The AAPS Journal

, 20:66 | Cite as

Integrative Pharmacology: Advancing Development of Effective Immunotherapies

  • Mohammad Tabrizi
  • Daping Zhang
  • Vaishnavi Ganti
  • Glareh Azadi
Review Article Theme: Cancer Immunotherapy: Promises and Challenges
Part of the following topical collections:
  1. Theme: Cancer Immunotherapy: Promises and Challenges


With the recent advances in cancer immunotherapy, it is now evident that the antigen-specific activation of the patients’ immune responses can be utilized for achieving significant therapeutic benefits. Novel molecules have been developed and promising advances have been achieved in cancer therapy. The recent success of cancer immunotherapy clearly reflects the novelty of the approach and importance of this class of therapeutics. Due to the nature of immunotherapy, i.e., harnessing the patient’s immune system, it becomes critical to evaluate the important variables that can guide preclinical development, translational strategies, patient selection, and effective clinical dosing paradigms following single and combination therapies. To further boost the durability and efficacy profiles of IO (immuno-oncology) drugs following single agent therapy, novel combination therapies are being sought. Combination strategies have become critical for enhancing the anti-tumor immunity in broader cancer indications. Comprehensive methods are being developed to quantify the synergistic combination effect profiles at various development phases. Further evaluation of the signaling and pathway components can potentially establish a unique “signature” characteristic for specific combination therapies following modulation of various immunomodulatory pathways. In this article, critical topics related to preclinical, translational, and clinical development of IO agents are discussed.


cancer immunotherapy first-in-human dose immuno-oncology (IO) preclinical development systems pharmacology tumor modeling 


  1. 1.
    Tabrizi MA, Bornstein GG, Klakamp SL, editors. Development of antibody-based therapeutics: translational considerations. New York: Springer; 2012.Google Scholar
  2. 2.
    Trame MN, et al. Systems pharmacology to predict drug safety in drug development. Eur J Pharm Sci. 2016;94:93–5.PubMedGoogle Scholar
  3. 3.
    Danhof M. Systems pharmacology—towards the modeling of network interactions. Eur J Pharm Sci. 2016;94:4–14.PubMedGoogle Scholar
  4. 4.
    de Greef R, et al. Pembrolizumab: role of modeling and simulation in bringing anovel immunotherapy to patients with melanoma. CPT Pharmacometrics Syst Pharmacol. 2017;6(1):5–7.PubMedGoogle Scholar
  5. 5.
    Elassaiss-Schaap J, et al. Using model-based “learn and confirm” to reveal the pharmacokinetics-pharmacodynamics relationship of pembrolizumab in the KEYNOTE-001 Trial. CPT Pharmacometrics Syst Pharmacol. 2017;6(1):21–8.PubMedGoogle Scholar
  6. 6.
    Masoud V, Pages G. Targeted therapies in breast cancer: new challenges to fight against resistance. World J Clin Oncol. 2017;8(2):120–34.PubMedPubMedCentralGoogle Scholar
  7. 7.
    Guo C, et al. Therapeutic cancer vaccines: past, present, and future. Adv Cancer Res. 2013;119:421–75.PubMedPubMedCentralGoogle Scholar
  8. 8.
    Lollini PL, et al. Vaccines for tumour prevention. Nat Rev Cancer. 2006;6(3):204–16.PubMedGoogle Scholar
  9. 9.
    Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321–30.PubMedGoogle Scholar
  10. 10.
    Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity cycle. Immunity. 2013;39(1):1–10.PubMedGoogle Scholar
  11. 11.
    Chen L, Han X. Anti-PD-1/PD-L1 therapy of human cancer: past, present, and future. J Clin Invest. 2015;125(9):3384–91.PubMedPubMedCentralGoogle Scholar
  12. 12.
    Finn OJ. Immuno-oncology: understanding the function and dysfunction of the immune system in cancer. Ann Oncol. 2012;23(Suppl 8):viii6–9.PubMedPubMedCentralGoogle Scholar
  13. 13.
    Finn OJ. Cancer immunology. N Engl J Med. 2008;358(25):2704–15.PubMedGoogle Scholar
  14. 14.
