Analyzing Somatic Genome Rearrangements in Human Cancers by Using Whole-Exome Sequencing

Abstract

Although exome sequencing data are generated primarily to detect single-nucleotide variants and indels, they can also be used to identify a subset of genomic rearrangements whose breakpoints are located in or near exons. Using >4,600 tumor and normal pairs across 15 cancer types, we identified over 9,000 high confidence somatic rearrangements, including a large number of gene fusions. We find that the 5' fusion partners of functional fusions are often housekeeping genes, whereas the 3' fusion partners are enriched in tyrosine kinases. We establish the oncogenic potential of ROR1-DNAJC6 and CEP85L-ROS1 fusions by showing that they can promote cell proliferation in vitro and tumor formation in vivo. Furthermore, we found that ∼4% of the samples have massively rearranged chromosomes, many of which are associated with upregulation of oncogenes such as ERBB2 and TERT. Although the sensitivity of detecting structural alterations from exomes is considerably lower than that from whole genomes, this approach will be fruitful for the multitude of exomes that have been and will be generated, both in cancer and in other diseases.

A Pan-Cancer Compendium of Genes Deregulated by Somatic Genomic Rearrangement across More Than 1,400 Cases

“…Cases with low-pass (~6–8×) WGS had 33.9 SVs detected on average, while cases with high-pass (~30–60×) WGS had 164.5 SVs detected on average. Although they were subjected to high-pass WGS, the three kidney cancer types showed relatively fewer detected SVs (average 27.3), which is consistent with previous findings (Chen et al, 2016; Yang et al, 2013, 2016). As compared to somatic SVs as detectable by whole-exome sequencing (Yang et al, 2016), low-pass WGS detected 10 times as many SVs on average.…”

“…Although they were subjected to high-pass WGS, the three kidney cancer types showed relatively fewer detected SVs (average 27.3), which is consistent with previous findings (Chen et al, 2016; Yang et al, 2013, 2016). As compared to somatic SVs as detectable by whole-exome sequencing (Yang et al, 2016), low-pass WGS detected 10 times as many SVs on average. Based on comparisons between SV calls by either low-pass or high-pass WGS for a subset of cases (Table S2), ~20% of SVs identifiable by high-pass WGS were identified by low-pass WGS with the Meerkat algorithm, and ~75% of SVs identified by low-pass WGS were identifiable by high-pass WGS.…”

“…As may have been anticipated (Weischenfeldt et al, 2013; Yang et al, 2016), genomic rearrangements could be associated here with widespread patterns of CNAs. While SVs may be balanced or unbalanced in terms of CNAs within the immediate vicinity of the breakpoint (e.g., involving deletions, insertions, tandem duplications), here, we considered SV associations with CNAs at a broad level, by cytoband region.…”

“…With data from both tumor and germline samples for each patient to distinguish germline and somatic variants, a total of 85,560 high-confidence somatic SVs were detected, using the Meerkat algorithm (Yang et al, 2013, 2016) (Table S2). As would be expected, cases sequenced with higher coverage had more SVs detected.…”

“…While low-pass WGS may involve decreased sensitivity of detection, >1,200 cases in TCGA have low-pass data, representing a rich resource, with no pan-cancer study to date using these data. Previous studies have surveyed genomic rearrangements in TCGA using whole-exome or SNP array platforms (Weischenfeldt et al, 2017; Yang et al, 2016); the platforms are more limited in terms of sensitivity of detection as compared to WGS. The unified datasets and larger sample numbers offered by TCGA would allow us to identify robust associations between WGS-inferred SVs and expression that would cut across multiple cancer types of various lineages.…”