which increases the interferon-γ response to HCV in vitro (MacDonald et al. 2008). Even if the identified component communities do not highlight these two particular species, it seems worth to expect that similar mechanisms could apply to other helminth species (McSorley and Maizels 2012). Furthermore, our results suggest the absence of protective component communities (all guilds confounded) in temperate countries. However, the redistribution analysis showed that this last result may frequently occur by chance (in about 40% of redistribution simulations). Therefore, the interpretation of this finding in the context of liver cancer epidemiology is highly speculative. Furthermore, our results reveal the absence of a protective component community (all guilds confounded) in temperate countries. Redistribution analysis showed that this last result may frequently occur by chance (around 40%); nevertheless, the interpretation of this result could give insights into our understanding of liver cancer epidemiology. Indeed, temperate countries benefit from high vaccination coverage (e.g., 76% for Europe according to the WHO) which may decrease the number of cancer cases attributable to HBV and thus the strength of the interaction between infectious agents and cancer incidences.

As the oncogenic agent *H. pylori* is highly prevalent worldwide (Atherton and Blaser 2009), Stomach cancer had a particular likelihood to be associated with large species-rich component communities in both biomes, as the oncogenic agent *H. pylori* is highly prevalent worldwide (Atherton and Blaser 2009). This was the case, with the largest number and diversity of species across guilds associating both positively and negatively with this cancer. The principal result for stomach cancer is that component communities of viruses have an opposite effect between the two biomes. We hypothesize that it could rely on different sequences of infections between these two regions. In temperate countries, the highest incidence of viral diseases occurs among children younger than 10 years (Seward et al. 2002). In temperate biomes, early activation of Th1 responses by viruses, even before *H. pylori* infection, could thus bring protection against *H. pylori* through cross immunity (Quiding-Järbrink et al. 2001). Conversely, viruses are acquired at older ages in tropical countries with a higher proportion of cases and higher susceptibility among adults (Lee 1998). In this situation, viral infections may temporarily divert the Th1 responses from *H. pylori* and allow it to proliferate (Figure 3). Viruses have numerous mechanisms to escape immune system and especially they are able to down-regulate interferon signaling (Gale and Sen 2009). However, IFN-γ responses produced by CD8+ cells contribute significantly to the elimination of *H*.
pylori infection (Quiding-Järbrink et al. 2001). Thus, viruses could allow the persistence of H. pylori and consequent pro-tumoral inflammation by protecting it from immune destruction. According to this hypothesis, five viruses have a consistent facilitating role between biomes (Cowpox virus, Western and Eastern equine encephalitis virus, St Louis encephalitis virus, Hantavirus).

As for any statistical approach dealing with large scale database, our study is susceptible to a number of issues which need to be discussed. First, some diseases based on observation of symptoms have multiple potential causative agents. When this was the case, we included only the agent responsible for the majority of cases. Second, a principal weakness of our study is that infectious assemblages were based on presence/absence data where even imported and isolated cases (observed rarely) can result in a presence assignation for the whole country. In addition, all worldwide-distributed infectious agents have been removed from the database because they would not be discriminating at all. These two last points suggested that it would be helpful to consider prevalence data in order to assess the proportion of the population which is really in contact with the infectious agent. Such approach should be facilitated by future development of openly available data sources by open date area in the future and may allow assessing the global component community, including worldwide-distributed infectious agents, which may actually have a clearer impact on cancer incidence. This consideration, however, do not necessarily negate the indirect impact of the communities we have identified. Finally, we defined guilds according to their taxonomic classifications, which could be not biologically meaningful, but which allows to deal with the high number of infectious agents considered here. In fact, it has been recommended that guilds should be based on functional similarity of species or based on their life-cycle categories instead of their taxonomy (McGill et al. 2006).

Considering cancer incidence data, the Globocan database (compiled by the IARC) do not account for the different cancer subtypes. As oncogenic agents are implied in subtypes that are only a proportion of the total number of cases, our conclusions may be more robust when infection is associated with a prevalent cancer (stomach adenocarcinoma and hepatocellular carcinoma) as opposed to rarer subtypes (e.g., SCCs of the bladder). Furthermore, the detection of cancer cases could be better in temperate countries because of the higher investment in health and disease surveillance. Furthermore, the detection of cancer

Formatted: Font: Not Bold
pylori infection (Quiding-Järbrink et al. 2001). Thus, viruses could allow the persistence of H. pylori and consequent pro-tumoral inflammation by protecting it from immune destruction. According to this hypothesis, five viruses have a consistent facilitating role between biomes (Cowpox virus, Western and Eastern equine encephalitis virus, St Louis encephalitis virus, Hantavirus).

As for any statistical approach dealing with large scale database, our study is susceptible to a number of issues which need to be discussed. First, some diseases based on observation of symptoms have multiple potential causative agents. When this was the case, we included only the agent responsible for the majority of cases. Second, a principal weakness of our study is that infectious assemblages were based on presence/absence data where even imported and isolated cases (observed rarely) can result in a presence assignation for the whole country. In addition, all worldwide-distributed infectious agents have been removed from the database because they would not be discriminating at all. These two last points suggested that it would be helpful to consider prevalence data in order to assess the proportion of the population which is really in contact with the infectious agent. Such approach should be facilitated by future development of openly available data sources by open date area in the future and may allow assessing the global component community, including worldwide-distributed infectious agents, which may actually have a clearer impact on cancer incidence. This consideration, however, do not necessarily negate the indirect impact of the communities we have identified. Finally, we defined guilds according to their taxonomic classifications, which could be not biologically meaningful, but which allows to deal with the high number of infectious agents considered here. In fact, it has been recommended that guilds should be based on functional similarity of species or based on their life-cycle categories instead of their taxonomy (McGill et al. 2006).

