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Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC

Nature Reviews Clinical Oncology volume 16, pages 341–355 (2019)Cite this article

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Abstract

The era of personalized medicine for advanced-stage non-small-cell lung cancer (NSCLC) began when biomarker-based evidence of molecular pathway and/or oncogene addiction of the tumour became mandatory for the allocation of specific targeted therapies. More recently, the immunotherapy revolution, specifically, the development of immune-checkpoint inhibitors (ICIs), has dramatically altered the NSCLC treatment landscape. Herein, we compare and contrast the clinical development of immunotherapy and oncogene-directed therapy for NSCLC, focusing on the role of predictive biomarkers. Immunotherapy biomarkers are fundamentally different from oncogene biomarkers in that they are continuous rather than categorical (binary), spatially and temporally variable and reliant on multiple complex interactions rather than a single, dominant determinant. The performance of predictive biomarkers for ICIs might be improved by combining different markers to reduce the assumptive risks associated with each one. Novel combinations with chemotherapy and ICIs complicate biomarker discovery but do not decrease the value of the markers identified. Perfectly predictive biomarkers of benefit from immunotherapy are unlikely to be identified, although exclusionary biomarkers of minimal benefit or an unacceptable risk of toxicity might be feasible. The clinical adoption and applicability of such biomarkers might vary depending on line of treatment, the available therapeutic alternatives and health economic considerations.

Key points

  • Immunotherapy biomarkers are fundamentally different from the core driver oncogene biomarkers identified for molecularly targeted therapies: they are continuous rather than categorical (binary), spatially and temporally variable and influenced by multiple complex interactions rather than a single, dominant determinant.

  • Immunotherapy biomarkers enrich for clinical benefit but are currently unable to guarantee or exclude benefit and, therefore, have predominantly been used to identify subgroups of patients with a sufficient likelihood of benefit to suggest immunotherapy as the preferred option in a particular line of therapy.

  • Biomarkers related to the initiation of an immune cascade (such as tumour mutational burden) could be used to enrich for benefit from any potential immunotherapy; those more towards the final effector phase of the immune cascade (such as programmed cell death 1 ligand 1 (PD-L1)) are more likely to enrich for drug-specific effects.

  • The next steps in improving the performance of predictive biomarkers of responsiveness to immunotherapy in patients with non-small-cell lung cancer might involve combining different markers to lessen the assumptive risks associated with each one.

  • The use of chemotherapy–immunotherapy combinations adds additional complexities to the study of immunotherapy biomarkers but does not negate the value of such markers.

  • Currently, perfectly predictive biomarkers of benefit from immunotherapy seem unattainable, but exclusionary biomarkers based on minimal benefit or maximal toxicity risk might be attainable; their adoption and applicability might vary by line of therapy, available therapeutic alternatives and health economic considerations.

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Fig. 1: Confounding of predictive biomarkers can complicate identification of the drug-sensitive population.
Fig. 2: Categorical versus continuous biological variables as predictive biomarkers of therapeutic benefit.
Fig. 3: Assumptions and associated predictive biomarkers of TKI activity in patients with oncogene-addicted NSCLC and of anti-PD-1 or anti-PD-L1 therapy in otherwise unselected patients with NSCLC.
Fig. 4: Schematic representation of ‘rule in’ and ‘rule out’ predictive biomarker approaches for anti-PD-1 or anti-PD-L1 therapy.

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References

  1. Mok, T. S. et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N. Engl. J. Med. 361, 947–957 (2009).

    Article  CAS  PubMed  Google Scholar 

  2. Rosell, R. et al. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomised phase 3 trial. Lancet Oncol. 13, 239–246 (2012).

    Article  CAS  PubMed  Google Scholar 

  3. Sequist, L. V. et al. Phase III study of afatinib or cisplatin plus pemetrexed in patients with metastatic lung adenocarcinoma with EGFR mutations. J. Clin. Oncol. 31, 3327–3334 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. Solomon, B. J. et al. First-line crizotinib versus chemotherapy in ALK-positive lung cancer. N. Engl. J. Med. 371, 2167–2177 (2014).

