Searching for Pharmacogenomic Markers: The Synergy between Omic and Hypothesis‐Driven Research

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.

Clinical course and outcome in children with acute lymphoblastic leukemia and asparaginase‐associated pancreatitis

“…Extensive ongoing research investigating the pharmacogenomics of chemotherapy-induced toxicities may help to identify patients at high risk of developing this complication. [29]…”

B.E.A.R. GeneInfo: A tool for identifying gene-related biomedical publications through user modifiable queries

“…This corpus provides a large dataset of gene information that can be mined for supplementary information related to genomic data analysis. Approaches have sought to create comprehensive datasets that identify all the relationships present in the biomedical literature between genes, genes and drug targets, and along metabolic pathways [9,27,28]. These approaches have used either curated or automated data compilation.…”

Conducting Scientific Research: Research Hypothesis and Null Hypothesis

“…In other words, many of the developments we saw in the past century with respect to microelectronics are being recapitulated in biology. All of these developments reflect what can be termed 'omic' research [1][2][3][4][5]. Omic research includes studies in genomics, proteomics, transcriptomics, kinomics (for the kinases), CHOmics (for the carbohydrates) and epigenomics, among many others.…”