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Spotlight on Molecular Profiling Article

Transcript and protein expression profiles of the NCI-60 cancer cell panel: an integromic microarray study

¹ Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, Maryland; ² Office of Information Technology, Center for Drug Evaluation and Research, Food and Drug Administration, Rockville, Maryland; ³ Gene Logic, Inc., Gaithersburg, Maryland; ⁴ Science Applications International Corp.-National Cancer Institute-Frederick Cancer Research and Development Center, Frederick, Maryland; and ⁵ George Mason University, Manassas, Virginia

Requests for reprints: John N. Weinstein, Genomics and Bioinformatics Group, Laboratory of Molecular Pharmacology, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892. Phone: 301-496-9571. E-mail: weinstein{at}dtpax2.ncifcrf.gov

Abstract

To evaluate the utility of transcript profiling for prediction of protein expression levels, we compared profiles across the NCI-60 cancer cell panel, which represents nine tissues of origin. For that analysis, we present here two new NCI-60 transcript profile data sets (A based on Affymetrix HG-U95 and HG-U133A chips; Affymetrix, Santa Clara, CA) and one new protein profile data set (based on reverse-phase protein lysate arrays). The data sets are available online at http://discover.nci.nih.gov in the CellMiner program package. Using the new transcript data in combination with our previously published cDNA array and Affymetrix HU6800 data sets, we first developed a "consensus set" of transcript profiles based on the four different microarray platforms. Using that set, we found that 65% of the genes showed statistically significant transcript-protein correlation, and the correlations were generally higher than those reported previously for panels of mammalian cells. Using the predictive analysis of microarray nearest shrunken centroid algorithm for functional prediction of tissue of origin, we then found that (a) the consensus mRNA set did better than did data from any of the individual mRNA platforms and (b) the protein data seemed to do somewhat better (P = 0.027) on a gene-for-gene basis in this particular study than did the consensus mRNA data, but both did well. Analysis based on the Gene Ontology showed protein levels of structure-related genes to be well predicted by mRNA levels (mean r = 0.71). Because the transcript-based technologies are more mature and are currently able to assess larger numbers of genes at one time, they continue to be useful, even when the ultimate aim is information about proteins. [Mol Cancer Ther 2007;6(3):820–32]

Grant support: Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research. Cells for the various profiling studies were supplied under material transfer agreement by the NCI Developmental Therapeutics Program.

Note: Current address for S. Nishizuka: Molecular Therapeutics Program, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD; current address for D. Morita: Department of Pathology II, National Defense Medical College, Saitama, Japan; current address for U. Scherf: Division of Microbiology Devices, Center for Devices and Radiological Health, Food and Drug Administration, Rockville, MD; current address for J. Cossman: The Critical Path Institute, Rockville, MD and Tucson, AZ; current address for J.K. Lee: Department of Public Health Sciences, University of Virginia, Charlottesville, VA.

⁶ http://discover.nci.nih.gov

⁷ http://abs.cit.nih.gov/index.html

⁸ Supplementary material for this article are available at Molecular Cancer Therapeutics Online (http://mct.aacrjournals.org/).

⁹ http://www-stat.stanford.edu/~tibs/PAM/index.html

Received 10/10/06; revised 12/21/06; accepted 2/ 1/07.

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