SpringerLink
Log in

Classification and Evolution of Tumor Ecosystem

  • Chapter
  • First Online:
Tumor Ecosystem

  • 351 Accesses

Abstract

The development of neoplasms has been documented since decades ago, and this documentation illustrates the mechanisms of acquired treatment resistance and carcinogenesis. The natural selection forces put on neoplasms by their microenvironmental ecology shape the tumors’ ongoing metamorphosis. Tumors may, however, show variances in the dynamics of cancer ecology and evolution, including the rates of emergence and extinction of new clones, the degree of divergence among the clones, and whether they develop at a more periodic pace or in bursts, both within and across forms of cancer. Many ecological and evolutionary traits of a neoplasm have clinical significance, and in most instances, their clinical significance has not been explored. There is a need for conceptual frameworks and a shared lingo for creating clinical distinctions that reflect the relevant environmental, genetic, and kinetic factors that influence tumor adaption, progression, and therapeutic response. In this chapter, we introduce the quantification of neoplasm using Evo and Eco-index in measuring cancer diversity and how these cancer changes over time.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 117.69
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 160.49
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Maley CC, Aktipis A, Graham TA, Sottoriva A, Boddy AM, Janiszewska M et al (2017) Classifying the evolutionary and ecological features of neoplasms. Nat Rev Cancer 17(10):605–619

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Greaves M, Maley CC (2012) Clonal evolution in cancer. Nature 481(7381):306–313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Mroz EA, Tward AD, Pickering CR, Myers JN, Ferris RL, Rocco JW (2013) High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma. Cancer 119(16):3034–3042

    Article  PubMed  Google Scholar 

  4. Andor N, Graham TA, Jansen M, **a LC, Aktipis CA, Petritsch C et al (2016) Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat Med 22(1):105–113

    Article  CAS  PubMed  Google Scholar 

  5. Wangsa D, Chowdhury SA, Ryott M, Gertz EM, Elmberger G, Auer G et al (2016) Phylogenetic analysis of multiple FISH markers in oral tongue squamous cell carcinoma suggests that a diverse distribution of copy number changes is associated with poor prognosis. Int J Cancer 138(1):98–109

    Article  CAS  PubMed  Google Scholar 

  6. Schwarz RF, Ng CK, Cooke SL, Newman S, Temple J, Piskorz AM et al (2015) Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. PLoS Med 12(2):e1001789

    Article  PubMed  PubMed Central  Google Scholar 

  7. Urbschat S, Rahnenführer J, Henn W, Feiden W, Wemmert S, Linsler S et al (2011) Clonal cytogenetic progression within intratumorally heterogeneous meningiomas predicts tumor recurrence. Int J Oncol 39(6):1601–1608

    PubMed  Google Scholar 

  8. Birkbak NJ, Eklund AC, Li Q, McClelland SE, Endesfelder D, Tan P et al (2011) Paradoxical relationship between chromosomal instability and survival outcome in cancer. Cancer Res 71(10):3447–3452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Roylance R, Endesfelder D, Gorman P, Burrell RA, Sander J, Tomlinson I et al (2011) Relationship of extreme chromosomal instability with long-term survival in a retrospective analysis of primary breast cancer. Cancer Epidemiol Biomarkers Prev 20(10):2183–2194

    Article  PubMed  PubMed Central  Google Scholar 

  10. Maley CC, Galipeau PC, Finley JC, Wongsurawat VJ, Li X, Sanchez CA et al (2006) Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat Genet 38(4):468–473

    Article  CAS  PubMed  Google Scholar 

  11. Merlo LM, Shah NA, Li X, Blount PL, Vaughan TL, Reid BJ et al (2010) A comprehensive survey of clonal diversity measures in Barrett's esophagus as biomarkers of progression to esophageal adenocarcinoma. Cancer Prev Res (Phila) 3(11):1388–1397

    Article  PubMed  Google Scholar 

  12. Martinez P, Timmer MR, Lau CT, Calpe S, Sancho-Serra Mdel C, Straub D et al (2016) Dynamic clonal equilibrium and predetermined cancer risk in Barrett's oesophagus. Nat Commun 7:12158

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lipinski KA, Barber LJ, Davies MN, Ashenden M, Sottoriva A, Gerlinger M (2016) Cancer evolution and the limits of predictability in precision cancer medicine. Trends Cancer 2(1):49–63

