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Early change of plasma Epstein-Barr virus DNA load and the viral lytic genome level could positively predict clinical outcome in recurrent or metastatic nasopharyngeal carcinoma receiving anti-programmed cell death 1 monotherapy

BMC Cancer volume 24, Article number: 797 (2024) Cite this article

Abstract

Purpose

Patients with recurrent or metastatic nasopharyngeal carcinoma (RM-NPC) have proven benefit from anti-programmed cell death 1 (anti-PD-1) monotherapy. Here, we retrospectively analyze the association of plasma Epstein-Barr virus (EBV) DNA load and tumor viral lytic genome with clinical outcome from 2 registered phase I trials.

Methods

Patients with RM-NPC from Checkmate 077 (nivolumab phase I trial in China) and Camrelizumab phase I trial between March 2016 and January 2018 were enrolled. Baseline EBV DNA titers were tested in 68 patients and EBV assessment was performed in 60 patients who had at least 3 post-baseline timepoints of EBV data and at least 1 post-baseline timepoint of radiographic assessment. We defined “EBV response” as 3 consecutive timepoints of load below 50% of baseline, and “EBV progression” as 3 consecutive timepoints of load above 150% of baseline. Whole-exome sequencing was performed in 60 patients with available tumor samples.

Results

We found that the baseline EBV DNA load was positively correlated with tumor size (spearman p < 0.001). Both partial response (PR) and stable disease (SD) patients had significantly lower EBV load than progression disease (PD) patients. EBV assessment was highly consistent with radiographic evaluation. Patients with EBV response had significantly improved overall survival (OS) than patients with EBV progression (log-rank p = 0.004, HR = 0.351 [95% CI: 0.171–0.720], median 22.5 vs. 11.9 months). The median time to initial EBV response and progression were 25 and 36 days prior to initial radiographic response and progression, respectively. Patients with high levels of EBV lytic genomes at baseline, including BKRF2, BKRF3 and BKRF4, had better progression-free survival (PFS) and OS.

Conclusion

In summary, early clearance of plasma EBV DNA load and high levels of lytic EBV genes were associated with better clinical outcome in patients with RM-NPC receiving anti-PD-1 monotherapy.

Peer Review reports

Background

Nasopharyngeal carcinoma (NPC) is a head and neck cancer endemic to southern China and Southeast Asia [1]. Patients with recurrent or metastatic NPC (RM-NPC) have limited effective treatment options and poor clinical outcome [2]. With the development of immune-checkpoint inhibitors (ICIs), patients with RM-NPC have obtained significant clinical benefit [3,4,5]. In RM-NPC patients who had progressed on first-line treatment, anti-programmed cell death 1 (anti-PD-1) monotherapy showed promising antitumor activity, with objective response rates (ORRs) of 13–43% and median duration of response longer than 8 months [6]. However, thorough analysis of the connection of plasma and tissue Epstein–Barr virus (EBV) biomarkers with ICI therapy outcome is lacking [7, 8].

EBV infection is highly associated with NPC [9]. Latent infection is the predominant program of EBV infection in NPC, in contrast to lytic infection, which is the default mode of EBV infection in normal epithelium [10]. Type II latency of EBV infection is commonly observed in NPC, with the expression of various latent EBV genes, including LMP1, LMP2A, BARTs, EBNA1, and EBERs [11]. Expression of latency II EBV genes, notably LMP1, alters multiple cellular pathways and drives NPC pathogenesis [12, 13]. In comparison, the expression of lytic EBV genes is detected in small islets of NPC cells [14]. The BKRF2, BKRF3 and BKRF4 genes are lytic EBV genes located close to one another in the viral genome [15]. The BKRF2 gene is a true-late lytic gene during EBV reactivation which encodes glycoprotein L [16]. The BKRF3 and BKRF4, considered as early lytic genes, encode uracil-DNA glyosylase and a tegument protein, respectively. The BKRF3 and BKRF4 are direct transcriptional targets of Rta and Zta, two transactivators which trigger viral reactivation [17, 18]. However, little is known about the impact of BKRF2, BKRF3 and BKRF4 genes in NPC.

