At least 20% of all cancers are attributable to viral, bacterial, or parasitic infections (de Martel et al., 2020). The advent of high-throughput deep sequencing has provided unprecedented opportunities to learn how infectious agents are involved in cancer in an unbiased manner. Several previous studies have searched for microbial nucleotide sequences in The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and PanCancer Analysis of Whole Genomes (PCAWG) datasets (Zapatka et al., 2020; Cantalupo et al., 2018). In addition to confirming known associations, such as the presence of human papillomaviruses (HPVs) in cervical cancer, these studies also uncovered rare cases in which viral sequences were unexpectedly found in other major cancers affecting the general population (Cantalupo et al., 2018).

Despite the immense amount of tumor sequencing data generated to date, the identification of microorganisms in common cancers through these studies has been limited. A more focused assessment of groups at increased risk for virus-associated cancers may be needed. In particular, oncogenic viruses may contribute to a larger fraction of cancer cases among immunosuppressed individuals, such as those with human immunodeficiency virus (HIV) infection and organ transplant recipients. These populations have been previously shown to be at increased risk for developing papillomavirus-mediated cancers, and oncogenic viruses, such as Kaposi’s sarcoma-associated herpesvirus and Merkel cell polyomavirus (MCPyV), were discovered in these patient populations (Chang et al., 1994; Feng et al., 2008; D’Arcy et al., 2021).

Roughly a dozen ‘high-risk’ HPV types cause nearly all cervical cancers, a large majority of other anogenital cancers, and about half of all oropharyngeal cancers (Graham, 2017). The carcinogenic effects of these small circular double-stranded DNA viruses are primarily dependent on the expression of the E6 and E7 oncogenes which, among a wide range of other functions, inactivate the tumor suppressor proteins p53 and Rb, respectively (Rosty et al., 2005; Crook et al., 1991; Mirabello et al., 2017; DeCaprio, 2014; Barbosa et al., 1990).

Polyomaviruses share many biological features with papillomaviruses. In particular, polyomavirus T antigens perform many of the same functions as papillomavirus oncoproteins and are similarly oncogenic in cellular and animal models (Moens and Macdonald, 2019). MCPyV has been identified as an etiological factor in a rare skin cancer, Merkel cell carcinoma (Feng et al., 2008; Shuda et al., 2008; Kassem et al., 2008). Another human polyomavirus, BKPyV, has a long-debated history as a candidate cancer-causing virus. Several case reports have described the detection of BKPyV in bladder cancers arising in transplant recipients, and kidney recipients who develop BKPyV viremia or BKPyV-induced nephropathy (BKVN) after transplant are at increased risk of bladder cancer (Gupta et al., 2018; Vajdic and van Leeuwen, 2009; Li et al., 2021; Papadimitriou et al., 2016).

Similar to HPV-induced cervical and oropharyngeal cancers, bladder cancers exhibit somatic point mutations that are largely attributable to the activity of APOBEC3 family cytosine deaminases (Robertson et al., 2017; Burns et al., 2013; Roberts et al., 2013). These enzymes normally function as innate immune defenses against viruses by deaminating cytosines in single-stranded DNA, leading to hypermutation of the viral genome (Poulain et al., 2020). Commonly, APOBEC3 enzymes, particularly APOBEC3A and APOBEC3B, can become dysregulated and cause carcinogenic damage to cellular DNA during the development of various types of cancer (Swanton et al., 2015). APOBEC3A and APOBEC3B are upregulated in response to the expression of HPV E6 and E7, and APOBEC3A can restrict HPV replication (Mori et al., 2017; Warren et al., 2017; Ahasan et al., 2015; Warren et al., 2015b; Vieira et al., 2014). The large T antigens (LTags) of BKPyV and JC polyomavirus (JCPyV, a close relative of BKPyV) also upregulate APOBEC3B expression and activity (Starrett et al., 2019; Peretti et al., 2018; Verhalen et al., 2016).

To characterize the mutational, transcriptomic, and viral landscapes of bladder cancers arising in immunosuppressed individuals, we evaluated archived tissues from 43 solid organ transplant recipients who developed this malignancy. We performed total RNA sequencing and whole genome sequencing (WGS) from these tissues. We utilized high-sensitivity methods and comprehensive reference databases of sequences for conserved viral proteins to identify known viral species and to search for divergent viruses. Once viruses were identified, we further evaluated the sequence data for integration events, point mutations, mutation signatures, and differentially expressed genes to identify differences correlating with the presence of these viruses and their integration state.

This report presents a comprehensive molecular assessment of 43 bladder cancers arising in solid organ transplant recipients by WGS and total RNA sequencing. DNA and/or RNA sequences of human BK or JC polyomaviruses were detected in 16 tumors (37%). Expression of the polyomavirus LTag was documented immunohistochemically in 10 cases. HPV sequences were detected in six cases, including four cases with HPV types known to cause cervical cancer. Overall, this is a much higher frequency of small DNA tumor virus sequence detection compared to prior surveys of bladder cancers affecting the general population, where fewer than 5% of tumors harbor small DNA tumor virus sequences (Cantalupo et al., 2018; Llewellyn et al., 2018). The results suggest that human polyomaviruses and papillomaviruses can play a carcinogenic role in the development of bladder cancer, particularly among transplant recipients.