    Article  PubMed  PubMed Central  Google Scholar 

  14. Morris LG, Riaz N, Desrichard A, Şenbabaoğlu Y, Hakimi AA, Makarov V et al (2016) Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 7(9):10051–10063

    Article  PubMed  PubMed Central  Google Scholar 

  15. Williams MJ, Werner B, Barnes CP, Graham TA, Sottoriva A (2016) Identification of neutral tumor evolution across cancer types. Nat Genet 48(3):238–244

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. McFarland CD, Korolev KS, Kryukov GV, Sunyaev SR, Mirny LA (2013) Impact of deleterious passenger mutations on cancer progression. Proc Natl Acad Sci U S A 110(8):2910–2915

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. McFarland CD, Mirny LA, Korolev KS (2014) Tug-of-war between driver and passenger mutations in cancer and other adaptive processes. Proc Natl Acad Sci U S A 111(42):15138–15143

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Axelrod R, Axelrod DE, Pienta KJ (2006) Evolution of cooperation among tumor cells. Proc Natl Acad Sci U S A 103(36):13474–13479

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Marusyk A, Tabassum DP, Altrock PM, Almendro V, Michor F, Polyak K (2014) Non-cell-autonomous driving of tumor growth supports sub-clonal heterogeneity. Nature 514(7520):54–58

    Article  CAS  PubMed  Google Scholar 

  20. Marusyk A, Almendro V, Polyak K (2012) Intra-tumor heterogeneity: a looking glass for cancer? Nat Rev Cancer 12(5):323–334

    Article  CAS  PubMed  Google Scholar 

  21. Chapman A, Fernandez del Ama L, Ferguson J, Kamarashev J, Wellbrock C, Hurlstone A (2014) Heterogeneous tumor subpopulations cooperate to drive invasion. Cell Rep 8(3):688–695

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Cleary AS, Leonard TL, Gestl SA, Gunther EJ (2014) Tumor cell heterogeneity maintained by cooperating subclones in Wnt-driven mammary cancers. Nature 508(7494):113–117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Driscoll WW, Pepper JW (2010) Theory for the evolution of diffusible external goods. Evolution 64(9):2682–2687

    Article  PubMed  Google Scholar 

  24. Alizadeh AA, Aranda V, Bardelli A, Blanpain C, Bock C, Borowski C et al (2015) Toward understanding and exploiting tumor heterogeneity. Nat Med 21(8):846–853

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Magurran AE (2004) Measuring biological diversity. Blackwell, Durham

    Google Scholar 

  26. Whittaker RH (1972) Evolution and measurement of species diversity. Taxon 21:213–251

    Article  Google Scholar 

  27. Mroz EA, Tward AD, Hammon RJ, Ren Y, Rocco JW (2015) Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the cancer genome atlas. PLoS Med 12(2):e1001786

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R (2011) UniFrac: an effective distance metric for microbial community comparison. ISME J 5(2):169–172

    Article  PubMed  Google Scholar 

  29. KrĂĽger S, Piro RM (2019) decompTumor2Sig: identification of mutational signatures active in individual tumors. BMC Bioinformatics 20(Suppl 4):152

    Article  PubMed  PubMed Central  Google Scholar 

  30. dos Reis M, Donoghue PC, Yang Z (2016) Bayesian molecular clock dating of species divergences in the genomics era. Nat Rev Genet 17(2):71–80

    Article  PubMed  Google Scholar 

  31. Stephens PJ, Greenman CD, Fu B, Yang F, Bignell GR, Mudie LJ et al (2011) Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144(1):27–40

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Stevens JB, Abdallah BY, Liu G, Ye CJ, Horne SD, Wang G et al (2011) Diverse system stresses: common mechanisms of chromosome fragmentation. Cell Death Dis 2(6):e178

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Dewhurst SM, McGranahan N, Burrell RA, Rowan AJ, Grönroos E, Endesfelder D et al (2014) Tolerance of whole-genome doubling propagates chromosomal instability and accelerates cancer genome evolution. Cancer Discov 4(2):175–185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Li X, Galipeau PC, Paulson TG, Sanchez CA, Arnaudo J, Liu K et al (2014) Temporal and spatial evolution of somatic chromosomal alterations: a case-cohort study of Barrett's esophagus. Cancer Prev Res (Phila) 7(1):114–127

