Integrated pan-cancer map of EBV-associated neoplasms reveals functional host-virus interactions

Abstract

Epstein-Barr virus (EBV) is a complex oncogenic symbiont. The molecular mechanisms governing EBV carcinogenesis remain elusive and the functional interactions between virus and host cells are incompletely defined. Here we present a comprehensive map of the host cell-pathogen interactome in EBV-associated cancers. We systematically analyzed RNA-sequencing from >1000 patients with 15 different cancer types, comparing virus and host factors of EBV⁺ to EBV^– tissues. EBV preferentially integrated at highly accessible regions of the cancer genome with significant enrichment in super-enhancer architecture. Twelve EBV transcripts, including LMP1 and LMP2, correlated inversely with EBV reactivation signature. Over-expression of these genes significantly suppressed viral reactivation, consistent with a “virostatic” function. In cancer samples, hundreds of novel frequent missense and nonsense variations in virostatic genes were identified, and variant genes failed to regulate their viral and cellular targets in cancer. For example, one-third of EBV⁺ NK/T-cell lymphoma patients carried two novel nonsense variants (Q322X, G342X) of LMP1 and both variant proteins failed to restrict viral reactivation, confirming loss of virostatic function. Host cell transcriptional changes in response to EBV infection classified tumors into two molecular sub-types based on patterns of interferon signature genes and immune checkpoint markers, such as PD-L1 and IDO1. Overall, these findings uncover novel points of interaction between a common oncovirus and the human genome and identify novel regulatory nodes and druggable targets for individualized EBV and cancer-specific therapies.

Introduction

EBV is ubiquitous, infecting approximately 90% of the world’s adult population (1). Most acute infections are asymptomatic, following which EBV establishes latency. In the latency phase, EBV infection is associated with substantial morbidity and mortality as it causes a wide range of lymphocytic and epithelial malignancies, such as Burkitt’s and diffuse large B cell lymphomas (BL, DLBCL), nasopharyngeal carcinoma (NPC) and stomach adenocarcinoma (STAD), in both immune-competent and immune-compromised hosts (2). Nearly 150,000 cancer-related deaths can be attributed to EBV annually (3), partially due to the lack of effective, EBV-specific treatment options tailored to individualized cancers. Over the five decades since the discovery of this multi-disease associated oncovirus, various aspects of its life cycle and host immune response in individual tumor types or cell lines have been studied (4–11). However, the generalized cancer and tumor type-specific characteristics of EBV and the host cells’ response, i.e. the host-virus interaction in a large cohort of cancer patients, have not been systematically delineated.

Several factors affect the life cycle of EBV, including viral integration into the host genome, expression and mutation(s) of viral genes and the host response to the presence of virus. Although some of these factors in individual tumor types or cell lines have been studied, how they interact to promote tumorigenesis remains poorly understood. For example, despite typically being maintained in an episomal state within infected cells, several studies have demonstrated EBV integration into the genome of individual cancer types and cell lines (4,12,13), but the extent and the location of these integrations across different cancer types remain poorly understood. Moreover, EBV reactivation is one of the major risk factors for developing cancers and several viral and non-viral factors are known to reactivate EBV (8,14,15). However, how genetic factors such as variants in viral coding genes influence this has been less explored. Moreover, to date there has been no systematic pan-cancer genomic analysis of this widespread, multi-disease associated oncovirus to delineate shared and tumor-specific characteristics of EBV and the host response.

High throughput DNA and RNA sequencing technologies capture genetic and transcriptional profiles in host cells, which can be leveraged to interrogate not only the host genome but also any pathogen genomes infecting the host cells (4,7,16,17). Since EBV establishes a long-term persistence within host cells, we reasoned that large numbers of RNA-sequencing data from multiple EBV associated tumors would provide a platform to interrogate EBV characteristics and interactions within the tumor environment. Such integrated pan-cancer genomic analysis could establish a host-virus “interactome map” essential for the design of novel generic and/or cancer-specific treatments for EBV-associated cancers. Here, we constructed a comprehensive “interactome map” by analyzing publicly available RNA-sequencing samples from more than 1000 samples across multiple cancer types. This comprised detailed viral and host expression and mutation profiles for each cancer and cancer type associated with EBV as a resource for discovery-led science. We present our initial findings exploring new facets of host-virus interaction in cancer. Notably, we identified a set of EBV integration loci across multiple cancer types, with association of those integrations to the most active regulators of the human genome, i.e. super enhancers. In addition, we found large numbers of novel missense and nonsense variants in a subset of viral genes that are negatively linked to EBV reactivation. Finally, we uncovered that EBV⁺ cancer types could be dichotomously classified according to activation and repression of interferon signature genes in response to EBV infection.

Methods and Materials

Data sources

RNA-seq samples were collected from different sources publicly available databases as of November 2018 (Table S1). The samples include Angioimmunoblastic T cell Lymphoma (18), Nasal NK/T-cell Lymphoma (19), Endemic Burkitt Lymphoma (5), Sporadic Burkitt Lymphoma (20), Cutaneous T-Cell Lymphoma (21), Mantle Cell Lymphoma (22), Nasopharyngeal Cancer (23), and healthy donor lymph nodes (24,25). Normal nasopharyngeal tissue samples were from BioProject (accession: PRJNA283839). The samples from Diffuse Large B-Cell Lymphoma, Acute Myeloid Leukemia, and Stomach Adenocarcinoma were collected from The Cancer Genome Atlas (TCGA) (dbGaP study accession: phs000178). Additional samples for Diffuse Large B-Cell Lymphoma were from dbGaP (study accession: phs000532). The samples for Peripheral T-Cell Lymphoma and Anaplastic Large Cell Lymphoma were obtained from dbGaP (study accession: phs000689). The samples for Follicular Lymphoma were from dbGaP (study accession: phs000533) and BioProject (accession: PRJNA278311). Samples for Chronic Lymphocytic Leukemia were from dbGaP (study accession: phs000767). EBV-transformed Lymphocytes and healthy donor blood samples were from Genotype-Tissue Expression Project (GTEx) (dbGaP study accession: phs000424).