The role of plasma EBV DNA as a biomarker for patients with RM-NPC receiving anti-PD-1 therapy has been reported. However, controversial results were found in different studies. Notably, the association of early clearance of plasma EBV DNA with clinical outcome in RM-NPC patients treated with anti-PD-1 immunotherapy was positive in POLARIS-02 and CAPTAIN-1st trials but negative in the Mayo Clinic Phase 2 Consortium study of nivolumab [3, 5, 19, 20]. Moreover, the value of circulating viral DNA titer for distinguishing patients with RM-NPC receiving anti-PD-1 monotherapy who could obtain long-term clinical benefit has not been comprehensively investigated. Given the controversial evidence, more studies are needed to verify the use of EBV surveillance for RM-NPC patients treated with anti-PD-1 monotherapy.

In this study, we aimed to explore the role of plasma and tissue EBV biomarkers, including plasma EBV DNA load, tumor neoantigen burden (TNB), lytic EBV genes and EBV strains, for predicting RM-NPC patients’ response to anti-PD-1 monotherapy by analyzing the data from two phase I clinical trials evaluating camrelizumab and nivolumab.

Methods

The current study was approved by the institutional ethics committee of Sun Yat-sen University Cancer Center and written informed consent were obtained from all patients. The study methods referred partially to our previous report, which detailly analyzed the survival outcomes and the association between copy number loss in either GZMB or GZMH genes and survival within the same population as this present study [21].

Study design and patients

The design and results of Camrelizumab phase I trial and Checkmate 077 (nivolumab phase I trial in China) from March 2016 to January 2018 (ClinicalTrials.gov identifiers: NCT02721589 and NCT02593786) have been previously reported [4, 22]. Patients with RM-NPC from these 2 clinical trials were enrolled in current study. The distribution of treatments and the screen process of participants in current study were displayed in supplementary Figure S1 [see supplementary file]. The baseline tumor tissues and blood samples were obtained before anti-PD-1 immunotherapy.

Responses were evaluated by investigators using Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at baseline and approximately every 6 weeks. Durable clinical benefit (DCB) was calculated as the percentage of patients who achieved complete response (CR) or partial response (PR) or stable disease (SD) lasted more than 6 months; non-durable clinical benefit (NDB) was defined as progression disease (PD) or SD lasted 6 months or less. Progression-free survival (PFS) was defined as the duration from the first treatment to PD or death. OS was calculated as the time from first dose to death.

Sample collection and analysis

Plasma EBV DNA level was measured by real-time quantitative polymerase chain reaction (PCR) with probes against EBV genes before and every 2 weeks until disease progression. Real-time quantitative polymerase chain reaction was conducted using the ABI Prism 7500 Sequence Detection System from Applied Biosystems. The reagents for the DNA extraction and detection kit were commercially available assays provided by Sansure Biotech. This particular DNA extraction and detection kit has obtained approval from both the National Medical Products Administration and Conformité Européenne. For EBV assessment, we defined “EBV response” as 3 consecutive timepoints of load below 50% of baseline, and “EBV progression” as 3 consecutive timepoints of load above 150% of baseline.

Whole-exome Sequencing (WES) was performed in 60 patients with available tumor samples on Illumina HiSep4000 platform (Illumina, USA). QIAamp DNA FFPE Tissue Kit and DNeasy Blood and tissue kit (Qiagen, USA) were used to extract DNA from Formalin-fixed, paraffin-embedded (FFPE) or biopsy tumor tissues and blood samples, respectively. Genomic DNA was then quantified by a Qubit Fluorometer (Invitrogen) using the dsDNA HS Assay Kit (ThermoFisher Scientific, USA). The sequence reads were aligned to the human reference genome (hg19) using BWA-mem [23]. Somatic Single Nucleotide Variant and insertion/deletions were called with Mutect [24] and Scalpel [25], respectively. TNB referred to the number of tumor-mutated antigens. The association between EBV lytic genes read counts and clinical outcomes (DCB vs. NDB) were displayed by heatmaps. Levels of BKRF2, BKRF3 and BKRF4 read counts were grouped binarily as high versus low by median. As for assembly of EBV genome, all sequencing reads were aligned to hg19 using Browtie2 and human sequences were removed. Following the elimination of human sequences, the remaining reads underwent assembly via SOAPdenovo [26]. SOAPdenovo amalgamated overlapping reads utilizing the de Bruijn graph algorithm to produce contigs. The paired-end information was then leveraged to connect these contigs into scaffolds. Subsequently, assembled scaffolds exceeding 100 base pairs in length were aligned to EBV strain reference genomes, including HQ020558_GD2_NPC- tumor_China_2009 and KF373730_M81_NPC_China_1970 strains. GD2 was acquired as a minor subset of sequence data from next-generation sequencing of the complete DNA sequences originating from a biopsy specimen of NPC [27], and M81 was a valid representative of the EBV strains that were found in Chinese NPC [28].