    Article  PubMed  Google Scholar 

  35. Haigh J (1978) The accumulation of deleterious genes in a population—Muller's Ratchet. Theor Popul Biol 14(2):251–267

    Article  CAS  PubMed  Google Scholar 

  36. Drummond AJ, Pybus OG, Rambaut A, Forsberg R, Rodrigo AG (2003) Measurably evolving populations. Trends Ecol 18:481–488

    Article  Google Scholar 

  37. Chmielecki J, Foo J, Oxnard GR, Hutchinson K, Ohashi K, Somwar R et al (2011) Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling. Sci Transl Med 3(90):90ra59

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Maley CC, Galipeau PC, Li X, Sanchez CA, Paulson TG, Reid BJ (2004) Selectively advantageous mutations and hitchhikers in neoplasms: p16 lesions are selected in Barrett's esophagus. Cancer Res 64(10):3414–3427

    Article  CAS  PubMed  Google Scholar 

  39. Gerlinger M, Horswell S, Larkin J, Rowan AJ, Salm MP, Varela I et al (2014) Genomic architecture and evolution of clear cell renal cell carcinomas defined by multiregion sequencing. Nat Genet 46(3):225–233

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366(10):883–892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Beerenwinkel N, Schwarz RF, Gerstung M, Markowetz F (2015) Cancer evolution: mathematical models and computational inference. Syst Biol 64(1):e1–e25

    Article  CAS  PubMed  Google Scholar 

  42. Schwartz R, Schäffer AA (2017) The evolution of tumor phylogenetics: principles and practice. Nat Rev Genet 18(4):213–229

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Amend SR, Pienta KJ (2015) Ecology meets cancer biology: the cancer swamp promotes the lethal cancer phenotype. Oncotarget 6(12):9669–9678

    Article  PubMed  PubMed Central  Google Scholar 

  44. Amend SR, Roy S, Brown JS, Pienta KJ (2016) Ecological paradigms to understand the dynamics of metastasis. Cancer Lett 380(1):237–242

    Article  CAS  PubMed  Google Scholar 

  45. Pienta KJ, Robertson BA, Coffey DS, Taichman RS (2013) The cancer diaspora: metastasis beyond the seed and soil hypothesis. Clin Cancer Res 19(21):5849–5855

    Article  PubMed  Google Scholar 

  46. Yang KR, Mooney SM, Zarif JC, Coffey DS, Taichman RS, Pienta KJ (2014) Niche inheritance: a cooperative pathway to enhance cancer cell fitness through ecosystem engineering. J Cell Biochem 115(9):1478–1485

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Brown JSK, B. P. (2004) Hazardous duty pay and the foraging cost of predation. Ecol Lett 7:999–1014

    Article  Google Scholar 

  48. Aktipis CA, Boddy AM, Gatenby RA, Brown JS, Maley CC (2013) Life history trade-offs in cancer evolution. Nat Rev Cancer 13(12):883–892

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. de Groot AE, Roy S, Brown JS, Pienta KJ, Amend SR (2017) Revisiting seed and soil: examining the primary tumor and cancer cell foraging in metastasis. Mol Cancer Res 15(4):361–370

    Article  PubMed  PubMed Central  Google Scholar 

  50. Lloyd MC, Rejniak KA, Brown JS, Gatenby RA, Minor ES, Bui MM (2015) Pathology to enhance precision medicine in oncology: lessons from landscape ecology. Adv Anat Pathol 22(4):267–272

    Article  PubMed  PubMed Central  Google Scholar 

  51. Maley CC, Koelble K, Natrajan R, Aktipis A, Yuan Y (2015) An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer. Breast Cancer Res 17(1):131

    Article  PubMed  PubMed Central  Google Scholar 

  52. Kirilovsky A, Marliot F, El Sissy C, Haicheur N, Galon J, Pagès F (2016) Rational bases for the use of the Immunoscore in routine clinical settings as a prognostic and predictive biomarker in cancer patients. Int Immunol 28(8):373–382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mlecnik B, Bindea G, Kirilovsky A, Angell HK, Obenauf AC, Tosolini M et al (2016) The tumor microenvironment and Immunoscore are critical determinants of dissemination to distant metastasis. Sci Transl Med 8(327):327ra26