Cell lines and cell culture

The EBV^– Gastric Carcinoma (GC) cell line SNU-1 (CRL-5971, ATCC), EBV⁺ GC cell line SNU-719 (CSC-C9698L, Creative Bioarray), and B-Lymphoblastic cell line (LCL-358, cat #1038–3754NV17, Astarte Biologics and GTEX-UPJH-0001-SM-3YRE9, GTEx) were cultured in RPMI 1640 (VWRL0105–0500) media supplemented with 10% heat inactivated Fetal Bovine Serum (FBS; Hyclone) and 1% penicillin/streptomycin (Life Technologies). Cells were freshly purchased, were tested negative for mycoplasma contamination according to PCR Mycoplasma Detection Kit (ABM Inc.), and were used at low passage (<10) number, but were not independently authenticated.

RNA isolation and quantitative Real Time PCR (qRT-PCR)

Total RNA was extracted using Direct-zol RNA extraction kit with DNase I treatment (Zymo Research) and reverse transcribed into cDNA using OneScript® Plus cDNA Synthesis SuperMix (ABM Inc.). The qRT-PCR was performed on Bio-Rad CFX connect. All experiments were performed in independent triplicates in total reaction volumes of 15 uL using BrightGreen 2X qPCR MasterMix-No Dye (ABM Inc.). The relative quantities for each sample were first determined using a standard calibration curve generated by four 10-fold serial dilutions, then normalized to that of indicated housekeeping gene (e.g. UBC) in the same sample, and finally calibrated to the control samples. All gene-specific primers used in this study are listed in Table S2.

Transient Transfection

All plasmids were purified using ZymoPURE™ Plasmid MidiPrep Kit (Catalog # D4201). LCLs were resuspended in pre-warmed complete media at confluency of 1 million/ml a day prior to the transfection. At the day of transfection, cells were washed twice with 1X PBS to get rid of residual antibiotics. For each experiment, 5–7 × 10⁶ cells were transfected with concentration of 1ug plasmid per million cells using Kit V, Amaxa Nucleofector IIb, program T-20, according to the manufacturer’s instructions. Following electroporation, cells were recovered in pre-warmed antibiotic free RPMI media supplemented with 20% h.i FBS. After 5–6 hours, equal volume of pre-warmed complete media supplemented with 20% heat inactivated FBS was added to the cells and incubated in 5% CO₂ at 37° C. 24 hours post-transfection cells were stimulated with Doxorubicin.

EBV reactivation and cell treatment

To induce EBV lytic cycle, LCLs were treated with 200nM Doxorubicin (Millipore Sigma, catalog# 44583) or control carrier DMSO (VWR, catalog# 97063–136) for 48 hours after which cells were collected, fixed, permeabilized and stained for intracellular antibodies. EBV reactivation in Gastric carcinoma cells (SNU-1, SNU-719) was triggered with 20 ng/ml of 12-O-Tetradecanoylphorbol-13-acetate (TPA; Millipore Sigma, catalog# 1585) and 3 mM Sodium butyrate (NaB; Millipore Sigma, catalog# B5887) for 12 hours. IFNγ treatment was performed using 100 IU/mL Recombinant Human IFNγ (carrier free, Biolegend, catalog# 570206) for 12 hrs prior to staining cells for flow cytometry.

Primary Human B cell culture and infection

Discarded, de-identified human peripheral blood mononuclear cells (PBMCs) left in apheresis tube after platelet donation were obtained from Dana-Farber Cancer Institute blood donor center. PBMCs were first purified by centrifugation over a density gradient. Primary B lymphocytes were negatively selected using EasySep Human B Cell Enrichment Kits (StemCell Technologies, #19054 and 15064) following the manufacturer’s instruction. Cells were suspended in RPMI 1640 medium (Life Technologies) supplemented with 10% FCS. Equal amount of EBV^WT (B958) and EBV^{ΔEBNA2/ΔEBNALP} (P3HR1; entire EBNA2 and the last two exons of EBNALP deleted) were used to infect B lymphocytes. Total RNAs were prepared three days post infection. qRT-PCR was used to quantitate the expression of lytic genes (see Table S2 for primers). EBNA1 was used as internal control to normalize the RNA loading and B958 levels were set to 1.

CRISPR mediated EBNA1 knockout

sgRNAs targeting EBNA1 were cloned into lentiGuide-Puro vector and packaged into lentiviruses by co-transfecting with VSVG and psPAX2. Harvested lentiviruses were used to infect GM12878 stably expressing CAS9 for 2 days. Cells infected with lentiviruses were selected with puromycin. The efficiency of EBNA1 knockout was validated by Western blotting at 3 days after puromycin selection. After knockout EBNA1 for 7 days, cells were harvested and used for qRT-PCR validations.

Plasmids

EBV genes EBNA3A, EBNA3C, LMP1, LMP2A were PCR amplified from commercially available plasmids from Addgene (Catalog #37956, 37958, 37962, 37963 respectively). LMP2B was separately amplified from LCL. All genes were cloned into pLVX-EF1a-IRES-zGreen (Catalog #631982, Clontech Laboratories, Inc.) using HiFi DNA Assembly cloning (Catalog# E2621, New England Biolabs; NEB) and verified using Sanger sequencing. The LMP1 point mutations were generated using specific primers (Table S2) by the Q5 Site-Directed Mutagenesis Kit (Catalog# E0554S, NEB) according to the manufacturer’s instructions.

Antibodies and Flow cytometry

All data acquisition was carried out on Attune NxT Flow Cytometer (Thermo Fisher Scientific) in a final staining volume of 200uL. The flow data was analyzed using FlowJo (v10.4.2). Appropriate internal controls were used to assign gates. Fluorescence from multiple antibodies were compensated using AbC Total Compensation beads (ThermoFisher, catalog# A10497). 2% human serum (H4522, Millipore Sigma) was used for Fc blockade in all experiments before surface and intercellular staining. In all experiments, cells were assessed for viability by staining with Fixable viability dye eFluor780 (Life Technologies, Catalog# 65–0865-14; 1:2000 dilution) as per manufacturer’s instructions. PD-L1 surface marker staining (CD274; clone 29E.2A3; Biolegend catalog# 329714; 1:60 dilution) was performed using standard protocol in 1X PBS. Cells were then fixed with 4% methanol-free Formaldehyde (ThermoFisher, catalog# 28908) for 20 minutes to retain intracellular levels of GFP proteins prior to permeabilization using FoxP3/Transcription Factor Staining Buffer Set (eBioscience, Catalog# 5523). Antibodies for staining for intracellular proteins were IDO1 (clone eyedio; eBioscience, catalog# 12–9477-41; 1:60 dilution), BZLF1 (Santa Cruz, catalog# sc-53904; 1:60 dilution), LMP1 (clone LMPO24; Novus biologicals, catalog# NBP2–50383; 1:60 dilution) and anti-HA.11 (clone 16B121; Biolegend, catalog# 901509; 1:500 dilution).

