Potential prognostic impact of EBV RNA‐seq reads in gastric cancer: a reanalysis of The Cancer Genome Atlas cohort

Abstract

Epstein–Barr virus (EBV)‐associated gastric cancer (EBVaGC), whose prognosis remains controversial, is diagnosed by in situ hybridization of EBV‐derived EBER1/2 small RNAs. In The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma (STAD) project, the EBV molecular subtype was determined through a combination of multiple next‐generation sequencing methods, but not by the gold standard in situ hybridization method. This leaves unanswered questions regarding the discordance of EBV positivity detected by different approaches and the threshold of sequencing reads. Therefore, we reanalyzed the TCGA‐STAD RNA sequencing (RNA‐seq) dataset including 375 tumor and 32 normal samples, using our analysis pipeline. We defined a reliable threshold for EBV‐derived next‐generation sequencing reads by mapping them to the EBV genome with three different random arbitrary alignments. We analyzed the prognostic impact of EBV status on the histopathological subtypes of gastric cancer. EBV‐positive cases identified by reanalysis comprised nearly half of the cases (49.6%) independent from infiltrating lymphocyte signatures, and showed significantly longer overall survival for adenocarcinomas of the ‘not‐otherwise‐specified’ type [P = 0.016 (log‐rank test); hazard ratios (HR): 0.476; 95% CI: 0.260–0.870, P = 0.016 (Cox univariate analysis)], but shorter overall survival for the tubular adenocarcinoma type [P = 0.005 (log‐rank test); HR: 3.329; 95% CI: 1.406–7.885, P = 0.006 (Cox univariate analysis)]. These results demonstrate that the EBV positivity rates were higher when determined by RNA‐seq than when determined by EBER1/2 in situ hybridization. The RNA‐seq‐based EBV positivity demonstrated distinct results for gastric cancer prognosis depending on the histopathological subtype, suggesting its potential to be used in clinical prognoses.

We reanalyzed the large‐scale RNA sequencing (RNA‐seq) dataset of gastric cancer (The Cancer Genome Atlas‐Stomach Adenocarcinoma). Epstein–Barr virus (EBV)‐positive cases identified by reanalysis encompassed almost half of the cases, and showed longer overall survival for adenocarcinomas of the ‘not‐otherwise‐specified’ type, but shorter overall survival for the intestinal type. Thus, the RNA‐seq‐based EBV positivity might be useful in clinical prognoses.

Abbreviations

EBV

Epstein–Barr virus

EBVaGC

EBV‐associated GC

GC

gastric cancer

IPW

inverse probability weighting

NGS

next‐generation sequencing

RNA‐seq

RNA sequencing

STAD

Stomach Adenocarcinoma

Epstein–Barr virus (EBV), formally designated as human herpesvirus 4, whose genome length is ~ 172 kbp, infects ~ 90% of Homo sapiens worldwide and establishes a lifelong persistent infection, typically with no observable symptoms in immunocompetent hosts 1, 2, 3, 4. Memory B cells are usually the host cell species responsible for establishing latent infections 4. Interestingly, EBV can be detected in lymphoma cells that originate from B cells, and in the rare type of lymphomas originating from T cells or NK cells, nasopharyngeal cancer, and gastric cancer (GC) 3, 4, suggesting that EBV is capable of infecting normal and/or malignant cells, including B, T, NK, and epithelial cells. Numerous studies on EBV have shown carcinogenic activities in EBV genome‐encoded products 4, 5. Hypermethylation of tumor suppressor genes has been observed in EBV‐associated cancers 4, 6, 7, 8, suggesting that aberrant methylation might be a critical mechanism of EBV‐related tumorigenesis.

Gastric cancer is a heterogenous disease, with subtypes, such as diffuse or intestinal, generally being based on histopathological criteria 9. EBV is detected in tumor cells in 2–20% of GC cases 5, 7, and most cases of classical EBV‐associated (EBVa) GC exhibit diffuse histology accompanied by lymphocyte accumulation, which is known as lymphoepithelioma‐like carcinoma 10, 11. However, EBV‐associated GC (EBVaGC) includes other histological subtypes 12, indicating its intrinsic heterogeneity.