Considering cancer incidence data, the Globocan database (compiled by the IARC) do not account for the different cancer subtypes. As oncogenic agents are implied in subtypes that are only a proportion of the total number of cases, our conclusions may be more robust when infection is associated with a prevalent cancer (stomach adenocarcinoma and hepatocellular carcinoma) as opposed to rarer subtypes (e.g., SCCs of the bladder). Furthermore, the detection of cancer cases could be better in temperate countries because of the higher investment in health and disease surveillance. Furthermore, the detection of cancer

Formatted: Font: Not Bold
cases could be better in temperate countries because of the higher investment in health and disease surveillance. However, we assessed this disparity using two summarizing indices provided by the IARC, which considers the methods and data quality. These indices have been considered as confounding variables (see supplementary materials). Finally, reporting bias could also affect our dataset on confounding variables. However, we have selected data from reference organizations such as the WHO and the World Bank which are susceptible to be the more accurate. Even if it could quantitatively impact our results, it is unlikely to influence them qualitatively as the information has been compiled in a unique variable used as random factor. However, we assess this disparity using indices provided by the IARC, which described the methods and data quality. These indices have been considered among the confounding variables used as random factors in our analyses (see supplementary materials).

We have based our interpretations here on the hypothesis that infectious agents may modify the circulation or the persistence of oncogenic agents. However, our population-scale data do not reveal whether human individuals with cancer are actually infected or have been in contact with the identified diseases. In addition, our data do not allow assessing the variation in co-infection by infectious species at the individual level. While our this study does not claim to provide a definitive answer, it calls for designing cohort studies of cancer patients considering personal infection history to determine the causality, and potentially the mechanism, of such indirect link between non-oncogenic infectious agents and cancer development. However, our population-scale data do not reveal whether human individuals with cancer are actually infected or have been in contact with the identified diseases. One alternative, though not mutually exclusive, hypothesis is that infectious agents belonging to the community could play a role at the cellular level by disturbing immunosurveillance, introducing viral genes, promoting mutation or providing an environment where cancer growth can go unchecked (Dalton-Griffin and Kellam 2009). Some of these oncogenic mechanisms require that infectious agents may have a specific tropism for cancer cells, i.e., stomach, bladder or liver cells, but to the best of our knowledge species identified by our analysis do not necessarily show such tropism and cohort studies of cancer patients at the individual scale would be necessary to detect this mechanism. Finally, the inclusion of a cancer for which no infectious agent is suspected to play a role would have served as an evidence-boosting negative control. However, the assemblage identified in these controls
cases could be better in temperate countries because of the higher investment in health and disease surveillance. However, we assessed this disparity using two summarizing indices provided by the IARC, which considers the methods and data quality. These indices have been considered as confounding variables (see supplementary materials). Finally, reporting bias could also affect our dataset on confounding variables. However, we have selected data from reference organizations such as the WHO and the World Bank which are susceptible to be the more accurate. Even if it could quantitatively impact our results, it is unlikely to influence them qualitatively as the information has been compiled in a unique variable used as random factor. However, we assess this disparity using indices provided by the IARC, which described the methods and data quality. These indices have been considered among the confounding variables used as random factors in our analyses (see supplementary materials).

We have based our interpretations here on the hypothesis that infectious agents may modify the circulation or the persistence of oncogenic agents. However, our population-scale data do not reveal whether human individuals with cancer are actually infected or have been in contact with the identified diseases. In addition, our data do not allow assessing the variation in co-infection by infectious species at the individual level. While our this study does not claim to provide a definitive answer, it calls for designing cohort studies of cancer patients considering personal infection history to determine the causality, and potentially the mechanism, of such indirect link between non-oncogenic infectious agents and cancer development. However, our population-scale data do not reveal whether human individuals with cancer are actually infected or have been in contact with the identified diseases. One alternative, though not mutually exclusive, hypothesis is that infectious agents belonging to the community could play a role at the cellular level by disturbing immunosurveillance, introducing viral genes, promoting mutation or providing an environment where cancer growth can go unchecked (Dalton-Griffin and Kellam 2009). Some of these oncogenic mechanisms require that infectious agents may have a specific tropism for cancer cells, i.e., stomach, bladder or liver cells, but to the best of our knowledge species identified by our analysis do not necessarily show such tropism and cohort studies of cancer patients at the individual scale would be necessary to detect this mechanism. Finally, the inclusion of a cancer for which no infectious agent is suspected to play a role would have served as an evidence-boosting negative control. However, the assemblage identified in these controls
441 could also have biological relevance as they could include species that indirectly alter
442 immunosurveillance as suggested in (Jacqueline et al. 2017).

443

444 **Conclusion**

445 Despite the further steps necessary to validate the biological relevance of our findings,
446 these results draw attention to the need and potential benefits of a “pathocenosis approach”
447 for the management of infection-derived cancers, in a global health perspective. Indeed,
448 acknowledging the indirect role of infectious agents may allow for the adaptation of public
449 health strategies (such as vaccination) to improve prevention against cancer as well as the
450 targeted infectious diseases. As cancers with an infectious origin are predominant in tropical
451 countries, our study raises the perspective to used current strategies of infectious diseases
452 control (vaccination, antibiotics...) to decrease cancer burden in this region. Our approach,
453 which serves to determine associations at the largest scale (Khoury and Ioannidis 2014), is a
454 necessary first step to motivate further experimental and cohort-based studies to confirm our
455 findings, to identify the mechanisms implicated and thus to reveal new therapeutic
456 opportunities.

457

458 **Acknowledgments**

459 The authors thank CREEC sponsors CNRS and André HOFFAMNN (Fondation
460 Mava). This paper is a contribution of the EVOCAN and STORY projects funded by the
461 Agence Nationale de la Recherche. Post-doctoral support for JLA was provided by ANR JC
462 ‘STORY’ granted to Benjamin Roche. JFG and BR were funded by an “Investissement
463 d’Avenir” Laboratoire d’Excellence Centre d’Etude de la Biodiversité Amazonienne Grant
464 (ANR-10-LABX-25-01).

465

466 **Competing interests:** No conflict of interests to declare.

467
441 could also have biological relevance as they could include species that indirectly alter
442 immunosurveillance as suggested in (Jacqueline et al. 2017).

