Clinical characteristics of patient selection and imaging predictors of outcome in solid tumors treated with checkpoint-inhibitors

  • Sabrina Rossi
  • Luca Toschi
  • Angelo Castello
  • Fabio Grizzi
  • Luigi Mansi
  • Egesta Lopci
Review Article


The rapidly evolving knowledge on tumor immunology and the continuous implementation of immunotherapy in cancer have recently led to the FDA and EMA approval of several checkpoint inhibitors as immunotherapic agents in clinical practice. Anti-CTLA-4, anti-PD-1, and anti-PDL-1 antibodies are becoming standard of care in advanced melanoma, as well as in relapsed or metastatic lung and kidney cancer, demonstrating higher and longer response compared to standard chemotherapy. These encouraging results have fostered the evaluation of these antibodies either alone or in combination with other therapies in several dozen clinical trials for the treatment of multiple tumor types. However, not all patients respond to immune checkpoint inhibitors, hence, specific biomarkers are necessary to guide and monitor therapy. The utility of PD-L1 expression as a biomarker has varied in different clinical trials, but, to date, no consensus has been reached on whether PD-L1 expression is an ideal marker for response and patient selection; approximately 20–25% of patients will respond to immunotherapy with checkpoint inhibitors despite a negative PD-L1 staining. On the other hand, major issues concern the evaluation of objective response in patients treated with immunotherapy. Pure morphological criteria as commonly used in solid tumors (i.e. RECIST) are not sufficient because change in size is not an early and reliable marker of tumor response to biological therapies. Thus, the scientific community has required a continuous adaptation of immune-related response criteria (irRC) to overcome the problem. In this context, metabolic information and antibody-based imaging with positron emission tomography (PET) have been investigated, providing a powerful approach for an optimal stratification of patients at staging and during the evaluation of the response to therapy. In the present review we provide an overview on the clinical characteristics of patient selection when using imaging predictors of outcome in solid tumors treated with checkpoint-inhibitors.


Immunotherapy Checkpoint inhibitors Predictive biomarkers Immune-related response criteria FDG-PET  Immune-PET 


  1. 1.
    Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12(4):252–64.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Hodi FS, et al. Improved survival with Ipilimumab in patients with metastatic melanoma. N Engl J Med. 2010;363:711–23.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Robert C, Thomas L, Bondarenko I, et al. Ipilimumab plus dacarbazine for previously untreated metastatic melanoma. N Engl J Med. 2011;364:2517–26.CrossRefPubMedGoogle Scholar
  4. 4.
    Margolin K, Wong SL, Penrod JR, et al. Effectiveness and safety of first-line ipilimumab (IPI) 3mg/kg therapy for advanced melanoma (AM): Evidence from a U.S. multisite retrospective chart review. Presented at: The European Cancer Congress. 2013; September 27–October 1; Amsterdam, The Netherlands (P488).Google Scholar
  5. 5.
    Patt D, Wong SL, Juday T, et al. Community-based, real-world, study of treatment-naïve advanced melanoma (AM) patients treated with 3mg/kg ipilimumab (IPI) in the United States. Presented at: The European Cancer Congress. 2013; September 27–October 1; Amsterdam, The Netherlands (P497).Google Scholar
  6. 6.
    Wang X, Teng F, Kong L, Yu J. PD-L1 expression in human cancers and its association with clinical outcomes. Onco Targets Ther. 2016;9:5023–39.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Robert C, Long GV, Brady B, et al. Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med. 2015;372:320–30.CrossRefPubMedGoogle Scholar
  8. 8.
    Larkin J, Chiarion-Sileni V, Gonzalez R, et al. Combined nivolumab and ipilimumab or monotherapy in untreated melanoma. N Engl J Med. 2015;373:23–34.CrossRefPubMedGoogle Scholar
  9. 9.
    Brahmer J, Reckamp KL, Baas P, et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med. 2015;373(2):123–35.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Borghaei H, Paz-Ares L, Horn L, et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med. 2015;373(17):1627–39.CrossRefPubMedGoogle Scholar
  11. 11.
    Motzer RJ, Escudier B, McDermott DF, et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med. 2015;373:1803–13.CrossRefPubMedGoogle Scholar
  12. 12.
    Ribas A, Puzanov I, Dummer R, et al. Pembrolizumab versus investigator-choice chemotherapy for ipilimumab-refractory melanoma (KEYNOTE-002): A randomised, controlled, phase 2 trial. Lancet Oncol. 2015;16:908–18.CrossRefPubMedGoogle Scholar
  13. 13.
