Biomedical subjects

Markus Ringnér

Publications and source records attributed to Markus Ringnér.

16 recordsLinked to original sources

PubMed2006

Detection and identification of protein isoforms using cluster analysis of MALDI-MS mass spectra.

We describe an approach to screen large sets of MALDI-MS mass spectra for protein isoforms separated on two-dimensional electrophoresis gels. Mass spectra are matched against each other by utilizing extracted peak mass lists and hierarchical clustering. The output is presented as dendrograms in which protein isoforms cluster together. Clustering could be applied to mass spectra from different sample sets, dates, and instruments, revealed similarities between mass spectra, and was a useful tool to highlight peptide peaks of interest for further investigation. Shared peak masses in a cluster could be identified and were used to create novel peak mass lists suitable for protein identification using peptide mass fingerprinting. Complex mass spectra consisting of more than one protein were deconvoluted using information from other mass spectra in the same cluster. The number of peptide peaks shared between mass spectra in a cluster was typically found to be larger than the number of peaks that matched to calculated peak masses in databases, thus modified peaks are probably among the shared peptides. Clustering increased the number of peaks associated with a given protein.

Arabidopsis↗

PubMed2005

Folding free energies of 5'-UTRs impact post-transcriptional regulation on a genomic scale in yeast.

Using high-throughput technologies, abundances and other features of genes and proteins have been measured on a genome-wide scale in Saccharomyces cerevisiae. In contrast, secondary structure in 5'-untranslated regions (UTRs) of mRNA has only been investigated for a limited number of genes. Here, the aim is to study genome-wide regulatory effects of mRNA 5'-UTR folding free energies. We performed computations of secondary structures in 5'-UTRs and their folding free energies for all verified genes in S. cerevisiae. We found significant correlations between folding free energies of 5'-UTRs and various transcript features measured in genome-wide studies of yeast. In particular, mRNAs with weakly folded 5'-UTRs have higher translation rates, higher abundances of the corresponding proteins, longer half-lives, and higher numbers of transcripts, and are upregulated after heat shock. Furthermore, 5'-UTRs have significantly higher folding free energies than other genomic regions and randomized sequences. We also found a positive correlation between transcript half-life and ribosome occupancy that is more pronounced for short-lived transcripts, which supports a picture of competition between translation and degradation. Among the genes with strongly folded 5'-UTRs, there is a huge overrepresentation of uncharacterized open reading frames. Based on our analysis, we conclude that (i) there is a widespread bias for 5'-UTRs to be weakly folded, (ii) folding free energies of 5'-UTRs are correlated with mRNA translation and turnover on a genomic scale, and (iii) transcripts with strongly folded 5'-UTRs are often rare and hard to find experimentally.

5' Untranslated Regions↗

PubMed2005

Distinct genomic profiles in hereditary breast tumors identified by array-based comparative genomic hybridization.

Mutations in BRCA1 and BRCA2 account for a significant proportion of hereditary breast cancers. Earlier studies have shown that inherited and sporadic tumors progress along different somatic genetic pathways and that global gene expression profiles distinguish between these groups. To determine whether genomic profiles similarly discriminate among BRCA1, BRCA2, and sporadic tumors, we established DNA copy number profiles using comparative genomic hybridization to BAC-clone microarrays providing <1 Mb resolution. Tumor DNA was obtained from BRCA1 (n = 14) and BRCA2 (n = 12) mutation carriers, as well as sporadic cases (n = 26). Overall, BRCA1 tumors had a higher frequency of copy number alterations than sporadic breast cancers (P = 0.00078). In particular, frequent losses on 4p, 4q, and 5q in BRCA1 tumors and frequent gains on 7p and 17q24 in BRCA2 tumors distinguish these from sporadic tumors. Distinct amplicons at 3q27.1-q27.3 were identified in BRCA1 tumors and at 17q23.3-q24.2 in BRCA2 tumors. A homozygous deletion on 5q12.1 was found in a BRCA1 tumor. Using a set of 169 BAC clones that detect significantly (P < 0.001) different frequencies of copy number changes in inherited and sporadic tumors, these could be discriminated into separate groups using hierarchical clustering. By comparing DNA copy number and RNA expression for genes in these regions, several candidate genes affected by up- or down-regulation were identified. Moreover, using support vector machines, we correctly classified BRCA1 and BRCA2 tumors (P < 0.0000004 and 0.00005, respectively). Further validation may prove this tumor classifier to be useful for selecting familial breast cancer cases for further mutation screening, particularly, as these data can be obtained using archival tissue.

