Advertisement

Current Pathobiology Reports

, Volume 6, Issue 4, pp 209–218 | Cite as

Cancer Foraging Ecology: Diet Choice, Patch Use, and Habitat Selection of Cancer Cells

  • Sarah R. Amend
  • Robert A. Gatenby
  • Kenneth J. Pienta
  • Joel S. Brown
The Evolutionary and Ecological Pathology of Cancer (C Maley, Section Editor)
  • 30 Downloads
Part of the following topical collections:
  1. Topical Collection on The Evolutionary and Ecological Pathology of Cancer

Abstract

Purpose of Review

Here we connect theories of diet choice, patch use, and habitat selection with cancer biology. Key and only partially answered questions include: Do cancer cells’ uptake of nutrients conform to theory? What are the supply and total mass of resources within tumors? Can cancer cell foraging strategies provide indicators for tumor dynamics and therapies? We advocate for a new research subdiscipline of cancer foraging ecology.

Recent Findings

Foraging ecology studies feeding behaviors of organisms as adaptations. Virtually all of life exhibits adaptations relating to diet, patch use, and habitat selection. Cancer cells likely exhibit selective nutrient uptake (diet), local depletion of resources (patch use), and motility (habitat selection). In fact, the evolution of adaptive feeding strategies by cancer cells may be an additional hallmark of cancer. In aggregate, the feeding behaviors of cancer cells can be devastating—acidosis, hypoxia, cachexia, necrosis, tissue invasion, and metastasis. While these are well known, little is known regarding the nutrient uptake strategies of individual cancer cells. Foraging theory provides a strong theoretical basis for anticipating what cancer cells might do and how research on cancer foraging ecology—with impact on metastasis research and therapeutic intervention—should proceed.

Summary

Normal cells, as “servants” to the whole organism, should not conform to the principles of optimal foraging theory. Cancer cells in response to fluctuating resource supplies, nutrient limitations, and hazards should evolve resource acquisition strategies that are more optimal-foraging-like. Two areas of research make cancer foraging ecology a particularly propitious emerging field. From behavioral and evolutionary ecology, there is a well-developed body of theory suggesting how organisms, including cancer cells, should forage. From cancer cell metabolomics there is a large body of knowledge regarding how cancer cells process and utilize different nutrients as fuel, material, buffers and messenger molecules. We suggest the time is ripe for conjoining foraging ecology with cancer cell metabolomics.

Keywords

Cancer Foraging ecology Consumer-resource dynamics Tumor ecosystems Diet choice Patch use Habitat selection Metabolomics 

Notes

Acknowledgments

We thank Chris Whelan and Mark Lloyd with insightful discussion and pointing us to important papers in the literature.

Funding Information

SRA is supported by American Cancer Society Postdoctoral Fellowship PF-16-025-01-CSM, a Prostate Cancer Foundation Young Investigator award and Patrick C. Walsh Prostate Cancer Research Award; KJP is supported by the NIH/NCI (CA093900 and U54CA210173) and the Prostate Cancer Foundation. JSB and RAG were supported by the European Union’s Horizon 2020 research and innovation program (Marie Sklodowska-Curie grant agreement no. 690817), the James S. McDonnell Foundation grant, “Cancer therapy: Perturbing a complex adaptive system,” a V Foundation grant, NIH/National Cancer Institute (NCI) R01CA170595, Application of Evolutionary Principles to Maintain Cancer Control (PQ21), and NIH/NCI U54CA143970-05 [Physical Science Oncology Network (PSON)] “Cancer as a complex adaptive system.”