Classical EBVaGC has traditionally been diagnosed via in situ hybridization, which detects the presence of EBV genome‐derived EBV‐encoded RNA (EBER)1/2 small RNAs in GC cells 13, 14. Based on molecular evidence, as outlined by The Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma (STAD) study 15, four molecular subtypes of GC have been proposed, including tumors positive for EBV, microsatellite‐unstable tumors, genomically stable tumors, and tumors with chromosomal instability. The EBV‐positive subtype in the TCGA‐STAD study was determined by pairwise comparisons of EBV read counts obtained using four sequencing platforms [whole genome, exome, RNA sequencing (RNA‐seq), and microRNA‐seq] but not by EBER in situ hybridization 15, 16. Despite the discordant results for EBV positivity among these sequencing methods, positivity was defined by dichotomous values from pairwise plots between two sequencing methods without careful consideration of the small number of sequencing reads 15. Although the concordance between EBV positivity obtained by the TCGA methods and in situ hybridization has been validated 16, it remains unclear whether the small amounts of EBV sequencing reads are sufficient for determining prognosis. Additionally, in some reanalysis studies of the TCGA RNA‐seq data, the cutoff levels of next‐generation sequencing (NGS) reads for EBV positivity were determined by arbitrary criteria that excluded small amounts of EBV reads 17, 18, 19.

Several studies have shown that the overall survival (OS) for EBVaGC is longer than in other types of GC that are not associated with EBV 12, 20, 21. However, it has shown that the prognosis of EBVaGC is identical 22, 23, 24, 25, or shorter 26, 27. The first report based on the TCGA‐STAD dataset showed that GC prognosis did not differ between the EBV molecular subtype and the other three subtypes 15. However, when a gene expression data‐based subtype prediction model was used to define the EBV molecular subtype, this subtype was associated with a better prognosis in two large independent cohorts 28. The underlying cause of these discrepancies has yet to be determined.

Recently, Liu et al. 29 reported a high‐quality validated survival outcome based on the entire TCGA dataset: The median follow‐up time of STAD was calculated as 14.0 months. Like other TCGA datasets, the OS data from the STAD study were used for survival analysis. Furthermore, ‘primary diagnosis’ data corresponding to histopathological information in the clinical TCGA datasets were harmonized with the terms in the WHO International Classification of Diseases for Oncology, 3rd Edition (ICD‐O‐3; GDC data release 13.0 on September 27, 2018), which allowed us to perform analysis based on current histopathological classifications, rather than on the Lauren scheme. We thus reanalyzed the TCGA‐STAD RNA‐seq data using our own analysis pipeline and successfully obtained wide‐ranging EBV‐derived NGS reads. The reliability of a small number of these reads was examined with the randomness properties of the mapping tool. Considering the heterogeneity of EBVaGC, we performed survival analysis on the EBV‐positive and EBV‐negative groups based on the high‐quality survival outcome 29 and the updated histopathological diagnosis data.

Materials and methods

Ethics statement

This study was approved by the Ethics Review Board of Tokyo Metropolitan Institute of Medical Science (IRB#18‐18) and the Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital (IRB#1563). Approval for access to anonymized RNA‐seq BAM files was obtained from dbGaP 30.

TCGA‐STAD data and molecular subtype

Sequence data in BAM file format (407 files) and accompanying clinical data were downloaded from the GDC data portal (https://portal.gdc.cancer.gov). We then linked the obtained files using patient identifiers. Among these files, 375 files were derived from tumor samples and the rest from normal samples. BAM files were converted into FASTQ with bam2fastq (https://github.com/jts/bam2fastq). The data used were from cancer specimens and noncancer control specimens.