    Garon EB, Rizvi NA, Hui R, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015;372:2018–28.CrossRefPubMedGoogle Scholar
  14. 14.
    Reck M, Rodriguez-Abreu D, Robinson AG, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med. 2016;375:1823–33.CrossRefPubMedGoogle Scholar
  15. 15.
    Ghiotto M, Gauthier L, Serriari N, et al. PD-L1 and PD-L2 differ in their molecular mechanisms of interaction with PD-1. Int Immunol. 2010;22:651–60.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Rosenberg JE, Hoffman-Censits J, Powles T, 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;387:1909–20.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Fehrenbacher L, Spira A, Ballinger M, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): A multicentre, open-label, phase 2 randomised controlled trial. Lancet. 2016;387:1837–46.CrossRefPubMedGoogle Scholar
  18. 18.
    Rittmeyer A, Barlesi F, Waterkamp D, et al. OAK study group. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): A phase 3, open-label, multicentre randomised controlled trial. Lancet. 2017;389(10066):255–65.CrossRefPubMedGoogle Scholar
  19. 19.
    Rittmeyer A, Barlesi F, Waterkamp D, Park K, et al. OAK study group. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): A phase 3, open-label, multicentre randomised controlled trial. Lancet. 2017;389(10066):255–65.CrossRefPubMedGoogle Scholar
  20. 20.
    Goldman JW, Crino L, Vokes EE, et al. Nivolumab (nivo) in patients (pts) with advanced (adv) NSCLC and central nervous system (CNS) metastases (mets): Track: Immunotherapy. J Thorac Oncol. 2016;11(10S):S238–9.CrossRefPubMedGoogle Scholar
  21. 21.
    Herbst RS, Baas P, Kim DW, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): A randomised controlled trial. Lancet. 2016;387(10027):1540–50.CrossRefPubMedGoogle Scholar
  22. 22.
    Topalian SL, Taube JM, Anders RA, Pardoll DM. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer. 2016;16(5):275–87.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Chatterjee S, Lesniak WG, Nimmagadda S. Noninvasive imaging of immune checkpoint ligand PD-L1 in tumors and metastases for guiding immunotherapy. Mol Imaging. 2017;16:1536012117718459.CrossRefPubMedGoogle Scholar
  24. 24.
    Nicolazzo C, Raimondi C, Mancini M, et al. Monitoring PD-L1 positive circulating tumor cells in non-small cell lung cancer patients treated with the PD-1 inhibitor Nivolumab. Sci Rep. 2016;6:31726.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Anantharaman A, Friedlander T, Lu D, et al. Programmed death-ligand 1 (PD-L1) characterization of circulating tumor cells (CTCs) in muscle invasive and metastatic bladder cancer patients. BMC Cancer. 2016;16(1):744.CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Kelderman S, Heemskerk B, van Tinteren H, et al. Lactate dehydrogenase as a selection criterion for ipilimumab treatment in metastatic melanoma. Cancer Immunol Immunother. 2014;63:449–58.PubMedGoogle Scholar
  27. 27.
    Simeone E, Gentilcore G, Giannarelli D, et al. Immunological and biological changes during ipilimumab treatment and their potential correlation with clinical response and survival in patients with advanced melanoma. Cancer Immunol Immunother. 2014;63:675–83.CrossRefPubMedGoogle Scholar
  28. 28.
    Delyon J, Mateus C, Lefeuvre D, et al. Experience in daily practice with ipilimumab for the treatment of patients with metastatic melanoma: An early increase in lymphocyte and eosinophil counts is associated with improved survival. Ann Oncol. 2013;24:1697–703.CrossRefPubMedGoogle Scholar
  29. 29.
    Yuan J, Adamow M, Ginsberg BA, et al. Integrated NY-ESO-1 antibody and CD8+ T-cell responses correlate with clinical benefit in advanced melanoma patients treated with ipilimumab. Proc Natl Acad Sci U S A. 2011;108:16723–8.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Ferrucci PF, Gandini S, Battaglia A, et al. Baseline neutrophil-to-lymphocyte ratio is associated with outcome of ipilimumab-treated metastatic melanoma patients. Br J Cancer. 2015;112(12):1904–10.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Bagley SJ, Kothari S, Aggarwal C, et al. Pretreatment neutrophil-to-lymphocyte ratio as a marker of outcomes in nivolumab-treated patients with advanced non-small-cell lung cancer. Lung Cancer. 2017;106:1–7.CrossRefPubMedGoogle Scholar
  32. 32.