Compliance with Ethical Standards

Conflict of Interest

Sarah R. Amend, Robert A. Gatenby, Kenneth J. Pienta, and Joel S. Brown declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This review did not generate, use, or analyze any primary data with human or animal subjects.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Gillies RJ, Brown JS, Anderson ARA, Gatenby RA. Evo-evolutionary causes and consequences of temporal variations in intratumoral blood flow. Nat Rev Cancer. 2018;18(9):576–85Google Scholar
  2. 2.
    Brown JS. Vigilance, patch use and habitat selection: foraging under predation risk. Evol Ecol Res. 1999;1(1):49–71.Google Scholar
  3. 3.
    Thompson CB. Rethinking the regulation of cellular metabolism. Cold Spring Harb Symp Quant Biol. 2011;76:23–9.CrossRefPubMedGoogle Scholar
  4. 4.
    •• Pavlova NN, Thompson CB. The emerging hallmarks of cancer metabolism. Cell Metab. 2016;23(1):27–47. The authors recognize that cancer cells are under selection to acquire nutrients quickly, efficiently and in novel ways. All of this enhances survival and proliferation under conditions of severe nutrient limitation. Hence, they propose a relationship between six hallmarks and metabolic reprogamming: (1) ‘deregulated uptake of glucose and amino acids”’ (2) “use of opportunistic modes of nutrient acquisition”, (3) “use of glycolysis/TCA cycle intermediates for biosynthesis and NADPH production”, (4) “increased demand for nitrogen” [or phosphorus?], (5) ”alterations in metabolite-driven gene regulation”, and (6) “metabolic interactions with the microenvironment”. We see these evolving traits as of the mechanisms propelling cancer cells towards optimal foraging. CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Palm W, Thompson CB. Nutrient acquisition strategies of mammalian cells. Nature. 2017;546(7657):234–42.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    •• Maley CC, Aktipis A, Graham TA, Sottoriva A, Boddy AM, Janiszewska M, et al. Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer. 2017;17(10):605–19. This consensus paper recognizes the eco-evolutionary dynamics that drive changes in cancer cell numbers, the frequency and coexistence of different cancer cell types within the tumor, metastatic potential, and the evolution of traits that promote survivorship and proliferation under what may be fairly homogeneous or heterogeneous tumors. An Evo-Index is defined based on cell type diversity within the tumor and changes in tumor heterogeneity. An Eco-Index considers both resource availability (food) and hazards (predation) that represent a universal property of all ecological systems. The goal of having 16 combinations of low and high Eco- and Evo-indices is to have a straightforward and sound means for clinically scoring cancers. The indices are meant for evaluating prognoses, treatment options and anticipated outcomes. Google Scholar
  7. 7.
    Pulliam HR. On the theory of optimal diets. Am Nat. 1974;108(959):59–74.CrossRefGoogle Scholar
  8. 8.
    Charnov EL. Optimal foraging, the marginal value theorem. Theor Popul Biol. 1976;9(2):129–36.CrossRefGoogle Scholar
  9. 9.
    Morris DW. Toward an ecological synthesis: a case for habitat selection. Oecologia. 2003;136(1):1–13.CrossRefPubMedGoogle Scholar
  10. 10.
    Yang KR, Mooney SM, Zarif JC, Coffey DS, Taichman RS, Pienta KJ. Niche inheritance: a cooperative pathway to enhance cancer cell fitness through ecosystem engineering. J Cell Biochem. 2014;115(9):1478–85.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Ibrahim-Hashim A, J. Gillies R, S. Brown J, A. Gatenby R. Coevolution of tumor cells and their microenvironment: “niche construction in cancer”. In: Ecology and evolution of Cancer. Amsterdam: Elsevier; 2017. p. 111–7.CrossRefGoogle Scholar
  12. 12.
