About half of all cancers have somatic integrations of retrotransposons. Here, to characterize their role in oncogenesis, we analyzed the patterns and mechanisms of somatic retrotransposition in 2,954 cancer genomes from 38 histological cancer subtypes within the framework of the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. We identified 19,166 somatically acquired retrotransposition events, which affected 35% of samples and spanned a range of event types. Long interspersed nuclear element (LINE-1; L1 hereafter) insertions emerged as the first most frequent type of somatic structural variation in esophageal adenocarcinoma, and the second most frequent in head-and-neck and colorectal cancers. Aberrant L1 integrations can delete megabase-scale regions of a chromosome, which sometimes leads to the removal of tumor-suppressor genes, and can induce complex translocations and large-scale duplications. Somatic retrotranspositions can also initiate breakage–fusion–bridge cycles, leading to high-level amplification of oncogenes. These observations illuminate a relevant role of L1 retrotransposition in remodeling the cancer genome, with potential implications for the development of human tumors.
L1 retrotransposons are widespread repetitive elements in the human genome, representing 17% of the entire DNA content1,2. Using a combination of cellular enzymes and self-encoded proteins with endonuclease and reverse transcriptase activity, L1 elements copy and insert themselves at new genomic sites, in a process called retrotransposition. Most of the approximately 500,000 L1 copies in the human reference genome are truncated, inactive elements that are unable to retrotranspose. A small subset of them, around 100–150 L1 loci, remain active in the average human genome, acting as source elements, a small number of which consists of highly active copies termed hot-L1s3,4,5. These L1 source elements are usually transcriptionally repressed, but epigenetic changes that occur in tumors may promote their expression and allow them to retrotranspose6,7. Somatic L1 retrotransposition usually introduces a new copy of the 3′ end of the L1 sequence, and can also mobilize unique DNA sequences located immediately downstream of the source element, in a process called 3′ transduction7,8,9. L1 retrotransposons can also promote the somatic trans-mobilization of Alu elements, SINE-VNTR-Alu (SVA) elements and processed pseudogenes, which are copies of mRNAs that have been reverse transcribed into DNA and inserted into the genome with the machinery of active L1 elements10,11,12.
Approximately 50% of human tumors contain somatic retrotranspositions of L1 elements7,13,14,15. Previous analyses indicate that although a fraction of somatically acquired L1 insertions in cancer may influence gene function, the majority of retrotransposon integrations in a single tumor represent passenger mutations with little or no effect on cancer development7,13. Nonetheless, L1 elements are capable of promoting other types of genomic structural alterations in the germline and somatically, in addition to canonical L1 insertion events16,17,18; the effect of these alterations remains largely unexplored in the context of human cancer19,20.
To further understand the roles of retrotransposons in cancer, we developed strategies to analyze the patterns and mechanisms of somatic retrotransposition in 2,954 cancer genomes from 38 histological cancer subtypes within the framework of the PCAWG project21, many of which had not been evaluated for retrotransposition. On the basis of the robustness of the retrotransposition calls, we retained 296 tumors that were preliminarily excluded by the PCAWG Consortium21 (see Methods). Our analyses identify patterns and mutational mechanisms of structural variation in human cancers that are mediated by L1 retrotransposition. We found that the aberrant integration of L1 retrotransposons has a relevant role in remodeling the architecture of the cancer genome in some human tumors, mainly by promoting megabase-scale deletions that, occasionally, generate genomic consequences that may promote cancer development through the removal of tumor-suppressor genes, such as CDKN2A, or trigger the amplification of oncogenes, such as CCND1.
The landscape of somatic retrotransposition in a large cancer whole-genome dataset
We ran our bioinformatic pipelines (Methods and Supplementary Note) to explore somatic retrotransposition on whole-genome sequencing data from 2,954 tumors and their matched normal pairs, across 38 cancer types (Supplementary Fig. 1 and Supplementary Table 1). The analysis retrieved a total of 19,166 somatically acquired retrotranspositions that were classified into six categories (Fig. 1a and Supplementary Table 2). Comprising 98% (18,739 out of 19,166) of the events, L1 integrations (14,967 solo-L1, 3,669 L1-transductions, and 103 L1-mediated rearrangements, which mainly comprised deletions) overwhelmingly dominate the landscape of somatic retrotransposition in the PCAWG dataset (Fig. 1a,b). By contrast, elements of the lineages Alu (Supplementary Fig. 2) and SVA (comprising 130 and 23 somatic copies, respectively) and processed pseudogenes, with 274 events, represent minor categories.
The core pipeline, TraFiC-mem (Supplementary Fig. 3)—which was used to explore somatic retrotransposition in PCAWG—was validated by single-molecule whole-genome sequencing data analysis of one cancer cell line with high retrotransposition rate and its matched normal sample, confirming the somatic acquisition of 295 out of 308 retrotransposition events (false discovery rate <5%, Supplementary Fig. 4a,b). To further evaluate TraFiC-mem, we reanalyzed a mock cancer genome into which we had previously7 seeded somatic retrotransposition events at different levels of tumor clonality, and then simulated sequencing reads to the average level of coverage of the PCAWG dataset. The results confirmed a high precision (>99%) of TraFiC-mem, and a recall ranging from 90 to 94% for tumor clonalities from 25 to 100%, respectively (Supplementary Fig. 4c–e).
We observed marked variation in the retrotransposition rate across PCAWG tumor types (Fig. 1c and Supplementary Table 3). Overall, 35% (1,046 out of 2,954) of all cancer genomes have at least one retrotransposition event. However, esophageal adenocarcinoma, head-and-neck squamous carcinoma, lung squamous carcinoma and colorectal adenocarcinoma are significantly enriched in somatic retrotranspositions (Mann–Whitney U-test, P < 0.05; Fig. 1c,d and Supplementary Fig. 5). These four tumor types alone account for 70% (13,373 out of 19,166) of all somatic events in the PCAWG dataset, although they represent just 9% (266 out of 2,954) of the samples. This is particularly noticeable in esophageal adenocarcinoma, in which 27% (27 out of 99) of the samples show more than 100 separate somatic retrotranspositions (Fig. 1c), making L1 insertions the most frequent type of structural variation in esophageal adenocarcinoma (Fig. 1e). Furthermore, retrotranspositions are the second-most frequent type of structural variants in head-and-neck squamous and colorectal adenocarcinomas (Fig. 1e). To gain insights into the genetic causes that make some cancers more prone to retrotransposition than others, we looked for associations between retrotransposition and driver mutations in cancer-related genes. This analysis revealed an increased L1 retrotransposition rate in tumors with TP53 mutations (Mann–Whitney U-test, P < 0.05; Supplementary Fig. 6), and supports previous analyses that have suggested that TP53 functions to restrain mobile elements22,23. We also observe a widespread correlation between L1 retrotransposition and other types of structural variation (Spearman’s ρ = 0.44, P < 0.01; Supplementary Fig. 7), a finding that is most likely a consequence of a confounding effect of TP53-mutated genotypes (Supplementary Fig. 6).
We identified 43% (7,979 out of 18,636) somatic retrotranspositions of L1 inserted within gene regions including promoters, of which 66 events hit cancer-associated genes. The analysis of expression levels in samples with available transcriptome data, revealed four genes—including the ABL oncogene—with L1 retrotranspositions in the proximity of promoter regions that showed significant overexpression compared with the expression in the remaining samples of the same tumor type (Student’s t-test, q < 0.10; Supplementary Fig. 8a–c). The structural analysis of RNA-sequencing data identified instances in which portions of a somatic retrotransposition within a gene exonize, a process that sometimes involves cancer-associated genes (Supplementary Fig. 8d). In addition, we found evidence of aberrant fusion transcripts arising from the inclusion of processed pseudogenes in the target host gene and expression of processed pseudogenes landing in intergenic regions (Supplementary Fig. 8e).
