Infectious causes of cancer and their detection
© BioMed Central Ltd 2009
Published: 11 August 2009
Molecular techniques for identifying pathogens associated with cancer continue to be developed, including one reported recently in BMC Medical Genomics. Identifying a causal infectious agent helps in understanding the biology of these cancers and can lead ultimately to the development of antimicrobial drugs and vaccines for their treatment and prevention.
In 1911, Peyton Rous used cell-free filtered extract of a chicken sarcoma to establish an association between cancer and an infectious agent – the Rous sarcoma virus. Almost 100 years later, developments in the techniques used to detect microbial genomes and investigate their biological properties have led to a definitive role for viral, bacterial and parasitic infection in human carcinogenesis. Recent estimates indicate that the average proportion of malignancies worldwide that could be avoided in the absence of an infectious agent is 17.8%, with this figure being higher in developing countries, at 26.3% .
Indeed, several of the key mediators of pathways and networks proposed by Hanahan and Weinberg were discovered through the study of viruses. Many oncogenes, for example Ras and Myc, were identified originally in cancer-causing retroviruses. Similarly, the study of DNA virus proteins, such as SV40 large T-antigen, was instrumental in discovering tumor suppressor genes such as p53. Understanding the biology of oncogenic infections will most likely continue to inform cancer biology in general. One challenge is that of identifying potentially oncogenic agents and proving their causal connection with cancer. A recent paper in BMC Medical Genomics by Duncan et al.  describes the new computational technique of digital karyotyping microbe identification (DK-MICROBE), and its application to identifying pathogens. Their paper further exemplifies both the potential of molecular and bioinformatics methods for identifying pathogen DNA in tumor samples, but underlines the necessity of establishing a causal association rather than just a pathogen presence.
How infectious agents cause cancer
Second, oncogenesis can occur through virus-induced transformation. This is due to the persistence of the viral genome in a latent form in an infected cell, either without replication, as with Epstein-Barr virus (EBV), which infects B lymphocytes, or through integration of the viral genome into a host-cell chromosome, as with human papillomavirus (HPV), the cause of cervical cancer. EBV is frequently detected in childhood Burkitt's lymphoma, post-transplant B-cell lymphomas, non-Hodgkin's lymphoma, Hodgkin's disease and nasopharyngeal carcinoma . The transforming capability of this virus is exemplified further by its ability to transform resting B cells in vitro at high efficiency to obtain stable proliferating lymphoblastoid cell lines. This process is driven by EBV-encoded latent proteins that directly promote cell growth and survival – for example, lymphocyte membrane-associated protein-1 (LMP-1) .
The third mechanism is the chronic suppression of the immune system by the infectious agent, such as the immunodeficiency (AIDS) caused by HIV infection. The presence of natural mechanisms of immunosurveillance for cancer cells, which in the case of an infectious etiology will also involve immune mechanisms that routinely control the infection, suggests why pathogens with oncogenic potential do not rapidly cause malignancy. A compromised immune system can result in an increased incidence of infection-driven tumors by weakening the immune control. Such an increase is seen, for example, in transplant patients, who are being treated with immunosuppressants, or in individuals with AIDS .
Pathogens associated with cancer exemplify many of these mechanisms; persistent infection involves evading the immune response as well as chronic inflammation, which even in the immune-competent leads to chronic cell proliferation and a greater risk of oncogenic transformation. However, many non-oncogenic pathogens are equally adept at these processes, indicating that other factors must be involved. For example, the risk of an infectious agent causing cancer may also depend on the cell type infected, as certain cell lineages could be more 'prone' to transformation than others. For example, the increased prevalence of lympho mas and leukemias in children and young adults suggests that lymphocytes are more susceptible to transformation.
Detecting infectious agents in cancer
'The harder you look the more you find' seems to be true for infectious agents in disease. The ability of some agents to remain latent, as well as the existence of new and emerging infections , make detection and proving causality challenging. An infectious agent may trigger the initial events of oncogenesis but be absent in the final tumor, which adds timing of detection to the problem. However, once the causal agent has been unequivocally found, development of a preventive treatment can be relatively rapid, as has been the case for cervical cancer. Human papilloma viruses, especially types HPV16 and HPV18, are now firmly associated with cervical cancer after their discovery in 1983, and this has led to the development and widespread use of an HPV vaccine within 25 years – a timescale not dissimilar to that of drug discovery. Similarly, recovery of immune function in AIDS by inhibiting HIV replication by antiretroviral therapy can lead to a regression of Kaposi's sarcoma, an endothelial tumor caused by the herpesvirus KSHV, and a decrease in incidence of other AIDS-related cancers.