Statistical analysis

Association between EBV load and tumor size was examined by Spearman correlation test. Kruskal-Wallis H test was performed to compare baseline EBV load among cohorts with different responses. Logistic regression analysis was performed between plasma EBV DNA load and response, as well as being adjusted by other basic characteristics, including age, gender, stage, performance status, and previous treatment lines. We stratified quantitative variables such as TNB at different thresholds to identify their association with clinical outcomes. Kaplan-Meier analysis along with log-rank test were used to estimate the survival. Hazard ratios (HRs) were calculated using Cox proportional hazards regression model. R software (version 3.3.2) was used for statistical analysis. A two-tailed p value < 0.05 was considered statistically different.

Results

Patient cohort

Sixty-eight patients (45 in camrelizumab trial and 23 in nivolumab trial) were included in EBV-DNA analysis, and 60 patients with available tumor issues (42 in camrelizumab trial and 18 in nivolumab trial) were included in WES analysis. All baseline characteristics were displayed in Table 1.

Table 1 Patients’ demographics and baseline clinical characteristics
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Association between EBV DNA titers and clinical outcomes

For 68 patients with baseline EBV DNA titers, the absolute viral load before immunotherapy was positively correlated with tumor size (spearman p < 0.001, Fig. 1A), and both PR and SD patients had significantly lower EBV load than PD patients (Fig. 1B).

Fig. 1
figure 1

Baseline Epstein-Barr virus (EBV) load and clinical outcome. A. Spearman correlation between tumor size and plasma EBV copy number at baseline. B. Boxplot of log10 baseline viral load by Response Evaluation Criteria in Solid Tumors group. ORR, objective response rate; PD, progression disease; PR, partial remission; SD, stable disease

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For the following analysis, we only included patients who had at least 3 post-baseline timepoints of EBV data and at least 1 post-baseline timepoint of radiographic assessment (n = 60). EBV assessment was highly consistent with RECIST evaluation (Fig. 2A). PR patients showed a stable or decreased viral load while PD patients displayed an increasing but highly fluctuating load within 50 days of treatment initiation (Fig. 2B). EBV assessment accurately distinguished patients who benefited from immunotherapy and those who did not. EBV responders (N = 23) had significantly improved OS (log-rank p = 0.004, HR = 0.351 [95% CI: 0.171–0.720], median 222.5 vs. 11.9 months) compared to the EBV progression patients (N = 33) (Fig. 2C, Figure S2A). A multi-variate analysis confirmed that the EBV assessment is independent compared to other clinical characteristics including sex, age, stage, ECOG performance status score and prior lines of treatment (log-rank p = 0.004, HR = 0.331 [95% CI: 0.158–0.696], Figure S2B). We compared the difference between the timing of EBV response/progression and RECIST response/progression in PR and PD patients (n = 28, Fig. 2D). The median time to initial EBV response was 28 days from the start of treatment whereas the median time to initial RECIST response was 53 days from the start of treatment. The median time to initial EBV progression and initial RECIST progression were 14 and 50 days, respectively. We included representative cases from 1 PD patient and 1 PR patient in Fig. 3 and the change curves revealed satisfactory consistency between radiographic evaluation and EBV load assessment.

Fig. 2
figure 2

Change of EBV load during treatment and response to immunotherapy. A. Waterfall plot of change from baseline in tumor size for patients with nasopharyngeal carcinoma. B. Change from baseline in EBV viral load for patients with PD and PR. C. OS curve of patients with EBV response vs. those with EBV progression. D. Swimmer plot that shows time to initial EBV response/progression and RECIST response/progression in PR and PD patients. RECIST, Response Evaluation Criteria in Solid Tumors; PD, progression disease; PR, partial remission; SD, stable disease; EBV, Epstein-Barr virus; OS, overall survival

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Fig. 3
figure 3

EBV and RECIST assessment of 1 PR and 1 PD patients. A. Tumor size and plasma EBV load change over time in a PD patient. EBV load was on log10 scale. B. Tumor size and plasma EBV load change over time in a PR patient. EBV load was on log10 scale. EBV: Epstein-Barr virus; RECIST: Response Evaluation Criteria in Solid Tumors; PD: progression disease; PR: partial remission

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Association between molecular markers in WES data and clinical outcomes

The median TNB in 60 patients with WES data was 21. The distribution of TNB was not statistically different among patients with PR, SD or PD (supplementary Figure S3A) [see supplementary file]. Patients were divided into TNB high and low by median and no impact were showed on neither PFS nor OS between groups (supplementary Figure S3B, C) [see supplementary file].