    Article  PubMed  Google Scholar 

  54. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313(5795):1960–1964

    Article  CAS  PubMed  Google Scholar 

  55. Sato E, Olson SH, Ahn J, Bundy B, Nishikawa H, Qian F et al (2005) Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer. Proc Natl Acad Sci U S A 102(51):18538–18543

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Loi S, Sirtaine N, Piette F, Salgado R, Viale G, Van Eenoo F et al (2013) Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02-98. J Clin Oncol 31(7):860–867

    Article  CAS  PubMed  Google Scholar 

  57. Adams S, Gray RJ, Demaria S, Goldstein L, Perez EA, Shulman LN et al (2014) Prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancers from two phase III randomized adjuvant breast cancer trials: ECOG 2197 and ECOG 1199. J Clin Oncol 32(27):2959–2966

    Article  PubMed  PubMed Central  Google Scholar 

  58. Motz GT, Coukos G (2013) Deciphering and reversing tumor immune suppression. Immunity 39(1):61–73

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Galon J, Angell HK, Bedognetti D, Marincola FM (2013) The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity 39(1):11–26

    Article  CAS  PubMed  Google Scholar 

  60. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ et al (2015) Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348(6230):124–128

    Article  CAS  PubMed  Google Scholar 

  61. Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L et al (2015) Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350(6257):207–211

    Article  PubMed  PubMed Central  Google Scholar 

  62. Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM, Desrichard A et al (2014) Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med 371(23):2189–2199

    Article  PubMed  PubMed Central  Google Scholar 

  63. McGranahan N, Furness AJ, Rosenthal R, Ramskov S, Lyngaa R, Saini SK et al (2016) Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351(6280):1463–1469

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Carmona-Fontaine C, Bucci V, Akkari L, Deforet M, Joyce JA, Xavier JB (2013) Emergence of spatial structure in the tumor microenvironment due to the Warburg effect. Proc Natl Acad Sci U S A 110(48):19402–19407

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Fang JS, Gillies RD, Gatenby RA (2008) Adaptation to hypoxia and acidosis in carcinogenesis and tumor progression. Semin Cancer Biol 18(5):330–337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Gatenby RA, Gawlinski ET (2003) The glycolytic phenotype in carcinogenesis and tumor invasion: insights through mathematical models. Cancer Res 63(14):3847–3854

    CAS  PubMed  Google Scholar 

  67. Vander Heiden MG, Cantley LC, Thompson CB (2009) Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324(5930):1029–1033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Riehl A, NĂ©meth J, Angel P, Hess J (2009) The receptor RAGE: bridging inflammation and cancer. Cell Commun Signal 7:12

    Article  PubMed  PubMed Central  Google Scholar 

  69. Lv L, Shao X, Chen H, Ho CT, Sang S (2011) Genistein inhibits advanced glycation end product formation by trap** methylglyoxal. Chem Res Toxicol 24(4):579–586

    Article  CAS  PubMed  Google Scholar 

  70. Grimm EA, Sikora AG, Ekmekcioglu S (2013) Molecular pathways: inflammation-associated nitric-oxide production as a cancer-supporting redox mechanism and a potential therapeutic target. Clin Cancer Res 19(20):5557–5563

    Article  CAS  PubMed  Google Scholar 

  71. Fukumura D, Kashiwagi S, Jain RK (2006) The role of nitric oxide in tumor progression. Nat Rev Cancer 6(7):521–534

    Article  CAS  PubMed  Google Scholar 

  72. Antognelli C, Mezzasoma L, Fettucciari K, Talesa VN (2013) A novel mechanism of methylglyoxal cytotoxicity in prostate cancer cells. Int J Biochem Cell Biol 45(4):836–844

    Article  CAS  PubMed  Google Scholar 

  73. Ghosh M, Talukdar D, Ghosh S, Bhattacharyya N, Ray M, Ray S (2006) In vivo assessment of toxicity and pharmacokinetics of methylglyoxal. Augmentation of the curative effect of methylglyoxal on cancer-bearing mice by ascorbic acid and creatine. Toxicol Appl Pharmacol 212(1):45–58

    Article  CAS  PubMed  Google Scholar 

  74. Schwabe RF, Jobin C (2013) The microbiome and cancer. Nat Rev Cancer 13(11):800–812

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Swidsinski A, Khilkin M, Kerjaschki D, Schreiber S, Ortner M, Weber J et al (1998) Association between intraepithelial Escherichia coli and colorectal cancer. Gastroenterology 115(2):281–286

