Abstract
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.
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 1–3. Genomic rearrangements in cancer are common and often associated with copy number alterations4,5. 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 leukemia6, activation of GFI1 family oncogenes in medulloblastoma7, and 5p15.33 rearrangements in neuroblastoma juxtaposing strong enhancer elements to TERT8. By integrating somatic copy alterations, gene expression data, and information on topologically associating domains (TADs), a recent pan-cancer study uncovered 18 genes with over-expression 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-regulation10.
The Pan-Cancer Analysis of Whole Genomes (PCAWG) initiative has recently assembled over 2600 whole cancer genomes 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), based on “consensus” calling across three independent algorithmic pipelines, together with initial basic filtering, quality checks, and merging11,12. Whole genome sequencing offers much better resolution in SV inference over that of whole exome data or SNP arrays4,9. 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, e.g. SVs 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.
Results
Widespread impact of somatic SVs on gene expression patterns in cancer
Inspired by recent observations in kidney cancer3,14, neuroblastoma8,15, and B-cell malignancies16, 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 TERT3), 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). (SVs 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 signature17, and as genomic rearrangements may be involved in copy alterations4, 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 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 rates18 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 found to diminish considerably when examining regions beyond 100 kb upstream of the gene (Supplementary Figure 1c).
Key driver genes in cancer impacted by nearby SVs
Genes with increased expression associated with nearby SVs included many genes with important roles in cancer (Table 1), such as TERT (significant with p<0.001 for regions from 020kb downstream to 20-50kb upstream of the gene), MYC (significant for gene body SVs), 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 SVs located within the gene included PTEN (n=50 cases with an SV 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, SVs within the gene could potentially impact intronic regulatory elements, or could represent potential fusion events (though in a small fraction of cases)13. Examining the set of genes positively correlated (p<0.001) with occurrence of SV upstream of the gene (for either 0-20kb, 20-50kb, or 50-100kb SV sets), enriched gene categories (Figure 1d) included G-protein coupled receptor activity (70 genes), telomerase holoenzyme complex (TERT, PTGES3, SMG6), eukaryotic translation initiation factor 2B complex (EIF2S1, EIF2B1, EIF2B5), keratin filament (15 genes), and insulin receptor binding (DOK6, DOK7, IGF2, IRS4, FRS2, FRS3, PTPN11). When taken together, SVs involving the above categories of genes would potentially impact a substantial fraction of cancer cases, e.g. on the order of 2-5% of cases across various types (Figure 1e). Gene amplification events (defined as five or more copies) could be observed for a number of genes associated with SVs, but amplification alone in many cases would not account for the elevated gene expression patterns observed (Figure 1e).
Translocations involving the region 0-100kb upstream of TERT were both inter- and intrachromosomal (Figure 2a and Table S3) and included 170 SV breakpoints and 84 cancer cases, with the most represented cancer types including liver-biliary (n=29 cases), melanoma (n=17 cases), sarcoma (n=15 cases), and kidney (n=9 cases). Most of these SV breakpoints were found within 20kb of the TERT start site (Figure 2b), which represented the region where correlation between SV events and TERT expression was strongest (Figures 2c and 2d, p<1E14, linear regression model). In neuroblastoma, translocation of enhancer regulatory elements near the promoter was previously associated with TERT up-regulation 8,15. Here, in a global analysis, we examined the number of enhancer elements19 within a 0.5 Mb region upstream of each rearrangement breakpoint occurring in proximity to TERT (for breakpoints where the SV mate was oriented away from TERT). While for unaltered TERT, 21 enhancer elements are located 0.5 Mb upstream of the gene, on the order of 30 enhancer elements on average were within the 0.5 Mb region adjacent to the TERT SV breakpoint (Figure 2e), representing a significant increase (p<1E-6, paired t-test). A trend was also observed, by which SVs closer to the TERT start site were associated with a larger number of enhancer elements (Figure 2d, p<0.03, Spearman’s correlation).
Consistent with observations elsewhere4, genomic rearrangements could be associated here with copy alterations for a large number of genes (Figure 1b), including genes of particular interest such as TERT and MDM2 (Figure 3a). However, copy alteration alone would not account for all observed cases of increased expression in conjunction with SV event. For example, with a number of key genes (including TERT, MDM2, ERBB2, CDK4), when all amplified cases (i.e. with five or more gene copies) were grouped into a single category, regardless of SV breakpoint occurrence, the remaining SV-involved cases showed significantly increased expression (Figure 3b). Regarding TERT in particular, a number of types of genomic alteration may act upon transcription, including upstream SV, TERT amplification20, promoter mutations1,2, promoter viral integration21, and MYC amplification22. Within the PCAWG cohort of 2658 cancer cases, 933 (35%) were altered according to at least one of the above alteration classes, with each class being associated with increased TERT mRNA expression (Figure 3c). Upstream SVs in particular were associated with higher TERT as compared to promoter mutation or amplification events.