Next, we explored the association between EBV lytic genomes, including BKRF2, BKRF3 and BKRF4, and clinical outcomes. Figure 4 showed that patients with DCB (n = 18) tended to have higher levels of BKRF2, BKRF3 and BKRF4 compared with patients with NDB (n = 42). The association between other genes and clinical efficacy were also analyzed as shown in supplemental Figure S4 [see supplementary file]. Levels of lytic EBV genes were grouped binarily as high versus low by median and further analyses demonstrated that high levels of BKRF2, BKRF3 and BKRF4 were associated with better PFS and OS (Fig. 5A-F).

Fig. 4
figure 4

Levels of BKRF2, BKRF3 and BKRF4 in patients with durable clinical benefit (DCB) and non-durable clinical benefit (NDB)

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Fig. 5
figure 5

The effect of levels of BKRF2, BKRF3 and BKRF4 on patients’ progression-free survival (PFS) and overall survival (OS). PFS (A) and OS (B) curve stratified by median levels of BKRF2. PFS (C) and OS (D) curve stratified by median levels of BKRF3. PFS (E) and OS (F) curve stratified by median levels of BKRF4

Full size image

In addition, we explored the association between EBV strains and clinical outcomes. EBV strains were determined by direct sequencing of EBV genomes in tumor tissues using WES. Our results showed that there were 23 and 25 EBV genome sequences mapped to known HQ020558_GD2_NPC-tumor_China_2009 [27] and KF373730_M81_NPC_China_1970 strains [28], and the others (n = 12) were not unique. We further compared the PFS and OS between GD2 and M81 EBV strains and the results showed no difference between groups (PFS: p = 0.13; OS: p = 0.66).

Discussion

This study comprehensively investigated the role of plasma and tissue EBV biomarkers for predicting patients with RM-NPC receiving anti-PD-1 monotherapy. Overall, we found that patients with a lower baseline EBV DNA titer was positively correlated with smaller tumor size and better clinical response. Besides, EBV assessment was highly consistent with RECIST evaluation and patients with early clearance of plasma EBV DNA, namely EBV response, experienced superior survival outcomes. Furthermore, patients with high levels of BKRF2, BKRF3 and BKRF4 genes had significant longer median PFS and OS. These findings highlighted the value of plasma and tissue EBV biomarkers of anti-PD-1 therapy which could be incorporated into the guidance of individual treatment selection for patients with RM-NPC.

The role of plasma EBV DNA copy number as a biomarker for patients with RM-NPC receiving anti-PD-1 immunotherapy remains controversial. In POLARIS-02 study, the association of baseline EBV DNA titers with ORR was not statistically different but patients with RM-NPC with low baseline EBV copy number had a significantly higher DCB rate and improved survival [5, 20]. In our study, the baseline EBV load was positively correlated with tumor size and patients with PR and SD had significantly lower baseline EBV load than patients with PD, indicating that the blood EBV DNA might be derived from the tumor mass and could serve as an indicator of the tumor burden [29]. Previous studies have reported that large baseline tumor size was an independent prognostic marker of unfavorable efficacy in patients treated with pembrolizumab monotherapy [30, 31]. We speculated that the association between baseline EBV DNA level and clinical efficacy might result from the tumor burden. As for dynamic EBV titers, the Mayo Clinic Phase 2 Consortium study of nivolumab in RM-NPC observed a decreasing trend in EBV DNA titers in 87.5% of the responders, but found no association of plasma EBV DNA clearance with response rate and survival in RM-NPC patients receiving nivolumab [3]. However, positive results were observed in other reports [5, 19, 20]. A phase 3 trial (CAPTAIN-1st) showed that there was an association between the early clearance of EBV DNA and the response rate of camrelizumab in combination with gemcitabine and cisplatin as first-line treatment for RM-NPC [19]. In POLARIS-02 study, a large clinical study of toripalimab in RM-NPC patients, the ORR of patients with ≥ 50% decrease of plasma EBV DNA copy number on day 28 was significantly better than those with < 50% decrease. Additionally, improved survival was observed in patients with early decreases in EBV DNA titers [5, 20]. Here, we found that early change of EBV load, defined as EBV response/progression, was able to assess the treatment efficacy of anti-PD-1 monotherapy, with high consistency with RECIST evaluation. The change curve of EBV load assessment revealed satisfactory consistency with the curve of radiographic evaluation in PR and PD patients. Additionally, the median time to initial EBV response and progression were 25 days and 36 days prior to initial RECIST response and progression, respectively. The evaluation of response to anticancer therapy in patients with solid tumors presently depends on radiological assessments. Nevertheless, recurrent radiological evaluations are not devoid of constraints, such as increased radiation exposure for the patient and difficulties in assessing bone lesions [32]. Given the fact that the radiographic evaluation was difficult and inappropriate to be conducted so early and frequently in clinical practice, EBV assessment proved to be a convenient method for predicting the clinical efficacy of NPC patients receiving anti-PD-1 immunotherapy prior to radiologic review. Patients assessed as EBV progression who were unlikely to benefit from immunotherapy could be promptly scheduled for additional radiographic confirmation and expedited transition to new treatment. Moreover, patients who obtained EBV response had significantly improved survival than those who obtained EBV progression. These findings demonstrated that EBV assessment could predict clinical effects early and identify RM-NPC patients who could benefit from anti-PD-1 immunotherapy accurately. The monitoring of plasma EBV DNA levels during immunotherapy facilitated ongoing surveillance and enabled timely modification of treatment. To our knowledge, we are the first to use EBV assessment as a valuable tool for evaluating tumor response in patients with RM-NPC receiving anti-PD-1 monotherapy.