BKPyV infection in organotypic urothelial cell culture has been shown to promote cellular proliferation (Schneidewind et al., 2020). This is most likely through the transforming effects of viral T antigens, as is supported here and in previous studies by the loss of late region transcripts and enrichment of early region transcription in primary tumors, and maintenance of these transcripts in metastatic lesions (Müller et al., 2018). Interestingly, we observed frequent clonal loss of the p53-inactivating helicase domain of BKPyV LTag due to deletions and point mutations in the integrated virus. While such deletions in LTag are commonly observed in MCPyV-positive Merkel cell carcinoma (Shuda et al., 2008), MCPyV LTag lacks the p53-inactivating activity of the C-terminal helicase domain of BKPyV. One might thus have expected the C-terminal portion of BKPyV LTag to be preserved in tumor cells. We speculate that the loss of the BKPyV helicase domain is driven by negative selection against deleterious effects of LTag on tumor survival (e.g. LTag might unwind the integrated BKPyV origin of replication and initiate ‘onion skin’ DNA structures leading to chromosomal instability and cell death). The absence of the p53-binding domain may be compensated for in some BKPyV-positive tumors through the significantly increased expression of the ubiquitin ligase TRIM71 that we observed. TRIM71 has been shown to bind and poly-ubiquitinate p53 for proteasomal degradation and prevent apoptosis during stem cell differentiation (Nguyen et al., 2017).

We also observed amplification of the host genome surrounding BKPyV integration sites, consistent with circular DNA intermediates and/or MMEJ break-induced replication. Similar findings have been reported for HPV and MCPyV-associated tumors (Starrett et al., 2020; Czech-Sioli et al., 2020). These amplification events result in a variable number of tandem head-to-tail copies of the virus and host genome that are thought to create super-enhancers affecting viral and host gene expression (Warburton et al., 2018; Dooley et al., 2016). In cervical cancer, frequently only one integration event is transcriptionally active; however, in tumors carrying integrated BKPyV sequences, the abundance of viral DNA and RNA are positively correlated, suggesting that each integrated copy produces viral transcripts. While the observed integration sites in this study are unique and have not been observed in Merkel cell carcinoma, HPV16 integration has been reported previously in BCAR3 (Jeannot et al., 2018). Elevated expression of BCAR3 has been shown to increase the proliferation, motility, and invasiveness of estrogen receptor-positive breast cancer cells after treatment with antiestrogens (Wallez et al., 2014; Wilson et al., 2013).

In five tumors harboring integrated BKPyV sequences (TBC02, TBC03, TBC05, TBC06, and TBC08), we observed significant upregulation of genes associated with cell cycle progression, DNA damage, histones, and the mitotic spindle. Tumors with evidence of BKPyV integration also exhibited significant downregulation of keratins and cell adhesion genes. The latter may contribute to the high-grade and invasive behavior of BKPyV-positive tumors observed in this study and others (Alexiev et al., 2013; Sirohi et al., 2018; Kenan et al., 2015; Kenan et al., 2017; Nickeleit et al., 2018).

Many of the observed gene expression changes are consistent with known effects of BKPyV infection and the specific activities of LTag, which binds Rb-family proteins and alters the active pool of E2F transcription factors in the cell (Caller et al., 2019; Harris et al., 1996). Recent studies have shown that APOBEC3B expression is repressed by the DREAM complex (which is composed of Rb-family proteins and E2F transcription factors Starrett et al., 2019) and, accordingly, we found that APOBEC3B is more highly expressed in BKPyV-positive tumors compared to normal tissues and tumors without tumor virus sequences, likely due to LTag activity. However, despite this increased expression, the mutation signature commonly attributed to APOBEC3B did not appear enriched in BKPyV-positive tumors. It is possible that tumors expressing BKPyV LTag and increased APOBEC3B manifested greater intratumor mutational heterogeneity, but we were unable to detect possible low-frequency APOBEC3-mediated variants from FFPE tissue without deeper and more accurate sequencing. Additionally, consistent with the disruption of the DREAM complex in these tumors, we observed higher expression of MYBL2, a key component of the MMB complex, and one of its targets, FOXM1, which regulates numerous genes required for G2/M progression. We also observed increased expression of FOXM1 downstream targets associated with the centromere and kinetochore, which have been shown to promote improper chromosome segregation and tumorigenesis (Fischer and Müller, 2017; Sadasivam et al., 2012; Schade et al., 2019).

BKPyV-positive tumors in our study had significantly higher expression of a number of genes that promote homologous recombination (e.g. RAD51, RAD54L, BRCA1, and BRCA2) and protect against replication fork stalling and collapse (e.g. RAD51, XRCC2, and FANCB) relative to virus-negative tumors (Tye et al., 2021). Claspin (CLSPN) and TIMELESS, which interact with replicative polymerases and helicases, are also highly expressed in BKPyV-positive tumors, further promoting replication fork progression and genome stability (Bianco et al., 2019). This expression pattern might promote cell survival in the face of genomic damage caused by viral genome integration, oncogene expression, and APOBEC3B upregulation.

