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Annual Review of Cancer Biology

Volume 4, 2020

Biomarkers for Response to Immune Checkpoint Blockade

Abstract

Immune checkpoint blockade (ICB) has significant clinical activity in diverse cancer classes and can induce durable remissions in even refractory advanced disease. However, only a minority of cancer patients treated with ICB have long-term benefits, and ICB treatment is associated with significant, potentially life-threatening, autoimmune side effects. There is a great need to develop biomarkers of response to guide patient selection to maximize the chance of benefit and prevent unnecessary toxicity, and current biomarkers do not have optimal positive or negative predictive value. A variety of potential biomarkers are currently being developed, including those based on assessment of checkpoint protein expression, evaluation of tumor-intrinsic features including mutation burden and viral infection, evaluation of features of the tumor immune microenvironment including nature of immune cell infiltration, and features of the host such as composition of the gut microbiome. Better understanding of the underlying fundamental mechanisms of immune response and resistance to ICB, along with the use of complementary assays that interrogate distinct features of the tumor, the tumor microenvironment, and host immune system, will allow more precise use of these therapies to optimize patient outcomes.

Keyword(s): biomarker, cancer therapy, immune checkpoint blockade, PD-1, PD-L1
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/content/journals/10.1146/annurev-cancerbio-030419-033604
2020-03-04
2024-04-19
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Literature Cited

  1. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S et al. 2013. Signatures of mutational processes in human cancer. Nature 500:415–21
     [Google Scholar]
  2. Ancevski Hunter K, Socinski MA, Villaruz LC 2018. PD-L1 testing in guiding patient selection for PD-1/PD-L1 inhibitor therapy in lung cancer. Mol. Diagn. Ther. 22:1–10
     [Google Scholar]
  3. Ansell SM, Lesokhin AM, Borrello I, Halwani A, Scott EC et al. 2015. PD-1 blockade with nivolumab in relapsed or refractory Hodgkin's lymphoma. N. Engl. J. Med. 372:311–19
     [Google Scholar]
  4. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A et al. 2017. IFN-gamma-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Investig. 127:2930–40
     [Google Scholar]
  5. Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C et al. 2018. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174:1293–308.e36
     [Google Scholar]
  6. Balachandran VP, Luksza M, Zhao JN, Makarov V, Moral JA et al. 2017. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551:512–16
     [Google Scholar]
  7. Balar AV, Castellano D, O'Donnell PH, Grivas P, Vuky J et al. 2017. First-line pembrolizumab in cisplatin-ineligible patients with locally advanced and unresectable or metastatic urothelial cancer (KEYNOTE-052): a multicentre, single-arm, phase 2 study. Lancet Oncol 18:1483–92
     [Google Scholar]
  8. Balli D, Rech AJ, Stanger BZ, Vonderheide RH 2017. Immune cytolytic activity stratifies molecular subsets of human pancreatic cancer. Clin. Cancer Res. 23:3129–38
     [Google Scholar]
  9. Basavanhally AN, Ganesan S, Agner S, Monaco JP, Feldman MD et al. 2010. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Trans. Biomed. Eng. 57:642–53
     [Google Scholar]