443

444 **Conclusion**

445 Despite the further steps necessary to validate the biological relevance of our findings,
446 these results draw attention to the need and potential benefits of a “pathocenosis approach”
447 for the management of infection-derived cancers, in a global health perspective. Indeed,
448 acknowledging the indirect role of infectious agents may allow for the adaptation of public
449 health strategies (such as vaccination) to improve prevention against cancer as well as the
450 targeted infectious diseases. As cancers with an infectious origin are predominant in tropical
451 countries, our study raises the perspective to used current strategies of infectious diseases
452 control (vaccination, antibiotics...) to decrease cancer burden in this region. Our approach,
453 which serves to determine associations at the largest scale (Khoury and Ioannidis 2014), is a
454 necessary first step to motivate further experimental and cohort-based studies to confirm our
455 findings, to identify the mechanisms implicated and thus to reveal new therapeutic
456 opportunities.

457

458 **Acknowledgments**

459 The authors thank CREEC sponsors CNRS and André HOFFAMNN (Fondation
460 Mava). This paper is a contribution of the EVOCAN and STORY projects funded by the
461 Agence Nationale de la Recherche. Post-doctoral support for JLA was provided by ANR JC
462 ‘STORY’ granted to Benjamin Roche. JFG and BR were funded by an “Investissement
463 d’Avenir” Laboratoire d’Excellence Centre d’Etude de la Biodiversité Amazonienne Grant
464 (ANR-10-LABX-25-01).

465

466 **Competing interests:** No conflict of interests to declare.

467
468 References

469 Adeyeba OA, Ojeaga SGT. 2002. URINARY SCHISTOSOMIASIS AND CONCOMITANT URINARY TRACT PATHOGENS AMONG SCHOOL CHILDREN IN METROPOLITAN IBADAN, . Afr J Biomed Res 5: 103-108.

472 Atherton JC, Blaser MJ. 2009. Review series Coadaptation of Helicobacter pylori and humans: ancient history, modern implications. J. Clin. Invest. 119; doi:10.1172/JCI38605DS1.

475 Borkow G, Weisman Z, Leng Q, Stein M, Kalinkovich A, Wolday D, et al. 2001. Helminths, Human Immunode ciency Virus and Tuberculosis. 568-571.

477 Brenner H, Rothenbacher D, Arndt V. 2009. Epidemiology of Stomach Cancer. 467-477.

478 Cain ML (Michael L, Bowman WD, Hacker SD. 2011. Ecology. Sinauer Associates.

479 Chêne A, Donati D, Guerreiro-Cacais AO, Levitsky V, Chen Q, Falk KI, et al. 2007. A molecular link between malaria and Epstein-Barr virus reactivation. PLoS Pathog. 3:e80; doi:10.1371/journal.ppat.0030080.

482 Chitsulo L, Engels D, Montresor A, Savioli L. 2000. The global status of schistosomiasis and its control. Acta Trop. 77:41-51; doi:10.1016/S0001-706X(00)00122-4.

484 Davis CP, Cohen MS, Gruber MB, Anderson MD, Warren MM. 1984. Urothelial hyperplasia and neoplasia: a response to chronic urinary tract infection in rats. J Urol 132: 1025-31.

486 Doll R, Payne P, Waterhouse JA. 1996. Cancer Incidence in Five Continents, Vol. I Union Internationale Contre le Cancer. Geneva.

488 Ezenwa VO, Jolles AE. 2014. Opposite effects of anthelmintic treatment on microbial infection at individual versus population scales. 8-11.

490 Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. 2010. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer 127:2893-2917; doi:10.1002/ijc.25516.

493 Fox J, Weisberg S. 2011. An [R] Companion to Applied Regression, Second Edition. Thousand O.

Formatted: English (United States)
468 References

469 Adeyeba OA, Ojeaga SGT. 2002. URINARY SCHISTOSOMIASIS AND CONCOMITANT URINARY TRACT PATHOGENS AMONG SCHOOL CHILDREN IN METROPOLITAN IBADAN, . Afr J Biomed Res 5: 103–108.

472 Atherton JC, Blaser MJ. 2009. Review series Coadaptation of Helicobacter pylori and humans: ancient history, modern implications. J. Clin. Invest. 119; doi:10.1172/JCI38605DS1.

475 Borkow G, Weisman Z, Leng Q, Stein M, Kalinkovich A, Wolday D, et al. 2001. Helminths, Human Immunode ciency Virus and Tuberculosis. 568–571.

477 Brenner H, Rothenbacher D, Arndt V. 2009. Epidemiology of Stomach Cancer. 467–477.

478 Cain ML (Michael L, Bowman WD, Hacker SD. 2011. Ecology. Sinauer Associates.

479 Chêne A, Donati D, Guerreiro-Cacais AO, Levitsky V, Chen Q, Falk KI, et al. 2007. A molecular link between malaria and Epstein-Barr virus reactivation. PLoS Pathog. 3:e80; doi:10.1371/journal.ppat.0030080.

482 Chitsulo L, Engels D, Montresor A, Savioli L. 2000. The global status of schistosomiasis and its control. Acta Trop. 77:41–51; doi:10.1016/S0001-706X(00)00122-4.

484 Davis CP, Cohen MS, Gruber MB, Anderson MD, Warren MM. 1984. Urothelial hyperplasia and neoplasia: a response to chronic urinary tract infection in rats. J Urol 132: 1025–31.

486 Doll R, Payne P, Waterhouse JA. 1996. Cancer Incidence in Five Continents, Vol. I Union Internationale Contre le Cancer. Geneva.

488 Ezenwa VO, Jolles AE. 2014. Opposite effects of anthelmintic treatment on microbial infection at individual versus population scales. 8–11.

490 Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. 2010. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer 127:2893–2917; doi:10.1002/ijc.25516.

493 Fox J, Weisberg S. 2011. An [R] Companion to Applied Regression, Second Edition. Thousand O.

Formatted: English (United States)
495 Guernier V, Hochberg ME, Guégan J-F. 2004. Ecology drives the worldwide distribution of human diseases. PLoS Biol. 2:e141; doi:10.1371/journal.pbio.0020141.

497 Hotez PJ, Molyneux DH, Fenwick A, Kumaresan J, Sachs SE, Sachs JD, et al. 2007. Control of Neglected Tropical Diseases. 1018–1027.

499 Husson F, Josse J, Le S, Maintainer JM. 2017. Package "FactoMineR" Title Multivariate Exploratory Data Analysis and Data Mining.

