Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity

Key Points

  • Immunotherapy using antibodies that block immune checkpoints is an emerging success story for some patients with cancer; however, the majority of patients gain no benefit while they can experience considerable toxicity.

  • Biomarkers to predict whether a patient will respond or not would therefore be extremely helpful. Current biomarkers are assessed from tumour tissue or peripheral blood, usually taken before treatment, and many studies have used archival samples.

  • The antitumour immune response after checkpoint blockade displays features of a critical state transition, similar to other complex systems.

  • Complex systems are highly sensitive to the initial conditions, and critical state transitions are notoriously difficult to predict far in advance.

  • Recent advances in mathematics and network biology are making it possible to identify dynamic states moving towards critical transitions.

  • We propose that mapping dynamic biomarkers will prove to be useful for differentiating responding from non-responding patients, and will facilitate the identification of new therapeutic targets to improve the efficacy of current treatments.

Abstract

Recently, there has been a coordinated effort from academic institutions and the pharmaceutical industry to identify biomarkers that can predict responses to immune checkpoint blockade in cancer. Several biomarkers have been identified; however, none has reliably predicted response in a sufficiently rigorous manner for routine use. Here, we argue that the therapeutic response to immune checkpoint blockade is a critical state transition of a complex system. Such systems are highly sensitive to initial conditions, and critical transitions are notoriously difficult to predict far in advance. Nevertheless, warning signals can be detected closer to the tipping point. Advances in mathematics and network biology are starting to make it possible to identify such warning signals. We propose that these dynamic biomarkers could prove to be useful in distinguishing responding from non-responding patients, as well as facilitate the identification of new therapeutic targets for combination therapy.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Dynamic biomarkers in immune checkpoint blockade.
Figure 2: Identifying biomarkers for response in responsive versus non-responsive tumours.
Figure 3: The rugged landscape of possible responses to immune checkpoint blockade: what routes are followed towards complete tumour regression?

References

  1. Poincarre, H. Science and Method (T. Nelson, London, 1914).

    Google Scholar 

  2. Wolchok, J. D. et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122–133 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Robert, C. et al. Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N. Engl. J. Med. 364, 2517–2526 (2011).

    CAS  PubMed  Google Scholar 

  4. Garon, E. B. et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N. Engl. J. Med. 372, 2018–2028 (2015).

    PubMed  Google Scholar 

  5. Motzer, R. J. et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. N. Engl. J. Med. 373, 1803–1813 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Ansell, S. M. et al. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin's lymphoma. N. Engl. J. Med. 372, 311–319 (2015).

    PubMed  Google Scholar 

  7. Hodi, F. S. et al. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363, 711–723 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Rosenberg, J. E. et al. Atezolizumab in patients with locally advanced and metastatic urothelial carcinoma who have progressed following treatment with platinum-based chemotherapy: a single-arm, multicentre, phase 2 trial. Lancet 387, 1909–1920 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Muro, K. et al. Pembrolizumab for patients with PD-L1-positive advanced gastric cancer (keynote-012): a multicentre, open-label, phase 1b trial. Lancet Oncol. 17, 717–726 (2016).

    CAS  PubMed  Google Scholar 

  10. Hamanishi, J. et al. Safety and antitumor activity of anti-PD-1 antibody, nivolumab, in patients with platinum-resistant ovarian cancer. J. Clin. Oncol. 33, 4015–4022 (2015).

    CAS  PubMed  Google Scholar 

  11. Seiwert, T. Y. et al. Safety and clinical activity of pembrolizumab for treatment of recurrent or metastatic squamous cell carcinoma of the head and neck (keynote-012): an open-label, multicentre, phase 1b trial. Lancet Oncol. 17, 956–965 (2016). References 2,3,4,5,6,7,8,9,10,11 are clinical trials demonstrating the tremendous efficacy of immune checkpoint blockade in some patients with cancer and relative inefficacy in others.

    CAS  PubMed  Google Scholar 

  12. Eggermont, A. M., Kroemer, G. & Zitvogel, L. Immunotherapy and the concept of a clinical cure. Eur. J. Cancer 49, 2965–2967 (2013).

    CAS  PubMed  Google Scholar 

  13. Michot, J. M. et al. Immune-related adverse events with immune checkpoint blockade: a comprehensive review. Eur. J. Cancer 54, 139–148 (2016).