While HPVs are not generally considered causative agents of bladder cancer, they have been detected in rare cases of bladder cancer affecting immunocompetent and immunosuppressed patients (Zapatka et al., 2020; Cantalupo et al., 2018; Guma et al., 2016). In the current study, we identified four tumors with carcinogenic Alphapapillomavirus sequences (HPV16 or HPV51). Alphapapillomaviruses are believed to cause cancer through the sustained expression of their E6 and E7 oncoproteins, which is frequently associated with the integration of the papillomavirus genome into the tumor genome.

One case in the panel carried sequences of HPV20, a Betapapillomavirus that can cause cutaneous squamous cell carcinoma in animal model systems (Michel et al., 2006). The possible involvement of Betapapillomaviruses in skin cancer in the general population remains controversial (Viarisio et al., 2018). In epidermodysplasia verruciformis, a rare syndrome caused by defects in zinc-binding proteins EVER1 and EVER2, patients frequently develop non-melanoma skin cancers containing Betapapillomaviruses (Dell’Oste et al., 2009). Expression of E6 and E7 from Betapapillomaviruses has been shown to promote cell survival in the face of ultraviolet radiation damage and other carcinogenic insults (Michel et al., 2006; Viarisio et al., 2018; Viarisio et al., 2016). In the context of bladder cancer, it is possible that cutaneous papillomaviruses likewise enable the accumulation of carcinogenic DNA damage. Additionally, the identification of HPV28, an Alphapapillomavirus that is not generally associated with cervical cancer, suggests more abundant papillomavirus infections of the bladder than previously assumed, with unknown implications for carcinogenesis.

An explanation for the observation that viruses are more prevalent in bladder cancers affecting solid organ transplant recipients compared to cases in the general population is that, in combination with immune suppression, transplant recipients may often become newly infected through transmission from the donor graft at the time of transplantation, perhaps with a different viral genotype than present in the host previously. This phenomenon is commonly observed in kidney transplantation and is associated with BKVN (Solis et al., 2018), but has not been documented for heart, lung, or liver transplant recipients, who are also included in the current study. Additionally, this study and one prior study (Querido et al., 2020) identified JCPyV in bladder tumors. Based on the high degree of similarity between JCPyV and BKPyV, it seems reasonable to expect that the two species would behave similarly. However, the low abundance of JCPyV RNA and DNA in these specimens and the absence of integration, together with the ubiquity of latent JCPyV infections in the urinary tract, raises the possibility that these observations reflect incidental detection events.

The data from this study and others suggest that in the context of strong immune the suppression BKPyV can cause bladder cancer through clonal integration but is rarely detected in tumors of the general population. While most adults are seropositive for BKPyV, with at least 10% having detectable BKPyV in the urine, BKPyV is only observed in upwards of 4% of NMIBC cases and less than 0.25% in muscle-invasive bladder cancers in the general population (Cantalupo et al., 2018; Llewellyn et al., 2018). This implies that, while BKPyV LTag can provide a growth advantage to cells in culture, the large multi-domain antigen may be relatively immunogenic compared to the much smaller oncoproteins encoded by high-risk HPVs or the highly truncated MCPyV LTag isoforms typically observed in Merkel cell carcinomas. Immunologic recognition of these tumors may also be impacted by the increased expression of APOBEC3B, which can generate immunogenic neoantigens (Serebrenik et al., 2019; Chen et al., 2019). Several reports of regression of patients’ BKPyV-positive tumors after reduction of immune suppression support the idea that tumors constitutively expressing BKPyV gene products are readily targeted and controlled by the immune system (Meier et al., 2021; Cuenca et al., 2020; Fu et al., 2018). The theoretical immunological costs of viral gene expression for a nascent tumor cell raise the possibility of ‘hit-and-run’ carcinogenesis. The hit-and-run hypothesis invokes the idea that a virus may play a causal role in the early stages of carcinogenesis but then become undetectable at more advanced stages of tumor development. Infection of a premalignant cell may promote its growth and survival through the expression of the viral oncogenes. Additionally, the expression of viral oncogenes may promote genome instability through the expression of the mutagenic APOBEC3 enzymes or other mechanisms that further push the cell towards transformation, as has been suggested by a recent study of BKPyV infection in differentiated urothelium (Baker et al., 2022).

The heterogeneous expression of LTag observed in this study could represent transcriptional silencing or loss of BKPyV DNA from one part of the tumor, supporting the idea that tumors can lose the need for LTag expression. Alternatively, our observations could be accounted for by a multistage integration and carcinogenesis process proposed by other recent studies on BKPyV-positive urinary tumors from kidney transplant recipients (Jin et al., 2021; Wang et al., 2020). However, our sequencing experiments support a dominant clonally integrated form likely established early during tumor development in most BKPyV-positive tumors in this study. The only exception to this observation is TBC01, which appears to exhibit a viable BKPyV episome with a rearranged regulatory region present in a small subset of tumor cells. This tumor likely represents a passenger infection of an existing tumor (Dalianis and Hirsch, 2013). Future studies should investigate the hypothesis that passenger infections might play an oncomodulatory role in tumor development. This also suggests that archetypal BKPyV, rather than the more pathogenic rearranged strains found in cases of nephropathy, is more likely to integrate and be preserved into nascent tumor cells. In support of the idea that integration may be a more common aspect of BKPyV infection than previously assumed, we identified a clonal BKPyV integrant in the normal bladder specimen from case TBC09 in both the RNA and WGS sequencing that was distinct from the BKPyV integrant observed in the tumor sample. The normal tissue integrant had multiple copies of small T antigen and a large deletion in the regulatory regions (Figure 1—figure supplement 1). Only a few reads from RNA sequencing mapped to the small T antigen region, and histology of the section indicates no tumor cells or LTag staining, suggesting that the virus did not integrate into the right genomic location or maintain the needed components to drive carcinogenesis.

