Abstract
The fight against cancer has drawn researchers from a wide variety of disciplines, ranging from molecular biology to physics, but the perspective of an ecological theorist has been mostly overlooked. By thinking about the cells that make up a tumour as an endangered species, cancer vulnerabilities become more apparent. Studies in conservation biology and microbial experiments indicate that extinction is a complex phenomenon, which is often driven by the interaction of ecological and evolutionary processes. Recent advances in cancer research have shown that tumours, like species striving for survival, harbour intricate population dynamics, which suggests the possibility to exploit the ecology of tumours for treatment.
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References
Howlader, N. et al. SEER cancer statistics review, 1975–2008. National Cancer Institute, Bethesda MD (2011).
Frank, S. A. Dynamics of cancer: incidence, inheritance, and evolution. (Princeton Univ. Press, 2007).
Nowak, M. A. Evolutionary dynamics: exploring the equations of life. (Harvard Univ. Press, 2006).
Fisher, J. C. & Hollomon, J. H. A hypothesis for the origin of cancer foci. Cancer 4, 916–918 (1951).
Armitage, P. & Doll, R. The age distribution of cancer and a multi-stage theory of carcinogenesis. Br. J. Cancer 8, 1 (1954).
Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).
Fearon, E. R. & Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 61, 759–767 (1990).
Hanahan, D. & Weinberg, R. A. The hallmarks of cancer. Cell 100, 57–70 (2000).
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Hornsby, C., Page, K. M. & Tomlinson, I. P. What can we learn from the population incidence of cancer? Armitage and Doll revisited. Lancet Oncol. 8, 1030–1038 (2007).
Pleasance, E. D. et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2009).
Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010).
Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).
Fröhling, S. et al. Identification of driver and passenger mutations of FLT3 by high-throughput DNA sequence analysis and functional assessment of candidate alleles. Cancer Cell 12, 501–513 (2007).
Attolini, C. S.-O. et al. A mathematical framework to determine the temporal sequence of somatic genetic events in cancer. Proc. Natl Acad. Sci. 107, 17604–17609 (2010).
S. Datta, R., Gutteridge, A., Swanton, C., Maley, C. C. & Graham, T. A. Modelling the evolution of genetic instability during tumour progression. Evol. Appl. 6, 20–33 (2013).
Iwami, S., Haeno, H. & Michor, F. A race between tumor immunoescape and genome maintenance selects for optimum levels of (epi) genetic instability. PLoS Comput. Biol. 8, e1002370 (2012).
Michor, F. Chromosomal instability and human cancer. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 360, 631–635 (2005).
Michor, F., Iwasa, Y., Komarova, N. L. & Nowak, M. A. Local regulation of homeostasis favors chromosomal instability. Curr. Biol. 13, 581–584 (2003).
Iwasa, Y., Nowak, M. A. & Michor, F. Evolution of resistance during clonal expansion. Genetics 172, 2557–2566 (2006).
Mumenthaler, S. M. et al. Evolutionary modeling of combination treatment strategies to overcome resistance to tyrosine kinase inhibitors in non-small cell lung cancer. Mol. Pharm. 8, 2069–2079 (2011).
Hammer, S. M. et al. A controlled trial of two nucleoside analogues plus indinavir in persons with human immunodeficiency virus infection and CD4 cell counts of 200 per cubic millimeter or less. N. Engl. J. Med. 337, 725–733 (1997).
Gulick, R. M. et al. Treatment with indinavir, zidovudine, and lamivudine in adults with human immunodeficiency virus infection and prior antiretroviral therapy. N. Engl. J. Med. 337, 734–739 (1997).
Ferguson, A. L. et al. Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design. Immunity 38, 606–617 (2013).
Merlo, L. M., Pepper, J. W., Reid, B. J. & Maley, C. C. Cancer as an evolutionary and ecological process. Nature Rev. Cancer 6, 924–935 (2006).