Breast Neoplasms↗

PubMed2005

Gene expression profiling demonstrates that TGF-beta1 signals exclusively through receptor complexes involving Alk5 and identifies targets of TGF-beta signaling.

Transforming growth factor-beta1 (TGF-beta) regulates cellular functions like proliferation, differentiation, and apoptosis. On the cell surface, TGF-beta binds to receptor complexes consisting of TGF-beta receptor type II (TbetaRII) and activin-like kinase receptor-5 (Alk5), and the downstream signaling is transduced by Smad and MAPK proteins. Recent data have shown that alternative receptor combinations aside from the classical pairing of TbetaRII/Alk5 can be relevant for TGF-beta signaling. We have screened for alternative receptors for TGF-beta and also for gene targets of TGF-beta signaling, by performing functional assays and microarray analysis in murine embryonic fibroblast (MEF) cell lines lacking Alk5. Data from TGF-beta-stimulated Alk5(-/-) cells show them to be completely unaffected by TGF-beta. Additionally, 465 downstream targets of Alk5 signaling were identified when comparing Alk5(-/-) or TGF-beta-stimulated Alk5(+/+) MEFs with unstimulated Alk5(+/+) cells. Our results demonstrate that, in MEFs, TGF-beta signals exclusively through complexes involving Alk5, and give insight to its downstream effector genes.

Activin Receptors, Type I↗

PubMed2004

Multiclass discovery in array data.

BACKGROUND: A routine goal in the analysis of microarray data is to identify genes with expression levels that correlate with known classes of experiments. In a growing number of array data sets, it has been shown that there is an over-abundance of genes that discriminate between known classes as compared to expectations for random classes. Therefore, one can search for novel classes in array data by looking for partitions of experiments for which there are an over-abundance of discriminatory genes. We have previously used such an approach in a breast cancer study. RESULTS: We describe the implementation of an unsupervised classification method for class discovery in microarray data. The method allows for discovery of more than two classes. We applied our method on two published microarray data sets: small round blue cell tumors and breast tumors. The method predicts relevant classes in the data sets with high success rates. CONCLUSIONS: We conclude that the proposed method is accurate and efficient in finding biologically relevant classes in microarray data. Additionally, the method is useful for quality control of microarray experiments. We have made the method available as a computer program.

Breast Neoplasms↗

PubMed2004

Microarray expression profiling in melanoma reveals a BRAF mutation signature.

We have used microarray gene expression profiling and machine learning to predict the presence of BRAF mutations in a panel of 61 melanoma cell lines. The BRAF gene was found to be mutated in 42 samples (69%) and intragenic mutations of the NRAS gene were detected in seven samples (11%). No cell line carried mutations of both genes. Using support vector machines, we have built a classifier that differentiates between melanoma cell lines based on BRAF mutation status. As few as 83 genes are able to discriminate between BRAF mutant and BRAF wild-type samples with clear separation observed using hierarchical clustering. Multidimensional scaling was used to visualize the relationship between a BRAF mutation signature and that of a generalized mitogen-activated protein kinase (MAPK) activation (either BRAF or NRAS mutation) in the context of the discriminating gene list. We observed that samples carrying NRAS mutations lie somewhere between those with or without BRAF mutations. These observations suggest that there are gene-specific mutation signals in addition to a common MAPK activation that result from the pleiotropic effects of either BRAF or NRAS on other signaling pathways, leading to measurably different transcriptional changes.

Amino Acid Substitution↗

PubMed2004

ACID: a database for microarray clone information.

SUMMARY: Array Close Information Database is an online database for information about microarray cDNA clones. For each clone, the database contents include assigned UniGene cluster(s), location in the full-length transcript, assigned gene ontology terms and position in the genome assembly. AVAILABILITY: http://bioinfo.thep.lu.se/acid.html

Animals↗

PubMed2004

Predicting continuous values of prognostic markers in breast cancer from microarray gene expression profiles.