Treating infection is therefore a valuable addition to antitumor therapy if the infectious agent can be identified. Today, two main techniques are used to detect microbial genomes in disease, one based on hunting for acknowledged candidates and the other on removing (either physically or computationally) known human sequences to reveal any foreign nucleic acid. PCR and microarray-based strategies are limited by a finite number of probes and sequences available but can be very sensitive. Subtractive methods include representational difference analysis (RDA), which was used to detect KSHV in Kaposi's sarcoma in 1994. They do, however, require isogenic controls, which are not always readily available.
One new approach to finding pathogen genomes is that of Duncan et al. , which applies computational subtraction to digital karyotyping to hunt for virus genomes in several primary colorectal cancer samples and metastases as well as in normal tissue. The technique of DK-MICROBE described by Duncan et al.  aims to circumvent limitations on detection imposed by the different mechanisms by which pathogens contribute to disease. In DK-MICROBE, genomic DNA from the tumor is digested enzymatically into fragments of less than 10 kb in size that are processed to yield 21-bp tags for amplification, concate nation and sequencing. Human sequences are compu tationally removed; the remaining unidentified 'pathogen' tags are then studied further. However, DK-MICROBE in its present form can only detect the genomes of DNA viruses. Duncan et al.  were able to detect the human herpesvirus 6 (HHV6) genome in samples from tumor tissue, but the fact that they also identified the viral DNA in healthy tissues well illustrates the difficulties of causally associating a particular virus with a particular cancer.
Another subtractive method known as digital transcript subtraction (DTS) attempts to identify exogenous pathogen transcripts via high-throughput sequencing, and can thus potentially identify the presence of RNA and DNA viruses . This method involves developing a long serial analysis of gene expression (L-SAGE) library from the tumor cells by quantitatively joining 21-bp tags composed of cDNA copied from the 3' end of mRNAs. It can therefore detect all transcripts that are expressed in the tumor. As all human tumor viruses to date express part of their genome in the transformed cells, this has proved effective in virus discovery. The pioneers of this technique, Yuan Chang and Patrick Moore, have validated DTS by identifying sequences from KSHV in the primary effusion lymphoma cell line BCBL-1 (Feng et al. ). More recently, DTS has been used to identify a new polyomavirus in an uncommon but aggressive human skin cancer, Merkel cell carcinoma (MCC) . A fusion transcript between an unknown virus T-antigen and a human receptor tyrosine kinase was detected. The new virus was named Merkel cell polyoma-virus (MCV) and was detected in 80% of MCC tumors and also in 16% of normal skin biopsies. In 75% of the MCV-related MCCs viral DNA was integrated in a clonal pattern, suggesting a potential mechanism for transformation. MCC occurs predominantly in the elderly and immuno-suppressed, two of the key features that indicate an infectious etiological agent.
Where to find associations?
The question of where to look for other cancer-causing infectious agents is partly answered by the example of MCC. Cancers with increased incidence in HIV-infected individuals  or in transplant recipients are an ideal place to look. One example is squamous cell conjunctival carcinoma (SCCC), which has emerged with the AIDS epidemic and is common in parts of sub-Saharan Africa. Papillomaviruses have been implicated, but the evidence from PCR-based studies and serology testing is controversial. Feng et al.  attempted to identify viral transcripts using DTS, finding 21 candidate sequences that did not align with the human genome. However, further analysis revealed that they were all most likely of human origin . This does not rule out an infectious aetiology for SCCC, however. DTS is a powerful tool, but it is important to realize that its sensitivity is governed by the depth of sequencing, that is, the number of sequence reads analysed by DTS or DK MICROBE, which may have to be increased in tumor samples, in which the ratio of viral mRNA to human mRNA is low. Nevertheless, the combination of carefully selected tumors with deep sequencing and computational identification of non-human sequences is set to uncover more tumor viruses.
