Publication:
Rubinstein, L.V., Shoemaker, R.H., Paull, K.D., Simon, R.M.,
Tosini, S., Skehan, P., Scudiero, D.A., Monks, A., and Boyd,
M.R.: Comparison of in vitro anticancer drug screening data
generated with a tetrazolium assay versus a protein assay against
a diverse panel of human tumor cell lines. J. Nat. Cancer
Inst. 82: 1113-1118, 1990.
Abstract:
The National Cancer Institute (NCI) is implementing a
large-scale in vitro drug-screening program that requires a very
efficient automated assay of drug effects on tumor cell viability
or growth. Many laboratories worldwide have adopted a
microculture assay based on metabolic reduction of
3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide
(MTT). However, because of certain technical advantages to use of
the protein-binding dye sulforhodamine B (SRB) in a large-scale
screening application, a detailed comparison of data generated by
each type of assay was undertaken. The MTT and SRB assays were
each used to test 197 compounds, on simultaneous days, against up
to 38 human tumor cell lines representing seven major tumor
categories. On subsequent days, 38 compounds were retested with
the SRB assay and 25 compounds were retested with the MTT assay.
For each of these three comparisons, we tabulated the differences
between the two assays in the ratios of test group values to
control values (T/C) for cell survival; calculated correlation
coefficients for various T/C ratios; and estimated the bivariate
distribution of the values for IC50 (concentration of drug
resulting in T/C values of 50%, or 50% growth inhibition) for the
two assays. The results indicate that under the experimental
conditions used and within the limits of the data analyses, the
assays perform similarly. Because the SRB assay has practical
advantages for large-scale screening, however, it has been
adopted for routine use in the NCI in vitro antitumor screen.
Publication:
Hodes, L., Paull, K., Koutsoukos, A., Rubinstein, L.: Measures
of selectivity and similarity for drug responses in a panel of
human tumor cell lines. J. Biopharm.
Statist. 2: 31-48, 1992.
Abstract:
Information theory is used to provide a measure of
selectivity, i.e., the degree to which a drug has preferential
toxicity or growth inhibition for one or a few cell lines from a
large panel. The selectivity measure is intended to complement a
measure of differential growth inhibition in evaluating the drug
development potential of a new compound. Also, a similarity
measure obtained from information theory is used to classify
drugs according to their pattern of responses on the panel. Some
structure-activity relations emerge. This work is applied to 176
agents selected to be tested by the National Cancer Institute in
about 50 cell lines.
Publication:
Weinstein, J.N., Kohn, K.W., Grever, M.R., Viswanadhan, V.N.,
Rubinstein, L.V., Monks, A., Scudiero, D.A., Welch L.,
Koutsoukos, A., Paull, K.D.: Neural computing in cancer drug
development: Predicting mechanism of action, Science
258: 447-451, 1992.
Abstract:
Described here are neural networks capable of predicting a
drug's mechanism of action from its pattern of activity against a
panel of 60 malignant cell lines in the National Cancer
Institute's drug screening program. Given six possible classes of
mechanism, the network misses the correct category for only 12
out of 141 agents (8.5 percent), whereas linear discriminant
analysis, a standard statistical technique, misses 20 out of 141
(14.2 percent). The success of the neural net indicates several
things. (i) The cell line response patterns are rich in
information about mechanism. (ii) Appropriately designed neural
networks can make effective use of that information. (iii)
Trained networks can be used to classify prospectively the more
than 10,000 agents per year tested by the screening program.
Related networks, in combination with classical statistical
tools, will help in a variety of ways to move new anticancer
agents through the pipeline from in vitro studies to clinical
application.
Publication:
Koutsoukos, A.D. Rubinstein, L.V., Faraggi, D., Simon, R.M.,
Kalyandrug, S., Weinstein, J.N., Paull, K.D., Kohn, K.W.,
Discrimination techniques applied to the NCI in vitro antitumor
drug screen: predicting biochemical mechanism of action, Stat.
in Med. 13: 719-730, 1994.
Abstract:
The National Cancer Institute currently tests approximately
400 compounds per week against a panel of human tumour cell lines
in order to identify potential anti-cancer drugs. We describe
several approaches, based on these in vitro data, to the problem
of identifying the primary biochemical mechanism of action of a
compound. Using linear and non-parametric discriminant procedures
and cross-validation, we find that accurate identification of the
mechanism of action is achieved for approximately 90 per cent of
a diverse collection of 141 known compounds, representing six
different mechanistic categories. We demonstrate that
two-dimensional graphical displays of the compounds in terms of
the initial three principal components (of the original data)
result in suggestive visual clustering according to mechanism of
action. Finally, we compare the classification accuracy of the
statistical discrimination procedures with the accuracy obtained
from a neural network approach and, for our example, we find that
the results obtained from the various approaches are similar.
Publication:
Korn, E.L., Midthune, D., Chen, T.T., Rubinstein, L.V.,
Christian, M.C., Simon, R.M.: A comparison of two phase I trial
designs, Stat. in Med. 13:1799-1806,
1994.
Abstract:
Phase I cancer chemotherapy trials are designed to determine
rapidly the maximum tolerated dose of a new agent for further
study. A recently proposed Bayesian method, the continual
reassessment method, has been suggested to offer an improvement
over the standard design of such trials. We find the previous
comparisons did not completely address the relative performance
of the designs as they would be used in practice. Our results
indicate that with the continual reassessment method, more
patients will be treated at very high doses and the trials will
take longer to complete. We offer some suggested improvements to
both the standard design and the Bayesian method.
Publication:
Weinstein, J.N., Myers, T,, O’Connor, P.M., Friend, S.H.,
Fornace, A.J., Kohn, K.W., Fojo, A., Bates, S.E., Rubinstein,
L.V., Anderson, N.L., Buolamwini, J.K., van Osdol, W.W., Monks,
A.P., Scudiero, D.A., Sausville, E.A., Zaharevitz, D.G., Bunow,
B., Viswanadhan, V.N., Johnson, G.S., Wittes, R.E., Paull, K., An
information-intensive approach to the molecular pharmacology of
cancer. Science 275: 343-349, 1997.
Abstract:
Since 1990, the National Cancer Institute (NCI) has screened
more than 60,000 compounds against a panel of 60 human cancer
cell lines. The 50-percent growth-inhibitory concentration (GI50)
for any single cell line is simply an index of cytotoxicity or
cytostasis, but the patterns of 60 such GI50 values encode
unexpectedly rich, detailed information on mechanisms of drug
action and drug resistance. Each compound's pattern is like a
fingerprint, essentially unique among the many billions of
distinguishable possibilities. These activity patterns are being
used in conjunction with molecular structural features of the
tested agents to explore the NCI's database of more than 460,000
compounds, and they are providing insight into potential target
molecules and modulators of activity in the 60 cell lines. For
example, the information is being used to search for candidate
anticancer drugs that are not dependent on intact p53 suppressor
gene function for their activity. It remains to be seen how
effective this information-intensive strategy will be at
generating new clinically active agents.