    D’Angelo SP, Larkin J, Weber J, et al. Efficacy and safety of nivolumab vs investigator’s choice chemotherapy (ICC) in subgroups of patients with advanced melanoma after prior anti-CTLA-4 therapy. Presented at the Society for Melanoma Research (SMR). 2014 Congress; November 13–16, 2014; Zurich, Switzerland.Google Scholar
  33. 33.
    Larkin J, Lao CD, Urba WJ, et al. Efficacy and safety of Nivolumab in patients with BRAF V600 mutant and BRAF wild-type advanced melanoma: A pooled analysis of 4 clinical trials. JAMA Oncol. 2015;1(4):433–40.CrossRefPubMedGoogle Scholar
  34. 34.
    Ackerman A, Klein O, McDermott DF. Waet al. Outcomes of patients with metastatic melanoma treated with immunotherapy prior to or after BRAF inhibitors. Cancer. 2014;120(11):1695–701.CrossRefPubMedGoogle Scholar
  35. 35.
    Johnson DB, Pectasides E, Feld E, et al. Sequencing treatment in BRAFV600 mutant melanoma: Anti-PD-1 before and after BRAF inhibition. J Immunother. 2017;40(1):31–5.CrossRefPubMedGoogle Scholar
  36. 36.
    Robert C, Schachter J, Long GV, et al. KEYNOTE-006 investigators. Pembrolizumab versus Ipilimumab in advanced melanoma. N Engl J Med. 2015;372(26):2521–32.CrossRefPubMedGoogle Scholar
  37. 37.
    Lee CK, Man J, Lord S, et al. Checkpoint inhibitors in metastatic EGFR-mutated non-small cell lung cancer-a meta-analysis. J Thorac Oncol. 2017;12(2):403–7.CrossRefPubMedGoogle Scholar
  38. 38.
    Chan TA, Wolchok JD, Snyder A. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2015;373:1984.CrossRefPubMedGoogle Scholar
  39. 39.
    Rizvi NA, Hellmann MD, Snyder A, et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015;348:124–8.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Spigel DR, Schrock AB, Fabrizio D, et al. Total mutation burden (TMB) in lung cancer (LC) and relationship with response to PD-1/PD-L1 targeted therapies. J Clin Oncol. 2016;34:9017.Google Scholar
  41. 41.
    Schabath MB, Welsh EA, Fulp WJ, et al. Differential association of STK11 and TP53 with KRAS mutation-associated gene expression, proliferation and immune surveillance in lung adenocarcinoma. Oncogene. 2016;35(24):3209–16.CrossRefPubMedGoogle Scholar
  42. 42.
    Skoulidis F, Byers LA, Diao L. Paet al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities. Cancer Discov. 2015;5(8):860–77.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Nelson D, Fisher S, Robinson B. The “Trojan horse” approach to tumor immunotherapy: Targeting the tumor microenvironment. J Immunol Res. 2014;2014:789069.PubMedPubMedCentralGoogle Scholar
  44. 44.
    Kluger HM, Zito CR, Barr ML, et al. Characterization of PD-L1 expression and associated T-cell infiltrates in metastatic melanoma samples from variable anatomic sites. Clin Cancer Res. 2015;21:3052–60.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Topalian SL, Hodi FS, Brahmer JR, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med. 2012;366:2443–54.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Tumeh PC, Harview CL, Yearley JH, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568–71.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    Herbst RS, Soria J-C, Kowanetz M, et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 2014;515:563–7.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Teng MW, Ngiow SF, Ribas A, Smyth MJ. Classifying cancers based on T-cell infiltration and PD-L1. Cancer Res. 2015;75(11):2139–45.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Taube JM, Klein A, Brahmer JR, 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.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Wesolowski R, Markowitz J, Carson WE. Myeloid derived suppressor cells – A new therapeutic target in the treatment of cancer. J Immunother Cancer. 2013;1:10.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Biswas SK, Mantovani A. Macrophage plasticity and interaction with lymphocyte subsets: Cancer as a paradigm. Nat Immunol. 2010;11:889–96.CrossRefPubMedGoogle Scholar
  52. 52.
