Quantitative Biology

, Volume 2, Issue 3, pp 85–99 | Cite as

Population dynamics inside cancer biomass driven by repeated hypoxia-reoxygenation cycles

  • Chi Zhang
  • Sha Cao
  • Ying XuEmail author
Research Article


A computational analysis of genome-scale transcriptomic data collected on ∼1,700 tissue samples of three cancer types: breast carcinoma, colon adenocarcinoma and lung adenocarcinoma, revealed that each tissue consists of (at least) two major subpopulations of cancer cells with different capabilities to handle fluctuating O2 levels. The two populations have distinct genomic and transcriptomic characteristics, one accelerating its proliferation under hypoxic conditions and the other proliferating faster with higher O2 levels, referred to as the hypoxia and the reoxygenation subpopulations, respectively. The proportions of the two subpopulations within a cancer tissue change as the average O2 level changes. They both contribute to cancer development but in a complementary manner. The hypoxia subpopulation tends to have higher proliferation rates than the reoxygenation one as well as higher apoptosis rates; and it is largely responsible for the acidic environment that enables tissue invasion and provides protection against attacks from T-cells. In comparison, the reoxygenation subpopulation generates new extracellular matrices in support of further growth of the tumor and strengthens cell-cell adhesion to provide scaffolds to keep all the cells connected. This subpopulation also serves as the major source of growth factors for tissue growth. These data and observations strongly suggest that these two major subpopulations within each tumor work together in a conjugative relationship to allow the tumor to overcome stresses associated with the constantly changing O2 level due to repeated growth and angiogenesis. The analysis results not only reveal new insights about the population dynamics within a tumor but also have implications to our understanding of possible causes of different cancer phenotypes such as diffused versus more tightly connected tumor tissues.


cancer population dynamics intratumor heterogeneity cancer cell subpopulations hypoxia reoxygenation cancer evolution 

Supplementary material

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Supplementary material, approximately 7.97 MB.
40484_2014_32_MOESM2_ESM.pdf (917 kb)
Supplementary material, approximately 916 KB.