    Mellman I, Coukos G, Dranoff G. Cancer immunotherapy comes of age. Nature. 2011;480(7378):480–9.PubMedPubMedCentralGoogle Scholar
  15. 15.
    Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12(4):252–64.PubMedPubMedCentralGoogle Scholar
  16. 16.
    Ribas A. Releasing the brakes on cancer immunotherapy. N Engl J Med. 2015;373(16):1490–2.PubMedGoogle Scholar
  17. 17.
    Ribas A. Tumor immunotherapy directed at PD-1. N Engl J Med. 2012;366(26):2517–9.PubMedGoogle Scholar
  18. 18.
    Ahamadi M, et al. Model-based characterization of the pharmacokinetics of pembrolizumab: a humanized anti-PD-1 monoclonal antibody in advanced solid tumors. CPT Pharmacometrics Syst Pharmacol. 2017;6(1):49–57.PubMedGoogle Scholar
  19. 19.
    Bajaj G, et al. Model-based population pharmacokinetic analysis of nivolumab in patients with solid tumors. CPT Pharmacometrics Syst Pharmacol. 2017;6(1):58–66.PubMedGoogle Scholar
  20. 20.
    Chatterjee MS, et al. Population pharmacokinetic/pharmacodynamic modeling of tumor size dynamics in pembrolizumab-treated advanced melanoma. CPT Pharmacometrics Syst Pharmacol. 2017;6(1):29–39.PubMedGoogle Scholar
  21. 21.
    Lindauer A, et al. Translational pharmacokinetic/pharmacodynamicmodeling of tumor growth inhibition supports dose-range selection of the anti-PD-1 antibody pembrolizumab. CPT Pharmacometrics Syst Pharmacol. 2017;6(1):11–20.PubMedGoogle Scholar
  22. 22.
    Stroh M, et al. Clinical pharmacokinetics and pharmacodynamics of atezolizumab in metastatic urothelial carcinoma. Clin Pharmacol Ther. 2017;102(2):305–12.PubMedGoogle Scholar
  23. 23.
    Wang X, et al. Quantitative characterization of the exposure-response relationship for cancer immunotherapy: a case study of nivolumab in patients with advanced melanoma. CPT Pharmacometrics Syst Pharmacol. 2017;6(1):40–8.PubMedGoogle Scholar
  24. 24.
    Westerhoff HV, et al. Systems pharmacology: an opinion on how to turn the impossible into grand challenges. Drug Discov Today Technol. 2015;15:23–31.PubMedGoogle Scholar
  25. 25.
    Pennock ND, et al. T cell responses: naive to memory and everything in between. Adv Physiol Educ. 2013;37(4):273–83.PubMedPubMedCentralGoogle Scholar
  26. 26.
    Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: a common denominator approach to cancer therapy. Cancer Cell. 2015;27(4):450–61.PubMedPubMedCentralGoogle Scholar
  27. 27.
    Garon EB. Current perspectives in immunotherapy for non-small cell lung cancer. Semin Oncol. 2015;42(Suppl 2):S11–8.PubMedGoogle Scholar
  28. 28.
    Garon EB. Cancer immunotherapy trials not immune from imprecise selection of patients. N Engl J Med. 2017;376(25):2483–5.PubMedGoogle Scholar
  29. 29.
    Janssen LME, et al. The immune system in cancer metastasis: friend or foe? J Immunother Cancer. 2017;5(1):79.PubMedPubMedCentralGoogle Scholar
  30. 30.
    Pauken KE, et al. Epigenetic stability of exhausted T cells limits durability of reinvigoration by PD-1 blockade. Science. 2016;354(6316):1160–5.PubMedPubMedCentralGoogle Scholar
  31. 31.
    Pitt JM, et al. Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy. Ann Oncol. 2016;27(8):1482–92.PubMedGoogle Scholar
  32. 32.
    Tang H, Qiao J, Fu YX. Immunotherapy and tumor microenvironment. Cancer Lett. 2016;370(1):85–90.PubMedGoogle Scholar
  33. 33.
    Wei SC, et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell. 2017;170(6):1120–1133e17.PubMedGoogle Scholar
  34. 34.
