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Whole genome and RNA sequencing of 1,220 cancers reveals hundreds of genes deregulated by rearrangement of cis-regulatory elements

Using a dataset of somatic Structural Variants (SVs) in cancers from 2658 patients—1220 with corresponding gene expression data—we identified hundreds of genes for which the nearby presence (within 100kb) of an SV breakpoint was associated with altered expression. For the vast majority of these genes, expression was increased rather than decreased with corresponding SV event. Well-known up-regulated cancer-associated genes impacted by this phenomenon included TERT, MDM2, CDK4, ERBB2, CD274, PDCD1LG2, and IGF2. SVs upstream of TERT involved ~3% of cancer cases and were most frequent in liver-biliary, melanoma, sarcoma, stomach, and kidney cancers. SVs associated with up-regulation of PD1 and PDL1 genes involved ~1% of non-amplified cases. For many genes, SVs were significantly associated with either increased numbers or greater proximity of enhancer regulatory elements near the gene. DNA methylation near the gene promoter was often increased with nearby SV breakpoint, which may involve inactivation of repressor elements. Abbreviations PCAWG the Pan-Cancer Analysis of Whole Genomes project SV Structural Variant


Introduction
Functionally relevant DNA alterations in cancer extend well beyond exomic boundaries. One notable example of this involves TERT, for which both non-coding somatic point mutations in the promoter or genomic rearrangements in proximity to the gene have been associated with TERT up-regulation [1][2][3] . Genomic rearrangements in cancer are common and often associated with copy number alterations 4,5 . Breakpoints associated with rearrangement can potentially alter the regulation of nearby genes, e.g. by disrupting specific regulatory elements or by translocating cis-regulatory elements from elsewhere in the genome into close proximity to the gene. Recent examples of rearrangements leading to "enhancer hijacking"-whereby enhancers from elsewhere in the genome are juxtaposed near genes, leading to overexpression-include a distal GATA2 enhancer being rearranged to ectopically activate EVI1 in leukemia 6 , activation of GFI1 family oncogenes in medulloblastoma 7 , and 5p15.33 rearrangements in neuroblastoma juxtaposing strong enhancer elements to TERT 8 . By integrating somatic copy alterations, gene expression data, and information on topologically associating domains (TADs), a recent pan-cancer study uncovered 18 genes with overexpression resulting from rearrangements of cis-regulatory elements (including enhancer hijacking) 9 . Genomic rearrangement may also disrupt the boundary sites of insulated chromosome neighborhoods, resulting in gene up-regulation 10 .
The Pan-Cancer Analysis of Whole Genomes (PCAWG) initiative has recently assembled over 2600 whole cancer genomes, with high sequencing coverage, from multiple independent studies representing a wide range of cancer types. These data involve a comprehensive and unified identification of somatic substitutions, indels, and structural variants (SVs, representing genomic rearrangement events, each event involving two breakpoints from different genomic coordinates becoming fused together), based on "consensus" calling across three independent algorithmic pipelines, together with initial basic filtering, quality checks, and merging 11,12 . Whole genome sequencing offers much better resolution in SV inference over that of whole exome data or SNP arrays 4,9 . These data represent an opportunity for us to survey this large cohort of cancers for somatic SVs with breakpoints located in proximity to genes. For a sizeable subset of cases in the PCAWG cohort, data from other platforms in addition to whole genome sequencing, such as RNA expression or DNA methylation, are available for integrative analyses, with 1220 cases having both whole genome and RNA sequencing.
While SVs can result in two distant genes being brought together to form fusion gene rearrangements (e.g. BCR-ABL1 or TMPRSS2-ERG) 13 , this present study focuses on SVs impacting gene regulation in the absence of fusion events or copy number alterations, e.g. SVs with breakpoints occurring upstream or downstream of the gene and involving rearrangement of cis-regulatory elements. With a genome-wide analysis involving a large sample size, information from multiple genes may be leveraged effectively, in order to identify common features involving the observed disrupted regulation of genes impacted by genomic rearrangement.