The lytic genes of EBV are less well characterized, unlike the latent genes of EBV, which have been studied extensively [33]. Here, we focused on the EBV lytic genes and investigated their association with clinical outcomes in RM-NPC patients treated with ICIs. Our results demonstrated that patients with high levels of BKRF2, BKRF3 and BKRF4 obtained long-lasting clinical benefit. Latent infection, rather than lytic infection, is the predominant program of EBV infection in NPC and lytic EBV genes are expressed only in small islets of NPC cells [34]. The proportion of lytic/latent EBV genes in patients expressing lytic genes might have changed. There could be an elevation in both the levels of lytic and latent EBV genes. Another potential scenario involves the transition from latent to lytic state in malignant cells [35]. Most of the EBV-infected tumor cells harbor latent virus, so the lytic reactivation was considered as part of an oncolytic treatment repertoire [36]. The latent state of EBV infection switches off the expression of most viral genes with strong immunogenicity, which makes EBV invisible to the cellular immunity, especially the CD8+ T cell response [35]. Here, we hypothesized that patients with high levels of BKRF2, BKRF3 and BKRF4 were supposed to experience lytic reactivation from latency and release abundant lytic antigens, which were recognized by immune effector peripheral T cells, particularly CD8+ T cells awakened by ICIs [37], thus achieving clinical response. The understanding of EBV-specific host immunity in RM-NPC patients receiving an-ti-PD-1 immunotherapy remains to be elucidated and our hypothesis also needs further validation.

There were some limitations in current study. First, the data were based on 2 phase I clinical studies with limited sample size and the risk of patient selection bias should be noticed. Second, the mechanisms of EBV DNA load and lytic EBV genes predicting the clinical outcome of anti-PD-1 immunotherapy in RM-NPC patients were unclear and needed further investigation.

Conclusions

In summary, this study provided valuable EBV biomarkers predictive for RM-NPC patients’ response to anti-PD-1 monotherapy. Our results showed that the baseline EBV DNA load corresponded with clinical response and EBV assessment could predict clinical effects prior to radiographic evaluation and identify RM-NPC patients who could obtain long-term clinical benefit from anti-PD-1 monotherapy. We demonstrated that early clearance of plasma EBV DNA load and high levels of lytic EBV genes, including BKRF2, BKRF3 and BKRF4, were associated with improved survival. Future prospective studies with larger sample size are required to validate our findings.

Data availability

The data of this study are available upon reasonable request from the corresponding author, H Zhao.