    CAS  PubMed  Google Scholar 

  14. Zafar, S. Y. Financial toxicity of cancer care: it's time to intervene. J. Natl Cancer Inst. 108, djv370 (2016).

    PubMed  Google Scholar 

  15. Cohen, J. V. et al. Melanoma brain metastasis pseudoprogression after pembrolizumab treatment. Cancer Immunol. Res. 4, 179–182 (2016).

    PubMed  Google Scholar 

  16. Ribas, A. et al. New challenges in endpoints for drug development in advanced melanoma. Clin. Cancer Res. 18, 336–341 (2012).

    CAS  PubMed  Google Scholar 

  17. Sutmuller, R. P. et al. Synergism of cytotoxic T lymphocyte-associated antigen 4 blockade and depletion of CD25+ regulatory T cells in antitumor therapy reveals alternative pathways for suppression of autoreactive cytotoxic T lymphocyte responses. J. Exp. Med. 194, 823–832 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Simpson, T. R. et al. Fc-dependent depletion of tumor-infiltrating regulatory T cells co-defines the efficacy of anti-CTLA-4 therapy against melanoma. J. Exp. Med. 210, 1695–1710 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhu, Y. et al. CSF1/CSF1R blockade reprograms tumor-infiltrating macrophages and improves response to T-cell checkpoint immunotherapy in pancreatic cancer models. Cancer Res. 74, 5057–5069 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. van Elsas, A. et al. Elucidating the autoimmune and antitumor effector mechanisms of a treatment based on cytotoxic T lymphocyte antigen-4 blockade in combination with a B16 melanoma vaccine: comparison of prophylaxis and therapy. J. Exp. Med. 194, 481–489 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Lesterhuis, W. J. et al. Synergistic effect of CTLA-4 blockade and cancer chemotherapy in the induction of anti-tumor immunity. PLoS One 8, e61895 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Santegoets, S. J. et al. T cell profiling reveals high CD4+CTLA-4+ T cell frequency as dominant predictor for survival after prostate GVAX/ipilimumab treatment. Cancer Immunol. Immunother. 62, 245–256 (2013).

    CAS  PubMed  Google Scholar 

  23. Armand, P. et al. Programmed death-1 blockade with pembrolizumab in patients with classical Hodgkin lymphoma after brentuximab vedotin failure. J. Clin. Oncol. 34, 3733–3739 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Santegoets, S. J. et al. Myeloid derived suppressor and dendritic cell subsets are related to clinical outcome in prostate cancer patients treated with prostate GVAX and ipilimumab. J. Immunother. Cancer 2, 31 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. Pico de Coana, Y. et al. Ipilimumab treatment results in an early decrease in the frequency of circulating granulocytic myeloid-derived suppressor cells as well as their Arginase1 production. Cancer Immunol. Res. 1, 158–162 (2013).

    CAS  PubMed  Google Scholar 

  26. Martinez-Lostao, L., Anel, A. & Pardo, J. How do cytotoxic lymphocytes kill cancer cells? Clin. Cancer Res. 21, 5047–5056 (2015).

    CAS  PubMed  Google Scholar 

  27. Halle, S. et al. In vivo killing capacity of cytotoxic T cells is limited and involves dynamic interactions and T cell cooperativity. Immunity 44, 233–245 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015). References 28,29,30 report on the correlation between pre-treatment biomarkers and the response to immune checkpoint blockade.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Larkin, J. et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N. Engl. J. Med. 373, 23–34 (2015).

    PubMed  PubMed Central  Google Scholar 

  32. Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366, 2443–2454 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Borghaei, H. et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N. Engl. J. Med. 373, 1627–1639 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Blank, C. U., Haanen, J. B., Ribas, A. & Schumacher, T. N. Cancer immunology. The “cancer immunogram”. Science 352, 658–660 (2016).

    CAS  PubMed  Google Scholar 

  36. Sacher, A. G. & Gandhi, L. Biomarkers for the clinical use of PD-1/PD-L1 inhibitors in non-small-cell lung cancer: a review. JAMA Oncol. 2, 1217–1222 (2016).

