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

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]

A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery

“…In our first application of the COXEN algorithm, for example, cell sets 1 and 2 were the NCI-60 and BLA-40 cell panels, respectively; the step 1 drug activities were those assessed by the DTP in the NCI-60; the ''molecular characteristics'' in steps 2 and 4 were transcript expression levels, as assessed using Affymetrix HG-U133A microarrays (13); the algorithm in step 3 was significance analysis of microarrays (14) or similar statistical testing for differential expression; and step 5 was a coexpression extrapolation algorithm we developed. This coexpression extrapolation procedure is conceptually illustrated in SI Fig.…”

Tandem Analysis of Transcriptome and Proteome Changes after a Single Dose of Corticosteroid: A Systems Approach to Liver Function in Pharmacogenomics

“…Although information on mRNA expression changes helps in understanding mechanisms of drug action, some studies show that message expression changes may not correlate well with protein changes and hence might not accurately reflect drug effects (as the majority of pathway modulators and drug targets are proteins) (Nishizuka et al, 2003;Shankavaram et al, 2007). Hence characterization of changes in protein expression at the proteome level (along with gene expression profiling) will not only reveal the dynamic and temporal features of drug-induced protein changes, but will also provide rich biological information that may lead to improved understanding of diverse drug effects at both transcriptional and translational levels.…”

KRAS Mutation Status Is Associated with Enhanced Dependency on Folate Metabolism Pathways in Non–Small Cell Lung Cancer Cells

“…The statistical analyses were conducted using GraphPad Prism 5.0 software. analyzed microarray data from the publically available NCI-60 dataset from the study of Shankavaram and colleagues (23). In this study, basal gene expression across all NCI-60 cell lines was determined using gene expression microarrays (Affymetrix HGU-95).…”

“…23) basal gene expression microarray data for nonsquamous NSCLC cell lines from the study of Shankavaram and colleagues (23) were compiled and grouped according to KRAS mutation status. Genes showing significant differences (Student t test P < 0.05, >1.5-fold cutoff) in expression between KRASmutant and KRAS wild-type cells were analyzed using ingenuity pathway analysis (IPA) software (Ingenuity Systems).…”