Crespi, B. & Summers, K. Evolutionary biology of cancer. Trends Ecol. Evol. 20, 545–552 (2005).
Basanta, D. & Anderson, A. R. Exploiting ecological principles to better understand cancer progression and treatment. Interface Focus 3, 20130020 (2013).
Gatenby, R. A., Silva, A. S., Gillies, R. J. & Frieden, B. R. Adaptive therapy. Cancer Res. 69, 4894–4903 (2009).
Silva, A. S. et al. Evolutionary approaches to prolong progression-free survival in breast cancer. Cancer Res. 72, 6362–6370 (2012).
Basanta, D., Gatenby, R. A. & Anderson, A. R. Exploiting evolution to treat drug resistance: combination therapy and the double bind. Mol. Pharm. 9, 914–921 (2012).
Courchamp, F., Clutton-Brock, T. & Grenfell, B. Inverse density dependence and the Allee effect. Trends Ecol. Evol. 14, 405–410 (1999).
Allee, W. C. Animal aggregations: a study in general sociology. (AMS Press, 1978).
Kramer, A. M., Dennis, B., Liebhold, A. M. & Drake, J. M. The evidence for Allee effects. Popul. Ecol. 51, 341–354 (2009).
Weinberg, R. The biology of cancer. (Garland Science, 2013).
Greig, D. & Travisano, M. The Prisoner's Dilemma and polymorphism in yeast SUC genes. Proc Biol Sci. 271, S25–S26 (2004).
Gore, J., Youk, H. & van Oudenaarden, A. Snowdrift game dynamics and facultative cheating in yeast. Nature 459, 253–256 (2009).
Axelrod, R., Axelrod, D. E. & Pienta, K. J. Evolution of cooperation among tumor cells. Proc. Natl Acad. Sci. 103, 13474–13479 (2006).
West, S. A., Diggle, S. P., Buckling, A., Gardner, A. & Griffin, A. S. The social lives of microbes. Annu. Rev. Ecol. Evol. Syst. 38, 53–77 (2007).
Elias, S. & Banin, E. Multi-species biofilms: living with friendly neighbors. FEMS Microbiol. Rev. 36, 990–1004 (2012).
Thomlinson, R. H. & Gray, L. H. The histological structure of some human lung cancers and the possible implications for radiotherapy. Br. J. Cancer 9, 539 (1955).
Li, C. et al. Identification of pancreatic cancer stem cells. Cancer Res. 67, 1030–1037 (2007).
Szczepariski, T., Orfão, A., van der Valden, V. H., San Miguel, J. F. & van Dongen, J. J. Minimal residual disease in leukaemia patients. Lancet Oncol. 2, 409–417 (2001).
Ferriere, R. & Legendre, S. Eco-evolutionary feedbacks, adaptive dynamics and evolutionary rescue theory. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 368 1610 (2013).
Gans, J., Wolinsky, M. & Dunbar, J. Computational improvements reveal great bacterial diversity and high metal toxicity in soil. Science 309, 1387–1390 (2005).
Hekstra, D. R. & Leibler, S. Contingency and statistical laws in replicate microbial closed ecosystems. Cell 149, 1164–1173 (2012).
Kiskowski, M. A. et al. Role for stromal heterogeneity in prostate tumorigenesis. Cancer Res. 71, 3459–3470 (2011).
Olumi, A. F. et al. Carcinoma-associated fibroblasts direct tumor progression of initiated human prostatic epithelium. Cancer Res. 59, 5002–5011 (1999).
Goswami, S. et al. Macrophages promote the invasion of breast carcinoma cells via a colony-stimulating factor-1/epidermal growth factor paracrine loop. Cancer Res. 65, 5278–5283 (2005).
Gocheva, V. et al. IL-4 induces cathepsin protease activity in tumor-associated macrophages to promote cancer growth and invasion. Genes Dev. 24, 241–255 (2010).
