Retroelement co-option disrupts the cancer transcriptional programme

Retroelement co-option disrupts the cancer transcriptional programme | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Retroelement co-option disrupts the cancer transcriptional programme Jane Loong , Rachael Thompson , Callum Hall , Laura Doglio , Judith Pape , View ORCID Profile George Kassiotis doi: https://doi.org/10.1101/2025.02.21.639580 Jane Loong 1 Retroviral Immunology Laboratory, The Francis Crick Institute , 1 Midland Road, London NW1 1AT, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rachael Thompson 1 Retroviral Immunology Laboratory, The Francis Crick Institute , 1 Midland Road, London NW1 1AT, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Callum Hall 1 Retroviral Immunology Laboratory, The Francis Crick Institute , 1 Midland Road, London NW1 1AT, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Laura Doglio 1 Retroviral Immunology Laboratory, The Francis Crick Institute , 1 Midland Road, London NW1 1AT, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Judith Pape 1 Retroviral Immunology Laboratory, The Francis Crick Institute , 1 Midland Road, London NW1 1AT, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site George Kassiotis 1 Retroviral Immunology Laboratory, The Francis Crick Institute , 1 Midland Road, London NW1 1AT, UK 2 Department of Infectious Disease, Faculty of Medicine, Imperial College London , London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for George Kassiotis For correspondence: george.kassiotis{at}crick.ac.uk Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Background Transcriptional activation of otherwise repressed endogenous retroelements (RTEs) is a hallmark of cancer, shaping tumour progression and immunogenicity by multifaceted, yet incompletely understood mechanisms. Methods We used an extended pan-cancer transcriptome assembly to identify potential effects of RTEs on the genes near or within which they have integrated. These were subsequently verified in test cases by further analysis of transcriptional profiles in cancer patient data, and by in vitro studies involving restoration of gene activity, and proliferation and migration assays in cancer cell lines. Results We report that cancer-specific transcriptional activation of RTEs causes frequent reduction or loss of gene function. Exonisation and alternative splicing of RTEs creates non-functional RNA and protein isoforms and derepressed RTE promoter activity initiates antisense transcription, both at the expense of the canonical isoforms. Contrary to theoretical expectation, transcriptionally activated RTEs affect genes with established tumour-promoting function, including the common essential RNGTT and the lung cancer-promoting CHRNA5 genes. Furthermore, the disruptive effect of RTE activation on adjacent tumour-promoting genes is associated with slower disease progression in clinical data, whereas experimental restoration of gene activity enhances tumour cell in vitro growth and invasiveness Conclusions These findings underscore the gene-disruptive potential of seemingly innocuous germline RTE integrations, unleashed only by their transcriptional utilisation in cancer. They further suggest that such metastable RTE integrations are co-opted as sensors of the epigenetic and transcriptional changes occurring during cellular transformation and as executors that disrupt the function of tumour-promoting genes. Introduction Similar to all mammalian genomes, the human genome has amassed over 4 million recognisable integrations of retrotransposable elements (RTEs) of diverse subfamilies, some of which are primate-specific, such as the human endogenous retrovirus H ( HERVH ) subfamily of long terminal repeat (LTR) RTEs and the Alu subfamily of non-LTR RTEs [ 1 ]. While the vast majority of human RTEs have lost the ability to retrotranspose, new germline integrations of active RTEs are acquired slowly but continually [ 2 ]. RTE integration poses a significant risk of insertional mutagenesis, which is higher for the recently-acquired or active subfamilies. Highly deleterious mutations may be quickly counterselected, whereas less damaging integrations are retained for longer evolutionary times. The genetic diversity generated by RTE integration, as well as the regulatory sequences they carry, can be co-opted in the evolution of new physiological host functions and transcriptional networks [ 3 , 4 ]. Indeed, RTE enhancer and promoter activities are co-opted in the regulatory networks of placentation or the interferon (IFN) response genes [ 5 ], and alternative RTE exons in the diversification of protein isoforms and function [ 4 ]. In addition to evolutionary selection, epigenetic control of RTEs further mitigates the potentially damaging effect of RTE integrations on host gene function by preventing their independent transcription or inclusion in host gene transcripts [ 6 ]. Epigenetic repression acts faster than evolutionary processes and allows for the latter to determine the ultimate fate of a given RTE integration. However, epigenetic repression is reversible and can be lost, particularly following the major epigenetic changes seen in cancer [ 6 ]. In turn, RTE release from epigenetic control may allow for previously hidden effects on the gene near or within which they have integrated to manifest. Altered transcriptional activity of RTEs has been consistently observed in most cancer types, associated with substantial effects on the cancer transcriptome [ 6 , 7 ]. RTE transcriptional inclusion in cancer results in aberrant transcription and splicing patterns, often in a cancer type-specific fashion [ 8 ]. However, given its high degree of complexity, the functional consequences of the aberrant cancer transcriptome created by RTE derepression is not yet fully understood. RTE can be co-opted in driving elevated or ectopic expression of genes with tumour-promoting, or in creating tumorigenic protein isoforms. Instances of such onco-exaptation events include the elevated expression of CSF1R and IRF5 in Hodgkin’s lymphoma [ 9 , 10 ], the ectopic expression of CALB1 in squamous lung cancer [ 11 ], and the creation of an oncogenic form of anaplastic lymphoma kinase (ALK) in melanoma [ 12 ]. Moreover, comprehensive analysis of epigenetically reactivated RTE promoter activity has identified >100 potential onco-exaptation events involving oncogenes in diverse cancer types [ 13 ]. Owing to the increase in tumour cell fitness they confer, RTE-mediated activation of tumour-promoting genes would be increasingly enriched over the course of tumour progression and molecular evolution, making such onco-exaptation events easier to identify in cancer transcriptomes. In contrast, effects of epigenetically reactivated RTEs that compromise the function of an essential gene would also compromise tumour cell fitness and would, therefore, be counterselected during tumour evolution, giving the appearance of rarer occurrence. However, the potential of transcriptionally reactivated RTEs to disrupt the function of tumour-promoting genes may be a selectable trait in host species evolution. Here, we investigated the degree to which gene function may be compromised by transcriptionally reactivated RTEs in cancer. We made use of our increasing understanding of the complexity of cancer transcriptomes and identified cancer-specific transcript isoforms, created by transcriptional inclusion of RTEs at the expense of the protein-coding canonical isoform of adjacent genes. Counterintuitively, many of the affected genes have an established tumour-promoting role, and, in test cases, restoration of their expression accelerates tumour cell-intrinsic growth and invasiveness. Methods Transcript identification, read mapping and quantitation RNA-seq samples were downloaded from The Genotype-Tissue Expression (GTEx) project and TCGA (poly(A) selected RNA), CCLE and other publicly available RNA-seq repositories listed in the Data availability statement. Samples from TCGA were downloaded through the gdc-client application. The .bam files were parsed with a custom Bash pipeline using GNU parallel [ 14 ], and converted to .fasta files using SAMtools v1.8 [ 15 ]. RNA-seq data from TCGA, GTEx, CCLE and listed previous studies were mapped to our de novo cancer transcriptome assembly and counted as previously described [ 8 ]. Briefly, transcripts per million (TPM) values were calculated for all transcripts in the transcript assembly [ 8 ] with a custom