Dissecting the genomic features that influence the landscape of L1 retrotranspositions in cancer
The genome-wide analysis of the distribution of somatic L1 insertions across the cancer genome revealed considerable variation in the rate of L1 retrotransposition (Fig. 2a and Supplementary Table 4). To understand the reasons behind such variation, we studied the association of L1 event rates with various genomic features. We first investigated whether the distribution of somatic L1s across the cancer genome could be determined by the occurrence of L1-endonuclease target-site motifs. We used a statistical approach based on negative binomial regression to deconvolute the influence of multiple overlapping genomic variables24; this analysis showed that close matches to the motif have a 244-fold increased L1 rate, compared with non-matched motifs (Fig. 2b and Supplementary Fig. 9a). Adjusting for this effect, we found a strong association with DNA replication time; the latest-replicating quarter of the genome was 8.9-fold enriched in L1 events (95% confidence interval, 8.25–9.71) compared with the earliest-replicating quarter (Fig. 2b,c and Supplementary Fig. 9b). Recent work25 has shown that L1 retrotransposition has a strong cell-cycle bias, and preferentially occurs during S phase. Our results are in agreement with these findings and suggest that L1 retrotransposition peaks in the later stages of nuclear DNA synthesis.
Next, we examined L1 rates in open chromatin measured using DNase hypersensitivity and, conversely, in closed heterochromatic regions by analyzing K9-trimethylated histone H3 (H3K9me3)26. When adjusting for the confounding effects of L1 motif content and replication time24, we found that somatic L1 events are enriched in open chromatin (1.27-fold in the top DNase hypersensitivity bin; 95% confidence interval, 1.14–1.41; Fig. 2b) and depleted in heterochromatin (1.72-fold, 95% confidence interval, 1.57–1.99; Fig. 2b). This finding differs from previous analyses, which have suggested that L1 insertions favored heterochomatin7—a discrepancy that we believe to be due to the confounding effect between heterochromatin and late-replicating DNA regions, which was not addressed in previous analyses. We also found a negative association of L1 rate with features of active transcription of chromatin, characterized by fewer L1 events at active promoters (1.63-fold; Supplementary Fig. 9c), a slight but significant reduction in L1 rates in highly expressed genes (1.25-fold lower; 95% confidence interval, 1.16–1.34; Fig. 2b) and a further depletion at H3K36me3 (1.90-fold reduction in the highest tertile; 95% confidence interval, 1.59–2.29; Fig. 2b), a mark of actively transcribed regions deposited in the body and at the 3′ end of active genes26. Further details on these associations are shown in Supplementary Fig. 9c–e and described in the Supplementary Note.
The contribution of L1 source elements to the pan-cancer retrotransposition burden
We used somatically mobilized L1 3′ transduction events to trace L1 activity to specific source elements7. This strategy revealed 124 germline L1 loci in the human genome that are responsible for most of the genomic variation generated by retrotransposition in the PCAWG dataset7,21 (Supplementary Table 5). To our knowledge, 52 of these loci represent previously unreported source elements in human cancer21. We analyzed the relative contribution of individual source elements to retrotransposition burden across cancer types, and found that retrotransposition is generally dominated by five hot-L1 source elements that alone give rise to half of all somatic transductions (Fig. 3a). This analysis revealed a dichotomous pattern of hot-L1 activity, with source elements that we have termed Strombolian and Plinian, given their similarity to these two types of volcanoes (Fig. 3b). Strombolian source elements are relatively indolent and produce small numbers of retrotranspositions in individual tumor samples, although they are often active and contribute substantially to overall retrotransposition in the PCAWG dataset. By contrast, Plinian elements are rarely active across tumors, but in these isolated cases, their activity is fulminant, causing large numbers of retrotranspositions.
At the individual tumor level, although we observed that the number of active source elements in a single cancer genome varied from 1 to 22, typically only 1 to 3 loci were operative (Fig. 3c). There is a correlation between somatic retrotranspositions and the number of active germline L1 source elements among PCAWG samples (Fig. 3d); this is likely one of the factors that explains why esophageal adenocarcinoma, lung and head-and-neck squamous carcinoma account for higher retrotransposition rates—in these three tumor types we also observed higher numbers of active germline L1 loci (Fig. 3c). Occasionally, somatic L1 integrations that retain their full length may also act as a source for subsequent somatic retrotransposition events7,27, and may reach high activity rates, leading them to dominate retrotransposition in a given tumor. For example, in a remarkable head-and-neck tumor sample, SA197656, we identified one somatic L1 integration at 4p16.1 that then triggered 18 transductions from its new site, with the next most active element being a germline L1 locus at 22q12.1, which accounted for 15 transductions (Supplementary Table 5).
Genomic deletions mediated by somatic L1 retrotransposition
In cancer genomes with high somatic L1 activity rates, we observed that some L1 retrotransposition events followed a distinctive pattern that consisted of a single cluster of reads, associated with copy-number loss, for which the mates unequivocally identified one extreme of a somatic L1 integration with, apparently, no local, reciprocal cluster that supported the other extreme of the L1 insertion (Fig. 4a). Analysis of the associated copy-number changes identified the missing L1 reciprocal cluster at the far end of the copy-number loss, indicating that this pattern represents a deletion that occurred in conjunction with the integration of an L1 retrotransposon (Fig. 4b; see the Supplementary Note for additional information on how to interpret the paired-end mapping data from this and other figures). These rearrangements—called L1-mediated deletions—have been observed to occur somatically with engineered L1s in cultured human cells16,17 and naturally in the brain18, and are most likely the consequence of an aberrant mechanism of L1 integration.
We developed specific algorithms to systematically identify L1-mediated deletions, and applied these methods across all PCAWG tumors. We identified 90 somatic events that matched the patterns described above, causing deletions of different size, which ranged in size from around 0.5 kb to 53.4 Mb (Fig. 4c and Supplementary Table 6). The reconstruction of the sequence at the breakpoint junctions in each case supports the presence of an L1-element—or L1-transduction—sequence and its companion polyadenylate tract, indicative of passage through an RNA intermediate. No target site duplication was found, which is also the typical pattern for L1-mediated deletions17. One potential mechanism for these events is that a molecule of L1 cDNA pairs with a distant 3′ overhang from a pre-existing double-strand DNA break generated upstream of the initial integration site, and the DNA region between the break and the original target site is subsequently removed by aberrant repair17 (Fig. 4d). Indeed, in 75% (47 out of 63) of L1-mediated deletions with a 5′-end breakpoint characterized to base-pair resolution, the analysis of the sequences at the junction revealed short (1–5 bp long, with median at 3 bp) microhomologies between the pre-integration site and the 5′ L1 sequence integrated right there (Supplementary Table 6). Furthermore, we found 14% (9 out of 63) instances in which short insertions (1–33 bp long, with median at 9 bp) are found at the 5′-breakpoint junction of the insertion. Both signatures are consistent with a non-homologous end-joining mechanism28, or other type of microhomology-mediated repair, for the 5′-end attachment of the L1 cDNA to a 3′ overhang from a pre-existing double-strand DNA break located upstream. L1-mediated deletions in which microhomologies or insertions are not found may follow alternative models17,29,30,31.
To confirm that these rearrangements are mediated by the integration of a single intervening retrotransposition event, we explored the PCAWG dataset for somatic L1-mediated deletions in which the L1 sequences at both breakpoints of the deletion could unequivocally be assigned to the same L1 insertion. These include small deletions and associated L1 insertions that were shorter than the library size, allowing sequencing read pairs to overlay the entire structure. For example, in a lung tumor sample, SA313800, we identified a deletion involving a 1-kb region of 19q12 with hallmarks of being generated by an L1 element (Fig. 4e). In this rearrangement, we found two different types of discordant read pairs at the deletion breakpoints: one cluster that supported the insertion of an L1 element and a second that spanned the L1 event and supported the deletion. Another type of L1-mediated deletion that could unequivocally be assigned to a single L1 insertion event is represented by those deletions generated by the integration of orphan L1 transductions. These transductions represent fragments of unique DNA sequence located downstream of an active L1 locus, which are mobilized without the companion L1 (refs. 7,15). For example, in one esophageal tumor sample, SA528932, we found a deletion of 2.5 kb on chromosome 3 mediated by the orphan transduction of a sequence downstream of an L1 locus on chromosome 7 (Fig. 4f).
Owing to the unavailability of PCAWG DNA specimens, we performed a validation of 16 additional somatic L1-mediated deletions that were identified by TraFiC-mem in two head-and-neck cancer cell lines with high retrotransposition rates, NCI-H2009 and NCI-H20877. We carried out two independent validation approaches, including PCR followed by single-molecule sequencing of amplicons, and Illumina whole-genome sequencing using mate-pair libraries with long insert size (3 kb and 10 kb). The results confirmed the somatic status of the rearrangements and a single L1-derived retrotransposition as the cause of the associated copy-number loss (Supplementary Figs. 10–12 and Supplementary Table 7).