    PubMed  Google Scholar 

  37. Topalian, S. L., Taube, J. M., Anders, R. A. & Pardoll, D. M. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat. Rev. Cancer 16, 275–287 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. van Elsas, A., Hurwitz, A. A. & Allison, J. P. Combination immunotherapy of B16 melanoma using anti-cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and granulocyte/macrophage colony-stimulating factor (GM-CSF)-producing vaccines induces rejection of subcutaneous and metastatic tumors accompanied by autoimmune depigmentation. J. Exp. Med. 190, 355–366 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Lesterhuis, W. J. et al. Network analysis of immunotherapy-induced regressing tumours identifies novel synergistic drug combinations. Sci. Rep. 5, 12298 (2015). Network analysis of transcriptomic data pinpointed response-associated molecular modules and hubs that could be targeted to improve the response rate.

    PubMed  PubMed Central  Google Scholar 

  40. Grosso, J. F. & Jure-Kunkel, M. N. CTLA-4 blockade in tumor models: an overview of preclinical and translational research. Cancer Immun. 13, 5 (2013).

    PubMed  PubMed Central  Google Scholar 

  41. Woo, S. R. et al. Immune inhibitory molecules LAG-3 and PD-1 synergistically regulate T-cell function to promote tumoral immune escape. Cancer Res. 72, 917–927 (2012).

    CAS  PubMed  Google Scholar 

  42. Koues, O. I. et al. Distinct gene regulatory pathways for human innate versus adaptive lymphoid cells. Cell 165, 1134–1146 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Hasbold, J., Corcoran, L. M., Tarlinton, D. M., Tangye, S. G. & Hodgkin, P. D. Evidence from the generation of immunoglobulin G-secreting cells that stochastic mechanisms regulate lymphocyte differentiation. Nat. Immunol. 5, 55–63 (2004).

    CAS  PubMed  Google Scholar 

  44. Germain, R. N. The art of the probable: system control in the adaptive immune system. Science 293, 240–245 (2001).

    CAS  PubMed  Google Scholar 

  45. Feinerman, O., Veiga, J., Dorfman, J. R., Germain, R. N. & Altan-Bonnet, G. Variability and robustness in T cell activation from regulated heterogeneity in protein levels. Science 321, 1081–1084 (2008). This paper provides an example of how randomness at a cellular level is managed to enable controlled variability at a population level.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Sallusto, F. et al. Switch in chemokine receptor expression upon TCR stimulation reveals novel homing potential for recently activated T cells. Eur. J. Immunol. 29, 2037–2045 (1999).

    CAS  PubMed  Google Scholar 

  47. Gerner, M. Y., Torabi-Parizi, P. & Germain, R. N. Strategically localized dendritic cells promote rapid T cell responses to lymph-borne particulate antigens. Immunity 42, 172–185 (2015).

    CAS  PubMed  Google Scholar 

  48. Duffy, K. R. & Hodgkin, P. D. Intracellular competition for fates in the immune system. Trends Cell Biol. 22, 457–464 (2012).

    CAS  PubMed  Google Scholar 

  49. Duffy, K. R. et al. Activation-induced B cell fates are selected by intracellular stochastic competition. Science 335, 338–341 (2012).

    CAS  PubMed  Google Scholar 

  50. Ashkenazi, A. & Salvesen, G. Regulated cell death: signaling and mechanisms. Annu. Rev. Cell Dev. Biol. 30, 337–356 (2014).

    CAS  PubMed  Google Scholar 

  51. Janes, K. A. et al. A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310, 1646–1653 (2005).

    CAS  PubMed  Google Scholar 

  52. Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Pepys, M. B. & Hirschfield, G. M. C-Reactive protein: a critical update. J. Clin. Invest. 111, 1805–1812 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Dinarello, C. A. Proinflammatory and anti-inflammatory cytokines as mediators in the pathogenesis of septic shock. Chest 112, 321S–329S (1997).

    CAS  PubMed  Google Scholar 

  55. Matzinger, P. & Kamala, T. Tissue-based class control: the other side of tolerance. Nat. Rev. Immunol. 11, 221–230 (2011).

    CAS  PubMed  Google Scholar 

  56. Amit, I., Winter, D. R. & Jung, S. The role of the local environment and epigenetics in shaping macrophage identity and their effect on tissue homeostasis. Nat. Immunol. 17, 18–25 (2016).