  1. 1.
    Xu, X., Hou, Y., Yin, X., Bao, L., Tang, A., Song, L., Li, F., Tsang, S., Wu, K., Wu, H., et al. (2012) Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell, 148, 886–895PubMedCrossRefGoogle Scholar
  2. 2.
    Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med., 366, 883–892PubMedCrossRefGoogle Scholar
  3. 3.
    Hou, Y., Song, L., Zhu, P., Zhang, B., Tao, Y., Xu, X., Li, F., Wu, K., Liang, J., Shao, D., et al. (2012) Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell, 148, 873–885PubMedCrossRefGoogle Scholar
  4. 4.
    Axelson, H., Fredlund, E., Ovenberger, M., Landberg, G. and Påhlman, S. (2005) Hypoxia-induced dedifferentiation of tumor cells—a mechanism behind heterogeneity and aggressiveness of solid tumors. Semin. Cell Dev. Biol., 16, 554–563PubMedCrossRefGoogle Scholar
  5. 5.
    Malec, V., Gottschald, O. R., Li, S., Rose, F., Seeger, W. and Hänze, J. (2010) HIF-1 alpha signaling is augmented during intermittent hypoxia by induction of the Nrf2 pathway in NOX1-expressing adenocarcinoma A549 cells. Free Radic. Biol. Med., 48, 1626–1635PubMedCrossRefGoogle Scholar
  6. 6.
    Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., et al. (2011) Tumour evolution inferred by single-cell sequencing. Nature, 472, 90–94PubMedCrossRefGoogle Scholar
  7. 7.
    The Cancer Genome Atlas Network. (2012) Comprehensive molecular characterization of human colon and rectal cancer. Nature, 487, 330–337PubMedCentralCrossRefGoogle Scholar
  8. 8.
    The Cancer Genome Atlas Network. (2012) Comprehensive molecular portraits of human breast tumours. Nature, 490, 61–70PubMedCentralCrossRefGoogle Scholar
  9. 9.
    Cui, J., Mao, X., Olman, V., Hastings, P. J. and Xu, Y. (2012) Hypoxia and miscoupling between reduced energy efficiency and signaling to cell proliferation drive cancer to grow increasingly faster. J. Mol. Cell Biol., 4, 174–176PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Huang, W., Sherman, B. T. and Lempicki, R. A. (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 4, 44–57CrossRefGoogle Scholar
  11. 11.
    Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M. and Tanabe, M. (2014) Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res., 42, D199–D205PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA, 102, 15545–15550PubMedCentralPubMedCrossRefGoogle Scholar
  13. 13.
    Matsumoto, S., Yasui, H., Mitchell, J. B. and Krishna, M. C. (2010) Imaging cycling tumor hypoxia. Cancer Res., 70, 10019–10023PubMedCentralPubMedCrossRefGoogle Scholar
  14. 14.
    Dewhirst, M.W. (2009) Relationships between cycling hypoxia, HIF-1, angiogenesis and oxidative stress. Radiat. Res., 172, 653–665PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Polotsky, V. Y., Savransky, V., Bevans-Fonti, S., Reinke, C., Li, J., Grigoryev, D. N. and Shimoda, L. A. (2010) Intermittent and sustained hypoxia induce a similar gene expression profile in human aortic endothelial cells. Physiol. Genomics, 41, 306–314PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Dewhirst, M. W. (2007) Intermittent hypoxia furthers the rationale for hypoxia-inducible factor-1 targeting. Cancer Res., 67, 854–855PubMedCrossRefGoogle Scholar
  17. 17.
    Toffoli, S. and Michiels, C. (2008) Intermittent hypoxia is a key regulator of cancer cell and endothelial cell interplay in tumours. FEBS J., 275, 2991–3002PubMedCrossRefGoogle Scholar
  18. 18.
    Weis, S. M. and Cheresh, D. A. (2011) Tumor angiogenesis: molecular pathways and therapeutic targets. Nat. Med., 17, 1359–1370PubMedCrossRefGoogle Scholar
  19. 19.
    Carmeliet, P. and Jain, R. K. (2000) Angiogenesis in cancer and other diseases. Nature, 407, 249–257PubMedCrossRefGoogle Scholar
  20. 20.
    Li, G., Ma, Q., Tang, H., Paterson, A. H. and Xu, Y. (2009) QUBIC: a qualitative biclustering algorithm for analyses of gene expression data. Nucleic Acids Res., 37, e101PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Gao, Y. and Church, G. (2005) Improving molecular cancer class discovery through sparse non-negative matrix factorization. Bioinformatics, 21, 3970–3975PubMedCrossRefGoogle Scholar
  22. 22.
    Brunet, J. P., Tamayo, P., Golub, T. R. and Mesirov, J. P. (2004) Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. USA, 101, 4164–4169PubMedCentralPubMedCrossRefGoogle Scholar