    Article  CAS  PubMed  Google Scholar 

  76. Perez-Chanona E, Trinchieri G (2016) The role of microbiota in cancer therapy. Curr Opin Immunol 39:75–81

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D et al (2015) The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med 21(8):938–945

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Horn HS (1966) Measurement of "Overlap" in comparative ecological studies. Am Nat 100(914):419–424

    Article  Google Scholar 

  79. Swietach P, Vaughan-Jones RD, Harris AL, Hulikova A (2014) The chemistry, physiology and pathology of pH in cancer. Philos Trans R Soc Lond Ser B Biol Sci 369(1638):20130099

    Article  Google Scholar 

  80. Damaghi M, Tafreshi NK, Lloyd MC, Sprung R, Estrella V, Wojtkowiak JW et al (2015) Chronic acidosis in the tumor microenvironment selects for overexpression of LAMP2 in the plasma membrane. Nat Commun 6:8752

    Article  CAS  PubMed  Google Scholar 

  81. Burns MB, Lynch J, Starr TK, Knights D, Blekhman R (2015) Virulence genes are a signature of the microbiome in the colorectal tumor microenvironment. Genome Med 7(1):55

    Article  PubMed  PubMed Central  Google Scholar 

  82. Gatenby RA, Gillies RJ (2004) Why do cancers have high aerobic glycolysis? Nat Rev Cancer 4(11):891–899

    Article  CAS  PubMed  Google Scholar 

  83. Shiraishi T, Verdone JE, Huang J, Kahlert UD, Hernandez JR, Torga G et al (2015) Glycolysis is the primary bioenergetic pathway for cell motility and cytoskeletal remodeling in human prostate and breast cancer cells. Oncotarget 6(1):130–143

    Article  PubMed  Google Scholar 

  84. Stephens DW, Brown JS, Ydenberg RC (2007) Foraging: behavior and ecology. University of Chicago Press, Chicago

    Book  Google Scholar 

  85. Schmidt M, Voelker HU, Kapp M, Krockenberger M, Dietl J, Kammerer U (2010) Glycolytic phenotype in breast cancer: activation of Akt, up-regulation of GLUT1, TKTL1 and down-regulation of M2PK. J Cancer Res Clin Oncol 136(2):219–225

    Article  CAS  PubMed  Google Scholar 

  86. Perera RM, Bardeesy N (2015) Pancreatic cancer metabolism: breaking it down to build it back up. Cancer Discov 5(12):1247–1261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Jung B, Lee S, Yang IH, Good T, Coté GL (2002) Automated on-line noninvasive optical glucose monitoring in a cell culture system. Appl Spectrosc 56:51–57

    Article  CAS  Google Scholar 

  88. Chen J, Sprouffske K, Huang Q, Maley CC (2011) Solving the puzzle of metastasis: the evolution of cell migration in neoplasms. PLoS One 6(4):e17933

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Aktipis CA, Maley CC, Pepper JW (2012) Dispersal evolution in neoplasms: the role of dysregulated metabolism in the evolution of cell motility. Cancer Prev Res (Phila) 5(2):266–275

    Article  PubMed  Google Scholar 

  90. Lloyd MC, Cunningham JJ, Bui MM, Gillies RJ, Brown JS, Gatenby RA (2016) Darwinian dynamics of Intratumoral heterogeneity: not solely random mutations but also variable environmental selection forces. Cancer Res 76(11):3136–3144

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Vincent TLS, Scheel. (1996) Trade-offs and coexistence in consumer-resource models: it all depends on what and where you eat. Am Nat 148(6):1038–1058

    Article  Google Scholar 

  92. Ferreira SC Jr, Martins ML, Vilela MJ (2002) Reaction-diffusion model for the growth of avascular tumor. Phys Rev E Stat Nonlinear Soft Matter Phys 65(2 Pt 1):021907

    Article  Google Scholar 

  93. DeNicola GM, Cantley LC (2015) Cancer's fuel choice: new flavors for a picky eater. Mol Cell 60(4):514–523

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Ornitz DM, Itoh N (2015) The fibroblast growth factor signaling pathway. Wiley Interdiscip Rev Dev Biol 4(3):215–266