Bash pipeline and Salmon v0.8.2 [ 16 ], which uses a probabilistic model for assigning reads aligning to multiple transcript isoforms, based on the abundance of reads unique to each isoform [ 16 ]. Read count tables were additionally imported into Qlucore Omics Explorer v3.9 (Qlucore, Lund, Sweden) for further downstream expression analyses and visualization. Splice junctions were visualized using the Integrative Genome Viewer (IGV) v2.4.19 [ 17 ]. Long-read RNA-seq samples were aligned to the GRCh38/hg38 human genome using minimap2 v2.17 [ 18 ]. For assemblies of long-read RNA-seq reads, the obtained .bam files were first converted to bed12 using bam2bed12.py script from FLAIR suit [ 19 ]. High-confidence isoforms were selected using “collapse” function from flair.py script [ 19 ]. Hypoxia scores The hypoxia scores [ 20 ] for all TCGA samples were kindly provided by Prof. David Mole (Nuffield Department of Medicine, University of Oxford). The hypoxia scores for KIRC samples were filtered, however mismatches in sample names due to updates by TCGA to the naming system of files meant the mean hypoxia score per patient was used here. Of the 479 KIRC patients with hypoxia scores calculated for tumour samples, 406 had hypoxia scores for one sample, 70 for two samples, and three patients had the mean hypoxia score calculated from three samples. Repeat annotation and enrichment analysis Repeat regions were annotated as previously described [ 21 ]. Briefly, hidden Markov models (HMMs) representing known Human repeat families (Dfam 2.0 library v150923) were used to annotate GRCh38 using RepeatMasker, configured with nhmmer. RepeatMasker annotates LTR and internal regions separately, thus tabular outputs were parsed to merge adjacent annotations for the same element. Enrichment analysis of repeat types was performed using Fisher’s Exact test in MATLAB (version R2022b, TheMathWorks), followed by the Bonferroni-Holm method to correct for multiple testing. Functional gene annotation by gene ontology Pathway analyses were performed using g:Profiler ( https://biit.cs.ut.ee/gprofiler ) with genes ordered by the degree of differential expression. P values were estimated by hypergeometric distribution tests and adjusted by multiple testing correction using the g:SCS (set counts and sizes) algorithm, integral to the g:Profiler server [ 22 ]. Survival analysis and hazard ratio calculations Overall survival time was downloaded for TCGA data alongside all other metadata. Survival analysis was run using R and RStudio (version 2023.12.0 Build 369). Univariate and multivariate analyses, hazard ratio calculations and survival curve plotting were performed using GraphPad Prism (version 10.3). Cell lines All cell lines were obtained from the Cell Services facility of The Francis Crick Institute and verified as mycoplasma-free. All human cell lines were further validated by DNA fingerprinting. View this table: View inline View popup Download powerpoint Cell transfections HEK293T and A549 cells were seeded at a density of 200,000 cells/well in 2ml of culture media 24 hours prior transfection in 6-well plates. Cells were then transfected with 5 µg of plasmid each expressing the following sequence (see table): CHRNA5 (pcDNA3.1-CHRNA5, Genewiz) or CHRNA5[AluSz] (pcDNA3.1-CHRNA5[AluSz], Genewiz) or CHRNA5-Full intron 5 (pcDNA3.1-CHRNA5-Full intron 5, Genewiz) or CHRNA5-RTE deleted (pcDNA3.1-CHRNA5-RTE deleted, Genewiz) using Lipofectamine 3000 transfection reagent (Thermo Fisher). Cell were then seeded for immunofluorescence staining. Reverse transcriptase-based quantitative PCR (RT-qPCR) RNA was extracted using the RNeasy kit (Qiagen). cDNA was synthesized using the Maxima First Strand cDNA Synthesis Kit (Thermo Fisher), and qPCR performed using Applied Biosystems Fast SYBR Green (Thermo Fisher) using the following primers: View this table: View inline View popup PCR PCR was performed using KOD Hot Start Master Mix (Sigma) with the following primers: View this table: View inline View popup Download powerpoint Cas9-mediated editing The HERVE provirus in the RNGTT locus was targeted by the following guide RNA (gRNA) sequences: View this table: View inline View popup Download powerpoint Amplicon Sanger sequencing Genomic DNA PCR products from A498 cells were sent to Genewiz for PCR clean-up and Sanger sequencing with the following sequencing primers: View this table: View inline View popup Download powerpoint Amplicon next-generation sequencing Amplicons from HCC4006 cell cDNA specific for the HERVH Xp22.2-AS isoforms were amplified using the following primers: View this table: View inline View popup Download powerpoint Immunofluorescence HEK293T cells transfected with pcDNA3.1-CHRNA5 or pcDNA3.1-CHRNA5[AluSz] were grown on 1.5mm coverglass dishes (MatTek, Cat #P35G-1.5-20-C) were fixed using 10% neutrally buffered formalin (Genta Medical) for 15 min. Non-specific staining of non-permeabilized cells was blocked with 1% bovine serum albumin (Sigma-Aldrich). Primary antibody incubation for HA-tag antibody (Santa Cruz Biotechnology, sc-7392) was performed overnight at 4°C at a 1:100 dilution. Secondary antibody incubation using Goat Anti-Mouse IgG H&L AlexaFluor594 Antibody (Abcam, Cat #ab150116) was carried out the next day for 1 hour at room temperature at a 1:1,000 dilution. Nuclear staining was performed using Hoechst 33342 (Thermo Fisher). Samples were imaged on the Zeiss Observer.Z1 (Carl Zeiss Meditec AG) using Micro-Manager 2.0 software. Retroviral transduction Stably transduced cell lines were produced through viral infection and single cell sorting on green fluorescent protein (GFP) and mCherry using the MoFLO XDP cell sorter (BD Biosciences, Flow Cytometry Team, The Francis Crick Institute). Virus was generated using HEK293T cells plated at a density of 1.5×10 6 cells per 60 mm well incubated with a mixture of serum-free IMDM (Sigma-Aldrich, I3390), GeneJuice (VWR International Ltd., 70967-4), and 5 μL of plasmid DNA. Plasmids used were vesicular stomatitis virus glycoprotein (VSVg) plasmid (pcVG-wt), pHIT60, and the open reading frames of the sequences of interest cloned into the pRV-IRES-GFP or pRV-IRES-mCherry vector (see table). Cloning the open reading frames into the vector was carried out Genewiz LLC, and was followed by sequencing to verify the plasmid structure. Virus-containing supernatant was collected and added to HEK293T cells (plated at a density of 8.5×10 4 cells per 35 mm well) along with polybrene (Sigma-Aldrich, TR-1003-G), and spun at 1200 RPM for 45 minutes. After three days, populations were single cell sorted on GFP, or mCherry expression using a BD FACSAria II (BD Biosciences) (Flow Cytometry STP, The Francis Crick Institute). Protein preparation for Western Blot Cells were washed twice with phosphate-buffered saline (PBS) stored at 4°C before being incubated on ice with radioimmunoprecipitation assay (RIPA, Sigma-Aldrich, R0278-50ML) buffer for 30 minutes to lyse the cells. The mixture was then spun at 14000 RPM for 10 minutes at 4°C. The protein concentration of the lysate was measured using the Pierce TM BCA protein assay kit (Thermo Scientific, 23225). Stock solutions at a protein concentration of 500 μg/mL were made by mixing 100 μL of sample buffer (Laemmli 2x concentrate, Sigma-Aldrich, S3401-10VL), with 100 μg of protein lysate and RIPA buffer to a final volume of 200 μL. Stock solutions were heat denatured at 95°C for 5 minutes before being frozen at −20°C. Western Blot Sample stock solutions were thawed on ice before being boiled at 95°C for 5 minutes. 