Data Mining the NCI Cancer Cell Line Compound GI<sub>50</sub> Values: Identifying Quinone Subtypes Effective Against Melanoma and Leukemia Cell Classes.

Marx, Kenneth A.; O'Neil, Philip; Hoffman, Patrick; Ujwal, M. L.

doi:10.1002/chin.200351221

Cited by 10 publications

(11 citation statements)

References 2 publications

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“…Since labeled compounds were not available, we could not determine whether they were taken up by the cells and thus could become available to the cytosolic MAT. In addition both 1 and 2 were evaluated by the Developmental Therapeutics Program of the National Cancer Institute for potential inhibition of the growth of their panel of 60 mammalian tumor cell lines (http://dtp.nci.nih.gov/branches/btb/ivclsp.html);31, 32 concentrations up to 100 μM were tested. Neither compound caused detectable growth inhibition of any of the cell lines; as in the case of the trials with E. coli and yeast, it was not possible to determine whether the compounds were transported into the cells which would be required for MAT inhibition.…”

Section: Resultsmentioning

confidence: 99%

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Discovery of Novel Types of Inhibitors ofS-Adenosylmethionine Synthesis by Virtual Screening

et al. 2009

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S-adenosylmethionine (AdoMet) lies at an intersection of nucleotide and amino acid metabolism, and performs a multitude of metabolic functions. AdoMet formation is catalyzed by S-adenosylmethionine synthetase (ATP : L-methionine S-adenosyltransferase (MAT)) which is a target for development of anti-cancer and antimicrobial agents. High affinity MAT inhibitors have been found through computational docking of more than 200,000 compounds for predicted binding to the crystallographically-defined nucleotide binding region of the enzyme's active site. Two of the top scoring candidate compounds had IC50 values less than 10 nM,, more than 10,000-fold lower than the substrates' KM values. The compounds are structurally unrelated to the natural ligands of the enzyme. The enzyme is protected from inhibition by ATP, but not by methionine, consistent with binding at the adenosyl region of the active site. These results validate in silico screening as a robust approach to the discovery of inhibitors of this chemotherapeutically relevant enzyme.

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Section: Resultsmentioning

confidence: 99%

“…The potential effects of 1 and 2 on the growth of the NCI-60 cell line panel were evaluated at the Division of Chemical Therapeutics of the National Cancer Institute 32. Concentrations up to 100 μM were tested in their standard protocol.…”

Section: Experimental Methodsmentioning

confidence: 99%

Discovery of Novel Types of Inhibitors ofS-Adenosylmethionine Synthesis by Virtual Screening

et al. 2009

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“…In order to ascertain the uniqueness of the 2 quinone subsets, we first determined the extent of occurrence of p ‐quinones of all types in our starting data set via substructure searching using the ChemFinder 7.0 ultra software. The internal and external quinone subtypes represent a significant fraction: 25% (10/41) of all the internal quinones and 40% (6/15) of all the external quinones in the entire data set 38…”

Section: Resultsmentioning

confidence: 99%

“…More recently, gene expression analysis has been added to the data mining activity of the NCI compound data set36 to predict chemosensitivity, using the GI 50 test data for each compound, for a few hundred compound subset of the NCI data set 37. After we completed our initial data mining analysis using the GI 50 values,38 gene expression data on the 60 cancer cell lines were combined with NCI compound GI 50 data and also with a 27,000 chemical feature database computed for the NCI compounds 39…”

Section: Applicationsmentioning

confidence: 99%

Applications of Machine Learning and High‐Dimensional Visualization in Cancer Detection, Diagnosis, and Management

