Biomedical Engineering Reference
In-Depth Information
IDENTIFICATION OF TUMOR-SPECIFIC CAMs
RESPONSIBLE FOR DRUG RESISTANCE
Dif erent tumor types can exhibit dif erential responses to a given drug, irrespective
of their tissue of origin, partly because of dif erential expression of CAMs (Stein
et al
. 2004, 2005). Stein
et al
. used an approach called 'intractability measurement'
that quantitatively dei nes how tumors from dif erent tissues are dif erentially
sensitive to currently used chemotherapies. h ey used a bioinformatics approach
to illustrate how CAMs are involved in tumor intractability. To measure the
intractability, they used survival data for dif 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 dif erent tumor types to various
drugs as surveyed from literature (Stein
et al
. 2004, 2005). h 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 dif erent tumor types to identify
molecules that could predict the intractability of tumors from dif erent tissues.
h 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). h 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). h 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.
CONCLUSION
h e identii cation of novel CAMs involved in tumor progression and metastasis
becomes important because of their role in various processes of malignancy.
Identii cation of tumor-associated CAMs by experimental biology techniques such
as RT-PCR and chromatography is time consuming and low-throughput. However,
the search for CAMs involved in various processes including tumorigenicity,
metastasis and organ-specii c homing can be abetted by bioinformatics analysis of
high-throughput data generated from various models of cancer. Nevertheless, one
must bear in mind that the results obtained from the high-throughput techniques
and bioinformatics analysis are limited to the tumor models used. Moreover,
predictions made by bioinformatic techniques need to be validated using proper
experimental techniques. Because each high-throughput technique has its own
limitations, we suggest an integrated bioinformatics approach using dif erent
