-60
Abstract: The proposed models were tested on four published datasets: (1) Leukemia (2) Colon cancer (3) Brain tumors and (4) NCI cancer cell lines. The models gave class prediction with markedly reduced error rates compared to other class prediction approaches, and the importance of feature selection on microarray data analysis was also emphasized.Our models identify marker genes with predictive potential, often better than other available methods in the literature. The models are potentially useful for medical diagnostics and may reveal some insights into cancer classification. Additionally, we illustrated two limitations in tumor classification from microarray data related to the biology underlying the data, in terms of (1) the class size of data, and (2) the internal structure of classes. These limitations are not specific for the classification models used.Generally, cancer classification has been based primarily on the morphological appearance of the tumor, but tumors with similar histopathological appearance can follow significantly different clinical courses and show different responses to therapy. Current microarray technology (such as high density oligonucleotide arrays and cDNA arrays) enables researchers to partially overcome this limitation, by enabling tumor subclass identification through global gene expression analysis. Research in this direction has gained wide attention, as illustrated by molecular classification of various clinical samples, such as in acute leukemia, human cancer cell lines and brain tumors [9,12,16], and in tumor subclass prediction, e.g. in diffuse large B-cell lymphoma and breast cancer [1,18]. Several analytical approaches have been applied for this task, such as k-nearest neighbours, weighted voting [9], support vector machines [23], partial least squares [14], hierarchical clustering, artificial neural networks [12], and supervised clustering [5]. Even if these approaches show promising results, classification of clinical samples remai
Clustering of the SOM easily reveals distinct gene expression patterns: results of a reanalysis of lymphoma study
Junbai Wang, Jan Delabie, Hans Aasheim, Erlend Smeland, Ola Myklebost
BMC Bioinformatics , 2002, DOI: 10.1186/1471-2105-3-36
Abstract: We tested the two-level analysis on public data from diffuse large B-cell lymphomas. The analysis easily distinguished major gene expression patterns without the need for supervision: a germinal center-related, a proliferation, an inflammatory and a plasma cell differentiation-related gene expression pattern. The first three patterns matched the patterns described in the original publication using supervised clustering analysis, whereas the fourth one was novel.Our study shows that by using SOM as an intermediate step to analyze genome-wide gene expression data, the gene expression patterns can more easily be revealed. The "expression display" by the SOM component plane summarises the complicated data in a way that allows the clinician to evaluate the classification options rather than giving a fixed diagnosis.The development and progression of cancer is accompanied by complex changes in the patterns of gene expression. That can be revealed by DNA microarrays analysis [1]. However, to reliably identify expression patterns associated with tumor type, prognosis or therapy, hundreds of samples need to be studied, and powerful data mining tools are needed. Microarray experiments are generally performed without a priori hypothesis. Therefore, the data mining tools have to be developed that reveal a maximum of information to generate new hypotheses [9] with minimal supervision. Hierarchical clustering is a frequently used method [2-4], but has a number of shortcomings [5,6]. Notably, the most important genes defining the branches of the clustering tree are not readily recognized, and important patterns can be lost due to the deterministic nature of clustering or the high dimensionality of data. To solve this problem, we propose a two-level analysis [14] for the study of complex gene expression data. This analysis summarizes the data by the SOM component plane, and then clusters the SOM to investigate the feature gene expression patterns. The SOM reduces the dimensionality
Characterization of Liposarcoma Cell Lines for Preclinical and Biological Studies
Eva W. Stratford,Russell Castro,Jeanette Daffinrud,Magne Sk rn,Silje Lauvrak,Else Munthe,Ola Myklebost
Sarcoma , 2012, DOI: 10.1155/2012/148614
Abstract: Liposarcoma cell lines represent in vitro models for studying disease mechanisms at the cellular level and for preclinical evaluation of novel drugs. To date there are a limited number of well-characterized models available. In this study, nine immortal liposarcoma cell lines were evaluated for tumor-forming ability, stem cell- and differentiation potential, and metastatic potential, with the aim to generate a well-characterized liposarcoma cell line panel. Detailed stem cell and differentiation marker analyses were also performed. Five of the liposarcoma cell lines were tumorigenic, forming tumors in mice. Interestingly, tumor-forming ability correlated with high proliferative capacity in vitro. All the cell lines underwent adipocytic differentiation, but the degree varied. Surprisingly, the expression of stem cell and differentiation markers did not correlate well with function. Overall, the panel contains cell lines suited for in vivo analyses (LPS141, SA-4, T778, SW872, and LISA-2), for testing novel drugs targeting cancer stem cells (LPS141) and for studying tumor progression and metastasis (T449 and T778).
