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Eco-evolutionary causes and consequences of temporal changes in intratumoural blood flow

Nature Reviews Cancervolume 18pages576585 (2018) | Download Citation


Temporal changes in blood flow are commonly observed in malignant tumours, but the evolutionary causes and consequences are rarely considered. We propose that stochastic temporal variations in blood flow and microenvironmental conditions arise from the eco-evolutionary dynamics of tumour angiogenesis in which cancer cells, as individual units of selection, can influence and respond only to local environmental conditions. This leads to new vessels arising from the closest available vascular structure regardless of the size or capacity of this parental vessel. These dynamics produce unstable vascular networks with unpredictable spatial and temporal variations in blood flow and microenvironmental conditions. Adaptations of evolving populations to temporally varying environments in nature include increased diversity, greater motility and invasiveness, and highly plastic phenotypes, allowing for broad metabolic adaptability and rapid shifts to high rates of proliferation and profound quiescence. These adaptive strategies, when adopted in cancer cells, promote many commonly observed phenotypic properties including those found in the stem phenotype and in epithelial-to-mesenchymal transition. Temporal variations in intratumoural blood flow, which occur through the promotion of cancer cell phenotypes that facilitate both metastatic spread and resistance to therapy, may have substantial clinical consequences.

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  1. 1.

    Pienta, K. J., McGregor, N., Axelrod, R. & Axelrod, D. E. Ecological therapy for cancer: defining tumors using an ecosystem paradigm suggests new opportunities for novel cancer treatments. Transl Oncol. 1, 158–164 (2008).

  2. 2.

    Greaves, M. Evolutionary determinants of cancer. Cancer Discov. 5, 806–820 (2015).

  3. 3.

    Pries, A. R. & Secomb, T. W. Making microvascular networks work: angiogenesis, remodeling, and pruning. Physiology (Bethesda) 29, 446–455 (2014).

  4. 4.

    Pries, A. R., Reglin, B. & Secomb, T. W. Structural adaptation of microvascular networks: functional roles of adaptive responses. Am. J. Physiol. Heart Circ. Physiol. 281, H1015–H1025 (2001).

  5. 5.

    Sherwood, L., & Cengage Learning (Firm). Human physiology: From Cells to Systems. 7th edn (Brooks/Cole, Cengage Learning, 2010).

  6. 6.

    Mankoff, D. A., Dunnwald, L. K., Partridge, S. C. & Specht, J. M. Blood flow-metabolism mismatch: good for the tumor, bad for the patient. Clin. Cancer Res. 15, 5294–5296 (2009).

  7. 7.

    Betof, A. S. et al. Modulation of murine breast tumor vascularity, hypoxia and chemotherapeutic response by exercise. J. Natl Cancer Inst. 107, djv040 (2015).

  8. 8.

    Brizel, D. M. et al. A comparison of tumor and normal tissue microvascular hematocrits and red cell fluxes in a rat window chamber model. Int. J. Radiat. Oncol. Biol. Phys. 25, 269–276 (1993).

  9. 9.

    Dewhirst, M. W. et al. Microvascular studies on the origins of perfusion-limited hypoxia. Br. J. Cancer Suppl. 27, S247–S251 (1996).

  10. 10.

    Yu, B. et al. Measuring tumor cycling hypoxia and angiogenesis using a side-firing fiber optic probe. J. Biophotonics 7, 552–564 (2014).

  11. 11.

    Eskey, C. J., Koretsky, A. P., Domach, M. M. & Jain, R. K. 2H-nuclear magnetic resonance imaging of tumor blood flow: spatial and temporal heterogeneity in a tissue-isolated mammary adenocarcinoma. Cancer Res. 52, 6010–6019 (1992).

  12. 12.

    Gatenby, R. A. & Brown, J. Mutations, evolution and the central role of a self-defined fitness function in the initiation and progression of cancer. Biochim. Biophys. Acta 1867, 162–166 (2017).

  13. 13.

    Gatenby, R. Cancer biology and Mr. Darwin. Biochim. Biophys. Acta 1867, 67–68 (2017).

  14. 14.

    Gillies, R. J., Schornack, P. A., Secomb, T. W. & Raghunand, N. Causes and effects of heterogeneous perfusion in tumors. Neoplasia 1, 197–207 (1999).

  15. 15.

    Gilead, A., Meir, G. & Neeman, M. The role of angiogenesis, vascular maturation, regression and stroma infiltration in dormancy and growth of implanted MLS ovarian carcinoma spheroids. Int. J. Cancer 108, 524–531 (2004).