EBV detection

We used hg38, constructed by the Genome Reference Consortium, as a human genome reference sequence. We utilized virus detection methods for cancer RNA‐seq data as described elsewhere 17, 18, 31, 32. Briefly, reads that were not mapped to the human genome reference sequence were excised from the BAM files and mapped to virus references. Raw sequencing reads were mapped to reference human genome hg38 with star v.2.4.2a 33, and unmapped reads from the first step were again mapped to hg38 with bowtie2 v.2.2.8 34 using the ‘‐‐very‐sensitive‐local’ option to perform high sensitivity mapping. After trimming low‐quality bases and adaptor sequences with cutadapt v.1.8.1 35, unmapped reads from the previous step were mapped to the virus reference FASTA files listed in Table S2 using bowtie2 with the ‘‐‐very‐sensitive‐local’ option. samtools v.1.8 36 was used to convert the SAM file to BAM and sort the resulting BAM file.

Verification of EBV reads

In order to verify the small number of detected EBV reads obtained from the analysis pipeline in this study, we implemented our pipeline three times for all samples using the ‘‐‐non‐deterministic’ option of bowtie2, which yielded different results due to the arbitrary choice of alignment (http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml).

Visualization of EBV reads

Mapped EBV reads were visualized using the Integrative Genomics Viewer 37 with the EBV reference genome.

Evaluation of lymphocyte infiltration into tumors

We obtained TCGA‐Pan‐Cancer Clinical Data, including extended prognostic data from the supplementary information of a previous report 29 and TCGA immunogenomics data reported in the supplemental table in Thorsson et al. 38, which includes intratumoral infiltrating lymphocyte data and intratumoral immune cell scores estimated using cibersort 39.

Statistical analysis

Statistical analyses were performed with r (The R Foundation for Statistical Computing, Vienna, Austria). In multigroup comparisons, we used the Kruskal–Wallis test with a post hoc Dunn's test to estimate the difference between two groups. To test for correlation between EBV read and intratumoral immune cell scores, we used the Spearman correlation coefficients. Survival curves with mortality hazard ratios (HRs) were generated using the Kaplan–Meier method and the univariate Cox proportional hazard model unless otherwise mentioned, applying inverse probability weighting (IPW) in order to adjust for potential imbalances due to confounding factors 40, 41. Propensity scores for calculating IPW were obtained from logistic regression using a set of covariates deemed likely to have affected the outcome, including age, gender, race, tissue or organ of origin, and tumor stage. We acknowledge that there may have been other differences that the TCGA‐STAD project did not measure. The log‐rank test was used to estimate statistical significance in survival analyses.

Results

Reanalysis of TCGA‐STAD RNA‐seq data and threshold of EBV reads

Clinicopathological characteristics of patients in the TCGA‐STAD dataset are shown in Table S1. We first mapped TCGA‐STAD RNA‐seq reads to the human genome and obtained nonhuman reads; the analysis pipeline is shown in Fig. 1. Unmapped reads were mapped to the EBV reference genomes listed in Table S2. Figure 2A shows the number of EBV‐positive cases both in the TCGA‐STAD study and in our analysis. Our analysis pipeline detected EBV in 49.6% cases, in contrast to the TCGA‐STAD frequency of 9% (Fig. 2A). Cumulative transcriptional profiling of the EBV genome revealed EBV reads that were in accordance with a previous report (Fig. S1) 15. To confirm the reliability of the small number of EBV reads in this study, we focused on the mapping process of our analysis pipeline by using bowtie2, which employs a random process (bowtie2 manual, URL: http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml) that yields different results that are filtered out for each calculation. To exclude ambiguous mapping results, we carried out three rounds of mapping with bowtie2 for all samples; Fig. 2B shows the proportion of results in which EBV was absent. The proportion of cases with negative results was the highest (35%) when the maximum EBV read count was 1 (Fig. 2B). On the other hand, if the maximum count of EBV reads was 2 or more, the proportion of negative results was 16% and 5%, respectively. Figure 2C shows corrected EBV read counts with respect to the total read counts in each case (particles per billion reads, ppb). The maximum corrected EBV level of one‐read cases was equivalent to approximately half that of the two‐read cases and was lower than that of all three‐read cases. We thus provisionally adopted the maximum level in one‐read cases as the threshold (7.1 ppb) (Fig. 2C), which resulted in the identification of 186 EBV‐positive cases and 189 EBV‐negative cases (Fig. 2D). Furthermore, we analyzed the normal sample BAM files in TCGA‐STAD using the same analysis pipeline (Fig. 2E). The obtained EBV reads from the normal samples were comparable to those from the EBV‐negative tumor samples. The number of EBV reads from EBV‐positive tumor samples was significantly higher than that of the normal samples and EBV‐negative tumor samples (P < 0.001). The lymphocyte infiltration score of EBV‐positive cases in the TCGA‐STAD report was significantly higher than that of negative cases. The lymphocyte infiltration scores in the present study, excluding positive cases from the TCGA‐STAD report and > 200 ppb cases in this study, did not differ from those of the negative cases (Fig. 2F). Available intratumoral immune cell scores showed that there was no correlation between EBV reads and the number of intratumoral B‐cell indicators 38 (Fig. 2G,H). We further analyzed the correlation between EBV reads and CD4⁺ T‐cell (Fig. S2A), CD8⁺ T‐cell (Fig. S2B), and natural killer (NK) cell (Fig. S2C) scores, but no correlation was found.