  23. 23.
    Lee, D. D. and Seung, H. S. (1999) Learning the parts of objects by non-negative matrix factorization. Nature, 401, 788–791PubMedCrossRefGoogle Scholar
  24. 24.
    Kong, X. Z., Zheng, C. H., and Wu, Y, Q (2007) Molecular cancer class discovery using non-negative matrix factorization with sparseness constraint. Advanced Intelligent Computing Theories and Applications: With Aspects of Theoretical and Methodological Issues, 4681. Berlin: Springer-Verlag 792–802CrossRefGoogle Scholar
  25. 25.
    Evangelou, M., Rendon, A., Ouwehand, W. H., Wernisch, L. and Dudbridge, F. (2012) Comparison of methods for competitive tests of pathway analysis. PLoS ONE, 7, e41018PubMedCentralPubMedCrossRefGoogle Scholar
  26. 26.
    Tusher, V. G., Tibshirani, R. and Chu, G. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci. USA, 98, 5116–5121PubMedCentralPubMedCrossRefGoogle Scholar
  27. 27.
    Liao, D. and Johnson, R. S. (2007) Hypoxia: a key regulator of angiogenesis in cancer. Cancer Metastasis Rev., 26, 281–290PubMedCrossRefGoogle Scholar
  28. 28.
    Dewhirst, M. W., Cao, Y. and Moeller, B. (2008) Cycling hypoxia and free radicals regulate angiogenesis and radiotherapy response. Nat. Rev. Cancer, 8, 425–437PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Hanahan, D. and Weinberg, R. A. (2011) Hallmarks of cancer: the next generation. Cell, 144, 646–674PubMedCrossRefGoogle Scholar
  30. 30.
    Luoto, K. R., Kumareswaran, R. and Bristow, R. G. (2013) Tumor hypoxia as a driving force in genetic instability. Genome Integr., 4, 5PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Gatenby, R. A., Smallbone, K., Maini, P. K., Rose, F., Averill, J., Nagle, R. B., Worrall, L. and Gillies, R. J. (2007) Cellular adaptations to hypoxia and acidosis during somatic evolution of breast cancer. Br. J. Cancer, 97, 646–653PubMedCentralPubMedCrossRefGoogle Scholar
  32. 32.
    Bondar, T. and Medzhitov, R. (2010) p53-mediated hematopoietic stem and progenitor cell competition. Cell Stem Cell, 6, 309–322PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Vaupel, P. and Mayer, A. (2007) Hypoxia in cancer: significance and impact on clinical outcome. Cancer Metastasis Rev., 26, 225–239PubMedCrossRefGoogle Scholar
  34. 34.
    Vaupel, P. (2008) Hypoxia and aggressive tumor phenotype: implications for therapy and prognosis. Oncologist, 13, 21–26PubMedCrossRefGoogle Scholar
  35. 35.
    Lai, L. C. (2002) Role of steroid hormones and growth factors in breast cancer. Clin. Chem. Lab. Med., 40, 969–974PubMedCrossRefGoogle Scholar
  36. 36.
    Evangelou, A. I., Winter, S. F., Huss, W. J., Bok, R. A. and Greenberg, N. M. (2004) Steroid hormones, polypeptide growth factors, hormone refractory prostate cancer, and the neuroendocrine phenotype. J. Cell. Biochem., 91, 671–683.PubMedCrossRefGoogle Scholar
  37. 37.
    Quatromoni, J. G. and Eruslanov, E. (2012) Tumor-associated macrophages: function, phenotype, and link to prognosis in human lung cancer. Am. J. Transl. Res., 4, 376–389PubMedCentralPubMedGoogle Scholar
  38. 38.
    Hirschhaeuser, F., Sattler, U. G. and Mueller-Klieser, W. (2011) Lactate: a metabolic key player in cancer. Cancer Res., 71, 6921–6925PubMedCrossRefGoogle Scholar
  39. 39.
    Fischer, K., Hoffmann, P., Voelkl, S., Meidenbauer, N., Ammer, J., Edinger, M., Gottfried, E., Schwarz, S., Rothe, G., Hoves, S., et al. (2007) Inhibitory effect of tumor cell-derived lactic acid on human T cells. Blood, 109, 3812–3819PubMedCrossRefGoogle Scholar
  40. 40.
    Greaves, M. and Maley, C. C. (2012) Clonal evolution in cancer. Nature, 481, 306–313PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Gutierrez, A., Laureti, L., Crussard, S., Abida, H., Rodríguez-Rojas, A., Blázquez, J., Baharoglu, Z., Mazel, D., Darfeuille, F., Vogel, J., et al. (2013) β-Lactam antibiotics promote bacterial mutagenesis via an RpoS-mediated reduction in replication fidelity. Nat. Commun., 4, 1610PubMedCentralPubMedCrossRefGoogle Scholar
  42. 42.
    Tompkins, J. D., Nelson, J. L., Hazel, J. C., Leugers, S. L., Stumpf, J. D. and Foster, P. L. (2003) Error-prone polymerase, DNA polymerase IV, is responsible for transient hypermutation during adaptive mutation in Escherichia coli. J. Bacteriol., 185, 3469–3472PubMedCentralPubMedCrossRefGoogle Scholar
  43. 43.