Analysis of L1 3′-extreme insertion breakpoint sequences from L1-mediated deletions found in the PCAWG dataset revealed that 82% (74 out of 90) of the L1 events that caused deletions preferentially inserted into sequences that resemble L1-endonuclease consensus cleavage sites (for example, 5′-TTTT/A-3′ and related sequences32) (Supplementary Table 6). This confirms that the L1 machinery, through a target-primed reverse-transcription mechanism, is responsible for the integration of most of the L1 events that cause neighboring DNA loss32. Notably, in 16% (14 out of 90) of the events endonucleotidic cleavage occurred at the phosphodiester bond between a T and G instead of between the standard T and A site. In addition, we observed 8% (7 out of 90) instances in which the endonuclease motif was not found and the integrated element was truncated at both the 5′ and 3′ ends, suggesting that a small fraction of L1-associated deletions are the consequence of an L1-endonuclease-independent insertion mechanism30,31,32. Whatever mechanism of L1 integration is effective in each case, taken together, these data indicate that the somatic integration of L1 elements induces the associated deletions.
Megabase-size L1-mediated deletions cause loss of tumor-suppressor genes
Most L1-mediated deletions ranged from a few hundred to thousands of base pairs, although occasionally megabase-long regions of a chromosome were deleted (Fig. 4c and Supplementary Table 6). For example, in esophageal tumor sample SA528901, we found a 45.5-Mb interstitial deletion that involved the p31.3–p13.3 regions of chromosome 1 (Fig. 5a), in which both breakpoints of the rearrangement showed the hallmarks of a deletion mediated by integration of an L1 element. Here, the L1 element is 5′ truncated, which generated a small L1 insertion, allowing a fraction of the sequencing read pairs to span both breakpoints of the rearrangement. This unequivocally supports the model that the observed copy-number change is indeed a deletion mediated by retrotransposition of an L1 element. Similarly, in a lung tumor sample, SA313800, we found an interstitial L1-mediated deletion that induced the loss of 51.1 Mb from chromosome X, which included the centromere (Fig. 5b).
L1-mediated deletions were, on occasion, driver events and caused the loss of tumor-suppressor genes. In esophageal tumor sample SA528932, the integration of an L1 transduction from chromosome 7p12.3 to the short arm of chromosome 9 caused a 5.3-Mb clonal deletion that involved the 9p21.3–9p21.2 region. This led to the loss of one copy of a key tumor-suppressor gene, CDKN2A (Fig. 5c), which is deleted in many cancer types including esophageal tumors33,34,35,36. Notably, the sequencing data revealed a somatic transduction that arose from this L1 element at its new insertion site, demonstrating that L1 events that promote deletions can be competent for retrotransposition (Supplementary Fig. 13). In a second esophageal tumor sample, SA528899, an L1 element integrated into chromosome 9 promoted an 8.6-Mb clonal deletion that encompasses the 9p22.1–9p21.1 region that removes one copy of the same tumor-suppressor gene, CDKN2A (Fig. 5d). Thus, L1-mediated deletions have clear oncogenic potential.
L1 retrotransposition generates other types of structural variation in human tumors
Somatic retrotransposition can also be involved in mediating or repairing more complex structural variants. In one esophageal tumor sample, SA528896, two separate L1-mediated structural variants were present within a complex cluster of rearrangements (Fig. 6a). In the first, an L1 transduction from a source element on chromosome 14q23.1 bridged an unbalanced translocation from chromosome 1p to 5q. A second somatic retrotransposition event bridged from chromosome 5p to an unknown part of the genome, completing a large interstitial copy-number loss on chromosome 5 that involves the centromere. This case suggests that retrotransposon transcripts and their reverse-transcriptase machinery can mediate breakage and repair of complex dsDNA breaks, spanning two chromosomes.
To explore this further, we identified single-L1 clusters with no reciprocal cluster in the cancer cell lines that were sequenced by using mate pairs with 3 kb and 10 kb inserts. Such events may correspond to hidden genomic translocations leading to the linkage of two different chromosomes, in which L1 retrotransposition is involved. One of the samples, NCI-H2087, showed translocation breakpoints at 1q31.1 and 8q24.12, both of which had the hallmarks of L1-mediated deletions, for which the mate-pair sequencing data identified an orphan L1 transduction from chromosome 6p24 that bridged both chromosomes (Fig. 6b). The configuration has also been confirmed by using long-read single-molecule sequencing (Supplementary Fig. 11). This interchromosomal rearrangement is likely mediated by the aberrant operation of L1-integration mechanism, in which the L1-transduced cDNA is wrongly paired with a second 3′ overhang from a pre-existing double-strand break generated in a second chromosome32 (Fig. 6c).
We also found evidence that L1 integrations can cause duplications of large genomic regions in human cancer. In esophageal tumor sample SA528848 (Fig. 7a), we identified two independent read clusters that support the integration of a small L1 event, coupled with a coverage drop at both breakpoints. Copy-number analysis revealed that the two L1 clusters demarcate the boundaries of a 22.6-Mb duplication that involves the 6q14.3–q21 region, suggesting that the L1 insertion could be the cause of such rearrangement by bridging sister chromatids during or after DNA replication (Fig. 7b). The analysis of the rearrangement data at the breakpoints identified read pairs that traverse the length of the L1 insertion breakpoints, and the L1-endonuclease motif is the L1 3′ insertion breakpoint, both confirming a single L1 event as the cause of a tandem duplication (Fig. 7a). Notably, this duplication increases the copy number of the cyclin C gene, CCNC, which is dysregulated in some tumors37.
L1-mediated rearrangements can induce breakage–fusion–bridge cycles that trigger oncogene amplification
L1 retrotranspositions can also induce genomic instability by triggering breakage–fusion–bridge cycles. This form of genetic instability starts with end-to-end fusion of broken sister chromatids, and lead to a dicentric chromosome that forms an anaphase bridge during mitosis. Classically, the end-to-end chromosome fusions are thought to arise from telomere attrition38,39,40. We found, however, that somatic retrotransposition can induce the first inverted rearrangement that generates end-to-end fusion of sister chromatids. In lung tumor sample SA313800 (Fig. 7c), we found a small L1 event inserted on chromosome 14q that demarcates a copy-number change that involves a 79.6-Mb amplification of the 14q arm. Analysis of the sequencing data at the breakpoint revealed two discordant read clusters with the same orientation, which are 5.5 kb apart and support the integration of an L1. Both discordant clusters demarcate an increment of the sequencing coverage, for which the density is much greater in the right cluster. The only genomic structure that can explain this pattern is a fold-back inversion in which the two sister chromatids are bridged by an L1 retrotransposition in head-to-head (inverted) orientation (Fig. 7d).
In the example described above (Fig. 7c,d), no further breaks occurred, and the L1 retrotransposition generated an isochromosome (14q). In addition, we found examples in which the fusion of two chromatids by an L1 bridge induced further cycles of breakage–fusion–bridge repair. In esophageal tumor sample SA528848, we identified a cluster of reads on the long arm of chromosome 11 that had the typical hallmarks of an L1-mediated rearrangement (Fig. 8a). Copy-number data analysis showed that this L1 insertion point demarcated a 53-Mb deletion, which involved the loss of the telomeric region, from a region of massive amplification on chromosome 11. The amplified region on chromosome 11 contains the CCND1 oncogene, which is amplified in many human cancers41. The other end of this amplification was bound by a conventional fold-back inversion rearrangement (Fig. 8a), which is indicative of breakage–fusion–bridge repair42,43.
These patterns suggest the following sequence of events. During or soon after S phase, a somatic L1 retrotransposition bridges across sister chromatids in inverted orientation, breaking off the telomeric ends of 11q, which are then lost to the clone during the subsequent cell division (fold-back inversion model, Fig. 8b). The chromatids bridged by the L1 insertion now produce a dicentric chromosome. During mitosis, the two centromeres are pulled to opposite poles of the dividing cell, creating an anaphase bridge, which is resolved by further dsDNA breakage. This induces a second cycle of breakage–fusion–bridge repair, albeit not one mediated by L1 retrotransposition. These cycles lead to rapid-fire amplification of the CCND1 oncogene. Alternatively, an interchromosomal rearrangement mediated by L1 retrotransposition (interchromosomal rearrangement model, Fig. 8b) followed by two cycles of breakage–fusion–bridge repair could generate similar copy-number patterns with telomere loss and amplification of CCND1.