  10. Behr DS, Peitsch WK, Hametner C, Lasitschka F, Houben R et al. 2014. Prognostic value of immune cell infiltration, tertiary lymphoid structures and PD-L1 expression in Merkel cell carcinomas. Int. J. Clin. Exp. Pathol. 7:7610–21
     [Google Scholar]
  11. Bensch F, van der Veen EL, Lub-de Hooge MN, Jorritsma-Smit A, Boellaard R et al. 2018. 89Zr-atezolizumab imaging as a non-invasive approach to assess clinical response to PD-L1 blockade in cancer. Nat. Med. 24:1852–58
     [Google Scholar]
  12. Boichard A, Tsigelny IF, Kurzrock R 2017. High expression of PD-1 ligands is associated with kataegis mutational signature and APOBEC3 alterations. Oncoimmunology 6:e1284719
     [Google Scholar]
  13. Carbone DP, Reck M, Paz-Ares L, Creelan B, Horn L et al. 2017. First-line nivolumab in stage IV or recurrent non-small-cell lung cancer. N. Engl. J. Med. 376:2415–26
     [Google Scholar]
  14. Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM et al. 2017. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med 9:34
     [Google Scholar]
  15. Chan TA, Wolchok JD, Snyder A 2015. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 373:1984
     [Google Scholar]
  16. Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA 2018. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol. Biol. 1711:243–59
     [Google Scholar]
  17. Chen DS, Mellman I. 2013. Oncology meets immunology: the cancer-immunity cycle. Immunity 39:1–10
     [Google Scholar]
  18. Chiappinelli KB, Strissel PL, Desrichard A, Li H, Henke C et al. 2015. Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses. Cell 162:974–86
     [Google Scholar]
  19. Chowell D, Morris LGT, Grigg CM, Weber JK, Samstein RM et al. 2018. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359:582–87
     [Google Scholar]
  20. Chung HC, Ros W, Delord JP, Perets R, Italiano A et al. 2019. Efficacy and safety of pembrolizumab in previously treated advanced cervical cancer: results from the phase II KEYNOTE-158 study. J. Clin. Oncol. 37:1470–78
     [Google Scholar]
  21. Conroy JM, Pabla S, Nesline MK, Glenn ST, Papanicolau-Sengos A et al. 2019. Next generation sequencing of PD-L1 for predicting response to immune checkpoint inhibitors. J. Immunother. Cancer 7:18
     [Google Scholar]
  22. Cremonesi E, Governa V, Garzon JFG, Mele V, Amicarella F et al. 2018. Gut microbiota modulate T cell trafficking into human colorectal cancer. Gut 67:1984–94
     [Google Scholar]
  23. Cristescu R, Mogg R, Ayers M, Albright A, Murphy E et al. 2018. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science 362:eaar3593
     [Google Scholar]
  24. Davies H, Glodzik D, Morganella S, Yates LR, Staaf J et al. 2017. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med. 23:517–25
     [Google Scholar]
  25. Deinlein T, Lax SF, Schwarz T, Giuffrida R, Schmid-Zalaudek K, Zalaudek I 2017. Rapid response of metastatic cutaneous squamous cell carcinoma to pembrolizumab in a patient with xeroderma pigmentosum: case report and review of the literature. Eur. J. Cancer 83:99–102
     [Google Scholar]
  26. Deng J, Wang ES, Jenkins RW, Li S, Dries R et al. 2018. CDK4/6 inhibition augments antitumor immunity by enhancing T-cell activation. Cancer Discov 8:216–33
     [Google Scholar]
  27. Derosa L, Hellmann MD, Spaziano M, Halpenny D, Fidelle M et al. 2018. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann. Oncol. 29:1437–44
     [Google Scholar]
  28. Diem S, Kasenda B, Spain L, Martin-Liberal J, Marconcini R et al. 2016. Serum lactate dehydrogenase as an early marker for outcome in patients treated with anti-PD-1 therapy in metastatic melanoma. Br. J. Cancer 114:256–61