Figure 1.

Scheme for detection of viral sequences in TCGA‐STAD RNA‐seq data. Analysis pipeline for detecting viral sequence reads. Each mapped read of EBV was normalized by the total reads mapped to the human genome.

Figure 2.

Optimizing the EBV threshold value and scores of tumor‐infiltrating lymphocytes. (A) EBV reads detected in this study (left) and the original report (right). (B) The ambiguity of EBV read counts. Three independent bowtie2 mappings to the EBV genome were implemented for all samples with an option (‐‐non‐deterministic) that led to different results due to the arbitrary choice of alignments (http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml). X‐axis values show the detected maximum values of EBV reads. (C) Normalized EBV expression levels in all cases and in cases where EBV expression level ranges from 0 to 3. The dotted line indicates a threshold corresponding to the maximum value of cases in which only one EBV read was detected. (D) Dot plots of normalized EBV reads in normal and tumor samples. A newly defined threshold was applied. (E) Comparison of EBV reads in normal and tumor samples. (F) Lymphocyte infiltration scores in cases grouped by EBV status and each corrected EBV read range. To estimate the statistical difference, the Kruskal–Wallis test with a post hoc Dunn's test was used. Scatter plots of EBV reads, and scores of intratumoral naïve B cells (G) or intratumoral memory B cells (H) are shown. Box plots show the median (central line), first and third quartiles (box), and minimum and maximum values (whiskers above and below the boxes, respectively). The Spearman correlation coefficients were calculated for testing correlation between EBV read and intratumoral immune cell scores.

Survival analysis in reclassified groups according to histopathological type

We performed a Kaplan–Meier survival analysis of whole cases. Covariates were adjusted by IPW. There was no difference in adjusted OS between the EBV‐positive and EBV‐negative groups in this study [P = 0.956 (log‐rank test); HR: 1.008, 95% confidence interval (CI) 0.723–1.406, P = 0.961 (Cox univariate analysis); Fig. S3A]. We confirmed that there were no differences between the molecular EBV subtype and others, as previously reported 15 (Fig. S3B). Since EBVaGC is associated with diffuse‐type GC 42, 43, 44, we examined whether EBV positivity in each histopathological type affects survival. According to the criterion of whether a luminal structure (i.e., intestinal) was involved, we provisionally reclassified the ICD‐O‐3‐harmonized classification into diffuse, intestinal, mixed, and not‐otherwise‐specified (NOS) types (Table S3). This reclassification is used hereinafter. The average number of EBV reads in EBV‐positive cases did not differ among the reclassified pathological groups (Fig. 3A). Lymphocyte infiltration scores 38 were higher in the diffuse type of EBV‐positive GC than in the intestinal type, irrespective of age (Fig. S4). Statistically significant differences in lymphocyte infiltration scores were observed between EBV‐positive and EBV‐negative cases only in the intestinal type (Fig. 3B–E).

Figure 3.