Widespread impact of somatic SVs on gene expression patterns in cancer
Inspired by recent observations in kidney cancer 3,14 , neuroblastoma 8,15 , and B-cell malignancies 16 , of recurrent genomic rearrangements affecting the chromosomal region proximal to TERT and resulting in its up-regulation, we sought to carry out a pan-cancer analysis of all coding genes, for ones appearing similarly affected by rearrangement. We referred to a dataset of somatic SVs called for whole cancer genomes of 2658 patients, representing more than 20 different cancer types and compiled and harmonized by the PCAWG initiative from 47 previous studies (Table S1). Gene expression profiles were available for 1220 of the 2658 patients. We set out to systematically look for genes for which the nearby presence of an SV breakpoint could be significantly associated with changes in expression. In addition to the 0-20 kb region upstream of each gene (previously involved with rearrangements near TERT 3 ), we also considered SV breakpoints occurring 20-50kb upstream of a gene, 50-100kb upstream of a gene, within a gene body, or 0-20kb downstream of a gene (Figure 1a). (SV breakpoints located within a given gene were not included in the other upstream or downstream SV sets for that same gene.) For each of the above SV groups, we assessed each gene for correlation between associated SV event and expression. As each cancer type as a group would have a distinct molecular signature 17 , and as genomic rearrangements may be involved in copy alterations 4 , both of these were factored into our analysis, using linear models.
For each of the genomic regions relative to genes that were considered (i.e. genes with at least three samples associated with an SV breakpoint within the given region), we found widespread associations between SV event and expression, after correcting for expression patterns associated with tumor type or copy number (Figure 1b and Supplementary Figure 1a and Table   S2). For gene body, 0-20kb upstream, 20-50kb upstream, 50-100kb upstream, and 0-20kb downstream regions, the numbers of significant genes at p<0.001 (corresponding to estimated false discovery rates 18 of less than 5%) were 518, 384, 416, 496, and 302, respectively. For each of these gene sets, many more genes were positively correlated with SV event (i.e. expression was higher when SV breakpoint was present) than were negatively correlated (on the order of 95% versus 5%). Permutation testing of the 0-20kb upstream dataset (randomly shuffling the SV event profiles and computing correlations with expression 1000 times) indicated that the vast majority of the significant genes observed using the actual dataset would not be explainable by random chance or multiple testing (with permutation results yielding an average of 30 "significant" genes with standard deviation of 5.5, compared to 384 significant genes found for the actual dataset). Without correcting for copy number, even larger numbers of genes with SVs associated with increased expression were found (Figure 1b), indicating that many of these SVs would be strongly associated with copy gain. Many of the genes found significant for one SV group were also significant for other SV groups (Figure 1c). Tumor purity and total number of SV breakpoints were not found to represent significant confounders (Supplementary Figure   1b). The numbers of statistically significant genes were also found when examining regions further upstream or downstream of genes, up to 1Mb (Supplementary Figure 1c).

Key driver genes in cancer impacted by nearby SV breakpoints
Genes with increased expression associated with nearby SV breakpoints included many genes with important roles in cancer (Table 1), such as TERT (significant with p<0.001 for regions from 0-20kb downstream to 20-50kb upstream of the gene), MYC (significant for gene body SV breakpoints), MDM2 (regions from 0-20kb downstream to 50-100kb upstream), CDK4 (0-20kb downstream and 20-100kb upstream), ERBB2 (gene body to 50-100kb upstream), CD274 (0-20kb downstream to 50-100kb upstream), PDCD1LG2 (0-20kb downstream to 20-50kb upstream), and IGF2 (0-20kb downstream and 50-100kb upstream). Genes with decreased expression associated with SV breakpoints located within the gene included PTEN 19 (n=50 cases with an SV breakpoint out of 1220 cases with expression data available), STK11 (n=15), KEAP1 (n=5), TP53 (n=22), RB1 (n=55), and SMAD4 (n=18), where genomic rearrangement would presumably have a role in disrupting important tumor suppressors; for other genes, SV breakpoints within the gene could potentially impact intronic regulatory elements, or could represent potential fusion events (though in a small fraction of cases) 13  SVs associated with CD274 (PD1) and PDCD1LG2 (PDL1)-genes with important roles in the immune checkpoint pathway-were associated with increased expression of these genes ( Figure 4a and Table S4). Out of the 1220 cases with gene expression data, 19 harbored an SV breakpoint in the region involving the two genes, both of which reside on chromosome 9 in proximity to each other (Figure 4b, considering the region 50kb upstream of CD274 to 20kb downstream of PDCD1LG2). These 19 cases included lymphoma (n=5), lung (4), breast (2), head and neck (2), stomach (2), colorectal (1), and sarcoma (1). Six of the 19 cases had amplification of one or both genes, though on average cases with associated SV had higher expression than cases with amplification but no SV breakpoint (Figure 4a, p<0.0001 t-test on log-transformed data). For most of the 19 cases, the SV breakpoint was located within the boundaries of one of the genes (Figure 4a), while both genes tended to be elevated together regardless of the SV breakpoint position (Figure 4b). We examined the 19 cases with associated SVs for fusions involving either CD274 or PDCD1LG2, and we identified a putative fusion transcript for RNF38->PDCD1LG2 involving three cases, all of which were lymphoma. No fusions were identified involving CD274.