     [Google Scholar]
  29. Edwards J, Wilmott JS, Madore J, Gide TN, Quek C et al. 2018. CD103+ tumor-resident CD8+ T cells are associated with improved survival in immunotherapy-naive melanoma patients and expand significantly during anti-PD-1 treatment. Clin. Cancer Res. 24:3036–45
     [Google Scholar]
  30. Engels B, Engelhard VH, Sidney J, Sette A, Binder DC et al. 2013. Relapse or eradication of cancer is predicted by peptide-major histocompatibility complex affinity. Cancer Cell 23:516–26
     [Google Scholar]
  31. Erber R, Stohr R, Herlein S, Giedl C, Rieker RJ et al. 2017. Comparison of PD-L1 mRNA expression measured with the CheckPoint Typer® assay with PD-L1 protein expression assessed with immunohistochemistry in non-small cell lung cancer. Anticancer Res 37:6771–78
     [Google Scholar]
  32. Ferris RL, Blumenschein G Jr., Fayette J, Guigay J, Colevas AD et al. 2016. Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 375:1856–67
     [Google Scholar]
  33. Galanina N, Goodman AM, Cohen PR, Frampton GM, Kurzrock R 2018. Successful treatment of HIV-associated Kaposi sarcoma with immune checkpoint blockade. Cancer Immunol. Res. 6:1129–35
     [Google Scholar]
  34. Goel S, DeCristo MJ, Watt AC, BrinJones H, Sceneay J et al. 2017. CDK4/6 inhibition triggers anti-tumour immunity. Nature 548:471–75
     [Google Scholar]
  35. Goh G, Walradt T, Markarov V, Blom A, Riaz N et al. 2016. Mutational landscape of MCPyV-positive and MCPyV-negative Merkel cell carcinomas with implications for immunotherapy. Oncotarget 7:3403–15
     [Google Scholar]
  36. Gong J, Chehrazi-Raffle A, Placencio-Hickok V, Guan M, Hendifar A, Salgia R 2019. The gut microbiome and response to immune checkpoint inhibitors: preclinical and clinical strategies. Clin. Transl. Med. 8:9
     [Google Scholar]
  37. Goodman AM, Kato S, Bazhenova L, Patel SP, Frampton GM et al. 2017. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol. Cancer Ther. 16:2598–608
     [Google Scholar]
  38. Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC et al. 2018. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359:97–103
     [Google Scholar]
  39. Gubin MM, Zhang X, Schuster H, Caron E, Ward JP et al. 2014. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515:7528577–81
     [Google Scholar]
  40. Härtlova A, Erttmann SF, Raffi FA, Schmalz AM, Resch U et al. 2015. DNA damage primes the type I interferon system via the cytosolic DNA sensor STING to promote anti-microbial innate immunity. Immunity 42:2332–43
     [Google Scholar]
  41. Hause RJ, Pritchard CC, Shendure J, Salipante SJ 2016. Classification and characterization of microsatellite instability across 18 cancer types. Nat. Med. 22:1342–50
     [Google Scholar]
  42. Havel JJ, Chowell D, Chan TA 2019. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19:133–50
     [Google Scholar]
  43. Heeren AM, Rotman J, Stam AGM, Pocorni N, Gassama AA et al. 2019. Efficacy of PD-1 blockade in cervical cancer is related to a CD8+FoxP3+CD25+ T-cell subset with operational effector functions despite high immune checkpoint levels. J. Immunother. Cancer 7:43
     [Google Scholar]
  44. Heitzer E, Tomlinson I. 2014. Replicative DNA polymerase mutations in cancer. Curr. Opin. Genet. Dev. 24:107–13
     [Google Scholar]
  45. Hellmann MD, Callahan MK, Awad MM, Calvo E, Ascierto PA et al. 2018. Tumor mutational burden and efficacy of nivolumab monotherapy and in combination with ipilimumab in small-cell lung cancer. Cancer Cell 33:853–61.e4