    Article  PubMed  CAS  Google Scholar 

  5. Peters, S. et al. Alectinib versus crizotinib in untreated ALK-positive non-small-cell lung cancer. N. Engl. J. Med. 377, 829–838 (2017).

    Article  CAS  PubMed  Google Scholar 

  6. Shaw, A. T. et al. Crizotinib in ROS1-rearranged non-small cell lung cancer. N. Engl. J. Med. 371, 1963–1971 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Planchard, D. et al. Dabrafenib plus trametinib in patients with previously treated BRAF(V600E)-mutant metastatic non-small cell lung cancer: an open-label, multicentre phase 2 trial. Lancet Oncol. 17, 984–993 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Brahmer, J. et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N. Engl. J. Med. 373, 123–135 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Herbst, R. S. et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet 387, 1540–1550 (2016).

    Article  CAS  PubMed  Google Scholar 

  11. Rittmeyer, A. et al. Atezolizumab versus docetaxel in patients with previously treated non-small-cell lung cancer (OAK): a phase 3, open-label, multicentre randomised controlled trial. Lancet 389, 255–265 (2017).

    Article  PubMed  Google Scholar 

  12. Fehrenbacher, L. 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 387, 1837–1846 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Reck, M. et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N. Engl. J. Med. 375, 1823–1833 (2016).

    Article  CAS  PubMed  Google Scholar 

  14. Carbone, D. P. et al. First-line nivolumab in stage IV or recurrent non-small-cell lung cancer. N. Engl. J. Med. 376, 2415–2426 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cohen, M. H., Johnson, J. R., Chen, Y. F., Sridhara, R. & Pazdur, R. FDA drug approval summary: erlotinib (Tarceva) tablets. Oncologist 10, 461–466 (2005).

    Article  CAS  PubMed  Google Scholar 

  16. Shepherd, F. A. et al. Erlotinib in previously treated non-small-cell lung cancer. N. Engl. J. Med. 353, 123–132 (2005).

    Article  CAS  PubMed  Google Scholar 

  17. Zhu, C. Q. et al. Role of KRAS and EGFR as biomarkers of response to erlotinib in National Cancer Institute of Canada Clinical Trials Group Study BR.21. J. Clin. Oncol. 26, 4268–4275 (2008).

    Article  CAS  PubMed  Google Scholar 

  18. Bunn, P. A. Jr et al. Biological markers for non-small cell lung cancer patient selection for epidermal growth factor receptor tyrosine kinase inhibitor therapy. Clin. Cancer Res. 12, 3652–3656 (2006).

    Article  CAS  PubMed  Google Scholar 

  19. Cappuzzo, F., Camidge, D. R. & Varella-Garcia, M. Is FISH floating or still swimming in the lung cancer ocean? Ann. Oncol. 22, 493–499 (2011).

    Article  CAS  PubMed  Google Scholar 

  20. Garassino, M. C. et al. Erlotinib versus docetaxel as second-line treatment of patients with advanced non-small-cell lung cancer and wild-type EGFR tumours (TAILOR): a randomised controlled trial. Lancet Oncol. 14, 981–988 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Rulli, E. et al. Value of KRAS as prognostic or predictive marker in NSCLC: results from the TAILOR trial. Ann. Oncol. 26, 2079–2084 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. National Comprehensive Cancer Network. NCCN Guidelines. NCCN https://www.nccn.org/professionals/physician_gls/default.aspx (2019).

  23. US Department of Health and Human Services. Erlotinib (Tarceva). FDA https://www.fda.gov/drugs/informationondrugs/approveddrugs/ucm525739.htm (2016).

  24. Jänne, P. A. et al. AZD9291 in EGFR inhibitor-resistant non-small-cell lung cancer. N. Engl. J. Med. 372, 1689–1699 (2015).

    Article  PubMed  Google Scholar 

  25. Barlesi, F. et al. Avelumab versus docetaxel in patients with platinum-treated advanced non-small-cell lung cancer (JAVELIN Lung 200): an open-label, randomised, phase 3 study. Lancet Oncol. https://doi.org/10.1016/S1470-2045(18)30673-9 (2018).