DNA Copy Number Changes in Human Malignant Fibrous Histiocytomas by Array Comparative Genomic Hybridisation
Stine H. Kresse,Hege O. Ohnstad,Bodil Bjerkehagen,Ola Myklebost,Leonardo A. Meza-Zepeda
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0015378
Abstract: Malignant fibrous histiocytomas (MFHs), or undifferentiated pleomorphic sarcomas, are in general high-grade tumours with extensive chromosomal aberrations. In order to identify recurrent chromosomal regions of gain and loss, as well as novel gene targets of potential importance for MFH development and/or progression, we have analysed DNA copy number changes in 33 MFHs using microarray-based comparative genomic hybridisation (array CGH).
M-CGH: Analysing microarray-based CGH experiments
Junbai Wang, Leonardo A Meza-Zepeda, Stine H Kresse, Ola Myklebost
BMC Bioinformatics , 2004, DOI: 10.1186/1471-2105-5-74
Abstract: M-CGH is a MATLAB toolbox with a graphical user interface designed specifically for the analysis of array CGH experiments, with multiple approaches to ratio normalization. Specifically, the distributions of three classes of DNA copy numbers (gains, normal and losses) can be estimated using a maximum likelihood method. Amplicon boundaries are computed by either the fuzzy K-nearest neighbour method or a wavelet approach. The program also allows linking each genomic clone with the corresponding genomic information in the Ensembl database http://www.ensembl.org webcite.M-CGH, which encompasses the basic tools needed for analysing array CGH experiments, is freely available for academics http://www.uio.no/~junbaiw/mcgh webcite, and does not require any other MATLAB toolbox.In cancer, gene amplification and deletion frequently contribute to alterations in the expression of oncogenes and tumour-suppressor genes, respectively. Thus, detection and mapping of these DNA copy number changes are important for both the basic understanding of cancer and its diagnosis [1]. Comparative genomic hybridisation to DNA microarrays (array CGH) allows efficient, genome-wide analyses of relative genome copy number in a single experiment. In array CGH [1,2], copy numbers would be related to the Cy3:Cy5 fluorescence ratios (hereafter called CGH ratios) of the microarray targets bound to each probe spot. There are some public available tools for array CGH analysis, but they either only run in Excel [3] or the software does not support the pre-processing (filtering or normalization) of array CGH data [4]. Therefore, there is a need for tools, preferably platform independent, which are capable of assessing the quality of CGH arrays as well as identifying the DNA copy number changes and link these with relevant genome information. We describe here the development of M-CGH, a MATLAB toolbox for analysing CGH ratios, which has the ability to reliably locate the copy number changes.M-CGH can directly
Profound influence of microarray scanner characteristics on gene expression ratios: analysis and procedure for correction
Heidi Lyng, Azadeh Badiee, Debbie H Svendsrud, Eivind Hovig, Ola Myklebost, Trond Stokke
BMC Genomics , 2004, DOI: 10.1186/1471-2164-5-10
Abstract: All scanners showed a limited intensity range from 200 to 50 000 (mean spot intensity), for which the expression ratios were independent of PMT voltage. This usable intensity range was considerably less than the maximum detection range of the PMTs. The use of spot and background intensities outside this range led to errors in the ratios. The errors at high intensities were caused by saturation of pixel intensities within the spots. An algorithm was developed to correct the intensities of these spots, and, hence, extend the upper limit of the usable intensity range.It is suggested that the PMT voltage should be increased to avoid intensities of the weakest spots below the usable range, allowing the brightest spots to reach the level of saturation. Subsequently, a second set of images should be acquired with a lower PMT setting such that no pixels are in saturation. Reliable data for spots with saturation in the first set of images can easily be extracted from the second set of images by the use of our algorithm. This procedure would lead to an increase in the accuracy of the data and in the number of data points achieved in each experiment compared to traditional procedures.Microarray technology is widely used for large scale studies of gene expression levels in cells [1-3]. When spotted cDNA microarrays are used, the expression level is generally measured as a ratio between the fluorescence intensity of two cDNA samples. The samples are labeled with different fluorescent dyes and co-hybridized to an array of DNA probes. The fluorescence intensities are measured by imaging the array in an optical scanner. Many sources of variation are associated with each step of the experimental procedure [4-6]. Efforts have been made to optimize laboratory protocols and image analysis to increase the accuracy of the results [7-11]. Moreover, non-linear normalization methods have been developed to account for asymmetry in the data [12-14]. Little attention has, however, been paid to