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Hanahan D, Coussens LM (2012) Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21(3):309–322

    Article  CAS  PubMed  Google Scholar 

  96. Martinez-Outschoorn UE, Sotgia F, Lisanti MP (2012) Power surge: supporting cells "fuel" cancer cell mitochondria. Cell Metab 15(1):4–5

    Article  CAS  PubMed  Google Scholar 

  97. Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR et al (2011) Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat Med 17(11):1498–1503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Kalluri R (2016) The biology and function of fibroblasts in cancer. Nat Rev Cancer 16(9):582–598

    Article  CAS  PubMed  Google Scholar 

  99. Ostman A, Augsten M (2009) Cancer-associated fibroblasts and tumor growth—bystanders turning into key players. Curr Opin Genet Dev 19(1):67–73

    Article  PubMed  Google Scholar 

  100. Franco OE, Shaw AK, Strand DW, Hayward SW (2010) Cancer associated fibroblasts in cancer pathogenesis. Semin Cell Dev Biol 21(1):33–39

    Article  CAS  PubMed  Google Scholar 

  101. Hirata E, Girotti MR, Viros A, Hooper S, Spencer-Dene B, Matsuda M et al (2015) Intravital imaging reveals how BRAF inhibition generates drug-tolerant microenvironments with high integrin β1/FAK signaling. Cancer Cell 27(4):574–588

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Folkman J (1971) Tumor angiogenesis: therapeutic implications. N Engl J Med 285(21):1182–1186

    Article  CAS  PubMed  Google Scholar 

  103. Folkman J, Watson K, Ingber D, Hanahan D (1989) Induction of angiogenesis during the transition from hyperplasia to neoplasia. Nature 339(6219):58–61

    Article  CAS  PubMed  Google Scholar 

  104. Richards CH, Mohammed Z, Qayyum T, Horgan PG, McMillan DC (2011) The prognostic value of histological tumor necrosis in solid organ malignant disease: a systematic review. Future Oncol 7(10):1223–1235

    Article  CAS  PubMed  Google Scholar 

  105. Anderson AR, Weaver AM, Cummings PT, Quaranta V (2006) Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 127(5):905–915

    Article  CAS  PubMed  Google Scholar 

  106. Ferriere II, Belthoff JR, Olivieri II, Krackow II (2000) Evolving dispersal: where to go next? Trends Ecol Evol 15(1):5–7

    Article  CAS  PubMed  Google Scholar 

  107. Johnson ML, Gaines MS (1990) Evolution of dispersal: theoretical models and empirical tests using birds and mammals. Annu Rev Ecol Syst 21(1):449–480

    Article  Google Scholar 

  108. Bowler DE, Benton TG (2005) Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics. Biol Rev Camb Philos Soc 80(2):205–225

    Article  PubMed  Google Scholar 

  109. Brizel DM, Scully SP, Harrelson JM, Layfield LJ, Bean JM, Prosnitz LR et al (1996) Tumor oxygenation predicts for the likelihood of distant metastases in human soft tissue sarcoma. Cancer Res 56(5):941–943

    CAS  PubMed  Google Scholar 

  110. Cairns RA, Hill RP (2004) Acute hypoxia enhances spontaneous lymph node metastasis in an orthotopic murine model of human cervical carcinoma. Cancer Res 64(6):2054–2061

    Article  CAS  PubMed  Google Scholar 

  111. Hockel M, Schlenger K, Aral B, Mitze M, Schaffer U, Vaupel P (1996) Association between tumor hypoxia and malignant progression in advanced cancer of the uterine cervix. Cancer Res 56(19):4509–4515

    CAS  PubMed  Google Scholar 

  112. Nordsmark M, Bentzen SM, Rudat V, Brizel D, Lartigau E, Stadler P et al (2005) Prognostic value of tumor oxygenation in 397 head and neck tumors after primary radiation therapy. An international multi-center study. Radiol Oncol 77(1):18–24

    Article  Google Scholar 

  113. Rofstad EK, Galappathi K, Mathiesen B, Ruud EB (2007) Fluctuating and diffusion-limited hypoxia in hypoxia-induced metastasis. Clin Cancer Res 13(7):1971–1978