    Millar, T. M., Phan, V. and Tibbles, L. A. (2007) ROS generation in endothelial hypoxia and reoxygenation stimulates MAP kinase signaling and kinase-dependent neutrophil recruitment. Free Radic. Biol. Med., 42, 1165–1177PubMedCrossRefGoogle Scholar
  44. 44.
    Jastroch, M., Divakaruni, A. S., Mookerjee, S., Treberg, J. R. and Brand, M. D. (2010) Mitochondrial proton and electron leaks. Essays Biochem., 47, 53–67PubMedCentralPubMedCrossRefGoogle Scholar
  45. 45.
    Zulueta, J. J., Yu, F. S., Hertig, I. A., Thannickal, V. J. and Hassoun, P. M. (1995) Release of hydrogen peroxide in response to hypoxiareoxygenation: role of an NAD(P)H oxidase-like enzyme in endothelial cell plasma membrane. Am. J. Respir. Cell Mol. Biol., 12, 41–49PubMedCrossRefGoogle Scholar
  46. 46.
    Tas, F., Hansel, H., Belce, A., Ilvan, S., Argon, A., Camlica, H. and Topuz, E. (2005) Oxidative stress in breast cancer. Med. Oncol., 22, 11–15PubMedCrossRefGoogle Scholar
  47. 47.
    Kim, B. M., Choi, J. Y., Kim, Y. J., Woo, H. D. and Chung, H. W. (2007) Reoxygenation following hypoxia activates DNA-damage checkpoint signaling pathways that suppress cell-cycle progression in cultured human lymphocytes. FEBS Lett., 581, 3005–3012PubMedCrossRefGoogle Scholar
  48. 48.
    Sullivan, R., Paré, G. C., Frederiksen, L. J., Semenza, G. L. and Graham, C. H. (2008) Hypoxia-induced resistance to anticancer drugs is associated with decreased senescence and requires hypoxia-inducible factor-1 activity. Mol. Cancer Ther., 7, 1961–1973PubMedCrossRefGoogle Scholar
  49. 49.
    Louie, E., Nik, S., Chen, J. S., Schmidt, M., Song, B., Pacson, C., Chen, X. F., Park, S., Ju, J. and Chen, E. I. (2010) 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, R94PubMedCentralPubMedCrossRefGoogle Scholar
  50. 50.
    Kim, Y., Lin, Q., Glazer, P. M. and Yun, Z. (2009) Hypoxic tumor microenvironment and cancer cell differentiation. Curr. Mol. Med., 9, 425–434PubMedCentralPubMedCrossRefGoogle Scholar
  51. 51.
    Teppo, S., Sundquist, E., Vered, M., Holappa, H., Parkkisenniemi, J., Rinaldi, T., Lehenkari, P., Grenman, R., Dayan, D., Risteli, J., et al. (2013) The hypoxic tumor microenvironment regulates invasion of aggressive oral carcinoma cells. Exp. Cell Res., 319, 376–389PubMedCrossRefGoogle Scholar
  52. 52.
    Weljie, A. M. and Jirik, F. R. (2011) Hypoxia-induced metabolic shifts in cancer cells: moving beyond the Warburg effect. Int. J. Biochem. Cell Biol., 43, 981–989PubMedCrossRefGoogle Scholar
  53. 53.
    Sergeant, G., van Eijsden, R., Roskams, T., Van Duppen, V. and Topal, B. (2012) Pancreatic cancer circulating tumour cells express a cell motility gene signature that predicts survival after surgery. BMC Cancer, 12, 527PubMedCentralPubMedCrossRefGoogle Scholar
  54. 54.
    Ramsköld, D., Luo, S., Wang, Y. C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol., 30, 777–782PubMedCentralPubMedCrossRefGoogle Scholar
  55. 55.
    Denko, N. C. (2008) Hypoxia, HIF1 and glucose metabolism in the solid tumour. Nat. Rev. Cancer, 8, 705–713PubMedCrossRefGoogle Scholar
  56. 56.
    Gillies, R. J., Verduzco, D. and Gatenby, R. A. (2012) Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat. Rev. Cancer, 12, 487–493PubMedCentralPubMedCrossRefGoogle Scholar
  57. 57.
    Scott, B., Sun, C. L., Mao, X., Yu, C., Vohra, B. P., Milbrandt, J. and Crowder, C. M. (2013) Role of oxygen consumption in hypoxia protection by translation factor depletion. J. Exp. Biol., 216, 2283–2292PubMedCentralPubMedCrossRefGoogle Scholar
  58. 58.
    Wheaton, W. W. and Chandel, N. S. (2011) Hypoxia. 2. Hypoxia regulates cellular metabolism. Am. J. Physiol. Cell Physiol., 300, C385–C393PubMedCentralPubMedCrossRefGoogle Scholar
  59. 59.
    Yuan, G., Nanduri, J., Khan, S., Semenza, G. L. and Prabhakar, N. R. (2008) Induction of HIF-1alpha expression by intermittent hypoxia: involvement of NADPH oxidase, Ca2+ signaling, prolyl hydroxylases, and mTOR. J. Cell. Physiol., 217, 674–685PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH 2014

Authors and Affiliations

  1. 1.Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology, Institute of BioinformaticsUniversity of GeorgiaAthensUSA
  2. 2.College of Computer Science and TechnologyJilin UniversityChangchunChina

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