It remains to be seen whether TTVs contribute to disease in the context of immune suppression. A general model is that these ubiquitous viruses establish a chronic infection that the immune system generally keeps in check, but immune suppression results in increases not only in the abundance but also in the diversity of TTVs observed in hosts (De Vlaminck et al., 2013). Indeed, the detection of TTVs can serve as an indicator of the degree of overall immune suppression in transplant recipients (Blatter et al., 2018). Interestingly, these viruses, like papillomaviruses and polyomaviruses, also appear to be depleted for APOBEC3 target motifs, consistent with the effects of an evolutionary virus-host arms race (Poulain et al., 2020; Verhalen et al., 2016; Warren et al., 2015a).

Until recently, this type of molecular assessment from FFPE tissues would have been nearly impossible or badly muddled by the highly damaging effects of formalin fixation and oxidation of nucleic acids over time. Recent advancements in the isolation of nucleic acids, such as low temperature and organic solvent-free deparaffinization, combined with efficient library preparation from low-concentration highly degraded sources, yielded sufficiently high-quality material for WGS variant calling and total RNA sequencing (Robbe et al., 2018). To address the difficulty of accurately calling somatic variants (which can be problematic even from flash-frozen or fresh tissues), we called variants using the consensus of three modern variant callers. The lack of matched normal tissues for most cases is a limitation of this work, but our analytical approach accounted for this by focusing the analysis on mutations with >10% allele frequency and those with potential functional effects, and by excluding known germline variants. Our methods were validated internally through the sequencing of separate regions from the same tumor and of primary-metastatic pairs, which reveal similar concordance of mutations as has been reported from flash-frozen tissues (Zhang et al., 2014). Our variant calling approach was also validated by the observation that we detected four deconvoluted mutation signatures that match those expected from prior surveys of bladder cancer. However, the low overall coverage of our WGS remains a limitation of this study.

We identified four bladder cancers in kidney transplant recipients that exhibited abundant mutations attributable to aristolochic acid-mediated DNA adducts. Aristolochic acid is a highly nephrotoxic and mutagenic compound produced by birthwort plants, which sometimes contaminates certain types of herbal medicines and grains (Poon et al., 2015). Exposure to this compound likely contributed to the patients’ need for kidney transplantation, as well as their eventual development of bladder cancer. Highlighting the highly mutagenic nature of this compound, the four cases with dominant aristolochic acid signatures were in the top seven for total mutation burden (Figure 6). None of the three tumors had detectable oncogenic viral sequences, but one had detectable TTV. We also identified likely ganciclovir-mediated mutations (de Kanter et al., 2021) in most patients indicating that this common treatment to prevent reactivation of cytomegalovirus in solid organ transplant recipients may promote mutagenesis in the urinary bladder. Unfortunately, ganciclovir treatment history was unavailable to confirm that this is the origin of this mutation signature in these cases. Ever-decreasing sequencing costs will facilitate additional studies of this type and shed light on rare and understudied tumor types, as well as analyses of lower-grade and pre-cancerous lesions.

All refseqs for human papillomaviruses were downloaded from PaVE and refseqs for human polyomaviruses were downloaded from NCBI as of November 2018. All sequencing data generated in this study are available from dbGaP under accession phs003012.v1.p1. Viral contigs from this study are deposited in GenBank under accessions OQ469311 and OQ469312. All other contigs and larger IHC images are deposited in figshare (https://figshare.com/projects/Common_Mechanisms_of_Virus-Mediated_Oncogenesis_in_Bladder_Cancers_Arising_in_Solid_Organ_Transplant_Recipients/132833). Code used in this manuscript are available from https://github.com/gstarrett/oncovirus_tools (Starrett, 2020).

1. 1. Starrett GJ

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   ID OQ469312. Anellovirus sp. isolate TBC35T_k141_22964 ORF1 (ORF1) gene, partial cds; and ORF2 (ORF2) gene, complete cds.

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Decision letter after peer review:

Thank you for submitting your article "Evidence for Virus-Mediated Oncogenesis in Bladder Cancers Arising in Solid Organ Transplant Recipients" for consideration by eLife. Your article has been reviewed by 2 peer reviewers, including Nicholas Wallace as Reviewing Editor and Reviewer #2, and the evaluation has been overseen by Wafik El-Deiry as the Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

(1) Despite showing strong evidence that viruses are altering the tumor cell environment, it is unclear if these changes are necessary for tumorigenesis. The heterogeneity of the LT expression suggests that the presence of the viral DNA and RNA may not be enough to assess whether it is actively contributing to the tumor. Is an increased frequency of viral protein staining linked with any evidence of an active contribution to tumorigenesis (fewer tumor-suppressor/oncogene mutations). that they reduced mutations in tumor suppressors. This might be easiest to assess with the tumors that have oncogenic HPV DNA. (If those tumors lacked p53 and RB mutations, it would support a causative role for the virus.) For BKPyV+ cancers, is the virus playing a role as a driver, hit and run, or by-stander as described in Dalianis et al., 2013 (https://pubmed.ncbi.nlm.nih.gov/23357733/).