McCarthy¹,

Marx²,

Hoffman³

et al. 2004

Annals of the New York Academy of Sciences

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112

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Recent technical advances in combinatorial chemistry, genomics, and proteomics have made available large databases of biological and chemical information that have the potential to dramatically improve our understanding of cancer biology at the molecular level. Such an understanding of cancer biology could have a substantial impact on how we detect, diagnose, and manage cancer cases in the clinical setting. One of the biggest challenges facing clinical oncologists is how to extract clinically useful knowledge from the overwhelming amount of raw molecular data that are currently available. In this paper, we discuss how the exploratory data analysis techniques of machine learning and high-dimensional visualization can be applied to extract clinically useful knowledge from a heterogeneous assortment of molecular data. After an introductory overview of machine learning and visualization techniques, we describe two proprietary algorithms (PURS and RadViz) that we have found to be useful in the exploratory analysis of large biological data sets. We next illustrate, by way of three examples, the applicability of these techniques to cancer detection, diagnosis, and management using three very different types of molecular data. We first discuss the use of our exploratory analysis techniques on proteomic mass spectroscopy data for the detection of ovarian cancer. Next, we discuss the diagnostic use of these techniques on gene expression data to differentiate between squamous and adenocarcinoma of the lung. Finally, we illustrate the use of such techniques in selecting from a database of chemical compounds those most effective in managing patients with melanoma versus leukemia.

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“…Details of the laboratory methods used to carry out these screens have been published [2,3] and a range of di erent data reduction techniques, including cluster analysis and neural network methods, have been explored for summarizing and analysing the results in order to gain insights into the modes of action of di erent anticancer agents [4][5][6][7][8][9]. More recently gene expression data has become available for the 60 cell lines [10,11], and this has prompted more analyses that make use of the activity estimates in relation to the genetic proÿles of the cell lines and the agents [12,13]. The activity proÿle for each agent used in these analyses is obtained from linear interpolation applied to the dose-response curves of cell growth against concentration of the agent for each of the 60 cell lines.…”

Section: Introductionmentioning

confidence: 99%

Cytotoxic assays for screening anticancer agents

Baharith¹,

Al-Khouli²,

Raab

2005

Statistics in Medicine

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In the process of identifying potential anticancer agents, the ability of a new agent is tested for cytotoxic activity against a panel of standard cancer cell lines. The National Cancer Institute (NCI) present the cytotoxic profile for each agent as a set of estimates of the dose required to inhibit the growth of each cell line. The NCI estimates are obtained from a linear interpolation method applied to the dose-response curves. In this paper non-linear fits are proposed as an alternative to interpolation. This is illustrated with data from two agents recently submitted to NCI for potential anticancer activity. Fitting of individual non-linear curves proved difficult, but a non-linear mixed model applied to the full set of cell lines overcame most of the problems. Two non-linear functional forms were fitted using random effect models by both maximum likelihood and a full Bayesian approach. Model-based toxicity estimates have some advantages over those obtained from interpolation. They provide standard errors for toxicity estimates and other derived quantities, allow model comparisons. Examples of each are illustrated.

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Data Mining the NCI Cancer Cell Line Compound GI₅₀ Values: Identifying Quinone Subtypes Effective Against Melanoma and Leukemia Cell Classes.

Cited by 10 publications

References 2 publications

Discovery of Novel Types of Inhibitors ofS-Adenosylmethionine Synthesis by Virtual Screening

Discovery of Novel Types of Inhibitors ofS-Adenosylmethionine Synthesis by Virtual Screening

Applications of Machine Learning and High‐Dimensional Visualization in Cancer Detection, Diagnosis, and Management

Cytotoxic assays for screening anticancer agents

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Data Mining the NCI Cancer Cell Line Compound GI50 Values: Identifying Quinone Subtypes Effective Against Melanoma and Leukemia Cell Classes.

Cited by 10 publications

References 2 publications

Discovery of Novel Types of Inhibitors ofS-Adenosylmethionine Synthesis by Virtual Screening

Discovery of Novel Types of Inhibitors ofS-Adenosylmethionine Synthesis by Virtual Screening

Applications of Machine Learning and High‐Dimensional Visualization in Cancer Detection, Diagnosis, and Management

Cytotoxic assays for screening anticancer agents

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Data Mining the NCI Cancer Cell Line Compound GI₅₀ Values: Identifying Quinone Subtypes Effective Against Melanoma and Leukemia Cell Classes.