For
cancer one of the primary aims of molecular epidemiology is to identify the endogenous or exogenous cause of mutations within a gene. Regarding exogenous
mutagens, many mutation data have become available via in vitro and in vivo mutation assays and become publicly available through mutation databases such as the Mammalian Gene Mutation Database (http://lisntweb.swan.ac.uk/cmgt/index.htm). One particular mutation assay incorporates the bacterial
supF tRNA gene which allows selection of mutations at virtually all
nucleotides. We have developed an algorithm called LwPy53 that utilizes mutation data from supF that can be used to predict chemically induced hot-spots along the p53 gene. The prediction is based on a number of parameters: the mutability of supF dinucleotides
after treatment with a
mutagen of interest;
DNA curvature along the p53 gene; the selectability of a mutation along the gene; the likelihood of a site being within a
nucleosome. We applied LwPy53 to exons 5, 7 and 8 of p53 using
benzo[a]pyrene diol
epoxide (
BPDE)-induced mutation data for supF to obtain a predicted
BPDE G-->T transversion spectrum after hypothetical treatment with
BPDE. The resulting predicted mutation distribution reveals strong mutation hot-spots at
codons 157, 248 and 273 that correlate with known
BPDE adduct hot-spots within p53. The predicted
BPDE spectrum strongly resembles the G-->T mutation spectrum compiled from known
lung cancer mutation data from smokers and further supports evidence that
BPDE contributes to the overall smoking-related mutation distribution in
lung cancer. The algorithm shows how
BPDE target sequence specificity and
DNA curvature both shape the overall mutation distribution.