Characteristics of EBV‐positive and EBV‐negative cases classified according to provisional histopathological categories. (A–E) Normalized EBV expression levels in histopathological categories. Lymphocyte infiltration scores in patients with EBV‐negative or EBV‐positive GC, including diffuse (B), intestinal (C), NOS (D), or mixed (E) types. Box plots show the median (central line), first and third quartiles (box), and minimum and maximum values (whiskers above and below the boxes). (F–I) The Kaplan–Meier plots of patients distinguished by RNA‐seq‐based EBV positivity in cases showing four differentiated types. Covariates were adjusted by IPW. (J, K) The Kaplan–Meier plots of patients classified according to the number of EBV reads.

In the Kaplan–Meier survival analysis, EBV positivity demonstrated an unfavorable prognostic effect in the intestinal type [P = 0.043 (log‐rank test); HR 1.736; 95% CI: 1.019–2.958, P = 0.043 (Cox univariate analysis); Fig. 3G] but a favorable prognostic effect in the NOS type [P = 0.027 (log‐rank test); HR 0.520; 5% CI: 0.2907–0.9291, P = 0.027 (Cox univariate analysis); Fig. 3H]. The statistical power in the mixed type was insufficient due to the small sample size (Fig. 3I). The unadjusted Kaplan–Meier curves were comparable to the IPW‐adjusted results described above (Fig. S5). To validate the prognostic effects of a small number of EBV reads, we carried out a Kaplan–Meier survival analysis for groups classified by the number of EBV reads: those with a small number (7.1–100 ppb) and a large number (more than 100 ppb) of reads. Unlike in Fig. 3G,H, there was no statistically significant difference between the two groups, likely due to the reduction in sample size (Fig. 3J,K). In multivariate Cox survival analysis, the EBV‐positive group with a small number of reads (7.1–100 ppb) showed favorable prognosis in NOS‐type GC, as shown in Fig. 3H (HR: 0.528, 95% CI: 0.283–0.983, P = 0.044) (Table 1).

Table 1.

Multivariate Cox survival analysis in patient groups distinguished by the amount of EBV reads.

EBV reads	HR	95% CI	P
Intestinal
Negative	1.000
7.1–100	1.634	0.923–2.900	0.092
> 100	2.103	0.956–4.627	0.065
NOS
Negative	1.000
7.1–100	0.528	0.283–0.983	0.044*
> 100	0.489	0.161–1.485	0.207

^* P < 0.05.

Another histopathological criterion: an analysis by TCGA‐provided tumor grade in combination with the provisional histopathological groups

Another histopathology‐based criterion, ‘tumor grade’, is provided by TCGA‐STAD. This information is in the TCGA‐STAD clinical data and shows the degree of abnormality of cancer cells, including a numeric histological grade (G1, well differentiated; G2, moderately differentiated; G3, poorly differentiated; and GX, grade cannot be assessed). Thus, we employed this grade as an alternative classifier in addition to our provisional histopathological categories. Figure S6 shows distinct proportions of G1–GX in the provisional histopathological categories with their original ICD‐O‐3‐harmonized histopathological types, suggesting a difference between the histopathological classification and tumor grade.

The Kaplan–Meier curves in Fig. 3F–I were further subdivided by G1/G2 (well/moderately differentiated) and G3 (poorly differentiated). Statistically significant differences in prognosis (shorter OS in intestinal, longer OS in NOS) were observed only in G1/G2 (P = 0.008, 0.015, respectively) but not in G3 (P = 0.441, 0.275, respectively; Fig. 4). The unadjusted Kaplan–Meier curves (Fig. S7) were comparable to the IPW‐adjusted results (Fig. 4).

Figure 4.

Prognostic effects of EBV‐positive and EBV‐negative cases classified according to tumor grade and the provisional histopathological categories. The Kaplan–Meier plots of patients distinguished by EBV positivity in cases showing the provisional histopathological categories (diffuse, intestinal, NOS, and mixed) and tumor grades (G1/G2, upper; and G3, lower). Covariates were adjusted by IPW. Differences between survival curves were tested for significance by the log‐rank test.

