- Open Access
- Open Peer Review
Natural resistance to cancers: a Darwinian hypothesis to explain Peto’s paradox
© Roche et al.; licensee BioMed Central Ltd. 2012
- Received: 30 April 2012
- Accepted: 27 August 2012
- Published: 3 September 2012
Peto's paradox stipulates that there is no association between body mass (a surrogate of number of cells and longevity) and cancer prevalence in wildlife species. Resolving this paradox is a very promising research direction to understand mechanisms of cancer resistance. As of present, research has been focused on the consequences of these evolutionary pressures rather than of their causes.
Here, we argue that evolution through natural selection may have shaped mechanisms of cancer resistance in wildlife species and that this can result in a threshold in body mass above which oncogenic and tumor suppressive mechanisms should be increasingly purified and positively selected, respectively.
We conclude that assessing wildlife species in their natural ecosystems, especially through theoretical modeling, is the most promising way to understand how evolutionary processes can favor one or the other pathway. This will provide important insights into mechanisms of cancer resistance.
- Body Size
- Natural Selection
- Multicellular Organism
- Life History Strategy
- Increase Cancer Risk
Here, we propose that oncogenic or tumor suppressive mechanisms should be increasingly purified or positively selected, respectively, above a given threshold in body mass (Figure 1). There is correlative evidence for a within-species increase in cancer frequency with body mass . For instance, cohort studies in humans have shown that leg lengths 3-4 mm above the average is associated with a 80% higher risk of non-smoking cancers (Figure 1; reference 4, but see 5). Despite the prediction that more cells in a body should be associated with a higher probability of cancer, current knowledge indicates that cancers do not increase across wildlife species as a function of body mass (incidences are typically between 20 and 40%, 2).
We suggest that these contrasting patterns are produced by large wildlife species undergoing stronger selection against cell deregulation and for tumor suppression than smaller ones. Analyzing the selective pressures within a species’ ecosystem (i.e., biotic and a biotic environments) will inform if natural selection targets oncogenic and/or tumor suppressive mechanisms. Identifying patterns across species in cancer resistance evolution would be an important insight into resolving Peto’s paradox.
We need to understand the selective (biotic and a biotic) landscapes in which species evolved and continue to evolve. Indeed, natural selection is the product of how environments favor specific heritable phenotypes. Cancer vulnerability amongst wildlife species is likely to have been shaped by natural selection, depending on which fitness-reducing risks predominate (somatic diseases including cancer, infectious and parasitic diseases, predation and adverse environmental conditions). For instance, small rodents in natura may succumb to cancer, but only if they do not first die from any one of numerous other causes, such as predators, infectious diseases, or environmental vagaries such as floods, temperature extremes, etc. Natural selection will tend to promote resistance to sources of mortality prior to reproduction, meaning that for blue whales to grow so large and live so long, they need to both develop defenses against predators and resistance to somatic diseases like cancer. There is clearly a chicken-and-egg problem here, since changes in body size, longevity and life history strategy will alter the selective influence of different mortality factors, including the probability of cancer emergence.
Since wildlife species are subjected to a large variety of selective pressures and are found in a diverse range of habitats, it should be possible to use comparative genomics  to understand how proto-oncogenes and TSGs covary with certain environmental characteristics. Indeed, comparing genomic regions of interest for cancer research, e.g., proto-oncogenes or Tumor Suppressor Genes widespread in mammals, according to the biotic and a biotic environments where these species are found can give important insights into how these classes of genes have been shaped by natural selection as a function of the environment.
Addressing these considerations is undoubtedly relevant for human populations living in different environmental conditions (e.g., presence or absence of pathogens). Indeed, evidence suggests that many human populations lack alleles with enhanced protection against certain cancers, possibly because their short life-spans have precluded selection for those alleles [4, 7, 8].
Given the complex nature of interactions arbitrating cancer (Figure 2), mathematical and statistical approaches will be necessary to tease apart causal mechanisms and their interactions [9–19], especially for the optimal use of chemotherapies . In particular, quantitative approaches will help understand the selective forces acting on oncogene dynamics in animals in nature. Although cancer is an important cause of morbidity and mortality in several wildlife species , data are difficult to collect because most wild animals live and die unseen . In this context, quantitative approaches will be of considerable interest to understand and predict wildlife cancers, and to see to what extent insights can instruct on the prevention of cancer in humans.
This work is a contribution from the “Centre de Recherche Ecologique et Evolutive sur le Cancer” (CREEC) and the PEPii program funded by the CNRS. BR, MEH and FT thank the RNSC (Réseau National des Systèmes Complexes) for financial support.