Abbreviations

RM-NPC:

Recurrent or metastatic nasopharyngeal carcinoma

Anti-PD-1:

Anti-programmed cell death 1

EBV:

Epstein-Barr virus

PR:

Partial response

SD:

Stable disease

PD:

Progression disease

OS:

Overall survival

PFS:

Progression-free survival

ICI:

Immune-checkpoint inhibitor

ORR:

Objective response rate

TNB:

Tumor neoantigen burden

RECIST:

Response Evaluation Criteria in Solid Tumors

DCB:

Durable clinical benefit

CR:

Complete response

NDB:

Non-durable clinical benefit

PCR:

Polymerase chain reaction

WES:

Whole-exome Sequencing

HR:

Hazard ratio

WHO:

World health organization

References

  1. Chen YP, Chan ATC, Le QT, Blanchard P, Sun Y, Ma J. Nasopharyngeal carcinoma. Lancet. 2019;394(10192):64–80.

    Article  PubMed  Google Scholar 

  2. Caudell JJ, Gillison ML, Maghami E, Spencer S, Pfister DG, Adkins D, Birkeland AC, Brizel DM, Busse PM, Cmelak AJ, et al. NCCN Guidelines® insights: Head and Neck cancers, Version 1.2022. J Natl Compr Canc Netw. 2022;20(3):224–34.

    Article  PubMed  Google Scholar 

  3. Ma BBY, Lim WT, Goh BC, Hui EP, Lo KW, Pettinger A, Foster NR, Riess JW, Agulnik M, Chang AYC, et al. Antitumor Activity of Nivolumab in recurrent and metastatic nasopharyngeal carcinoma: an International, Multicenter Study of the Mayo Clinic Phase 2 Consortium (NCI-9742). J Clin Oncol. 2018;36(14):1412–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Fang W, Yang Y, Ma Y, Hong S, Lin L, He X, Xiong J, Li P, Zhao H, Huang Y, et al. Camrelizumab (SHR-1210) alone or in combination with gemcitabine plus cisplatin for nasopharyngeal carcinoma: results from two single-arm, phase 1 trials. Lancet Oncol. 2018;19(10):1338–50.

    Article  CAS  PubMed  Google Scholar 

  5. Wang FH, Wei XL, Feng J, Li Q, Xu N, Hu XC, Liao W, Jiang Y, Lin XY, Zhang QY, et al. Efficacy, Safety, and Correlative Biomarkers of Toripalimab in previously treated recurrent or metastatic nasopharyngeal carcinoma: a phase II clinical trial (POLARIS-02). J Clin Oncol. 2021;39(7):704–12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Adkins DR, Haddad RI. Clinical trial data of Anti-PD-1/PD-L1 therapy for recurrent or metastatic nasopharyngeal carcinoma: a review. Cancer Treat Rev. 2022;109:102428.

    Article  CAS  PubMed  Google Scholar 

  7. Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Rutkowski P, Lao CD, Cowey CL, Schadendorf D, Wagstaff J, Dummer R, et al. Five-year survival with combined Nivolumab and Ipilimumab in Advanced Melanoma. N Engl J Med. 2019;381(16):1535–46.

    Article  CAS  PubMed  Google Scholar 

  8. Tang LQ, Chen QY, Guo SS, Chen WH, Li CF, Zhang L, Lai XP, He Y, Xu YX, Hu DP, et al. The impact of plasma Epstein-Barr virus DNA and fibrinogen on nasopharyngeal carcinoma prognosis: an observational study. Br J Cancer. 2014;111(6):1102–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Münz C. Latency and lytic replication in Epstein-Barr virus-associated oncogenesis. Nat Rev Microbiol. 2019;17(11):691–700.

    Article  PubMed  Google Scholar 

  10. Jha HC, Pei Y, Robertson ES. Epstein-Barr Virus: diseases linked to infection and Transformation. Front Microbiol. 2016;7:1602.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Tsao SW, Tsang CM, Lo KW. Epstein-Barr virus infection and nasopharyngeal carcinoma. Philos Trans R Soc Lond B Biol Sci 2017, 372(1732).

  12. Li YY, Chung GT, Lui VW, To KF, Ma BB, Chow C, Woo JK, Yip KY, Seo J, Hui EP, et al. Exome and genome sequencing of nasopharynx cancer identifies NF-κB pathway activating mutations. Nat Commun. 2017;8:14121.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yang CF, Yang GD, Huang TJ, Li R, Chu QQ, Xu L, Wang MS, Cai MD, Zhong L, Wei HJ, et al. EB-virus latent membrane protein 1 potentiates the stemness of nasopharyngeal carcinoma via preferential activation of PI3K/AKT pathway by a positive feedback loop. Oncogene. 2016;35(26):3419–31.

    Article  CAS  PubMed  Google Scholar 

  14. Sengupta S, den Boon JA, Chen IH, Newton MA, Dahl DB, Chen M, Cheng YJ, Westra WH, Chen CJ, Hildesheim A, et al. Genome-wide expression profiling reveals EBV-associated inhibition of MHC class I expression in nasopharyngeal carcinoma. Cancer Res. 2006;66(16):7999–8006.

