Literature DB >> 2733715

A statistical model to estimate variance in long term-low dose mutation assays: testing of the model in a human lymphoblastoid mutation assay.

A R Oller1, P Rastogi, S Morgenthaler, W G Thilly.   

Abstract

Long term-low dose mutation assays offer a means to study the genetic effects of environmental mutagens at concentrations relevant to human exposure. These assays involve continuous induction of mutants, serial dilution of cultures and sampling to determine the mutant fraction as a function of time and mutagen concentration. An arithmetic model for the expected variance among identically treated cultures is presented. This model provides means to calculate a predicted variance of the mutant fractions and mutation rates in typical long term-low dose experiments. We have calculated the expected variances of the mutant fraction with this model and compared them to the observed variances among 4 independent experiments in which human lymphoblastoid cells were treated for 5, 10, 15 and 20 days with a non-toxic concentration of the mutagen 4-aminobiphenyl. Mutations at the HPRT locus were measured by determining the 6-thioguanine-resistant mutant fraction. The expected and observed variances of the mutant fractions are in close agreement. This model is adequate to predict the variance of the mutant fraction and should be useful in experimental design and objective evaluation of long term-low dose mutation assays.

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Year:  1989        PMID: 2733715     DOI: 10.1016/0165-1161(89)90001-0

Source DB:  PubMed          Journal:  Mutat Res        ISSN: 0027-5107            Impact factor:   2.433


  12 in total

1.  Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling.

Authors:  Juliann Chmielecki; Jasmine Foo; Geoffrey R Oxnard; Katherine Hutchinson; Kadoaki Ohashi; Romel Somwar; Lu Wang; Katherine R Amato; Maria Arcila; Martin L Sos; Nicholas D Socci; Agnes Viale; Elisa de Stanchina; Michelle S Ginsberg; Roman K Thomas; Mark G Kris; Akira Inoue; Marc Ladanyi; Vincent A Miller; Franziska Michor; William Pao
Journal:  Sci Transl Med       Date:  2011-07-06       Impact factor: 17.956

Review 2.  The mutation rate and cancer.

Authors:  A L Jackson; L A Loeb
Journal:  Genetics       Date:  1998-04       Impact factor: 4.562

3.  Expression of p14(ARF), p15(INK4b), p16(INK4a) and skp2 increases during esophageal squamous cell cancer progression.

Authors:  Peng Bai; Xue Xiao; Juan Zou; Lin Cui; Tri M Bui Nguyen; Jinsong Liu; Jianguo Xiao; Bin Chang; Jin Wu; He Wang
Journal:  Exp Ther Med       Date:  2012-03-22       Impact factor: 2.447

4.  Intratumor heterogeneity in evolutionary models of tumor progression.

Authors:  Rick Durrett; Jasmine Foo; Kevin Leder; John Mayberry; Franziska Michor
Journal:  Genetics       Date:  2011-03-15       Impact factor: 4.562

5.  A progenitor cell origin of myeloid malignancies.

Authors:  Hiroshi Haeno; Ross L Levine; D Gary Gilliland; Franziska Michor
Journal:  Proc Natl Acad Sci U S A       Date:  2009-09-21       Impact factor: 11.205

6.  Complex frameshift mutations mediated by plasmid pKM101: mutational mechanisms deduced from 4-aminobiphenyl-induced mutation spectra in Salmonella.

Authors:  J G Levine; R M Schaaper; D M DeMarini
Journal:  Genetics       Date:  1994-03       Impact factor: 4.562

7.  The probable cell of origin of NF1- and PDGF-driven glioblastomas.

Authors:  Dolores Hambardzumyan; Yu-Kang Cheng; Hiroshi Haeno; Eric C Holland; Franziska Michor
Journal:  PLoS One       Date:  2011-09-09       Impact factor: 3.240

8.  Eradication of chronic myeloid leukemia stem cells: a novel mathematical model predicts no therapeutic benefit of adding G-CSF to imatinib.

Authors:  Jasmine Foo; Mark W Drummond; Bayard Clarkson; Tessa Holyoake; Franziska Michor
Journal:  PLoS Comput Biol       Date:  2009-09-11       Impact factor: 4.475

Review 9.  The inflammatory network: bridging senescent stroma and epithelial tumorigenesis.

Authors:  Weiwei Shan; Gong Yang; Jinsong Liu
Journal:  Front Biosci (Landmark Ed)       Date:  2009-01-01

10.  Biologically based epidemiological studies of electric power and cancer.

Authors:  R G Stevens
Journal:  Environ Health Perspect       Date:  1993-12       Impact factor: 9.031

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