Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 45 (10), 1134-40

Pan-cancer Patterns of Somatic Copy Number Alteration

Affiliations

Pan-cancer Patterns of Somatic Copy Number Alteration

Travis I Zack et al. Nat Genet.

Abstract

Determining how somatic copy number alterations (SCNAs) promote cancer is an important goal. We characterized SCNA patterns in 4,934 cancers from The Cancer Genome Atlas Pan-Cancer data set. 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 underlying their 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 were enriched for genes involved in epigenetic regulation. When levels of genomic disruption were accounted for, 7% of region pairs were anticorrelated, and these regions tended to encompass genes whose proteins physically interact, suggesting related functions. These results provide insights into mechanisms of generation and functional consequences of cancer-related SCNAs.

Figures

Figure 1
Figure 1. Distribution of SCNAs across lineages
(a) Sample purities (top panel) and ploidies (bottom panel) across lineages (see Supplementary Table 1 for a list of lineage abbreviations). Near-diploid samples are designated in purple; cancers that have undergone one or more than one WGD event are designated by green and red, respectively. Summarized data across all lineages are indicated on the right. (b) Numbers of arm-level (top) and focal (bottom) amplifications (left) and deletions (right) across lineages. For each lineage, near-diploid and WGD samples are indicated by bars on the left and right, respectively; events among WGD samples are resolved according to their timing relative to WGD.
Figure 2
Figure 2. Characteristics of different types of SCNA
(a) The distribution of lengths of SCNAs originating at telomeres (black line) compared to SCNAs that are internal to the chromosome. (b) Rates of chromothripsis across lineages. (c) Rates of chromothripsis across chromosomes. Chromothripsis events that involved peak regions of amplification and deletion (see below) are indicated in blue (dark blue: amplifications >4.4 copies or deletions<−1; light blue: low-level events involving smaller changes); events that do not involve peak regions are indicated in grey.
Figure 3
Figure 3. Significantly recurrent focal SCNAs
(a) Frequencies of amplification minus frequencies of deletion (red and blue indicated propensity to amplifications and deletions, respectively) across lineages (x-axis; see Supplementary Table 1 for a list of lineage abbreviations) for all 84 significant peak regions of SCNA, arranged in order of significance (y-axis). The ordering of lineages reflects the results of unsupervised hierarchical clustering of these data. Magnified views of the values for the ten most significant amplification and deletion peaks, respectively, are shown to the right, alongside candidate targets for these regions. Criteria for selecting the indicated candidates are described in the Methods. (b) Associated terms in literature in peak regions containing fewer than 25 genes, according to a GRAIL analysis of (top) all peak regions and (bottom) peak regions without known cancer genes or large genes. (c) Illustration of locations of peak regions within chromosomes four and eight (other chromosomes are displayed in Supplementary Figure 3) across cancer types (designated by boxes on top and bottom colored according to the scheme in panel a) and the Pan-Cancer analysis (right-most column, denoted by a black line). Peaks are designated by candidate targets for each region, selected according to criteria described in the Methods.
Figure 4
Figure 4. Correlations between SCNAs
(a) Illustration of question, displaying a heatmap of copy-number profiles across 4934 cancers (x-axis), arranged in order of increasing genomic disruption. (b) Fraction of region pairs exhibiting significant positive correlation (left), negative correlation (right), or neither (middle), using standard analysis techniques (top) and after controlling for variations in genomic disruption (bottom). (c) Fraction of genome involved in focal SCNAs in samples displayed in panel (a) among observed data (red line), permutations generated by standard techniques (blue line) and permutations that maintain levels of genomic disruption (black dashed line). (d) Genetic interactome map for high-level SCNAs. Nodes represent peak regions with fewer than 25 genes and are connected by edges if focal high-level SCNAs (amplifications to >4.4 copies and deletions to <1 copy) are significantly anticorrelated. (e) The number of significant anticorrelations that overlap known protein-protein interactions in the observed genetic interactome network (red arrow) and permuted networks (blue bars). These results are from the analysis of all SCNAs; results from the high-level analysis are displayed in Supplementary Figure 4d. (f) Distribution of connectivity values (number of nodes to which each node is connected) for the observed genetic interactome network (red dots) and permuted networks (box plots) in the all-SCNAs analysis.

Comment in

  • A four-lane highway to cancer.
    Galluzzi L, Kroemer G. Galluzzi L, et al. Nat Rev Mol Cell Biol. 2016 Jul;17(7):398. doi: 10.1038/nrm.2016.73. Epub 2016 May 25. Nat Rev Mol Cell Biol. 2016. PMID: 27220642 No abstract available.

Similar articles

See all similar articles

Cited by 575 articles

See all "Cited by" articles

References

    1. Beroukhim R, et al. The landscape of somatic copy-number alteration across human cancers. Nature. 2010;463:899–905. - PMC - PubMed
    1. Baudis M. Genomic imbalances in 5918 malignant epithelial tumors: an explorative meta-analysis of chromosomal CGH data. Bmc Cancer. 2007;7 - PMC - PubMed
    1. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature. 2009;458:719–724. - PMC - PubMed
    1. Kim TM, et al. Functional genomic analysis of chromosomal aberrations in a compendium of 8000 cancer genomes. Genome Research. 2013;23:217–227. - PMC - PubMed
    1. Bignell GR, et al. Signatures of mutation and selection in the cancer genome. Nature. 2010;463 893-U61. - PMC - PubMed

Publication types

LinkOut - more resources

Feedback