Basanta, D. et al. Investigating prostate cancer tumour–stroma interactions: clinical and biological insights from an evolutionary game. Br. J. Cancer 106, 174–181 (2011).
Dingli, D., Chalub, F., Santos, F. C., Van Segbroeck, S. & Pacheco, J. M. Cancer phenotype as the outcome of an evolutionary game between normal and malignant cells. Br. J. Cancer 101, 1130–1136 (2009).
Powles, T. et al. Randomized, placebo-controlled trial of clodronate in patients with primary operable breast cancer. J. Clin. Oncol. 20, 3219–3224 (2002).
Diel, I. J. et al. Adjuvant oral clodronate improves the overall survival of primary breast cancer patients with micrometastases to the bone marrow—a long-term follow-up. Ann. Oncol. 19, 2007–2011 (2008).
Mundy, G. Preclinical models of bone metastases. Semin. Oncol. 28 (suppl. 11), 2–8 (2001).
Yoneda, T. et al. Actions of bisphosphonate on bone metastasis in animal models of breast carcinoma. Cancer 88, 2979–2988 (2000).
Ryder, M. et al. Genetic and pharmacological targeting of CSF-1/CSF-1R inhibits tumor-associated macrophages and impairs BRAF-induced thyroid cancer progression. PLoS ONE 8, e54302 (2013).
Pyonteck, S. M. et al. CSF-1R inhibition alters macrophage polarization and blocks glioma progression. Nature Med. 19, 1264–1272 (2013).
Joyce, J. A. Therapeutic targeting of the tumor microenvironment. Cancer Cell 7, 513–520 (2005).
Desai, M. M., Fisher, D. S. & Murray, A. W. The speed of evolution and maintenance of variation in asexual populations. Curr. Biol. 17, 385–394 (2007).
Kao, K. C. & Sherlock, G. Molecular characterization of clonal interference during adaptive evolution in asexual populations of Saccharomyces cerevisiae. Nature Genet. 40, 1499–1504 (2008).
Lang, G. I., Botstein, D. & Desai, M. M. Genetic variation and the fate of beneficial mutations in asexual populations. Genetics 188, 647–661 (2011).
Lang, G. I. et al. Pervasive genetic hitchhiking and clonal interference in forty evolving yeast populations. Nature 500, 571–574 (2013).
Martens, E. A., Kostadinov, R., Maley, C. C. & Hallatschek, O. Spatial structure increases the waiting time for cancer. New J. Phys. 13, 115014 (2011).
Maley, C. C. et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nature Genet. 38, 468–473 (2006).
Tilman, D., Reich, P. B. & Knops, J. M. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).
Scheffer, M. et al. Anticipating critical transitions. Science 338, 344–348 (2012).
Clarke, B. Balanced polymorphism and the diversity of sympatric species. Taxon. Geogr. Syst. Assoc. Oxf. 4, 47–70 (1962).
Borghans, J. A., Beltman, J. B. & De Boer, R. J. MHC polymorphism under host-pathogen coevolution. Immunogenetics 55, 732–739 (2004).
Le Gac, M., Plucain, J., Hindré, T., Lenski, R. E. & Schneider, D. Ecological and evolutionary dynamics of coexisting lineages during a long-term experiment with Escherichia coli. Proc. Natl Acad. Sci. 109, 9487–9492 (2012).
Blount, Z. D., Barrick, J. E., Davidson, C. J. & Lenski, R. E. Genomic analysis of a key innovation in an experimental Escherichia coli population. Nature 489, 513–518 (2012).
Poltak, S. R. & Cooper, V. S. Ecological succession in long-term experimentally evolved biofilms produces synergistic communities. ISME J. 5, 369–378 (2010).
Thliveris, A. T. et al. Transformation of epithelial cells through recruitment leads to polyclonal intestinal tumors. Proc. Natl Acad. Sci. 110, 11523–11528 (2013).