Our data show that L1-mediated retrotransposition is an alternative mechanism of creating the first dicentric chromosome that induces subsequent rounds of chromosomal breakage and repair. If this occurs near an oncogene, the resulting amplification can provide a powerful selective advantage to the clone. We searched the PCAWG dataset for other rearrangements that included copy-number amplifications from telomeric deletions that were mediated by L1 integration. We found four more such events across three cancer samples (Supplementary Fig. 14). In a lung tumor sample, SA503541, we found almost identical rearrangements to the one described above (Fig. 8c). In this case, a somatic L1 event also generated telomere loss that induced a second cycle of breakage–fusion–bridge repair. The megabase-size amplification of chromosomal regions also targeted the CCND1 oncogene, in which the boundaries were demarcated by the L1 insertion breakpoint and a fold-back inversion, which indicates breakage–fusion–bridge repair. The independent occurrence of these patterns, which involve the amplification of CCND1, in two different tumor samples (SA528848 and SA503541) demonstrates a mutational mechanism mediated by L1 retrotransposition, which likely contributes to the development of human cancer.
Here we characterize the patterns and mechanisms of cancer retrotransposition on a multidimensional scale, across 2,954 cancer genomes, integrated with rearrangement, transcriptomic and copy-number data. Our analyses provide a new perspective on the long-standing question of whether the activation of retrotransposons is relevant in human oncogenesis. Our findings demonstrate that major restructuring of cancer genomes can sometimes emerge from aberrant L1 retrotransposition events in tumors with high retrotransposition rates, particularly in esophageal, lung and head-and-neck cancers. L1-mediated deletions can promote the loss of megabase-scale regions of a chromosome that may involve centromeres and telomeres. It is likely that the majority of such genomic rearrangements would be harmful for a cancer clone. However, occasionally, L1-mediated deletions may promote cancer-driving rearrangements that involve the loss of tumor-suppressor genes and/or the amplification of oncogenes, representing another mechanism by which cancer clones acquire new mutations that help them to survive and grow. We expect that structural variants induced by somatic retrotransposition in human cancer are more frequent than we could unambiguously characterize here, given the constraints on the fragment sizes of paired-end sequencing libraries. Long-read sequencing technologies should be able to provide a more comprehensive picture of how frequent such events are. Relatively few germline L1 loci in a given tumor, typically one to three copies, are responsible for such marked structural remodeling. Given the role that these L1 copies may have in some cancer types, this work underscores the importance of characterizing cancer genomes for patterns of L1 retrotransposition.
Whole-genome sequencing dataset
We analyzed Illumina whole-genome paired-end sequencing reads (100–150 bp) from 2,954 tumors and matched normal samples across 38 cancer types21. On the basis of the robustness of the retrotransposition calls (false discovery rate of <5%), we opted to retain all samples that were preliminarily excluded by the PCAWG Consortium21, as they were excluded from SNV and structural variation analyses on the basis of read direction biases from PCR artifacts or poor sequence quality, but were not found to be problematic for retrotransposition analysis. For the majority of donors, the tumor specimens consisted of a fresh frozen sample, whereas the normal specimens consisted of a blood sample. Most of the tumor samples came from treatment-free primary cancers, although there was also a small number of donors with multiple samples of primary, metastatic and/or recurrent tumors. The average coverage was 30 reads per genome for normal samples, whereas tumor samples had a bimodal coverage distribution with maxima at 38 and 60 reads per bp (Supplementary Fig. 1 and Supplementary Table 1). BWA-mem44 v.0.7.8-r455 was used to align each tumor and normal sample to human reference build GRCh37. Additional technical details of the sequencing metrics are provided in Supplementary Table 1 and in the PCAWG lead paper21. The Ethics oversight for the PCAWG protocol was undertaken by the TCGA Program Office and the Ethics and Governance Committee of the ICGC.
About half of the donors (1,188) with whole-genome data in PCAWG had at least one tumor specimen with whole transcriptome obtained by RNA sequencing (RNA-seq). Mapping onto the reference was carried out using two independent read aligners, STAR45 v.2.4.0i, two-pass and TopHat2 (ref. 46) v.2.0.12. Gene expression was quantified with HTSeq47 v.0.6.1p1 and consensus normalized expression values, in fragments per kilobase of transcript per million mapped reads (FPKM), were obtained by averaging the expression from STAR and TopHat2. A more detailed description of RNA-seq data processing is provided by the PCAWG Integration of Transcriptome and Genome Working Group48.
We analyzed copy-number profiles obtained by the PCAWG Evolution and Heterogeneity Working Group, using a consensus approach that combines six different state-of-the-art copy-number calling methods49. GC content corrected logR values were extracted using the Battenberg algorithm50, smoothed using a running median and transformed into copy-number space according to n = [(2(1 − ρ) + ψρ)2logR − 2(1 − ρ)]/ρ where ρ and ψ are consensus tumor purity and ploidy, respectively.
Structural variant dataset
The structural variation call set was generated by the PCAWG Somatic Structural Variation Working Group by merging the structural variant calls from four independent calling pipelines51. The merged structural variant calls were further required to have a consistent change in copy number.
Analysis of somatic retrotransposition
Detection of mobile element insertions using TraFiC-mem
BAM files from tumor and matched normal pairs were processed with TraFiC-mem v.1.1.0 to identify somatic mobile element insertions (MEIs) including solo-L1, L1-mediated transductions, Alu, SVA and ERV-K using Illumina paired-end mapping data. TraFiC-mem starts by identifying candidate somatic MEIs by analyzing discordant read pairs. In contrast to a previous version of the algorithm7, the new pipeline uses BWA-mem v.0.7.17 instead of RepeatMasker as the search engine for the identification of retrotransposon-like sequences in the sequencing reads. Calls obtained at this step are preliminary, in which MEI features are outlined and insertion coordinates represent ranges that surround the breakpoints. Then, a new module of TraFiC-mem, called MEIBA (from Mobile Element Insertion Breakpoint Analyzer), is used to identify the integration breakpoints to base-pair resolution and to perform a detailed characterization of MEI features, including structure, subfamily assignment and insertion site annotation. TraFiC-mem is illustrated in Supplementary Fig. 3. Detailed information about the pipeline is provided in the Supplementary Note.
Identification of germline and somatic L1 source elements
Because L1-mediated transductions are defined by the retrotransposition of unique, non-repetitive genomic sequences, we can unambiguously identify the L1 source element from which they derive7. The method relies on the detection of unique DNA regions retrotransposed somatically elsewhere in the cancer genome from a single locus that matches the 10-kb downstream region of a reference full-length L1 element or a putative non-reference polymorphic L1 element detected by TraFiC-mem across the matched normal samples in the PCAWG cohort21. When transduced regions were derived from the downstream region of a putative L1 event present in the tumor genome but not in the matched normal genome, we catalogued these elements as somatic L1 source loci.
Identification of processed pseudogene insertions
An additional separate module of TraFiC-mem was implemented for the identification of somatic insertions of processed pseudogenes. The method relies on the same principle as for the identification of somatic MEI events, through the detection of two reciprocal clusters of discordant read pairs, namely positive and negative, that supports an insertion event in the reference genome. However, the method differs from standard MEI calling to which the read mates map, as in this case mates are required to map to exons that belong to the same source protein-coding gene in GENCODE v.19. To avoid misclassification with standard genomic rearrangements that involve coding regions, we use MEIBA—described above—to reconstruct the insertion breakpoint junctions looking for hallmarks of retrotransposition, including the poly(A) tract and duplication of the target site. Candidate insertions without a poly(A) tail were discarded.