We continued to investigate the correlation between EBV reads and tumor grade. There was no statistically significant difference between G1/G2 and G3 based on the number of EBV reads (Fig. S8A). The lymphocyte infiltration score also demonstrated no significant difference between EBV‐negative and EBV‐positive samples in G1/G2 and G3 (Fig. S8B,C). The Kaplan–Meier survival analysis demonstrated no difference in OS between the EBV‐positive and EBV‐negative groups in this study both in G1/G2 and in G3, regardless of IPW adjustment (Fig. S8D–I).

Survival analysis in original ICD‐O‐3‐harmonized histopathological types with TCGA‐provided tumor grade

We next analyzed the prognosis of the original ICD‐O‐3‐harmonized histopathological types that correspond to intestinal and NOS types. Of the assigned ICD‐O‐3‐harmonized histopathological types in each case, we excluded ‘Adenocarcinoma with mixed type’ and ‘Papillary adenocarcinoma NOS’ because of their extremely small sample sizes (n = 1 and 5, respectively). Survival analysis was performed for the remaining cases after adjusting for explanatory variables, although some types had low statistical power as a result of their very small sample size (mucinous adenocarcinoma, n = 19; signet ring cell carcinoma, n = 12). For ‘tubular adenocarcinoma’ (n = 65), which corresponds to intestinal‐type GC, EBV‐positive cases demonstrated significantly shorter OS [P = 0.005 (log‐rank test); HR 3.329; 95% CI: 1.406–7.885, P = 0.006 (Cox univariate analysis); Fig. 5B], but this was not the case for the ‘adenocarcinoma intestinal’ type (n = 71; Fig. 5A). For ‘Adenocarcinoma NOS’ (n = 120), EBV‐positive cases showed significantly longer OS [P = 0.016 (log‐rank test); HR: 0.476; 95% CI: 0.260–0.870, P = 0.016 (Cox univariate analysis); Fig. 5C]. As seen in the analysis of provisional histopathological categories (Fig. 4), these statistically significant differences in prognoses were observed only in G1/G2 (P = 0.004, 0.008, respectively) but not in G3 (P = 0.148, 0.275, respectively; Fig. 5D–I). The unadjusted Kaplan–Meier curves were comparable to the IPW‐adjusted results (Fig. S9A–I). For the ‘carcinoma diffuse’ type (n = 59), which was placed in the diffuse classification, we observed no difference between EBV‐positive and EBV‐negative groups [P = 0.376 (log‐rank test); HR: 0.416, 95% CI: 0.156–1.109, P = 0.079 (Cox univariate analysis); Fig. S10A]. In the ‘signet ring cell carcinoma’ and the ‘mucinous adenocarcinoma’ types, we were unable to obtain statistically meaningful results due to the small sample size and because patients were censored early on (Fig. S10B,C). The unadjusted Kaplan–Meier curves were once again comparable to the IPW‐adjusted results (Fig. S10A–C).

Figure 5.

Survival analysis of EBV‐positive and EBV‐negative cases stratified by histopathological features (ICD‐O‐3‐harmonized) and the tumor grade. The Kaplan–Meier plots of patients distinguished by RNA‐seq‐based EBV positivity in cases stratified by ICD‐O‐3‐harmonized histopathology [adenocarcinoma intestinal type (A, D, G), tubular adenocarcinoma (B, E, H), and adenocarcinoma NOS (C, F, I)]. Groups stratified by tumor grade are shown in the middle row (G1/G2) and the lower row (G3). Covariates were adjusted by IPW. P values were determined with the log‐rank test.

Discussion

This is the first report to examine the reliability of EBV reads in very small quantities and the prognostic impact of RNA‐seq‐determined EBV positivity. Our reanalysis of the TCGA‐STAD RNA‐seq dataset yielded a wide range of EBV reads, irrespective of the degree of intratumoral lymphocyte infiltration. EBV positivity had contrasting prognostic effects on the ‘tubular adenocarcinoma’ and ‘adenocarcinoma NOS’ types of GC, which may explain previous conflicting reports on the prognostic value of EBV in GC.