SVs associated with translocated enhancers and altered DNA methylation near genes
Similar to analyses focusing on TERT (Figure 2d), we examined SVs involving other genes for potential translocation of enhancer elements. For example, like TERT, SVs with breakpoints 0-20kb upstream of CDK4 were associated with an increased number of upstream enhancer elements as compared to that of the unaltered gene ( Figure 5a); however, SV breakpoints upstream of MDM2 were associated with significantly fewer enhancer elements compared to that of the unaltered region ( Figure 5a). For the set of 1233 genes with at least 7 SV breakpoints 0-20kb upstream and with breakpoint mate on the distal side from the gene, the numbers of enhancer elements 0.5 Mb region upstream of rearrangement breakpoints was compared with the number for the unaltered gene ( Figure 5b and Table S5). Of these genes, 24% showed differences at a significance level of p<0.01 (paired t-test, with ~12 nominally significant genes being expected by chance). However, for most of these genes, the numbers of enhancer elements was decreased on average with the SV breakpoint rather than increased (195 versus 103 genes, respectively), indicating that translocation of greater numbers of enhancers might help explain the observed upregulation for some but not all genes. For other genes (e.g.  Table S6). However, for the subset of 263 genes positively correlated in expression with occurrence of upstream SV breakpoint (p<0.001, 0-20kb, 20-50kb, or 50-100kb SV sets), the methylation-expression correlations were less skewed towards negative (p=0.0001 by t-test, comparing the two sets of correlation distributions in Figure 5a). Genes positively correlated between expression and methylation included TERT and MDM2, with many of the same genes also showing a positive correlation between DNA methylation and nearby SV breakpoint ( Figure 6a). Regarding TERT, a CpG site located in close proximity to its core promotor is known to contain a repressive element 8,25 ; nonmethylation results in the opening of CTCF binding sites and the transcriptional repression of TERT 25 . In the PCAWG cohort, SV breakpoints occurring 0-20kb upstream of the gene were associated with increased CpG island methylation (Figure 6b), while SV breakpoints 20-50kb upstream were not; TERT promoter mutation was also associated with increased methylation (Figure 6c).

Discussion
Using a unique dataset of high coverage whole genome sequencing and gene expression on tumors from a large number of patients and involving a wide range of cancer types, we have shown here how genomic rearrangement of regions nearby genes, leading to gene upregulation-a phenomenon previously observed for individual genes such as TERT-globally impacts a large proportion of genes and of cancer cases. Genomic rearrangements involved with up-regulation of TERT in particular have furthermore been shown here to involve a wide range of cancer types, expanded from previous observations made in individual cancer types such as kidney chromophobe and neuroblastoma. While many of the genes impacted by genomic rearrangement in this present study likely represent passengers rather than drivers of the disease, many other genes with canonically established roles in cancer would be impacted.
Though any given gene may not be impacted in this way in a large percentage of cancer cases (the more frequently SV-altered gene TERT involving less than 3% of cancers surveyed), the multiple genes involved leads to a large cumulative effect in terms of absolute numbers of patients. The impact of somatic genomic rearrangements on altered cis-regulation should therefore be regarded as an important driver mechanism in cancer, alongside that of somatic point mutations, copy number alteration, epigenetic silencing, gene fusions, and germline polymorphisms.
While the role of genomic rearrangements in altering the cis-regulation of specific genes within specific cancer types has been previously observed, our present pan-cancer study demonstrates that this phenomenon is more extensive and impacts a far greater number of genes than may have been previously thought. A recent study by Weischenfeldt et al. 9 , utilizing SNP arrays to estimate SV breakpoints occurring within TADs (which confine physical and regulatory interactions between enhancers and their target promoters), uncovered 18 genes (including TERT and IRS4) in pan-cancer analyses and 98 genes (including IGF2) in cancer type-specific analyses with over-expression associated with rearrangements involving nearby or surrounding TADs. Our present study using PCAWG datasets identifies hundreds of genes impacted by SV-altered regulation, far more than the Weischenfeldt study. In contrast to the Weischenfeldt study, our study could take advantage of whole genome sequencing over SNP arrays, with the former allowing for much better resolution in identifying SVs, including those not associated with copy alterations. In addition, while TADs represent very large genomic regions, often extending over 1Mb, our study pinpoints SV with breakpoints acting within relatively close distance to the gene, e.g. within 20kb for many genes. In principle, genomic rearrangements could impact a number of regulatory mechanisms, not necessarily limited to enhancer hijacking, and genes may be altered differently in different samples. The analytical approach of our present study has the advantage of being able to identify robust associations between SVs and expression, without making assumptions as to the specific mechanism.
Future efforts can further explore the mechanisms involved with specific genes deregulated by nearby genomic rearrangements. Regarding TERT-associated SVs, for example, previously