10 μg of protein per sample was loaded into a 4–20% Mini-PROTEAN® TGX™ precast polyacrylamide gel (Bio-Rad, 4561094) alongside a protein ladder (PageRuler Plus Prestained Protein Ladder, 10 kDa to 250 kDa, ThermoFisher, 26619). The gel electrophoresis was run in a Mini-PROTEAN® Tetra Vertical Electrophoresis Cell (Bio-Rad) filled with protein running buffer (Media Team, The Francis Crick Institute) at 180 V for 40 minutes. Samples were transferred to a 0.2 μm nitrocellulose membrane (Trans-Blot Turbo Mini 0.2 μm Nitrocellulose Transfer Pack, Bio-Rad, 1704158) using the Trans-Blot Turbo dry transfer system (Bio-Rad, 1704150) turbo setting for mini TGX gels before blocking with 5% skimmed milk (Marvel) in Tris-buffered saline with 0.5% Tween-20 (TBS-T, Media Team, The Francis Crick Institute) for 1 hour. Membranes were stained overnight at 4°C with the anti-FLAG antibody (Sigma-Aldrich, F1804) diluted at 1:1000 in 5% skimmed milk in TBS-T. Membranes were washed with TBS-T at room temperature before the horseradish peroxidase (HRP)-conjugated anti-mouse secondary antibody (Abcam, ab6728 Error! Reference source not found. ) was added, diluted at 1:1000 in 3% skimmed milk in TBS-T and incubated at room temperature for 1 hour. Membranes were then washed in TBS-T and visualised by enhanced chemiluminescence using Clarity™ Western ECL Substrate (Bio-Rad, 1705060) on a ChemiDoc XRS+ (Bio-Rad). Transwell migration assay Cell migratory capacity was examined by transwell migration assay that uses chemotactic gradient to assess how cells migrate through a porous membrane. Cells were trypsinised and resuspended in serum-free media. They were then seeded into Millicell Hanging Cell Culture Insert (Millipore, PTEP24H48) at a density of 20×10 3 for A498 and A549 clones, while the lower chamber contained fresh culture media with 30% FBS. The cells were allowed to migrate for at least 48 hours. The inserts were washed with PBS and fixed with 10% neutrally buffered formalin (Genta Medical) for 10 minutes. Those cells that did not migrate were removed. The cells on the lower surface of the insert were washed with PBS again, stained with crystal violet solution (Sigma Aldrich, V5265). The cells on each insert were counted in 4 random fields under Zeiss Observer.Z1 (Carl Zeiss Meditec AG) using Micro-Manager 2.0 software. Cell growth assays Growth and proliferation of A498 parental control (1E7), A498 HERVE 6q15 -/- (2D11) clone, A549 parental and canonical CHRNA5 and CHRNA5[AluSz] expressing A549 clones was assessed by real-time quantitative live-cell imaging using the Incucyte Live-Cell Analysis System (Sartorius). Cells were seeded into 96-well plates 24 hours prior measurement in Incucyte system at a density of 2000 and 1000 cells per well for A498 and A549, respectively. Image and confluency measurement were taken every 3 hours for at least 72 hours. Cell growth data for RNGTT-deficient cell lines in CCLE were downloaded from Dependency Map (DepMap) portal ( https://depmap.org/portal ) [ 23 ]. Statistical analyses Statistical comparisons were made using GraphPad Prism 10.3 (GraphPad Software), SigmaPlot 14.0, Qlucore Omics Explorer v3.9, or R (versions 3.6.1-4.0.0). Parametric comparisons of normally distributed values that satisfied the variance criteria were made by unpaired or paired Student’s t -tests or One Way Analysis of variance (ANOVA) tests with Bonferroni correction for multiple comparisons. Data that did not pass the variance test were compared with non-parametric two-tailed Mann-Whitney Rank Sum tests (for unpaired comparisons) or Kruskal-Wallis test with Dunn’s multiple comparisons correction. Results Widespread potential for RTE-mediate disruption of adjacent gene function To identify cases where aberrant transcriptional inclusion of RTEs may affect local gene function, we searched for gene loci that exhibit a specific switch in RNA isoform expression. To this end, we used a cancer transcriptome assembly that captures diverse RTE-overlapping transcripts [ 8 ], and selected transcripts that were either upregulated or downregulated in a given cancer type compared with its respective normal tissue ( Figure 1A-C ). Intersection of the two lists identified transcripts that, despite exhibiting contrasting transcriptional behaviour (referred to here as discordant transcripts), were transcribed from the same locus ( Figure 1A, B ). For example, from a total of 225,544 transcripts differentially expressed between kidney renal clear cell carcinoma (KIRC) and normal kidney tissue, 62.9% were from loci that produced transcriptionally discordant RNA isoforms ( Figure 1A-C ), suggesting extensive changes in RNA isoform balance in this cancer type. Similar results were also obtained from analysis of colon adenocarcinoma (COAD), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and esophageal adenocarcinoma (EAC), although the proportion of discordant transcripts was lower (11.3%-34.3%) in these cancer types ( Figure 1C ). Moreover, nearly half of the loci producing transcriptionally discordant RNA isoforms in KIRC were also found in at least one other cancer type, and this fraction was much higher (82.5%-92.2%) for the remaining types ( Figure 1C ). Compared with the entire previously assembled transcriptome, Alu , MIR and L1 elements were particularly enriched in discordant transcripts from all types of cancer analysed, whereas LTR elements displayed a cancer type-specific pattern of enrichment ( Figure 1D ). These findings suggested extensive imbalances in RNA isoform expression in cancer through increased RTE utilisation and indicated common underlying mechanisms operating in multiple cancer types. Given the potential of alternative isoforms generated by RTE co-option to affect gene function or create new function, we next investigated specific effects on nearby or encompassing genes in detail. Download figure Open in new tab Figure 1. Widespread shifts in the balance of RNA isoform expression in cancer (A) Heatmap of expression of 87,861 and 54,202 discordant transcripts that are upregulated and downregulated (≥1.5 fold-change, p≤0.05, q≤0.05), respectively, in TCGA KIRC samples, compared with normal kidney tissue, and overlap with loci producing transcripts in both categories. (B) Total number of differentially-expressed transcripts (≥1.5 fold-change, p≤0.05, q≤0.05) ( top ), differentially-expressed discordant transcripts ( middle ), and loci producing discordant transcripts ( bottom ) between the indicated cancer types and their respective normal tissue (KIRC n=538, normal kidney n=71; COAD n=239, normal colon n=39; LUAD n=419, normal lung n=24; LUSC n=362, normal lung n=24; EAC n=78, normal esophagus n=9). (C) Fold-change of individual discordant transcripts (symbols) overlapping with the indicated loci in KIRC samples. (D) Fold-enrichment for the indicated RTE subgroups in discordant transcripts, compared with the entire transcriptome. All indicated RTE subgroups were significantly enriched (p≤0.05). Downregulation of RNGTT transcription by an intronic HERVE integration An example of a locus producing discordant transcripts in KIRC was RNGTT , encoding the mRNA capping enzyme with RNA 5’-triphosphate monophosphatase and guanylyltransferase activities. In its penultimate intron, RNGTT harbours a HERVE provirus, integrated in reverse orientation relative to RNGTT ( Figure 2A ). This HERVE provirus, referred to here as HERVE 6q15 , is known to be highly expressed in KIRC and to encode immunogenic retroviral proteins, which contribute to tumour immune control [ 24 – 27 ]. However, the consequences of its transcriptional induction on RNGTT have not been previously considered. We found that transcription of RNGTT and the intronic HERVE provirus exhibited an inverse pattern in KIRC ( Figure 2B-D ). HERVE 6q15 was not expressed in normal kidney tissue but was highly induced in ∼50% of KIRC cases, with significantly higher expression in later stages of the disease ( Figure 2B, C ). In contrast, RNGTT expression was significantly reduced in KIRC, compared with normal kidney tissue, and this reduction was more pronounced in cases with higher HERVE 6q15 expression ( Figure 2B, D ), suggesting that transcriptional activation of the HERVE integration negatively impacted RNGTT expression. A similar negative correlation between RNGTT and HERVE 6q15 transcription was also observed in RNA-seq data from individual renal cell carcinoma cell lines obtained from the Cancer Cell Line Encyclopedia (CCLE) ( Figure 1E, F ), where possible confounding effects of non-tumour cells in tumour samples can be excluded. Download figure Open in new tab Figure 2. Effect of HERVE activation on RNGTT transcription (A) Gene structure and integrated HERVE provirus, GENCODE-annotated and assembled transcripts, and RNA-seq traces of 24 combined KIRC and kidney renal papillary cell carcinoma (KIRP) samples at the RNGTT locus. (B) Expression of transcripts overlapping HERVE 6q15 or the canonical RNGTT in normal kidney tissue and KIRC samples, ordered according to HERVE 6q15 expression. (C) HERVE 6q15 expression in TPM in the same samples as in b (p value calculated with Mann-Whitney test), and according to tumour stage (I n=271, II n=59, III n=123, IV n=82; p values calculated with Kruskal-Wallis test with Dunn’s multiple comparisons correction). (D) RNGTT expression (TPM) in normal kidney tissue (n=71) and in KIRC samples with low (n=322) and high (n=216) HERVE 