Limitations of mRNA amplification from small-size cell samples
Vigdis Nygaard, Marit Holden, Anders L?land, Mette Langaas, Ola Myklebost, Eivind Hovig
BMC Genomics , 2005, DOI: 10.1186/1471-2164-6-147
Abstract: From expression data, TransCount provided estimates of absolute transcript concentrations in each examined sample. The results from TransCount were used to calculate the Pearson correlation coefficient between transcript concentrations for different sample sizes. The correlations were clearly transcript copy number dependent. A critical level was observed where stochastic fluctuations became significant. The analysis allowed us to pinpoint the gene specific number of transcript templates that defined the limit of reliability with respect to number of cells from that particular source. In the sample amplifying from 1000 cells, transcripts expressed with at least 121 transcripts/cell were statistically reliable and for 250 cells, the limit was 1806 transcripts/cell. Above these thresholds, correlation between our data sets was at acceptable values for reliable interpretation.These results imply that the reliability of any amplification experiment must be validated empirically to justify that any gene exists in sufficient quantity in the input material. This finding has important implications for any experiment where only extremely small samples such as single cell analyses or laser captured microdissected cells are available.Standard protocols for microarray analysis are generally based on samples with more than 1–5 μg of total RNA. However, there is an increasing interest in transcription profiling of small samples, as large amounts of material can be difficult, if not impossible, to obtain in both clinical and experimental settings. Fine needle aspirates (FNA) (~1–2 μg) and fine needle core biopsies (~2 μg of total RNA) offer feasible, atraumatic clinical sampling procedures of limited material. Advances in technology designed for selective collection of specialized cells such as laser capture microdissection (LCM), yields homogenous minute material for further analysis. Following standard protocols in a pilot study, Assersohn et al. [1] had to exclude 85% of the FN
Epigenetic Regulation and Functional Characterization of MicroRNA-142 in Mesenchymal Cells
Magne Sk?rn, Tale Bar?y, Eva Wessel Stratford, Ola Myklebost
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0079231
Abstract: The transcripts encoded by the microRNA mir-142 gene are highly active in hematopoietic cells, but expressed at low levels in many other cell types. Treatment with the demethylating agent 5-Aza-2′-deoxycytidine increased both the 1,636 nucleotide primary transcript and mature miR-142-5p/3p in mesenchymal cells, indicating that mir-142 is epigenetically repressed by DNA methylation. The transcription start site was determined to be located 1,205 base pairs upstream of the precursor sequence within a highly conserved CpG island. In addition, a second CpG island overlapped with the precursor. A TATA-box, several promoter-proximal elements and enrichment of conserved transcription factor binding sites within the first 100 base pairs upstream of the transcription start site, suggests that this region represents the core/proximal mir-142 promoter. Moreover, both CpG islands were heavily methylated in mesenchymal cells, having low levels of miR-142-5p/3p, and unmethylated in hematopoietic cells where both miRNAs were abundantly expressed. We show that treatment with 5-Aza-2′-deoxycytidine significantly reduced the DNA methylation of the upstream CpG island, which led to increased expression, and that in vitro DNA methylation of the upstream region of the mir-142 precursor repressed its transcriptional activity. When overexpressed, miR-142-5p/3p reduced proliferation of cells with epigenetic silencing of endogenous mir-142. This finding is interesting as miR-142-5p/3p have been reported to be deregulated in tumors of mesenchymal origin. We provide the first experimental evidence that transcription of mir-142 is directly repressed by DNA methylation. In addition, we discovered that the antisense strand of mir-142 might act as a precursor for functional mature antisense miRNAs. Thus, our study expands the current knowledge about the regulation of mir-142 and function of miR-142-5p/3p, and adds novel insight into the rapidly increasing field of microRNA regulation.