Molecular guidelines for establishing microbial causation of disease
Putative pathogen genome is present in most cases of disease
Microbial nucleic acid should be found preferentially in diseased sites in combination with anatomic, histologic, chemical or clinical evidence of pathology and not in areas lacking the pathology
Only diseased tissue should harbor putative pathogen genome
Fewer, or no, copy numbers of pathogen-associated nucleic acid sequences should occur in non-diseased host or tissue
Disease resolution should be accompanied by a reduction in copy number of pathogen genome
Disease resolution perhaps due to effective clinical treatment should lead to undetectable or reduced pathogen-associated nucleic acid. Any relapse in disease should see an increase in copy number
Microbial sequence may be detected before disease or may correlate with disease severity
A causal relationship can be more strongly inferred when pathogen-associated nucleic acid is present before disease onset and copy number correlates with disease severity
The nature of the microbial organism associated by detection of its nucleic acid should be consistent with known biological characteristics of that group of organisms
When phenotypes such as pathology, microbial morphology and clinical features are predicted by sequence-based phylogeny the meaningfulness of the detected sequence can be enhanced
Microbe-associated sequences detected in disease tissue should be corroborated at the cellular level
In situ hybridization of microbial sequences in an area of tissue pathology (or where microorganisms are thought to be located) should be attempted
Molecular evidence should be reproducible
Any sequence-based evidence for microbial causation must be replicated
We wish to thank Robin A Weiss for reading a draft manuscript and making helpful comments.
- Parkin DM: The global health burden of infection-associated cancers in the year 2002. Int J Cancer. 2006, 118: 3030-3044. 10.1002/ijc.21731.View ArticlePubMedGoogle Scholar
- Hanahan D, Weinberg RA: The hallmarks of cancer. Cell. 2000, 100: 57-70. 10.1016/S0092-8674(00)81683-9.View ArticlePubMedGoogle Scholar
- Duncan CG, Leary RJ, Lin JC, Cummins J, Di C, Schaefer CF, Wang TL, Riggins GJ, Edwards J, Bigner D, Kopelovich L, Vogelstein B, Kinzler KW, Velculescu VE, Yan H: Identification of microbial DNA in human cancer. BMC Med Genomics. 2009, 2: 22-10.1186/1755-8794-2-22.PubMed CentralView ArticlePubMedGoogle Scholar
- Lax AJ, Thomas W: How bacteria could cause cancer: one step at a time. Trends Microbiol. 2002, 10: 293-299. 10.1016/S0966-842X(02)02360-0.View ArticlePubMedGoogle Scholar
- Stein L, Urban MI, O'Connell D, Yu XQ, Beral V, Newton R, Ruff P, Donde B, Hale M, Patel M, Sitas F: The spectrum of human immunodeficiency virus-associated cancers in a South African black population: results from a case-control study, 1995–2004. Int J Cancer. 2008, 122: 2260-2265. 10.1002/ijc.23391.View ArticlePubMedGoogle Scholar
- Morens DM, Folkers GK, Fauci AS: The challenge of emerging and re-emerging infectious diseases. Nature. 2004, 430: 242-249. 10.1038/nature02759.View ArticlePubMedGoogle Scholar
- Feng H, Taylor JL, Benos PV, Newton R, Waddell K, Lucas SB, Chang Y, Moore PS: Human transcriptome subtraction by using short sequence tags to search for tumor viruses in conjunctival carcinoma. J Virol. 2007, 81: 11332-11340. 10.1128/JVI.00875-07.PubMed CentralView ArticlePubMedGoogle Scholar
- Feng H, Shuda M, Chang Y, Moore PS: Clonal integration of a polyomavirus in human Merkel cell carcinoma. Science. 2008, 319: 1096-1100. 10.1126/science.1152586.PubMed CentralView ArticlePubMedGoogle Scholar
- Hill AB: The environment and disease: association or causation?. Proc R Soc Med. 1965, 58: 295-300.PubMed CentralPubMedGoogle Scholar
- Fredericks DN, Relman DA: Sequence-based identification of microbial pathogens: a reconsideration of Koch's postulates. Clin Microbiol Rev. 1996, 9: 18-33.PubMed CentralGoogle Scholar