    CAS  PubMed  Google Scholar 

  57. Doedens, A. L. et al. Hypoxia-inducible factors enhance the effector responses of CD8+ T cells to persistent antigen. Nat. Immunol. 14, 1173–1182 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Buck, M. D., O'Sullivan, D. & Pearce, E. L. T cell metabolism drives immunity. J. Exp. Med. 212, 1345–1360 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Woo, S. R., Corrales, L. & Gajewski, T. F. Innate immune recognition of cancer. Annu. Rev. Immunol. 33, 445–474 (2015).

    CAS  PubMed  Google Scholar 

  60. Sivan, A. et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350, 1084–1089 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Holland, J. H. Studying complex adaptive systems. J. Syst. Sci. Complex. 19, 1–8 (2006).

    Google Scholar 

  62. Subramanian, V. G., Duffy, K. R., Turner, M. L. & Hodgkin, P. D. Determining the expected variability of immune responses using the cyton model. J. Math. Biol. 56, 861–892 (2008).

    PubMed  Google Scholar 

  63. Hodgkin, P. D., Dowling, M. R. & Duffy, K. R. Why the immune system takes its chances with randomness. Nat. Rev. Immunol. 14, 711 (2014).

    CAS  PubMed  Google Scholar 

  64. Buchholz, V. R., Schumacher, T. N. & Busch, D. H. T. Cell fate at the single-cell level. Annu. Rev. Immunol. 34, 65–92 (2016).

    CAS  PubMed  Google Scholar 

  65. Hodgkin, P. D. A probabilistic view of immunology: drawing parallels with physics. Immunol. Cell Biol. 85, 295–299 (2007). References 44, 63 and 65 provide insightful perspectives on how the immune system functions on a systems level and how this function is shaped on a lower level through random and probabilistic events.

    CAS  PubMed  Google Scholar 

  66. Angus, D. C. & van der Poll, T. Severe sepsis and septic shock. N. Engl. J. Med. 369, 840–851 (2013).

    CAS  PubMed  Google Scholar 

  67. Stevens, D. L. The flesh-eating bacterium: what's next? J. Infect. Dis. 179 (Suppl. 2), S366–S374 (1999).

    PubMed  Google Scholar 

  68. Suntharalingam, G. et al. Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N. Engl. J. Med. 355, 1018–1028 (2006).

    CAS  PubMed  Google Scholar 

  69. Chapman, P. B., D'Angelo, S. P. & Wolchok, J. D. Rapid eradication of a bulky melanoma mass with one dose of immunotherapy. N. Engl. J. Med. 372, 2073–2074 (2015).

    PubMed  Google Scholar 

  70. Scheffer, M. et al. Anticipating critical transitions. Science 338, 344–348 (2012).

    CAS  PubMed  Google Scholar 

  71. Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009). References 70 and 71 provide an excellent overview of critical transitions in many complex systems in nature and society.

    CAS  PubMed  Google Scholar 

  72. Chen, L., Liu, R., Liu, Z. P., Li, M. & Aihara, K. Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Sci. Rep. 2, 342 (2012).

    PubMed  PubMed Central  Google Scholar 

  73. Liu, R., Aihara, K. & Chen, L. Dynamical network biomarkers for identifying critical transitions and their driving networks of biologic processes. Quant. Biol. 1, 105–114 (2013).

    CAS  Google Scholar 

  74. Liu, R. et al. Identifying critical transitions and their leading biomolecular networks in complex diseases. Sci. Rep. 2, 813 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Liu, R., Chen, P., Aihara, K. & Chen, L. Identifying early-warning signals of critical transitions with strong noise by dynamical network markers. Sci. Rep. 5, 17501 (2015). References 72,73,74,75 report on the development of mathematical models to identify dynamic network biomarkers that can predict a critical transition from a healthy to a disease state.

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Wu, F. X., Wu, L., Wang, J., Liu, J. & Chen, L. Transittability of complex networks and its applications to regulatory biomolecular networks. Sci. Rep. 4, 4819 (2014).

    PubMed  PubMed Central  Google Scholar 

  77. Veraart, A. J. et al. Recovery rates reflect distance to a tipping point in a living system. Nature 481, 357–359 (2012).

    CAS  Google Scholar 

  78. Boccaletti, S., Grebogi, C., Lai, Y. C., Mancini, H. & Maza, D. The control of chaos: theory and applications. Phys. Rep. 329, 103–197 (2000).