Western Blotting

Nearly 4 million cells per sample were collected and lysed using 100ul of Pierce RIPA buffer (ThermoScientific, cat#89900) containing 1X Halt Protease Inhibitor Cocktail, EDTA-free (ThermoScientific, cat#78425) as per manufacturer’s instructions. The cell lysates were additionally sonicated 30 seconds at 80% amplitude. Protein concentration in the lysate was determined using the Pierce BCA Assay Kit (ThermoScientific, cat#23227) and ~10ug of protein was loaded per well of a 10% SDS-PAGE gel (1mm). Proteins were then transferred to a PVDF membrane using the Semi Dry Transfer Apparatus (Bio-Rad). To prevent non-specific antibody binding, membranes were incubated with 5% milk in 1X TBS blocking solution for 1h at RT. Membranes were washed three times for 10 min with 1X TBST and were then incubated overnight with primary antibodies. BZLF1 (1:500, sc-53904, SCBT), Vinculin (1:1000, sc-73614, SCBT), or GAPDH antibodies were commercially obtained (cat:#ab8245). OT1X monoclonal antibody against EBNA1 was a gift from Dr. Jaap Middeldorp (Amsterdam University Medical School). The following day, membranes were washed and incubated with HRP-linked anti-mouse secondary antibody (1:3000, 7076S, CST) for 1h at RT, followed by washing and developing the blots using ECL (ThermoScientific, cat#32209).

Results

EBV integrates at highly accessible genomic loci of the host enriched in super-enhancer architecture

We collected 291 control and 1051 RNA-seq samples across 15 different cancer types from publicly available databases as of November 2018 (5,18–25) (Table S1A). We constructed a comprehensive host-virus interactome map for each cancer type by analyzing sequencing reads aligned to the EBV and to the human genomes (Fig. 1A and also Methods). We first defined viral reactivation signature (viral RNA load) by counting the fraction of sequencing reads mapping to the EBV genome (parts per million reads of library size, P.P.M. (16)) in each cancer sample (Table S1B). An EBV detection threshold of 20 P.P.M. was selected to denote EBV infected samples because at this threshold 100% of the 57 positive controls (EBV-transformed lymphoblastoid cell lines (LCLs)) and 234 negative control samples (healthy blood cells and lymph nodes) were detected as EBV⁺ and EBV^– respectively, corresponding to 100% specificity and sensitivity (Fig. S1A). In other words, all EBV⁺ positive samples had p.p.m > 20 and all negative control samples (healthy donor blood samples from GTEx and healthy donor lymph nodes from (24,25)) had p.p.m < 20 (mean p.p.m. = 0.5). The frequency of EBV infection depended on cancer type, being highest in nasopharyngeal carcinoma (NPC; 100%), endemic Burkitt’s lymphoma (eBL; 80%) and NKT cell lymphoma (NKTCL; 71%) and absent in eight other tested cancers (peripheral T cell lymphoma (PTCL), cutaneous T cell lymphoma (CTCL), anaplastic large cell lymphoma (ALCL), follicular lymphoma (FL), mantle cell lymphoma (MCL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and multiple myeloma (MM)) (Fig. 1B; Table S1A). The percentage of EBV⁺ tumors was consistent with the known frequency of EBV in these tumors (26). In EBV⁺ cancers, the highest and lowest EBV RNA burden was in NPC (mean = 613 P.P.M.) and angioimmunoblastic T cell lymphoma (AITL) (mean = 69 P.P.M.), respectively (Fig. 1B).

Figure 1. EBV is integrated at limited loci across multiple tissues and a subset of its genes are preferentially expressed in cancer.

A, schematic showing pipeline for generation and analysis of EBV-host interaction map. B, EBV RNA load (dotplot, top) and the percentage of samples (heatmap, bottom) with P.P.M. ≥20 across multiple cancers (shown are mean and min-to-max range). C, Circos plot showing integration of EBV genes (black bar) into human DNA (human chromosomes shown in gray). Connecting lines are color-coded to differentiate lymphoblastoid cell lines (LCL; blue) and cancer (red) samples; nearest human genes to the site of integration are annotated. D, expression distribution of all expressed human transcripts adjacent to EBV integration sites across all EBV⁺ samples (median is shown; two-tailed Wilcoxon test). E, enrichment odds ratios and p-values (Fisher exact test) for super-enhancer (SE) regulation of genes that are preferred sites for EBV integration. Shown are data from multiple healthy tissue/cell types assessed or EBV⁺ cancer cells (data from (31,34,65)). F, expression of EBV genes (columns) across multiple cancers and LCL samples (rows) (below) with bar-chart (above) showing the log fold-change between the mean gene expression in cancer versus LCLs. AITL, angioimmunoblastic T cell lymphoma; eBL, endemic Burkitt’s lymphoma; sBL, sporadic Burkitt’s lymphoma; DLBCL, diffuse large B cell lymphoma; NKTCL, NKT cell lymphoma; NPC, nasopharyngeal carcinoma; STAD, stomach adenocarcinoma. ****p<0.0001