Identification of L1-mediated deletions
Independent read clusters, identified with TraFic-mem, supporting an L1 event (that is, clusters of discordant read pairs with no apparent reciprocal cluster within the proximal 500 bp, and for which the mates support a somatic L1 retrotransposition event) were interrogated for the presence of an associated change in copy number in its proximity. In brief, we looked for copy-number loss calls from the PCAWG Evolution and Heterogeneity Working Group for which the following conditions were fulfilled: (1) the upstream breakpoint matches an independent L1 cluster in positive orientation, (2) the corresponding downstream breakpoint, if any, from the same change in copy number matches an independent L1 cluster in negative orientation, and (3) the reconstruction of the structure of the putative insertion causing the deletion is compatible with one-single retrotransposition event. We used MEIBA (Supplementary Note) to reconstruct the insertion breakpoint junctions to confirm the ends of the events and identify hallmarks of retrotransposition, including the poly(A) tract and duplication of the target site.
An additional strategy was used for L1-mediated deletions that were shorter than 100 kb. Read-depth drops in the proximity of independent clusters were detected by, first, normalizing the read depth on each side of the cluster, using the matched normal sample as a reference. Then, the ratio between the normalized read depth on both sides of the cluster was computed for windows of 200–5,000 bp. Adjacent buffer regions of 300 bp on each side of the cluster were omitted from read-depth calculations to avoid false positives caused by sequence repeats. Pairs of independent reciprocal (positive–negative) clusters were selected such that: (1) both clusters were located less than 100 kb apart, (2) a potential drop in the read-depth ratio was identified, extending from the positive cluster to the negative cluster, and (3) the reconstruction of the structure of the putative insertion that caused the deletion was compatible with a single L1 event. For each cluster pair, the continuity and reliability of the copy-number drop was assessed by measuring the normalized read-depth ratio between non-overlapping 500-bp windows that spanned the region between the positive and negative clusters (that is the putative deletion) and windows upstream and downstream of the positive and negative clusters, respectively. The significance of each read-depth ratio drop was estimated nonparametrically using a null distribution of normalized read-depth ratios. This distribution was obtained for each tumor sample by randomly sampling 100,000 genomic locations (from copy-number segments showing the predominant copy number), and calculating read-depth ratios between both sides of each position. Nonparametric P values were calculated by comparing observed read-depth ratios with this null distribution, and adjusted using the Benjamini–Hochberg correction. Two cluster groups were produced: tier 1, pairs of reciprocal clusters with both clusters that had P < 0.1, and tier 2, pairs of reciprocal clusters with only one of both clusters having P < 0.1.
Retrotransposition rate enrichment and depletion across tumor types
For each tumor type with a minimum sample size of 15, we assessed whether it was enriched or depleted in retrotransposition compared to the overall retrotransposition burden using zero-inflated negative binomial regression, as implemented in the zeroinfl function of the pscl R package. This type of model takes into account the excess of zeros and the overdispersion that is present in this dataset. The MEI counts per sample were regressed on a binary factor that expressed whether they belonged to that particular type of cancer or to any other cancer type. On each regression, the magnitude and sign of the z-score indicates the effect size and directionality of the association. More specifically, positive z-scores indicate that a higher number of counts in the samples belongs to a particular cancer type compared with the rest (enrichment), whereas negative scores indicate a lower number of counts (depletion). Each z-score is accompanied by its P value to indicate the level of statistical significance.
Association between mutation in tumor-suppressor genes and retrotransposition and structural variantion rates
To assess whether the disruption of a particular tumor-suppressor gene was associated with a high level of retrotransposition, we used the whole-genome panorama of cancer driver events per sample produced by the PCAWG Drivers and Functional Interpretation Working Group21. This panorama includes coding and non-coding SNVs, insertions and deletions, copy-number alterations, structural variants and potentially predisposing germline variants. For each tumor-suppressor gene in the Cancer Gene Census database with mutational data, we stratified the samples into two groups—mutated tumor-suppressor genes and non-mutated tumor-suppressor genes. Then, we compared the distribution of MEI counts between both groups using a Mann–Whitney U-test to identify significant differences. P values were corrected for multiple testing using the Benjamini–Hochberg procedure. Adjusted P < 0.05 were considered significant. This analysis was done at both the level of the individual cancer type and the level of pan-cancer to identify tumor-type-specific associations. We further investigated whether there was a TP53 dosage effect as follows: every PCAWG sample was classified into three groups according to TP53 mutational status, namely wild-type, monoallelic and biallelic driver mutation. Then, the MEI counts distribution was compared for all possible group pair combinations using a Mann–Whitney U-test. The same analysis described above was applied to investigate the association between TP53 mutation and other types of structural variation.
Correlation between L1 insertion and structural variation rate
For each sample, we computed the number of MEIs, the total number of structural variants and the number of five different structural variant classes: deletions, duplications, translocations, head-to-head inversions and tail-to-tail inversions, when data were available. Then, the correlation between the number of MEIs and the structural variant burden was assessed at both the level of the individual tumor type and the level of the pan-cancer using a Spearman’s rank test.
Association between L1 insertion rate and genomic features
The L1 insertion rate was calculated as the total number of somatic L1 insertions, identified across the complete PCAWG cohort per 1-Mb window. The density of L1 endonuclease motifs was computed as the number of canonical endonuclease motifs, here defined as TTTT|R (where R is A or G) or Y|AAAA (where Y is C or T) per 1-Mb window. To study the association of L1 insertion rate with multiple variables at single-nucleotide resolution, we used a statistical framework based on negative binomial regression, as described in detail previously24, and adapted to adjust for the genome-wide distribution of the L1 endonuclease motif; we stratified the genome into four bins (0–3) by the closeness of match to the motif. Bin 0 contains dissimilar DNA motifs, with four or five (out of five) mismatches, encompassing 1,150 Mb. Bins 1, 2 and 3 contain loci with three, two and at most one mismatches, encompassing 749 Mb, 380 Mb and 114 Mb, respectively. The closer match of either of the two DNA strands at each locus was considered. Histone mark data and DNase hypersensitivity data were obtained from the Roadmap Epigenomics Consortium by averaging the fold-enrichment signal across eight cell types and processed by stratifying into four genomic bins as described previously24: bin 0 contains regions with below-baseline signal (fold enrichment versus input <1), while bins 1–3 are approximately equal-sized bins that cover the remainder of the genome. RNA-seq data from Roadmap were processed by averaging across eight cell types; bin 0 contains non-expressed genes (FPKM = 0) and intergenic DNA not listed as expressed, while bins 1–3 included genes with up to 0.59, 5.68 and above 5.68 FPKM, respectively. Replication time was averaged across the eight ENCODE cell types and divided into four equal-sized genomic bins, where bin 0 is latest and bin 3 is earliest replicating. Essential genes were estimated from CRISPR screens in cell lines52. All enrichment scores shown compare bins 1–3 for a particular feature (replication time, histone marks, gene expression, L1 motif) versus bin 0 of the same feature. Bin 0 therefore always has log enrichment = 0 by definition and is not shown on plots. The analyses were restricted to regions of the genome with perfect CRG75 alignability.
Impact of retrotransposition insertions on gene expression
To study the transcriptional impact of a somatic L1 insertion within COSMIC cancer genes and promoters, we used RNA-seq data to compare gene-expression levels in samples with and without somatic L1 insertion. For each somatic L1 insertion within a cancer gene or promoter, we compared the gene FPKM between the sample having the insertion (study sample) and the remaining samples of the same tumor type (control samples). Using the distribution of gene-expression levels in control samples, we calculated the normalized gene expression differences using a Student’s t-test. To overcome the problems due to multiple testing, false discovery rate–adjusted P values (q values) were calculated using the Benjamini–Hochberg procedure, and adjusted P < 0.1 was considered to be significant.
Analysis of processed pseudogene expression
We analyzed the PCAWG RNA-seq data to identify and characterize the transcriptional consequences of somatic integrations of processed pseudogenes (PSD). We interrogated RNA-seq split reads and discordant read pairs, looking for chimeric retrocopies that involved PSDs and target genomic regions. For each PSD insertion somatic call, we extracted all of the RNA-seq reads (when available), mapping the source gene and the insertion target region, together with the RNA-seq unmapped reads for the corresponding sample. Then, we used these reads as a query in BLASTn53 v.2.7.1 searches against a database that contained all isoforms of the source gene described in RefSeq54, together with the genomic sequence ranging from −5 kb to +5 kb around the PSD integration site. Finally, we looked for RNA-seq discordant read pairs and/or RNA-seq clipped reads that supported the joint expression of PSD and target site. Only read pairs with one of the mates aligned to the host gene mRNA with >98% identity were considered. All expression signals were confirmed by visual inspection with Integrative Genomics Viewer v.2.4.10.