6q15 expression (p values calculated with Kruskal-Wallis test with Dunn’s multiple comparisons correction). (E) Expression of transcripts overlapping HERVE 6q15 or the canonical RNGTT in renal cell carcinoma cell lines in CCLE, in columns ordered according to HERVE 6q15 expression. (F) RNGTT expression (TPM) in renal cell carcinoma cell lines with low (n=9) and high (n=13) HERVE 6q15 expression (p value calculated with two-tailed Student’s t-test). (G) Ratio of HERVE 6q15 to RNGTT expression in renal cell carcinoma cell lines in CCLE ( left ), and RNGTT expression (assessed by RT-PCR and plotted relatively to HPRT1 expression) in A498 cells with (clone 1E7, HERVE 6q15 +/+ ) or without (clone 2D11, HERVE 6q15 -/- ) the HERVE 6q15 provirus ( right ). Symbols represent replicates of 4 independent experiments, connected with lines (p value calculated with two-tailed paired Student’s t-test). (H) Heatmap of differential gene expression (≥2 fold-change, p≤0.05, q≤0.05) of between HERVE 6q15 +/+ and HERVE 6q15 -/- A498 cells ( left ). Columns represent independent replicates. Gene ontology (GO) functional annotation of the differentially expressed genes ( right ) (p values calculated with g:Profiler using hypergeometric distribution tests and adjusted for multiple hypothesis testing using the g:SCS (set counts and sizes) algorithm, integral to the g:Profiler server ( https://biit.cs.ut.ee/gprofiler )). i , Mean confluency (±SD, n=6 from 1 experiment) ( left ) and mean cell number (±SEM, n=3 from 1 experiment) ( right ) of HERVE 6q15 +/+ and HERVE 6q15 -/- A498 cell cultures over time (p values calculated by two-tailed Student’s t-test of the area under the curve (AUC) values and by two-tailed Student’s t-test for day 10, respectively). (J) Representative images of of HERVE 6q15 +/+ and HERVE 6q15 -/- A498 cell morphology 2 or 3 days after plating. (K) In vitro migration of HERVE 6q15 +/+ (1E7) and HERVE 6q15 -/- (2D11) A498 cells. Symbols represent independent measurements (n=12, 4 fields of view from 3 independent experiments; p value calculated with two-tailed Student’s t-test). In agreement with prior reports [ 25 ], HERVE 6q15 transcription in KIRC was directly correlated with the degree of hypoxia (Figure S1A, B), although the strength of this correlation was likely affected by the ability of current hypoxia scores to reflect the pseudohypoxic state of this cancer type [ 20 ]. However, a direct effect of hypoxia on HERVE 6q15 transcription was evident by analysis of RNA-seq data from renal cell carcinoma RCC4 cells with restored expression of the von Hippel-Lindau (VHL) tumour suppressor [ 28 ] (GSE120887) (Figure S1C, D). Hypoxia significantly increased HERVE 6q15 expression in VHL-expressing RCC4 cells, but did not affect levels of RNGTT transcription, which were already very low and substantially lower than those of HERVE 6q15 in these cells (Figure S1C, D). The enzymatic activities encoded by RNGTT play an indispensable role in RNA capping, by catalysing the first step of a complex series of reactions leading to the addition of the methyl-7-guanosine cap on the 5’ ends of nascent RNAs [ 29 ]. In turn, RNA capping affects all subsequent aspects of RNA processing and function, including translation potential [ 29 ]. Accordingly, RNGTT is a common essential gene, required for growth of virtually all cancer cell lines (Figure S2), and its direct pro-tumour activity is associated with worse prognosis on most cancer types [ 29 – 31 ]. Given that high levels of HERVE 6q15 transcription in most renal cell carcinoma cell lines may have reduced RNGTT transcription to levels that cannot be further reduced without compromising cell viability (Figure S2), we investigated a direct effect of HERVE 6q15 on RNGTT , with the reverse experiment. We selected renal cell carcinoma A498 cells, which express a high HERVE 6q15 to RNGTT ratio ( Figure 1G ), and used CRISPR/Cas9 editing to remove the HERVE provirus, together with two immediately adjacent L1 integrations (ED. Figure 3 ). Assessed by RT-qPCR and compared with the HERVE 6q15 +/+ clone (1E7), expression of RNGTT was upregulated by ∼63% in the HERVE 6q15 -/- clone (2D11) (Fig, 1G). RNGTT upregulation following HERVE 6q15 deletion in A498 cells was accompanied by extensive transcriptional changes, with upregulation of genes involved in cell adhesion and migration, and downregulation of genes involved in metabolic processes ( Figure 1H ). Consistent with their transcriptional profile and the pro-tumour activities of RNGTT , HERVE 6q15 -/- 2D11 cells exhibited increased in vitro growth, altered morphology and increased migration ( Figure 1I-K ). Lastly, supporting opposing transcriptional profiles, RNGTT and HERVE 6q15 levels also showed the opposite correlation with KIRC survival, which was however an indirect result of HERVE 6q15 association with later stages of the disease (Figure S4). Together, these results support a model of HERVE 6q15 transcriptional induction during KIRC progression, which reduces, but does not abolish pro-tumour RNGTT expression. Download figure Open in new tab Figure 3. Effect of THE1A activation on CDH4 transcription (A) Expression of transcripts overlapping THE1A[CDH4-AS] or the canonical CDH4 in primary and metastatic SKCM samples. (B) THE1A[CDH4-AS] and canonical CDH4 expression (TPM) in normal skin tissue (n=36) and in primary (n=101) and metastatic (n=224) SKCM samples (p values calculated with Kruskal-Wallis tests with Dunn’s multiple comparisons correction). (C) CDH4 expression (TPM) in primary and metastatic SKCM samples with low (n=66 and n=161, respectively) and high (n=35 and n=89, respectively) THE1A[CDH4-AS] expression (p value calculated with Mann-Whitney test). (D) Overall survival of primary and metastatic SKCM patients, stratified by THE1A[CDH4-AS] expression (p values calculated with log-rank tests). Regulation of Cadherin 4 by THE1A -driven antisense transcription We have previously identified an antisense transcript, initiated by a THE1A retroelement and referred to as THE1A[CDH4-AS] , which is spanning two introns of the CDH4 gene and expressed specifically in cutaneous and uveal melanomas [ 8 ]. To examine a possible effect of transcriptional activation of the intronic THE1A element on CDH4 transcription we correlated the two in RNA-seq data from The Cancer Genome Atlas (TCGA) skin cutaneous melanoma (SKCM) samples ( Figure 3 ). This analysis revealed a pattern of mutual exclusivity between THE1A[CDH4-AS] and CDH4 transcription in both primary and metastatic SKCM, irrespective of disease stage ( Figure 3A ). However, compared with normal skin samples, the transcriptional activation of the THE1A element and concomitant downregulation of CDH4 transcription were considerably more pronounced in primary than in metastatic disease ( Figure 3B, C ). Indeed, whereas significantly reduced in primary melanoma, overall CDH4 transcription remained high in metastatic melanoma, particularly in samples with low THE1A[CDH4-AS] transcription ( Figure 3B, C ). CDH4 encodes Cadherin 4, also known as R-cadherin (retinal), a member of the calcium-dependent adhesion molecule superfamily, important in forming adherens junctions and in organ development [ 32 , 33 ]. Cadherin-regulated cellular adhesion and detachment also determines migration and invasiveness of tumour cells and, consequently, the ability of several cancer types to metastasise [ 34 , 35 ]. In the skin, E-cadherin (epidermal) and P-cadherin (placental) mediate heterotypic adhesion of neural crest-derived melanocytes with the surrounding epithelial cells [ 36 ]. Loss of E-cadherin and P-cadherin, and gain of N-cadherin (neuronal), known as a cadherin switching [ 35 ], facilitates melanoma cell metastasis and is associated with worse prognosis of SKCM [ 37 ]. While, its role in melanoma is less well studied, R-cadherin mediates adhesion with N-cadherin [ 32 ] and its overexpression in epidermoid carcinoma A431 cells causes the loss of E-cadherin and P-cadherin, through competition for the intracellular adaptor proteins catenins [ 38 ]. An involvement of R-cadherin in cadherin switching is further supported by reports of an essential role in tumorigenesis and metastasis in human osteosarcoma [ 39 ] and in a murine model of glioma [ 40 ], and of a tumour suppressive function in human colorectal and gastric cancers [ 41 ]. Consistent with a role for CDH4 in the metastatic process suggested by findings in other cancer types, we found that transcriptional co-option of