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K-RAS mutation is being developed as a cancer biomarker and tumor K-RAS is being used to predict therapeutic response. Yet, levels of K-RAS mutation in normal and pathological tissue samples have not been determined rigorously, nor inter-individual variation in these levels characterized. Therefore, K-RAS codon 12 GAT and GTT mutant fractions were measured in colonic mucosa of individuals without colon cancer, tumor-distal mucosa, tumor-proximal mucosa, normal tumor-adjacent tissues, colonic adenomas, and carcinomas. The results indicate K-RAS codon 12 GAT mutation is present at measurable levels in normal appearing mucosa. All tumors carried K-RAS mutation, in most cases as a mutant subpopulation.

The utility of liver H-ras codon 61 CAA to AAA mutant fraction as a biomarker of liver tumor development was investigated using neonatal male mice treated with 4-aminobiphenyl (4-ABP). Treatment with 0.1, 0.3, or 1.0 mumol 4-ABP produced dose-dependent increases in liver DNA adducts in B6C3F(1) and C57BL/6N mice. Eight months after treatment with 0.3 mumol 4-ABP or the DMSO vehicle, H-ras codon 61 CAA to AAA mutant fraction was measured in liver DNA samples (n = 12) by allele-specific competitive blocker-polymerase chain reaction (ACB-PCR). A significant increase in average mutant fraction was found in DNA of 4-ABP-treated mice, with an increase from 1.3 x 10(-5) (control) to 44.9 x 10(-5) (treated) in B6C3F(1) mice and from 1.4 x 10(-5) to 7.0 x 10(-5) in C57BL/6N mice. Compared with C57BL/6N mutant fractions, B6C3F(1) mutant fractions were more variable and included some particularly high mutant fractions, consistent with the more rapid development of liver foci expected in B6C3F(1) mouse liver. Twelve months after treatment, liver tumors developed in 79.2% of 4-ABP-treated and 22.2% of control B6C3F(1) mice; thus measurement of H-ras mutant fraction correlated with subsequent tumor development. This study demonstrates that ACB-PCR can directly measure background levels of somatic oncogene mutation and detect a carcinogen-induced increase in such mutation., (Published 2005 Wiley-Liss, Inc.)

The use of transgenic targets for measuring mutant frequencies in mammalian tissue requires an estimate of the mutant frequency that results from recovery of the transgene in bacterial recovery systems. In this study, we have determined the spontaneous mutant frequency, estimated the mutation rate, and ascertained the mutation spectrum for gene A of phiX174 grown in E. coli strain CQ2 from 156 small independent cultures. The mutant frequency of 12 of the 156 cultures was 17 +/- 1.0 x 10(-6) and the estimated mutation rate per gene replication was 7.4 +/- 2.3 x 10(-6). The mutant frequency and spectrum from E. coli were not significantly different from that of solvent-treated embryonic mouse cells in culture, 19 +/- 0.5 x 10(-6) (Valentine CR et al. [2002]: Environ Mol Mutagen 39:55-68), indicating that those spontaneous mutants were primarily derived from E. coli. The E. coli spectrum was heavily weighted toward two major target sites (hot spots), 4225A-->G (56%) and 4218G-->A or C (20%). Four new target sites and one new mutational event were recovered by the gene A forward assay. A mutant spectrum from an expanded phage stock was also determined to assess the effects of propagating the virus. This mutant frequency was higher (6 x 10(-4)), contained more double mutants (15% compared to 0.6%), and had a significantly different spectrum from the spectrum for independent cultures (fewer A:T-->G:C and G:C-->C:G changes and more G:C-->A:T; P < 0.002). The E. coli mutation spectrum will be useful for determining the origin of gene A mutation in tissues of phiX174 transgenic mice., (Copyright 2004 Wiley-Liss, Inc.)

Single-burst analysis was applied to a forward assay for gene A mutation in splenic lymphocytes of phiX174 transgenic mice for the purpose of optimizing analytical parameters for identifying in vivo mutations. The effect of varying the cutoff value for an in vivo burst on induced mutant frequency, fold increase, and the significance of the difference between control and N-ethyl-N-nitrosourea (ENU)-treated mice was calculated by two different methods. The plating density was reduced to an average of less than 10 background mutant plaques per aliquot in order to separate in vitro bursts. The spectrum of mutations contributing < 60 plaques per aliquot from control animals was not significantly different from the control spectra from E. coli or transgenic phiX174 cells in culture. The mutant spectra from ENU-treated animals was highly different between mutant bursts of > 80 plaques per aliquot compared to mutations contributing < 60 plaques per aliquot (P < 0.000001), the former fitting the spectrum expected for ENU-induced mutations. The latter spectrum was also different from control animals and E. coli (P < 0.000001), suggesting the difference was caused by ex vivo mutation. With the mutations found in this study, the total number of reported target sites for gene A is now 33. The results support the interpretation that, in contrast to results for the lacI transgene, 100% of mutants isolated in gene A from control animals and cells were fixed in E. coli. We attribute the difference between the two genes to hot-spot sites for mutation in gene A and to a testable hypothesis that the mosaic plaque assay for the lacI transgene underestimates the frequency of ex vivo mutants., (Copyright 2004 Wiley-Liss, Inc.)

