Identification of Novel Candidate Oncogenes and Tumor Suppressors in Malignant Pleural Mesothelioma Using Large-Scale Transcriptional Profilin

The American Journal of Pathology has just published, and featured on its cover, the work of two MARF-funded (www.marf.org) researchers, Gavin J. Gordon and Raphael Bueno (see below).

With the support of MARF, Drs. Gordon and Bueno and their colleagues have developed a sophisticated network of molecular pathways identifying genes likely related to mesothelioma tumor development. This network, which could help identify targets for therapy, is featured on the Journals cover. June 02, 2005

American Journal of Pathology. 2005;166:1827-1840.)
) 2005 American Society for Investigative Pathology

http://ajp.amjpathol.org/cgi/content/abstract/166/6/1827

Gavin J. Gordon^*, Graham N. Rockwell^{[if gte vml 1]> , Roderick V. Jensen}[if gte vml 1]> , James G. Rheinwald^{[if gte vml 1]>} , Jonathan N. Glickman^¶, Joshua P. Aronson^*, Brian J. Pottorf^*, Matthew D. Nitz^*, William G. Richards^*, David J. Sugarbaker^* and Raphael Bueno^*

From the Thoracic Surgery Oncology Laboratory, the Division of Thoracic Surgery,^* and the Departments of Neurology,^{[if gte vml 1]>} Dermatology,^{[if gte vml 1]>} and Pathology,^¶ Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts; and the Department of Physics,^{[if gte vml 1]>} University of Massachusetts, Boston, Massachusetts

Malignant pleural mesothelioma (MPM) is a highly lethal, poorly understood neoplasm that is typically associated with asbestos exposure. We performed transcriptional profiling using high-density oligonucleotide microarrays containing [if gte vml 1]> 22,000 genes to elucidate potential molecular and pathobiological pathways in MPM using discarded human MPM tumor specimens (n = 40), normal lung specimens (n = 4), normal pleura specimens (n = 5), and MPM and SV40-immortalized mesothelial cell lines (n = 5). In global expression analysis using unsupervised clustering techniques, we found two potential subclasses of mesothelioma that correlated loosely with tumor histology. We also identified sets of genes with expression levels that distinguish between multiple tumor subclasses, normal and tumor tissues, and tumors with different morphologies. Microarray gene expression data were confirmed using quantitative reverse transcriptase-polymerase chain reaction and protein analysis for three novel candidate oncogenes (NME2, CRI1, and PDGFC) and one candidate tumor suppressor (GSN). Finally, we used bioinformatics tools (ie, software) to create and explore complex physiological pathways. Combined, all of these data may advance our understanding of mesothelioma tumorigenesis, pathobiology, or both.

*** POSTED JUNE 7, 2005 ***