THE1A[CDH4-AS] specifically in SCKM, differentiates primary and metastatic disease and is differentially associated with survival in the two subtypes ( Figure 3D ). This association indicated that THE1A[CDH4-AS] -mediated suppression of CDH4 in primary melanomas restrains their metastatic potential, whereas the failure to establish or the loss of such transcriptional effect facilitates metastasis. HERVH -driven downregulation of endosomal single-stranded RNA sensors TLR7 and TLR8 The TLR7 and TLR8 paralogue genes are arranged in tandem on chromosome Xp22.2, followed by a HERVH provirus, referred to here as HERVH Xp22.2 ( Figure 4A ). Our assembly identified a number of antisense transcripts, initiated by the bidirectional promoter activity of the HERVH Xp22.2 provirus, using a total of 10 alternative exons spread throughout the locus and extending to the upstream gene PRPS2 ( Figure 4A ). Some of the assembled transcripts partially matched the annotated TLR8-AS1 transcripts in GENCODE 46 (ENST00000451564) and RefSeq (NR_030727), which appeared incomplete ( Figure 4A ). Splice junction analysis of RNA-seq data from TCGA LUAD samples revealed two main groups of HERVH Xp22.2 -initated antisense transcripts (collectively referred to as HERVH Xp22.2-AS ), one terminating between the TLR7 and TLR8 loci and one terminating within the PRPS2 locus ( Figure 4A ). To validate the complex splicing pattern, we amplified the corresponding cDNAs from key splicing isoforms expressed in lung adenocarcinoma HCC4006 cells. Deep-sequencing of the amplicons confirmed the alternative use of middle and terminal exons, as well as the balance of shorter and longer HERVH Xp22.2-AS isoforms ( Figure 4A ). Download figure Open in new tab Figure 4. Effect of HERVH activation on TLR7 and TLR8 transcription (A) Gene structure and integrated HERVH provirus at the PRPS2-TLR7-TLR8 locus, GENCODE- and RefSeq-annotated and assembled transcripts, splice junction analysis of LUAD RNA-seq data, amplicons used for transcript validation, splice junction analysis of amplicon sequencing, and RNA-seq traces of LUAD samples with ( top two ) or without ( bottom two ) HERVH transcriptional activation. (B) Expression of transcripts overlapping HERVH Xp22.2 or the canonical TLR7 or TLR8 in normal lung (n=36) and ovary (n=12) tissue, and in MESO (n=24), LUAD (n=419), LUSC (n=362) and OV (n=419) samples. (C) Overall survival of LUAD ( left ) and OV ( right ) patients, stratified by HERVH Xp22.2 expression (p values calculated with log-rank tests). Across several cancer types and respective normal tissues, HERVH Xp22.2-AS was transcriptionally activated in a substantial proportion of samples from ovarian serous cystadenocarcinoma (OV), mesothelioma (MESO) and testicular germ cell tumours (TGCT), and a smaller proportion of samples from other cancers, including LUAD and LUSC, but remained inactive in normal tissues, with the possible exception of a small number of EBV-transformed B cell lines (Figure S5A). Notably, HERVH Xp22.2-AS transcription was strongly anti-correlated with TLR7 and TLR8 transcription is cancers where it was expressed ( Figure 4A , B; Extended Data Fig, 5B). Indeed, whereas normal lung tissue expressed TLR7 and TLR8 highly and proportionally, without detectable HERVH Xp22.2-AS expression, ∼13% of LUAD samples expressed high levels of HERVH Xp22.2-AS , but not of TLR7 and TLR8 (Figure S5B), indicating an inhibitory effect of HERVH Xp22.2-AS transcriptional activation on sense transcription. This effect appeared to extend also to the PRPS2 locus, which was expressed only in LUAD samples, but not in normal lung tissue, and exhibited a significant anti-correlation with HERVH Xp22.2-AS expression (Figure S5B). Similar results were obtained also in LUSC, as well as MESO and OV, where HERVH Xp22.2-AS was highly expressed and TLR7 and TLR8 were downregulated in nearly half of the cases ( Figure 4B ). TLR7 and TLR8 are endosomal sensors of single-stranded RNA [ 42 ] that play essential roles in the defence against viral infection and in the induction of B cell systemic autoimmunity [ 43 ]. Recent studies have indicated an essential, yet dual role for TLR7 and TLR8 also in cancer progression and immune control [ 44 , 45 ]. Whereas their ligation in immune cells may enhance anti-tumour activity, TLR7 and TLR8 are also expressed in tumour cells where the mediate a pro-tumour effect [ 44 , 45 ]. Indeed, tumour cell-intrinsic expression of TLR7 and TLR8 promotes their growth and survival in vitro [ 46 , 47 ] and is associated with poor clinical outcome in non-small cell lung carcinomas (NSCLC) [ 48 – 50 ]. This pro-tumour effect of TLR7 is further supported by studies in animal models [ 48 ]. A pro-tumour effect of tumour cell-intrinsic TLR7 and TLR8 expression would predict that their downregulation by HERVH Xp22.2-AS transcriptional activation has an anti-tumour effect. Although survival analyses of TLR7 and TLR8 expression are confounded by their expression both in tumour cells and in immune cells, the strict tumour specificity of HERVH Xp22.2-AS expression reflects tumour cell-intrinsic transcriptional states. Indeed, we found that higher HERVH Xp22.2-AS expression in tumour samples is significantly associated with better prognosis in both LUAD and OV ( Figure 4B ), consistent with a protective effect of HERVH Xp22.2-AS transcriptional activation. Downregulation of APOBEC3B expression by MER11C element co-option Similar to TLR7 and TLR8 paralogues, members of the Apolipoprotein B editing complex 3 (APOBEC3) family of enzymes are encoded by genes arranged in a cluster on chromosome 22 ( Figure 5A, B ). They catalyse cytidine deamination in DNA or RNA substrates, which can potently inhibit virus and RTE replication, but can also drive genomic diversity and instability in cancer [ 51 , 52 ]. Current evidence implicates APOBEC3A and APOBEC3B as the two enzymes primarily responsible for the mutational signatures in human cancers and indicate a role for APOBEC3B in the regulation of APOBEC3A [ 53 ]. Expression of human APOBEC3B in mice enhances their susceptibility to tumours and also causes male infertility [ 54 ], supporting a pro-tumour role, as well as a detrimental effect on the genetic integrity of the male germline. Download figure Open in new tab Figure 5. Effect of MER11C activation on APOBEC3B transcription (A) Gene structure and integrated MER11C, L2c and MER96B RTEs at the APOBEC3B locus, and RNA-seq traces of TGCT samples with ( top two ) or without ( bottom two ) MER11C transcriptional activation. (B) Gene structure at the extended APOBEC3 gene cluster. (C) Expression (TPM) of APOBEC3B-AS1 and the indicated APOBEC3 genes in TGCT samples with low (n=14) and high (n=10) APOBEC3B-AS1 expression (p values calculated with Mann-Whitney test and Student’s t-test for APOBEC3B-AS1 and APOBEC3B , respectively). In this locus, we have identified a transcript matching annotated transcript APOBEC3B-AS1 (ENST00000513758) and transcribed in the reverse orientation in relation to the APOBEC3 genes ( Figure 5A, B ). This transcript was initiated by a MER11C element integrated between the APOBEC3B and APOBEC3C genes and extended over the first 3 APOBEC3B exons ( Figure 5A, B ). High APOBEC3B-AS1 expression was highly specific to TGCT samples, with very low expression in other cancer types or normal tissues (Figure S6). Importantly, transcriptional activation of the MER11C element in TGCT samples was accompanied by reduction specifically in transcription of the overlapping APOBEC3B gene, whereas transcription of all other APOBEC3 genes in this cluster remained unaffected ( Figure 5C ). Reduction of ENPP3 potential by a switch to non-functional isoforms The ENPP3 locus produced multiple discordant transcripts, the majority of which were highly upregulated ( Figure 1B ). In addition to ENPP3 , the locus also contains two other annotated genes, CTAGE9 and OR2A4 , both located in intron 16 of ENPP3 and transcribed in the reverse orientation ( Figure 6A ). Inspection of the locus identified several novel isoforms created by transcriptional inclusion of RTEs ( Figure 6A ). These included a transcript using L2a and AluSx elements ( ENPP3[L2a/AluSx] ) also in intron 16 as alternative terminal exon and polyadenylation site ( Figure 6A ). They also included a transcript using AluSx3 and L2a elements as alternative second and terminal exons, respectively ( ENPP3[AluSx3/L2a] ), partially matching annotated GENCODE 46 transcript ENST00000427707 ( Figure 