    Article  PubMed  Google Scholar 

  26. Antonia, S. J. et al. Durvalumab after chemoradiotherapy in stage III non-small-cell lung cancer. N. Engl. J. Med. 377, 1919–1929 (2017).

    Article  CAS  PubMed  Google Scholar 

  27. Camidge, D. R. et al. Native and rearranged ALK copy number and rearranged cell count in non-small cell lung cancer: implications for ALK inhibitor therapy. Cancer 119, 3968–3975 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. de Bruin, E. C. et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 346, 251–256 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Schwaederlé, M. C. et al. Utility of genomic assessment of blood-derived circulating tumor DNA (ctDNA) in patients with advanced lung adenocarcinoma. Clin. Cancer Res. 23, 5101–5111 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. US Department of Health and Human Services. Premarket approval (PMA) — Cobas EGFR Mutation Test V2, Cobas DNA And CfDNA Sample Preparation Kit. FDA https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P120019s019 (2019).

  31. Camidge, D. R., Pao, W. & Sequist, L. V. Acquired resistance to TKIs in solid tumours: learning from lung cancer. Nat. Rev. Clin. Oncol. 11, 473–481 (2014).

    Article  CAS  PubMed  Google Scholar 

  32. Shaw, A. T. et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N. Engl. J. Med. 368, 2385–2394 (2013).

    Article  CAS  PubMed  Google Scholar 

  33. Aisner, D. L. et al. The impact of smoking and TP53 mutations in lung adenocarcinoma patients with targetable mutations-lung cancer mutation consortium (LCMC2). Clin. Cancer Res. 24, 1038–1047 (2018).

    Article  CAS  PubMed  Google Scholar 

  34. Drilon, A., Cappuzzo, F., Ignatius Ou, S. H. & Camidge, D. R. Targeting MET in lung cancer: will expectations finally be MET? J. Thorac. Oncol. 12, 15–26 (2017).

    Article  PubMed  Google Scholar 

  35. Noonan, S. A. et al. Identifying the appropriate FISH criteria for defining MET copy number driven lung adenocarcinoma through oncogene overlap analysis. J. Thorac. Oncol. 11, 1293–1304 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Camidge, D. R. et al. Efficacy and safety of crizotinib in patients with advanced c-MET-amplified non-small cell lung cancer (NSCLC). J. Clin. Oncol. 32 (Suppl), 8001 (2014).

    Article  Google Scholar 

  37. Gros, A. et al. Prospective identification of neoantigen-specific lymphocytes in the peripheral blood of melanoma patients. Nat. Med. 22, 433–438 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Verdegaal, E. M. et al. Neoantigen landscape dynamics during human melanoma-T cell interactions. Nature 536, 91–95 (2016).

    Article  CAS  PubMed  Google Scholar 

  39. Tran, E. et al. Immunogenicity of somatic mutations in human gastrointestinal cancers. Science 350, 1387–1390 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Roemer, M. G. M. et al. Major histocompatibility complex class II and programmed death ligand 1 expression predict outcome after programmed death 1 blockade in classic Hodgkin lymphoma. J. Clin. Oncol. 36, 942–950 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Pardoll, D. M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12, 252–264 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  44. Büttner, R. et al. Programmed death-ligand 1 immunohistochemistry testing: a review of analytical assays and clinical implementation in non-small-cell lung cancer. J. Clin. Oncol. 35, 3867–3876 (2017).

    Article  PubMed  Google Scholar 

  45. Hirsch, F. R. 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. 12, 208–222 (2017).

    Article  PubMed  Google Scholar 

  46. Rimm, D. L. et al. A prospective, multi-institutional, pathologist-based assessment of 4 immunohistochemistry assays for PD-L1 expression in non-small cell lung cancer. JAMA Oncol. 3, 1051–1058 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Tsao, M. S., Kerr, K. M., Yatabe, Y. & Hirsch, F. R. Blueprint 2: PD-L1 immunohistochemistry comparability study in real-life, clinical samples. J. Thorac Oncol. 12 (Suppl. 2), 1606 (2017).

