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

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.

AbMiner: a bioinformatic resource on available monoclonal antibodies and corresponding gene identifiers for genomic, proteomic, and immunologic studies.

BACKGROUND: Monoclonal antibodies are used extensively throughout the biomedical sciences for detection of antigens, either in vitro or in vivo. We, for example, have used them for quantitation of proteins on "reverse-phase" protein lysate arrays. For those studies, we quality-controlled > 600 available monoclonal antibodies and also needed to develop precise information on the genes that encode their antigens. Translation among the various protein and gene identifier types proved non-trivial because of one-to-many and many-to-one relationships. To organize the antibody, protein, and gene information, we initially developed a relational database in Filemaker for our own use. When it became apparent that the information would be useful to many other researchers faced with the need to choose or characterize antibodies, we developed it further as AbMiner, a fully relational web-based database under MySQL, programmed in Java. DESCRIPTION: AbMiner is a user-friendly, web-based relational database of information on > 600 commercially available antibodies that we validated by Western blot for protein microarray studies. It includes many types of information on the antibody, the immunogen, the vendor, the antigen, and the antigen's gene. Multiple gene and protein identifier types provide links to corresponding entries in a variety of other public databases, including resources for phosphorylation-specific antibodies. AbMiner also includes our quality-control data against a pool of 60 diverse cancer cell types (the NCI-60) and also protein expression levels for the NCI-60 cells measured using our high-density "reverse-phase" protein lysate microarrays for a selection of the listed antibodies. Some other available database resources give information on antibody specificity for one or a couple of cell types. In contrast, the data in AbMiner indicate specificity with respect to the antigens in a pool of 60 diverse cell types from nine different tissues of origin. CONCLUSION: AbMiner is a relational database that provides extensive information from our own laboratory and other sources on more than 600 available antibodies and the genes that encode the antibodies' antigens. The data will be made freely available at http://discover.nci.nih.gov/abminer.

Proteomic profiling of the NCI-60 cancer cell lines using new high-density reverse-phase lysate microarrays.

Because most potential molecular markers and targets are proteins, proteomic profiling is expected to yield more direct answers to functional and pharmacological questions than does transcriptional profiling. To aid in such studies, we have developed a protocol for making reverse-phase protein lysate microarrays with larger numbers of spots than previously feasible. Our first application of these arrays was to profiling of the 60 human cancer cell lines (NCI-60) used by the National Cancer Institute to screen compounds for anticancer activity. Each glass slide microarray included 648 lysate spots representing the NCI-60 cell lines plus controls, each at 10 two-fold serial dilutions to provide a wide dynamic range. Mouse monoclonal antibodies and the catalyzed signal amplification system were used for immunoquantitation. The signal levels from the >30,000 data points for our first 52 antibodies were analyzed by using p-scan and a quantitative dose interpolation method. Clustered image maps revealed biologically interpretable patterns of protein expression. Among the principal early findings from these arrays were two promising pathological markers for distinguishing colon from ovarian adenocarcinomas. When we compared the patterns of protein expression with those we had obtained for the same genes at the mRNA level by using both cDNA and oligonucleotide arrays, a striking regularity appeared: cell-structure-related proteins almost invariably showed a high correlation between mRNA and protein levels across the NCI-60 cell lines, whereas non-cell-structure-related proteins showed poor correlation.

Diagnostic markers that distinguish colon and ovarian adenocarcinomas: identification by genomic, proteomic, and tissue array profiling.

Colon and ovarian cancers can be difficult to distinguish in the abdomen, and the distinction is important because it determines which drugs will be used for therapy. To identify molecular markers for that differential diagnosis, we developed a multistep protocol starting with the 60 human cancer cell lines used by the National Cancer Institute to screen for new anticancer agents. The steps included: (a) identification of candidate markers using cDNA microarrays; (b) verification of clone identities by resequencing; (c) corroboration of transcript levels using Affymetrix oligonucleotide chips; (d) quantitation of protein expression by "reverse-phase" protein microarray; and (e) prospective validation of candidate markers on clinical tumor sections in tissue microarrays. The two best candidates identified were villin for colon cancer cells and moesin for ovarian cancer cells. Because moesin stained stromal elements in both types of cancer, it would probably not have been identified as a marker if we had started with mRNA or protein profiling of bulk tumors. Villin appears at least as useful as the currently used colon cancer marker cytokeratin 20, and moesin also appears to have utility. The multistep process introduced here has the potential to produce additional markers for cancer diagnosis, prognosis, and therapy.