EBV typically persists as an episome in the host cells but can also integrate into the cancer genome (4,12,27). Chimeric RNA-seq reads that partially map to the EBV genome and partially to the human genome represent viral integration sites (28). We used these chimeric reads to determine the frequency and location of EBV integration into the human genome across different cancers (see supplementary methods). Sixty out of 112 EBV⁺ cancers (corresponding to 56 unique genomic loci) showed evidence of viral integration (median of 3 integration events per sample; Fig. 1C; Table S1C–D), with significant over-representation near ribosomal RNA genes and in proximity to promoters (3.8 fold-enrichment). We annotated these 56 specific integration loci with their nearest genes and analyzed their functional properties. These include integration at B2M, CD74, and HLA-C loci, which are part of the major histocompatibility class I and II complexes (Table S1C). Since the integration events were inferred from RNA-sequencing data in LCLs or EBV⁺ cancer samples of any origin, we decided to use an orthogonal method to verify these events. To this end, we turned to an existing whole-genome long-read DNA-sequencing (as opposed to RNA-sequencing) data from an independent LCL (GM12878; (29)). We identified chimeric viral-human reads that were mapped to 20 of these integration loci, independently verifying these events (Table S1C, see supplementary methods). The top three pathways enriched in these genes were viral processes (p-value ≤ 1e-10), response to type I interferons (p-value ≤ 1e-9), and regulation of apoptotic pathways in response to DNA damage (p-value ≤ 1e-5) (Fig. S1B). Enrichment for viral process genes was attributable to integration near genes encoding human ribosomal proteins controlling RNA translation (RPL8, RPL28, RPS3, RPS6, RPS19, and RPLP1), which is intriguing since EBV recruits and subverts this machinery to translate its own mRNA as a survival strategy (30). Host genes near preferred EBV integration sites (integration sites supported by more than one sample) were expressed on average 20-fold higher than other expressed genes (p-value < 1e-16; Fig. 1D). Integration events not only did not interrupt the expression of these already highly expressed genes but seemed to slightly augment their expression (Fig. S1C). suggesting that highly accessible chromatin may harbor more recurrent EBV integration events. In cancer cells, clusters of regulatory elements called super enhancers (SE) are generated near highly expressed genes important to tumorigenesis (31). We reasoned, therefore, that SE regions could be attractive to EBV integration, as seen in other viruses, e.g. human hepatitis and herpes viruses (32,33). EBV integration sites significantly overlapped with SEs annotated in multiple immune cells and tissues (31), as well as EBV⁺ cancer cells (34) (Fig. 1E, see supplementary methods). For example, SE architecture existed in the vicinity of ~12% of all genes in at least one screened tissue type; this compares to 45% (25 out of 56) of EBV-integrated genes using the same screening (Fisher exact p-value = 1e-9; Odds ratio = 4.6). Collectively, these data indicate that EBV preferentially integrates near highly expressed genes with SE architecture.

Virostatic EBV genes are frequently mutated in cancer

EBV establishes latent and lytic infection during its lifecycle, which is regulated by expression of specific viral genes. To determine how EBV behaves in cancerous tissues, we next compared expression of EBV genes in cancerous cells to EBV-transformed lymphoblastoid cell lines (LCLs). Most EBV genes expressed in LCLs derived from healthy donors were either transcriptionally silent or extremely low-expressed in EBV⁺ cancer samples of any origin (Fig. 1F). These included EBV nuclear antigen genes EBNA2, 3A, 3B, and 3C, all of which are essential for EBV transformation (35) but potentially redundant for other EBV life cycle stages. While expression of some EBV genes appeared to be cancer-specific, such as EBV encoded small RNAs EBER1 and EBER2 in stomach adenocarcinoma (STAD) and eBL, a handful of genes including LF2, BALF5, BILF1, BARF0, RPMS1, and A73 were ubiquitously expressed in all cancer types (but minimally or infrequently expressed in LCLs) and thus have potential as prognostic and/or diagnostic markers for these diseases (Fig. 1F; Table S1E).

EBV controls the lytic phase of its life cycle by orchestrating the expression of various viral genes that have the capacity to prevent EBV reactivation from latency (15,36,37). To determine which viral genes are the most influential to this regulation and to what extent, we initially correlated expression of EBV genes to viral RNA load in 57 LCLs. Twelve genes were strongly inversely correlated with viral RNA load in LCLs (Bonferroni adjusted p-value < 0.0005 of Spearman correlation; Fig. 2A–B), suggesting potential function as inhibitors of virus replication. EBNA1 had the highest negative correlation with viral RNA load, consistent with its reported function in inhibiting spontaneous viral reactivation (15). Other “virostatic” genes in order of their inverse correlation to the viral RNA load included EBNA3C, EBNA2, EBNA-LP, latent membrane proteins LMP2B, LMP2A, BHRF1, BARF0, RPMS1, LMP1, BALF5, and EBNA3A, some of which were also previously shown to inhibit virus lytic cycle induction (38,39). To confirm the ability of these genes to suppress lytic viral replication, we performed three experiments. First, we cloned five of them (LMP1, LMP2A, LMP2B, EBNA3A, EBNA3C) that represented a mixture of high and low negative correlations with viral RNA load. We overexpressed these genes in LCL-358 and induced EBV lytic replication with doxorubicin (Fig. S2A–B). All five genes significantly suppressed BZLF1 (Fig. 2C) and BRLF1 expression (Fig. 2D), both of which are immediate-early viral lytic genes. The same results were obtained in an independent LCL, GTEX-UPJH (Fig. S2C). Second, we infected primary human B cells with P3HR1 EBV, an EBV strain with whole gene deletion of EBNA2 and deletion of the last two exons of EBNA-LP, and showed that P3HR1 expressed higher levels of viral lytic genes, compared to cells infected with wild-type EBV (Fig. S2D), confirming the virostatic function of these EBNA genes. Finally, we knocked out EBNA1 expression in another independent LCL, GM12878, using two separate single guide RNAs, and showed that cells with EBNA1 knockout have higher levels of viral lytic genes (Fig. S2E). Collectively, these data indicate that a subset of EBV-encoded genes have virostatic function.

Figure 2. A subset of EBV genes are negatively correlated with EBV RNA load in 57 LCLs.