  16. 16.

    Semenza, G. L. Hypoxia and cancer. Cancer Metastasis Rev. 26, 223–224 (2007).

  17. 17.

    Kato, Y. et al. Acidic extracellular microenvironment and cancer. Cancer Cell. Int. 13, 89 (2013).

  18. 18.

    Ergon, T. & Ergon, R. When three traits make a line: evolution of phenotypic plasticity and genetic assimilation through linear reaction norms in stochastic environments. J. Evol. Biol. 30, 486–500 (2017).

  19. 19.

    Kivela, S. M., Valimaki, P. & Gotthard, K. Evolution of alternative insect life histories in stochastic seasonal environments. Ecol. Evol. 6, 5596–5613 (2016).

  20. 20.

    Schreiber, S. J. The evolution of patch selection in stochastic environments. Am. Nat. 180, 17–34 (2012).

  21. 21.

    Via, S. & Lande, R. Genotype-environment interaction and the evolution of phenotypic plasticity. Evolution 39, 505–522 (1985).

  22. 22.

    Muller, J., Hense, B. A., Fuchs, T. M., Utz, M. & Potzsche, C. Bet-hedging in stochastically switching environments. J. Theor. Biol. 336, 144–157 (2013).

  23. 23.

    Nichol, D., Robertson-Tessi, M., Jeavons, P. & Anderson, A. R. Stochasticity in the genotype-phenotype map: implications for the robustness and persistence of bet-hedging. Genetics 204, 1523–1539 (2016).

  24. 24.

    Schwinning, S. & Sala, O. E. Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 141, 211–220 (2004).

  25. 25.

    Gordon, C. E. Movement patterns of wintering grassland sparrows in Arizona. Auk 117, 748–759 (2000).

  26. 26.

    Basanta, D. & Anderson, A. R. A. Homeostasis back and forth: an ecoevolutionary perspective of cancer. Cold Spring Harb. Perspect. Med. 7, a028332 (2017).

  27. 27.

    Heeger, D. J. & Ress, D. What does fMRI tell us about neuronal activity? Nat. Rev. Neurosci. 3, 142–151 (2002).

  28. 28.

    Merlo, L. M., Pepper, J. W., Reid, B. J. & Maley, C. C. Cancer as an evolutionary and ecological process. Nat. Rev. Cancer 6, 924–935 (2006).

  29. 29.

    Maley, C. C. et al. Classifying the evolutionary and ecological features of neoplasms. Nat. Rev. Cancer 17, 605–619 (2017).

  30. 30.

    Rieger, H. & Welter, M. Integrative models of vascular remodeling during tumor growth. Wiley Interdiscip. Rev. Syst. Biol. Med. 7, 113–129 (2015).

  31. 31.

    Barker, G. & Odling-Smee, F. J. in Entangled Life: Organisms and Environment in the Biological and Social Sciences: History, Philosophy and Theory of the Life Sciences (eds Desjardins, G. E., Barker, G. & Pearce, T.) 187–211 (Springer, 2014).

  32. 32.

    Odling-Smee, F. J., Laland, K. N. & Feldman, M. W. Niche Construction: the Neglected Process in Evolution (Princeton Univ. Press, 2003).

  33. 33.

    You, L. et al. Spatial versus non-spatial eco-evolutionary dynamics in a tumor growth model. J. Theor. Biol. 435, 78–97 (2017).

  34. 34.

    Laland, K. N., Odling-Smee, F. J. & Feldman, M. W. Evolutionary consequences of niche construction and their implications for ecology. Proc. Natl Acad. Sci. USA 96, 10242–10247 (1999).

  35. 35.

    Fukumura, D. & Jain, R. K. Tumor microvasculature and microenvironment: targets for anti-angiogenesis and normalization. Microvasc. Res. 74, 72–84 (2007).

  36. 36.

    Secomb, T. W., Dewhirst, M. W. & Pries, A. R. Structural adaptation of normal and tumour vascular networks. Basic Clin. Pharmacol. Toxicol. 110, 63–69 (2013).

  37. 37.

    Macklin, P. et al. Multiscale modelling and nonlinear simulation of vascular tumour growth. J. Math. Biol. 58, 765–798 (2009).

  38. 38.

    Liou, G. Y. & Storz, P. Reactive oxygen species in cancer. Free Radic. Res. 44, 479–496 (2010).

  39. 39.