(2) The data presented here and previously published data (https://doi.org/10.1002/path.5012) hint at Chromosome 1 as a potential "hot spot" for viral integration. Alternatively, this could be due to the larger relative size of the chromosome or some other feature of the chromosome. This should be discussed.

(3) There are multiple places where the organization and presentation of the data needs to be improved. These are noted in individual reviewer comments provided below.

Reviewer #1 (Recommendations for the authors):

In general, there seem to be multiple mistakes in terms of sample numbers, figure references and figure legends, which complicated the review extensively. In addition, in many of the shown data, it was not clear why only specific samples have been chosen or why some samples have just been left out or have been "forgotten". Moreover, for some of the statements in the Results sections, literature references are missing. Abbreviations should always be explained and be consistent throughout the manuscript and according to current state of the art (DeCaprio, J. A., et al. (2020). Fields Virolgy: DNA Viruses). All of these points need to be properly addressed before publishing the manuscript.

Title:

The title should be adapted as data does not support "viral-mediated oncogenesis".

Related to Table 1:

The table should be adapted for a better overview with characteristics and statistics being clearly labeled (see below). Suggest structuring it as follows: age in years at diagnosis, years from transplant to diagnosis, race, sex, transplanted organ, grade, summary stage (also following text).

Characteristic Statistic

Median IQR

Age in years at diagnosis

N %

Race

In the category "race" seems 1 patient missing as the sum only results in 42 cases. Please adapt the table and text.

Page 7 line 3-6:

It is not clear why not 43 primary tumors have been analyzed (corresponding to Table 1 and Figure 1)? Why are numbers between WGS and total RNAseq different? From Figure 1 it seems that multiple samples have been used from one patient, is this correct? If yes, this is not clear in the results description, please adapt. What does normal tissue mean = healthy?

Page 7 line 18 and Figure S1:

Please reference Figure S1 in the text. Please show coverage plots of TBC07 and separate runs of TBC03 (as for TBC09).

Figure 1 and Materials and methods:

What does "no data" mean? Was there no material available or was the quantity/quality of DNA not enough? Please clarify this in the text and Materials and methods.

"We obtained twenty 10-micron sections from formal-fixed paraffin-embedded (FFPE) blocks for cases with available material." How does that fit with Table 1 talking about 43 primary tumors?

How do the authors explain that for some tumors DNA signatures but no latency dependent transcripts could be detected? Please integrate in the text.

Material and methods:

How long were the DNA and RNA samples stored at 4{degree sign}C? Prolonged storage at 4{degree sign}C, especially for RNA samples, could contribute to low sample quality. Please adapt in the material and methods.

Which polyomavirus LTags are recognized by the used antibody? Please mention.

Related to Table 2:

Please sort Table 2 according to the most frequent chromosome location so overrepresentation of e.g., chromosome 1 is obvious. Might this integration sites be a contamination or a real hot-spot? Please discuss.

All abbreviations used in the table should be explained.

Page 9 line 10:

How was tumor purity immunohistochemically assessed? Should be explained in the Materials and methods section.

Figure 4B:

How were the stabilized counts calculated? This should be described in the Materials and methods.

Was TBC01 included in the BKPyV positive tumors? There should be only 9 BKPyV-positive tumors rather than 10. The authors should show one graph with exclusion of TBC01.

Figure S2:

Wrong figure reference and figure legend. It is very hard to interpret the data if figure references and legends are not correctly cited. Authors should carefully review the whole manuscript.

Page 11 lines 9-15:

Is there any figure associated to the statement? It is unclear what this result means.

Figure 5:

TBC02 seems to be missing in the BKPyV positive tumors. All BKPyV-negative tumors and all 43 primary tumors should be displayed.

Page 12 lines 6-12 and Figure S3:

Paragraph should refer to Figure S3 and not S4. "A possible HPV51 integration site…": there is no data showing that.

Figure 6A:

What are the black bars representing? Why is TBC36 now BKPyV positive? Why is Figure 1 showing EBV but Figure 6 not? Why is Figure 6 showing TTV but Figure 1 not? Please explain and adapt.

Figure 6B:

Please show this analysis also for HPV-positive tumors.

Figure 6E:

It is suggested that the results are sorted according to the percentages and to cluster all BKPyV-positive tumors together.

Page 16 lines 1-22:

Paragraph about APOBUCK3B seems to be overrepresented in the discussion and consistency in statements failed. Some information is non-informative or even highly speculative. This paragraph should be extensively revised.

