Proteomic profiling of the cancer microenvironment by antibody arrays.

Critical changes in protein expression that enable tumors to initiate and progress originate in the local tissue microenvironment, and there are increasing indications that these microenvironmental alterations in protein expression play critical roles in shaping and directing this process. As a model to better understand how patterns of protein expression shape the tissue microenvironment, we analyzed protein expression in tissue derived from squamous cell carcinoma of the oral cavity through an antibody microarray approach for high-throughput proteomic analysis. Utilizing laser capture microdissection to procure total protein from specific microscopic cellular populations, we demonstrate that quantitative, and potentially qualitative, differences in expression patterns of multiple proteins within epithelial cells reproducibly correlate with oral cavity tumor progression. Furthermore, differential expression of multiple proteins was also found in stromal cells surrounding and adjacent to regions of diseased epithelium that directly correlated with tumor progression of the epithelium. Most of the proteins identified in both cell types are involved in signal transduction pathways, thus we hypothesize that extensive molecular communication involving complex cellular signaling between epithelium and stroma play a key role in driving oral cavity cancer progression.

Development of a prostate cDNA microarray and statistical gene expression analysis package.

A cDNA microarray comprising 5184 different cDNAs spotted onto nylon membrane filters was developed for prostate gene expression studies. The clones used for arraying were identified by cluster analysis of > 35 000 prostate cDNA library-derived expressed sequence tags (ESTs) present in the dbEST database maintained by the National Center for Biotechnology Information. Total RNA from two cell lines, prostate line 8.4 and melanoma line UACC903, was used to make radiolabeled probe for filter hybridizations. The absolute intensity of each individual cDNA spot was determined by phosphorimager scanning and evaluated by a bioinformatics package developed specifically for analysis of cDNA microarray experimentation. Results indicated 89% of the genes showed intensity levels above background in prostate cells compared with only 28% in melanoma cells. Replicate probe preparations yielded results with correlation values ranging from r = 0.90 to 0.93 and coefficient of variation ranging from 16 to 28%. Findings indicate that among others, the keratin 5 and vimentin genes were differentially expressed between these two divergent cell lines. Follow-up northern blot analysis verified these two expression changes, thereby demonstrating the reliability of this system. We report the development of a cDNA microarray system that is sensitive and reliable, demonstrates a low degree of variability, and is capable of determining verifiable gene expression differences between two distinct human cell lines. This system will prove useful for differential gene expression analysis in prostate-derived cells and tissue.

Analysis of gene expression in mutiple sclerosis lesions using cDNA microarrays.

In multiple sclerosis (MS) patients, a coordinated attack of the immune system against the primary constituents of oligodendrocytes and/or the myelin sheath of oligodendrocytes results in the formation of lesions in the brain and spinal cord. Thus far, however, a limited number of genes that potentially contribute to lesion pathology have been identified. Using cDNA microarray technology, we have performed experiments on MS tissue monitoring the expression pattern of over 5,000 genes and compared the gene expression profile of normal white matter with that found in acute lesions from the brain of a single MS patient. Sixty-two differentially expressed genes were identified, including the Duffy chemokine receptor, interferon regulatory factor-2, and tumor necrosis factor alpha receptor-2 among others. Thus, cDNA microarray technology represents a powerful new tool for the identification of genes not previously associated with the MS disease process.