Statistical method on nonrandom clustering with application to somatic mutations in cancer

Background: Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention. Results: We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, whi...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Jingjing Ye, Adam Pavlícek, Elizabeth A. Lunney, Paul A. Rejto, Chi-Hse Teng
• Keywords: Amino Acid Mutations, BMCBI 2010, Driver Mutations, Mutations