Essential revisions:

(1) Despite showing strong evidence that viruses are altering the tumor cell environment, it is unclear if these changes are necessary for tumorigenesis. The heterogeneity of the LT expression suggests that the presence of the viral DNA and RNA may not be enough to assess whether it is actively contributing to the tumor. Is an increased frequency of viral protein staining linked with any evidence of an active contribution to tumorigenesis (fewer tumor-suppressor/oncogene mutations). that they reduced mutations in tumor suppressors. This might be easiest to assess with the tumors that have oncogenic HPV DNA. (If those tumors lacked p53 and RB mutations, it would support a causative role for the virus.) For BKPyV+ cancers, is the virus playing a role as a driver, hit and run, or by-stander as described in Dalianis et al., 2013 (https://pubmed.ncbi.nlm.nih.gov/23357733/).

We thank the reviewers for their thoughtful review. Viral oncoprotein heterogeneity has previously been reported for HPV and MCPyV and can be affected by both methodology and biology, for example tumor hypoxia has been shown to affect E6 and E7 expression. Our evidence for clonal integration in primary tumors and a metastasis and recurrent helicase-inactivating mutations support selection in tumorigenesis. As suggested, in Figure 6 we indeed show that no BKPyV-positive or HPV-positive tumor harbored mutations in RB1. Additionally, only one BKPyV-positive tumor and none of the HPV-positive tumors had a mutation in TP53. We have added further emphasis to this point on page 14,

“None of the HPV-positive tumors with WGS harbored mutations in TP53 or RB1. Similarly, none of the polyomavirus-positive tumors harbored mutations in RB1 and only TBC08 had a frameshift mutation in TP53.”

We have also added speculation about the unique aspects of TBC01:

“This tumor likely represents a passenger infection of an existing tumor. Future studies should investigate the hypothesis that passenger infections might play an oncomodulatory role in tumor development.”

(2) The data presented here and previously published data (https://doi.org/10.1002/path.5012) hint at Chromosome 1 as a potential "hot spot" for viral integration. Alternatively, this could be due to the larger relative size of the chromosome or some other feature of the chromosome. This should be discussed.

In the current study, the events on chromosome 1 do not represent a hotspot since all of the integration events are from one patient and within 1000 bp of each other. These events instead appear to represent a single integration event with complex rearrangements. Two observations of integration on this large chromosome is not suggestive of a hotspot.

(3) There are multiple places where the organization and presentation of the data needs to be improved. These are noted in individual reviewer comments provided below.

We have addressed this and corrected the supplemental table order, which was due to a previous version being uploaded with the submission. Please see the detailed responses to individual comments.

Reviewer #1 (Recommendations for the authors):

In general, there seem to be multiple mistakes in terms of sample numbers, figure references and figure legends, which complicated the review extensively. In addition, in many of the shown data, it was not clear why only specific samples have been chosen or why some samples have just been left out or have been "forgotten". Moreover, for some of the statements in the Results sections, literature references are missing. Abbreviations should always be explained and be consistent throughout the manuscript and according to current state of the art (DeCaprio, J. A., et al. (2020). Fields Virolgy: DNA Viruses). All of these points need to be properly addressed before publishing the manuscript.

We apologize for this error and thank Reviewer #1 for pointing it out. It appears that a previous version of the supplemental material was uploaded with this manuscript, which led to most supplemental materials being off by one. We have checked the text regarding the issues raised by the reviewer, and we added references and defined abbreviations where we thought these changes were indicated.

Title:

The title should be adapted as data does not support "viral-mediated oncogenesis".

We agree with Reviewer #1’s view that the data can be seen as falling short of conclusive proof of virus-mediated oncogenesis. But the finding of clonally integrated viruses in tumors and their metastases, the detection of distinctive transcriptomic signatures, and the absence of mutations in TP53 and RB1 – are accepted traditional lines of evidence for virus-mediated oncogenesis (for more detail, see: https://pubmed.ncbi.nlm.nih.gov/31336246/). We believe that starting the title with “Evidence for…” provides readers with an accurate description of the type of data that the manuscript is reporting without indicating that we are providing definitive proof.

Related to Table 1:

The table should be adapted for a better overview with characteristics and statistics being clearly labeled (see below). Suggest structuring it as follows: age in years at diagnosis, years from transplant to diagnosis, race, sex, transplanted organ, grade, summary stage (also following text).

Characteristic Statistic

Median IQR

Age in years at diagnosis

N %

Race

We have reordered the table to bundle the common patient statistics (i.e. N % and Median IQR, respectively) together and clearly labeled the columns above the statistics.

In the category "race" seems 1 patient missing as the sum only results in 42 cases. Please adapt the table and text.

This has been corrected.

Page 7 line 3-6:

It is not clear why not 43 primary tumors have been analyzed (corresponding to Table 1 and Figure 1)? Why are numbers between WGS and total RNAseq different? From Figure 1 it seems that multiple samples have been used from one patient, is this correct? If yes, this is not clear in the results description, please adapt. What does normal tissue mean = healthy?

As described in the Materials and methods on page 22, lines 24-25 and page 24, lines 9-10 samples that didn’t yield enough nucleic acids for library preparation were not sequenced. This should hopefully be clearer with the addition of the suggested Supplemental table 1 (supplementary file 1a) checkerboard. Please see Results page 7, line 18-19, and Figure 2 legend. We have added, on page 7 line 4, an additional definition of normal as “histologically non-malignant.” The legends for Figures 2 and 6 also explain that for samples TBC03, TBC09, and TBC28 two separate sections of the tumor were sequenced.

