ABSTRACT

Diff erent tumor types can exhibit diff erential responses to a given drug, irrespective of their tissue of origin, partly because of diff erential expression of CAMs (Stein et al. 2004, 2005). Stein et al. used an approach called ‘intractability measurement’ that quantitatively defi nes how tumors from diff erent tissues are diff erentially sensitive to currently used chemotherapies. Th ey used a bioinformatics approach to illustrate how CAMs are involved in tumor intractability. To measure the intractability, they used survival data for diff erent types of tumors from the Survival Epidemiology and End Results (SEER) project (Ries et al. 1983). Treatment success was gauged by response rates of diff erent tumor types to various drugs as surveyed from literature (Stein et al. 2004, 2005). Th eir analysis showed pancreas, liver, lung and colon as the most intractable cancers, breast, ovary and prostate as intermediately intractable cancers, and testis as the least intractable tumor. Based on the evidence, they performed bioinformatics analysis using serial analysis of gene expression (SAGE) databases and diff erent tumor types to identify molecules that could predict the intractability of tumors from diff erent tissues. Th ey found numerous genes that were either overexpressed or underexpressed in intractable tumors compared to tractable tumors. Later, for each tissue, they performed correlation analysis of each gene to the SEER 5-year ‘distant tumors’ survival numbers (Stein et al. 2004). Th ey performed similar analysis using cDNA gene expression microarray data and SEER survival data. Based on the analysis, they found that most of the genes that correlate negatively with survival in intractable tumors were CAMs and cytoskeletal genes (Stein et al. 2005). Th e survival outcome and intractability measures from this study suggest that CAMs are responsible for drug resistance found in poor survival tumors, irrespective of their tissue of origin.