    Redente EF, Dwyer-Nield LD, Merrick DT, et al. Tumor progression stage and anatomical site regulate tumor-associated macrophage and bone marrow-derived monocyte polarization. Am J Pathol. 2010;176:2972–85.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Coussens LM, Zitvogel L, Palucka AK. Neutralizing tumor-promoting chronic inflammation: A magic bullet? Science. 2013;339:286–91.CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Qian BZ, Pollard JW. Macrophage diversity enhances tumor progression and metastasis. Cell. 2010;141:39–51.CrossRefPubMedPubMedCentralGoogle Scholar
  55. 55.
    Noman MZ, Desantis G, Janji B, et al. PD-L1 is a novel direct target of HIF-1alpha, and its blockade under hypoxia enhanced MDSC-mediated T cell activation. J Exp Med. 2014;211:781–90.CrossRefPubMedPubMedCentralGoogle Scholar
  56. 56.
    Weber JS, Kudchadkar RR, Yu B, et al. Safety, efficacy, and biomarkers of nivolumab with vaccine in ipilimumab-refractory or -naive melanoma. J Clin Oncol. 2013;31(34):4311–8.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Miller AB, Hoogstraten B, Staquet M, Winkler A. Reporting results of cancer treatment. Cancer. 1981;47:207–14.CrossRefPubMedGoogle Scholar
  58. 58.
    Therasse P, Arbuck SG, Eisenhauer EA, et al. New guidelines to evaluate the response to treatment in solid tumors. JNCI: J Natl Cancer Inst. 2000;92(3):205–16.CrossRefPubMedGoogle Scholar
  59. 59.
    Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–47.CrossRefPubMedGoogle Scholar
  60. 60.
    Subbiah V, Chuang HH, Gambhire D, Kairemo K. Defining clinical response criteria and early response criteria for precision oncology: Current state-of-the-art and future perspectives. Diagnostics (Basel). 2017;7(1):E10.CrossRefGoogle Scholar
  61. 61.
    Wong AN, McArthur GA, Hofman MS, et al. The advantages and challenges of using FDG PET/CT for response assessment in melanoma in the era of targeted agents and immunotherapy. Eur J Nucl Med Mol Imaging. 2017; doi:10.1007/s00259-017-3691-7.
  62. 62.
    Motzer RJ, Rini BI, McDermott DF, et al. Nivolumab for metastatic renal cell carcinoma: Results of a randomized phase II trial. J Clin Oncol. 2015;33(13):1430–7.CrossRefPubMedGoogle Scholar
  63. 63.
    Hamid O, Robert C, Daud A, et al. Safety and tumor responses with lambrolizumab (anti-PD-1) in melanoma. N Engl J Med. 2013;369(2):134–44.CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    Hodi FS, Hwu WJ, Kefford R, et al. Evaluation of immune-related response criteria and RECIST v1.1 in patients with advanced melanoma treated with Pembrolizumab. J Clin Oncol. 2016;34(13):1510–7.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    Daud A, Ribas A, Robert C, et al. Long-term efficacy of pembrolizumab (pembro; MK-3475) in a pooled analysis of 655 patients (pts) with advanced melanoma (MEL) enrolled in KEYNOTE-001. J Clin Oncol. 2015;33 (suppl; abstr 9005).Google Scholar
  66. 66.
    Robert C, Ribas A, Wolchok JD, et al. Anti-programmed-death-receptor-1 treatment with pembrolizumab in ipilimumab-refractory advanced melanoma: A randomised dose-comparison cohort of a phase 1 trial. Lancet. 2014;384(9948):1109–17.CrossRefPubMedGoogle Scholar
  67. 67.
    Powles T, Eder JP, Fine GD, et al. MPDL3280A (anti-PD-L1) treatment leads to clinical activity in metastatic bladder cancer. Nature. 2014;515(7528):558–62.CrossRefPubMedGoogle Scholar
  68. 68.
    Herbst RS, Soria JC, Kowanetz M, et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature. 2014;515(7528):563–7.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Wolchok JD, Kluger H, Callahan MK, et al. Nivolumab plus ipilimumab in advanced melanoma. N Engl J Med. 2013;369(2):122–33.CrossRefPubMedGoogle Scholar
  70. 70.
    Weber JS, D’Angelo SP, Minor D, et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): A randomised, controlled, open-label, phase 3 trial. Lancet Oncol. 2015;16(4):375–84.CrossRefPubMedGoogle Scholar
  71. 71.