    Article  Google Scholar 

  48. Fujimoto, D. et al. Predictive performance of four programmed cell death ligand 1 assay systems on nivolumab response in previously treated patients with non-small cell lung cancer. J. Thorac Oncol. 13, 377–386 (2018).

    Article  PubMed  Google Scholar 

  49. Rozali, E. N., Hato, S. V., Robinson, B. W., Lake, R. A. & Lesterhuis, W. J. Programmed death ligand 2 in cancer-induced immune suppression. Clin. Dev. Immunol. 2012, 656340 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Meng, X., Huang, Z., Teng, F., Xing, L. & Yu, J. Predictive biomarkers in PD-1/PD-L1 checkpoint blockade immunotherapy. Cancer Treat. Rev. 41, 868–876 (2015).

    Article  CAS  PubMed  Google Scholar 

  51. Gainor, J. F. et al. EGFR mutations and ALK rearrangements are associated with low response rates to PD-1 pathway blockade in non-small cell lung cancer: a retrospective analysis. Clin. Cancer Res. 22, 4585–4593 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Garassino, M. C. et al. Durvalumab as third-line or later treatment for advanced non-small-cell lung cancer (ATLANTIC): an open-label, single-arm, phase 2 study. Lancet Oncol. 19, 521–536 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mazieres, J. et al. Efficacy of immune-checkpoint inhibitors in non-small cell lung cancer patients harboring activating molecular alterations (ImmunoTarget) [abstract]. J. Clin. Oncol. 36(Suppl. 15), 9010 (2018).

    Article  Google Scholar 

  54. Parra, E. R. et al. Immunohistochemical and image analysis-based study demonstrate that several immune checkpoints are co-expressed in non-small cell lung carcinoma tumors. J. Thorac Oncol. 13, 779–791 (2018).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  56. Garon, E. B. Cancer immunotherapy trials not immune from imprecise selection of patients. N. Engl. J. Med. 376, 2483–2485 (2017).

    Article  PubMed  Google Scholar 

  57. Jin, J. Y. et al. Higher radiation dose to immune system is correlated with poorer survival in patients with stage III non-small cell lung cancer: a secondary study of a phase 3 cooperative group trial (NRG Oncology RTOG 0617). Int. J. Radiat. Oncol. Biol. Phys. 99, S151–S152 (2017).

    Article  Google Scholar 

  58. International Association for the Study of Lung Cancer. IASLC Atlas of PD-L1 Testing in Lung Cancer (eds Tsao, M. S., Kerr, K. M., Dacic, S., Yatabe, Y. & Hirsch, F. R.) (IASLC, Aurora, CO, 2017).

  59. Aguilar, E. J. et al. Comparison of outcomes with PD-L1 tumor proportion score of 50–74% versus 75–100% in patients with non-small cell lung cancer treated with first-line PD-1 inhibitors [abstract]. J. Clin. Oncol. 36 (Suppl. 15), 9037 (2018).

    Article  Google Scholar 

  60. Lopes, G. et al. Pembrolizumab versus platinum-based chemotherapy as first-line therapy for advanced/metastatic NSCLC with a PDL-1 TPS 1%: open-label, phase 3, KEYNOTE-042 study [abstract]. J. Clin. Oncol. 36 (Suppl. 18), LBA4 (2018).

    Article  Google Scholar 

  61. Rizvi, H. et al. Molecular determinants of response to anti-programmed cell death (PD)-1 and anti-programmed death-ligand 1 (PD-L1) blockade in patients with non-small-cell lung cancer profiled with targeted next-generation sequencing. J. Clin. Oncol. 36, 633–641 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Peters, S. et al. Impact of tumor mutation burden on the efficacy of first-line nivolumab in stage IV or recurrent non-small cell lung cancer: an exploratory analysis of CheckMate 026 [abstract]. Cancer Res. 77, (Suppl. 13) CT082 (2017).