    Article  CAS  PubMed  Google Scholar 

  15. Chen LW, Hung CH, Wang SS, Yen JB, Liu AC, Hung YH, Chang PJ. Expression and regulation of the BKRF2, BKRF3 and BKRF4 genes of Epstein-Barr virus. Virus Res. 2018;256:76–89.

    Article  CAS  PubMed  Google Scholar 

  16. Johannsen E, Luftig M, Chase MR, Weicksel S, Cahir-McFarland E, Illanes D, Sarracino D, Kieff E. Proteins of purified Epstein-Barr virus. Proc Natl Acad Sci U S A. 2004;101(46):16286–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Su MT, Liu IH, Wu CW, Chang SM, Tsai CH, Yang PW, Chuang YC, Lee CP, Chen MR. Uracil DNA glycosylase BKRF3 contributes to Epstein-Barr virus DNA replication through physical interactions with proteins in viral DNA replication complex. J Virol. 2014;88(16):8883–99.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Masud H, Watanabe T, Yoshida M, Sato Y, Goshima F, Kimura H, Murata T. Epstein-Barr Virus BKRF4 gene product is required for efficient progeny production. J Virol 2017, 91(23).

  19. Yang Y, Qu S, Li J, Hu C, Xu M, Li W, Zhou T, Shen L, Wu H, Lang J, et al. Camrelizumab versus placebo in combination with gemcitabine and cisplatin as first-line treatment for recurrent or metastatic nasopharyngeal carcinoma (CAPTAIN-1st): a multicentre, randomised, double-blind, phase 3 trial. Lancet Oncol. 2021;22(8):1162–74.

    Article  CAS  PubMed  Google Scholar 

  20. Xu JY, Wei XL, Ren C, Zhang Y, Hu YF, Li JY, Chen JL, Wang YQ, Han F, Wang FH. Association of plasma Epstein-Barr Virus DNA with outcomes for patients with recurrent or metastatic nasopharyngeal carcinoma receiving anti-programmed cell death 1 immunotherapy. JAMA Netw Open. 2022;5(3):e220587.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ma Y, Chen X, Wang A, Zhao H, Lin Q, Bao H, Zhang Y, Hong S, Tang W, Huang Y et al. Copy number loss in granzyme genes confers resistance to immune checkpoint inhibitor in nasopharyngeal carcinoma. J Immunother Cancer 2021, 9(3).

  22. Ma Y, Fang W, Zhang Y, Yang Y, Hong S, Zhao Y, Tendolkar A, Chen L, Xu D, Sheng J, et al. A phase I/II open-label study of Nivolumab in previously treated Advanced or recurrent nasopharyngeal carcinoma and other solid tumors. Oncologist. 2019;24(7):891–e431.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Gabriel S, Meyerson M, Lander ES, Getz G. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31(3):213–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Fang H, Bergmann EA, Arora K, Vacic V, Zody MC, Iossifov I, O’Rawe JA, Wu Y, Jimenez Barron LT, Rosenbaum J, et al. Indel variant analysis of short-read sequencing data with Scalpel. Nat Protoc. 2016;11(12):2529–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chou LS, Liu CS, Boese B, Zhang X, Mao R. DNA sequence capture and enrichment by microarray followed by next-generation sequencing for targeted resequencing: neurofibromatosis type 1 gene as a model. Clin Chem. 2010;56(1):62–72.

    Article  CAS  PubMed  Google Scholar 

  27. Liu P, Fang X, Feng Z, Guo YM, Peng RJ, Liu T, Huang Z, Feng Y, Sun X, Xiong Z, et al. Direct sequencing and characterization of a clinical isolate of Epstein-Barr virus from nasopharyngeal carcinoma tissue by using next-generation sequencing technology. J Virol. 2011;85(21):11291–9.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Kwok H, Wu CW, Palser AL, Kellam P, Sham PC, Kwong DL, Chiang AK. Genomic diversity of Epstein-Barr virus genomes isolated from primary nasopharyngeal carcinoma biopsy samples. J Virol. 2014;88(18):10662–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhou X, Yang Y, Ou X, Xu T, Shen C, Hu C. Interplay of Tumor Spread, volume and Epstein-Barr Virus DNA in nasopharyngeal carcinoma: feasibility of an integrative risk stratification Scheme. J Cancer. 2018;9(22):4271–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bureau M, Chatellier T, Perennec T, Goronflot T, Greilsamer C, Chene AL, Affi R, Frampas E, Bennouna J, Pons-Tostivint E. Baseline tumour size is an independent prognostic factor for overall survival in PD-L1 ≥ 50% non-small cell lung cancer patients treated with first-line pembrolizumab. Cancer Immunol Immunotherapy: CII. 2022;71(7):1747–56.

