Skip to main content

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

Radiotherapy induces responses of lung cancer to CTLA-4 blockade

Abstract

Focal radiation therapy enhances systemic responses to anti-CTLA-4 antibodies in preclinical studies and in some patients with melanoma1,2,3, but its efficacy in inducing systemic responses (abscopal responses) against tumors unresponsive to CTLA-4 blockade remained uncertain. Radiation therapy promotes the activation of anti-tumor T cells, an effect dependent on type I interferon induction in the irradiated tumor4,5,6. The latter is essential for achieving abscopal responses in murine cancers6. The mechanisms underlying abscopal responses in patients treated with radiation therapy and CTLA-4 blockade remain unclear. Here we report that radiation therapy and CTLA-4 blockade induced systemic anti-tumor T cells in chemo-refractory metastatic non-small-cell lung cancer (NSCLC), where anti-CTLA-4 antibodies had failed to demonstrate significant efficacy alone or in combination with chemotherapy7,8. Objective responses were observed in 18% of enrolled patients, and 31% had disease control. Increased serum interferon-β after radiation and early dynamic changes of blood T cell clones were the strongest response predictors, confirming preclinical mechanistic data. Functional analysis in one responding patient showed the rapid in vivo expansion of CD8 T cells recognizing a neoantigen encoded in a gene upregulated by radiation, supporting the hypothesis that one explanation for the abscopal response is radiation-induced exposure of immunogenic mutations to the immune system.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Patients survival and clinical response to radiotherapy and ipilimumab.
Fig. 2: Increase in interferon-β levels and TCR clonal dynamics predict response to treatment.
Fig. 3: Expansion of tumor-derived TCR clones in peripheral blood after treatment with radiotherapy and ipilimumab.
Fig. 4: Expansion of neoantigen-reactive CD8 T cells in a patient with NSCLC with complete response to radiotherapy and ipilimumab.

Data availability

The data reported are tabulated in the manuscript and supplementary figures and tables, and Supplementary Dataset 2 and 3. Raw data for soluble markers and flow cytometry are available in Supplementary Dataset 1. The raw TCR sequence data have been deposited into the ImmuneACCESS project repository of the Adaptive Biotechnology database (https://doi.org/10.21417/B7BW6X). WES and RNA-seq data have been deposited at the NCBI Sequence Read Archive (accession number SRP136187; http://www.ncbi.nlm.nih.gov/bioproject/439205).

References

  1. 1.

    Formenti, S. C. & Demaria, S. Systemic effects of local radiotherapy. Lancet. Oncol. 10, 718–726 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Demaria, S., Coleman, C. N. & Formenti, S. C. Radiotherapy: changing the game in immunotherapy. Trends Cancer 2, 286–294 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Postow, M. A. et al. Immunologic correlates of the abscopal effect in a patient with melanoma. N. Engl. J. Med. 366, 925–931 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Burnette, B. C. et al. The efficacy of radiotherapy relies upon induction of type i interferon-dependent innate and adaptive immunity. Cancer Res. 71, 2488–2496 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Deng, L. et al. STING-dependent cytosolic DNA sensing promotes radiation-induced type I interferon-dependent anti-tumor immunity in immunogenic tumors. Immunity 41, 843–852 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Vanpouille-Box, C. et al. DNA exonuclease Trex1 regulates radiotherapy-induced tumour immunogenicity. Nat. Commun. 8, 15618 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Zatloukal, P. et al. Randomized phase II clinical trial comparing tremelimumab (CP-675, 206) with best supportive care (BSC) following first-line platinum-based therapy in patients (pts) with advanced non-small cell lung cancer (NSCLC). J. Clin. Oncol. 27, 8071–8071 (2009).

    Google Scholar 

  8. 8.

    Lynch, T. J. et al. Ipilimumab in combination with paclitaxel and carboplatin as first-line treatment in stage IIIB/IV non-small-cell lung cancer: results from a randomized, double-blind, multicenter phase II study. J. Clin. Oncol. 30, 2046–2054 (2012).

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Golden, E. B., Demaria, S., Schiff, P. B., Chachoua, A. & Formenti, S. C. An abscopal response to radiation and ipilimumab in a patient with metastatic non-small cell lung cancer. Cancer Immunol. Res. 1, 365–372 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Golden, E. B. et al. Local radiotherapy and granulocyte-macrophage colony-stimulating factor to generate abscopal responses in patients with metastatic solid tumours: a proof-of-principle trial. Lancet. Oncol. 16, 795–803 (2015).

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Spigel, D. R. et al. Total mutation burden (TMB) in lung cancer (LC) and relationship with response to PD-1/PD-L1 targeted therapies. J. Clin. Oncol. 34, 9017–9017 (2016).

    Article  Google Scholar 

  12. 12.

    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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Ng Tang, D. et al. Increased frequency of ICOS+ CD4 T cells as a pharmacodynamic biomarker for anti-CTLA-4 therapy. Cancer. Immunol. Res. 1, 229–234 (2013).

    Article  CAS  PubMed  Google Scholar 

  14. 14.

    Wang, W. et al. Biomarkers on melanoma patient T cells associated with ipilimumab treatment. J. Transl. Med. 10, 146 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Sim, G. C. et al. IL-2 therapy promotes suppressive ICOS+ Treg expansion in melanoma patients. J. Clin. Invest. 124, 99–110 (2014).

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Kuo, P. et al. Galectin-1 mediates radiation-related lymphopenia and attenuates NSCLC radiation response. Clin. Cancer Res. 20, 5558–5569 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Koguchi, Y. et al. Serum immunoregulatory proteins as predictors of overall survival of metastatic melanoma patients treated with ipilimumab. Cancer Res. 75, 5084–5092 (2015).