    Harding FA, et al. CD28-mediated signalling co-stimulates murine T cells and prevents induction of anergy in T-cell clones. Nature. 1992;356(6370):607–9.PubMedGoogle Scholar
  35. 35.
    McIntyre BW, Allison JP. The mouse T cell receptor: structural heterogeneity of molecules of normal T cells defined by xenoantiserum. Cell. 1983;34(3):739–46.PubMedGoogle Scholar
  36. 36.
    Leach DR, Krummel MF, Allison JP. Enhancement of antitumor immunity by CTLA-4 blockade. Science. 1996;271(5256):1734–6.PubMedGoogle Scholar
  37. 37.
    Furness AJ, et al. Impact of tumour microenvironment and Fc receptors on the activity of immunomodulatory antibodies. Trends Immunol. 2014;35(7):290–8.PubMedGoogle Scholar
  38. 38.
    Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013;14(10):1014–22.PubMedPubMedCentralGoogle Scholar
  39. 39.
    Gandini S, Massi D, Mandala M. PD-L1 expression in cancer patients receiving anti PD-1/PD-L1 antibodies: a systematic review and meta-analysis. Crit Rev Oncol Hematol. 2016;100:88–98.PubMedGoogle Scholar
  40. 40.
    Garon EB, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015;372(21):2018–28.PubMedGoogle Scholar
  41. 41.
    Gniadek TJ, et al. Heterogeneous expression of PD-L1 in pulmonary squamous cell carcinoma and adenocarcinoma: implications for assessment by small biopsy. Mod Pathol. 2017;30(4):530–8.PubMedGoogle Scholar
  42. 42.
    Gridelli C, et al. Immunotherapy of non-small cell lung cancer: report from an international experts panel meeting of the Italian association of thoracic oncology. Expert Opin Biol Ther. 2016;16(12):1479–89.PubMedGoogle Scholar
  43. 43.
    Herbst RS, et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 2014;515(7528):563–7.PubMedPubMedCentralGoogle Scholar
  44. 44.
    Keir ME, et al. PD-1 and its ligands in tolerance and immunity. Annu Rev Immunol. 2008;26:677–704.PubMedGoogle Scholar
  45. 45.
    Keir ME, Francisco LM, Sharpe AH. PD-1 and its ligands in T-cell immunity. Curr Opin Immunol. 2007;19(3):309–14.PubMedGoogle Scholar
  46. 46.
    Keir ME, Freeman GJ, Sharpe AH. PD-1 regulates self-reactive CD8+ T cell responses to antigen in lymph nodes and tissues. J Immunol. 2007;179(8):5064–70.PubMedGoogle Scholar
  47. 47.
    Keir ME, et al. Programmed death-1 (PD-1):PD-ligand 1 interactions inhibit TCR-mediated positive selection of thymocytes. J Immunol. 2005;175(11):7372–9.PubMedPubMedCentralGoogle Scholar
  48. 48.
    Nguyen LT, Ohashi PS. Clinical blockade of PD1 and LAG3—potential mechanisms of action. Nat Rev Immunol. 2015;15(1):45–56.PubMedGoogle Scholar
  49. 49.
    Workman CJ, et al. Lymphocyte activation gene-3 (CD223) regulates the size of the expanding T cell population following antigen activation in vivo. J Immunol. 2004;172(9):5450–5.PubMedGoogle Scholar
  50. 50.
    Wherry EJ. T cell exhaustion. Nat Immunol. 2011;12(6):492–9.PubMedGoogle Scholar
  51. 51.
    Wherry EJ, Kurachi M. Molecular and cellular insights into T cell exhaustion. Nat Rev Immunol. 2015;15(8):486–99.PubMedPubMedCentralGoogle Scholar
  52. 52.
    Norris S, et al. PD-1 expression on natural killer cells and CD8(+) T cells during chronic HIV-1 infection. Viral Immunol. 2011;25(4):329–32.Google Scholar
  53. 53.
    Church SE, Galon J. Regulation of CTL infiltration within the tumor microenvironment. Adv Exp Med Biol. 2017;1036:33–49.PubMedGoogle Scholar
  54. 54.
    Church SE, Galon J. Tumor microenvironment and immunotherapy: the whole picture is better than a glimpse. Immunity. 2015;43(4):631–3.PubMedGoogle Scholar
  55. 55.