    Khramtsov, V. V. & Gillies, R. J. Janus-faced tumor microenvironment and redox. Antioxid. Redox Signal. 21, 723–729 (2014).

  40. 40.

    Skala, M. C., Fontanella, A., Lan, L., Izatt, J. A. & Dewhirst, M. W. Longitudinal optical imaging of tumor metabolism and hemodynamics. J. Biomed. Opt. 15, 011112 (2010).

  41. 41.

    Wang, J. W. et al. Quantitative assessment of tumor blood flow changes in a murine breast cancer model after adriamycin chemotherapy using contrast-enhanced destruction-replenishment sonography. J. Ultrasound Med. 32, 683–690 (2013).

  42. 42.

    Milosevic, M. F., Fyles, A. W. & Hill, R. P. The relationship between elevated interstitial fluid pressure and blood flow in tumors: a bioengineering analysis. Int. J. Radiat. Oncol. Biol. Phys. 43, 1111–1123 (1999).

  43. 43.

    Jain, R. K. Determinants of tumor blood flow: a review. Cancer Res. 48, 2641–2658 (1988).

  44. 44.

    Pahernik, S. et al. Quantitative imaging of tumour blood flow by contrast-enhanced magnetic resonance imaging. Br. J. Cancer 85, 1655–1663 (2001).

  45. 45.

    Matsumoto, S., Yasui, H., Mitchell, J. B. & Krishna, M. C. Imaging cycling tumor hypoxia. Cancer Res. 70, 10019–10023 (2010).

  46. 46.

    Herman, A. B., Savage, V. M. & West, G. B. A quantitative theory of solid tumor growth, metabolic rate and vascularization. PLoS ONE 6, e22973 (2011).

  47. 47.

    Cairns, R. A., Kalliomaki, T. & Hill, R. P. Acute (cyclic) hypoxia enhances spontaneous metastasis of KHT murine tumors. Cancer Res. 61, 8903–8908 (2001).

  48. 48.

    Dewhirst, M. W. Relationships between cycling hypoxia, HIF-1, angiogenesis and oxidative stress. Radiat. Res. 172, 653–665 (2009).

  49. 49.

    Zhang, G., Palmer, G. M., Dewhirst, M. W. & Fraser, C. L. A dual-emissive-materials design concept enables tumour hypoxia imaging. Nat. Mater. 8, 747–751 (2009).

  50. 50.

    Buckling, A., Brockhurst, M. A., Travisano, M. & Rainey, P. B. Experimental adaptation to high and low quality environments under different scales of temporal variation. J. Evol. Biol. 20, 296–300 (2006).

  51. 51.

    Trotter, M. J., Chaplin, D. J. & Olive, P. L. Use of a carbocyanine dye as a marker of functional vasculature in murine tumours. Br. J. Cancer 59, 706–709 (1989).

  52. 52.

    Durand, R. E. & LePard, N. E. Contribution of transient blood flow to tumour hypoxia in mice. Acta Oncol. 34, 317–323 (1995).

  53. 53.

    Durand, R. E. & Aquino-Parsons, C. Clinical relevance of intermittent tumour blood flow. Acta Oncol. 40, 929–936 (2001).

  54. 54.

    Durand, R. E. Intermittent blood flow in solid tumours — an under-appreciated source of ‘drug resistance’. Cancer Metastasis Rev. 20, 57–61 (2001).

  55. 55.

    Wong, T. Z. et al. PET of hypoxia and perfusion with 62Cu-ATSM and 62Cu-PTSM using a 62Zn/62Cu generator. AJR Am. J. Roentgenol. 190, 427–432 (2008).

  56. 56.

    Benjaminsen, I. C., Brurberg, K. G., Ruud, E. B. & Rofstad, E. K. Assessment of extravascular extracellular space fraction in human melanoma xenografts by DCE-MRI and kinetic modeling. Magn. Reson. Imaging 26, 160–170 (2008).

  57. 57.

    Brurberg, K. G., Benjaminsen, I. C., Dorum, L. M. & Rofstad, E. K. Fluctuations in tumor blood perfusion assessed by dynamic contrast-enhanced MRI. Magn. Reson. Med. 58, 473–481 (2007).

  58. 58.

    Cardenas-Navia, L. I. et al. The pervasive presence of fluctuating oxygenation in tumors. Cancer Res. 68, 5812–5819 (2008).

  59. 59.

    Helmlinger, G., Yuan, F., Dellian, M. & Jain, R. K. Interstitial pH and pO2 gradients in solid tumors in vivo: high-resolution measurements reveal a lack of correlation. Nat. Med. 3, 177–182 (1997).