    Google Scholar 

  79. Ott, E., Grebogi, C. & Yorke, J. A. Controlling chaos. Phys. Rev. Lett. 64, 1196–1199 (1990). This seminal paper introduces the concept (now known as OGY control) of exploiting chaos to drive a chaotic system to an arbitrary desired state.

    CAS  PubMed  Google Scholar 

  80. Page, D. B., Postow, M. A., Callahan, M. K., Allison, J. P. & Wolchok, J. D. Immune modulation in cancer with antibodies. Annu. Rev. Med. 65, 185–202 (2014).

    CAS  PubMed  Google Scholar 

  81. Pitt, J. M. et al. Resistance mechanisms to immune-checkpoint blockade in cancer: tumor-intrinsic and -extrinsic factors. Immunity 44, 1255–1269 (2016).

    CAS  PubMed  Google Scholar 

  82. Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Saenger, Y. et al. Blood mRNA expression profiling predicts survival in patients treated with tremelimumab. Clin. Cancer Res. 20, 3310–3318 (2014).

    CAS  PubMed  Google Scholar 

  84. Das, R. et al. Combination therapy with anti-CTLA-4 and anti-PD-1 leads to distinct immunologic changes in vivo. J. Immunol. 194, 950–959 (2015).

    CAS  PubMed  Google Scholar 

  85. Ji, R. R. et al. An immune-active tumor microenvironment favors clinical response to ipilimumab. Cancer Immunol. Immunother. 61, 1019–1031 (2012).

    CAS  PubMed  Google Scholar 

  86. Herbst, R. S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Hegde, P. S., Karanikas, V. & Evers, S. The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition. Clin. Cancer Res. 22, 1865–1874 (2016).

    CAS  PubMed  Google Scholar 

  88. Chen, P. L. et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).

    PubMed  PubMed Central  Google Scholar 

  89. Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Hudson, N. J., Dalrymple, B. P. & Reverter, A. Beyond differential expression: the quest for causal mutations and effector molecules. BMC Genomics 13, 356 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Alvarez, M. J. et al. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat. Genet. 48, 838–847 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Barabasi, A. L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nat. Rev. Genetics 5, 101–113 (2004).

    CAS  PubMed  Google Scholar 

  93. Vidal, M., Cusick, M. E. & Barabasi, A. L. Interactome networks and human disease. Cell 144, 986–998 (2011). References 92 and 93 review the principles underlying and the application of network science in the field of biology.

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Talukdar, H. A. et al. Cross-tissue regulatory gene networks in coronary artery disease. Cell Syst. 2, 196–208 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Yu, H., Kim, P. M., Sprecher, E., Trifonov, V. & Gerstein, M. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput. Biol. 3, e59 (2007).

    PubMed  PubMed Central  Google Scholar 

  96. Albert, R., Jeong, H. & Barabasi, A. L. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000).

    CAS  PubMed  Google Scholar 

  97. Jeong, H., Mason, S. P., Barabasi, A. L. & Oltvai, Z. N. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    CAS  PubMed  Google Scholar 

  98. Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. From molecular to modular cell biology. Nature 402, C47–C52 (1999).

    CAS  PubMed  Google Scholar 

  99. Segal, E., Friedman, N., Koller, D. & Regev, A. A module map showing conditional activity of expression modules in cancer. Nat. Genet. 36, 1090–1098 (2004).

    CAS  PubMed  Google Scholar 

  100. Costanzo, M. et al. The genetic landscape of a cell. Science 327, 425–431 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257–263 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Bosco, A., Ehteshami, S., Panyala, S. & Martinez, F. D. Interferon regulatory factor 7 is a major hub connecting interferon-mediated responses in virus-induced asthma exacerbations in vivo. J. Allergy Clin. Immunol. 129, 88–94 (2012).

    CAS  PubMed  Google Scholar 

  103. Bosco, A., McKenna, K. L., Firth, M. J., Sly, P. D. & Holt, P. G. A network modeling approach to analysis of the Th2 memory responses underlying human atopic disease. J. Immunol. 182, 6011–6021 (2009).