Datasets
Datasets of structural variants (SVs), RNA expression, somatic mutation, and copy number were generated as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. 11,12 In all, 2671 patients with whole genome data were represented in the PCAWG datasets, spanning a range of cancer types (bladder, sarcoma, breast, liver-biliary, cervix, leukemia, colorectal, lymphoma, prostate, eosophagus, stomach, central nervous system or "cns", head/neck, kidney, lung, skin, ovary, pancreas, thyroid, uterus). For SVs, calls were made by three different data centers using different algorithms; calls made by at least two algorithms were used in the downstream analyses, along with additional filtering criteria being used as described elsewhere 11,26 . The consensus SV calls are available from synapse (https://www.synapse.org/#!Synapse:syn7596712).
For copy number, the calls made by the Sanger group were used. Gene copies of five or more were called as amplification events. For somatic mutation of TERT promoter, PCAWG variant calls, as well as any additional data available from the previous individual studies 3,22,27,28 , were used. TERT promoter viral integrations were obtained from ref 22 . Of the 2658 cases, RNA-seq data were available for 1220 cases. For RNA-seq data, alignments by both STAR and TopHat2 were used to generated a combined set of expression calls; FPKM-UQ values (where UQ= upper quartile of fragment count to protein coding genes) were used (dataset available at https://www.synapse.org/#!Synapse:syn5553991). Where a small number of patients had multiple tumor sample profiles, one profile was randomly selected to represent the patient.
DNA methylation profiles had been generated for 771 cases by The Cancer Genome Atlas using either the Illumina Infinium HumanMethylation450 (HM450) or HumanMethylation27 (HM27) BeadChips (Illumina, San Diego, CA), as previously described 29 . To help correct for batch effects between methylation data platforms (HM450 versus HM27), we used the combat software 30 . For each of 8226 represented genes, an associated methylation array probe mapping to a CpG island was assigned; where multiple probes referred to the same gene, the probe with the highest variation across samples was selected for analysis. Correlations between DNA methylation and gene expression were assessed using logit-transformed methylation data and log-transformed expression data and Pearson's correlations.

Integrative analyses between SVs and gene expression
For each of a number of specified genomic region windows in relation to genes, we constructed a somatic SV breakpoint matrix by annotating for every sample the presence or absence of at least one SV breakpoint within the given region. For the set of SVs associated with a given gene within a specified region in proximity to the gene (e.g. 0-20kb upstream, 20-50kb upstream, 50-100kb upstream, 0-20kb downstream, or within the gene body), correlation between expression of the gene and the presence of at least one SV breakpoint was assessed using a linear regression model (with log-transformed expression values). In addition to modeling expression as a function of SV event, models incorporating cancer type (one of the 20 major types listed above) as a factor in addition to SV, and models incorporating both cancer type and copy number in addition to SV, were also considered. For these linear regression models, genes with at least three samples associated with an SV breakpoint within the given region were considered. Genes for which SVs were significant (p<0.001) after correcting for both cancer type and copy were explored in downstream analyses. Results from both the SV only model and results from the SV+cancer type models were also highlighted in Figure 1 and provided in Table   S2, but the p-values from those models were not used in selecting for genes or SVs of interest for follow-up analyses.
The method of Storey and Tibshirani 18 was used to estimate false discovery rates (FDR) for significant genes. For purposes of FDR, only genes that had SV breakpoints falling within the given region relative to the gene in at least 3 cases were tested; for example, for the 0-20kb upstream region, 6257 genes were tested, where 384 genes were significant at a nominal pvalue of <0.001 (using a stringent cutoff, with ~6 genes expected by chance due to multiple testing, or FDR<2%); the other genomic region windows yielded similar results. For each genomic region window, the FDR for genes significant at the p<0.001 level did not exceed 4%.
In addition, permutation testing of the 0-20kb upstream dataset was carried out, whereby the SV events were randomly shuffled (by shuffling the patient ids) and the linear regression models (incorporating both cancer type and copy number) were used to compute expression versus permuted SV breakpoint associations; for each of 1000 permutation tests, the number of nominally significant genes at p<0.001 was computed and compared with results from the actual datasets.

Integrative analyses using enhancer genomic coordinates
Gene boundaries and locations of enhancer elements were obtained from Ensembl (GRCh37 build). Enhancer elements found in multiple cell types (using Ensembl "Multicell" filter, accessed April 1, 2016) were used 20 . As previously described 20 , the Ensembl team first reduced all available experimental data for each cell type into a cell type-specific annotation of the genome; consensus "Multicell" regulatory features of interest, including predicted enhancers, were then defined. For each SV breakpoint 0-20kb upstream of a gene, the number of enhancer elements near the gene that would be represented by the rearrangement was determined (based on the orientation of the SV breakpoint mate). Only SVs with breakpoints on the distal side from the gene were considered in this analysis; in other words, for genes on the negative strand, the upstream sequence of the breakpoint should be fused relative to the breakpoint coordinates, and for genes on the positive strand, the downstream sequence of the breakpoint (denoted as negative orientation) should be fused relative to the breakpoint coordinates.

Statistical Analysis
All P-values were two-sided unless otherwise specified.