    Article  CAS  Google Scholar 

  31. Joseph RW, Elassaiss-Schaap J, Kefford R, Hwu WJ, Wolchok JD, Joshua AM, Ribas A, Hodi FS, Hamid O, Robert C, et al. Baseline tumor size is an independent prognostic factor for overall survival in patients with Melanoma Treated with Pembrolizumab. Clin cancer Research: Official J Am Association Cancer Res. 2018;24(20):4960–7.

    Article  CAS  Google Scholar 

  32. Litière S, Collette S, de Vries EG, Seymour L, Bogaerts J. RECIST - learning from the past to build the future. Nat Reviews Clin Oncol. 2017;14(3):187–92.

    Article  Google Scholar 

  33. Ahmed N, Abusalah M, Farzand A, Absar M, Yusof NY, Rabaan AA, AlSaihati H, Alshengeti A, Alwarthan S, Alsuwailem HS et al. Updates on Epstein-Barr Virus (EBV)-Associated Nasopharyngeal Carcinoma: emphasis on the latent Gene products of EBV. Med (Kaunas) 2022, 59(1).

  34. Martel-Renoir D, Grunewald V, Touitou R, Schwaab G, Joab I. Qualitative analysis of the expression of Epstein-Barr virus lytic genes in nasopharyngeal carcinoma biopsies. J Gen Virol. 1995;76(Pt 6):1401–8.

    Article  CAS  PubMed  Google Scholar 

  35. Shen Y, Zhang S, Sun R, Wu T, Qian J. Understanding the interplay between host immunity and Epstein-Barr virus in NPC patients. Emerg Microbes Infect. 2015;4(3):e20.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. McKenzie J, El-Guindy A. Epstein-Barr Virus Lytic Cycle Reactivation. Curr Top Microbiol Immunol. 2015;391:237–61.

    CAS  PubMed  Google Scholar 

  37. Valpione S, Galvani E, Tweedy J, Mundra PA, Banyard A, Middlehurst P, Barry J, Mills S, Salih Z, Weightman J, et al. Immune-awakening revealed by peripheral T cell dynamics after one cycle of immunotherapy. Nat Cancer. 2020;1(2):210–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

Not applicable.

Funding

This work was supported by National Nature Science Foundation of China (82073396 and 81872201 for H Zhao, 82002409 for Y Ma), Guangdong Basic and Applied Basic Research Foundation (2018A0303130243 for H Zhao, 2020A1515010020 for Y Ma).

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Author notes

  1. Shaoyan Lin, Huaqiang Zhou and Gang Chen contributed equally to this work.

Authors and Affiliations

  1. Department of Clinical Research, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, P. R. China

    Shaoyan Lin, Jinhui Xue, Qianwen Liu, Jianing Li, Yanhua Yang, Yuxiang Ma & Hongyun Zhao

  2. Department of Medical Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, P. R. China

    Huaqiang Zhou, Gang Chen, Yuanyuan Zhao & Yan Huang

  3. Geneseeq Research Institute, Nanjing Geneseeq Technology Inc, Nanjing, China

    Hua Bao

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  1. Shaoyan Lin
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Contributions

H Zhao, Y Ma and Y Huang designed and supervised the study. All authors collected the data. S Lin, H Zhou and G Chen analyzed the data and drafted the manuscript. All authors gave critical revision of the manuscript and approved the final manuscript.

Corresponding authors

Correspondence to Yan Huang, Yuxiang Ma or Hongyun Zhao.

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The study was approved by the ethics committee of Sun Yat-sen University Cancer Center and in accordance with international standards of good clinical practice. Informed consent was obtained from each patient.

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The authors declare no competing interests.

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Lin, S., Zhou, H., Chen, G. et al. Early change of plasma Epstein-Barr virus DNA load and the viral lytic genome level could positively predict clinical outcome in recurrent or metastatic nasopharyngeal carcinoma receiving anti-programmed cell death 1 monotherapy. BMC Cancer 24, 797 (2024). https://doi.org/10.1186/s12885-024-12564-4

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  • DOI: https://doi.org/10.1186/s12885-024-12564-4

Keywords

BMC Cancer

ISSN: 1471-2407

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