Showing papers by "Pablo Tamayo published in 2000"

Expression analysis with oligonucleotide microarrays reveals that MYC regulates genes involved in growth, cell cycle, signaling, and adhesion

Abstract: MYC affects normal and neoplastic cell proliferation by altering gene expression, but the precise pathways remain unclear. We used oligonucleotide microarray analysis of 6,416 genes and expressed sequence tags to determine changes in gene expression caused by activation of c-MYC in primary human fibroblasts. In these experiments, 27 genes were consistently induced, and 9 genes were repressed. The identity of the genes revealed that MYC may affect many aspects of cell physiology altered in transformed cells: cell growth, cell cycle, adhesion, and cytoskeletal organization. Identified targets possibly linked to MYC's effects on cell growth include the nucleolar proteins nucleolin and fibrillarin, as well as the eukaryotic initiation factor 5A. Among the cell cycle genes identified as targets, the G1 cyclin D2 and the cyclin-dependent kinase binding protein CksHs2 were induced whereas the cyclin-dependent kinase inhibitor p21(Cip1) was repressed. A role for MYC in regulating cell adhesion and structure is suggested by repression of genes encoding the extracellular matrix proteins fibronectin and collagen, and the cytoskeletal protein tropomyosin. A possible mechanism for MYC-mediated apoptosis was revealed by identification of the tumor necrosis factor receptor associated protein TRAP1 as a MYC target. Finally, two immunophilins, peptidyl-prolyl cis-trans isomerase F and FKBP52, the latter of which plays a role in cell division in Arabidopsis, were up-regulated by MYC. We also explored pattern-matching methods as an alternative approach for identifying MYC target genes. The genes that displayed an expression profile most similar to endogenous Myc in microarray-based expression profiling of myeloid differentiation models were highly enriched for MYC target genes.

Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks

Abstract: In an effort to find gene regulatory networks and clusters of genes that affect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer agents needed to inhibit growth of those same cell lines. Comprehensive pair-wise correlations were calculated between gene expression and measures of agent susceptibility. Associations weaker than a threshold strength were removed, leaving networks of highly correlated genes and agents called relevance networks. Hypotheses for potential single-gene determinants of anticancer agent susceptibility were constructed. The effect of random chance in the large number of calculations performed was empirically determined by repeated random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantages of this methodology over alternative approaches, such as phylogenetic-type tree clustering and self-organizing maps.

Class prediction and discovery using gene expression data

Abstract: Classification of patient samples is a crucial aspect of cancer diagnosis and treatment. We present a method for classifying samples by computational analysis of gene expression data. We consider the classification problem in two parts: class discovery and class prediction. Class discovery refers to the process of dividing samples into reproducible classes that have similar behavior or properties, while class prediction places new samples into already known classes. We describe a method for performing class prediction and illustrate its strength by correctly classifying bone marrow and blood samples from acute leukemia patients. We also describe how to use our predictor to validate newly discovered classes, and we demonstrate how this technique could have discovered the key distinctions among leukemias if they were not already known. This proof-of-concept experiment paves the way for a wealth of future work on the molecular classification and understanding of disease.

Methods for classifying samples and ascertaining previously unknown classes

Abstract: Methods and apparatus for classifying or predicting the classes for samples based on gene expression are described. Also described are methods and apparatus for ascertaining or discovering new, previously unknown classes based on gene expression.

Methods and apparatus for analyzing gene expression data

Abstract: The present invention relates to methods and apparatus for grouping or clustering gene expression patterns from a plurality of genes. The invention utilizes a Self Organizing Map to cluster the gene expression patterns into groups that exhibit similar patterns. The clustering enables one to easily analyze gene expression data from potentially thousands of genes.

Methods for defining MYC target genes and uses thereof

Abstract: Identification of MYC target genes whose expression is either induced or repressed by c-myc induction in human fibroblasts is disclosed. Also disclosed are methods of inducing or repressing expression of MYC target genes.

Method and device for analyzing gene expression data

Abstract: PROBLEM TO BE SOLVED: To analyze gene expression data by receiving the gene expression data of data points, clustering the data points by the use of a self-systematized image so that the data points expressing similar patterns are clustered into clusters, respectively, and then outputting the clusters of the data points. SOLUTION: This method for grouping plural data points which are a series of gene expression values, respectively, in a computer system comprises a step for receiving the gene expression values of the data points, a step for selecting any data point exhibiting a non-prominent change in the gene expression values so as to leave working data points, a step for normalizing the gene expression values of the working data points, a step for grouping the data points by the use of a self-systematized image so that the data points expressing similar patterns are grouped into clusters, respectively, and a step for outputting the groups of the data points. Thereby, a large quantity of the sets of the gene expression patterns can be analyzed.