    Article  CAS  Google Scholar 

  63. Hellmann, M. D. et al. Nivolumab plus ipilimumab in lung cancer with a high tumor mutational burden. N. Engl. J. Med. 378, 2093–2104 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Rizvi, N. A. et al. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Garon, E. B. et al. Safety and activity of durvalumab + tremelimumab in immunotherapy-pretreated advanced NSCLC patients [abstract]. J. Clin. Oncol. 36 (Suppl. 15), 9041 (2018).

    Article  Google Scholar 

  66. Gandara, D. R. et al. Blood-based biomarkers for cancer immunotherapy: tumor mutational burden in blood is associated with improved atezolizumab efficacy in 2L+ NSCLC (POPLAR and OAK) [abstract 12950]. Ann. Oncol. 28, (Suppl. 5), mdx380 (2017).

    Article  Google Scholar 

  67. Cabel, L. et al. Clinical potential of circulating tumour DNA in patients receiving anticancer immunotherapy. Nat. Rev. Clin. Oncol. 15, 639–650 (2018).

    Article  CAS  PubMed  Google Scholar 

  68. Borghaei, H. et al. Nivolumab + platinum-doublet chemotherapy versus chemo as first-line treatment for advanced non-small cell lung cancer with <1% tumor PD-L1 expression: results from CheckMate 227 [abstract]. J. Clin. Oncol. 36 (Suppl. 15), 9001 (2018).

    Article  Google Scholar 

  69. Duan, F. et al. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity. J. Exp. Med. 211, 2231–2248 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Turajilic, S. et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol. 18, 1009–1021 (2017).

    Article  Google Scholar 

  71. McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Gadgeel, S. et al. Clinical efficacy of atezolizumab in PD-L1 selected subgroups defined by SP142 and 22C3 IHC assays in 2L+ NSCLC: results from the randomized OAK trial [abstract 12960]. Ann. Oncol. 28 (Suppl. 5), mdx380.001 (2017).

    Google Scholar 

  73. Kowanetz, M. et al. Pre-existing immunity measured by teff gene expression in tumor tissue is associated with atezolizumab efficacy in NSCLC [abstract]. J. Thorac Oncol. 12, (Suppl. 2), 1817–1818 (2017).

    Article  Google Scholar 

  74. Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Higgs, B. W. et al. Interferon gamma messenger RNA signature in tumor biopsies predicts outcomes in patients with non-small cell lung carcinoma or urothelial cancer treated with durvalumab. Clin. Cancer Res. 24, 3857–3866 (2018).

    Article  CAS  PubMed  Google Scholar 

  76. Danaher, P. et al. Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA). J. Immunother. Cancer 22, 63 (2018).

    Article  Google Scholar 

  77. Daud, A. I. et al. Tumor immune profiling predicts response to anti-PD-1 therapy in human melanoma. J. Clin. Invest. 126, 3447–3452 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Kamphorst, A. O. et al. Proliferation of PD-1+ CD8 T cells in peripheral blood after PD-1-targeted therapy in lung cancer patients. Proc. Natl Acad. Sci. USA 114, 4993–4998 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Skoulidis, F. et al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune profiles, and therapeutic vulnerabilities. Cancer Discov. 5, 860–877 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Skoulidis, F. et al. STK11/LKB1 mutations and PD-1 inhibitor resistance in KRAS-mutant lung adenocarcinoma. Cancer Discov. 8, 822–835 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Routy, B. et al. Gut microbiome effects efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).

    Article  CAS  PubMed  Google Scholar 

  82. Gandhi, L. et al. Pembrolizumab plus chemotherapy in metastatic non-small-cell lung cancer. N. Engl. J. Med. 378, 2078–2092 (2018).

    Article  CAS  PubMed  Google Scholar 

  83. Kowanetz, M. et al. IMpower150: efficacy of atezolizumab plus bevacizumab and chemotherapy in 1L metastatic nonsquamous NSCLC across key subgroups [abstract]. Cancer Res. 79, CT076 (2019).

    Google Scholar 

  84. Socinski, M. A. et al. Overall survival analysis of IMpower150, a randomized Ph 3 study of atezolizumab + chemotherapy ± bevacizumab versus chemo + bev in 1L nonsquamous NSCLC [abstract]. J. Clin. Oncol. 36 (Suppl. 15), 9002 (2018).