- Greaves M: Darwinian medicine: a case for cancer. Nature Rev Canc. 2007, 7: 213-221. 10.1038/nrc2071.View ArticleGoogle Scholar
- Caulin AF, Maley CC: Peto’s paradox: evolution’s prescription for cancer prevention. TREE. 2011, 26: 175-182.PubMedPubMed CentralGoogle Scholar
- Peto R, Roe FJ, Lee PN, Levy L, Clack J: Cancer and ageing in mice and men. Br J Cancer. 1975, 32: 411-426. 10.1038/bjc.1975.242.View ArticlePubMedPubMed CentralGoogle Scholar
- Albanes D, Winic M: Are cell number and cell proliferation risk factors for cancer?. JNCI J Natl Cancer Inst. 1988, 80 (10): 772-775. 10.1093/jnci/80.10.772.View ArticlePubMedGoogle Scholar
- Diekmann O: A beginner’s guide to adpative dynamics. Banach Center Publ. 2003, 63: 47-86.View ArticleGoogle Scholar
- Hardison RC: Comparative genomics. PLoS Biol. 2003, 1 (2): e58-View ArticlePubMedPubMed CentralGoogle Scholar
- Calabrese P, Shibata D: A simple algebric cancer équation: calculating how cancers may arise with normal mutation rates. BMC Cancer. 2010, 10: 3-10.1186/1471-2407-10-3.View ArticlePubMedPubMed CentralGoogle Scholar
- Walker B, Figgs LW, Zahm SH: Differences in cancer incidence, mortality and survival between African Americans and whites. Environ Health Perpect. 1995, 103 (Suppl 8): 275-281. 10.1289/ehp.95103s8275.View ArticleGoogle Scholar
- Le Souëf PN, Goldblatt J, Lynch NR: Evolutionary adaptation of inflammatory immune responses in human beings. Lancet. 2000, 356: 242-244. 10.1016/S0140-6736(00)02491-0.View ArticlePubMedGoogle Scholar
- O’Byrne KJ, Dalgleish AG: Evolution, immune response, and cancer. Lancet. 2000, 356: 1033-View ArticlePubMedGoogle Scholar
- Maley CC, Forrest S: Exploring the relationship between neutral and selective mutations in cancer. Artif Life. 2000, 6 (4): 325-345. 10.1162/106454600300103665.View ArticlePubMedGoogle Scholar
- Luebeck EG, Moolgavkar SH: Multistage carcinogenesis and the incidence of colorectal cancer. Proc Natl Acad Sci U S A. 2002, 99 (23): 15095-15100. 10.1073/pnas.222118199.View ArticlePubMedPubMed CentralGoogle Scholar
- Nunney L: The population genetics of multistage carcinogenesis. Proc Biol Sci. 2003, 270 (1520): 1183-1191. 10.1098/rspb.2003.2351.View ArticlePubMedPubMed CentralGoogle Scholar
- Calabrese P, Tavare S, Shibata D: Pretumor progression: clonal evolution of human stem cell populations. Am J Pathol. 2004, 64 (4): 1337-1346.View ArticleGoogle Scholar
- Beerenwinkel N, Antal T, Dingli D, Traulsen A, Kinzler KW, Velculescu VE, Volgestein B, Nowak MA: Genetic progression and the waiting time to cancer. PLoS Comput Biol. 2007, 3 (11): e225-10.1371/journal.pcbi.0030225.View ArticlePubMedPubMed CentralGoogle Scholar
- Meza R, Jeon J, Moolgavkar SH, Luebeck EG: Age-specific incidence of cancer: phases, transitions, and biological implications. Proc Natl Acad Sci U S A. 2008, 105 (42): 16284-16289. 10.1073/pnas.0801151105.View ArticlePubMedPubMed CentralGoogle Scholar
- Maley CC, Lewis W, Reid BJ: Has cancer sculpted the genome?. Modeling linkage and the role of tetraploidy in neoplastic progression. in Multiscale Cancer Modeling. Edited by: Deisbock T, Stamakos GS. 2010, Boca Raton, FL: Chapman & Hall/CRC PressGoogle Scholar
- Sprouffske K, Pepper JW, Maley CC: Accurate reconstruction of the temporal order of mutations in neoplastic progression. Cancer Prevention Research. 2011, 4 (7): 1135-1144. 10.1158/1940-6207.CAPR-10-0374.View ArticlePubMedPubMed CentralGoogle Scholar
- Gatenby RA, Maini PK: Mathematical oncology: cancer summed up. Nature. 2003, 421: 321-10.1038/421321a.View ArticlePubMedGoogle Scholar
- Silva AS, Gatenby RA: A theoretical quantitative model for evolution of cancer chemotherapy resistance. Biol Direct. 2010, 5: 25-10.1186/1745-6150-5-25.View ArticlePubMedPubMed CentralGoogle Scholar
- McAloose D, AL N: Wildlife cancer: a conservation perspective. Nature Rev Canc. 2009, 9: 517-526. 10.1038/nrc2665.View ArticleGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2407/12/387/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.