Hierarchical Bayesian analysis of somatic mutation data in cancer

Jie Ding, Lorenzo Trippa, Xiaogang Zhong, Giovanni Parmigiani

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Identifying genes underlying cancer development is critical to cancer biology and has important implications across prevention, diagnosis and treatment. Cancer sequencing studies aim at discovering genes with high frequencies of somatic mutations in specific types of cancer, as these genes are potential driving factors (drivers) for cancer development. We introduce a hierarchical Bayesian methodology to estimate gene-specific mutation rates and driver probabilities from somatic mutation data and to shed light on the overall proportion of drivers among sequenced genes. Our methodology applies to different experimental designs used in practice, including one-stage, two-stage and candidate gene designs. Also, sample sizes are typically small relative to the rarity of individual mutations. Via a shrinkage method borrowing strength from the whole genome in assessing individual genes, we reinforce inference and address the selection effects induced by multistage designs. Our simulation studies show that the posterior driver probabilities provide a nearly unbiased false discovery rate estimate. We apply our methods to pancreatic and breast cancer data, contrast our results to previous estimates and provide estimated proportions of drivers for these two types of cancer.

Original languageEnglish (US)
Pages (from-to)883-903
Number of pages21
JournalAnnals of Applied Statistics
Issue number2
StatePublished - Jun 2013
Externally publishedYes


  • Drivers and passengers
  • Hierarchical Bayesian model
  • Pancreatic and breast cancer
  • Somatic mutations

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Modeling and Simulation
  • Statistics and Probability


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