    Article  Google Scholar 

  85. Galluzzi, L., Buqué, A., Kepp, O., Zitvogel, L. & Kroemer, G. Immunogenic cell death in cancer and infectious disease. Nat. Rev. Immunol. 17, 97–111 (2017).

    Article  CAS  PubMed  Google Scholar 

  86. Menzies, A. M. et al. Anti-PD-1 therapy in patients with advanced melanoma and preexisting autoimmune disorders or major toxicity with ipilimumab. Ann. Oncol. 28, 368–376 (2017).

    Article  CAS  PubMed  Google Scholar 

  87. Khan, S. A., Pruitt, S. L., Xuan, L., Makris, U. & Gerber, D. E. How does autoimmune disease impact treatment and outcomes among patients with lung cancer? A national SEER-Medicare analysis. Lung Cancer 115, 97–102 (2018).

    Article  PubMed  Google Scholar 

  88. Gowen, M. F. et al. Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors. J. Transl Med. 16, 82 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Champiat, S. et al. Hyperprogressive disease is a new pattern of progression in cancer patients treated by anti-PD-1/PD-L1. Clin. Cancer Res. 23, 1920–1928 (2017).

    Article  CAS  PubMed  Google Scholar 

  90. Kato, S. et al. Hyperprogressors after immunotherapy: analysis of genomic alterations associated with accelerated growth rate. Clin. Cancer Res. 23, 4242–4250 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Champiat, S. et al. Hyperprogressive disease: recognizing a novel pattern to improve patient management. Nat. Rev. Clin. Oncol. 15, 748–762 (2018).

    Article  CAS  PubMed  Google Scholar 

  92. Wu, Y. L. et al. First-line erlotinib versus gemcitabine/cisplatin in patients with advanced EGFR mutation-positive non-small-cell lung cancer: analyses from the phase III, randomized, open-label, ENSURE study. Ann. Oncol. 26, 1883–1889 (2015).

    Article  PubMed  Google Scholar 

  93. Mitsudomi, T. et al. Gefitinib versus cisplatin plus docetaxel in patients with non-small-cell lung cancer harbouring mutations of the epidermal growth factor receptor (WJTOG3405): an open label, randomised phase 3 trial. Lancet Oncol. 11, 121–128 (2010).

    Article  CAS  PubMed  Google Scholar 

  94. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The work of D.R.C. and R.C.D. is partially supported by the University of Colorado Lung Cancer SPORE (P50CA058187).

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Nature Reviews Clinical Oncology thanks R. Rosell, M. Reck and other anonymous reviewer(s) for their contribution to the peer review of this work.

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  1. Division of Medical Oncology, Department of Medicine, University of Colorado Cancer Center, Aurora, CO, USA

    D. Ross Camidge & Robert C. Doebele

  2. Department of Pathology, Aberdeen University Medical School, Aberdeen Royal Infirmary, Aberdeen, UK

    Keith M. Kerr

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D.R.C. has ad hoc advisory roles with ARIAD, Arrys/Kyn, AstraZeneca, Bio-Thera DSMB, Celgene, Clovis, Daiichi Sankyo (interstitial lung disease adjudication committee), G1 Therapeutics (DSMB), Genoptix, Hansoh SRC, Hengrui, Ignyta, Lycera, Mersana Therapeutics, Novartis, Orion, Regeneron, Revolution Med, Roche/Genentech and Takeda and has received research funding from Takeda. R.C.D. is an advisory board member for ARIAD, AstraZeneca, Ignyta, Spectrum and Takeda; has received research sponsorship from Ignyta and licensing fees from Abbott Molecular, Ignyta and Rain Therapeutics; and owns stock in Rain Therapeutics. K.M.K. has been a consultant for and has received speaker’s honoraria from AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, Merck Serono, Merck Sharp and Dohme, Novartis, Pfizer, Roche and Ventana and has been a consultant for AbbVie.

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Camidge, D.R., Doebele, R.C. & Kerr, K.M. Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC. Nat Rev Clin Oncol 16, 341–355 (2019). https://doi.org/10.1038/s41571-019-0173-9

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