Pan-cancer patterns of somatic copy number alteration

Abstract

Determining how somatic copy-number alterations (SCNAs) promote cancer is an important goal. We characterized SCNA patterns among 4934 cancers from The Cancer Genome Atlas Pan-Cancer dataset. Whole-genome doubling, observed in 37% of cancers, was associated with higher rates of every other type of SCNA, TP53 mutations, CCNE1 amplifications, and alterations of the PPP2R complex. SCNAs that were internal to chromosomes tended to be shorter than telomere-bounded SCNAs, suggesting different mechanisms of generation. Significantly recurrent focal SCNAs were observed in 140 regions, including 102 without known oncogene or tumor suppressor gene targets and 50 with significantly mutated genes. Amplified regions without known oncogenes are enriched for genes involved in epigenetic regulation. When levels of genomic disruption were accounted for, 7% of region pairs anticorrelated, and these tended to encompass genes whose proteins physically interact, suggesting related functions. These results provide insights into mechanisms of generation and functional consequences of cancer SCNAs.

Analysis of mutant allele fractions in driver genes in colorectal cancer – biological and clinical insights

“…Alternatively, repetitive copy number alterations co‐occurring with mutations in driver genes could reduce BRAF and PIK3CA adjMAFs counts. However, the published literature does not support this hypothesis: BRAF and PIK3CA mutations rarely co‐occur with copy number gains in wild‐type alleles (The Cancer Genome Atlas, 2012; Zack et al ., 2013). Arm‐level 7q gains ( BRAF locus) have been described in up to 40% of CRC samples, mainly chromosomally instable tumors lacking BRAF mutations (The Cancer Genome Atlas, 2012).…”

Impact of somatic copy number alterations on the glioblastoma miRNome: miR‐4484 is a genomically deleted tumour suppressor

“…SCNAs in particular are the most intriguing form of genetic variations to study in cancer, as they alter the dosage of a gene product independent of the transcription factors and other controls. Analyses of cancer genome by cytogenetic studies and more recently by array hybridization profiling‐based methodologies have assisted in the identification of recurrent alterations unique to various cancers as well as some common alterations (Beroukhim et al ., 2010; Zack et al ., 2013). In understanding the role of CNAs, it becomes imperative to characterize the CNAs as the driver events in a particular malignancy and distinguish them from the passenger CNAs that would have been imbued into the genome during cancer evolution and possibly do not contribute towards it (Zack et al ., 2013).…”

Identification of complex genomic rearrangements in cancers using CouGaR

“…It has been previously reported that CGRs are found in 5%-9% of all cancers (Malhotra et al 2013;Zack et al 2013) and ∼25% of bone tumors (Stephens et al 2011). However, these estimates depend on three factors: first, the type of data utilized (array or sequencing); second, the computational method implemented; and third, the specific types of cancer samples analyzed.…”

“…More recently, certain types of large-scale complex genomic rearrangements (CGRs), such as chromothripsis, breakage-fusion-bridges, and double minutes, have been discovered within tumor genomes and implicated in tumorigenesis ). CGRs involve three or more distant regions of the genome abnormally joining together and have been implicated in 5%-9% of all cancers (Malhotra et al 2013;Zack et al 2013) and ∼25% of bone tumors (Stephens et al 2011). These rearrangements can form distinct highly amplified contigs that harbor oncogenes, resulting in 10-to 100-fold increases in oncogene copy count, which may potentially drive tumorigenesis (Korbel and Campbell 2013).…”

“…These were filtered based on their likelihood to contain amplified CGRs. Since multiple amplifications in SNP array data are a good indicator of samples with CGRs (Zack et al 2013), we used the SNP data to build a simple discriminator with three parameters: the length of amplification l, the number of amplifications of at least such length n, and the threshold amplification value t. We then apply this discriminator by considering candidate samples to be those with at least n regions with log ratio of amplification greater than t and length at least l. Applying this discriminator with n = 2, t = 1.4, and l = 15 kb identified 482 candidate samples Table S9), and additionally, we removed one more sample (TCGA-50-5055; likely to be a sample mix up-female patient, male tumor). We therefore have 467 samples in total and present our analysis on these (Table 2).…”