Parsons, B. L. Many different tumor types have polyclonal tumor origin: evidence and implications. Mutat. Res. 659, 232–247 (2008).
Floor, S. L., Dumont, J. E., Maenhaut, C. & Raspe, E. Hallmarks of cancer: of all cancer cells, all the time? Trends Mol. Med. 18, 509–515 (2012).
Naumov, G. I., Naumova, E. S., Sancho, E. D. & Korhbla, M. P. Polymeric SUC genes in natural populations of Saccharomyces cerevisiae. FEMS Microbiol. Lett. 135, 31–35 (1996).
De Vargas Roditi, L., Boyle, K. E. & Xavier, J. B. Multilevel selection analysis of a microbial social trait. Mol. Syst. Biol. 9, 684 (2013).
Chao, H. X., Yurtsev, E., Datta, M., Artemova, T. & Gore, J. Bacterial cheating limits antibiotic resistance. Bull. Am. Phys. Soc. 57 (2012).
Nagy, J. D., Victor, E. M. & Cropper, J. H. Why don't all whales have cancer? A novel hypothesis resolving Peto's paradox. Integr. Comp. Biol. 47, 317–328 (2007).
Sanchez, A. & Gore, J. Feedback between population and evolutionary dynamics determines the fate of social microbial populations. PLoS Biol. 11, e1001547 (2013).
Hauert, C., Holmes, M. & Doebeli, M. Evolutionary games and population dynamics: maintenance of cooperation in public goods games. Proc. R. Soc. B Biol. Sci. 273, 2565–2571 (2006).
Hauert, C., Wakano, J. Y. & Doebeli, M. Ecological public goods games: cooperation and bifurcation. Theor. Popul. Biol. 73, 257–263 (2008).
Lindsey, H. A., Gallie, J., Taylor, S. & Kerr, B. Evolutionary rescue from extinction is contingent on a lower rate of environmental change. Nature 494, 463–467 (2013).
McFarland, C. D., Korolev, K. S., Kryukov, G. V., Sunyaev, S. R. & Mirny, L. A. Impact of deleterious passenger mutations on cancer progression. Proc. Natl Acad. Sci. 110, 2910–2915 (2013).
Beckman, R. A. & Loeb, L. A. Negative clonal selection in tumor evolution. Genetics 171, 2123–2131 (2005).
Grande-Pérez, A., Lázaro, E., Lowenstein, P., Domingo, E. & Manrubia, S. C. Suppression of viral infectivity through lethal defection. Proc. Natl Acad. Sci. USA 102, 4448–4452 (2005).
Lynch, M. Mutation accumulation in transfer RNAs: molecular evidence for Muller's ratchet in mitochondrial genomes. Mol. Biol. Evol. 13, 209–220 (1996).
Funchain, P. et al. The consequences of growth of a mutator strain of Escherichia coli as measured by loss of function among multiple gene targets and loss of fitness. Genetics 154, 959–970 (2000).
Gillespie, J. H. Population genetics: a concise guide. (JHU Press, 2010).
Gabriel, W., Lynch, M. & Burger, R. Muller's ratchet and mutational meltdowns. Evolution 47, 1744–1757 (1993).
Muller, H. J. Our load of mutations. Am. J. Hum. Genet. 2, 111 (1950).
Birkbak, N. J. et al. Paradoxical relationship between chromosomal instability and survival outcome in cancer. Cancer Res. 71, 3447–3452 (2011).
Jefferson, E. FDA approves Kyprolis for some patients with multiple myeloma. FDA [online], (2012).
Neckers, L. & Workman, P. Hsp90 molecular chaperone inhibitors: are we there yet? Clin. Cancer Res. 18, 64–76 (2012).
McConkey, D. J. & Zhu, K. Mechanisms of proteasome inhibitor action and resistance in cancer. Drug Resist. Updat. 11, 164–179 (2008).
Jego, G., Hazoumé, A., Seigneuric, R. & Garrido, C. Targeting heat shock proteins in cancer. Cancer Lett. 332, 275–285 (2013).
Kramer, A. M. & Drake, J. M. Experimental demonstration of population extinction due to a predator-driven Allee effect. J. Anim. Ecol. 79, 633–639 (2010).
Dai, L., Vorselen, D., Korolev, K. S. & Gore, J. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336, 1175–1177 (2012).
Dai, L., Korolev, K. S. & Gore, J. Slower recovery in space before collapse of connected populations. Nature 496, 355–358 (2013).
Carpenter, S. R. et al. Early warnings of regime shifts: a whole-ecosystem experiment. Science 332, 1079–1082 (2011).
Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).
Pal, M., Pal, A. K., Ghosh, S. & Bose, I. Early signatures of regime shifts in gene expression dynamics. Phys. Biol. 10, 036010 (2013).
Connolly, J. L., Schnitt, S. J., Wang, H. H., Dvorak, A. M. & Dvorak, H. F. in Cancer Medicine. 6th Edn. Ch. 35. (eds Kufe, D. W. et al.) (Hamilton., BC Decker Inc. 2003).
Gatenby, R. A., Grove, O. & Gillies, R. J. Quantitative imaging in cancer evolution and ecology. Radiology 269, 8–14 (2013).
Rietkerk, M., Dekker, S. C., de Ruiter, P. C. & van de Koppel, J. Self-organized patchiness and catastrophic shifts in ecosystems. Science 305, 1926–1929 (2004).
Phillips, B. L., Brown, G. P., Webb, J. K. & Shine, R. Invasion and the evolution of speed in toads. Nature 439, 803–803 (2006).
Tilman, D. Niche tradeoffs, neutrality, and community structure: a stochastic theory of resource competition, invasion, and community assembly. Proc. Natl Acad. Sci. USA 101, 10854–10861 (2004).
Lambert, G. et al. An analogy between the evolution of drug resistance in bacterial communities and malignant tissues. Nature Rev. Cancer 11, 375–382 (2011).
Ben-Jacob, E., S. Coffey, D. & Levine, H. Bacterial survival strategies suggest rethinking cancer cooperativity. Trends Microbiol. 20 403–410 (2012).
Brown, G. P., Kelehear, C. & Shine, R. The early toad gets the worm: cane toads at an invasion front benefit from higher prey availability. J. Anim. Ecol. 82 854–862 (2013).
Shilton, C. M., Brown, G. P., Benedict, S. & Shine, R. Spinal arthropathy associated with Ochrobactrum anthropi in free-ranging cane toads (Chaunus [Bufo] marinus) in Australia. Vet. Pathol. Online 45, 85–94 (2008).
Van Ditmarsch, D. et al. Convergent evolution of hyperswarming leads to impaired biofilm formation in pathogenic bacteria. Cell Rep. 4, 697–708 (2013).
Aktipis, C. A., Boddy, A. M., Gatenby, R. A., Brown, J. S. & Maley, C. C. Life history trade-offs in cancer evolution. Nature Rev. Cancer 13, 883–892 (2013).
Orlando, P. A., Gatenby, R. A. & Brown, J. S. Cancer treatment as a game: integrating evolutionary game theory into the optimal control of chemotherapy. Phys. Biol. 9, 065007 (2012).
Orlando, P. A., Gatenby, R. A. & Brown, J. S. Tumor evolution in space: the effects of competition colonization tradeoffs on tumor invasion dynamics. Front. Oncol. 3, 45 (2013).
Hallatschek, O., Hersen, P., Ramanathan, S. & Nelson, D. R. Genetic drift at expanding frontiers promotes gene segregation. Proc. Natl Acad. Sci. 104, 19926–19930 (2007).
Korolev, K. S., Xavier, J. B., Nelson, D. R. & Foster, K. R. A quantitative test of population genetics using spatiogenetic patterns in bacterial colonies. Am. Nat. 178, 538 (2011).
Buttery, N. J. et al. Structured growth and genetic drift raise relatedness in the social amoeba Dictyostelium discoideum. Biol. Lett. 8, 794–797 (2012).
González-García, I., Solé, R. V. & Costa, J. Metapopulation dynamics and spatial heterogeneity in cancer. Proc. Natl Acad. Sci. 99, 13085–13089 (2002).
Nadell, C. D., Foster, K. R. & Xavier, J. B. Emergence of spatial structure in cell groups and the evolution of cooperation. PLoS Comput. Biol. 6, e1000716 (2010).
Korolev, K. S. et al. Selective sweeps in growing microbial colonies. Phys. Biol. 9, 026008 (2012).
Korolev, K. S. The fate of cooperation during range expansions. PLoS Comput. Biol. 9, e1002994 (2013).
Datta, M. S., Korolev, K. S., Cvijovic, I., Dudley, C. & Gore, J. Range expansion promotes cooperation in an experimental microbial metapopulation. Proc. Natl Acad. Sci. USA 110, 7354–7359 (2013).
Van Dyken, J. D., Müller, M. J., Mack, K. M. & Desai, M. M. Spatial population expansion promotes the evolution of cooperation in an experimental prisoner's dilemma. Curr. Biol. 23, 919–923 (2013).
Vaupel, P., Kallinowski, F. & Okunieff, P. Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors: a review. Cancer Res. 49, 6449–6465 (1989).
Harris, A. L. Hypoxia—a key regulatory factor in tumour growth. Nature Rev. Cancer 2, 38–47 (2002).
Zhang, Q. et al. Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments. Science 333, 1764–1767 (2011).
Hermsen, R., Deris, J. B. & Hwa, T. On the rapidity of antibiotic resistance evolution facilitated by a concentration gradient. Proc. Natl. Acad. Sci. USA 109, 10775–10780 (2012).
Greulich, P., Waclaw, B. & Allen, R. J. Mutational pathway determines whether drug gradients accelerate evolution of drug-resistant cells. Phys. Rev. Lett. 109, 088101 (2012).
Gatenby, R. A. et al. Cellular adaptations to hypoxia and acidosis during somatic evolution of breast cancer. Br. J. Cancer 97, 646–653 (2007).
Carmona-Fontaine, C. et al. Emergence of spatial structure in the tumor microenvironment due to the Warburg effect. Proc. Natl Acad. Sci. USA 110, 19402–19407 (2013).
Anderson, A. R., Weaver, A. M., Cummings, P. T. & Quaranta, V. Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 127, 905–915 (2006).
Willi, Y., Van Buskirk, J. & Hoffmann, A. A. Limits to the adaptive potential of small populations. Annu. Rev. Ecol. Evol. Syst. 37, 433–458 (2006).
Bozic, I. et al. Accumulation of driver and passenger mutations during tumor progression. Proc. Natl Acad. Sci. USA 107, 18545–18550 (2010).
Jackson, A. L. & Loeb, L. A. The mutation rate and cancer. Genetics 148, 1483–1490 (1998).
Vermeulen, L. et al. Defining stem cell dynamics in models of intestinal tumor initiation. Science 342, 995–998 (2013).
Alcolea, M. P. & Jones, P. H. Tracking cells in their native habitat: lineage tracing in epithelial neoplasia. Nature Rev. Cancer 13, 161–171 (2013).
Schepers, A. G. et al. Lineage tracing reveals Lgr5+ stem cell activity in mouse intestinal adenomas. Science 337, 730–735 (2012).
Driessens, G., Beck, B., Caauwe, A., Simons, B. D. & Blanpain, C. Defining the mode of tumour growth by clonal analysis. Nature 488, 527–530 (2012).
Chen, J. et al. A restricted cell population propagates glioblastoma growth after chemotherapy. Nature 488, 522–526 (2012).
Lopez-Garcia, C., Klein, A. M., Simons, B. D. & Winton, D. J. Intestinal stem cell replacement follows a pattern of neutral drift. Science 330, 822–825 (2010).
Humphries, A. et al. Lineage tracing reveals multipotent stem cells maintain human adenomas and the pattern of clonal expansion in tumor evolution. Proc. Natl Acad. Sci. USA 110, E2490–E2499 (2013).
Momeni, B., Brileya, K. A., Fields, M. W., Shou, W. & Tautz, D. Strong inter-population cooperation leads to partner intermixing in microbial communities. ELife 2. e00230 (2013).
Murray, J. D. Mathematical Biology. 2, (Springer, 2002).
Cleary, A. S., Leonard, T. L., Gestl, S. A. & Gunther, E. J. Tumour cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature 508, 113–117 (2014).
Acknowledgements
J.B.X. is supported by National Cancer Institute Grant CA148967 through the Integrative Cancer Biology Program and by the Office of the Director, US National Institutes of Health, under Award DP2OD008440.
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Glossary
- Allee effect
-
A commonly observed deviation from logistic growth, with the per-capita growth rate reaching a maximum at an intermediate population size. One often distinguishes between a strong Allee effect, when the growth rate is negative at small population sizes, and a weak Allee effect, when the growth rate at small population sizes is small but positive.
- Auto-correlation time
-
The time that it takes deviations of a variable from its stable state to become statistically uncorrelated. This time is closely related to the recovery rate from perturbations.
- Coefficient of variation
-
The ratio of the standard deviation to the mean. The coefficient of variation measures the relative strength of fluctuations.
- Ecological dynamics
-
Describes interactions among species and the changes in their absolute abundances.
- Evolutionary dynamics
-
The emergence of new genotypes and the changes in relative abundances of the existing genotypes, including possible extinctions.
- Evolutionary game theory
-
Describes evolutionary dynamics in a polymorphic population consisting of organisms that use different strategies to succeed at a particular task and in which success depends on the strategies of other individuals, often conceptualized as a game. A typical example is a 'hawk–dove' game that describes a contest over mates. The success of an aggressive (hawk) strategy and a passive (dove) strategy depends on their relative abundance in the population and on how they fare in competition against other organisms with their own strategy and organisms with the opposite strategy.
- Frequency-dependent selection
-
Selection that occurs when the fitnesses of species or genotypes depend on their relative abundances in the population. This type of selection can lead to stable coexistence between two species when species A is more fit than species B; when species A is rare and species B is more fit than species A; or when species B is rare.
- Frequency-independent selection
-
Selection that occurs when the fitness of genotypes or species is independent of their relative abundance. In such situations, the genotype or species with the highest fitness takes over the population.
- Genetic drift
-
The random changes in relative frequencies of different genotypes in a population. The primary cause of genetic drift is the stochastic variation in the number of offspring among organisms with the same fitness. Genetic drifts makes natural selection less efficient: it enables fixation of deleterious mutations, as well as the loss of beneficial mutations.
- Logistic growth
-
A frequently used model of population growth, in which the net growth rate at population size N is rN(1 – N/K). At small population sizes, such populations grow exponentially at the per-capita growth rate r, whereas, at higher population sizes, the per-capita growth is diminished until it reaches zero at N = K. Here, K is the stationary population size, often termed the carrying capacity. Note that the per-capita growth rate is maximal at the smallest population sizes (N = 0).
- Population dynamics
-
An umbrella term that describes both ecological and evolutionary dynamics.
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Korolev, K., Xavier, J. & Gore, J. Turning ecology and evolution against cancer. Nat Rev Cancer 14, 371–380 (2014). https://doi.org/10.1038/nrc3712
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DOI: https://doi.org/10.1038/nrc3712
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