    Article  CAS  PubMed  Google Scholar 

  114. Mazzone M, Dettori D, de Oliveira RL, Loges S, Schmidt T, Jonckx B et al (2009) Heterozygous deficiency of PHD2 restores tumor oxygenation and inhibits metastasis via endothelial normalization. Cell 136(5):839–851

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Goel S, Duda DG, Xu L, Munn LL, Boucher Y, Fukumura D et al (2011) Normalization of the vasculature for treatment of cancer and other diseases. Physiol Rev 91(3):1071–1121

    Article  CAS  PubMed  Google Scholar 

  116. Zhang Q, Lambert G, Liao D, Kim H, Robin K, Tung CK et al (2011) Acceleration of emergence of bacterial antibiotic resistance in connected microenvironments. Science 333(6050):1764–1767

    Article  CAS  PubMed  Google Scholar 

  117. Tatum JL, Kelloff GJ, Gillies RJ, Arbeit JM, Brown JM, Chao KS et al (2006) Hypoxia: importance in tumor biology, noninvasive measurement by imaging, and value of its measurement in the management of cancer therapy. Int J Radiat Biol 82(10):699–757

    Article  CAS  PubMed  Google Scholar 

  118. Inai T, Mancuso M, Hashizume H, Baffert F, Haskell A, Baluk P et al (2004) Inhibition of vascular endothelial growth factor (VEGF) signaling in cancer causes loss of endothelial fenestrations, regression of tumor vessels, and appearance of basement membrane ghosts. Am J Pathol 165(1):35–52

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Wikström P, Lissbrant IF, Stattin P, Egevad L, Bergh A (2002) Endoglin (CD105) is expressed on immature blood vessels and is a marker for survival in prostate cancer. Prostate 51(4):268–275

    Article  PubMed  Google Scholar 

  120. Evans SM, Judy KD, Dunphy I, Jenkins WT, Nelson PT, Collins R et al (2004) Comparative measurements of hypoxia in human brain tumors using needle electrodes and EF5 binding. Cancer Res 64(5):1886–1892

    Article  CAS  PubMed  Google Scholar 

  121. Ljungkvist AS, Bussink J, Kaanders JH, van der Kogel AJ (2007) Dynamics of tumor hypoxia measured with bioreductive hypoxic cell markers. Radiat Res 167(2):127–145

    Article  CAS  PubMed  Google Scholar 

  122. Chida J, Yamane K, Takei T, Kido H (2012) An efficient extraction method for quantitation of adenosine triphosphate in mammalian tissues and cells. Anal Chim Acta 727:8–12

    Article  CAS  PubMed  Google Scholar 

  123. Chaudhury B, Zhou M, Goldgof DB, Hall LO, Gatenby RA, Gillies RJ et al (2015) Heterogeneity in intratumoral regions with rapid gadolinium washout correlates with estrogen receptor status and nodal metastasis. J Magn Reson Imaging 42(5):1421–1430

    Article  PubMed  PubMed Central  Google Scholar 

  124. Zhou M, Hall L, Goldgof D, Russo R, Balagurunathan Y, Gillies R et al (2014) Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. Transl Oncol 7(1):5–13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Gatenby RA, Grove O, Gillies RJ (2013) Quantitative imaging in cancer evolution and ecology. Radiology 269(1):8–15

    Article  PubMed  PubMed Central  Google Scholar 

  126. Kozak KH, Graham CH, Wiens JJ (2008) Integrating GIS-based environmental data into evolutionary biology. Trends Ecol Evol 23(3):141–148

    Article  PubMed  Google Scholar 

  127. Chan LM, Brown JL, Yoder AD (2011) Integrating statistical genetic and geospatial methods brings new power to phylogeography. Mol Phylogenet Evol 59(2):523–537

    Article  PubMed  Google Scholar 

  128. Millington AC, Walsh SJ, Osborne PE (2013) GIS and remote sensing applications in biogeography and ecology. Springer, Cham

    Google Scholar 

  129. Nawaz S, Heindl A, Koelble K, Yuan Y (2015) Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer. Mod Pathol 28(6):766–777

    Article  CAS  PubMed  Google Scholar 

  130. Lloyd MC, Alfarouk KO, Verduzco D, Bui MM, Gillies RJ, Ibrahim ME et al (2014) Vascular measurements correlate with estrogen receptor status. BMC Cancer 14:279