    Topalian SL, Sznol M, McDermott DF, et al. Survival, durable tumor remission, and long-term safety in patients with advanced melanoma receiving nivolumab. J Clin Oncol. 2014;32(10):1020–30.CrossRefPubMedPubMedCentralGoogle Scholar
  72. 72.
    Rizvi NA, Mazieres J, Planchard D, et al. Activity and safety of nivolumab, an anti-PD-1 immune checkpoint inhibitor, for patients with advanced, refractory squamous non-small-cell lung cancer (CheckMate 063): A phase 2, single-arm trial. Lancet Oncol. 2015;16(3):257–65.CrossRefPubMedGoogle Scholar
  73. 73.
    Brahmer JR, Tykodi SS, Chow LQ, et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med. 2012;366(26):2455–65.CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    Brahmer JR, Drake CG, Wollner I, et al. Phase I study of single-agent anti-programmed death-1 (MDX-1106) in refractory solid tumors: Safety, clinical activity, pharmacodynamics, and immunologic correlates. J Clin Oncol. 2010;28(19):3167–75.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    De Wolf K, Kruse V, Sundahl N, et al. A phase II trial of stereotactic body radiotherapy with concurrent anti-PD1 treatment in metastatic melanoma: Evaluation of clinical and immunologic response. J Transl Med. 2017;15(1):21.CrossRefPubMedPubMedCentralGoogle Scholar
  76. 76.
    Young H, Baum R, Cremerius U, et al. Measurement of clinical and subclinical tumour response using [18F]-fluorodeoxyglucose and positron emission tomography: Review and 1999 EORTC recommendations. Eur J Cancer. 1999;35(13):1773–82.CrossRefPubMedGoogle Scholar
  77. 77.
    Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: Evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50(Suppl 1):122S–50S.ù.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Pinker K, Riedl C, Weber WA. Evaluating tumor response with FDG PET: Updates on PERCIST, comparison with EORTC criteria and clues to future developments. Eur J Nucl Med Mol Imaging. 2017; doi:10.1007/s00259-017-3687-3.
  79. 79.
    Sachpekidis C, Larribere L, Pan L, et al. Predictive value of early 18F-FDG PET/CT studies for treatment response evaluation to ipilimumab in metastatic melanoma: Preliminary results of an ongoing study. Eur J Nucl Med Mol Imaging. 2015;42(3):386–96.CrossRefPubMedGoogle Scholar
  80. 80.
    Cheson BD, Ansell S, Schwartz L, et al. Refinement of the Lugano classification lymphoma response criteria in the era of immunomodulatory therapy. Blood. 2016;128:2489–96.CrossRefPubMedGoogle Scholar
  81. 81.
    Cho SY, Lipson EJ, Im HJ, ROwe SP, et al. Prediction of response to immune checkpoint inhibitor therapy early time-point FDG-PET/CT imaging in patients with advanced melanoma. J Nucl Med 2017.Google Scholar
  82. 82.
    Pearce EL, Pearce EJ. Metabolic pathways in immune cell activation and quiescence. Immunity. 2013;38:633–43.CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    Chang CH, Qiu J, O’Sullivan D, et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell. 2015;162:1229–41.CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Lopci E, Toschi L, Grizzi F, et al. Correlation of metabolic information on FDG-PET with tissue expression of immune markers in patienst with non-small cell lung cancer (NSCLC) who are candidate for upfront surgery. Eur J Nucl Med Mol Imaging. 2016;43(11):1954–61.CrossRefPubMedGoogle Scholar
  85. 85.
    Higashikawa K, Yagi K, Watanabe K, et al. 64Cu-DOTA-anti-CTLA-4 mAb enabled PET visualization of CTLA-4 on the T-cell infiltrating tumor tissues. PLoS One. 2014;9(11):e109866.CrossRefPubMedPubMedCentralGoogle Scholar
  86. 86.
    Ehlerding EB, England CG, Majewski RL, et al. ImmunoPET imaging of CTLA-4 expression in mouse models of non-small cell lung cancer. Mol Pharm. 2017;14(5):1782–9.CrossRefPubMedGoogle Scholar
  87. 87.
    Natarajan A, Mayer AT, Reeves RE, et al. Development of Novel ImmunoPET Tracers to Image Human PD-1 Checkpoint Expression on Tumor-Infiltrating Lymphocytes in a Humanized Mouse Model. Mol Imaging Biol. 2017.Google Scholar
  88. 88.