The sensitivity of in vivo transgenic mutation assays benefits from the sequencing of mutations, although the large number of possible mutations hinders high throughput sequencing. A forward mutational assay exists for Phi X174 that requires an altered, functional Phi X174 protein and therefore should have fewer targets (sense, base-pair substitutions) than forward assays that inactivate a protein. We investigated this assay to determine the number of targets and their suitability for detecting a known mutagen, N-ethyl-N-nitrosourea (ENU). We identified 25 target sites and 33 different mutations in Phi X174 gene A after sequencing over 350 spontaneous and ENU-induced mutants, mostly from mouse embryonic cell line PX-2 isolated from mice transgenic for Phi X174 am3, cs70 (line 54). All six types of base-pair substitution were represented among both the spontaneous and ENU-treated mutant spectra. The mutant spectra from cells treated with 200 and 400 microg/ml ENU were both highly different from the spontaneous spectrum (P < 0.000001) but not from each other. The dose trend was significant (P < 0.0001) for a linear regression of mutant frequencies (R(2) = 0.79), with a ninefold increase in mutant frequency at the 400 microg/ml dose. The spontaneous mutant frequency was 1.9 x 10(-5) and the spontaneous spectrum occurred at 11 target base pairs with 15 different mutations. Thirteen mutations at 12 targets were identified only from ENU-treated cells. Seven mutations had highly significant increases with ENU treatment (P < 0.0001) and 15 showed significant increases. The results suggest that the Phi X174 forward assay might be developed into a sensitive, inexpensive in vivo mutagenicity assay.

Genotypic selection methods detect rare sequence changes in populations of DNA molecules. These methods have been used to investigate the chemical induction of mutation and for the detection and diagnosis of cancer. The possible use of genotypic selection for improving current risk assessment practices is based on the premise that the frequency of somatic mutation is of critical importance in understanding and modeling carcinogenesis. If genotypic selection can measure the induction of specific mutations that disrupt normal cell/tissue homeostasis, then it could provide key mechanistic information for cancer risk assessment. For example, genotypic selection data might support a particular low-dose extrapolation method or characterize the relationship between rodent and human cancer risk. Strategies for evaluating the use of genotypic selection in cancer risk assessment include the concept of developing a battery of targets that detect a range of agent-specific effects. Ideal targets to examine by genotypic selection are the oncogene and tumor suppressor gene mutations frequently detected in human tumors because these are thought to represent tumor-initiating events. The most commonly occurring basepair (bp) substitutions within the ras and p53 genes are identified. Also, the battery of genotypic selection methods is defined in terms of the most important mutational specificities to include. In theory, the major basepair substitution mutations induced by 29 of 31 chemical carcinogens could be detected by analyzing three different mutations: G:C-->T:A, G:C-->A:T, and A:T-->T:A. Genotypic selection will have the greatest impact on risk assessment if measurement of spontaneous mutation is possible. Data from phenotypic selection assays suggest this corresponds to detection of mutant fractions of approximately 10(-7), and this would necessitate examining DNA samples containing >10(7) target molecules. Despite its apparent potential, considerable development and validation is needed before genotypic selection data can be applied to cancer risk assessment.

The Poisson distribution is a fundamental probability model for count data, and is a natural model for the observed plaque counts in mutation assays using animals with lambda or PhiX174 transgenes. The Poisson likelihood for observed counts is a function of the mutant fraction, and it is straightforward to derive the associated maximum likelihood estimate of the mutant fraction and its variance. The estimate is easy to calculate, and if not the same, very similar to ad hoc estimates in current use. The model indicates the proper way to combine data from a number of plates, possibly prepared with different sample dilutions. The estimator of the mutant fraction is biased as a consequence of dividing by a random variable, the plaque count used to calculate the total recovered plaque-forming units. Fortunately, the bias becomes negligible as this count becomes large. On the other hand, increasing this count can increase the variance by decreasing the amount of sample assayed for mutant phages. Concurrent heed to the bias and the variance provides some guidance as to the optimum allocation of a sample into portions assayed for mutant phages and total recovered phages. The distribution of the estimate of the mutant fraction is related to the binomial distribution. This relationship implies a binomial distribution for the mutant count conditional on an overall count (either the sum of mutant and counted total plaques or the sum of counted mutant and non-mutant plaques). A special but important case occurs when each plate can be evaluated for mutant plaques and non-mutant plaques. Then, the observed proportion of mutants estimates the mutant fraction. More generally, the relationship to a binomial distribution provides a procedure for calculating a confidence interval., (Copyright 1999 Elsevier Science B.V.)