The prognostic and treatment-predictive markers currently in use for breast cancer are commonly based on the protein levels of individual genes (e.g., steroid receptors) or aspects of the tumor phenotype, such as histological grade and percentage of cells in the DNA synthesis phase of the cell cycle. Microarrays have previously been used to classify binary classes in breast cancer such as estrogen receptor (ER)-alpha status. To test whether the properties and specific values of conventional prognostic markers are encoded within tumor gene expression profiles, we have analyzed 48 well-characterized primary tumors from lymph node-negative breast cancer patients using 6728-element cDNA microarrays. In the present study, we used artificial neural networks trained with tumor gene expression data to predict the ER protein values on a continuous scale. Furthermore, we determined a gene expression profile-directed threshold for ER protein level to redefine the cutoff between ER-positive and ER-negative classes that may be more biologically relevant. With a similar approach, we studied the prediction of other prognostic parameters such as percentage cells in the S phase of the cell cycle (SPF), histological grade, DNA ploidy status, and progesterone receptor status. Interestingly, there was a consistent reciprocal relationship in expression levels of the genes important for both ER and SPF prediction. This and similar studies may be used to increase our understanding of the biology underlying these markers as well as to improve the currently available prognostic markers for breast cancer.

Biomarkers↗

PubMed2003

The gene expression response of breast cancer to growth regulators: patterns and correlation with tumor expression profiles.

The effects of hormone and growth factor signaling on gene expression contribute significantly to breast tumorigenesis and disease progression; however, the targets of signaling networks associated with deregulated growth are not well understood. We defined the dynamic transcriptional effects elicited in MCF7, T-47D, and MDA-MB-436 breast cancer cell lines by nine regulators of growth and differentiation (17beta-estradiol, antiestrogens fulvestrant and tamoxifen, progestin R5020, antiprogestin RU486, all-trans-retinoic acid, epidermal growth factor, mitogen-activated protein/extracellular signal-regulated kinase 1/2 inhibitor U0126 and phorbol ester 12-O-tetradecanoylphorbol-13-acetate) and compared the patterns of gene regulation to published tumor expression profiles. The complex pattern of response to these agents revealed unexpected relationships between their effects, including a profound overlap in genes regulated by both steroids and epidermal growth factor, and striking overlaps between fulvestrant and all-trans-retinoic acid. Estrogen-responsive genes could be divided into two major clusters, only one of which is associated with cell proliferation. Gene ontology analysis was used to highlight functionally distinct biological responses to different mitogens. Significant correlations were identified between several clusters of drug-responsive genes and genes that discriminate estrogen receptor status or disease outcome in patient samples. The majority of estrogen receptor status discriminators were not responsive in our dataset and are therefore likely to reflect underlying differences in histogenesis and disease progression rather than growth factor signaling. This article highlights the overall impact at the gene expression level of diverse regulators of breast cancer growth and links the behavior of breast cancer cells in culture to important clinical properties of human breast tumors.

Algorithms↗

PubMed2003

Gene expression profile in multiple sclerosis patients and healthy controls: identifying pathways relevant to disease.

Multiple sclerosis (MS) and other T cell-mediated autoimmune diseases develop in individuals carrying a complex susceptibility trait, probably following exposure to various environmental triggers. Owing to the presumed weak influence of single genes on disease predisposition and the recognized genetic heterogeneity of autoimmune disorders in humans, candidate gene searches in MS have been difficult. In an attempt to identify molecular markers indicative of disease status rather than susceptibility genes for MS, we show that gene expression profiling of peripheral blood mononuclear cells by cDNA microarrays can distinguish MS patients from healthy controls. Our findings support the concept that the activation of autoreactive T cells is of primary importance for this complex organ-specific disorder and prompt further investigations on gene expression in peripheral blood cells aimed at characterizing disease phenotypes.

Adult↗

PubMed2003

Analyzing tumor gene expression profiles.

A brief introduction to high throughput technologies for measuring and analyzing gene expression is given. Various supervised and unsupervised data mining methods for analyzing the produced high-dimensional data are discussed. The main emphasis is on supervised machine learning methods for classification and prediction of tumor gene expression profiles. Furthermore, methods to rank the genes according to their importance for the classification are explored. The approaches are illustrated by exploratory studies using two examples of retrospective clinical data from routine tests; diagnostic prediction of small round blue cell tumors (SRBCT) of childhood and determining the estrogen receptor (ER) status of sporadic breast cancer. The classification performance is gauged using blind tests. These studies demonstrate the feasibility of machine learning-based molecular cancer classification.

Adult↗

PubMed2003

Microarray-based cancer diagnosis with artificial neural networks.

In recent years, the advent of experimental methods to probe gene expression profiles of cancer on a genome-wide scale has led to widespread use of supervised machine learning algorithms to characterize these profiles. The main applications of these analysis methods range from assigning functional classes of previously uncharacterized genes to classification and prediction of different cancer tissues. This article surveys the application of machine learning algorithms to classification and diagnosis of cancer based on expression profiles. To exemplify the important issues of the classification procedure, the emphasis of this article is on one such method, namely artificial neural networks. In addition, methods to extract genes that are important for the performance of a classifier, as well as the influence of sample selection on prediction results are discussed.

