Paul S. Meltzer

Bio: Paul S. Meltzer is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Cancer & Gene expression profiling. The author has an hindex of 108, co-authored 469 publications receiving 51064 citations. Previous affiliations of Paul S. Meltzer include University of Maryland, Baltimore.

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

Abstract: The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification. Expression of several of these genes has been reported in SRBCTs, but most have not been associated with these cancers. To test the ability of the trained ANN models to recognize SRBCTs, we analyzed additional blinded samples that were not previously used for the training procedure, and correctly classified them in all cases. This study demonstrates the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy.

Molecular classification of cutaneous malignant melanoma by gene expression profiling

Abstract: The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

Expression profiling using cdna microarrays

Abstract: cDNA microarrays are capable of profiling gene expression patterns of tens of thousands of genes in a single experiment. DNA targets, in the form of 3' expressed sequence tags (ESTs), are arrayed onto glass slides (or membranes) and probed with fluorescent- or radioactively-labelled cDNAs. Here, we review technical aspects of cDNA microarrays, including the general principles, fabrication of the arrays, target labelling, image analysis and data extraction, management and mining.

Vascular Channel Formation by Human Melanoma Cells in Vivo and in Vitro: Vasculogenic Mimicry

Abstract: Tissue sections from aggressive human intraocular (uveal) and metastatic cutaneous melanomas generally lack evidence of significant necrosis and contain patterned networks of interconnected loops of extracellular matrix. The matrix that forms these loops or networks may be solid or hollow. Red blood cells have been detected within the hollow channel components of this patterned matrix histologically, and these vascular channel networks have been detected in human tumors angiographically. Endothelial cells were not identified within these matrix-embedded channels by light microscopy, by transmission electron microscopy, or by using an immunohistochemical panel of endothelial cell markers (Factor VIII-related antigen, Ulex, CD31, CD34, and KDR[Flk-1]). Highly invasive primary and metastatic human melanoma cells formed patterned solid and hollow matrix channels (seen in tissue sections of aggressive primary and metastatic human melanomas) in three-dimensional cultures containing Matrigel or dilute Type I collagen, without endothelial cells or fibroblasts. These tumor cell-generated patterned channels conducted dye, highlighting looping patterns visualized angiographically in human tumors. Neither normal melanocytes nor poorly invasive melanoma cells generated these patterned channels in vitro under identical culture conditions, even after the addition of conditioned medium from metastatic pattern-forming melanoma cells, soluble growth factors, or regimes of hypoxia. Highly invasive and metastatic human melanoma cells, but not poorly invasive melanoma cells, contracted and remodeled floating hydrated gels, providing a biomechanical explanation for the generation of microvessels in vitro. cDNA microarray analysis of highly invasive versus poorly invasive melanoma tumor cells confirmed a genetic reversion to a pluripotent embryonic-like genotype in the highly aggressive melanoma cells. These observations strongly suggest that aggressive melanoma cells may generate vascular channels that facilitate tumor perfusion independent of tumor angiogenesis.

KEGG: Kyoto Encyclopedia of Genes and Genomes

Abstract: Kyoto Encyclopedia of Genes and Genomes (KEGG) is a knowledge base for systematic analysis of gene functions in terms of the networks of genes and molecules. The major component of KEGG is the PATHWAY database that consists of graphical diagrams of biochemical pathways including most of the known metabolic pathways and some of the known regulatory pathways. The pathway information is also represented by the ortholog group tables summarizing orthologous and paralogous gene groups among different organisms. KEGG maintains the GENES database for the gene catalogs of all organisms with complete genomes and selected organisms with partial genomes, which are continuously re-annotated, as well as the LIGAND database for chemical compounds and enzymes. Each gene catalog is associated with the graphical genome map for chromosomal locations that is represented by Java applet. In addition to the data collection efforts, KEGG develops and provides various computational tools, such as for reconstructing biochemical pathways from the complete genome sequence and for predicting gene regulatory networks from the gene expression profiles. The KEGG databases are daily updated and made freely available (http://www.genome.ad.jp/kegg/).

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes

Abstract: Gene-expression analysis is increasingly important in biological research, with real-time reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem. We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data. The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

Abstract: Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.