6A ). Two additional transcripts were created by the use of a PABL_B element as alternative promoter ( [PABL_B]ENPP3 ) or an AluSg element as alternative terminal exon ( ENPP3[AluSg] ) ( Figure 6A ). Compared with normal kidney tissue, expression of ENPP3 was found significantly elevated in KIRC samples, in agreement with prior reports [ 55 , 56 ], as was expression of alternative ENPP3 isoforms, as well as of CTAGE9 and an L1MDa integration straddling CTAGE9 , whereas expression of OR2A4 was similar ( Figure 6B ; Figure S7A). Alternative ENPP3 isoform expression accounted for a substantial proportion (∼32%) of total ENPP3 transcription, with stable balance through progressive stages of the disease, and was primarily driven by the ENPP3[L2a/AluSx] and ENPP3[AluSx3/L2a] transcripts ( Figure 6B ; Figure S7B). Notably, whereas other ENPP3 isoforms were expressed proportionally with the canonical isoform, expression of ENPP3[L2a/AluSx] and CTAGE9 did not follow this pattern and appeared to be independently regulated ( Figure 6C ). Download figure Open in new tab Figure 6. Effect of local RTEs on ENPP3 functional and non-functional isoform balance (A) Gene structure and exonised RTEs, GENCODE-annotated and assembled transcripts, and RNA-seq traces of 24 combined KIRC and KIRP samples at the ENPP3 locus. (B) Expression of transcripts overlapping the indicated ENNP3 isoforms or overlapping genes and RTEs in normal kidney tissue and KIRC samples, ordered according to canonical ENPP3 expression. (C) Correlation of ENPP3 isoform and CTAGE9 expression (TPM) in KIRC samples (n=538) (p values calculated with linear regression). CTAGE9 expression is capped at 30 TPM. (D) Overall survival hazard ratios (HRs) for the indicated variables in KIRC patients ( ENPP3 canonical high n=311, reference low n=210; ENPP3[L2a/AluSx] high n=214, reference low n=307; CTAGE9 high n=174, reference low n=347; Age n=521; Stage II n=56, III n=123, IV n=82, reference I n=257; Gender, male n=338, reference female n=183; Ethnicity, Asian n=8, African n=55, reference White n=450). Error bars represent 95% CIs (p values calculated with Cox proportional hazards regression). (E) Overall survival of KIRC patients, stratified by ENPP3 expression ( left ) or the fraction of the canonical ENPP3 isoform in total ENPP3 expression ( right ) (p values calculated with log-rank tests). ENPP3, also known as CD203c, is a type II transmembrane protein that catalyses the hydrolysis of extracellular nucleotides [ 57 ]. It was originally identified as a basophil and mast cell activation marker, regulating allergic inflammation by hydrolysing extracellular ATP [ 57 , 58 ]. More recently, ENPP3 has also been implicated in the regulation of extracellular levels of cGAMP (2′3′-cyclic guanosine monophosphate), a second messenger for the activation of the STING (stimulator of interferon genes) pathway and the production of type I IFNs in viral infection and cancer [ 59 ]. Supporting a pro-tumour role, loss of ENPP3 cGAMP hydrolase activity in mice renders them more resistant to primary tumour growth and metastasis [ 59 ]. In addition to regulating the tumour immune environment, cell-intrinsic expression of ENPP3 has been reported to promote cell migration [ 60 ] and to be essential for the growth of renal cell carcinoma cell lines [ 56 ], further supporting a pro-tumour function. We, therefore, considered the potential activity of the alternative ENPP3 isoforms. Exonisation of the AluSx3 element after the first coding exon of in the ENPP3[AluSx3/L2a] isoform creates a premature termination after codon 53, producing a severely truncated product (UniProt ID: E7ETI7), unlikely to retain any function. The ENPP3[L2a/AluSx] isoform has the potential to produce a larger protein, retaining the transmembrane helix and most of the phosphodiesterase domain, but missing the nuclease domain (Extended Data F. 8A), which could exert altered enzymatic activities. However, in contrast to the canonical isoform, which was readily detectable upon overexpression in HEK293T cells, the ENPP3[L2a/AluSx] isoform failed to produce a product of the expected or higher mass, indicative of protein instability (Figure S8B). These results suggested that alternative ENPP3 isoforms are non-functional and their production is, therefore, at the expense of the canonical, thereby compromising the maximum capacity of the locus to produce the ENPP3 enzymatic activity. In turn, the reduction in ENPP3 potential could impede tumour progression. In multivariate analyses, overall ENPP3 transcription was associated with favourable outcome in KIRC, as previously reported [ 55 , 56 ], whereas CTAGE9 , which encodes the cutaneous T cell lymphoma-associated antigen 9, showed the inverse association ( Figure 6D ). Pertinently, a low proportion of canonical ENPP3 among ENPP3 isoforms was significantly associated with better survival in KIRC ( Figure 6E ), supporting a model where the degree of switching to non-functional ENPP3 isoforms through RTE co-option correlated with disease outcome. Loss of tumour cell-intrinsic CHRNA5 function by Alu exonisation CHRNA5 encodes the alpha 5 subunit of heteropentameric nicotinic acetylcholine receptor (nAChR) complexes, which initiate signalling cascades upon ligand-gated ion influx [ 61 ]. Multiple studies have linked a genetic variant in CHRNA5 exon 5 (rs16969968) with susceptibility to lung cancer, both through indirect effects on nicotine dependence and smoking behaviour, and through direct tumour cell-intrinsic effects [ 62 – 65 ]. A direct effect of CHRNA5 expression on tumour cell-intrinsic growth, migration and invasion, has been supported by several in vitro studies, although the outcome is likely dependent on the expression pattern of other nAChR subunits expressed in each experimental system [ 66 – 69 ]. The regulated use of alternative splice donor sites within exon 5 generates several annotated CHRNA5 isoforms that all use the canonical terminal exon 6 [ 70 , 71 ] ( Figure 7A ). We have identified a novel transcript, referred to here as CHRNA5[AluSz] , which uses an intronic AluSz element as alternative terminal exon and polyadenylation site ( Figure 7A ). AluSz exonisation was confirmed by RT-PCR in HEK293T, lung adenocarcinoma A549 and esophageal adenocarcinoma OE19 cells (Figure S9), as well as by analysis of long-read RNA-seq data from HEK293T and A549 cells, and esophageal squamous cell carcinoma TE5 and normal immortalized esophageal squamous epithelial SHEE cells [ 72 ] (Figure S10A). Remarkably, CHRNA5[AluSz] appeared to be the dominant isoform in fully transformed A549 and TE5 cells, whereas the balance shifted in favour of the canonical isoform in HEK293T and non-transformed SHEE cells (Figure S10A). Predominant expression of the CHRNA5[AluSz] isoform was also apparent when assessed by RT-qPCR in A549 cells ( Figure 7B ), and was also observed in analysis of RNA-seq data from non-small cell lung carcinoma and esophageal squamous cell carcinoma cell lines in CCLE, whereas several neuroblastoma cell lines, originating from a tissue where CHRNA5 is physiologically highly expressed, exhibited expression additionally of the canonical isoform (Figure S10B). These results implied that, despite not being previously identified, CHRNA5[AluSz] was the major isoform expressed particularly in cancer. Consistent, with this notion, both the canonical and the CHRNA5[AluSz] isoforms were highly upregulated in several cancer types, compared with the respective normal tissues, with expression of the CHRNA5[AluSz] approaching or often exceeding that of the canonical isoform (Figure S11). Download figure Open in new tab Figure 7. Characterisation of the CHRNA5[AluSz] isoform (A) Gene structure and location of exonised AluSz , assembled CHRNA5[AluSz] transcript, and RNA-seq traces of 24 combined LUAD and LUSC samples at the CHRNA5 locus. (B) CHRNA5 and CHRNA5[AluSz] expression (assessed by RT-PCR and plotted relatively to HPRT1 expression) in A549 cells. Symbols represent replicates (n=3) from a single experiment (p value calculated with two-tailed Student’s t-test). (C) Left , Schematic representation of CHRNA5 cDNA minigene constructs retaining only intron 5 either with the reference complement of RTEs (full intron 5) or with the AluSz and adjacent L1PB1 and AluSx4 elements deleted (RTE-deleted), and of the amplicon used to measure expression. Right , total CHRNA5 expression (assessed by RT-PCR and plotted relatively to HPRT1 expression), and ratio of