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A protein expression database for the molecular pharmacology of cancer
Author(s) -
Myers Timothy G.,
Anderson N. Leigh,
Waltham Mark,
Li Guang,
Buolamwini John K.,
Scudiero Dominic A.,
Paull Kenneth D.,
Sausville Edward A.,
Weinstein John N.
Publication year - 1997
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.1150180351
Subject(s) - database , computational biology , biology , cancer , cancer cell , protein expression , cell growth , genetics , gene , computer science
In the last six years, the Developmental Therapeutics Program (DTP) of the US National Cancer Institute (NCI) has screened over 60,000 chemical compounds and a larger number of natural product extracts for their ability to inhibit growth of 60 different cancer cell lines representing different organs of origin. Whereas inhibition of the growth of one cancer cell type gives no information on drug specificity, the relative growth inhibitory activities against 60 different cells constitute patterns that encode detailed information on mechanisms of action and resistance (as reviewed in Boyd and Paull, Drug Devel. Res. 1995, 34, 19-109 and Weinstein et al., Science 1997, 275, 343-349). In order to correlate the patterns of activity with properties of the cells, we and other laboratories are characterizing the cells with respect to a large number of factors at the DNA, mRNA, and protein levels. As part of that effort, we have developed a two-dimensional gel electrophoresis (2-DE) protein expression database covering all 60 cell types (Buolamwini et al., submitted). Here we present analyses of the correlations among protein spots (i) in terms of their patterns of expression and (ii) in terms of their apparent relationships to the pharmacology of a set of 3989 screened compounds. The correlations tend to be stronger for the latter than for the former, suggesting that the spots have more robust signatures in terms of the pharmacology than in terms of expression levels. Links to pertinent databases and tools of analysis will be updated progressively at http:@www.nci.nih.gov/intra/lmp/jnwbio.htm and http:@epnwsl.ncifcrf.gov:2345/dis3d/dtp.++ +html.