     [Google Scholar]
  46. Hirsch FR, McElhinny A, Stanforth D, Ranger-Moore J, Jansson M et al. 2017. PD-L1 immunohistochemistry assays for lung cancer: results from phase 1 of the Blueprint PD-L1 IHC Assay Comparison Project. J. Thorac. Oncol. 12:208–22
     [Google Scholar]
  47. Hodi FS, O'Day SJ, McDermott DF, Weber RW, Sosman JA et al. 2010. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363:711–23
     [Google Scholar]
  48. Hsu C, Lee SH, Ejadi S, Even C, Cohen RB et al. 2017. Safety and antitumor activity of pembrolizumab in patients with programmed death-ligand 1-positive nasopharyngeal carcinoma: results of the KEYNOTE-028 study. J. Clin. Oncol. 35:4050–56
     [Google Scholar]
  49. Hsu J, Hodgins JJ, Marathe M, Nicolai CJ, Bourgeois-Daigneault MC et al. 2018. Contribution of NK cells to immunotherapy mediated by PD-1/PD-L1 blockade. J. Clin. Investig. 128:4654–68
     [Google Scholar]
  50. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ et al. 2018. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175:984–97.e24
     [Google Scholar]
  51. Johnson DB, Bordeaux J, Kim JY, Vaupel C, Rimm DL et al. 2018. Quantitative spatial profiling of PD-1/PD-L1 interaction and HLA-DR/IDO-1 predicts improved outcomes of anti-PD-1 therapies in metastatic melanoma. Clin. Cancer Res. 24:5250–60
     [Google Scholar]
  52. Kaufman HL, Russell J, Hamid O, Bhatia S, Terheyden P et al. 2016. Avelumab in patients with chemotherapy-refractory metastatic Merkel cell carcinoma: a multicentre, single-group, open-label, phase 2 trial. Lancet Oncol 17:1374–85
     [Google Scholar]
  53. Kim ST, Cristescu R, Bass AJ, Kim KM, Odegaard JI et al. 2018. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat. Med. 24:1449–58
     [Google Scholar]
  54. Krieg C, Nowicka M, Guglietta S, Schindler S, Hartmann FJ et al. 2018. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 24:144–53
     [Google Scholar]
  55. Kumar D, Lisok A, Dahmane E, McCoy M, Shelake S et al. 2019. Peptide-based PET quantifies target engagement of PD-L1 therapeutics. J. Clin. Investig. 129:616–30
     [Google Scholar]
  56. Lakhani SR, Jacquemier J, Sloane JP, Gusterson BA, Anderson TJ et al. 1998. Multifactorial analysis of differences between sporadic breast cancers and cancers involving BRCA1 and BRCA2 mutations. J. Natl. Cancer Inst. 90:1138–45
     [Google Scholar]
  57. Le DT, Durham JN, Smith KN, Wang H, Bartlett BR et al. 2017. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357:409–13
     [Google Scholar]
  58. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H et al. 2015. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372:2509–20
     [Google Scholar]
  59. Li X, Cheng Y, Zhang M, Yan J, Li L et al. 2018. Activity of pembrolizumab in relapsed/refractory NK/T-cell lymphoma. J. Hematol. Oncol. 11:15
     [Google Scholar]
  60. Loi S, Adams S, Schmid P, Cortes J, Cescon DW, Winer EP 2017. Relationship between tumor infiltration lymphocyte (TIL) levels and response to pembrolizumab in metatstatic triple-negative breast cancer (mTNBC): results from KEYNOTE-086. Ann. Oncol. 28:mdx440.005
     [Google Scholar]
  61. Loi S, Schmid P, Cortes J, Park Y, Munoz-Couselo E et al. 2019. Relationship between tumor infiltrating lymphocytes (TILs) and response to pembrolizumab (Pembro)+chemotherapy (Chemo) as neoadjuvant treatment (NAT) for triple-negative breast cancer (TNBC): phase Ib KEYNOTE-173 trial. Cancer Res 79:4 Suppl.P3–10-09 (Abstr.)
