Cancer characterization and feature set extraction by discriminative margin clustering

BACKGROUND: A central challenge in the molecular diagnosis and treatment of cancer is to define a set of molecular features that, taken together, distinguish a given cancer, or type of cancer, from all normal cells and tissues. RESULTS: Discriminative margin clustering is a new technique for analyzing high dimensional quantitative datasets, specially applicable to gene expression data from microarray experiments related to cancer. The goal of the analysis is find highly specialized sub-types of a tumor type which are similar in having a small combination of genes which together provide a unique molecular portrait for distinguishing the sub-type from any normal cell or tissue. Detection of the products of these genes can then, in principle, provide a basis for detection and diagnosis of a cancer, and a therapy directed specifically at the distinguishing constellation of molecular features can, in principle, provide a way to eliminate the cancer cells, while minimizing toxicity to any normal cell. CONCLUSIONS: The new methodology yields highly specialized tumor subtypes which are similar in terms of potential diagnostic markers

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Cell-type specific gene expression profiles of leukocytes in human peripheral blood

BACKGROUND: Blood is a complex tissue comprising numerous cell types with distinct functions and corresponding gene expression profiles. We attempted to define the cell type specific gene expression patterns for the major constituent cells of blood, including B-cells, CD4+ T-cells, CD8+ T-cells, lymphocytes and granulocytes. We did this by comparing the global gene expression profiles of purified B-cells, CD4+ T-cells, CD8+ T-cells, granulocytes, and lymphocytes using cDNA microarrays. RESULTS: Unsupervised clustering analysis showed that similar cell populations from different donors share common gene expression profiles. Supervised analyses identified gene expression signatures for B-cells (427 genes), T-cells (222 genes), CD8+ T-cells (23 genes), granulocytes (411 genes), and lymphocytes (67 genes). No statistically significant gene expression signature was identified for CD4+ cells. Genes encoding cell surface proteins were disproportionately represented among the genes that distinguished among the lymphocyte subpopulations. Lymphocytes were distinguishable from granulocytes based on their higher levels of expression of genes encoding ribosomal proteins, while granulocytes exhibited characteristic expression of various cell surface and inflammatory proteins. CONCLUSION: The genes comprising the cell-type specific signatures encompassed many of the genes already known to be involved in cell-type specific processes, and provided clues that may prove useful in discovering the functions of many still unannotated genes. The most prominent feature of the cell type signature genes was the enrichment of genes encoding cell surface proteins, perhaps reflecting the importance of specialized systems for sensing the environment to the physiology of resting leukocytes

Dissecting eukaryotic translation and its control by ribosome density mapping

Translation of an mRNA is generally divided into three stages: initiation, elongation and termination. The relative rates of these steps determine both the number and position of ribosomes along the mRNA, but traditional velocity sedimentation assays for the translational status of mRNA determine only the number of bound ribosomes. We developed a procedure, termed Ribosome Density Mapping (RDM), that uses site-specific cleavage of polysomal mRNA followed by separation on a sucrose gradient and northern analysis, to determine the number of ribosomes associated with specified portions of a particular mRNA. This procedure allows us to test models for translation and its control, and to examine properties of individual steps of translation in vivo. We tested specific predictions from the current model for translational control of GCN4 expression in yeast and found that ribosomes were differentially associated with the uORFs elements and coding region under different growth conditions, consistent with this model. We also mapped ribosome density along the ORF of several mRNAs, to probe basic kinetic properties of translational steps in yeast. We found no detectable decline in ribosome density between the 5′ and 3′ ends of the ORFs, suggesting that the average processivity of elongation is very high. Conversely, there was no queue of ribosomes at the termination site, suggesting that termination is not very slow relative to elongation and initiation. Finally, the RDM results suggest that less frequent initiation of translation on mRNAs with longer ORFs is responsible for the inverse correlation between ORF length and ribosomal density that we observed in a global analysis of translation. These results provide new insights into eukaryotic translation in vivo