Page 7 line 18 and Figure S1:

Please reference Figure S1 in the text. Please show coverage plots of TBC07 and separate runs of TBC03 (as for TBC09).

This has been added.

Figure 1 and Materials and methods:

What does "no data" mean? Was there no material available or was the quantity/quality of DNA not enough? Please clarify this in the text and Materials and methods.

See the response to comment 4 and changes on Page 7 lines 3-6.

"We obtained twenty 10-micron sections from formal-fixed paraffin-embedded (FFPE) blocks for cases with available material." How does that fit with Table 1 talking about 43 primary tumors?

This text is describing the amount of sample collected per case. This has been revised to read:

“We obtained twenty 10-micron sections from formal-fixed paraffin-embedded (FFPE) blocks for each specimen with available material.”

How do the authors explain that for some tumors DNA signatures but no latency dependent transcripts could be detected? Please integrate in the text.

We are uncertain about what the reviewer means by “DNA signatures” and “latency-dependent transcripts.” If we infer that some tumors have mutational signatures associated with viruses but do not have viral transcripts, our sense – as stated in the Discussion – is that this might represent transient suppression of viral transcription or it could reflect hypothetical hit-and-run effects.

Material and methods:

How long were the DNA and RNA samples stored at 4{degree sign}C? Prolonged storage at 4{degree sign}C, especially for RNA samples, could contribute to low sample quality. Please adapt in the material and methods.

Which polyomavirus LTags are recognized by the used antibody? Please mention.

The manuscript has been revised to note that all samples were processed within one month of receipt into our laboratory. Of note, with FFPE source material and this extraction method, RNAs are already highly fragmented immediately after extraction. We have observed that fragmentation does not significantly increase with storage time, as RNA integrity numbers are consistently between 2 to 4 immediately after RNA extraction. Additionally, the Takara total RNAseq kit used here takes advantage of the fragmented nature of the input RNA by not including a fragmentation step for FFPE RN. Subsequent library size selection removes very small fragments.

For the PAb416 antibody used for IHC, we have added the following text to the methods:

“clone Pab416 (Σ Millipore, cat. DP02), which detects LTag from multiple polyomaviruses, including SV40, BKPyV, JCPyV, WU, KI, 6, 7, 10, and 11 (84).”

Related to Table 2:

Please sort Table 2 according to the most frequent chromosome location so overrepresentation of e.g., chromosome 1 is obvious. Might this integration sites be a contamination or a real hot-spot? Please discuss.

All abbreviations used in the table should be explained.

We appreciate the comment, but we feel that sorting by chromosome will make the table difficult to interpret. Currently, it is sorted by patient ID, which we hope will make it easier for readers to recognize that all of the chromosome 1 integration junctions are from the same patient and within about 1000 basepairs of each other. We do not infer that chromosome 1 is a common hotspot; rather, within that patient there is a complex rearrangement that occurred on chromosome 1. Abbreviations have been added.

Page 9 line 10:

How was tumor purity immunohistochemically assessed? Should be explained in the Materials and methods section.

Tumor purity was determined by H and E staining and review by our pathologist. A description has been added to Materials and methods, page 22 line 26 and page 10, line 14.

Figure 4B:

How were the stabilized counts calculated? This should be described in the Materials and methods.

Our method for stabilizing the counts is described in Figure 4 legend: “Variance stabilized counts for APOBEC3B from DESeq2.” and in Material and Methods page 26, line 20.

Was TBC01 included in the BKPyV positive tumors? There should be only 9 BKPyV-positive tumors rather than 10. The authors should show one graph with exclusion of TBC01.

This plot also contained the metastatic tumor of TBC06. We have now removed the TBC06 metastatic tumor and highlighted TBC01 in this plot so readers can discern its difference versus the other BKPyV-positive tumors.

Figure S2:

Wrong figure reference and figure legend. It is very hard to interpret the data if figure references and legends are not correctly cited. Authors should carefully review the whole manuscript.

We thank Reviewer #1 for catching our inadvertent uploading of the wrong Supplementary files during the submission process. The correct supplements have been uploaded for the revision.

Page 11 lines 9-15:

Is there any figure associated to the statement? It is unclear what this result means.

We’ve added Figure 4-supplement 2 to support this statement. Virus integration commonly increases the expression of adjacent host genes, which can be an important selective pressure for a particular integration event. However, our one case with observed increased RNA coverage seems to originate from an amplification of a relatively small section host DNA rather than increased expression of the entire gene.

Figure 5:

TBC02 seems to be missing in the BKPyV positive tumors. All BKPyV-negative tumors and all 43 primary tumors should be displayed.

TBC02 has been added to panel B. All additional primary tumors with WGS have been added as panel E.

Page 12 lines 6-12 and Figure S3:

Paragraph should refer to Figure S3 and not S4. "A possible HPV51 integration site…": there is no data showing that.

We apologize for the misordered supplement file. We have now added a panel showing evidence for HPV51 integration to Figure S5.