    [Google Scholar]
  62. Luksza M, Riaz N, Makarov V, Balachandran VP, Hellmann MD et al. 2017. A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy. Nature 551:517–20
     [Google Scholar]
  63. Ma BBY, Lim WT, Goh BC, Hui EP, Lo KW et al. 2018. Antitumor activity of nivolumab in recurrent and metastatic nasopharyngeal carcinoma: an international, multicenter study of the Mayo Clinic Phase 2 Consortium (NCI-9742). J. Clin. Oncol. 36:1412–18
     [Google Scholar]
  64. Martens A, Wistuba-Hamprecht K, Geukes Foppen M, Yuan J, Postow MA et al. 2016. Baseline peripheral blood biomarkers associated with clinical outcome of advanced melanoma patients treated with ipilimumab. Clin. Cancer Res. 22:2908–18
     [Google Scholar]
  65. Matson V, Fessler J, Bao R, Chongsuwat T, Zha Y et al. 2018. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359:104–08
     [Google Scholar]
  66. McDermott DF, Huseni MA, Atkins MB, Motzer RJ, Rini BI et al. 2018. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat. Med. 24:749–57
     [Google Scholar]
  67. McGranahan N, Furness AJ, Rosenthal R, Ramskov S, Lyngaa R et al. 2016. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351:1463–69
     [Google Scholar]
  68. Mehnert JM, Panda A, Zhong H, Hirshfield K, Damare S et al. 2016. Immune activation and response to pembrolizumab in POLE-mutant endometrial cancer. J. Clin. Investig. 126:2334–40
     [Google Scholar]
  69. Miao D, Margolis CA, Gao W, Voss MH, Li W et al. 2018. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science 359:801–6
     [Google Scholar]
  70. Middha S, Yaeger R, Shia J, Stadler ZK, King S et al. 2019. Majority of B2M-mutant and -deficient colorectal carcinomas achieve clinical benefit from immune checkpoint inhibitor therapy and are microsatellite instability-high. JCO Precis. Oncol. 3: https://doi.org/10.1200/PO.18.00321
    [Crossref] [Google Scholar]
  71. Middha S, Zhang L, Nafa K, Jayakumaran G, Wong D et al. 2017. Reliable pan-cancer microsatellite instability assessment by using targeted next-generation sequencing data. JCO Precis. Oncol. 1: https://doi.org/10.1200/po.17.00084
    [Crossref] [Google Scholar]
  72. Mok TSK, Wu YL, Kudaba I, Kowalski DM, Cho BC et al. 2019. Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. Lancet 393:101831819–30
     [Google Scholar]
  73. Mouw KW, Goldberg MS, Konstantinopoulos PA, D'Andrea DA 2017. DNA damage and repair biomarkers of immunotherapy response. Cancer Discov 7:7675–93
     [Google Scholar]
  74. Narayanan S, Kawaguchi T, Yan L, Peng X, Qi Q, Takabe K 2018. Cytolytic activity score to assess anticancer immunity in colorectal cancer. Ann. Surg. Oncol. 25:2323–31
     [Google Scholar]
  75. Nghiem PT, Bhatia S, Lipson EJ, Kudchadkar RR, Miller NJ et al. 2016. PD-1 blockade with pembrolizumab in advanced merkel-cell carcinoma. N. Engl. J. Med. 374:2542–52
     [Google Scholar]
  76. Niemeijer AN, Leung D, Huisman MC, Bahce I, Hoekstra OS et al. 2018. Whole body PD-1 and PD-L1 positron emission tomography in patients with non-small-cell lung cancer. Nat. Commun. 9:4664
     [Google Scholar]
  77. Nirmal AJ, Regan T, Shih BB, Hume DA, Sims AH, Freeman TC 2018. Immune cell gene signatures for profiling the microenvironment of solid tumors. Cancer Immunol. Res. 6:1388–400
     [Google Scholar]
  78. Nishino M, Ramaiya NH, Hatabu H, Hodi FS 2017. Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat. Rev. Clin. Oncol. 14:655–68
     [Google Scholar]
  79. Ott PA, Bang YJ, Piha-Paul SA, Razak ARA, Bennouna J et al. 2019. T-cell-inflamed gene-expression profile, programmed death ligand 1 expression, and tumor mutational burden predict efficacy in patients treated with pembrolizumab across 20 cancers: KEYNOTE-028. J. Clin. Oncol. 37:318–27
     [Google Scholar]
  80. Pages F, Mlecnik B, Marliot F, Bindea G, Ou FS et al. 2018. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet 391:2128–39