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 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 (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 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, SVs 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 SVs 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 (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 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. HOXA13 and CCNE1), enhancer elements on average were positioned in closer proximity to the gene as a result of the genomic rearrangement (Figure 5c). Of 829 genes examined (with at least 5 SVs 0-20kb upstream and with breakpoint mate on the distal side from the gene, where the breakpoint occurs between the gene start site and its nearest enhancer in the unaltered scenario), 8.3% showed a significant decrease (p<0.01, paired t-test) in distance to the closest enhancer on average as a result of the SV, as compared to 1% showing a significance increase in distance.
We went on to examine genes impacted by nearby SVs for associated patterns of DNA methylation. Taking the entire set of 8256 genes with associated CpG island probes represented on the 27K DNA methylation array platform (available for samples from The Cancer Genome Atlas), the expected overall trend23 of inverse correlations between DNA methylation and gene expression were observed (Figure 6a and Table S6). However, for the subset of 263 genes positively correlated in expression with occurrence of upstream SV (p<0.001, 0-20kb, 2050kb, 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 element8,24; non-methylation results in the opening of CTCF binding sites and the transcriptional repression of TERT24. In the PCAWG cohort, SV breakpoints occurring 0-20kb upstream of the gene were associated with increase 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 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 up-regulation—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 SVs acting within relatively close distance to the gene, e.g. within 20kb for many genes. In principle, genomic rearrangements could impact a number of cis-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 observed increases in DNA methylation of the affected region had been previously thought to be the result of massive chromatin remodeling brought about by juxtaposition of the TERT locus to strong enhancer elements8, which is supported by observations made in this present study involving multiple cancer types. However, not all genes found here to be deregulated by SVs would necessarily follow the same patterns as those of TERT. For example, not all of the affected genes would have repressor elements being inactivated by DNA methylation, and some genes such as MDM2 do not show an increase in enhancer numbers with associated SVs but do correlate positively between expression and methylation. There is likely no single mechanism that would account for all of the affected genes, though some mechanisms may be common to multiple genes. Integration of other types of information (e.g. other genome annotation features, data from other platforms, or results of functional studies) may be combined with whole genome sequencing datasets of cancer, in order to gain further insights into the global impact of non-exomic alterations, where the datasets assembled by PCAWG in particular represent a valuable resource.
Methods
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 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. For copy number, the calls made by the Sanger group were used. For somatic mutation of TERT promoter, PCAWG variant calls, as well as any additional data available from the previous individual studies3,21,25,26, were used. TERT promoter viral integrations were obtained from ref21. 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 described27. To help correct for batch effects between methylation data platforms (HM450 versus HM27), we used the combat software28. 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.
Integrative Analyses
Gene boundaries and locations of enhancer elements were obtained from Ensembl (GRCh37 build). Enhancer elements found in multiple cell types (using Ensembl “Multicell” filter) were used19. For each SV 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 mate). Gene copies of five or more were called as amplification events.
For a given set of SVs associated with a given gene, correlation between expression of the gene and the presence of an SV 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 were also considered. For these linear regression models, genes with at least three samples associated with an SV within the given region were considered. Genes for which SVs were significant (p<0.001) after correcting for cancer type and copy numbers were explored in downstream analyses. The method of Storey and Tibshirini18 was used to estimate false discovery rates for significant genes. In addition, permutation testing of the 0-20kb upstream dataset was carried out, whereby the SV events were randomly shuffled and the linear regression models (incorporating both cancer type and copy number) were used to compute expression versus permuted SVs; 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.
Statistical Analysis
All P-values were two-sided unless otherwise specified.
Acknowledgements
This work was supported in part by National Institutes of Health (NIH) grant P30CA125123 (C. Creighton) and Cancer Prevention and Research Institute of Texas (CPRIT) grant RP120713 C2 (C. Creighton).
Footnotes
Abbreviations: PCAWG, the Pan-Cancer Analysis of Whole Genomes project; SV, Structural Variant;