CHRNA5[intron5] to CHRNA5[exon6] in parental A549 cells or those transfected with either construct. Symbols represent replicates (n=3) from a single experiment (p values calculated with two-tailed Student’s t-tests between the full intron 5 and RTE-deleted transfections). (D) Representative crystal violet staining of in vitro migrated parental A549 cells and A549 cells expressing the canonical CHRNA5 , the CHRNA5[AluSz] or both isoforms ( left ) (scale bar=200µm), and quantitation of the migrated cell number of each genotype ( right ). Symbols represent independent measurements (n=8, 4 fields of view from 2 independent experiments; p values calculated with Kruskal-Wallis test with Dunn’s multiple comparisons correction). (E) Correlation of CHRNA5 and CHRNA5[AluSz] expression in LUAD samples (n=419) (p value calculated with linear regression). (F) Overall survival of LUAD patients, stratified by CHRNA5 expression ( left ) or the ratio of CHRNA5[AluSz] to CHRNA5 expression ( right ) (p values calculated with log-rank tests). To examine the effect of the intronic RTEs on CHRNA5[AluSz] expression, we tested minigene constructs of CHRNA5 cDNA retaining only intron 5 with the reference complement of RTEs or with the AluSz and adjacent L1PB1 and AluSx4 elements deleted ( Figure 7C ). Overall CHRNA5 transcription from either minigene construct transfected into A549 cells exceeded endogenous CHRNA5 expression by at least an order of magnitude, but deletion of intronic RTEs resulted in higher levels of transcription compared with the full intron 5 ( Figure 7C ). Moreover, deletion of the intronic RTEs caused a significant shift in the balance of the two isoforms in favour of the canonical isoform terminating in exon 6 ( CHRNA5[exon6] ), although isoforms produced by continued transcription into intron 5 ( CHRNA5[intron5] ) still remained dominant ( Figure 7C ). These results suggested that, although not essential, the presence of the specific RTEs in CHRNA5 intron 5 favour the production of the intronically-terminated CHRNA5 isoform over the canonical. Given its high expression, we next assessed the potential of the CHRNA5[AluSz] isoform to produce a functional protein. At the protein level, AluSz exonisation replaces the last 52 amino acids, which are encoded by canonical exon 6 and include the 4 th transmembrane helix of CHRNA5, with as shorter sequence, predicted to remain cytoplasmic (Figure S12A, B). Expression of influenza hemagglutinin (HA)-tagged versions of the canonical CHRNA5 and CHRNA5[AluSz] protein isoforms showed equivalent cell-surface expression in HEK293T, visualised by immunofluorescence (Figure S12C), indicating efficient translation and plasma membrane trafficking of both. To examine the potential biological activity of the CHRNA5[AluSz] protein we stably expressed either isoform in A549 cells (Figure S13). Neither isoform significantly affected the in vitro growth rate of A549 cells (Figure S14). Consistent with prior reports [ 68 , 69 ], expression of the canonical isoform dramatically enhanced migration of A549 cells, whereas expression of CHRNA5[AluSz] had no apparent effect ( Figure 7D ). These results suggested that the loss of the last transmembrane helix resulted in a non-functional CHRNA5 isoform. Interestingly, acquired mutations resulting in an identical truncation of CHRNA6 have been found responsible for evolved insect resistance to insecticides [ 73 ], further highlighting the essential function of the last transmembrane helix. To test whether incorporation of the truncated CHRNA5[AluSz] protein into heteropentamers could potentially interfere with the function of the canonical, we co-expressed both isoforms in A549 cells (Figure S13). In this setting, cell migration was still significantly enhanced in doubly-expressing A549 cells, at levels comparable with those of A549 cells expressing the canonical isoform only ( Figure 7D ). These findings argued against a negative effect of CHRNA5[AluSz], in agreement with the lack of ligand binding by the CHRNA5 subunit [ 61 ]. Collectively, these results indicated that the switch to CHRNA5[AluSz] expression we observed in cancer would severely compromise the levels of canonical CHRNA5 that would otherwise be produced and, in turn, reduce the pro-tumour effects of CHRNA5 expression. A similar effect could also be achieved by alternative splicing within exon 5, causing a frame-shift in the translation of the exon 6 and producing a similarly truncated isoform (NCBI ID: NP_001382100). Although other alternative, non-functional isoforms can be detected [ 70 , 71 ], the CHRNA5[AluSz] appears to be the dominant isoform (Figure S10). Expression of CHRNA5[AluSz] was significantly correlated with that of the canonical isoform in LUAD samples, although their ratio varied considerably among individual cases ( Figure 7E ). In agreement with a pro-tumour role, high overall CHRNA5 transcription was associated with worse prognosis in LUAD ( Figure 7F ). However, a higher fraction of CHRNA5 transcription diverted to the CHRNA5[AluSz] isoform was associated with better prognosis in the same cohort ( Figure 7F ), suggesting a protective effect of a switch to the non-functional isoform. Discussion Evolutionary selection against deleterious effects is constantly depleting the germline of RTE integrations that pose a threat to nearby genes [ 74 ]. Nevertheless, numerous recent germline RTE integrations can adversely affect gene function, when the mechanisms that normally prevent their transcription and inclusion in gene transcripts fail. Our data indicate that transcriptional activation of RTEs causes widespread disruption of the transcriptional programme in cancer. Although they would be expected to be counterselected during tumour evolution, we identified several exemplar cases where transcriptional activation of embedded RTEs disrupts the function of a tumour-promoting or essential gene. Transcriptionally activated RTEs appear to disrupt the function of adjacent protein-coding genes by two main mechanisms. The first is reduction in the transcription of the protein-coding isoform by RTE-initiated antisense transcription, as exemplified here by RNGTT , CDH4 , TLR7 and APOBEC3B . Antisense transcription has long been recognised as a mechanism of gene regulation more broadly [ 75 ]. Furthermore, RTEs have also been implicated in the initiation of cis antisense transcripts that may regulate gene expression under physiological conditions [ 76 ]. Of note, RTE integrations driving cis natural antisense transcripts are enriched near the 3’ UTR of genes and belong to relatively older L2 and MIR subfamilies of non-LTR elements, implying they have been selected during evolution [ 76 ]. In contrast, RTEs identified here as regulators of cancer-promoting genes are primarily intergenic or intronic integrations of HERVs and other LTR elements, suggesting that the transcriptional activation of otherwise suppressed RTEs may extend regulation by antisense transcription to a new set of genes specifically in cancer. The second mechanism by which transcriptionally activated RTEs can disrupt gene function is a switch to the production of non-functional isoforms by RTE-exonisation and alternative splicing. Switch to a non-functional isoform can be at the expense of the canonical protein-coding isoform, with mutually exclusive expression of the two. However, non-functional RTE-exonising isoforms may also be expressed proportionally with the canonical, yet considerably reduce the functional output the gene would otherwise produce. Such an effect on gene function would still be strong, particularly when the non-functional isoform becomes the dominant isoform, as in the case of CHRNA5[AluSz] . The switch to non-functional isoforms appears to involve younger RTEs of the Alu and L1 subfamilies, the transcriptional utilisation of which is shared by diverse cancer types. A cancer-specific switch to non-functional isoforms may also explain the previously noted poor correlation between abundance of RNA transcripts, the quantitation of which often ignores the functional potential, and protein levels encoded from at least some genes in cancer [ 77 ]. Widespread RTE-mediated loss of function of tumour-promoting genes, as suggested by our findings, is seemingly at odds with the expected effect on tumour fitness that would disadvantage such events during tumour evolution, but may be further supported by recent evidence. A hypoxia-responsive LTR12B RTE has been reported to act as a cryptic