     [Google Scholar]
  81. Pan D, Kobayashi A, Jiang P, Ferrari de Andrade L, Tay RE et al. 2018. A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing. Science 359:770–75
     [Google Scholar]
  82. Panda A, Betigeri A, Subramanian K, Ross JS, Pavlick DC et al. 2017. Identifying a clinically applicable mutational burden threshold as a potential biomarker of response to immune checkpoint therapy in solid tumors. JCO Precis. Oncol. 1: https://www.doi.org/10.1200/po.17.00146
    [Crossref] [Google Scholar]
  83. Panda A, de Cubas AA, Stein M, Riedlinger G, Kra J et al. 2018a. Endogenous retrovirus expression is associated with response to immune checkpoint blockade in clear cell renal cell carcinoma. JCI Insight 3:16e121522
     [Google Scholar]
  84. Panda A, Mehnert JM, Hirshfield KM, Riedlinger G, Damare S et al. 2018b. Immune activation and benefit from avelumab in EBV-positive gastric cancer. J. Natl. Cancer Inst. 110:316–20
     [Google Scholar]
  85. Park VS, Pursell ZF. 2019. POLE proofreading defects: contributions to mutagenesis and cancer. DNA Repair 76:50–59
     [Google Scholar]
  86. Postow MA, Chesney J, Pavlick AC, Robert C, Grossmann K et al. 2015. Nivolumab and ipilimumab versus ipilimumab in untreated melanoma. N. Engl. J. Med. 372:2006–17
     [Google Scholar]
  87. Prat A, Navarro A, Pare L, Reguart N, Galvan P et al. 2017. Immune-related gene expression profiling after PD-1 blockade in non-small cell lung carcinoma, head and neck squamous cell carcinoma, and melanoma. Cancer Res 77:3540–50
     [Google Scholar]
  88. Ratner L, Waldmann TA, Janakiram M, Brammer JE 2018. Rapid progression of adult T-cell leukemia-lymphoma after PD-1 inhibitor therapy. N. Engl. J. Med. 378:1947–48
     [Google Scholar]
  89. Rayner E, van Gool IC, Palles C, Kearsey SE, Bosse T et al. 2016. A panoply of errors: polymerase proofreading domain mutations in cancer. Nat. Rev. Cancer 16:71–81
     [Google Scholar]
  90. Reck M, Rodriguez-Abreu D, Robinson AG, Hui R, Csoszi T et al. 2016. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N. Engl. J. Med. 375:1823–33
     [Google Scholar]
  91. Riaz N, Havel JJ, Kendall SM, Makarov V, Walsh LA et al. 2016. Recurrent SERPINB3 and SERPINB4 mutations in patients who respond to anti-CTLA4 immunotherapy. Nat. Genet. 48:1327–29
     [Google Scholar]
  92. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V et al. 2015. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348:124–28
     [Google Scholar]
  93. Rodig SJ, Gusenleitner D, Jackson DG, Gjini E, Giobbie-Hurder A et al. 2018. MHC proteins confer differential sensitivity to CTLA-4 and PD-1 blockade in untreated metastatic melanoma. Sci. Transl. Med. 10:eaar3342
     [Google Scholar]
  94. Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N 2015. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160:48–61
     [Google Scholar]
  95. Rosenberg JE, Hoffman-Censits J, Powles T, van der Heijden MS, Balar AV et al. 2016. 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 387:1909–20
     [Google Scholar]
  96. Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT et al. 2018. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359:91–97
     [Google Scholar]
  97. Sade-Feldman M, Yizhak K, Bjorgaard SL, Ray JP, de Boer CG et al. 2018. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175:998–1013.e20
     [Google Scholar]
  98. Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R et al. 2019. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51:202–6
     [Google Scholar]
  99. Schadendorf D, Nghiem P, Bhatia S, Hauschild A, Saiag P et al. 2017. Immune evasion mechanisms and immune checkpoint inhibition in advanced Merkel cell carcinoma. Oncoimmunology 6:e1338237
     [Google Scholar]
  100. Schaer DA, Beckmann RP, Dempsey JA, Huber L, Forest A et al. 2018. The CDK4/6 inhibitor abemaciclib induces a T cell inflamed tumor microenvironment and enhances the efficacy of PD-L1 checkpoint blockade. Cell Rep 22:2978–94