Validation of somatic retrotransposition algorithms
In silico validation of TraFiC-mem
To evaluate the precision and recall of our algorithm TraFiC-mem, we reanalyzed a mock cancer genome into which we had previously seeded known somatic retrotransposition events at different levels of tumor clonality7. To create the artificial, tumoral genome, 10,000 L1 insertion breakpoints—including solo-L1, partnered and orphan transductions—were randomly distributed in the standard reference genome using BedTools v.2.25.0, of which 9,227 were inserted out of un-sequenced gaps. Then, ART55 (v.MountRainier-2016-06-05) was used to generate paired-end read sequencing data for both the standard and the artificial reference genomes to a 38× coverage. The simulation FASTQ files were aligned into the standard reference genome with BWA-mem56 v.0.7.17. Reads from the normal and tumor BAM files were randomly subsampled and merged with samtools v.1.7 at three distinct proportions to also produce tumor samples with 25%, 50% and 75% clonalities. After that, the four possible tumor and matched normal pairs were processed with TraFiC-mem to call MEIs. For each clonality, the identified MEIs were compared with the list of simulated MEIs to compute the number of true-positive (TP), false-positive (FP), true-negative (TN) and false-negative (FN) calls. Finally, precision and recall were computed as follows: Precision = TP/(TP + FP); Recall = TP/(TP + FN).
Validation of TraFiC-mem calls using single-molecule sequencing
We performed validation of 308 putative somatic retrotranspositions identified with TraFiC-mem in one cancer cell line (NCI-H2087) with high retrotransposition rate, and absent in the matched normal cell line (NCI-BL2087) derived from blood, by single-molecule sequencing using Oxford Nanopore technology. Genomic DNA was sheared to 10-kb fragments using Covaris g-TUBEs, and cleaned with 0.4× Ampure XP Beads. After end-repairing and dA-tailing using the NEBNext End Repair/dA-tailing module (NEB), whole-genome libraries were constructed with the Oxford Nanopore Sequencing 1D ligation library prep kit (SQK-LSK108, Oxford Nanopore Technologies). Genomic libraries were loaded on MinION R9.4 flowcells. We used the Oxford Nanopore basecaller Albacore v.2.0.1 to generate fastq files. After quality filtering of the fastq files and read trimming of the data with Porechop v.0.2.3, we used minimap2 (ref. 57) v.2.10-r764-dirty to map sequencing reads onto the hs37d5 reference genome. Sequencing coverages were 8.2× (NCI-BL2087) and 9.17× (NCI-H2087), and average read sizes of mapped reads were ~4.5 kb (NCI-BL2087) and ~11 kb (NCI-H2087). After obtaining the whole-genome BAM files for each of the 308 putative somatic retrotransposition calls identified with TraFic-mem, we interrogated the long-read tumor BAM file to find reads that validated the event. MEIs supported by at least one Nanopore read in the tumor and absent in the matched normal sample were considered true-positive somatic events, while MEIs not supported by long reads in the tumor and/or present in the matched normal were considered false-positive calls. Overall, we found 4.22% (13/308) false-positive events. False discovery rate (FDR) was estimated as follows: FDR = FP/(TP + FP).
Validation of L1-mediated rearrangements with PCR and single-molecule sequencing
We performed validation of 20 somatic L1-mediated rearrangements, mostly deletions, identified in two cancer cell lines with high retrotransposition rates (NCI-H2009 and NCI-H2087). We carried out PCR followed by single-molecule sequencing of amplicons from the two tumor cell lines and their matched normal samples (NCI-BL2009 and NCI-BL2087) using a MinION from Oxford Nanopore. PCR primers were designed to amplify three regions from each event (namely, 5′-extreme, 3′-extreme and target sites) as shown in Supplementary Fig. 10.
Validation of L1-mediated rearrangements using mate pairs
To further validate and characterize L1-mediated rearrangements, we performed 10× mate-pair whole-genome sequencing using libraries with two different insert sizes (4 kb and 10 kb), which can span the integrated L1 element that caused the deletion, enabling the validation of the involvement of L1 in the generation of such rearrangements. Mate-pair reads (100 nucleotides long) were aligned to the human reference with BWA-mem v.0.7.17. Then, for each candidate L1-mediated rearrangement, we searched for discordant mate-pair clusters that spanned the breakpoints and supported the L1-mediated event. Each event was confirmed by visual inspection of the BAM files using Integrative Genomics Viewer v.2.4.10.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Somatic and germline variant calls, mutational signatures, subclonal reconstructions, transcript abundance, splice calls and other core data generated by the ICGC/TCGA PCAWG Consortium are available for download at https://dcc.icgc.org/releases/PCAWG. Additional information on accessing the data, including raw read files, can be found at https://docs.icgc.org/pcawg/data/. In accordance with the data access policies of the ICGC and TCGA projects, most molecular, clinical and specimen data are in an open tier, which does not require access approval. To access potentially identifying information, such as germline alleles and underlying sequencing data, researchers will need to apply to the TCGA Data Access Committee (DAC) via dbGaP (https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?page=login) for access to the TCGA portion of the dataset, and to the ICGC Data Access Compliance Office (DACO; http://icgc.org/daco) for the ICGC portion. In addition, to access somatic SNVs derived from TCGA donors, researchers will also need to obtain dbGaP authorization.
In addition, the analyses in this paper used a number of datasets that were derived from the raw sequencing data and variant calls (Supplementary Table 8). The individual datasets are available at Synapse (https://www.synapse.org/), which are also mirrored at DCC portal (https://dcc.icgc.org). Full links, filenames, accession numbers and descriptions are detailed in Supplementary Table 8. VCF files for somatic mobile element insertions described specifically in this manuscript can be found at Synapse, under accession number syn21052009, and in DCC portal at https://dcc.icgc.org/releases/PCAWG/retrotransposition.
The core computational pipelines used by the PCAWG Consortium for alignment, quality control and variant calling are available to the public at https://dockstore.org/search?search=pcawg under a GNU General Public License v.3.0, which allows for reuse and distribution. The algorithm for the identification of somatic retrotransposition events (TraFiC-mem) is available at https://gitlab.com/mobilegenomesgroup/TraFiC (v.1.2.0).
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J.M.C.T. is supported by European Research Council (ERC) Starting Grant 716290 ‘SCUBA CANCERS’, Ramon y Cajal grant RYC-2014-14999 and Spanish Ministry of Economy, Industry and Competitiveness (MINECO) grant SAF2015-66368-P. B.R.-M., E.G.A., M.S.G. and S.Z. are supported by PhD fellowships from Xunta de Galicia (Spain) ED481A-2016/151, ED481A-2017/299, ED481A-2017/306 and ED481A-2018/199, respectively. F.S. was supported by ERC Starting Grant 757700 ‘HYPER-INSIGHT’, MINECO grant BFU2017-89833-P ‘RegioMut’, and further acknowledges institutional funding from the MINECO Severo Ochoa award and from the CERCA Programme of the Catalan Government. Y.S.J. was supported by Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number HI16C2387). A.L.B. is supported by MINECO PhD fellowship BES-2016-078166. M.T. was supported by MINECO grant SAF2015-73916-JIN. R.B. received funding through the National Institutes of Health (U24CA210978 and R01CA188228). M.G.B. received funding through MINECO, AEI, Xunta de Galicia and FEDER (BFU2013-41554-P, BFU2016-78121-P, ED431F 2016/019). N.B. is supported by a My First AIRC grant from the Associazione Italiana Ricerca sul Cancro (number 17658). J.D. is a postdoctoral fellow of the Research Foundation Flanders (FWO) and the European Union’s Horizon 2020 research and innovation program (Marie Skłodowska-Curie grant agreement number 703594-DECODE). K.C. and Z.C. are supported by NIH R01 CA172652 and U41 HG007497. Z.C. is supported by an American Heart Association Institutional Data Fellowship Award (17IF33890015). P.A.W.E. is supported by Cancer Research UK. E.A.L. is supported by K01AG051791. I.M. is supported by Cancer Research UK (C57387/A21777). F.M. is supported by A.I.L. (Associazione Italiana Contro le Leucemie-Linfomi e Mieloma ONLUS) and by S.I.E.S. (Società Italiana di Ematologia Sperimentale). S.M.W. received funding through a SNSF Early Postdoc Mobility fellowship (P2ELP3_155365) and an EMBO Long-Term Fellowship (ALTF 755-2014). J.W. received funding from the Danish Medical Research Council (DFF-4183-00233). D.C.W. is funded by the Li Ka Shing foundation and the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. J.O.K. is supported by an ERC Starting Grant. P.V.L. is a Winton Group Leader in recognition of the Winton Charitable Foundation’s support towards the establishment of The Francis Crick Institute. This work is supported by The Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001202), the UK Medical Research Council (FC001202) and the Wellcome Trust (FC001202). H.H.K. is supported by grants from the National Institute of General Medical Sciences (P50GM107632 and 1R01GM099875). K.H.B. is supported by P50GM107632, R01CA163705 and R01GM124531. This work was supported by the TransTumVar project PN013600. R.C.F. thanks Cancer Research UK Programme Grant for esophageal ICGC, Cambridge BRC and ECMC infrastructure support. This work was supported by the Wellcome Trust grant 09805. We acknowledge the contributions of the many clinical networks across the ICGC and TCGA who provided samples and data to the PCAWG Consortium, and the contributions of the Technical Working Group and the Germline Working Group of the PCAWG Consortium for collation, realignment and harmonized variant calling of the cancer genomes used in this study. We thank the patients and their families for their participation in the individual ICGC and TCGA projects.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Integrated supplementary information
Supplementary Figure 1 Whole-genome sequencing coverage in tumours and matched-normal genomes in the PCAWG cohort.