promoter of an alternative isoform of POU5F1 , encoding the pluripotency transcription factor OCT4, producing a likely non-functional version of this tumour-promoting protein in renal cell carcinoma [ 78 ]. Similarly, antisense transcription has been reported to regulate levels of the E3 ubiquitin ligase HECTD2, which would otherwise exert a clear tumour-promoting effect in melanoma [ 79 ]. Transcriptional activation of intronic RTEs has also been linked with incomplete mRNA splicing, which reduces levels of fully-spliced, functional mRNA isoforms and, consequently, tumour cell fitness [ 80 ]. Although prior examples in cancer may be limited, similar events have been reported to affect gene function also in physiological conditions. For example, a truncated, non-functional form of ACE2 is produced during infection or inflammation by an IFN-responsive MIRb element, acting as an alternative promoter [ 81 ]. The use of an intronic L2a element as an alternative terminal exon creates a CD274 isoform that encodes a soluble version of PD-L1, which not only lacks suppressive activity, but also antagonises the membrane-bound canonical PD-L1 [ 82 ]. Similarly, the use of an intronic Alu element creates an isoform of IFNAR2 , encoding a truncated version of the type I IFN receptor subunit 2, acting as a decoy receptor [ 83 ]. Collectively, these findings underscore the mutagenic potential of RTE insertions, which may be higher than previously appreciated and further enhanced in cancer by their release from epigenetic control. Dysregulation of RTEs in cancer is considered to serve as a warning signal for the emergence of transformed cells. Transcriptional activation of RTEs creates immunogenic ligands that are recognised by innate immune sensors and adaptive antigen receptors, thereby contributing to tumour immunogenicity and immune control [ 84 , 85 ]. A potential effect of transcriptionally activated RTEs on the function of tumour-promoting or essential genes may represent an additional barrier to transformation. Similar to the immunogenic functions of transcriptionally activated RTEs, disruption of the cancer transcriptional programme would be subject to counterselection during evolution of individual tumours, but it may be positively selected during the evolution of the host species. Whereas the evolution of new function from RTE exaptation, particularly their utilisation in functional proteins is thought to be a slow evolutionary process [ 86 ], the regulation of adjacent gene function by co-option of transcriptionally metastable RTE integrations may evolve faster. Several of the genes affected by transcriptional activation of RTEs are known to exert strong cell-intrinsic pro-tumour effects. Considered in isolation, this finding would support a potential anti-tumour role for RTE dysregulation through the disruption of the function of those genes. However, there are a number of confounding factors that increase the complexity of these effects. Some of the affected genes are pleiotropic, with both tumour cell-intrinsic effects and effect on the immune or stroma microenvironment that can indirectly influence tumour grown. Direct indirect effects of an affected gene can synergise to promote tumour growth. For example, HECTD2 drives tumour cell-intrinsic proliferation of melanoma cells, as well as the production of immunosuppressive mediators [ 79 ], whereas ectopic expression of CALB1 prevents senescence of squamous lung carcinoma cells, but also prevents pro-tumour recruitment of neutrophils by cytokines that would otherwise be secreted as part of the senescence-associated secretory phenotype [ 11 ]. Similarly, ENPP3 promotes cell-intrinsic growth and migration renal cell carcinoma cells [ 56 , 60 ], but also regulates the availability of STING ligands for immune cells [ 59 ], and given its central role in RNA capping, RNGTT has the potential to affect many other genes with indirect effects on tumour growth. Moreover, while the function of a gene may be clearly pro-tumour in the context of an established tumour or cell line, it may play a different role at a different stage during cancer initiation and progression. It may also be the case that the relative fitness cost incurred by RTE-mediated disruption of pro-tumour gene function is a late event in tumour progression, by which time clonal competition between tumour cells has taken place. It may also be that such fitness costs are an unavoidable consequence of global RTE activation during tumour evolution, but offset by gains in tumour-promoting functions resulting from the same underlying epigenetic changes, so that the net effect on tumour growth is positive, and the effect on each gene has to be considered in the context of all other changes. Lastly, an overall negative effect on tumour cell-intrinsic growth caused of RTE-mediated disruption of the cancer transcriptional programme may still benefit tumours by restraining the exponential growth of late-stage tumours that would otherwise outrun or outpace available resources. Regardless of the ultimate effect on tumour growth, the identification and characterisation of specific cases of tumour-promoting genes affected by metastable RTE integrations highlights their potential to disrupt gene function in cancer, in turn increasing our understanding of tumour evolution and offering opportunities for intervention. Data availability The RNA-seq data generated in this study have been deposited at the EMBL-EBI repository ( www.ebi.ac.uk/arrayexpress ) (E-MTAB-14514). TCGA and GTEx data used for the analyses described in this manuscript were obtained from dbGaP ( https://dbgap.ncbi.nlm.nih.gov ) accession numbers phs000178.v10.p8.c1 and phs000424.v7.p2.c1 in 2017. Other publicly available dataset supporting the findings of this study included the following: RNA-seq data from a renal cell carcinoma cell line RCC4 with restored expression of the Von Hippel-Lindau (VHL) tumour suppressor protein (GSE120887) [ 28 ]; ISO-seq data from ESCC cell line TE5 and normal immortalized esophageal squamous epithelial cell line SHEE (PRJNA515570) [ 72 ]; Long-read RNA-seq data from HEK293T and A549 cells ( https://github.com/GoekeLab/sg-nex-data ). Competing interests G.K. is a scientific co-founder of EnaraBio and a member of its scientific advisory board. G.K. has consulted for EnaraBio, Repertoire Immune Medicines, ErVimmune and AdBio Partners. The other authors declare no competing interests. Acknowledgements We are grateful for assistance from the Advanced Sequencing, Cell Services, Flow cytometry, High Throughput Screening, Advanced Light Microscopy and Scientific Computing facilities at the Francis Crick Institute. The results shown here are in whole or part based upon data generated by the TCGA Research Network ( http://cancergenome.nih.gov ). The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. This work was supported by the Francis Crick Institute (CC2088), which receives its core funding from Cancer Research UK, the UK Medical Research Council, and the Wellcome Trust. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 101018670). For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. References 1. ↵ Wells JN , Feschotte C : A Field Guide to Eukaryotic Transposable Elements . Annu Rev Genet 2020 , 54 : 539 – 561 . 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Share Retroelement co-option disrupts the cancer transcriptional programme Jane Loong , Rachael Thompson , Callum Hall , Laura Doglio , Judith Pape , George Kassiotis bioRxiv 2025.02.21.639580; doi: https://doi.org/10.1101/2025.02.21.639580 Share This Article: Copy Citation Tools Retroelement co-option disrupts the cancer transcriptional programme Jane Loong , Rachael Thompson , Callum Hall , Laura Doglio , Judith Pape , George Kassiotis bioRxiv 2025.02.21.639580; doi: https://doi.org/10.1101/2025.02.21.639580 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genomics Subject Areas All Articles Animal Behavior and Cognition (7622) Biochemistry (17648) Bioengineering (13871) Bioinformatics (41880) Biophysics (21423) Cancer Biology (18561) Cell Biology (25461) Clinical Trials (138) Developmental Biology (13364) Ecology (19866) Epidemiology (2067) Evolutionary Biology (24290) Genetics (15590) Genomics (22475) Immunology (17713) Microbiology (40328) Molecular Biology (17148) Neuroscience (88473) Paleontology (666) Pathology (2827) Pharmacology and Toxicology (4816) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9815) Zoology (2268)