    de Groot AE, Roy S, Brown JS, Pienta KJ, Amend SR. Revisiting seed and soil: examining the primary tumor and cancer cell foraging in metastasis. Mol Cancer Res. 2017;15(4):361–70.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    • Hosios AM, Hecht VC, Danai LV, Johnson MO, Rathmell JC, Steinhauser ML, et al. Amino acids rather than glucose account for the majority of cell mass in proliferating mammalian cells. Dev Cell. 2016;36(5):540–9. The authors explore the contribution of consumed resources to cell mass. They find that amino acids, while consumed at lower rates than glucose and glutamine, compose the majority of proliferating cells’ carbon mass. Moreover, the authors trace the ultimate fates of consumed nutrients, demonstrating that different, seemingly similar, resources are used for different purposes (e.g., glutamine’s role in amino acid biosynthesis). Google Scholar
  14. 14.
    • De Vitto H, Perez-Valencia J, Radosevich JA. Glutamine at focus: versatile roles in cancer. Tumor Biol. 2016;37(2):1541–58. In this comprehensive review of glutamine metabolism in cancer, the authors discuss the myriad of roles for glutamine in proliferative cells, including contribution to protein biomass and its role in essential cellular bioenergetics. They also discuss glutamine addiction in oncogene- and tumor suppressor-driven cancer as a possible therapeutic target. CrossRefGoogle Scholar
  15. 15.
    Gentric G, Mieulet V, Mechta-Grigoriou F. Heterogeneity in cancer metabolism: new concepts in an old field. Antioxid Redox Signal. 2017;26(9):462–85.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Amelio I, Cutruzzolá F, Antonov A, Agostini M, Melino G. Serine and glycine metabolism in cancer. Trends Biochem Sci. 2014;39(4):191–8.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Spinelli JB, Yoon H, Ringel AE, Jeanfavre S, Clish CB, Haigis MC. Metabolic recycling of ammonia via glutamate dehydrogenase supports breast cancer biomass. Science. 2017;358(6365):941–6.CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.CrossRefGoogle Scholar
  19. 19.
    Tilman D. Resource competition and community structure. Princeton: Princeton University Press; 1982.Google Scholar
  20. 20.
    Biswas S, Lunec J, Bartlett K. Non-glucose metabolism in cancer cells-is it all in the fat? Cancer Metastasis Rev. 2012;31(3–4):689–98.CrossRefPubMedGoogle Scholar
  21. 21.
    Brown JS. Coexistence on a seasonal resource. Am Nat. 1989;133(2):168–82.CrossRefGoogle Scholar
  22. 22.
    Warburg O. The metabolism of carcinoma cells. J Cancer Res. 1925;9(1):148–63.CrossRefGoogle Scholar
  23. 23.
    Gatenby RA, Gillies RJ. Why do cancers have high aerobic glycolysis? Nat Rev Cancer. 2004;4(11):891–9.CrossRefPubMedGoogle Scholar
  24. 24.
    Gullino PM, Grantham FH, Courtney AH. Glucose consumption by transplanted tumors in vivo. Cancer Res. 1967;27(6):1031–40.PubMedGoogle Scholar
  25. 25.
    Sauer LA, Stayman JW 3rd, Dauchy RT. Amino acid, glucose, and lactic acid utilization in vivo by rat tumors. Cancer Res. 1982;42(10):4090–7.PubMedGoogle Scholar
  26. 26.
    Cooper GM, Hausman R. A molecular approach. In: The Cell. 2nd ed. Sunderland, MA: Sinauer Associates; 2000.Google Scholar
  27. 27.
    • Bhutia YD, Babu E, Ramachandran S, Ganapathy V. Amino acid transporters in cancer and their relevance to “glutamine addiction”: novel targets for the design of a new class of anticancer drugs. Cancer Res. 2015;75(9):1782–8. The authors highlight a possible new class of anti-cancer therapies aimed at exploiting cancer cells’ glutamine addiction. Specifically, they discuss preclinical studies inhibiting amino acid transporters SLC1A5, SLC7A5, SLC7A11, and SLC6A14, thus impeding resource consumption and leading to cell death. CrossRefPubMedGoogle Scholar
  28. 28.
