Abstract: With 35,000 genes and hundreds of thousands of protein states to
identify, correlate, and understand, it no longer suffices to rely on studies
of one gene, gene product, or process at a time. We have entered the ``omic''
era in biology. But large-scale omic studies of cellular molecules in aggregate
rarely can answer interesting questions without the assistance of information
from traditional hypothesis-driven research. The two types of science are
synergistic. A case in point is the set of pharmacogenomic studies that we and
our collaborators have done with the 60 human cancer cell lines of the National
Cancer Institute's drug discovery program. Those cells (the NCI-60) have been
characterized pharmacologically with respect to their sensitivity to
>70,000 chemical compounds. We are further characterizing
them at the DNA, RNA, protein, and functional levels. Our major aim is to
identify pharmacogenomic markers that can aid in drug discovery and design, as
well as in individualization of cancer therapy. The bioinformatic and
chemoinformatic challenges of this study have demanded novel methods for
analysis and visualization of high-dimensional data. Included are the
color-coded ``clustered image map'' and also the MedMiner program package,
which captures and organizes the biomedical literature on gene-gene and
gene-drug relationships. Microarray transcript expression studies of the 60
cell lines reveal, for example, a gene-drug correlation with potential clinical
implications -- that between the asparagine synthetase gene and the enzyme-drug
L-asparaginase in ovarian cancer cells.