A-B, inverse correlation between expression of 12 EBV gene transcripts and EBV RNA load in 57 LCLs. Shown are Spearman’s rank correlation coefficients (A, left) and p-values (A, right) and representative scatter plots for LMP1, LMP2A, LMP2B and BALF5 (B). C, immediate early viral lytic gene expression (BZLF1) in LCL-358 transfected with empty vector or vectors encoding five representative virostatic genes. Shown are representative flow cytometry plots (top) and cumulative data showing % BZLF1⁺ of transfected (GFP⁺) LCLs with the indicated over-expression plasmids (bottom). Data are from n ≥ 4 independent experiments; gating strategy is shown in Fig. S2A. D, expression of BRLF1 mRNA, another immediate early viral lytic gene, in LCLs transfected with empty vector (EV) or vectors encoding indicated genes. Bars show mean + sd from n=3 independent experiments; comparisons were against EV treated with doxorubicin. p-values are from two-tailed ratio paired t-test; *p<0.05, **p<0.01, ***p<0.001.

We reasoned that loss of virostatic genes, e.g. via inactivating variations, may be conducive to heightened virus expression in tumors. Five of these genes (EBNA-2, −3A, −3C, -LP, BHRF1) were either not expressed or very low in most cancers (Figs. 1F and 3A). To determine if the remaining seven harbor inactivating variations in cancer (i.e. gene mutations that occur in cancer or EBV strains that carry mutations), we catalogued all recurrent variants in EBV genes from 112 EBV⁺ cancer and 57 LCLs (all EBV-subtype I (40)) (Table S3). Variants were selected to be either homozygous (meaning that all the expressed EBV genomes in the cell contain the variant) or heterozygous (meaning that at least half of all the EBV genomes in the cell contain the variant). We detected 562 recurrent non-synonymous variations across all EBV⁺ cancers, half of which were shared across two or more cancer types (Fig. 3). As expected, the majority of LCLs had no, or scant EBV variations (Table S3). Across different cancers, EBV had a mean of 5.8 mutations/kb (SD=2.6), with NPC the highest (8.0 mutations/kb) (Fig. S3A). Most variations were missense (35.9%), noncoding (15.7%) or synonymous (47.5%) and only rarely nonsense (0.9%) (Fig. S3B). The mean transition/transversion ratio was 1.36 (Fig. S3C). Mutational signatures were not the commonly observed APOBEC-driven pattern (TCW>TT/GW, where W is enriched in T or A) (41,42) but, rather, substitutions of C>T at NpCpG (which may suggest a possible effect on methylation sites across the EBV genome) and T>C at NpTpG trinucleotides across multiple cancer types (Fig. S3D–E).

Figure 3. Virostatic EBV genes have frequent variations in cancer and affect expression of host genes.

A, bar heatmaps showing EBV genes correlated with increased viral RNA load (first bar; grey coded), samples in which missense or nonsense variation in EBV genes is associated with elevated viral RNA load (second bar; brown coded) and expression of EBV genes in cancer (third bar; orange coded). Table matrix below shows missense (red) or nonsense (blue) variations in all EBV genes (column) across all cancer samples (row) ordered by frequency. B, geneset enrichment analysis (GSEA) plot for EBV genes associated with 12 identified protein-coding virostatic genes, comparing EBV genes with frequent or infrequent variations in cancer. Normalized Enrichment Score (NES) is shown. C, representative example of EBV RNA load in EBV⁺ cancer samples of any origin with (Var) or without (WT) missense or nonsense variants in LMP1, LMP2A, LMP2B and BALF5. Shown are individual samples (dots) with median values. D and E GSEA plots for host genes up- (left) and down-regulated (right) by LMP2A (GSE46143) comparing LMP2A^WT versus LMP2A^variant NKTCL (D) or STAD (E) samples. ****p<0.0001 (two-tailed Wilcoxon test).

Virostatic genes harbored on average four times more variations than other EBV genes in cancer (Figs. 3A–B and S3F). Six of the virostatic EBV genes (Fig. 3A; top bar; gray coded), BARF0, BALF5, EBNA1, LMP1, LMP2B, and RPMS1, were both expressed (Fig. 3A; third bar; orange coded) and had frequent variations (Fig. 3A; variation matrix) in cancer. In the majority of these genes, samples with variations had elevated EBV burden (Fig. 3A; second bar; brown coded), suggesting that these are pathogenic viral proteins whose variation in cancer results in elevated viral RNA loads. For example, LMP1, LMP2A, LMP2B and BALF5 whose expressions were strongly inversely correlated with EBV RNA load in LCLs (Fig. 2B), had a high rate of nonsense or missense variations and cancer samples with variations in these genes had significantly higher EBV RNA load (Fig. 3C).

To determine the functional consequence of nonsense and missense variations in virostatic EBV genes to the host in vivo, we selected LMP2A as an exemplar and focused on NKTCL and STAD, which were the cancer types in our dataset for which we had both wild-type and variant LMP2A tissue samples. From the dataset of Vockerodt et al., GSE46143 (43), we sourced the top 200 host genes up-regulated and the top 200 host genes down-regulated by LMP2A. We determined whether expression of host genes regulated by this protein were different between NKTCL and STAD cancers that had wild-type or variant LMP2A, using geneset enrichment analysis (GSEA) (44). We found that NKTCL and STAD samples with wild-type LMP2A had higher expression of LMP2A up-regulated genes and lower expression of LMP2A down-regulated genes compared to samples with variant LMP2A, consistent with attenuation of LMP2A function in tissues containing the variant proteins (Fig. 3D–E). Taken together, these data indicated frequent missense and nonsense variations in virostatic EBV genes that potentially interrupt viral gene function and lead to higher viral RNA load in tumor samples.

