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Natural resistance to cancers: a Darwinian hypothesis to explain Peto’s paradox

Benjamin Roche18*, Michael E Hochberg238, Aleah F Caulin4, Carlo C Maley5, Robert A Gatenby6, Dorothée Misse78 and Frédéric Thomas78

Author Affiliations

1 IRD, UMMISCO (UMI IRD/UPMC), 32, avenue Henry Varagnat, 93143, Bondy Cedex, France

2 ISEM, Institut des Sciences de l’Evolution, Université Montpellier 2, CNRS, Case Postal 65, Place Eugène Bataillon, 34095, Montpellier cedex 5, France

3 Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA

4 Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA, 19104, USA

5 Center for Evolution and Cancer, Helen Diller Family Comprehensive Cancer Center and Department of Surgery, University of California San Francisco, San Francisco, CA, 94143, USA

6 Departments of Radiology and Integrated Mathematical Oncology, the Moffitt Cancer Center, 12902 Magnolia Dr, Tampa, FL, 33612, USA

7 IRD, MIVEGEC (UMR CNRS/IRD/UM1), 911 Ave. Agropolis, BP 64501, FR-34394, Montpellier cedex 5, France

8 CREEC, Universite Montpellier 2, CC, 06003, 95 rue de la Galera,, 34095, Montpellier Cedex 5,, France

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BMC Cancer 2012, 12:387  doi:10.1186/1471-2407-12-387

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.