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Characterization of anticancer agents by their growth inhibitory activity and relationships to mechanism of action and structure

  1. Author:
    Keskin, O.
    Bahar, I.
    Jernigan, R. L.
    Beutler, J. A.
    Shoemaker, R. H.
    Sausville, E. A.
    Covell, D. G.

  2. Author Address

    Covell DG NCI, SAIC, Lab Expt & Computat Biol, Mol Struct Sect,NIH Frederick, MD 21702 USA NCI, SAIC, Lab Expt & Computat Biol, Mol Struct Sect,NIH Frederick, MD 21702 USA Bogazici Univ, TUBITAK Adv Polymer Mat Res Ctr, Dept Chem Engn TR-80815 Bebek Turkey Bogazici Univ, TUBITAK Adv Polymer Mat Res Ctr, Ctr Polymer Res TR-80815 Bebek Turkey NCI, SAIC, DCTDC,DTP, Lab Drug Discovery Res & Dev Frederick, MD 21702 USA NCI, Div Canc Treatment & Diag, Dev Therapeut Program, NIH Frederick, MD 21702 USA
    1. Year: 2000
  2. Journal: Anti-Cancer Drug Design
    1. 15
    2. 2
    3. Pages: 79-98
  4. Type of Article: Article
  1. Abstract:

    An analysis of the growth inhibitory potency of 122 anticancer agents available from the National Cancer Institute anticancer drug screen is presented. Methods of singular value decomposition (SVD) were applied to determine the matrix of distances between all compounds. These SVD-derived dissimilarity distances were used to cluster compounds that exhibit similar tumor growth inhibitory activity patterns against 60 human cancer cell lines. Cluster analysis divides the 122 standard agents into 25 statistically distinct groups. The first eight groups include structurally diverse compounds with reactive functionalities that act as DNA-damaging agents while the remaining 17 groups include compounds that inhibit nucleic acid biosynthesis and mitosis. Examination of the average activity patterns across the 60 tumor cell lines reveals unique 'fingerprints' associated with each group. A diverse set of structural features are observed for compounds within these groups, with frequent occurrences of strong within-group structural similarities. Clustering of cell types by their response to the 122 anticancer agents divides the 60 cell types into 21 groups. The strongest within-panel groupings were found for the renal, leukemia and ovarian cell panels. These results contribute to the basis for comparisons between log(GI(50)) screening patterns of the 122 anticancer agents and additional tested compounds. [References: 64]

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