    Pham CD, et al. Differential immune microenvironments and response to immune checkpoint blockade among molecular subtypes of murine medulloblastoma. Clin Cancer Res. 2016;22(3):582–95.PubMedGoogle Scholar
  56. 56.
    Wang M, et al. Role of tumor microenvironment in tumorigenesis. J Cancer. 2017;8(5):761–73.PubMedPubMedCentralGoogle Scholar
  57. 57.
    Nelson BH. New insights into tumor immunity revealed by the unique genetic and genomic aspects of ovarian cancer. Curr Opin Immunol. 2015;33:93–100.PubMedGoogle Scholar
  58. 58.
    Hwang WT, et al. Prognostic significance of tumor-infiltrating T cells in ovarian cancer: a meta-analysis. Gynecol Oncol. 2012;124(2):192–8.PubMedGoogle Scholar
  59. 59.
    Curiel TJ, et al. Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat Med. 2004;10(9):942–9.PubMedGoogle Scholar
  60. 60.
    Bennett CL, et al. The immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (IPEX) is caused by mutations of FOXP3. Nat Genet. 2001;27(1):20–1.PubMedGoogle Scholar
  61. 61.
    Klages K, et al. Selective depletion of Foxp3+ regulatory T cells improves effective therapeutic vaccination against established melanoma. Cancer Res. 2010;70(20):7788–99.PubMedGoogle Scholar
  62. 62.
    Roychoudhuri R, Eil RL, Restifo NP. The interplay of effector and regulatory T cells in cancer. Curr Opin Immunol. 2015;33:101–11.PubMedGoogle Scholar
  63. 63.
    Fontenot JD, et al. A function for interleukin 2 in Foxp3-expressing regulatory T cells. Nat Immunol. 2005;6(11):1142–51.PubMedGoogle Scholar
  64. 64.
    Yu A, et al. A low interleukin-2 receptor signaling threshold supports the development and homeostasis of T regulatory cells. Immunity. 2009;30(2):204–17.PubMedPubMedCentralGoogle Scholar
  65. 65.
    Amado IF, et al. IL-2 coordinates IL-2-producing and regulatory T cell interplay. J Exp Med. 2013;210(12):2707–20.PubMedPubMedCentralGoogle Scholar
  66. 66.
    Chen ML, et al. Regulatory T cells suppress tumor-specific CD8 T cell cytotoxicity through TGF-beta signals in vivo. Proc Natl Acad Sci U S A. 2005;102(2):419–24.PubMedGoogle Scholar
  67. 67.
    Wesolowski R, et al. Circulating myeloid-derived suppressor cells increase in patients undergoing neo-adjuvant chemotherapy for breast cancer. Cancer Immunol Immunother. 2017;66(11):1437–47.PubMedGoogle Scholar
  68. 68.
    Wesolowski R, Markowitz J, Carson WE 3rd. Myeloid derived suppressor cells—a new therapeutic target in the treatment of cancer. J Immunother Cancer. 2013;1:10.PubMedPubMedCentralGoogle Scholar
  69. 69.
    Arina A, Bronte V. Myeloid-derived suppressor cell impact on endogenous and adoptively transferred T cells. Curr Opin Immunol. 2015;33:120–5.PubMedGoogle Scholar
  70. 70.
    Lanitis E, Irving M, Coukos G. Targeting the tumor vasculature to enhance T cell activity. Curr Opin Immunol. 2015;33:55–63.PubMedPubMedCentralGoogle Scholar
  71. 71.
    Martincorena I, Campbell PJ. Somatic mutation in cancer and normal cells. Science. 2015;349(6255):1483–9.PubMedGoogle Scholar
  72. 72.
    Dear AE. Epigenetic modulators and the newimmunotherapies. N Engl J Med. 2016;374(7):684–6.PubMedGoogle Scholar
  73. 73.
    Le DT, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372(26):2509–20.PubMedPubMedCentralGoogle Scholar
  74. 74.
    Schumacher TN, Hacohen N. Neoantigens encoded in the cancer genome. Curr Opin Immunol. 2015;41:98–103.Google Scholar
  75. 75.