    CAS  PubMed  Google Scholar 

  104. Kohanski, M. A., Dwyer, D. J., Wierzbowski, J., Cottarel, G. & Collins, J. J. Mistranslation of membrane proteins and two-component system activation trigger antibiotic-mediated cell death. Cell 135, 679–690 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Carro, M. S. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318–325 (2010).

    CAS  PubMed  Google Scholar 

  106. Quax, R., Apolloni, A. & Sloot, P. M. The diminishing role of hubs in dynamical processes on complex networks. J. R. Soc. Interface 10, 20130568 (2013).

    PubMed  PubMed Central  Google Scholar 

  107. Tanaka, G., Morino, K. & Aihara, K. Dynamical robustness in complex networks: the crucial role of low-degree nodes. Sci. Rep. 2, 232 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Watts, D. J. A simple model of global cascades on random networks. Proc. Natl Acad. Sci. USA 99, 5766–5771 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Watts, D. J. & Dodds, P. S. Influentials, networks, and public opinion formation. J. Consumer Res. 34, 441–458 (2007).

    Google Scholar 

  110. Duijn, P. A., Kashirin, V. & Sloot, P. M. The relative ineffectiveness of criminal network disruption. Sci. Rep. 4, 4238 (2014).

    PubMed  PubMed Central  Google Scholar 

  111. Luscombe, N. M. et al. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431, 308–312 (2004). References 106,107,108,109,110,111 provide examples of the limitations of using network analyses of static data to interpret dynamic processes in complex systems.

    CAS  PubMed  Google Scholar 

  112. Shi, J., Li, T. & Chen, L. Towards a critical transition theory under different temporal scales and noise strengths. Phys. Rev. E 93, 032137 (2016). Reference 112 provides a mathematical framework from which to develop a critical transition theory as a function of three time scales.

    PubMed  Google Scholar 

  113. Liu, R., Wang, X., Aihara, K. & Chen, L. Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers. Med. Res. Rev. 34, 455–478 (2014).

    PubMed  Google Scholar 

  114. Raj, A. & van Oudenaarden, A. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135, 216–226 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Banchereau, R. et al. Personalized immunomonitoring uncovers molecular networks that stratify lupus patients. Cell 165, 1548–1550 (2016).

    CAS  PubMed  Google Scholar 

  116. Jourde-Chiche, N., Chiche, L. & Chaussabel, D. Introducing a new dimension to molecular disease classifications. Trends Mol. Med. 22, 451–453 (2016).

    PubMed  Google Scholar 

  117. Lesterhuis, W. J., Bosco, A. & Lake, R. A. Comment on “Drug discovery: turning the Titanic”. Sci. Transl. Med. 6, 229le222 (2014).

    Google Scholar 

  118. Gettinger, S. N. et al. Overall survival and long-term safety of nivolumab (anti-programmed death 1 antibody, BMS-936558, ONO-4538) in patients with previously treated advanced non-small-cell lung cancer. J. Clin. Oncol. 33, 2004–2012 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Tanaka, G., Hirata, Y., Goldenberg, S. L., Bruchovsky, N. & Aihara, K. Mathematical modelling of prostate cancer growth and its application to hormone therapy. Philos. Trans. A Math. Phys. Eng. Sci. 368, 5029–5044 (2010).

    PubMed  Google Scholar 

  120. Ehlerding, E. B., England, C. G., McNeel, D. G. & Cai, W. Molecular imaging of immunotherapy targets in cancer. J. Nucl. Med. 57, 1487–1492 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Schadendorf, D. et al. Pooled analysis of long-term survival data from phase II and phase III trials of ipilimumab in unresectable or metastatic melanoma. J. Clin. Oncol. 33, 1889–1894 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  122. Euler, L. Solutio problematis ad geometriam situs pertinentis. Commentarii Academiae Scientarum Imperialis Petropolitanae 8, 128–140 (in Latin) (1736).

    Google Scholar 

  123. Watts, D. J. & Strogatz, S. H. Collective dynamics of 'small-world' networks. Nature 393, 440–442 (1998).

    CAS  PubMed  Google Scholar 

  124. Taylor, I. W. et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat. Biotechnol. 27, 199–204 (2009).

    CAS  PubMed  Google Scholar 

  125. Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

W.J.L is supported by a John Stocker Fellowship from the Australian Science and Industry Endowment Fund and by grants from Cure Cancer/Cancer Australia. R.A.L. is supported by the Insurance Commission of Western Australia. W.J.L., A.K.N. and R.A.L. are supported by project grants and Centre for Research Excellence funding from Australia's National Health and Medical Research Council.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to W. Joost Lesterhuis.