501 Jacqueline C, Tasiemski A, Sorci G, Ujvari B, Maachi F, Missé D, et al. 2017. Infections and cancer: the "fifty shades of immunity" hypothesis. BMC Cancer in press:1–11; doi:10.1186/s12885-017-3234-4.

504 Kaiser HF. 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23:187–200; doi:10.1007/BF02289233.

506 Kamal SM, Bianchi L, Al Tawil A, Koziel M, El Sayed Khalifa K, Peter T, et al. 2001. Specific Cellular Immune Response and Cytokine Patterns in Patients Coinfected with Hepatitis C Virus and *Schistosoma mansoni*. J. Infect. Dis. 184:972–982; doi:10.1086/323352.

510 Kamal SM, El Sayed Khalifa K. 2006. Immune modulation by helminthic infections: Worms and viral infections. Parasite Immunol. 28:483–496; doi:10.1111/j.1365-3024.2006.00909.x.

513 Kantor AF, Hartge P, Hoover RN, Fraumeni JF. 1988. Epidemiological characteristics of squamous cell carcinoma and adenocarcinoma of the bladder. Cancer Res. 48: 3853–5.

515 Khoury MJ, Ioannidis JP a. 2014. Big data meets public health. Science (80-. ). 346:1054–1055; doi:10.1126/science.aaa2709.

517 Lee BW. 1998. Review of varicella zoster seroepidemiology in India and South-east Asia. Trop. Med. Int. Heal. 3:886–890; doi:10.1046/j.1365-3156.1998.00316.x.

519 MacDonald AJ, Libri NA, Lustigman S, Barker SJ, Whelan MA, Semper AE, et al. 2008. A novel, helminth-derived immunostimulant enhances human recall responses to hepatitis C virus and tetanus toxoid and is dependent on CD56+ cells for its action. Clin. Exp. Immunol. 152:265–273; doi:10.1111/j.1365-2249.2008.03623.x.

Formatted: English (United States)

Formatted: English (United States)
495 Guernier V, Hochberg ME, Guégan J-F. 2004. Ecology drives the worldwide distribution of human diseases. PLoS Biol. 2:e141; doi:10.1371/journal.pbio.0020141.

497 Hotez PJ, Molyneux DH, Fenwick A, Kumaresan J, Sachs SE, Sachs JD, et al. 2007. Control of Neglected Tropical Diseases. 1018–1027.

499 Husson F, Josse J, Le S, Maintainer JM. 2017. Package "FactoMineR" Title Multivariate Exploratory Data Analysis and Data Mining.

501 Jacqueline C, Tasiemski A, Sorci G, Ujvari B, Maachi F, Missé D, et al. 2017. Infections and cancer: the "fifty shades of immunity" hypothesis. BMC Cancer in press:1–11; doi:10.1186/s12885-017-3234-4.

504 Kaiser HF. 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23:187–200; doi:10.1007/BF02289233.

506 Kamal SM, Bianchi L, Al Tawil A, Koziel M, El Sayed Khalifa K, Peter T, et al. 2001. Specific Cellular Immune Response and Cytokine Patterns in Patients Coinfected with Hepatitis C Virus and *Schistosoma mansoni*. J. Infect. Dis. 184:972–982; doi:10.1086/323352.

510 Kamal SM, El Sayed Khalifa K. 2006. Immune modulation by helminthic infections: Worms and viral infections. Parasite Immunol. 28:483–496; doi:10.1111/j.1365-3024.2006.00909.x.

513 Kantor AF, Hartge P, Hoover RN, Fraumeni JF. 1988. Epidemiological characteristics of squamous cell carcinoma and adenocarcinoma of the bladder. Cancer Res. 48: 3853–5.

515 Khoury MJ, Ioannidis JP a. 2014. Big data meets public health. Science (80-. ). 346:1054–1055; doi:10.1126/science.aaa2709.

517 Lee BW. 1998. Review of varicella zoster seroepidemiology in India and South-east Asia. Trop. Med. Int. Heal. 3:886–890; doi:10.1046/j.1365-3156.1998.00316.x.

519 MacDonald AJ, Libri NA, Lustigman S, Barker SJ, Whelan MA, Semper AE, et al. 2008. A novel, helminth-derived immunostimulant enhances human recall responses to hepatitis C virus and tetanus toxoid and is dependent on CD56+ cells for its action. Clin. Exp. Immunol. 152:265–273; doi:10.1111/j.1365-2249.2008.03623.x.

Formatted: English (United States)

Formatted: English (United States)
523 Magrath I, Steliarova-Foucher E, Epelman S, Ribeiro RC, Harif M, Li CK, et al. 2013. Paediatric cancer in low-income and middle-income countries. Lancet Oncol. 14; doi:10.1016/S1470-2045(13)70008-1.

526 McGill BJ, Enquist BJ, Weiher E, Westoby M. 2006. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21:178-85; doi:10.1016/j.tree.2006.02.002.

528 McSorley HJ, Maizels RM. 2012. Helminth infections and host immune regulation. Clin. Microbiol. Rev. 25:585-608; doi:10.1128/CMR.05040-11.

530 Mideo N. 2009. Parasite adaptations to within-host competition. Trends Parasitol. 25:261-8; doi:10.1016/j.pt.2009.03.001.

532 Mohd Hanafiah K, Groeger J, Flaxman AD, Wiersma ST. 2013. Global epidemiology of hepatitis C virus infection: New estimates of age-specific antibody to HCV seroprevalence. Hepatology 57:1333-1342; doi:10.1002/hep.26141.

535 Morrow RH, Gutensohn N, Smith PG. 1976. Epstein-Barr Virus-Malaria Interaction Models for Burkitt $\hat{a}\epsilon^{TM}$ s Lymphoma: Implications for Preventive Trials Epstein-Barr Virus-Malaria Interaction Models for Burkitt $\hat{a}\epsilon^{TM}$ s Lymphoma: Implications for Preventive Trials 1. 667-669.

539 Mostafa MH, Sheweita SA. 1999. Relationship between Schistosomiasis and Bladder Cancer EVIDENCE SUPPORTING THE RELATIONSHIP BETWEEN SCHISTOSOMIASIS AND BLADDER. 12: 97-111.

542 Oldstone MBA. 2006. Viral persistence: Parameters, mechanisms and future predictions. Virology 344:111-118; doi:10.1016/j.virol.2005.09.028.

544 Ossai OP, Dankoli R, Nwodo C, Tukur D, Nsubuga P, Ogbuabor D, et al. 2014. Bacteriuria and urinary schistosomiasis in primary school children in rural communities in Enugu State,. 18: 4-8.

547 Paavonen J, Naud P, Salmerón J, Wheeler CM, Chow SN, Apter D, et al. 2009. Efficacy of human papillomavirus (HPV)-16/18 AS04-adjuvanted vaccine against cervical infection and precancer caused by oncogenic HPV types (PATRICIA): final analysis of a double-blind, randomised study in young women. Lancet 374:301-314; doi:10.1016/S0140-

Formatted: English (United States)

Formatted: English (United States)
523 Magrath I, Steliarova-Foucher E, Epelman S, Ribeiro RC, Harif M, Li CK, et al. 2013. Paediatric cancer in low-income and middle-income countries. Lancet Oncol. 14; doi:10.1016/S1470-2045(13)70008-1.

526 McGill BJ, Enquist BJ, Weiher E, Westoby M. 2006. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21:178-85; doi:10.1016/j.tree.2006.02.002.

528 McSorley HJ, Maizels RM. 2012. Helminth infections and host immune regulation. Clin. Microbiol. Rev. 25:585-608; doi:10.1128/CMR.05040-11.

530 Mideo N. 2009. Parasite adaptations to within-host competition. Trends Parasitol. 25:261-8; doi:10.1016/j.pt.2009.03.001.

532 Mohd Hanafiah K, Groeger J, Flaxman AD, Wiersma ST. 2013. Global epidemiology of hepatitis C virus infection: New estimates of age-specific antibody to HCV seroprevalence. Hepatology 57:1333-1342; doi:10.1002/hep.26141.

535 Morrow RH, Gutensohn N, Smith PG. 1976. Epstein-Barr Virus-Malaria Interaction Models for Burkitt $\hat{a}\epsilon^{TM}$ s Lymphoma: Implications for Preventive Trials Epstein-Barr Virus-Malaria Interaction Models for Burkitt $\hat{a}\epsilon^{TM}$ s Lymphoma: Implications for Preventive Trials 1. 667-669.

539 Mostafa MH, Sheweita SA. 1999. Relationship between Schistosomiasis and Bladder Cancer EVIDENCE SUPPORTING THE RELATIONSHIP BETWEEN SCHISTOSOMIASIS AND BLADDER. 12: 97-111.

542 Oldstone MBA. 2006. Viral persistence: Parameters, mechanisms and future predictions. Virology 344:111-118; doi:10.1016/j.virol.2005.09.028.

544 Ossai OP, Dankoli R, Nwodo C, Tukur D, Nsubuga P, Ogbuabor D, et al. 2014. Bacteriuria and urinary schistosomiasis in primary school children in rural communities in Enugu State,. 18: 4-8.

547 Paavonen J, Naud P, Salmerón J, Wheeler CM, Chow SN, Apter D, et al. 2009. Efficacy of human papillomavirus (HPV)-16/18 AS04-adjuvanted vaccine against cervical infection and precancer caused by oncogenic HPV types (PATRICIA): final analysis of a double-blind, randomised study in young women. Lancet 374:301-314; doi:10.1016/S0140-

Formatted: English (United States)

Formatted: English (United States)
551 6736(09)61248-4.

552 Parkin DM. 2001. Global cancer statistics in the year 2000. Lancet Oncol. 2:533-543;
553 doi:10.1016/S1470-2045(01)00486-7.

554 Parkin DM. 2006. The global health burden of infection-associated cancers in the year 2002.
555 Int. J. Cancer 118:3030-44; doi:10.1002/ijc.21731.

556 Pathak AK, Pelensky C, Boag B, Cattadori IM. 2012. Immuno-epidemiology of chronic
557 bacterial and helminth co-infections: observations from the field and evidence from the
558 laboratory. Int. J. Parasitol. 42:647-55; doi:10.1016/j.ijpara.2012.04.011.

559 Pedersen AB, Fenton A. 2007. Emphasizing the ecology in parasite community ecology.
560 Trends Ecol. Evol. 22:133-139; doi:10.1016/j.tree.2006.11.005.

561 Peleteiro B, Bastos A, Ferro A, Lunet N. 2014. Prevalence of Helicobacter pylori infection
562 worldwide: A systematic review of studies with national coverage. Dig. Dis. Sci.
563 59:1698-1709; doi:10.1007/s10620-014-3063-0.

564 Quiding-Järbrink M, Lundin BS, Lönroth H, Svennerholm AM. 2001. CD4+ and CD8+ T cell
565 responses in Helicobacter pylori-infected individuals. Clin. Exp. Immunol. 123: 81-7.

566 Read AF, Taylor LH. 2001. The Ecology of Genetically Diverse Infections. 292: 1099-1103.

567 Seward JF, Watson BM, Peterson CL, Mascola L, Pelosi JW, Zhang JX, et al. 2002. Varicella
568 Disease After Introduction of Varicella Vaccine in the United States, 1995-2000. 287:
569 1995-2000.

570 Siegel R, Naishadham E, Jema A. 2013. Cancer Statistics, 2013. CA Cancer J Clin 37:408-
571 14; doi:10.3322/caac.21166.

572 Silins I, Ryd W, Strand A, Wadell G, Törnberg S, Hansson BG, et al. 2005. Chlamydia
573 trachomatis infection and persistence of human papillomavirus. Int. J. Cancer 116:110-
574 115; doi:10.1002/ijc.20970.

575 Zur Hausen H. 2009. The search for infectious causes of human cancers: where and why.
576 Virology 392:1-10; doi:10.1016/j.virol.2009.06.001.

577 Zur Hausen H, Villiers E De. 2015. Cancer "Causation" by Infections—Individual

20

Formatted: English (United States)
551 6736(09)61248-4.

552 Parkin DM. 2001. Global cancer statistics in the year 2000. Lancet Oncol. 2:533-543;
553 doi:10.1016/S1470-2045(01)00486-7.

554 Parkin DM. 2006. The global health burden of infection-associated cancers in the year 2002.
555 Int. J. Cancer 118:3030-44; doi:10.1002/ijc.21731.

556 Pathak AK, Pelensky C, Boag B, Cattadori IM. 2012. Immuno-epidemiology of chronic
557 bacterial and helminth co-infections: observations from the field and evidence from the
558 laboratory. Int. J. Parasitol. 42:647-55; doi:10.1016/j.ijpara.2012.04.011.

559 Pedersen AB, Fenton A. 2007. Emphasizing the ecology in parasite community ecology.
560 Trends Ecol. Evol. 22:133-139; doi:10.1016/j.tree.2006.11.005.

561 Peleteiro B, Bastos A, Ferro A, Lunet N. 2014. Prevalence of Helicobacter pylori infection
562 worldwide: A systematic review of studies with national coverage. Dig. Dis. Sci.
563 59:1698-1709; doi:10.1007/s10620-014-3063-0.

564 Quiding-Järbrink M, Lundin BS, Lönroth H, Svennerholm AM. 2001. CD4+ and CD8+ T cell
565 responses in Helicobacter pylori-infected individuals. Clin. Exp. Immunol. 123: 81-7.

566 Read AF, Taylor LH. 2001. The Ecology of Genetically Diverse Infections. 292: 1099-1103.

567 Seward JF, Watson BM, Peterson CL, Mascola L, Pelosi JW, Zhang JX, et al. 2002. Varicella
568 Disease After Introduction of Varicella Vaccine in the United States, 1995-2000. 287:
569 1995-2000.

570 Siegel R, Naishadham E, Jema A. 2013. Cancer Statistics, 2013. CA Cancer J Clin 37:408-
571 14; doi:10.3322/caac.21166.

572 Silins I, Ryd W, Strand A, Wadell G, Törnberg S, Hansson BG, et al. 2005. Chlamydia
573 trachomatis infection and persistence of human papillomavirus. Int. J. Cancer 116:110-
574 115; doi:10.1002/ijc.20970.

575 Zur Hausen H. 2009. The search for infectious causes of human cancers: where and why.
576 Virology 392:1-10; doi:10.1016/j.virol.2009.06.001.

577 Zur Hausen H, Villiers E De. 2015. Cancer "Causation" by Infections—Individual
578 Contributions and Synergistic Networks. Semin. Oncol. 41:860-875;
579 doi:10.1053/j.seminoncol.2014.10.003.

580

581

582

583

584

585

586

587
578
579
580
581
582
583
584
585
586
587
Contributions and Synergistic Networks. Semin. Oncol. 41:860-875;
doi:10.1053/j.seminoncol.2014.10.003.
<table><caption>**Table 1**| Results for the three cancers of interest in females for temperate and tropical countries. TE prefix represents temperate dimensions whereas the T prefix refers to tropical dimensions. Statistical significance and sign of association was obtained from analysis of variance. Range of significance describes results from the rarefaction analysis. Number of infectious agents (IA) was determined from the MCA analysis. Dimensions, *P*-value, sign of association and range of significance in italics mean that statistics are marginally significant.</caption><thead><tr><th rowspan="2">Type of cancer</th><th colspan="5">Temperate countries (TE)</th><th colspan="5">Tropical countries (T)</th></tr><tr><th>Dimensions</th><th><i>P</i>-value</th><th>Sign of association</th><th>Range of significance</th><th>Number of IA</th><th>Dimensions</th><th><i>P</i>-value</th><th>Sign of association</th><th>Range of significance</th><th>Number of IA</th></tr></thead><tbody><tr><td rowspan="3">Bladder</td><td>Bact TE1</td><td>0.008</td><td>-</td><td>60%-100%</td><td>10</td><td>Bact T4</td><td>0.017</td><td>+</td><td>70%-100%</td><td>13</td></tr><tr><td>Bact TE4</td><td>0.02</td><td>+</td><td>60%-100%</td><td>8</td><td>Helm T9</td><td>0.04</td><td>+</td><td>100%</td><td>6</td></tr><tr><td>Helm TE8</td><td>0.019</td><td>-</td><td>80%-100%</td><td>7</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Total</strong></td><td><strong>3</strong></td><td></td><td></td><td></td><td><strong>25</strong></td><td><strong>2</strong></td><td></td><td></td><td></td><td><strong>19</strong></td></tr><tr><td rowspan="2">Liver</td><td>Bact TE1</td><td>0.009</td><td>+</td><td>80%-100%</td><td>10</td><td>Bact T6</td><td>0.046</td><td>+</td><td>100%</td><td>6</td></tr><tr><td>Helm TE1</td><td>0.02</td><td>+</td><td>80%-100%</td><td>22</td><td>Helm T5</td><td>0.013</td><td>+</td><td>90%-100%</td><td>11</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Helm T6</td><td>0.002</td><td>-</td><td>60%-100%</td><td>9</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Virus T6</td><td>0.01</td><td>-</td><td>80%-100%</td><td>14</td></tr><tr><td><strong>Total</strong></td><td><strong>2</strong></td><td></td><td></td><td></td><td><strong>32</strong></td><td><strong>4</strong></td><td></td><td></td><td></td><td><strong>40</strong></td></tr><tr><td rowspan="8">Stomach</td><td>Bact TE1</td><td>0.0001</td><td>+</td><td>60%-100%</td><td>10</td><td>Bact T2</td><td>0.002</td><td>-</td><td>70%-100%</td><td>10</td></tr><tr><td>Bact TE4</td><td>0.001</td><td>-</td><td>70%-100%</td><td>8</td><td>Bact T7</td><td>0.007</td><td>-</td><td>70%-100%</td><td>5</td></tr><tr><td>Helm TE1</td><td>0.002</td><td>+</td><td>70%-100%</td><td>22</td><td>Helm T2</td><td>0.0002</td><td>+</td><td>40%-100%</td><td>10</td></tr><tr><td>Helm TE2</td><td>0.0002</td><td>-</td><td>70%-100%</td><td>20</td><td>Helm T3</td><td>0.006</td><td>-</td><td>60%-100%</td><td>9</td></tr><tr><td><i>Helm TE3</i></td><td><i>0.049</i></td><td>+</td><td><i>100%</i></td><td><i>13</i></td><td>Virus T1</td><td>0.00003</td><td>+</td><td>60%-100%</td><td>25</td></tr><tr><td>Helm TE7</td><td>0.0006</td><td>+</td><td>50%-100%</td><td>8</td><td>Virus T5</td><td>0.023</td><td>+</td><td>70%-100%</td><td>13</td></tr><tr><td>Virus TE3</td><td>0.02</td><td>-</td><td>90%-100%</td><td>10</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Virus TE8</td><td>0.038</td><td>-</td><td>100%</td><td>5</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Total</strong></td><td><strong>8</strong></td><td></td><td></td><td></td><td><strong>96</strong></td><td><strong>6</strong></td><td></td><td></td><td></td><td><strong>72</strong></td></tr></tbody></table>
<table><caption>**Table 1**| Results for the three cancers of interest in females for temperate and tropical countries. TE prefix represents temperate dimensions whereas the T prefix refers to tropical dimensions. Statistical significance and sign of association was obtained from analysis of variance. Range of significance describes results from the rarefaction analysis. Number of infectious agents (IA) was determined from the MCA analysis. Dimensions, *P*-value, sign of association and range of significance in italics mean that statistics are marginally significant.</caption><thead><tr><th rowspan="2">Type of cancer</th><th colspan="5">Temperate countries (TE)</th><th colspan="5">Tropical countries (T)</th></tr><tr><th>Dimensions</th><th><i>P</i>-value</th><th>Sign of association</th><th>Range of significance</th><th>Number of IA</th><th>Dimensions</th><th><i>P</i>-value</th><th>Sign of association</th><th>Range of significance</th><th>Number of IA</th></tr></thead><tbody><tr><td rowspan="3">Bladder</td><td>Bact TE1</td><td>0.008</td><td>-</td><td>60%-100%</td><td>10</td><td>Bact T4</td><td>0.017</td><td>+</td><td>70%-100%</td><td>13</td></tr><tr><td>Bact TE4</td><td>0.02</td><td>+</td><td>60%-100%</td><td>8</td><td>Helm T9</td><td>0.04</td><td>+</td><td>100%</td><td>6</td></tr><tr><td>Helm TE8</td><td>0.019</td><td>-</td><td>80%-100%</td><td>7</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Total</strong></td><td><strong>3</strong></td><td></td><td></td><td></td><td><strong>25</strong></td><td><strong>2</strong></td><td></td><td></td><td></td><td><strong>19</strong></td></tr><tr><td rowspan="2">Liver</td><td>Bact TE1</td><td>0.009</td><td>+</td><td>80%-100%</td><td>10</td><td>Bact T6</td><td>0.046</td><td>+</td><td>100%</td><td>6</td></tr><tr><td>Helm TE1</td><td>0.02</td><td>+</td><td>80%-100%</td><td>22</td><td>Helm T5</td><td>0.013</td><td>+</td><td>90%-100%</td><td>11</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Helm T6</td><td>0.002</td><td>-</td><td>60%-100%</td><td>9</td></tr><tr><td></td><td></td><td></td><td></td><td></td><td></td><td>Virus T6</td><td>0.01</td><td>-</td><td>80%-100%</td><td>14</td></tr><tr><td><strong>Total</strong></td><td><strong>2</strong></td><td></td><td></td><td></td><td><strong>32</strong></td><td><strong>4</strong></td><td></td><td></td><td></td><td><strong>40</strong></td></tr><tr><td rowspan="8">Stomach</td><td>Bact TE1</td><td>0.0001</td><td>+</td><td>60%-100%</td><td>10</td><td>Bact T2</td><td>0.002</td><td>-</td><td>70%-100%</td><td>10</td></tr><tr><td>Bact TE4</td><td>0.001</td><td>-</td><td>70%-100%</td><td>8</td><td>Bact T7</td><td>0.007</td><td>-</td><td>70%-100%</td><td>5</td></tr><tr><td>Helm TE1</td><td>0.002</td><td>+</td><td>70%-100%</td><td>22</td><td>Helm T2</td><td>0.0002</td><td>+</td><td>40%-100%</td><td>10</td></tr><tr><td>Helm TE2</td><td>0.0002</td><td>-</td><td>70%-100%</td><td>20</td><td>Helm T3</td><td>0.006</td><td>-</td><td>60%-100%</td><td>9</td></tr><tr><td><i>Helm TE3</i></td><td><i>0.049</i></td><td>+</td><td><i>100%</i></td><td><i>13</i></td><td>Virus T1</td><td>0.00003</td><td>+</td><td>60%-100%</td><td>25</td></tr><tr><td>Helm TE7</td><td>0.0006</td><td>+</td><td>50%-100%</td><td>8</td><td>Virus T5</td><td>0.023</td><td>+</td><td>70%-100%</td><td>13</td></tr><tr><td>Virus TE3</td><td>0.02</td><td>-</td><td>90%-100%</td><td>10</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Virus TE8</td><td>0.038</td><td>-</td><td>100%</td><td>5</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td><strong>Total</strong></td><td><strong>8</strong></td><td></td><td></td><td></td><td><strong>96</strong></td><td><strong>6</strong></td><td></td><td></td><td></td><td><strong>72</strong></td></tr></tbody></table>
Table 2 | Description of the specific associations which are supported by the random redistribution analysis (type I error). The combination observed in the principal analysis are described and the probability of finding them by chance was calculated over 10.000 reassortments normally distributed of cancer incidence data with the same mean and variance than in the original database in each biomes.

<table><thead><tr><td>Interactions</td><td>Description</td><td>Probability</td></tr></thead><tbody><tr><td>Bladder/Viruses</td><td>No significant association between bladder cancer incidence and dimension of viruses in both regions.</td><td>36%</td></tr><tr><td>Bladder/Bacteria</td><td>Significant association between bladder cancer incidence and dimension of bacteria in both regions.</td><td>12%</td></tr><tr><td>Liver/Negative associated dimension</td><td>No significant dimension of any guilds is negatively associated with liver cancer incidence in temperate countries. In addition, at least one significant dimension of at least one guild is negatively associated with liver cancer.</td><td>40%</td></tr><tr><td>Liver/Helminths</td><td>Significant association between liver cancer incidence and dimension of helminths in both regions.</td><td>16%</td></tr><tr><td>Stomach/Viruses</td><td>Virus dimensions negatively correlated to stomach incidence in temperate countries but positively correlated in tropical countries.</td><td>3%</td></tr></tbody></table>

599
Table 2 | Description of the specific associations which are supported by the random redistribution analysis (type I error). The combination observed in the principal analysis are described and the probability of finding them by chance was calculated over 10.000 reassortments normally distributed of cancer incidence data with the same mean and variance than in the original database in each biomes.

<table><thead><tr><td>Interactions</td><td>Description</td><td>Probability</td></tr></thead><tbody><tr><td>Bladder/Viruses</td><td>No significant association between bladder cancer incidence and dimension of viruses in both regions.</td><td>36%</td></tr><tr><td>Bladder/Bacteria</td><td>Significant association between bladder cancer incidence and dimension of bacteria in both regions.</td><td>12%</td></tr><tr><td>Liver/Negative associated dimension</td><td>No significant dimension of any guilds is negatively associated with liver cancer incidence in temperate countries. In addition, at least one significant dimension of at least one guild is negatively associated with liver cancer.</td><td>40%</td></tr><tr><td>Liver/Helminths</td><td>Significant association between liver cancer incidence and dimension of helminths in both regions.</td><td>16%</td></tr><tr><td>Stomach/Viruses</td><td>Virus dimensions negatively correlated to stomach incidence in temperate countries but positively correlated in tropical countries.</td><td>3%</td></tr></tbody></table>

599
<table><caption>**Table 3: Links between prevalence of oncogenic agents and the number of associated dimensions based on the scientific literature.** Mean prevalence for each region has been calculated according to data presented in the references.</caption><thead><tr><th rowspan="2">Oncogenic agents</th><th rowspan="2">Causality by cancer subtype</th><th rowspan="2">Global causality</th><th colspan="2">Temperate countries</th><th colspan="2">Tropical countries</th></tr><tr><th>Mean prevalence</th><th>Number of associated dimensions (Number of infectious agents)</th><th>Mean prevalence</th><th>Number of associated dimensions (Number of infectious agents)</th></tr></thead><tbody><tr><td>H. pylori</td><td>80%<br>of adenocarcinoma</td><td>70%<br>of stomach cancer</td><td>41%(Peleteiro et al. 2014)</td><td>8 (96)</td><td>53%(Peleteiro et al. 2014)</td><td>6 (72)</td></tr><tr><td>HCV/HBV</td><td>80%<br>of hepatocellular carcinoma</td><td>60%<br>of liver cancer</td><td>4.3%(Mohd Hanafiah et al. 2013)</td><td>2 (32)</td><td>4.2%(Mohd Hanafiah et al. 2013)</td><td>4 (40)</td></tr><tr><td>S. haematobium</td><td>30%<br>of squamous cells carcinoma</td><td>2%<br>of bladder cancer</td><td>0.000006%(Chitsulo et al. 2000)</td><td>3 (25)</td><td>4.5%(Chitsulo et al. 2000)</td><td>2 (19)</td></tr></tbody></table>

Field Code Changed
<table><caption>**Table 3: Links between prevalence of oncogenic agents and the number of associated dimensions based on the scientific literature.** Mean prevalence for each region has been calculated according to data presented in the references.</caption><thead><tr><th rowspan="2">Oncogenic agents</th><th rowspan="2">Causality by cancer subtype</th><th rowspan="2">Global causality</th><th colspan="2">Temperate countries</th><th colspan="2">Tropical countries</th></tr><tr><th>Mean prevalence</th><th>Number of associated dimensions (Number of infectious agents)</th><th>Mean prevalence</th><th>Number of associated dimensions (Number of infectious agents)</th></tr></thead><tbody><tr><td>H. pylori</td><td>80%<br>of adenocarcinoma</td><td>70%<br>of stomach cancer</td><td>41%(Peleteiro et al. 2014)</td><td>8 (96)</td><td>53%(Peleteiro et al. 2014)</td><td>6 (72)</td></tr><tr><td>HCV/HBV</td><td>80%<br>of hepatocellular carcinoma</td><td>60%<br>of liver cancer</td><td>4.3%(Mohd Hanafiah et al. 2013)</td><td>2 (32)</td><td>4.2%(Mohd Hanafiah et al. 2013)</td><td>4 (40)</td></tr><tr><td>S. haematobium</td><td>30%<br>of squamous cells carcinoma</td><td>2%<br>of bladder cancer</td><td>0.000006%(Chitsulo et al. 2000)</td><td>3 (25)</td><td>4.5%(Chitsulo et al. 2000)</td><td>2 (19)</td></tr></tbody></table>

Field Code Changed
603 **Figure 1**| Distinction between temperate and tropical countries. A) Infectious
604 communities in temperate and tropical biomes assessed by MCA. Tropical countries show a
605 different community of infectious agents from temperate countries even if we can observe
606 some overlaps. B) Values of the first dimension of the PCA on the confounding variables at
607 worldwide scale. Confounding variables of tropical countries largely group together in a
608 cohesive range of the parameter space, overlapping with those of temperate countries only at
609 high values. Vertical lines represent the geographical location of the tropics.

610

611 **Figure 12**| Flow diagram summarizing the main statistical analyses undertaken in this
612 study.

613

614 | **Figure 3**| Hypothesis on the contrasting role of viruses between the two biomes.
603 **Figure 1**| Distinction between temperate and tropical countries. A) Infectious
604 communities in temperate and tropical biomes assessed by MCA. Tropical countries show a
605 different community of infectious agents from temperate countries even if we can observe
606 some overlaps. B) Values of the first dimension of the PCA on the confounding variables at
607 worldwide scale. Confounding variables of tropical countries largely group together in a
608 cohesive range of the parameter space, overlapping with those of temperate countries only at
609 high values. Vertical lines represent the geographical location of the tropics.

610
611 **Figure 12**| Flow diagram summarizing the main statistical analyses undertaken in this
612 study.

613
614 **Figure 3**| Hypothesis on the contrasting role of viruses between the two biomes.