(a) Violin plot for the distribution of the mean coverage from all tumours (N=2,954) analyzed shows a bimodal distribution with maxima at 38 and 60 reads per base-pair. White point, median; box, 25th to 75th percentile (interquartile range, IQR); whiskers, data within 1.5 times the IQR. (b) Tumour samples mean depth of coverage distribution by cancer type. The number of samples per tumour type is shown in parenthesis. Violin plot features are as in ‘a’. (c) Violin plot for the distribution of the mean coverage from all PCAWG matched-normal samples (N=2,834) analyzed shows a mean coverage of 30 reads per genome. Violin plot features are as in ‘a’. (d) Matched-normal samples mean depth of coverage distribution by cancer type. Number of samples per tumour type is shown in parenthesis. Violin plot features are as in ‘a’.
(a) L1 subfamilies. Ta-1 and Ta-0 elements – the youngest subfamilies of L1 retrotransposons – represent 97.5% of all L1 somatic mobilizations that were characterized to subfamily level, although we also find 367 L1 events bearing the diagnostic hallmarks of pre-Ta elements, which have been shown to retain retrotransposition activity in modern humans5. The category “Ta-?” contains those L1-Ta events for which it was not possible to detect the Ta-0 or Ta-1 diagnostic nucleotides. (b) Alu subfamilies. (c) SVA subfamilies.
TraFiC-mem analyzes Illumina paired-end mapping data (see Supplementary Note). i) Identification of candidate somatic mobile element insertions (MEIs) by TraFiC-mem. Solo-retrotransposon insertions are detected by the identification of two reciprocal clusters (positive and negative, or head-to-head) of interchromosomal reads whose mates map onto retrotransposons of the same type located elsewhere in the genome. Partnered transductions are detected by the identification of one cluster of interchromosomal reads whose mates map onto L1 retrotransposons of the same family elsewhere in the genome, and one single reciprocal cluster of reads whose mates are clusterered at a unique region adjacent to a donor source L1 element (the example illustrates a transduction from chromosome 7). Orphan transductions are detected by the identification of two reciprocal clusters whose mates map downstream to a source element as described for partnered transductions. ii) MEI breakpoint analysis. TraFiC-mem seeks for two additional clusters (5’ brakpoint cluster and 3’ breakpoint cluster) of clipped reads in the candidate insertion region, in order to reveal the 5’ and 3’ insertion breakpoint coordinates to base-pair resolution. iii) MEI structural features annotation. iv) Subfamily assignment. Subfamily specific diagnostic nucleotides are used to determine the subfamily for L1 events. v) MEI locus annotation: The target genomic region is annotated and MEIs inserted within cancer genes, according to the COSMIC database, are flagged. Output is a VCF file.
(a) Retrotransposition breakpoint validation approach using long-reads with Oxford Nanopore Technologies (ONT). Illustrative example of a Solo-L1 insertion in cancer cell-line NCI-H2087 detected with short and long-reads. Top, TraFiC-mem relies on the identification of discordant read-pairs (DPs) and clipped reads (CRs) to detect a Solo-L1 insertion using Illumina paired-end data. Bottom, indel reads (IRs) and clipped reads confirmation using ONT. (b) For each type of insertion (solo-L1, partnered transductions, orphan transductions, Alu), the proportion of events that are supported by different counts of long-reads is represented (from zero to more than 5 reads). Events supported by at least one long-read and absent in the matched-normal sample were considered true positive (i.e., somatic), while those not supported by ONT and/or present in the matched-normal sample were considered false positive. The total number of events assessed for each retrotransposition category is shown in parenthesis. (c) Precision and recall of TraFiC-mem after in-silico simulation of 10,000 L1 insertions (Solo-L1, partnered and orphan transductions) in tumors of different clonalities at 25%, 50%, 75% and 100%. (d) Plot showing the correlation between the observed and expected lengths for 8,025 Solo-L1 insertions simulated in-silico. Sample size (N), Spearman’s rho and P-value are displayed above the panel. (e) Fraction of true positive Solo-L1 events with a predicted orientation consistent (green), and inconsistent (red), with the expected. Orientation consistency was assessed for four clonality levels (25%, 50%, 75%, 100%).
Violin plots showing the distribution of the number of retrotranspositions per sample across cancer types, for the six different categories of retrotranspositions that were analyzed (Solo-L1, L1-transductions, L1-mediated deletions, Alu, SVA and Processed pseudogenes). The number of samples per tumor type is shown in parenthesis. Y-axis is represented in a logarithmic scale. Black points, median; boxes, 25th to 75th percentile (interquartile range, IQR); whiskers, data within 1.5 times the IQR.
Supplementary Figure 6 TP53 mutation is associated with high rates of L1 retrotransposition and structural variants.
(a) Distribution of L1 counts of three sample groups according to their TP53 mutational status: wild-type, monoallelic and biallelic driver mutation. The number of samples per group is shown within parenthesis. Point, median; box, 25th to 75th percentile (interquartile range, IQR); whiskers, data within 1.5 times the IQR. P-values indicate significance from a two-tailed Mann–Whitney U-test. Y-axis is presented in a logarithmic scale. (b) The same for structural variant (SV) counts. (c) Distribution of L1 counts across tumor types from samples grouped in two categories: TP53 wild-type and TP53-mutated (monoallelic or biallelic). The number of samples per tumor type and TP53 status (green: wild-type; orange: mutated) is shown within parenthesis. Violin plot features are as in ‘a’. Outlier values outside 1.5 times the IQR are represented as diamonds. P-values indicate significance from a two-tailed Mann–Whitney U-test. Y-axis is presented in a logarithmic scale. (d) The same for structural variant (SV) counts.
(a) Heatmap showing the correlation between the number of L1 events, the total number of structural variants (SVs) and the number of 5 different types of SVs per sample: deletions (DEL), duplications (DUP), translocations (TRANS), head-2-head inversions (H2HINV) and tail-2-tail inversions (T2TINV). Correlation was assessed both at Pan-Cancer and tumor type levels. Spearman´s correlation coefficients are shown in a blue (negative) to a red (positive) colored gradient. P-values lower than 0.05 and 0.01 are represented as single and double asterisks, respectively. (b) Scatter plots showing correlations between the number of L1 events and the total number of SVs per sample at both Pan-Cancer and tumour type levels, for those comparisons that were significant in panel ‘a’. The sample size (N) together with Spearman’s rho and P-value are displayed above each chart. Both axes are displayed on a symlog scale.
Supplementary Figure 8 Some gene expression effects associated with somatic retrotransposition in PCAWG.