  60. 60.

    Fontanella, A. N. et al. Quantitative mapping of hemodynamics in the lung, brain, and dorsal window chamber-grown tumors using a novel, automated algorithm. Microcirculation 20, 724–735 (2013).

  61. 61.

    Neeman, M., Dafni, H., Bukhari, O., Braun, R. D. & Dewhirst, M. W. In vivo BOLD contrast MRI mapping of subcutaneous vascular function and maturation: validation by intravital microscopy. Magn. Reson. Med. 45, 887–898 (2001).

  62. 62.

    Menon, R. S. et al. BOLD based functional MRI at 4 Tesla includes a capillary bed contribution: echo-planar imaging correlates with previous optical imaging using intrinsic signals. Magn. Reson. Med. 33, 453–459 (1995).

  63. 63.

    Nevo, U. et al. Diffusion anisotropy MRI for quantitative assessment of recovery in injured rat spinal cord. Magn. Reson. Med. 45, 1–9 (2001).

  64. 64.

    Duyn, J. H. et al. 3-Dimensional functional imaging of human brain using echo-shifted FLASH MRI. Magn. Reson. Med. 32, 150–155 (1994).

  65. 65.

    Baudelet, C. & Gallez, B. Effect of anesthesia on the signal intensity in tumors using BOLD-MRI: comparison with flow measurements by Laser Doppler flowmetry and oxygen measurements by luminescence-based probes. Magn. Reson. Imaging 22, 905–912 (2004).

  66. 66.

    Goncalves, M. R. et al. Decomposition of spontaneous fluctuations in tumour oxygenation using BOLD MRI and independent component analysis. Br. J. Cancer 113, 1168–1177 (2015).

  67. 67.

    Yasui, H. et al. Low-field magnetic resonance imaging to visualize chronic and cycling hypoxia in tumor-bearing mice. Cancer Res. 70, 6427–6436 (2010).

  68. 68.

    Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

  69. 69.

    Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

  70. 70.

    Abzhanov, A. Darwin’s finches: analysis of beak morphological changes during evolution. Cold Spring Harb. Protoc. (2009).

  71. 71.

    Ibrahim-Hashim, A. et al. Defining cancer subpopulations by adaptive strategies rather than molecular properties provides novel insights into intratumoral evolution. Cancer Res. 77, 2242–2254 (2017).

  72. 72.

    Lloyd, M. C. et al. Darwinian dynamics of intratumoral heterogeneity: not solely random mutations but also variable environmental selection forces. Cancer Res. 76, 3136–3144 (2016).

  73. 73.

    Estrella, V. et al. Acidity generated by the tumor microenvironment drives local invasion. Cancer Res. 73, 1524–1535 (2013).

  74. 74.

    Thorn, C. C., Freeman, T. C., Scott, N., Guillou, P. J. & Jayne, D. G. Laser microdissection expression profiling of marginal edges of colorectal tumours reveals evidence of increased lactate metabolism in the aggressive phenotype. Gut 58, 404–412 (2009).

  75. 75.

    Robertson-Tessi, M., Gillies, R. J., Gatenby, R. A. & Anderson, A. R. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res. 75, 1567–1579 (2015).

  76. 76.

    Mitsui, H. et al. Gene expression profiling of the leading edge of cutaneous squamous cell carcinoma: IL-24-driven MMP-7. J. Invest. Dermatol. 134, 1418–1427 (2014).

  77. 77.

    Georgiou, L. et al. Angiogenesis and p53 at the invading tumor edge: prognostic markers for colorectal cancer beyond stage. J. Surg. Res. 131, 118–123 (2006).

  78. 78.

    Brown, G. P., Shilton, C., Phillips, B. L. & Shine, R. Invasion, stress, and spinal arthritis in cane toads. Proc. Natl Acad. Sci. USA 104, 17698–17700 (2007).

  79. 79.

    Martin, C. H. Context dependence in complex adaptive landscapes: frequency and trait-dependent selection surfaces within an adaptive radiation of Caribbean pupfishes. Evolution 70, 1265–1282 (2016).

  80. 80.

    Grime, J. P. & Pierce, S. The Evolutionary Strategies That Shape Ecosystems (Wiley-Blackwell, 2012).

  81. 81.

    Lytle, D. A. Disturbance regimes and life-history evolution. Am. Nat. 157, 525–536 (2001).

  82. 82.

