Literature DB >> 9463482

Mining the National Cancer Institute Anticancer Drug Discovery Database: cluster analysis of ellipticine analogs with p53-inverse and central nervous system-selective patterns of activity.

L M Shi1, T G Myers, Y Fan, P M O'Connor, K D Paull, S H Friend, J N Weinstein.   

Abstract

The United States National Cancer Institute conducts an anticancer drug discovery program in which approximately 10,000 compounds are screened every year in vitro against a panel of 60 human cancer cell lines from different organs. To date, approximately 62,000 compounds have been tested in the program, and a large amount of information on their activity patterns has been accumulated. For the current study, anticancer activity patterns of 112 ellipticine analogs were analyzed with the use of a hierarchical clustering algorithm. A dramatic coherence between molecular structures and their activity patterns could be seen from the cluster tree: the first subgroup (compounds 1-66) consisted principally of normal ellipticines, whereas the second subgroup (compounds 67-112) consisted principally of N2-alkyl-substituted ellipticiniums. Almost all apparent discrepancies in this clustering were explainable on the basis of chemical transformation to active forms under cell culture conditions. Correlations of activity with p53 status and selective activity against cells of central nervous system origin made this data set of special interest to us. The ellipticiniums, but not the ellipticines, were more potent on average against p53 mutant cells than against p53 wild-type ones (i.e., they seemed to be "p53-inverse") in this short term assay. This study strongly supports the hypothesis that "fingerprint" patterns of activity in the National Cancer Institute in vitro cell screening program encode incisive information on the mechanisms of action and other biological behaviors of tested compounds. Insights gained by mining the activity patterns could contribute to our understanding of anticancer drugs and the molecular pharmacology of cancer.

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Year:  1998        PMID: 9463482     DOI: 10.1124/mol.53.2.241

Source DB:  PubMed          Journal:  Mol Pharmacol        ISSN: 0026-895X            Impact factor:   4.436


  20 in total

1.  A multivariate insight into the in vitro antitumour screen database of the National Cancer Institute: classification of compounds, similarities among cell lines and the influence of molecular targets.

Authors:  G Musumarra; D F Condorelli; A S Costa; M Fichera
Journal:  J Comput Aided Mol Des       Date:  2001-03       Impact factor: 3.686

2.  Characterization and optimization of a novel protein-protein interaction biosensor high-content screening assay to identify disruptors of the interactions between p53 and hDM2.

Authors:  Drew D Dudgeon; Sunita N Shinde; Tong Ying Shun; John S Lazo; Christopher J Strock; Kenneth A Giuliano; D Lansing Taylor; Patricia A Johnston; Paul A Johnston
Journal:  Assay Drug Dev Technol       Date:  2010-08       Impact factor: 1.738

3.  Classification of a large anticancer data set by adaptive fuzzy partition.

Authors:  Nadège Piclin; Marco Pintore; Christophe Wechman; Jacques R Chrétien
Journal:  J Comput Aided Mol Des       Date:  2004 Jul-Sep       Impact factor: 3.686

4.  Induction of lysosomal membrane permeabilization by compounds that activate p53-independent apoptosis.

Authors:  Hamdiye Erdal; Maria Berndtsson; Juan Castro; Ulf Brunk; Maria C Shoshan; Stig Linder
Journal:  Proc Natl Acad Sci U S A       Date:  2004-12-23       Impact factor: 11.205

5.  Multistrain genetic comparisons reveal CCR5 as a receptor involved in airway hyperresponsiveness.

Authors:  Julia K L Walker; Adriana Ahumada; Bryan Frank; Renee Gaspard; Katherine Berman; John Quackenbush; David A Schwartz
Journal:  Am J Respir Cell Mol Biol       Date:  2006-02-10       Impact factor: 6.914

Review 6.  Targeting the p53 pathway.

Authors:  Vita M Golubovskaya; William G Cance
Journal:  Surg Oncol Clin N Am       Date:  2013-07-30       Impact factor: 3.495

Review 7.  An Interactive Resource to Probe Genetic Diversity and Estimated Ancestry in Cancer Cell Lines.

Authors:  Julie Dutil; Zhihua Chen; Alvaro N Monteiro; Jamie K Teer; Steven A Eschrich
Journal:  Cancer Res       Date:  2019-03-20       Impact factor: 12.701

Review 8.  Therapeutic targeting of p53: all mutants are equal, but some mutants are more equal than others.

Authors:  Kanaga Sabapathy; David P Lane
Journal:  Nat Rev Clin Oncol       Date:  2017-09-26       Impact factor: 66.675

9.  Investigating the correlations among the chemical structures, bioactivity profiles and molecular targets of small molecules.

Authors:  Tiejun Cheng; Yanli Wang; Stephen H Bryant
Journal:  Bioinformatics       Date:  2010-10-13       Impact factor: 6.937

10.  Structural similarity assessment for drug sensitivity prediction in cancer.

Authors:  Pavithra Shivakumar; Michael Krauthammer
Journal:  BMC Bioinformatics       Date:  2009-09-17       Impact factor: 3.169

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