  The purpose of cancer genome sequencing studies is to determine the nature
and types of alterations present in a typical cancer and to discover genes
mutated at high frequencies. In this article we discuss statistical methods for
the analysis of somatic mutation frequency data generated in these studies. We
place special emphasis on a two-stage study design introduced by Sj\"{o}blom et
al. [Science 314 (2006) 268--274]. In this context, we describe and compare
statistical methods for constructing scores that can be used to prioritize
candidate genes for further investigation and to assess the statistical
significance of the candidates thus identified. Controversy has surrounded the
reliability of the false discovery rates estimates provided by the
approximations used in early cancer genome studies. To address these, we
develop a semiparametric Bayesian model that provides an accurate fit to the
data. We use this model to generate a large collection of realistic scenarios,
and evaluate alternative approaches on this collection. Our assessment is
impartial in that the model used for generating data is not used by any of the
approaches compared. And is objective, in that the scenarios are generated by a
model that fits data. Our results quantify the conservative control of the
false discovery rate with the Benjamini and Hockberg method compared to the
empirical Bayes approach and the multiple testing method proposed in Storey [J.
R. Stat. Soc. Ser. B Stat. Methodol. 64 (2002) 479--498]. Simulation results
also show a negligible departure from the target false discovery rate for the
methodology used in Sj\"{o}blom et al. [Science 314 (2006) 268--274].
