Prediction of clonal chromosome aberration frequency in human blood lymphocytes

Radiat Res. 2004 Mar;161(3):282-9. doi: 10.1667/rr3134.

Abstract

We recently conducted a large-scale screening for clonal aberrations among atomic bomb survivors and proposed a model for the gross clonal composition of blood lymphocytes. Here we show an application of the model indicating that the number, m,of clones detectable by cytogenetic methods in an individual is predictable by the equation m= (1.8 + 6.4FG) x FP x n/500, where FG represents the estimated translocation frequency in the 46 chromosome set, FP is the observed translocation frequency with FISH or other methods, and nis the number of cells examined. Application of the equation to the results of seven other reports gave close agreement between the observed and calculated numbers of clones. Since the model assumes that clonal expansion is ubiquitous, and any translocation can be the constituent of a clone detectable by cytogenetic means, the vast majority of observed clonal expansions of these somatic cells are likely the result of random-hit events that are not detrimental to human health. Furthermore, since our model can predict the majority of clonal aberrations among Chernobyl workers who were examined 5-6 years after irradiation, clonal expansion seems to occur primarily within a few years after exposure to radiation, most likely being coupled with the process of recovery from radiation-induced injury in the lymphoid and hematopoietic systems.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Chromosome Aberrations / radiation effects*
  • Chromosomes / genetics*
  • Chromosomes / radiation effects*
  • Cloning, Organism / methods*
  • DNA Mutational Analysis / methods*
  • Gene Expression Profiling / methods*
  • Gene Frequency
  • Humans
  • In Situ Hybridization, Fluorescence / methods
  • Leukocytes, Mononuclear / radiation effects*
  • Models, Genetic*
  • Models, Statistical
  • Sensitivity and Specificity