    Article  PubMed  PubMed Central  Google Scholar 

  131. Yuan Y, Failmezger H, Rueda OM, Ali HR, Gräf S, Chin SF et al (2012) Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci Transl Med 4(157):157ra43

    Article  Google Scholar 

  132. Fujisawa T, Barraclough TG (2013) Delimiting species using single-locus data and the generalized mixed yule coalescent approach: a revised method and evaluation on simulated data sets. Syst Biol 62(5):707–724

    Article  PubMed  PubMed Central  Google Scholar 

  133. Prosperi MC, Prosperi L, Bruselles A, Abbate I, Rozera G, Vincenti D et al (2011) Combinatorial analysis and algorithms for quasispecies reconstruction using next-generation sequencing. BMC Bioinformatics 12:5

    Article  PubMed  PubMed Central  Google Scholar 

  134. Amir el AD, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC et al (2013) viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 31(6):545–552

    Article  Google Scholar 

  135. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M et al (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161(5):1202–1214

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Gatenby RA, Gillies RJ, Brown JS (2011) Of cancer and cave fish. Nat Rev Cancer 11(4):237–238

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Gatenby RA, Cunningham JJ, Brown JS (2014) Evolutionary triage governs fitness in driver and passenger mutations and suggests targeting never mutations. Nat Commun 5:5499

    Article  CAS  PubMed  Google Scholar 

  138. Win T, Miles KA, Janes SM, Ganeshan B, Shastry M, Endozo R et al (2013) Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer. Clin Cancer Res 19(13):3591–3599

    Article  CAS  PubMed  Google Scholar 

  139. Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ (2013) Quantifying tumor heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 40(1):133–140

    Article  PubMed  Google Scholar 

  140. Courchamp F, Clutton-Brock T, Grenfell B (1999) Inverse density dependence and the allee effect. Trends Ecol Evol 14(10):405–410

    Article  CAS  Google Scholar 

  141. Allee WC (1978) Animal aggregations: a study in general sociology. AMS Press, Brooklyn

    Google Scholar 

  142. Kramer AM, Dennis B, Liebhold AM, Drake JM (2009) The evidence for allee effects. Popul Ecol 51:341–354

    Article  Google Scholar 

  143. Weinberg R (2013) The biology of cancer. Garland Science, New York

    Book  Google Scholar 

  144. Greig D, Travisano M (2004) The Prisoner's dilemma and polymorphism in yeast SUC genes. Proc Biol Sci 271(Suppl 3(Suppl 3)):S25–S26

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Gore J, Youk H, van Oudenaarden A (2009) Snowdrift game dynamics and facultative cheating in yeast. Nature 459(7244):253–256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. West SA (2007) The social lives of microbes. Annu Rev Ecol Evol Syst 38:53–77

    Article  Google Scholar 

  147. Elias S, Banin E (2012) Multi-species biofilms: living with friendly neighbors. FEMS Microbiol Rev 36(5):990–1004

    Article  CAS  PubMed  Google Scholar 

  148. Thomlinson RH, Gray LH (1955) The histological structure of some human lung cancers and the possible implications for radiotherapy. Br J Cancer 9(4):539–549

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Li C, Heidt DG, Dalerba P, Burant CF, Zhang L, Adsay V et al (2007) Identification of pancreatic cancer stem cells. Cancer Res 67(3):1030–1037

    Article  CAS  PubMed  Google Scholar 

  150. Frank SA (2007) Dynamics of cancer: incidence, inheritance, and evolution. Princeton University Press, Princeton

    Book  Google Scholar 

  151. Szczepański T, Orfão A, van der Velden VH, San Miguel JF, van Dongen JJ (2001) Minimal residual disease in leukaemia patients. Lancet Oncol 2(7):409–417

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China

    Phei Er Saw & Erwei Song

  2. Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Foshan, China

    Phei Er Saw & Erwei Song

  3. Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China

    Erwei Song

Authors
  1. Phei Er Saw
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erwei Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erwei Song .

Editor information

Editors and Affiliations

  1. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China

    Erwei Song

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Saw, P.E., Song, E. (2023). Classification and Evolution of Tumor Ecosystem. In: Song, E. (eds) Tumor Ecosystem. Springer, Singapore. https://doi.org/10.1007/978-981-99-1183-7_29

Download citation

Publish with us

Policies and ethics

Search

Navigation