RNA‐seq‐based EBV‐positive GC may exist as a distinct entity that includes EBER1/2 in situ hybridization‐positive EBVaGC. The 9% positivity rate of EBV in the original TCGA‐STAD study 15 or the 7–10% as determined by in situ hybridization 45, 46 were much lower than the 49.6% observed here and the ~ 40% in another study 47 that were obtained by reanalysis of a portion of the TCGA‐STAD data. These variable results suggest that the assignment of an appropriate cutoff value is essential and that the proportion of EBVaGC is higher than previously recognized. We should note that the definition of EBV positivity depends on the EBV detection method—for example, EBER1/2 in situ hybridization vs. RNA‐seq. The latter detects various RNA species derived from the EBV genome, but not small RNAs such as EBER1/2. On the other hand, EBER1/2 in situ hybridization detects a predetermined target and may only detect small RNAs when there are a sufficient number of copies. There have been no previous reports that EBER expression is necessary and sufficient for confirming the existence of EBV in cancer tissue.

In our study, we could not detect any correlation between EBV transcripts and intratumoral immune scores of T cells, B cells, and NK cells, which are the primary or potential hosts of latent EBV 4, suggesting that EBV reads were not derived from these intratumoral immune cells. These results also imply that EBV reads could be obtained from tumor cells, even at low read counts. The small number of EBV reads in tumor tissues may be partly explained by the hit‐and‐run hypothesis of herpesvirus 48, 49, 50, which posits that the virus inflicts genetic or epigenetic injury to infected cells. The agreement between the reliable results obtained from repeated stochastic mapping and the transcriptional profile of the EBV genome 15 (Fig. S1) allowed us to consider the EBV reads in this study as actual EBV transcripts. To validate the method for both false‐positive and false‐negative EBV transcripts in the future, an internal standard RNA sample that includes the relevant EBV transcripts may be a prerequisite for EBV detection in cancer tissues.

The high rate (46.9%) of RNA‐seq‐based EBV positivity in this study may be useful for estimating GC prognosis. We have shown that the prognoses of cases with EBV reads were poor in the tubular adenocarcinoma type of GC, but were improved in adenocarcinoma NOS. The variable prognostic significance of EBV positivity may be attributable to the specific genetic and/or epigenetic features that underlie tumor histopathology and the immune response. Given the inconsistency between the G1/G2/G3 scale and the ICD‐O‐3‐harmonized histopathological type for ‘adenocarcinoma NOS’ (Figs 4 and 5, and Fig. S6), this GC type is most likely intrinsically heterogeneous and may be further classified by distinct tumor biology and immune features. Additional studies are needed to substantiate and confirm this at the molecular level.

It is important to note that the GC classifications made by the Lauren and WHO schemes are discordant. According to the Lauren classification, GCs are separated into two main histological types, diffuse and intestinal, in addition to the mixed and indeterminate types. In this study, we recategorized eight ICD‐O‐3‐derived groups into four new groups: diffuse, intestinal, NOS, and mixed (Table S3). These groups resemble but are not identical to the Lauren scheme. As such, extrapolation of the results of this study to GC studies performed in different pathological contexts must be done with caution.

Additionally, while IPW adjustment can reduce the impact of potentially confounding factors, it is subject to biases from unobserved differences. Furthermore, it cannot be ruled out that EBV read detection is due to systematic contamination from laboratory reagents and processes and/or the environment, although, considering the high EBV infection rate in humans, our results can be regarded as plausible. Moreover, EBV has not been detected in reports on contamination 51, 52, 53. Lastly, EBV positivity requires validation in different cohorts.

Conclusion

A reanalysis of the TCGA‐STAD RNA‐seq dataset revealed a wide range of EBV reads that are independent of the degree of infiltrating lymphocyte signatures. We found that EBV‐positive cases had variable prognoses depending on their histopathological subtype. Thus, RNA‐seq‐based EBV positivity may be a useful tool for estimating the prognosis of specific histopathological types of GC.

Conflicts of interest

The authors declare no conflict of interest.