Algorithms↗

PubMed2002

Impact of DNA amplification on gene expression patterns in breast cancer.

Genetic changes underlie tumor progression and may lead to cancer-specific expression of critical genes. Over 1100 publications have described the use of comparative genomic hybridization (CGH) to analyze the pattern of copy number alterations in cancer, but very few of the genes affected are known. Here, we performed high-resolution CGH analysis on cDNA microarrays in breast cancer and directly compared copy number and mRNA expression levels of 13,824 genes to quantitate the impact of genomic changes on gene expression. We identified and mapped the boundaries of 24 independent amplicons, ranging in size from 0.2 to 12 Mb. Throughout the genome, both high- and low-level copy number changes had a substantial impact on gene expression, with 44% of the highly amplified genes showing overexpression and 10.5% of the highly overexpressed genes being amplified. Statistical analysis with random permutation tests identified 270 genes whose expression levels across 14 samples were systematically attributable to gene amplification. These included most previously described amplified genes in breast cancer and many novel targets for genomic alterations, including the HOXB7 gene, the presence of which in a novel amplicon at 17q21.3 was validated in 10.2% of primary breast cancers and associated with poor patient prognosis. In conclusion, CGH on cDNA microarrays revealed hundreds of novel genes whose overexpression is attributable to gene amplification. These genes may provide insights to the clonal evolution and progression of breast cancer and highlight promising therapeutic targets.

Breast Neoplasms↗

PubMed2002

Expression profiling to predict outcome in breast cancer: the influence of sample selection.

Gene expression profiling of tumors using DNA microarrays is a promising method for predicting prognosis and treatment response in cancer patients. It was recently reported that expression profiles of sporadic breast cancers could be used to predict disease recurrence better than currently available clinical and histopathological prognostic factors. Having observed an overlap in those data between the genes that predict outcome and those that predict estrogen receptor-alpha status, we examined their predictive power in an independent data set. We conclude that it may be important to define prognostic expression profiles separately for estrogen receptor-alpha-positive and estrogen receptor-alpha-negative tumors.

Breast Neoplasms↗

PubMed2002

Gene expression in inherited breast cancer.

Large proportions of hereditary breast cancers are due to mutations in the two breast cancer susceptibility genes BRCA1 and BRCA2. Considerable effort has gone into studying the function(s) of these tumor suppressor genes, both in attempts to better understand why individuals with these inherited mutations acquire breast (and ovarian) cancer and to potentially develop better treatment strategies. The advent of tools such as cDNA microarrays has enabled researchers to study global gene expression patterns in, for example, primary tumors, thus providing more comprehensive overviews of tumor development and progression. Our recent study (Hedenfalk et al., 2001) strongly supports the principle that genomic approaches to classification of hereditary breast cancers are possible, and that further studies will likely identify the most significant genes that discriminate between subgroups and may influence prognosis and treatment. A large number of hereditary breast cancer cases cannot be accounted for by mutations in these two genes and are believed to be due to as yet unidentified breast cancer predisposition genes (BRCAx). Subclassification of these non-BRCA1/2 breast cancers using cDNA microarray-based gene expression profiling, followed by linkage analysis and/or investigation of genomic alterations, may help in the recognition of novel breast cancer predisposition loci. To summarize, gene expression-based analysis of hereditary breast cancer can potentially be used for classification purposes, as well as to expand upon our knowledge of differences between different forms of hereditary breast cancer. Initial studies indicate that a patient's genotype does in fact leave an identifiable trace on her/his cancer's gene expression profile.

BRCA1 Protein↗

PubMed2002

Analyzing array data using supervised methods.

Pharmacogenomics is the application of genomic technologies to drug discovery and development, as well as for the elucidation of the mechanisms of drug action on cells and organisms. DNA microarrays measure genome-wide gene expression patterns and are an important tool for pharmacogenomic applications, such as the identification of molecular targets for drugs, toxicological studies and molecular diagnostics. Genome-wide investigations generate vast amounts of data and there is a need for computational methods to manage and analyze this information. Recently, several supervised methods, in which other information is utilized together with gene expression data, have been used to characterize genes and samples. The choice of analysis methods will influence the results and their interpretation, therefore it is important to be familiar with each method, its scope and limitations. Here, methods with special reference to applications for pharmacogenomics are reviewed.

Artificial Intelligence↗