Gene expression patterns within cell lines are predictive of chemosensitivity. Poster at American Association of Cancer Research 2006
Brian Z. Ring1, Stella Chang1, Robert S. Seitz2, Douglas T. Ross1
1Applied Genomics Inc., Sunnyvale, CA; 2Applied Genomics Inc., Huntsville, AL
Abstract #3128
While novel protein and gene-expression based methods of prognostication and tumor subclassification are beginning to affect clinical oncology, too little headway is being made in identifying effective treatments for these newly identified patient groups. To this end we used the sixty cell lines which comprise the National Cancer Institute's screen for anti-cancer drugs, measuring expression of 40000 genes via cDNA microarrays. We employed the publicly available growth inhibition (GI50) data for 41000 compounds and calculated Spearman correlations for all pairs of gene expression and compound sensitivity patterns across the cell lines. Genes with patterns of expression which highly correlate with compound sensitivity are candidates for possible clinical markers of drug efficacy, and potentially even causal effectors of drug action. Comparison of our gene expression data to other published measures of expression of these same cell lines confirmed that our expression data was well measured. Large scale features of the gene expression and chemosensitivity data, such as tissue of origin and other physiological factors do not seem to explain the majority of correlations measured. To test the predictive power of these correlated patterns, we also measure gene expression on an additional seven cell lines for which additional GI50 data was available from the National Cancer Institute and confirmed that expected correlations were observed.
 |
 |
| |
(500 KB PDF) |
|