     [Google Scholar]
  101. Schmid P, Adams S, Rugo HS, Schneeweiss A, Barrios CH et al. 2018. Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. N. Engl. J. Med. 379:2108–21
     [Google Scholar]
  102. Seiwert TY, Burtness B, Mehra R, Weiss J, Berger R et al. 2016. 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 17:956–65
     [Google Scholar]
  103. Sharma P, Callahan MK, Bono P, Kim J, Spiliopoulou P et al. 2016. Nivolumab monotherapy in recurrent metastatic urothelial carcinoma (CheckMate 032): a multicentre, open-label, two-stage, multi-arm, phase 1/2 trial. Lancet Oncol 17:1590–98
     [Google Scholar]
  104. Shen X, Zhao B. 2018. Efficacy of PD-1 or PD-L1 inhibitors and PD-L1 expression status in cancer: meta-analysis. BMJ 362:k3529
     [Google Scholar]
  105. Sivan A, Corrales L, Hubert N, Williams JB, Aquino-Michaels K et al. 2015. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350:1084–89
     [Google Scholar]
  106. Skoulidis F, Goldberg ME, Greenawalt DM, Hellmann MD, Awad MM et al. 2018. STK11/LKB1 mutations and PD-1 inhibitor resistance in KRAS-mutant lung adenocarcinoma. Cancer Discov 8:822–35
     [Google Scholar]
  107. Smith CC, Beckermann KE, Bortone DS, De Cubas AA, Bixby LM et al. 2018. Endogenous retroviral signatures predict immunotherapy response in clear cell renal cell carcinoma. J. Clin. Investig. 128:4804–20
     [Google Scholar]
  108. Snyder A, Makarov V, Merghoub T, Yuan J, Zaretsky JM et al. 2014. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371:2189–99
     [Google Scholar]
  109. Solinas C, Garaud S, De Silva P, Boisson A, Van den Eynden G et al. 2017. Immune checkpoint molecules on tumor-infiltrating lymphocytes and their association with tertiary lymphoid structures in human breast cancer. Front. Immunol. 8:1412
     [Google Scholar]
  110. Solovyov A, Vabret N, Arora KS, Snyder A, Funt SA et al. 2018. Global cancer transcriptome quantifies repeat element polarization between immunotherapy responsive and T cell suppressive classes. Cell Rep 23:512–21
     [Google Scholar]
  111. Spranger S, Spaapen RM, Zha Y, Williams J, Meng Y et al. 2013. Up-regulation of PD-L1, IDO, and Tregs in the melanoma tumor microenvironment is driven by CD8+ T cells. Sci. Transl. Med. 5:200ra116
     [Google Scholar]
  112. Stadler ZK, Battaglin F, Middha S, Hechtman JF, Tran C et al. 2016. Reliable detection of mismatch repair deficiency in colorectal cancers using mutational load in next-generation sequencing panels. J. Clin. Oncol. 34:2141–47
     [Google Scholar]
  113. Strickland KC, Howitt BE, Shukla SA, Rodig S, Ritterhouse LL et al. 2016. Association and prognostic significance of BRCA1/2-mutation status with neoantigen load, number of tumor-infiltrating lymphocytes and expression of PD-1/PD-L1 in high grade serous ovarian cancer. Oncotarget 7:13587–98
     [Google Scholar]
  114. Su S, Zhao J, Xing Y, Zhang X, Liu J et al. 2018. Immune checkpoint inhibition overcomes ADCP-induced immunosuppression by macrophages. Cell 175:442–57.e23
     [Google Scholar]
  115. Subrahmanyam PB, Dong Z, Gusenleitner D, Giobbie-Hurder A, Severgnini M et al. 2018. Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J. Immunother. Cancer 6:18
     [Google Scholar]
  116. Surace M, DaCosta K, Huntley A, Zhao W, Bagnall C et al. 2019. Automated multiplex immunofluorescence panel for immuno-oncology studies on formalin-fixed carcinoma tissue specimens. J. Vis. Exp. 143:e58390
     [Google Scholar]
  117. Takada K, Toyokawa G, Shoji F, Okamoto T, Maehara Y 2018. The significance of the PD-L1 expression in non-small-cell lung cancer: trenchant double swords as predictive and prognostic markers. Clin. Lung Cancer 19:120–29
     [Google Scholar]
  118. Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC et al. 2012. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N. Engl. J. Med. 366:2443–54
