RAS genes are mutated in approximately 30% of all human cancers. Interestingly, there exists a strong bias in favor of mutation of only one of the three major RAS genes in tumors of different cellular origins. NRAS mutations occur in approximately 20% of human melanomas, whereas HRAS and KRAS mutations are rare in this disease. To define the mechanism(s) responsible for this preference in melanocytes, we compared the transformation efficiencies of mutant NRAS and KRAS in immortal, non-transformed Ink4a/Arf-deficient melanocytes. NRAS mutation leads to increased cellular proliferation and is potently tumorigenic. In contrast, KRAS mutation does not enhance melanocyte proliferation and is only weakly tumorigenic on its own. Although both NRAS and KRAS activate mitogen-activated protein kinase signaling, only NRAS enhances MYC activity in these cells. Our data suggest that the activity of specific RAS isoforms is context-dependent and provide a possible explanation for the prevalence of NRAS mutations in melanoma. In addition, understanding this mechanism will have important implications for cancer therapies targeting RAS pathways.

In this article we propose a multiple-inflation Poisson regression to model count response data containing excessive frequencies at more than one non-negative integer values. To handle multiple excessive count responses, we generalize the zero-inflated Poisson regression by replacing its binary regression with the multinomial regression, while Su et al. [Statist. Sinica 23 (2013) 1071-1090] proposed a multiple-inflation Poisson model for consecutive count responses with excessive frequencies. We give several properties of our proposed model, and do statistical inference under the fully Bayesian framework. We perform simulation studies and also analyze the data related to the number of infections collected in five major hospitals in Turkey, using our methodology.

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The assumption of Hardy-Weinberg equilibrium (HWE) among alleles in a nonevolving population is of fundamental importance in genetic studies. Deviation from HWE in a population usually indicates inbreeding, stratification and sometimes problems in genotyping. In populations of affected individuals, these deviations can also provide evidence for association. In this paper, we introduce a measure based on the Kullback-Leibler discrimination information function that quantifies the deviation from HWE in a population. We use this measure to order populations. We also propose a test for HWE based on an estimate of this measure. The test is a statistically consistent test of the null hypothesis for all alternatives and is very easy to implement. Our proposed test statistic is compared with an earlier, widely used, test. Finally, the use of the proposed new test is shown in an illustrative example.