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 Sjöblom 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 Sjöblom et al. [Science 314 (2006) 268–274].
"False discovery rates in somatic mutation studies of cancer." Ann. Appl. Stat. 5 (2B) 1360 - 1378, June 2011. https://doi.org/10.1214/10-AOAS438