    •• Duan G, Shi M, Xie L, Xu M, Wang Y, Yan H, et al. Increased glutamine consumption in cisplatin-resistant cells has a negative impact on cell growth. Sci Rep. 2018;8(1):4067. The authors demonstrate the cost of resistance in three cancer cell lines in which there are Cisplatin resistant and sensitive subclones. The mechanism of resistance appears to by the upregulation of glutamine metabolism in a manner that is inefficient yet protects the cells from antioxidant toxicity. The upregulated metabolism may actually permit faster growth rates but at the cost of equilibrium population sizes. The sensitive cells all have higher carrying capacities. In this example, it appears that the sensitive cells have evolved to be efficient foragers and utilizers of glutamine which is then sacrificed for safety in the face of therapy. Such a cost of resistance is essential in models of adaptive therapy. Google Scholar
  29. 29.
    Rathmell JC, Heiden MGV, Harris MH, Frauwirth KA, Thompson CB. In the absence of extrinsic signals, nutrient utilization by lymphocytes is insufficient to maintain either cell size or viability. Mol Cell. 2000;6(3):683–92.CrossRefPubMedGoogle Scholar
  30. 30.
    Barthel A, Okino ST, Liao J, Nakatani K, Li J, Whitlock JP Jr, et al. Regulation of GLUT1 gene transcription by the serine/threonine kinase Akt1. J Biol Chem. 1999;274(29):20281–6.Google Scholar
  31. 31.
    Vander Heiden MG, Plas DR, Rathmell JC, Fox CJ, Harris MH, Thompson CB. Growth factors can influence cell growth and survival through effects on glucose metabolism. Mol Cell Biol. 2001;21(17):5899–912.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Nicklin P, Bergman P, Zhang B, Triantafellow E, Wang H, Nyfeler B, et al. Bidirectional transport of amino acids regulates mTOR and autophagy. Cell. 2009;136(3):521–34.Google Scholar
  33. 33.
    Yanagida O, Kanai Y, Chairoungdua A, Kim DK, Segawa H, Nii T, et al. Human L-type amino acid transporter 1 (LAT1): characterization of function and expression in tumor cell lines. Biochim Biophys Acta. 2001;1514(2):291–302.Google Scholar
  34. 34.
    Gao P, Tchernyshyov I, Chang TC, Lee YS, Kita K, Ochi T, et al. c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature. 2009;458(7239):762–5.Google Scholar
  35. 35.
    Conrad M, Sato H. The oxidative stress-inducible cystine/glutamate antiporter, system x (c) (−) : cystine supplier and beyond. Amino Acids. 2012;42(1):231–46.CrossRefPubMedGoogle Scholar
  36. 36.
    Lloyd MC, Rejniak KA, Brown JS, Gatenby RA, Minor ES, Bui MM. Pathology to enhance precision medicine in oncology: lessons from landscape ecology. Adv Anat Pathol. 2015;22(4):267–72.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Zellmer VR, Zhang S. Evolving concepts of tumor heterogeneity. Cell Biosci. 2014;4(1):69.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Jain RK. Determinants of tumor blood flow: a review. Cancer Res. 1988;48(10):2641–58.PubMedGoogle Scholar
  39. 39.
    Vaupel P, Kallinowski F, Okunieff P. Blood flow, oxygen and nutrient supply, and metabolic microenvironment of human tumors: a review. Cancer Res. 1989;49(23):6449–65.PubMedGoogle Scholar
  40. 40.
    •• Amend SR, Pienta KJ. Ecology meets cancer biology: the cancer swamp promotes the lethal cancer phenotype. Oncotarget. 2015;6(12):9669–78. The authors introduce the concept of cancer-driven ecosystem engineering, autoeutrophication, leading to the formation of a cancer swamp. In particular, the authors highlight the resource poverty and toxin accumulation in a rapidly growing tumor. Importantly, the authors emphasize that a growing and changing tumor, and its associated tumor microenvironment, change over time and with passing generations of cells, thus influencing individual cell phenotype and selecting for more aggressive more motile phenotypes. In addition, the authors discuss how the lethal syndromes that ultimately cause cancer-related deaths are the result of the formation and maintenance of the cancer swamp(s). CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Brown JS. Patch use as an Indicator of habitat preference, predation risk, and competition. Behav Ecol Sociobiol. 1988;22(1):37–47.CrossRefGoogle Scholar
  42. 42.
    Kallinowski F, Runkel S, Fortmeyer HP, Förster H, Vaupel P. L-glutamine: a major substrate for tumor cells in vivo? J Cancer Res Clin Oncol. 1987;113(3):209–15.CrossRefPubMedGoogle Scholar
  43. 43.