Ethics declarations

Competing interests

W.J.L., A.B. and R.A.L. hold a patent on a method for the identification of immunotherapy and drug combinations using a network approach. the other authors declare no competing interests.

PowerPoint slides

Glossary

Immune checkpoint

A stimulatory or inhibitory pathway that regulates the scale of adaptive immune responses.

Cytotoxic T lymphocyte-associated antigen 4

(CTLA4). An inhibitory immune checkpoint molecule that is upregulated on effector T cells after activation. CTLA4 competes for ligands with the stimulatory receptor CD28.

Programmed cell death protein 1

(PD1). An inhibitory immune checkpoint receptor expressed on activated lymphocytes and highly expressed on exhausted T lymphocytes. Ligation results in an impairment of proliferation, cytokine production, cytolytic function and survival of T cells.

Biomarker

A measurable indicator of normal biological processes, pathogenic processes or biological responses to a therapeutic intervention. In oncology, biomarkers are often used as predictors of disease outcome, such as prognosis or response to treatment.

Pseudoprogression

This describes a phenomenon in which the tumour volume increases due to infiltrating immune cells and oedema, before a subsequent response occurs.

Cytotoxic T cells

A subset of T cells expressing CD8 that has the capacity to directly kill target cells, such as cancer cells or virus-infected cells, after recognizing antigenic peptides on that cell.

Regulatory T cells

A subset of CD4+ T cells that inhibit immune responses; they can down modulate the induction of a response and can limit the proliferation of effector T cells.

T cell receptor repertoires

T cell receptors are formed by the random recombination of a set of germline-encoded elements with mechanisms to enhance junctional diversity. Thus, genetically identical individuals can create unique T cell receptor repertoires.

Complex system

A system characterized by emergent behaviour. The system itself may consist of a large number of very simple parts interacting in simple ways, but with a large number of such interactions. The observed behaviour of the entire system is not what one would immediately expect from the individual components.

Chaotic system

A system that exhibits irregular and apparently random variation but is in fact completely described by a small number of deterministic equations. Chaotic systems are characterized by sensitive dependence on initial conditions (a small change in the initial state leads to a large change in outcome), extreme mixing (different initial conditions will eventually become arbitrarily close, for a short time), and, exhibiting bounded, deterministic, but aperiodic behaviour.

Critical state transition

The behaviour of complex systems typically changes smoothly (gradually) with changes in the system parameters. However, at a critical state transition a small perturbation to the system parameters will result in a sudden and dramatic change in the systems behaviour.

Tipping point

A tipping point is the (biological) state at which a critical state transition occurs.

Graph theory

The mathematical study of graphs, abstract structures used to model pairwise relations between members of a set. A graph in this context is made up of nodes that are connected by edges. Mathematical graph theory tends to focus on analysis of symmetry and structure, whereas in physics the same objects are called networks and the emphasis is on structures with a very large number of nodes and the description of the statistical interaction between them.

Nodes

Many complex systems are characterized by complex networks. A complex network is represented by a series of nodes connected by edges. One can visualize these nodes and edges as points (nodes) connected by line segments (edges). The edges determine which nodes are connected to which other nodes. Nodes that have a relatively large number of edges are called hubs.

Bottleneck nodes

A bottleneck node is a node with a central role in connecting other parts of a network. A large number of paths between random pairs of nodes will pass through a bottleneck node.

Subnetworks

Any subsets of the nodes in a network and the edges connecting nodes within that subset. They form a part a network.

Transient hubs

Hubs that rise to prominence only for a short time. In a time varying network, a transient hub will have a large number of edges only for some of the time.

Dynamic network biomarkers

Predictive biomarkers (that is, associated with response to treatment) that are obtained after treatment has been initiated.

Static biomarkers

Predictive biomarkers obtained at a single time point.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lesterhuis, W., Bosco, A., Millward, M. et al. Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity. Nat Rev Drug Discov 16, 264–272 (2017). https://doi.org/10.1038/nrd.2016.233

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrd.2016.233

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing