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To develop a comprehensive overview of copy number aberrations (CNAs) in stage-II/III colorectal cancer (CRC), we characterized 302 tumors from the PETACC-3 clinical trial. Microsatellite-stable (MSS) samples (n = 269) had 66 minimal common CNA regions, with frequent gains on 20 q (72.5%), 7 (41.8%), 8 q (33.1%) and 13 q (51.0%) and losses on 18 (58.6%), 4 q (26%) and 21 q (21.6%). MSS tumors have significantly more CNAs than microsatellite-instable (MSI) tumors: within the MSI tumors a novel deletion of the tumor suppressor WWOX at 16 q23.1 was identified (p<0.01). Focal aberrations identified by the GISTIC method confirmed amplifications of oncogenes including EGFR, ERBB2, CCND1, MET, and MYC, and deletions of tumor suppressors including TP53, APC, and SMAD4, and gene expression was highly concordant with copy number aberration for these genes. Novel amplicons included putative oncogenes such as WNK1 and HNF4A, which also showed high concordance between copy number and expression. Survival analysis associated a specific patient segment featured by chromosome 20 q gains to an improved overall survival, which might be due to higher expression of genes such as EEF1B2 and PTK6. The CNA clustering also grouped tumors characterized by a poor prognosis BRAF-mutantlike signature derived from mRNA data from this cohort. We further revealed non-random correlation between CNAs among unlinked loci, including positive correlation between 20 q gain and 8 q gain, and 20 q gain and chromosome 18 loss, consistent with co-selection of these CNAs. These results reinforce the non-random nature of somatic CNAs in stage-II/III CRC and highlight loci and genes that may play an important role in driving the development and outcome of this disease. [ABSTRACT FROM AUTHOR]

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, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and β-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors. Conclusions: Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences. [ABSTRACT FROM AUTHOR]