Activation of the Ki-ras protooncogene in spontaneously occurring and chemically induced lung tumors of the strain A mouse

The strain A mouse has a high incidence of spontaneous lung tumors and is susceptible to lung tumor induction by chemical carcinogens. By utilizing transfection assay, Southern blot analysis, and DNA amplification techniques, we have detected an activated Ki-ras gene in the DNAs of both spontaneously occurring and chemically induced lung tumors of strain A mice. The point mutations in the spontaneous lung tumors were in both codon 12 (60%) and codon 61 (30%). In contrast, 100% of the mutations in the Ki-ras gene detected in methylnitrosourea-induced lung tumors and 93% of the mutations in the Ki-ras genes detected in benzo[a]pyrene-induced lung tumors were in codon 12, whereas 90% of the mutations in the Ki-ras genes detected in ethyl carbamate-induced lung tumors were in codon 61. The selectivity of mutations in the Ki-ras oncogene observed in chemically induced tumors, as compared to spontaneous tumors, suggests that these chemicals directly induce point mutations in the Ki-ras protooncogene. These data indicate that the strain A mouse lung tumor model is a very sensitive system to detect the ability of chemicals to activate the Ki-ras protooncogene in lung tissue.

Detection of oncogene mRNA sequences in cultured cells by in situ hybridization

The present study was undertaken to determine the feasibility of using in situ hybridization techniques to identify oncogene transcription in cultured cells. Following in situ hybridization with 32P-labeled v-src and v-Ha-ras DNA probes, src and Ha-ras related transcripts were identified in cell lines transfected with v-src and Ha-ras, respectively. In both the v-src and c-Ha-ras transfected cell lines, the number of silver grains over individual cells were significantly higher (p less than 0.001, t-test) than in a non-transfected, non-tumorigenic, rat esophageal epithelial cell line. There was a highly variable number of silver grains above individual cells. Significantly fewer silver grains were counted over cells that had been preincubated with either non-labeled v-src or v-Ha-ras DNA or that were pretreated with RNase A. Both oncogene transfected cell lines contained approximately 10 times more oncogene related mRNAs than non-transfected cells as judged by the numbers of silver grains over individual cells. Filter-hybridization analysis of the transfected and non-transfected cell lines confirmed that the expression of src and Ha-ras transcripts was higher in the transfected cell lines than in the non-transfected cell line. Therefore, the in situ hybridization technique would appear useful for the identification of oncogene transcripts in single cells and could potentially be applied to cytological preparations of human cells and to human tumor cells in culture.

Lung tumor induction in strain A mice with benzotrichloride

Benzotrichloride (BTC) is used in the synthesis of benzoyl chloride and benzoyl peroxide. Epidemiological data suggest that BTC is a human lung carcinogen. In the present study, BTC was evaluated for its ability to induce lung adenomas in strain A/J mice. Four groups of 15 male and 15 female A/J mice were injected i.p. with either tricaprylin or BTC in tricaprylin three times a week for 8 weeks. BTC groups received doses totaling 1440 mg/kg, 719 mg/kg or 287 mg/kg. The mean number of lung tumors per mouse was 127 87 +/- 5.81, 43 +/- 2.44, and 17.73 +/- 1.09 in the groups treated with either 1440 mg/kg, 719 mg/kg, or 287 mg/kg, respectively. Tricaprylin-vehicle controls had a mean number of 0.46 +/- 0.15 lung tumors per mouse. Therefore, BTC produced a significant (P less than 0.001) and dose-related increase in the lung tumor response when compared to tricaprylin controls and is a potent carcinogen in the strain A mouse lung tumor bioassay.

Entropy and distance of random graphs with application to structural pattern recognition

The notion of a random graph is formally defined. It deals with both the probabilistic and the structural aspects of relational data. By interpreting an ensemble of attributed graphs as the outcomes of a random graph, we can use its lower order distribution to characterize the ensemble. To reflect the variability of a random graph, Shannon's entropy measure is used. To synthesize an ensemble of attributed graphs into the distribution of a random graph (or a set of distributions), we propose a distance measure between random graphs based on the minimum change of entropy before and after their merging. When the ensemble contains more than one class of pattern graphs, the synthesis process yields distributions corresponding to various classes. This process corresponds to unsupervised learning in pattern classification. Using the maximum likelihood rule and the probability computed for the pattern graph, based on its matching with the random graph distributions of different classes, we can classify the pattern graph to a class.