    Boussiotis VA. Somatic mutations and immunotherapy outcome with CTLA-4 blockade in melanoma. N Engl J Med. 2014;371(23):2230–2.PubMedPubMedCentralGoogle Scholar
  76. 76.
    Rizvi NA, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348(6230):124–8.PubMedPubMedCentralGoogle Scholar
  77. 77.
    Steinman L. Immune therapy for autoimmune diseases. Science. 2004;305(5681):212–6.PubMedGoogle Scholar
  78. 78.
    Bornstein GG, et al. Surrogate approaches in development of monoclonal antibodies. Drug Discov Today. 2009;14(23–24):1159–65.PubMedGoogle Scholar
  79. 79.
    Beers SA, Glennie MJ, White AL. Influence of immunoglobulin isotype on therapeutic antibody function. Blood. 2016;127(9):1097–101.PubMedPubMedCentralGoogle Scholar
  80. 80.
    Agrawal S, et al. Nivolumab dose selection: challenges, opportunities, and lessons learned for cancer immunotherapy. J Immunother Cancer. 2016;4:72.PubMedPubMedCentralGoogle Scholar
  81. 81.
    Li H, et al. Time dependent pharmacokinetics of pembrolizumab in patients with solid tumor and its correlation with best overall response. J Pharmacokinet Pharmacodyn. 2017;44(5):403–14.PubMedGoogle Scholar
  82. 82.
    Liu C, et al. Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis. Clin Pharmacol Ther. 2017;101(5):657–66.PubMedGoogle Scholar
  83. 83.
    Eisenhauer EA, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–47.PubMedGoogle Scholar
  84. 84.
    Gopalakrishnan V, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science. 2018;359(6371):97–103.PubMedGoogle Scholar
  85. 85.
    Sivan A, et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science. 2015;350(6264):1084–9.PubMedPubMedCentralGoogle Scholar
  86. 86.
    Adam J, Bellomo N. A survey of models for tumor-immune system dynamics. Boston: Birkhauser; 1996.Google Scholar
  87. 87.
    Ahmadzadeh M, et al. Tumor antigen-specific CD8 T cells infiltrating the tumor express high levels of PD-1 and are functionally impaired. Blood. 2009;114(8):1537–44.PubMedPubMedCentralGoogle Scholar
  88. 88.
    Sfanos KS, et al. Human prostate-infiltrating CD8+ T lymphocytes are oligoclonal and PD-1+. Prostate. 2009;69(15):1694–703.PubMedPubMedCentralGoogle Scholar
  89. 89.
    Dong H, et al. Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat Med. 2002;8(8):793–800.PubMedGoogle Scholar
  90. 90.
    Zou W, Chen L. Inhibitory B7-family molecules in the tumour microenvironment. Nat Rev Immunol. 2008;8(6):467–77.PubMedGoogle Scholar
  91. 91.
    Atefi M, et al. Effects of MAPK and PI3K pathways on PD-L1 expression in melanoma. Clin Cancer Res. 2014;20(13):3446–57.PubMedPubMedCentralGoogle Scholar
  92. 92.
    Zou W, Wolchok JD, Chen L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: mechanisms, response biomarkers, and combinations. Sci Transl Med. 2016;8(328):328rv4.PubMedPubMedCentralGoogle Scholar
  93. 93.
    Patel SP, Kurzrock R. PD-L1 expression as a predictive biomarker in cancer immunotherapy. Mol Cancer Ther. 2015;14(4):847–56.PubMedGoogle Scholar
  94. 94.
    Taube JM, et al. Colocalization of inflammatory response with B7-h1 expression in human melanocytic lesions supports an adaptive resistance mechanism of immune escape. Sci Transl Med. 2012;4(127):127ra37.PubMedPubMedCentralGoogle Scholar
  95. 95.
    Taube JM, et al. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res. 2014;20(19):5064–74.PubMedPubMedCentralGoogle Scholar
  96. 96.
    Topalian SL, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366(26):2443–54.PubMedPubMedCentralGoogle Scholar
  97. 97.
    Butte MJ, et al. Programmed death-1 ligand 1 interacts specifically with the B7-1 costimulatory molecule to inhibit T cell responses. Immunity. 2007;27(1):111–22.PubMedPubMedCentralGoogle Scholar
  98. 98.
