Biomedical subjects

Markus Ringnér

Publications and source records attributed to Markus Ringnér.

7 recordsLinked to original sources

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↗