Frequent nonsense variations in LMP1 fail to restrict viral reactivation

We showed that LMP1 represses EBV reactivation (Fig. 2B–C), has frequent variation in cancer (Fig. 3A), and tumor samples with variations in LMP1 have higher viral RNA load than those with wild-type LMP1 (LMP1^WT). Importantly, nearly all EBV⁺ NPC and DLBCL samples, and one-third of NKTCL samples, had one or two frequent nonsense variations in the LMP1 protein, Q322X and G342X. To elucidate the significance of these LMP1 variations, we reactivated EBV in LCLs engineered by electroporation to over-express wild-type (LMP1^WT) or variant (LMP1^Q322X or LMP1^Q342X) LMP1 and measured EBV reactivation by expression of BZLF1 protein (Fig. S4). Vectors used to over-express LMP1 were IRES-containing and bicistronic, allowing simultaneous expression of LMP1 proteins and green fluorescent protein (GFP). GFP was evident in LCLs expressing either LMP1^WT or its variants, showing successful expression of these genes in LCLs (Fig. 4A). LMP1 protein, however, was only detectable in LCLs electroporated with LMP1^WT, whereas cells transfected with variants were only positive for GFP, confirming that neither variant was translated to protein since both were mutants with pre-mature stop codons (Fig. 4A). Neither variant restricted viral reactivation, in contrast to wild-type LMP1, indicating that the consequence of both variants is loss of function (Fig. 4B). These observations were further confirmed by Western blotting (Fig. 4C). In NKTCL cancer transcriptomes, where we could directly compare tissues with wild-type versus any missense or nonsense variants of LMP1, we detected higher EBV RNA load in samples with LMP1 variation (Fig. 4D). These data confirm that these LMP1 proteins are loss of function variants in vivo.

Figure 4. LMP1 variants affect virostatic function.

A, representative flow cytometry plots (left) and cumulative data (right) from n=5 independent experiments showing LMP1 and GFP expression in LCLs cultured with doxorubicin (dox) or carrier alone (DMSO) after transfection with the indicated plasmids. B, representative flow cytometry plots (left) and cumulative data (right) from n=5 independent experiments showing % BZLF1⁺ of all live transfected (GFP⁺) LCL-358 with the indicated over-expression plasmids. C, representative immunoblot from n=2 independent experiments showing BZLF1 expression following transfection of LCL-358 cells with the wild-type or mutant LMP1. D, EBV RNA load in NKTCL samples with or without missense/nonsense variations in the LMP1 gene. EV, empty vector; bars show mean + sem. *p < 0.05 by paired ratio t-test (A, and B) and unpaired two-tailed t-test (D).

Higher viral RNA load is associated with mutation in cancer driver genes

To determine how tumor cells respond to EBV, we next switched our focus to study genetic and transcriptional factors of the host cell. We first performed mutational pattern analysis of the host genome in EBV⁺ and EBV^– cancer samples. We factorized the mutational patterns in to two de novo signatures, which we called “signature A” and “signature B” (Fig. S5A). Signature A resembled a previously identified observation in Hodgkin lymphoma, known as signature #25 from Mutational Signatures v2 (https://cancer.sanger.ac.uk/cosmic/signatures_v2). Signature B aligned with signatures #12 and #16 that were described in liver cancers. The etiology of these signatures remains unknown. We did not observe any significant difference in the usage of signature A or B in EBV⁺ vs EBV^– cancer types (Table S4A and Fig. S5B). Of note, the majority of stomach cancer samples show signature A regardless of EBV status, but other cancer types show the signature B mutational pattern.

We then focused on mutations in cancer driver genes. Although variation in cancer driver genes have previously been studied in the context of EBV-associated cancers (5,20), their prevalence in EBV⁺ versus EBV^– cancer is less explored. Moreover, their association with EBV RNA load is unknown. A recent large-scale genomics study in pediatric endemic and sporadic Burkitt lymphoma has shown aberrant somatic hypermutation in EBV⁺ tumors (45) supporting a link between EBV and tumor mutational burden. To estimate host cancer driver gene mutations and their association with viral RNA load, we implemented a previously validated pipeline (46). We compared EBV⁺ to EBV^– cancers, focusing on missense/nonsense coding SNPs in ~200 cancer driver genes curated by COSMIC (47) (see supplementary methods). We found that sixty-three cancer driver genes were mutated in at least 2 (out of 1051) samples (Table S4B–C). Among those, eight genes, DDX3X, MYC, BCOR, ARID1A, TRAF3, EP300, PTEN, and CASP8, were more frequently mutated in 112 EBV⁺ than 939 EBV^– cancers (Fisher exact test p-value < 0.05), with the remainder cancer driver genes (e.g. TP53 and ID3) not significantly different (Fig. 5A). DDX3X and MYC mutations were specific to EBV⁺ eBL cancers, consistent with previous reports on mutations of these genes in Burkitt lymphoma (20). Mutations in DDX3X, a protein that is highly intolerant of loss of function (ExAC pLOF score = 1 (48)) and missense mutations (ExAc Z-score = 5.13 (48)), predominantly affected the helicase domains at amino acids with almost universal phylogenic conservation (Fig. S5C, signifying that these are important residues. Indeed, seven out of the nine mutations we found were predicted to be deleterious using the Polyphen-2 algorithm (Fig. S5D) (49). Of note, eBL samples with higher EBV RNA load had four times higher mutation rate in either the DDX3X or MYC genes (Fisher exact test p-value < 0.05; Fig. 5B), suggesting an association between the frequency of mutations occurring in specific cancer driver genes and higher EBV burden.

Figure 5. EBV is associated with pathogenic mutations in cancer driver genes.

A, mutation frequency (left) and fold enrichment (right) of cancer driver genes in EBV⁺ (red) versus EBV^– (blue) cancers. B, EBV RNA load across all eBL cancer samples. Samples with DDX3X or MYC mutations are highlighted in red. Pie chart shows frequency of DDX3X or MYC mutations in samples with high and low EBV RNA loads. *p<0.05; **p<0.01 (Fisher Exact test).

EBV⁺ tumors can be dichotomously classified according to the host transcriptional response

To understand the molecular mechanisms of host responses to EBV infection, we first compared gene expression profiles of host cells between EBV⁺ and EBV^– cancers. NPC, eBL, and STAD had the highest and AITL and DLBCL had the lowest number of differentially expressed genes (DEGs) (fold change> 1.5; rpmk>4; FDR<0.05; Fig. S6A; Table S5A–G). Most DEGs were cancer-type specific; however, 556 up-regulated and 591 down-regulated genes were differentially expressed in at least 2 cancers, indicating that some of the response to EBV in cancer is shared (Fig. S6A). To functionally categorize the response, we performed integrative pathway analysis using Metascape (50), and focused on the top 20 statistically significant pathways (Fig. S6B). As expected, notable shared pathways in 4 or more cancer types included regulation of cell death, the immune system and responses to pathogens (Fig. 6A, S6A). Specific gene networks in individual cancer types are shown in Fig. S6C–I. For example, regulation of mitotic cell cycle pathway, as recently reported (5), was highly enriched in EBV-responsive DEGs of eBL (p-value = 1e-27; Fig. S6E), consistent with elevated expression of the cell cycle regulator CDKN2A. Killer Cell Lectin Like Receptor B1 (KLRB1), a protein with both inflammatory and regulatory functions, was markedly down-regulated in EBV⁺ NKTCL cancers, an observation that was also made in γδ T-cell lymphoproliferative disorders when compared to normal γδ T-cells (51) (Fig. S6H). Likewise, in NPC, cell-to-cell adhesion was significantly altered by EBV (p-value = 1e-44; Fig. S6D), including elevation of epithelial cell adhesion molecule (EPCAM), providing a rationale for anti-EpCAM-antibody as treatment.