(a) Volcano plot showing the impact of L1 integration in the expression of cancer genes. Gene expression fold-change (x axis) is represented versus inverted significance (y axis). Red dots indicate significant associations under Benjamini–Hochberg adjusted p-values < 0.1 from two-tailed Student’s t-test. Adjusted p-values for ABL2 and RB1 are 0.0017 and 0.0014, respectively. (b) Up-regulation of the ABL2 oncogene in tumour SA494343, a head-and-neck squamous carcinoma (Head-SCC), relative to the expression of the same oncogene in other Head-SCC samples (blue) from PCAWG. Expression levels measured as Fragments Per Kilobase Million (FPKM). (c) Gene expression differences for genes with L1-retrotranspositions in promoter regions reveals significant upregulation in four genes. Volcano plot features are as in ‘a’. Adjusted p-values for MTRNR2L1, ESRRG, ABL2 and MAGEA4 are 1.1294e-06, 0.0009, 0.0017 and 6.0019e-13, respectively. (d) Exonization of somatic retrotranspositions, including cancer genes PTPN11 and NCOR2. Green boxes are exons, thinner green blocks UTRs, lines introns and dotted lines means that a piece of the gene model is not shown for visualization purposes. Purple and orange boxes correspond to L1 and Alu integrations, respectively. Discordant read-pairs supporting fusion transcript expression are shown above the predicted transcript. (e) Host gene and processed pseudogene fusion transcripts. Arcs with arrows within the circos indicate the processed pseudogene events, connecting the source gene (underlined and bold) with the corresponding integration site. Predicted fusion transcript structures are shown in the outermost layer of the figure. Coding potential is shown underneath the fusion transcript representation. Start codon is denoted as ATG, termination codon as STOP, and uncertain termination is represented using dots.
(a) Correlation between the number of L1 events and L1 endonuclease (EN) motif instances per 1 Mb-windows. The sample size (N) together with Spearman’s rho and P-value is displayed above the plot. 2D Kernel density estimate (KDE) is displayed over the data points in a blue to red gradient. (b) Correlation between the number of somatic L1 insertions per Mb and replication timing, which is measured through Repli-seq wavelet-smoothed signal (late to early replication) and averaged per Mb. Plot features are as in ‘a’. (c-e) In each panel, enrichment scores are shown, adjusted for multiple covariates and comparing the L1 insertion rate in bins 1-3 for a particular genomic feature versus bin 0 of the same feature, which therefore always has log enrichment=0 by definition and is not shown. The error bars represent 95% confidence intervals. The number of observations per bin is provided between parenthesis whenever possible. For replication time, bin 0 is the latest-replicating quarter of the genome. For essentiality, bin 0 is the non-essential genes. For the L1 motif, bin 0 denotes a non-match (4 or more mismatches). MMs stands for the number of mismatches relative to the consensus L1 EN motif. Additional information in Supplementary Note. (d) Association between L1 insertion rate and multiple genomic features for those tumour types with at least 100 L1 events. Data colored according to tumor type. (e) Association between L1 insertion rate and multiple genomic features in samples with at least 100 L1 events. Each data point is colored according tumour type.
Gel showing PCR results on cancer cell-lines (NCI-H2009 and NCI-H2087) and their matched-normal cell-lines (NCI-BL2009 and NCI-BL2087). We performed validation of 20 L1-mediated rearrangements (for details, see Supplementary Table 7): 16 L1-mediated deletions (Rg1, Rg2, Rg3, Rg4, Rg6, Rg8, Rg9, Rg10, Rg11, Rg13, Rg14, Rg15, Rg16, Rg17, Rg18, Rg19), 1 L1-mediated translocation (Rg20) and 3 independent L1 breakpoints associated with a copy number change from an unknown rearrangement type (Rg5, Rg7, Rg12). For each rearrangement, except those where only one breakpoint is known, at least three regions were amplified in the tumours (see Online Methods): left breakpoint (L), right breakpoint (R), and the target site (T). Arrows are used to highlight the position of some somatic amplicons. Note that the target site could also amplify in the matched-normal sample if the deletion is not too long. “M” denotes the size marker. For illustrative purposes, the oligo design strategy is shown in a panel at the bottom of the figure: amplicons (L, R and T) and oligos – forward (F) and reverese (R) – are represented. This experiment was repeated 3 times with the same result, and results were further confirmed by single-molecule sequencing of the amplicons with ONT (see Online Methods).
Supplementary Figure 11 Single-molecule sequencing validation of somatic L1-mediated rearrangement calls.
We sequenced to high-coverage (>1,000x) the PCR amplicons shown in Supplementary Fig. 10 using single-molecule sequencing with a MinION sequencer (Oxford Nanopore Technologies). We also carried out whole-genome single-molecule sequencing to low coverage of the same two tumor cell-lines (NCI-H2009 and NCI-H2087) subjected to PCR validation. For illustrative purposes, this figure only shows the validation of four representative rearrangements (Rg18, Rg11, Rg13, Rg20). The sequences of the remaining PCR amplicons can be found in Supplementary Table 7. On the left side of each panel, paired-end and Oxford Nanopore reads supporting a given rearrangement are displayed over a virtual reconstruction of the rearrangement breakpoints. On the right side of each panel, nucleotide sequence obtained by single-molecule sequencing validating each event shown in left. Nucleotide colors match those in the virtual reconstruction of the rearrangement (blue for L1, bright-green for poly-A, grey for target region, light-green for transduction). (a) Solo-L1 insertion mediating a 642 bp deletion. (b) Partnered transduction promoting a 2.6 Kb long deletion. (c) A 1.5 Kb deletion generated through an endonuclease independent L1 integration. Long reads confirm the truncation of the L1 element at its 5′ and 3′ ends. (d) Translocation between 1q31.1 and 8q24.12 mediated by an orphan transduction (same rearrangement as in Fig. 6b). Nanopore reads validate the orphan transduction bridge between both chromosomes.
Supplementary Figure 12 Validation of L1-mediated rearrangements in cancer cell lines by mate-pairs sequencing.
In order to further validate L1-mediated deletions, we performed mate-pair sequencing of long-inserts libraries (3 kb and 10 kb) on two cancer cell-lines with high-retrotransposition rates. In these samples, our algorithms confirmed 16 events with the hallmarks of L1-mediated deletions, in which the mate-pair data confirmed a single L1-derived (i.e., solo-L1 or L1-transduction) retrotransposition as the cause of the copy number loss, and identified the sizes of the deletion and the associated insertion. For illustrative purposes, here it is shown the validation of a 10.4 kb long deletion promoted by integration of a 768 bp L1 insertion in the cancer cell-line NCI-H2009. The L1 element inserted within the deletion breakpoints is too long to be characterized using standard paired-end sequencing libraries, but the mate-pairs successfully span the breakpoints of the deletion and confirm a single L1 insertion associated with the rearrangement.
(a) Circos plot summarizing the three concatenated retrotransposition events shown in the panel b. First event, an L1 transduction mobilized from chromosome 7 is integrated into chromosome 9. Second event, this insertion concomitantly causes a 5.3 Mb deletion in the acceptor chromosome 9. Third event, the L1 element causing the deletion is subsequently able to promote a transduction that integrates into chromosome X. (b) Discordant read-pairs in chromosome 9 supports a 5.3 Mb deletion generated by the integration of a transduction from chromosome 7, and reveals an L1-event with full-length structure. Five kilobases downstream, a positive cluster of reads supports a transduction from this L1-retrotransposition event into chromosome X.
The PCAWG-11 consensus total copy number and the copy number of the minor allele are plotted as gold and gray bands, respectively. (a) In a head-and-neck tumor, SA494271, deletion of 1.9 Mb at the short arm of chromosome 10, which involves the telomeric region, is associated with the somatic integration of an L1 retrotransposon. (b) In another head-and-neck tumor, SA494351, two independent L1 events promote deletion of both ends of chromosome 5. (c) In a Lung squamous carcinoma, SA503541, the aberrant integration of an L1 event bearing 5’ and 3’ transductions causes a complex rearrangement with loss of 50.5 Mb from the long arm of chromosome 11 that includes the telomere. Only the two clusters supporting both extremes of a putative L1-mediated fold-back inversion are shown. Below, a detailed view of the 5’-transduction breakpoint.
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Rodriguez-Martin, B., Alvarez, E.G., Baez-Ortega, A. et al. Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition. Nat Genet 52, 306–319 (2020). https://doi.org/10.1038/s41588-019-0562-0
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