    Birsoy K, Possemato R, Lorbeer FK, Bayraktar EC, Thiru P, Yucel B, et al. Metabolic determinants of cancer cell sensitivity to glucose limitation and biguanides. Nature. 2014;508(7494):108–12.Google Scholar
  44. 44.
    Eagle H. The specific amino acid requirements of a human carcinoma cell (strain HeLa) in tissue culture. J Exp Med. 1955;102(1):37–48.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Pirkmajer S, Chibalin AV. Serum starvation: caveat emptor. Am J Phys Cell Phys. 2011;301(2):C272–9.CrossRefGoogle Scholar
  46. 46.
    Degenhardt K, Mathew R, Beaudoin B, Bray K, Anderson D, Chen G, et al. Autophagy promotes tumor cell survival and restricts necrosis, inflammation, and tumorigenesis. Cancer Cell. 2006;10(1):51–64.Google Scholar
  47. 47.
    • Shiraishi T, Verdone JE, Huang J, Kahlert UD, Hernandez JR, Torga G, et al. Glycolysis is the primary bioenergetic pathway for cell motility and cytoskeletal remodeling in human prostate and breast cancer cells. Oncotarget. 2015;6(1):130–43. The authors demonstrate that ATP production via aerobic glycolysis, but not via mitochondrial oxidative respiration, is required for cell motility and cytoskeletal rearrangements. Importantly, while aerobic glycolysis is increased in aggressive and motile cancer cell types, mitochondrial respiration – which supplies the majority of ATP – is unchanged between aggressive and less aggressive types, consistent with the Warburg Effect. Google Scholar
  48. 48.
    Alfarouk KO, Ibrahim ME, Gatenby RA, Brown JS. Riparian ecosystems in human cancers. Evol Appl. 2013;6(1):46–53.CrossRefPubMedGoogle Scholar
  49. 49.
    Aktipis CA, Maley CC, Pepper JW. Dispersal evolution in neoplasms: the role of disregulated metabolism in the evolution of cell motility. Cancer Prev Res. 2012;5(2):266–75.CrossRefGoogle Scholar
  50. 50.
    Zhou M, Chaudhury B, Hall LO, Goldgof DB, Gillies RJ, Gatenby RA. Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction. J Magn Reson Imaging. 2017;46(1):115–23.CrossRefPubMedGoogle Scholar
  51. 51.
    Alfonso JCL, Talkenberger K, Seifert M, Klink B, Hawkins-Daarud A, Swanson KR, et al. The biology and mathematical modelling of glioma invasion: a review. J R Soc Interface. 2017;14(136):20170490.Google Scholar
  52. 52.
    Stephens DW, Brown JS, Ydenberg RC. Foraging: behavior and ecology. Chicago: University of Chicago Press; 2007.CrossRefGoogle Scholar
  53. 53.
    Cantor JR, Sabatini DM. Cancer cell metabolism: one hallmark, many faces. Cancer Discovery. 2012;2(10):881–98.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Ward PS, Thompson CB. Metabolic reprogramming: a cancer hallmark even Warburg did not anticipate. Cancer Cell. 2012;21(3):297–308.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    •• Kotler BP, Morris DW, Brown JS. Direct behavioral indicators as a conservation and management tool. Conserv Behav. 2016;21:307. This review discusses how foraging behaviors (diet choice, patch use and habitat selection) can be used as leading indicators for managing wildlife populations and anticipating future changes in the wildlife’s population size. The paper provides detail on the foraging theories and behaviors that we present for cancer cells. At present we do not have the tools to completely measure such foraging behaviors, but this may have more to with a lack of interest than intrinsic technological hurdles. In addition to being a hallmark of cancer, optimal foraging behaviors may also provide leading indicators for cancer prognoses, therapy efficacies and rapid turnaround of information required to adjust therapies accordingly and successfully. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sarah R. Amend
    • 1
  • Robert A. Gatenby
    • 2
  • Kenneth J. Pienta
    • 1
  • Joel S. Brown
    • 2
  1. 1.The James Buchanan Brady Urological InstituteJohns Hopkins University School of MedicineBaltimoreUSA
  2. 2.Department of Integrated Mathematical OncologyMoffitt Cancer CenterTampaUSA

Personalised recommendations