    England CG, Ehlerding EB, Hernandez R, et al. Preclinical pharmacokinetics and biodistribution studies of 89Zr-labeled Pembrolizumab. J Nucl Med. 2017;58(1):162–16.CrossRefPubMedPubMedCentralGoogle Scholar
  89. 89.
    Natarajan A, Mayer At XL, et al. Novel radiotracer for ImmunoPET imaging of PD-1 checkpoint expression on tumor infiltrating lymphocytes. Bioconjug Chem. 2015;26(10):2062–9.CrossRefPubMedGoogle Scholar
  90. 90.
    Hettich M, Braun F, Bartholoma MD, et al. High-resolution PET imaging with therapeutic antibody-based PD-1/PD-L1 checkpoint tracers. Theranostics. 2016;6(10):1629–40.CrossRefPubMedPubMedCentralGoogle Scholar
  91. 91.
    Maute RL, Gordon SR, Mayer AT, et al. Engineering high-affinity PD-1 variants for optimized immunotherapy and immuno-PET imaging. Proc Natl Acad Sci U S A. 2015;112(47):E6506–14.CrossRefPubMedPubMedCentralGoogle Scholar
  92. 92.
    Larimer BM, Wehrenberg-Klee E, Caraballo A, Mahmood U. Quantitative CD3 PET imaging predicts tumor growth response to anti-CTLA-4 therapy. J Nucl Med. 2016;57(10):1607–11.CrossRefPubMedPubMedCentralGoogle Scholar
  93. 93.
    Tavare R, Escuin-Ordinas H, Mok S, et al. An effective Immuno-PET imaging method to monitor CD8-dependent responses to immunotherapy. Cancer Res. 2016;76(1):73–82.CrossRefPubMedGoogle Scholar
  94. 94.
    Knowles SM, Wu A. Advances in Immuno–positron emission tomography: Antibodies for molecular imaging in oncology. J Clin Oncol. 2012;30(31):3884–92.CrossRefPubMedPubMedCentralGoogle Scholar
  95. 95.
    Verel I, Visser GW, Boellaard R, et al. 89Zr immuno-PET: Comprehensive procedures for the production of 89Zr-labeled monoclonal antibodies. J Nucl Med. 2003;44(8):1271–81.PubMedGoogle Scholar
  96. 96.
    Tolmachev V, Stone-Elander V. Radiolabelled proteins for positron emission tomography: Pros and cons of labelling methods. Biochim Biophys Acta. 2010;1800(5):487–510.CrossRefPubMedGoogle Scholar
  97. 97.
    Boswell CA, Brechbiel MW. Development of radioimmunotherapeutic and diagnostic antibodies: An inside-out view. Nucl Med Biol. 2007;34(7):757–78.CrossRefPubMedPubMedCentralGoogle Scholar
  98. 98.
    Pentlow KS, Graham MC, Lambrecht RM, et al. Quantitative imaging of iodine-124 with PET. J Nucl Med. 1996;37(9):1557–62.PubMedGoogle Scholar
  99. 99.
    Pentlow KS, Finn RD, Larson SM, et al. Quantitative imaging of yttrium-86 with PET. The occurrence and correction of anomalous apparent activity in high density regions. Clin Positron Imaging. 2000;3(3):85–90.CrossRefPubMedGoogle Scholar
  100. 100.
    Kontermann RE. Strategies for extended serum half-life of protein therapeutics. Curr Opin Biotechnol. 2011;22(6):868–76.CrossRefPubMedGoogle Scholar
  101. 101.
    Wu AM. Engineered antibodies for molecular imaging of cancer. Methods. 2014;65(1):139–47.CrossRefPubMedGoogle Scholar
  102. 102.
    Ehlerding EB, England CG, McNeel DG, et al. Molecular imaging of immunotherapy targets in cancer. J Nucl Med. 2016;57(10):1487–92.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Medical OncologyHumanitas Clinical and Research HospitalRozzanoItaly
  2. 2.Nuclear MedicineHumanitas Clinical and Research HospitalRozzanoItaly
  3. 3.Immunology and InflammationHumanitas Clinical and Research HospitalRozzanoItaly
  4. 4.Nuclear MedicineSeconda Università di NapoliNaplesItaly
  5. 5.Nuclear MedicineHumanitas Cancer Center, Humanitas Clinical and Research HospitalRozzanoItaly

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