Characterization of Liposarcoma Cell Lines for Preclinical and Biological Studies
Eva W. Stratford,Russell Castro,Jeanette Daffinrud,Magne Sk?rn,Silje Lauvrak,Else Munthe,Ola Myklebost
Sarcoma , 2012, DOI: 10.1155/2012/148614
Abstract: Liposarcoma cell lines represent in vitro models for studying disease mechanisms at the cellular level and for preclinical evaluation of novel drugs. To date there are a limited number of well-characterized models available. In this study, nine immortal liposarcoma cell lines were evaluated for tumor-forming ability, stem cell- and differentiation potential, and metastatic potential, with the aim to generate a well-characterized liposarcoma cell line panel. Detailed stem cell and differentiation marker analyses were also performed. Five of the liposarcoma cell lines were tumorigenic, forming tumors in mice. Interestingly, tumor-forming ability correlated with high proliferative capacity in vitro. All the cell lines underwent adipocytic differentiation, but the degree varied. Surprisingly, the expression of stem cell and differentiation markers did not correlate well with function. Overall, the panel contains cell lines suited for in vivo analyses (LPS141, SA-4, T778, SW872, and LISA-2), for testing novel drugs targeting cancer stem cells (LPS141) and for studying tumor progression and metastasis (T449 and T778). 1. Introduction Liposarcoma is categorized into three main subtypes; well-differentiated/dedifferentiated liposarcomas (WD/DDLPSs), myxoid/round-cell liposarcomas, and undifferentiated high-grade pleomorphic liposarcomas (reviewed [1]). WDLPSs are local low-grade tumors, which do not metastasize unless they dedifferentiate. Progression to dedifferentiated liposarcoma (DDLPS) occurs in 25% of WDLPS [2], but the process is poorly understood. Ten to twenty?% of DDLPS undergo metastasis and overall mortality is 50–70% [2–4]. Both WDLPS and DDLPS have unique molecular characteristics, containing supernumerary ring and/or giant rod chromosomes containing amplified segments from 12q13–15 [5, 6]. The most common treatment for LPS is surgery, sometimes combined with radiotherapy and chemotherapy. Sensitivity to chemotherapy varies greatly between subtypes, with WD/DDLPS responding poorly (reviewed [1]). Well-characterized model systems are required for improved understanding of the molecular processes driving liposarcoma genesis, such as tumor formation, dedifferentiation, and metastasis and also for preclinical testing of novel therapies, but there is a lack of models, with only 1 LPS cell line (SW872) available commercially. However, a number of immortal LPS cell lines have been generated [7–11] and a small number of LPS cell lines and xenografts have been included in recent characterizations [12, 13]. This study initiates an effort in establishing an
Liposarcoma cells with aldefluor and CD133 activity have a cancer stem cell potential
Eva W Stratford, Russell Castro, Anna Wennerstr?m, Ruth Holm, Else Munthe, Silje Lauvrak, Bodil Bjerkehagen, Ola Myklebost
Clinical Sarcoma Research , 2011, DOI: 10.1186/2045-3329-1-8
Abstract: CSCs are described as a small population of tumour cells possessing stem-like properties, such as the ability to self-renew, as well as to differentiate into more mature cells that make up the bulk of the tumour, which usually to some extent resembles normal tissue. These cells are also referred to as tumour initiating [1].The CSCs are in many aspects similar to normal stem cells, and are thought to arise either when normal stem cells gain oncogenic mutations, which confer enhanced proliferation and lack of homeostatic control mechanisms, or alternatively when a progenitor or differentiated cell acquires mutations conferring de-differentiation to a malignant stem-like cell [2]. Since the integrity of stem cells is of critical importance for the organism, several mechanisms that ensure the survival of stem cells have evolved. These mechanisms include enhanced activity of membrane pumps which remove toxic substances [3], and enhanced activity of enzymes such as aldehyde dehydrogenase (ALDH), which confer resistance to toxic agents [4,5]. ALDH1 was also found to be implicated in regulating the stem cell fate in hematopoietic stem cells (HSCs) [6]. Properties and functions of normal stem cells can also be employed to enrich CSCs. In this respect, the Aldefluor assay, originally optimised to detect ALDH1 expression in HSCs [7] has been used to successfully enrich CSCs from breast cancer [8], leukemia [9], prostate cancer [10], colon cancer [11], bladder cancer [12] and liver cancer [13]. Because the Aldefluor substrate probably is not specific for this isoform [14], we refer only to ALDH-activity. ALDH-activity has also been associated with increased tumourigenicity in osteosarcoma [15]. Furthermore, several groups have reported that expression of ALDH is associated with high grade and poor prognosis in lung cancer [16], leukemia [9], ovarian cancer [17], breast cancer [8,18], colon cancer [11], prostate cancer [10], bladder cancer [12] and head and neck cancer [19]. ALD
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