Figure 6. EBV drives shared and cancer-specific gene networks in host cells to dichotomously classify tumors.

A, biological pathways significantly enriched in shared EBV response genes (see also Fig. S6A). Nodes indicate individual pathways, grouped by ellipses into functional category. Nodes are colored by fold enrichment and band thickness indicates number of shared response genes between nodes. B-C, Ingenuity Pathway Analysis™ showing top 10 upstream regulators (TFs and cytokines) of EBV-driven response genes (B) and heatmap showing expression of immune checkpoint genes in EBV⁺ compared to EBV^– tissues (C) in each cancer type. Columns are hierarchically clustered. Indicated are cancer types sub-classified into IFN⁺ and IFN^– groups; D, PD-L1 (left) and IDO1 (right) mRNA in EBV⁺ and EBV^– STAD and Burkitt lymphoma (BL, including both sporadic and endemic) samples. E, receiver operator curves (ROC) show performance of PD-L1 and IDO1 mRNA expression to predict EBV⁺ versus EBV^– status of samples in BL (left) and STAD (right). Area under the curve (AUC) and p-values are indicated; F, representative flow cytometry plots (left) and cumulative data (right) from n=4 independent experiments showing PD-L1 and IDO1 expression by EBV⁺ (SNU719) and EBV^– (SNU1) STAD cancer cell lines with or without IFN-γ treatment; G, representative flow cytometry plots (left) and cumulative data (right) from n=4 independent experiments showing PD-L1 expression in EBV⁺ and EBV^– STAD cancer cell lines with or without TPA/NaB-induced EBV reactivation (see also Fig. S6M). Bars are mean ± sem; p-values in D, F-G are two-tailed Wilcoxon test; *p<0.05, **p<0.01, ****p<0.0001.

To identify how EBV-response genes are regulated, we examined active regulatory regions in their vicinity. EBV-response genes were significantly enriched in SE architecture (Fig. S6J) - for example, ~25% of DEGs in NPC and ~25% of DEGs in NKTCL have associated SE architecture in at least one of the screened immune cells as compared to expected 12% (Fig. S6J; see supplementary methods; Fisher exact p-value < 1e-45; Odds ratio > 2) - identifying SEs as a shared feature of gene regulation in EBV-driven cancers (52). Thus, there is a connection between EBV and host super-enhancers at two levels. First, EBV preferentially integrates at highly accessible regions of the genome that are enriched in SEs (Fig. 1E). This may suggest a mechanism by which EBV subverts the highly active transcription machinery of the host to replicate its own genes. Second, genes that are differently expressed between EBV⁺ and EBV^– tumors are significantly enriched in SE architecture. These observations suggest that SEs play an important role in regulating EBV-driven host programs within tumor cells, an important observation as SE-inhibitors are currently undergoing clinical trials (53).

To identify upstream regulators (transcription factors (TFs) and cytokines) of the EBV-response genes, we next performed Ingenuity Pathway Analysis™. We found 10 regulators predicted to be significantly activated or inhibited in at least two cancer types (Fig. 6B; |Z-score| > 4; p-value < 1e-5). These were mostly type I and type II interferon-pathway proteins (e.g. IFN-γ, IFN-α, IFN-β), TFs that drive or transduce interferon signatures (e.g. STAT1 and IRF7) or that overlap with type I interferon responses (e.g. NF-κB; Fig. 6B). These upstream regulators classified EBV⁺ cancer types into two groups: IFN⁺, consisting of STAD, NPC, and DLBCL and characterized by an activated interferon signature and IFN^–, containing eBL, AITL, NKTCL and sBL and denoted by inhibited interferon response when comparing EBV⁺ to EBV^– tumors of the same origin. Literature-validated IFN-γ targets overlapped significantly with EBV-response genes in these two clusters and could explain up to 20% of response genes (Fig. S6K). To further quantify this observation, we calculated type I and type II interferon activity scores for each individual sample (Table S5I). EBV⁺ cancers in the interferon activated cluster over-expressed several immune checkpoint proteins, including the universal checkpoints Programmed Death-Ligand 1 (PD-L1) and indoleamine 2,3-dioxygenase (IDO)-1 (54) (Fig. 6C), which are both IFN-γ-induced and mediate immunosuppression and tumor evasion. For example, EBV⁺ STAD samples displayed strong activation of the IFN-γ pathway (Z-score>8; Fig. 6B), with an average of 3.9–5.6–fold higher mRNA levels of PD-L1 and IDO1 (Fig. 6D). Both these markers had strong predictive power to distinguish EBV⁺ from EBV^– stomach adenocarcinoma (AUC>0.8; p<0.0001; Fig. 6E).