    Boutros C, et al. Safety profiles of anti-CTLA-4 and anti-PD-1 antibodies alone and in combination. Nat Rev Clin Oncol. 2016;13(8):473–86.PubMedGoogle Scholar
  99. 99.
    Naidoo J, et al. Toxicities of the anti-PD-1 and anti-PD-L1 immune checkpoint antibodies. Ann Oncol. 2015;26(12):2375–91.PubMedGoogle Scholar
  100. 100.
    Wolchok JD, Saenger Y. The mechanism of anti-CTLA-4 activity and the negative regulation of T-cell activation. Oncologist. 2008;13(Suppl 4):2–9.PubMedGoogle Scholar
  101. 101.
    Hodi FS, et al. Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med. 2010;363(8):711–23.PubMedPubMedCentralGoogle Scholar
  102. 102.
    Ribas A, et al. Phase III randomized clinical trial comparing tremelimumab with standard-of-care chemotherapy in patients with advanced melanoma. J Clin Oncol. 2013;31(5):616–22.PubMedPubMedCentralGoogle Scholar
  103. 103.
    Fridman WH, et al. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12(4):298–306.PubMedGoogle Scholar
  104. 104.
    Cirenajwis H, et al. Molecular stratification of metastatic melanoma using gene expression profiling: prediction of survival outcome and benefit from molecular targeted therapy. Oncotarget. 2015;6(14):12297–309.PubMedPubMedCentralGoogle Scholar
  105. 105.
    Mlecnik B, et al. The tumor microenvironment and Immunoscore are critical determinants of dissemination to distant metastasis. Sci Transl Med. 2016;8(327):327ra26.PubMedGoogle Scholar
  106. 106.
    Ma W, et al. Current status and perspectives in translational biomarker research for PD-1/PD-L1 immune checkpoint blockade therapy. J Hematol Oncol. 2016;9(1):47.PubMedPubMedCentralGoogle Scholar
  107. 107.
    Topalian SL, et al. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer. 2016;16(5):275–87.PubMedPubMedCentralGoogle Scholar
  108. 108.
    Rosenberg JE, 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. 2016;387(10031):1909–20.PubMedPubMedCentralGoogle Scholar
  109. 109.
    Tumeh PC, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515(7528):568–71.PubMedPubMedCentralGoogle Scholar
  110. 110.
    Scheel AH, et al. Harmonized PD-L1 immunohistochemistry for pulmonary squamous-cell and adenocarcinomas. Mod Pathol. 2016;29(10):1165–72.PubMedGoogle Scholar
  111. 111.
    Hirsch FR, et al. PD-L1 immunohistochemistry assays for lung cancer: results from phase 1 of the blueprint PD-L1 IHC assay comparison project. J Thorac Oncol. 2017;12(2):208–22.PubMedGoogle Scholar
  112. 112.
    Rehman JA, et al. Quantitative and pathologist-read comparison of the heterogeneity of programmed death-ligand 1 (PD-L1) expression in non-small cell lung cancer. Mod Pathol. 2017;30(3):340–9.PubMedGoogle Scholar
  113. 113.
    Madore J, et al. PD-L1 expression in melanoma shows marked heterogeneity within and between patients: implications for anti-PD-1/PD-L1 clinical trials. Pigment Cell Melanoma Res. 2015;28(3):245–53.PubMedGoogle Scholar
  114. 114.
    McLaughlin J, et al. Quantitative assessment of the heterogeneity of PD-L1 expression in non-small-cell lung Cancer. JAMA Oncol. 2016;2(1):46–54.PubMedPubMedCentralGoogle Scholar
  115. 115.
    Obeid JM, et al. Heterogeneity of CD8(+) tumor-infiltrating lymphocytes in non-small-cell lung cancer: impact on patient prognostic assessments and comparison of quantification by different sampling strategies. Cancer Immunol Immunother. 2017;66(1):33–43.PubMedGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2018

Authors and Affiliations

  • Mohammad Tabrizi
    • 1
  • Daping Zhang
    • 1
  • Vaishnavi Ganti
    • 1
  • Glareh Azadi
    • 1
  1. 1.Merck & Co., Inc.Palo AltoUSA

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