    Nunney, L. Adapting to a changing environment: modeling the interaction of directional selection and plasticity. J. Hered. 107, 15–24 (2016).

  83. 83.

    Merila, J. & Hendry, A. P. Climate change, adaptation, and phenotypic plasticity: the problem and the evidence. Evol. Appl. 7, 1–14 (2014).

  84. 84.

    Crozier, L. G. & Hutchings, J. A. Plastic and evolutionary responses to climate change in fish. Evol. Appl. 7, 68–87 (2014).

  85. 85.

    Gravenmier, C. A., Siddique, M. & Gatenby, R. A. Adaptation to stochastic temporal variations in intratumoral blood flow: the Warburg effect as a bet hedging strategy. Bull. Math. Biol. 80, 954–970 (2018).

  86. 86.

    Hendry, A. P. Key questions on the role of phenotypic plasticity in eco-evolutionary dynamics. J. Hered. 107, 25–41 (2016).

  87. 87.

    Giesel, J. T. Reproductive strategeies as adaptations to life in temprally heterogeneous environments. Annu. Rev. Ecol. Syst. 7, 57–79 (1976).

  88. 88.

    Condon, C., Cooper, B. S., Yeaman, S. & Angilletta, M. J. Jr. Temporal variation favors the evolution of generalists in experimental populations of Drosophila melanogaster. Evolution 68, 720–728 (2014).

  89. 89.

    Egevang, C. et al. Tracking of Arctic terns Sterna paradisaea reveals longest animal migration. Proc. Natl Acad. Sci. USA 107, 2078–2081 (2010).

  90. 90.

    Yilmaz, M. & Christofori, G. EMT, the cytoskeleton, and cancer cell invasion. Cancer Metastasis Rev. 28, 15–33 (2009).

  91. 91.

    Haase, V. H. Oxygen regulates epithelial-to-mesenchymal transition: insights into molecular mechanisms and relevance to disease. Kidney Int. 76, 492–499 (2009).

  92. 92.

    Tilmon, K. J. Specialization, Speciation, and Radiation: the Evolutionary Biology of Herbivorous Insects (Univ. of California Press, 2008).

  93. 93.

    Hoffmann, A. A. & Hercus, M. J. Environmental stress as an evolutionary force. BioScience 50, 217–226 (2000).

  94. 94.

    Audo, M. C. & Diehl, W. Effect of quantity and quality of environmental stress on multilocus heterozygosity — growth relationships in Eisenia fetida (Annelida: Oligochaeta). Heredity 75, 98–105 (1995).

  95. 95.

    Kanarek, A. R. & Webb, C. T. Allee effects, adaptive evolution, and invasion success. Evol. Appl. 3, 122–135 (2010).

  96. 96.

    Brown, C. R. & Brown, M. B. Intense natural selection on body size and wing and tail asymmetry in cliff swallows during severe weather. Evolution 52, 1461–1475 (1998).

  97. 97.

    Prentis, P. J., Wilson, J. R., Dormontt, E. E., Richardson, D. M. & Lowe, A. J. Adaptive evolution in invasive species. Trends Plant Sci 13, 288–294 (2008).

  98. 98.

    Blacher, P., Huggins, T. J. & Bourke, A. F. G. Evolution of ageing, costs of reproduction and the fecundity-longevity trade-off in eusocial insects. Proc. Biol. Sci. (2017).

  99. 99.

    Aktipis, C. A., Boddy, A. M., Gatenby, R. A., Brown, J. S. & Maley, C. C. Life history trade-offs in cancer evolution. Nat. Rev. Cancer 13, 883–892 (2013).

  100. 100.

    Wadsworth, C. B., Woods, W. A. Jr, Hahn, D. A. & Dopman, E. B. One phase of the dormancy developmental pathway is critical for the evolution of insect seasonality. J. Evol. Biol. 26, 2359–2368 (2013).

  101. 101.

    Diniz, D. F. A., de Albuquerque, C. M. R., Oliva, L. O., de Melo-Santos, M. A. V. & Ayres, C. F. J. Diapause and quiescence: dormancy mechanisms that contribute to the geographical expansion of mosquitoes and their evolutionary success. Parasit. Vectors 10, 310 (2017).

  102. 102.

    Chen, E. H., Hou, Q. L., Wei, D. D., Jiang, H. B. & Wang, J. J. Phenotypic plasticity, trade-offs and gene expression changes accompanying dietary restriction and switches in Bactrocera dorsalis (Hendel) (Diptera: Tephritidae). Sci. Rep. 7, 1988 (2017).

  103. 103.

    Jia, D., Jolly, M. K., Kulkarni, P. & Levine, H. Phenotypic plasticity and cell fate decisions in cancer: insights from dynamical systems theory. Cancers (Basel) 9, E70 (2017).

  104. 104.

    Gade, T. P. F. et al. Ischemia induces quiescence and autophagy dependence in hepatocellular carcinoma. Radiology 283, 702–710 (2017).

  105. 105.

    Wang, X. et al. Exit from quiescence displays a memory of cell growth and division. Nat. Commun. 8, 321 (2017).

  106. 106.

    Kassen, R. The experimental evolution of specialists, generalists, and the maintenance of diversity. J. Evol. Biol. 15, 173–190 (2002).

  107. 107.

    Vamosi, J. C., Armbruster, W. S. & Renner, S. S. Evolutionary ecology of specialization: insights from phylogenetic analysis. Proc. Biol. Sci. (2014).

  108. 108.

    Van Tienderen, P. H. Evolution of generalists and specialists in spatially heterogeneous environments. Evolution 45, 1317–1331 (1991).

  109. 109.

    Johnson, K. P., Malenke, J. R. & Clayton, D. H. Competition promotes the evolution of host generalists in obligate parasites. Proc. Biol. Sci. 276, 3921–3926 (2009).

  110. 110.

    Folmes, C. D., Dzeja, P. P., Nelson, T. J. & Terzic, A. Metabolic plasticity in stem cell homeostasis and differentiation. Cell Stem Cell 11, 596–606 (2012).

  111. 111.

    Vander Heiden, M. G. & DeBerardinis, R. J. Understanding the intersections between metabolism and cancer biology. Cell 168, 657–669 (2017).

  112. 112.

    Chaudhury, B. et al. Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis. J. Magn. Reson. Imaging 42, 1421–1430 (2015).

  113. 113.

    Lloyd, M. C. et al. Vascular measurements correlate with estrogen receptor status. BMC Cancer 14, 279 (2014).

  114. 114.

    Brown, J. S., Arel, Y., Abramsky, Z. & Kotler, B. P. Patch use by gerbils (Gerbillus allenbyi) in sandy and rock habitats. J. Mammol. 73, 821–829 (1992).

  115. 115.

    Brown, J. S., Kotler, B. P. & Mitchell, W. A. Competition between birds and mammals: a comparison of giving-up densities between crested larks and gerbils. Evol. Ecol. 11, 757–771 (1997).

  116. 116.

    Berger-Tal, O. & Saltz, D. Conservation Behavior: Applying Behavioral Ecology to Wildlife Conservation and Management (Cambridge Univ. Press, 2016).

  117. 117.

    Steinmetz, R., Garshelis, D. L., Chutipong, W. & Seuaturien, N. Foraging ecology and coexistence of Asiatic bears and sun bears in a seasonal tropical forest in southeast Asia. J. Mammol. 94, 1–18 (2014).

  118. 118.

    Kneitel, J. M. & Chase, J. M. Trade-offs in community ecology: linking spatial scales and species coexistence. Ecol. Lett. 7, 69–80 (2004).

  119. 119.

    Swierniak, A., Krzeslak, M., Student, S. & Rzeszowska-Wolny, J. Development of a population of cancer cells: observation and modeling by a mixed spatial evolutionary games approach. J. Theor. Biol. 405, 94–103 (2016).

  120. 120.

    Marvier, M., Kareiva, P. & Neubert, M. G. Habitat destruction, fragmentation, and disturbance promote invasion by habitat generalists in a multispecies metapopulation. Risk Anal. 24, 869–878 (2004).

  121. 121.

    Sriswasdi, S., Yang, C. C. & Iwasaki, W. Generalist species drive microbial dispersion and evolution. Nat. Commun. 8, 1162 (2017).

  122. 122.

    Ebenhard, T. Colonization in metapopulations: a review of theory and observations. Biol. J. Linnean Soc. 42, 105121 (1991).

  123. 123.

    Lehuede, C., Dupuy, F., Rabinovitch, R., Jones, R. G. & Siegel, P. M. Metabolic plasticity as a determinant of tumor growth and metastasis. Cancer Res. 76, 5201–5208 (2016).

  124. 124.

    McLeman, R. A. & Hunter, L. M. Migration in the context of vulnerability and adaptation to climate change: insights from analogues. Wiley Interdiscip. Rev. Clim. Change 1, 450–461 (2010).

  125. 125.

    Lin, X., Yao, Y., Wang, B., Emlen, D. J. & Lavine, L. C. Ecological trade-offs between migration and reproduction are mediated by the nutrition-sensitive insulin-signaling pathway. Int. J. Biol. Sci. 12, 607–616 (2016).

  126. 126.

    Cannito, S. et al. Redox mechanisms switch on hypoxia-dependent epithelial-mesenchymal transition in cancer cells. Carcinogenesis 29, 2267–2278 (2008).

  127. 127.

    Chen, S. et al. Conversion of epithelial-to-mesenchymal transition to mesenchymal-to-epithelial transition is mediated by oxygen concentration in pancreatic cancer cells. Oncol. Lett. 15, 7144–7152 (2018).

  128. 128.

    Moen, I. et al. Hyperoxic treatment induces mesenchymal-to-epithelial transition in a rat adenocarcinoma model. PLoS ONE 4, e6381 (2009).

  129. 129.

    Bleuven, C. & Landry, C. R. Molecular and cellular bases of adaptation to a changing environment in microorganisms. Proc. Biol. Sci. (2016).

  130. 130.

    Sandberg, T. E., Lloyd, C. J., Palsson, B. O. & Feist, A. M. Laboratory evolution to alternating substrate environments yields distinct phenotypic and genetic adaptive strategies. Appl. Environ. Microbiol. 83, e00410–17 (2017).

  131. 131.

    Linde, N., Fluegen, G. & Aguirre-Ghiso, J. A. The relationship between dormant cancer cells and their microenvironment. Adv. Cancer Res. 132, 45–71 (2016).

  132. 132.

    Lorz, A., Botesteanu, D. A. & Levy, D. Modeling cancer cell growth dynamics in vitro in response to antimitotic drug treatment. Front. Oncol. 7, 189 (2017).

  133. 133.

    Kabraji, S. et al. AKT1(low) quiescent cancer cells persist after neoadjuvant chemotherapy in triple negative breast cancer. Breast Cancer Res. 19, 88 (2017).

  134. 134.

    Lozupone, F. & Fais, S. Cancer cell cannibalism: a primeval option to survive. Curr. Mol. Med. 15, 836–841 (2015).

  135. 135.

    Michalopoulou, E., Bulusu, V. & Kamphorst, J. J. Metabolic scavenging by cancer cells: when the going gets tough, the tough keep eating. Br. J. Cancer 115, 635–640 (2016).

  136. 136.

    Cantor, J. R. & Sabatini, D. M. Cancer cell metabolism: one hallmark, many faces. Cancer Discov. 2, 881–898 (2012).

  137. 137.

    DeBerardinis, R. J., Lum, J. J., Hatzivassiliou, G. & Thompson, C. B. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 7, 11–20 (2008).

  138. 138.

    Cai, C. et al. Intratumoral de novo steroid synthesis activates androgen receptor in castration-resistant prostate cancer and is upregulated by treatment with CYP17A1 inhibitors. Cancer Res. 71, 6503–6513 (2011).

  139. 139.

    Singh, A. & Settleman, J. EMT, cancer stem cells and drug resistance: an emerging axis of evil in the war on cancer. Oncogene 29, 4741–4751 (2010).

  140. 140.

    Chen, W., Dong, J., Haiech, J., Kilhoffer, M. C. & Zeniou, M. Cancer stem cell quiescence and plasticity as major challenges in cancer therapy. Stem Cells Int. 2016, 1740936 (2016).

  141. 141.

    Feramisco, J. R., Gross, M., Kamata, T., Rosenberg, M. & Sweet, R. W. Microinjection of the oncogene form of the human H-ras (T-24) protein results in rapid proliferation of quiescent cells. Cell 38, 109–117 (1984).

  142. 142.

    Fabian, A., Barok, M., Vereb, G. & Szollosi, J. Die hard: are cancer stem cells the Bruce Willises of tumor biology? Cytometry A 75, (67–74 (2009).

  143. 143.

    Friedl, P. & Wolf, K. Tumour-cell invasion and migration: diversity and escape mechanisms. Nat. Rev. Cancer 3, 362–374 (2003).

  144. 144.

    Cairns, R. A. & Hill, R. P. Acute hypoxia enhances spontaneous lymph node metastasis in an orthotopic murine model of human cervical carcinoma. Cancer Res. 64, 2054–2061 (2004).

  145. 145.

    Rofstad, E. K., Galappathi, K., Mathiesen, B. & Ruud, E. B. Fluctuating and diffusion-limited hypoxia in hypoxia-induced metastasis. Clin. Cancer Res. 13, 1971–1978 (2007).

  146. 146.

    Rofstad, E. K., Gaustad, J. V., Egeland, T. A., Mathiesen, B. & Galappathi, K. Tumors exposed to acute cyclic hypoxic stress show enhanced angiogenesis, perfusion and metastatic dissemination. Int. J. Cancer 127, 1535–1546 (2010).

  147. 147.

    Hsieh, C. H. et al. NADPH oxidase subunit 4-mediated reactive oxygen species contribute to cycling hypoxia-promoted tumor progression in glioblastoma multiforme. PLoS ONE 6, e23945 (2011).

  148. 148.

    Brurberg, K. G., Skogmo, H. K., Graff, B. A., Olsen, D. R. & Rofstad, E. K. Fluctuations in pO2 in poorly and well-oxygenated spontaneous canine tumors before and during fractionated radiation therapy. Radiother. Oncol. 77, 220–226 (2005).

  149. 149.

    Brurberg, K. G., Thuen, M., Ruud, E. B. & Rofstad, E. K. Fluctuations in pO2 in irradiated human melanoma xenografts. Radiat. Res. 165, 16–25 (2006).

  150. 150.

    Chaplin, D. J., Olive, P. L. & Durand, R. E. Intermittent blood flow in a murine tumor: radiobiological effects. Cancer Res. 47, 597–601 (1987).

  151. 151.

    Pigott, K. H., Hill, S. A., Chaplin, D. J. & Saunders, M. I. Microregional fluctuations in perfusion within human tumours detected using laser Doppler flowmetry. Radiother. Oncol. 40, 45–50 (1996).

  152. 152.

    Saad, F. et al. Impact of bone-targeted therapies in chemotherapy-naive metastatic castration-resistant prostate cancer patients treated with abiraterone acetate: post hoc analysis of study COU-AA-302. Eur. Urol. 68, 570–577 (2015).

  153. 153.

    Reynolds, T. Y., Rockwell, S. & Glazer, P. M. Genetic instability induced by the tumor microenvironment. Cancer Res. 56, 5754–5757 (1996).

  154. 154.

    Weinmann, M., Jendrossek, V., Guner, D., Goecke, B. & Belka, C. Cyclic exposure to hypoxia and reoxygenation selects for tumor cells with defects in mitochondrial apoptotic pathways. FASEB J. 18, 1906–1908 (2004).

  155. 155.

    Louie, E. et al. Identification of a stem-like cell population by exposing metastatic breast cancer cell lines to repetitive cycles of hypoxia and reoxygenation. Breast Cancer Res. 12, R94 (2010).

  156. 156.

    Nishida, N., Yano, H., Nishida, T., Kamura, T. & Kojiro, M. Angiogenesis in cancer. Vasc. Health Risk Manag. 2, 213–219 (2006).

  157. 157.

    Folkman, J. Tumor angiogenesis: therapeutic implications. N. Engl. J. Med. 285, 1182–1186 (1971).

  158. 158.

    Bergers, G. & Hanahan, D. Modes of resistance to anti-angiogenic therapy. Nat. Rev. Cancer 8, 592–603 (2008).

  159. 159.

    Jain, R. K. Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science 307, 58–62 (2005).

  160. 160.

    Ambrosetti, D. et al. The two glycolytic markers GLUT1 and MCT1 correlate with tumor grade and survival in clear-cell renal cell carcinoma. PLoS ONE 13, e0193477 (2018).

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This work was supported by the following grants from US National Institutes of Health (NIH) National Cancer Institute (NCI): U54CA143970-01, R01CA187532, RO1CA077575 and R01CA170595.

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Nature Reviews Cancer thanks R. M. H. Merks, J. W. Pepper and the anonymous reviewer(s) for their contribution to the peer review of this work.

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Author notes

  1. These authors contributed equally: Robert J. Gillies, Joel S. Brown, Alexander R. A. Anderson, Robert A. Gatenby.


  1. Cancer Biology and Evolution Program, Moffitt Cancer Center, Tampa, FL, USA

    • Robert J. Gillies
    • , Joel S. Brown
    • , Alexander R. A. Anderson
    •  & Robert A. Gatenby


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All authors researched data for the article, substantially contributed to discussion of the content, wrote the article and reviewed and/or edited the manuscript before submission.

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