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The dawn of the post-genome era is leading to extraordinary opportunities in biomedicine. Our group has embarked on a major effort to integrate genomics, transcriptomics and proteomics for the profiling of tumor tissues, an approach we refer to as operomics. Our major goals are the molecular classification of tumors and the identification of markers for the early detection of cancer. Molecular analyses of tumors rely on microdissected tissues, which are simultaneously investigated for genomic, transcriptomic and proteomic changes. Genomic alterations in tumor cells being investigated include deletions, amplifications and methylation changes across the entire genome as well as point mutations in specific genes. Expression analysis at the RNA level is being undertaken using oligonucleotide and cDNA based microarrays. An important aspect of our approach is the large-scale identification and quantitative analysis of tumor proteins in whole cell lysates as well as in protein compartments. Protein separation strategies include two-dimensional polyacrylamide gel electrophoresis and liquid chromatography. Specific protein subsets, of interest include membrane proteins, secreted proteins and antigenic proteins as sources of biomarkers for early detection of cancer. Our current approach is illustrated with findings stemming from our studies of human gliomas.

We have developed a comprehensive approach to identifying molecular changes in lung cancer that includes both genomic and proteomic analyses. The related effort has produced a large amount of data pertaining to gene expression at the RNA and protein levels. As a result, we have constructed a database that contains protein expression data on lung cancer as well as other relevant data including DNA microarray derived data. A large number of proteins that are expressed in different types of lung cancer have been identified and have been correlated with the expression measures for their corresponding genes at the RNA level. The database is intended to facilitate our effort at developing novel classification schemes for lung cancer and the identification of novel markers for early diagnosis.

A common therapy for nonorgan-confined prostate cancer involves androgen deprivation. To develop a better understanding of the effect of androgen on prostatic cells, we have analyzed gene expression changes induced by dihydrotestosterone (DHT) in the androgen responsive prostate cancer line LNCaP, at both RNA and protein levels. Changes at the RNA level induced by DHT were determined by means of serial analysis of gene expression (SAGE), and protein profiling was done by means of quantitative two-dimensional polyacrylamide gel electrophoresis. Among 123,371 transcripts analyzed, a total of 28,844 distinct SAGE tags were identified representing 16,570 genes. Some 351 genes were significantly affected by DHT treatment at the RNA level (p < 0.05), of which 147 were induced and 204 repressed by androgen. In two independent experiments, the integrated intensity of 32 protein spots increased and 12 decreased at least two-fold in response to androgen, out of a total of 1031 protein spots analyzed. The change in intensity for most of the affected proteins identified could not be predicted based on the level of their corresponding RNA. Our study provides a global assessment of genes regulated by DHT and suggests a need for profiling at both RNA and protein levels for a comprehensive evaluation of patterns of gene expression.

Rapamycin has been shown to affect translation. We have utilized two complementary approaches to identify genes that are predominantly affected by rapamycin in Jurkat T cells. One was to compare levels of polysome-bound and total RNA using oligonucleotide microarrays complementary to 6,300 human genes. Another was to determine protein synthesis levels using two-dimensional PAGE. Analysis of expression changes at the polysome-bound RNA levels showed that translation of most of the expressed genes was partially reduced following rapamycin treatment. However, translation of 136 genes (6% of the expressed genes) was totally inhibited. This group included genes encoding RNA-binding proteins and several proteasome subunit members. Translation of a set of 159 genes (7%) was largely unaffected by rapamycin treatment. These genes included transcription factors, kinases, phosphatases, and members of the RAS superfamily. Analysis of [(35)S]methionine-labeled proteins from the same cell populations using two-dimensional PAGE showed that the integrated intensity of 111 of 830 protein spots changed in rapamycin-treated cells by at least 3-fold (70 increased, 41 decreased). We identified 22 affected protein spots representing protein products of 16 genes. The combined microarray and proteomic approach has uncovered novel genes affected by rapamycin that may be involved in its immunosuppressive effect and other genes that are not affected at the level of translation in a context of general inhibition of cap-dependent translation.