     [Google Scholar]
  119. Tumeh PC, Harview CL, Yearley JH, Shintaku IP, Taylor EJ et al. 2014. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515:568–71
     [Google Scholar]
  120. Turajlic S, Litchfield K, Xu H, Rosenthal R, McGranahan N et al. 2017. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol 18:1009–21
     [Google Scholar]
  121. Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C et al. 2015. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350:207–11
     [Google Scholar]
  122. van Gool IC, Eggink FA, Freeman-Mills L, Stelloo E, Marchi E et al. 2015. POLE proofreading mutations elicit an antitumor immune response in endometrial cancer. Clin. Cancer Res. 21:3347–55
     [Google Scholar]
  123. Varn FS, Wang Y, Cheng C 2019. A B cell-derived gene expression signature associates with an immunologically active tumor microenvironment and response to immune checkpoint blockade therapy. Oncoimmunology 8:e1513440
     [Google Scholar]
  124. Wang L, Saci A, Szabo PM, Chasalow SD, Castillo-Martin M et al. 2018. EMT- and stroma-related gene expression and resistance to PD-1 blockade in urothelial cancer. Nat. Commun. 9:3503
     [Google Scholar]
  125. Wang Y, Shi C, Eisenberg R, Vnencak-Jones CL 2017. Differences in microsatellite instability profiles between endometrioid and colorectal cancers: a potential cause for false-negative results. ? J. Mol. Diagn. 19:57–64
     [Google Scholar]
  126. Wei SC, Levine JH, Cogdill AP, Zhao Y, Anang NAS et al. 2017. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170:61120–33.e17
     [Google Scholar]
  127. Weide B, Martens A, Hassel JC, Berking C, Postow MA et al. 2016. Baseline biomarkers for outcome of melanoma patients treated with pembrolizumab. Clin. Cancer Res. 22:5487–96
     [Google Scholar]
  128. Wong PF, Wei W, Smithy JW, Acs B, Toki MI et al. 2019. Multiplex quantitative analysis of tumor-infiltrating lymphocytes and immunotherapy outcome in metastatic melanoma. Clin. Cancer Res. 25:2442–49
     [Google Scholar]
  129. Woo SR, Fuertes MB, Corrales L, Spranger S, Furdyna MJ et al. 2014. STING-dependent cytosolic DNA sensing mediates innate immune recognition of immunogenic tumors. Immunity 41:830–42
     [Google Scholar]
  130. Yang W, Lee KW, Srivastava RM, Kuo F, Krishna C et al. 2019. Immunogenic neoantigens derived from gene fusions stimulate T cell responses. Nat. Med. 25:767–75
     [Google Scholar]
  131. Yarchoan M, Hopkins A, Jaffee EM 2017. Tumor mutational burden and response rate to PD-1 inhibition. N. Engl. J. Med. 377:2500–1
     [Google Scholar]
  132. Yi M, Jiao D, Xu H, Liu Q, Zhao W et al. 2018. Biomarkers for predicting efficacy of PD-1/PD-L1 inhibitors. Mol. Cancer 17:129
     [Google Scholar]
  133. Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W et al. 2016. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375:819–29
     [Google Scholar]
  134. Zhang J, Bu X, Wang H, Zhu Y, Geng Y et al. 2018. Cyclin D-CDK4 kinase destabilizes PD-L1 via cullin 3-SPOP to control cancer immune surveillance. Nature 553:91–95
     [Google Scholar]
  135. Zhang Y, Chen L. 2016. Classification of advanced human cancers based on tumor immunity in the microenvironment (TIME) for cancer immunotherapy. JAMA Oncol 2:1403–4
     [Google Scholar]
  136. Zitvogel L, Ma Y, Raoult D, Kroemer G, Gajewski TF 2018. The microbiome in cancer immunotherapy: diagnostic tools and therapeutic strategies. Science 359:1366–70
     [Google Scholar]
/content/journals/10.1146/annurev-cancerbio-030419-033604
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Biomarkers for Response to Immune Checkpoint Blockade
Annual Review of Cancer Biology 4, 331 (2020); https://doi.org/10.1146/annurev-cancerbio-030419-033604
/content/journals/10.1146/annurev-cancerbio-030419-033604
/content/journals/10.1146/annurev-cancerbio-030419-033604
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