Author contributions

DS and K Oboki designed the study concept. DS, MO, CH, and K Oboki analyzed the data. K Oboki was responsible for funding procurement. DS, MO, CH, TH, S‐IH, and K Oboki contributed to the investigation. DS and K Oboki contributed to the development of methodology. YH, TS, MI, K Ohashi, and K Oboki underwent supervision. DS and K Oboki contributed to the writing of the manuscript.

Supporting information

Fig. S1. Cumulative transcriptional profiling of the EBV genome. (A) Landscape of EBV reads on EBV Akata strain (KC207813.1). (B) Enlarged genomic region with mapped EBV reads for LF3. (C) Enlarged genomic region with mapped EBV reads for A73, BALF4, and BARF0.

Fig. S2. Scatter plots of EBV reads and scores of intratumoral T cells and NK cells. (A) CD4⁺ T cells (memory activated, memory resting, and naïve), (B) CD8⁺ T cells, and (C) NK cells (activated or resting). Spearman correlation coefficients were calculated for testing correlation between EBV read and intratumoral immune cell scores.

Fig. S3. Kaplan–Meier plot of whole cases. (A) Survival analysis based on RNA‐seq‐based EBV positivity in the entire patients of this study (left, unadjusted; right, IPW‐adjusted). (B) Survival analysis based on molecular EBV status in all TCGA‐STAD patients (left, unadjusted; right, IPW‐adjusted). P values were determined using the log‐rank test.

Fig. S4. Lymphocyte infiltration scores in TCGA GC patients reclassified based on histopathological features. Patient groups are as follows: all ages, 35–64 years old, and 65–90 years old. To estimate the statistical difference, Kruskal–Wallis test with a post hoc Dunn's test was used.

Fig. S5. Unadjusted Kaplan–Meier plot for the four provisional histopathological groups. These Kaplan–Meier plots (A–D) correspond to the data shown in Fig. 3F–I.

Fig. S6. Proportion of G1–GX in the provisional histopathological categories (diffuse, intestinal, NOS and mixed types) or their original ICD‐O‐3‐harmonized histopathological types.

Fig. S7. Unadjusted Kaplan–Meier curves corresponding to the data shown in Fig. 4. Prognostic effects of EBV‐positive and ‐negative cases classified according to tumor grade and the provisional histopathological categories are shown.

Fig. S8. Characteristics of EBV‐positive and ‐negative cases classified according to the TCGA‐provided tumor grade. (A) Normalized EBV expression levels in histopathological categories. Lymphocyte infiltration scores in patients with EBV‐negative or ‐positive GC, in G1/G2 (B), or G3 (C). Box plots show the median (central line), first and third quartiles (box), and minimum and maximum values (whiskers above and below the boxes). To estimate the statistical difference, Kruskal‐Wallis test with a post hoc Dunn's test was used. (D–G) Kaplan–Meier plots of patients distinguished by RNA‐seq‐based EBV positivity in G1/G2 or G3 cases. (H, I) Kaplan–Meier plots of patients classified according to the number of EBV reads. Covariates were adjusted by IPW.

Fig. S9. Unadjusted Kaplan–Meier curves corresponding to the data shown in Fig. 5. Prognostic effects of EBV‐positive and ‐negative case s classified according to tumor grade and the provisional histopathological categories. Intestinal type [adenocarcinoma intestinal type (A, D, G) and tubular adenocarcinoma (B, E, H)]. NOS type [adenocarcinoma NOS (C, F, I)].

Fig. S10. Kaplan–Meier curves of the remaining histopathological types (ICD‐O‐3‐harmonized) not listed in Fig. 5. (A) Carcinoma diffuse type, (B) signet ring cell carcinoma, and (C) mucinous adenocarcinoma with IPW‐adjustment (upper) or without IPW‐adjustment (lower).

Table S1. Baseline characteristics of patients in the Cancer Genome Atlas‐Stomach Adenocarcinoma (TCGA‐STAD) dataset.

Table S2. List of EBV DNA reference sequences for mapping next‐generation sequencing short reads.

Table S3. Provisional regrouped histopathological categories.