Figure 6A:

What are the black bars representing? Why is TBC36 now BKPyV positive? Why is Figure 1 showing EBV but Figure 6 not? Why is Figure 6 showing TTV but Figure 1 not? Please explain and adapt.

Black bars indicate that there is no determined etiologic agent or virus, which has now been added to the figure legend. Some of the bars were incorrectly colored in 6A and have been corrected. The calls and colors in 6C and 6E are correct. TTV was detected through a different method to look for divergent sequences, so it cannot readily be shown in Figure 1; this is explained in the results and methods. The very low depth and percent coverage of the EBV reads detected in TBC07 and TBC03 tumors and absence of RNA reads suggest that these are likely incidental infections; therefore, we did not classify these tumors as EBV positive in Figure 6.

Figure 6B:

Please show this analysis also for HPV-positive tumors.

This analysis was conducted on all tumors for which we were able to generate WGS data, including the HPV-positive tumors.

Figure 6E:

It is suggested that the results are sorted according to the percentages and to cluster all BKPyV-positive tumors together.

The results are sorted by the calculated selection enrichment by dNdScv R package (more details have been added to the methods on page 27), which takes into account total synonymous, missense, indel, and nonsynonymous mutations in a sample and gene sizes. This is an important calculation because simply analyzing mutations by frequency almost always results in the identification of very large genes (TTN is a key example) rather than finding loci with evidence for positive selection.

Page 16 lines 1-22:

Paragraph about APOBUCK3B seems to be overrepresented in the discussion and consistency in statements failed. Some information is non-informative or even highly speculative. This paragraph should be extensively revised.

In our view, an important purpose of scientific Discussion sections is to present plausible ideas that might help readers generate experimentally testable hypotheses and start building on the results. APOBEC3B has been shown to be upregulated by BKPyV infection and specifically by the activity of the suppression of the DREAM complex by LTag. APOBEC3-associated mutations are the second most abundant mutagenic process in all cancers and also the dominant mutation signature in bladder cancers arising in the general population. Furthermore, APOBEC3 mutagenesis has shaped the evolution of the BKPyV genome. It is of considerable interest to the field whether BKPyV infection contributes to APOBEC3 mutagenesis of the host genome in infected cells (see PMID 35194151). We see this as an important future direction for the field. For these reasons, we believe it is appropriate to provide a thorough discussion on APOBEC3B and have therefore not shortened the text.

Author details

1. Gabriel J Starrett

   CCR, NCI, NIH, Bethesda, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   gabe.starrett@nih.gov

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5871-5306

2. Kelly Yu

   DCEG, NCI, NIH, Rockville, United States

   Contribution

   Data curation, Validation, Project administration, Writing – review and editing

   Competing interests

   No competing interests declared

3. Yelena Golubeva

   Leidos Biomedical Research Inc, Frederick, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   is affiliated with Leidos Biomedical Research Inc. The author has no financial interests to declare

4. Petra Lenz

   Leidos Biomedical Research Inc, Frederick, United States

   Contribution

   Formal analysis, Validation, Writing – review and editing

   Competing interests

   is affiliated with Leidos Biomedical Research Inc. The author has no financial interests to declare

5. Mary L Piaskowski

   CCR, NCI, NIH, Bethesda, United States

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8453-6416

6. David Petersen

   CCR, NCI, NIH, Bethesda, United States

   Contribution

   Validation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Michael Dean

   DCEG, NCI, NIH, Rockville, United States

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

8. Ajay Israni

   Department of Medicine, Nephrology Division, Hennepin Healthcare System, University of Minnesota, Minneapolis, United States

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

9. Brenda Y Hernandez

   Cancer Center, University of Hawaii, Honolulu, United States

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

10. Thomas C Tucker

    The Kentucky Cancer Registry, University of Kentucky, Lexington, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

11. Iona Cheng

    Department of Epidemiology and Biostatistics,and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, Fremont, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

12. Lou Gonsalves

    Connecticut Tumor Registry, Connecticut Department of Public Health, Hartford, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

13. Cyllene R Morris

    California Cancer Reporting and Epidemiologic Surveillance Program, University of California, Davis, Davis, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

14. Shehnaz K Hussain

    Cedars-Sinai Cancer and Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

15. Charles F Lynch

    The Iowa Cancer Registry, University of Iowa, Iowa City, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

16. Reuben S Harris

    Howard Hughes Medical Institute, University of Minnesota, Minneapolis, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    is a co-founder, shareholder, and consultant of ApoGen Biotechnologies Inc

    "This ORCID iD identifies the author of this article:" 0000-0002-9034-9112

17. Ludmila Prokunina-Olsson

    DCEG, NCI, NIH, Rockville, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9622-2091

18. Paul S Meltzer

    CCR, NCI, NIH, Bethesda, United States

    Contribution

    Resources, Writing – review and editing

    Competing interests

    No competing interests declared

19. Christopher B Buck

    CCR, NCI, NIH, Bethesda, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

    Contributed equally with

    Eric A Engels

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3165-8094

20. Eric A Engels

    DCEG, NCI, NIH, Rockville, United States

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

    Contributed equally with

    Christopher B Buck

    Competing interests

    No competing interests declared

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