To better understand and verify the effect of interferon response pathways and EBV reactivation on PD-L1 and IDO1 expression in EBV⁺ cancers, we treated EBV⁺ (SNU719) and EBV^– (SNU1) STAD cancer cell lines with exogenous IFN-γ. This cytokine induced IDO1 only in the EBV⁺ cells and further induced PD-L1 in the EBV⁺, compared to the EBV^–, cells (Fig. 6F; see Fig. S6L for gating strategy) with no significant effect on EBV replication (measured by expression of the EBV lytic gene BMRF1; Fig. S6M), confirming interferon pathways as preferential drivers of these molecules in EBV⁺ cells. PD-L1 was induced by IFN-γ in both EBV⁺ and EBV^– cells, as previously reported (55). To directly assess the effect of EBV reactivation on PD-L1, we induced EBV lytic cycle by treating cells with 12-O-tetradecanoylphorbol-13-acetate and sodium butyrate (TPA/NaB) and measured PD-L1 expression. EBV reactivation induced PD-L1 only in EBV⁺ cells (Fig. 6G). We next examined two independent transcriptome datasets of DLBCL and NPC cancers (GSE12452 and GSE27255) (56,57). In both cases, PD-L1 was preferentially over-expressed in EBV⁺, compared to EBV^–, tumors (Fig. S6N–O), supporting the role of EBV in inducing immune checkpoint protein expression in EBV⁺ cancers. In summary, these data indicate that the transcriptomes of EBV⁺ tumors identify a shared transcriptional program regulated by interferons leading to a significant change in expression of immune checkpoint genes.

Discussion

We carried out a large-scale pan-cancer RNA-seq analysis to interrogate various aspects of host-virus interaction in EBV-associated cancers. Collectively, our work provides a comprehensive interactome map of EBV associated cancers that enables a broad interrogation of host-pathogen interactions, including expression and mutation of the pathogen genome as well as integration into the host genome, host gene mutations and transcriptional responses. There are other host-pathogen mechanisms that operate in different cancer types that have not been discussed here. However, detailed viral and host expression and mutation profiles for each cancer and cancer type have been provided in the supplementary materials as a resource for future discovery and validation. Our approach can be equally applied to the evaluation of other symbiotic, disease-associated pathogens, including cancer-causing viruses (5,7,17,33).

Using our interactome map, we made several novel observations. By evaluating viral gene expression within the host tumor environment, we uncovered pan-cancer and cancer-type specific expression of several virus genes. The pan-cancer expression of a few EBV genes make them prime candidates for designing early onset diagnostics. Our samples were obtained mainly from tumor sites (e.g. lymph node), thus the potential for EBV genes as biomarkers should first be evaluated by whether they can also be detected in blood or urine of patients using sensitive techniques. As EBV itself can be detected in whole blood and urine (58), we predict that these markers should also be detectable in these samples by PCR (59).

We identified a reliable set of 56 loci that have EBV integration signatures in more than one EBV⁺ cell type. The rate of integration in each sample could be rare because of two reasons: one, rather than most tumor cells, only a small fraction of them could have the integration, and two, only one or small number of several EBV copies present in each cell may be integrated. Nonetheless, the high throughput sequencing method utilized here is extremely sensitive and could detect such rare events and as such the integration signature was evident in almost half of the tumor samples.

This meta-analysis identified frequent missense and nonsense variations in virostatic genes in cancer. These variations were associated with elevated viral RNA load in cancer samples harboring the variation as well as the host transcriptional response to changes in these genes. As high EBV RNA load increases the risk of cancer, inactivating mutation(s) in these genes could heighten the risk of cancer development. There are three issues when considering variations in viral genes. The first issue is that there are generally several copies of EBV genomes per cell and thus variations may be present in a subset of EBV genomes. To address this issue, our variants are selected to be either homozygous (meaning that all the expressed EBV genomes in the cell contain the variant), and in several cases heterozygous (meaning that at least half of all the EBV genomes in the cell contain the variant). The second issue is that since EBV associated cancers occur in different parts of the world, there are known geographic inter-strain variations (60) that could complicate the interpretation of any identified variation. To be able to parse which variations are less likely to be due to strain differences, we have looked at the distribution of all our de novo cataloged variations and determined the proportion that are present in a single individual cancer type and the proportion present in more than one cancer type. Those variations present only in a single cancer type could represent strain differences while those shared among different, geographically distinct, cancer types are more likely to be related to biology. Nearly half of our identified variants are shared between multiple cancer types, suggesting potential relevance to cancer rather than inter-strain differences. This is further supported by elevated viral RNA load in tumor samples with variations and host transcriptional responses to these mutations. The third issue is that our analysis relied on RNA-sequencing, primarily due to the wider availability of such data. However, mutations in non-expressed/low-expressed EBV genes cannot be detected using this approach – the same limitation also exists for the EBV integration analysis. Future whole genome or targeted sequencing approaches are required to overcome this limitation. Nevertheless, the identification of viral genes that have virostatic function and that are frequently mutated in cancer also raises the possibility that these predicates can be leveraged as diagnostic/prognostic disease markers and/or form the basis of therapeutic drug development, for example, using mimetic agents. We have provided some evidence on the function of virostatic genes and their mutations, but subsequent experimental verifications of these observations directly in primary cancer cells remain to be done.

A shared transcriptional program regulated by interferons was also evident, leading to a significant change in expression of immune checkpoint genes. This enabled us to propose a dichotomous molecular classifier for EBV⁺ cancers – IFN⁺ or IFN^– group. Based on this classification, the treatments and mechanisms identified in one cancer type could potentially be applied to the linked group. The up-regulation of immune checkpoints in individual cancer types of IFN⁺ group have previously been shown in separate studies (61,62). There has also been a report on a selective loss of EBNA1-specific T cell responses in children with eBL (63), which could be explained by repression of interferon signatures identified in this IFN^– cancer type. Using our unified classification, one could anticipate distinct clinical treatment options. For example, inhibitors of interferon signaling (antibody therapy, or JAK inhibitors) or checkpoint blockade (e.g. PD-L1 inhibitors (64)) would be more efficacious in EBV⁺ cancers in IFN⁺ group whereas immunostimulants and adjuvants such as interferons might improve survival in IFN^– group. Thus, this distinction could help individualize treatment options and minimize consequent toxicities of treatments such as checkpoint inhibitors, which have a strong association with development of de novo autoimmunity.

In conclusion, we have presented here findings from a pan-cancer interactome atlas of host cells with EBV, highlighting the following: a catalog of reliable EBV integration sites genome-wide, an association between regulatory regions with high activity and EBV integration, a list of virostatic EBV genes and their susceptibility to nonsense and missense variation in cancer. Furthermore, we identified shared and unique host cell transcriptional responses to virus, allowing dichotomous classification of EBV⁺ cancer types. These responses were regulated by interferon pathway signaling, resulting in differential immune checkpoint expression.