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Jinushi, M., Hodi, F. S. & Dranoff, G. Therapy-induced antibodies to MHC class I chain-related protein A antagonize immune suppression and stimulate antitumor cytotoxicity. Proc. Natl Acad. Sci. USA 103, 9190–9195 (2006).

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Ferrari de Andrade, L., et al. Antibody-mediated inhibition of MICA and MICB shedding promotes NK cell–driven tumor immunity. Science 30, 1537–1542 (2018).

    Article  CAS  Google Scholar 

  20. 20.

    Gasser, S., Orsulic, S., Brown, E. J. & Raulet, D. H. The DNA damage pathway regulates innate immune system ligands of the NKG2D receptor. Nature 436, 1186–1190 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Ruocco, M. G. et al. Suppressing T cell motility induced by anti-CTLA-4 monotherapy improves antitumor effects. J. Clin. Invest. 122, 3718–3730 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Rudqvist, N. P. et al. Radiotherapy and CTLA-4 blockade shape the TCR repertoire of tumor-infiltrating T cells. Cancer Immunol. Res. 6, 139–150 (2018).

    CAS  Article  PubMed  Google Scholar 

  23. 23.

    Sidhom, J. W. et al. ImmunoMap: a bioinformatics tool for T-cell repertoire analysis. Cancer Immunol. Res. 6, 151–162 (2018).

    CAS  Article  PubMed  Google Scholar 

  24. 24.

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Snyder, A. et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 371, 2189–2199 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Song, K. H. et al. Induction of immunogenic cell death by radiation-upregulated karyopherin alpha 2 in vitro. Eur. J. Cell Biol. 95, 219–227 (2016).

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Twyman-Saint Victor, C. et al. Radiation and dual checkpoint blockade activate non-redundant immune mechanisms in cancer. Nature 520, 373–377 (2015).

    CAS  Article  Google Scholar 

  30. 30.

    Harding, S. M. et al. Mitotic progression following DNA damage enables pattern recognition within micronuclei. Nature 548, 466–470 (2017).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Konno, H. et al. Suppression of STING signaling through epigenetic silencing and missense mutation impedes DNA damage mediated cytokine production. Oncogene 37, 2037–2051 (2018).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    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).

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Wolchok, J. D. et al. Guidelines for the evaluation of immune therapy activity in solid tumors: immune-related response criteria. Clin. Cancer Res. 15, 7412–7420 (2009).

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Formenti, S. C. et al. Results of a phase I‒II study of adjuvant concurrent carboplatin and accelerated radiotherapy for triple negative breast cancer. Oncoimmunology 6, e1274479 (2017).

    Article  CAS  PubMed  Google Scholar 

  35. 35.

    Carlson, C. S. et al. Using synthetic templates to design an unbiased multiplex PCR assay. Nat. Commun. 4, 2680 (2013).

    Article  CAS  PubMed  Google Scholar 

  36. 36.

    Yousfi Monod, M., Giudicelli, V., Chaume, D. & Lefranc, M. P. IMGT/JunctionAnalysis: the first tool for the analysis of the immunoglobulin and T cell receptor complex V-J and V-D-J JUNCTIONs. Bioinformatics 20, i379–i385 (2004).

    Article  CAS  PubMed  Google Scholar 

  37. 37.

    Wu, D. et al. Detection of minimal residual disease in B lymphoblastic leukemia by high-throughput sequencing of IGH. Clin. Cancer Res. 20, 4540–4548 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Sherwood, A. M. et al. Tumor-infiltrating lymphocytes in colorectal tumors display a diversity of T cell receptor sequences that differ from the T cells in adjacent mucosal tissue. Cancer Immunol. Immunother. 62, 1453–1461 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    DeWitt, W. S. et al. Dynamics of the cytotoxic T cell response to a model of acute viral infection. J. Virol. 89, 4517–4526 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Pages, H., Aboyoun, P., Gentleman, R. & DebRoy, S. Biostrings: efficient manipulation of biological strings. https://doi.org/10.18129/B9.bioc.Biostrings (R package version 2.46.0, 2017).

  41. 41.

    Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).

    CAS  Article  Google Scholar 

  42. 42.

    Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. Y. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).

    Article  Google Scholar 

  43. 43.

    Ishwaran, H. & Kogalur, U. Random forests for survival, regression and classification (RF-SRC). https://cran.r-project.org/web/packages/randomForestSRC/index.html (R package version 2.4.1, 2017).

  44. 44.

    Ishwaran, H. Variable importance in binary regression trees and forests. Electron, J. Stat 1, 519–537 (2007).

    Article  Google Scholar 

  45. 45.

    Tang, F. & Ishwaran, H. Random forest missing data algorithms. Stat. Anal. Data Min. 10, 363–377 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Annal. Appl. Stat. 2, 841–860 (2008).

    Article  Google Scholar 

  47. 47.

    Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at http://arxiv.org/abs/1303.3997 (2013).

  48. 48.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Article  Google Scholar 

  51. 51.

    Trapnell, C. et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Roberts, A., Trapnell, C., Donaghey, J., Rinn, J. L. & Pachter, L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome. Biol. 12, R22 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Szolek, A. et al. OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30, 3310–3316 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    McLaren, W. et al. The ensembl variant effect predictor. Genome. Biol. 17, 122 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Hundal, J. et al. pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Med. 8, 11 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Nielsen, M. et al. Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12, 1007–1017 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Andreatta, M. & Nielsen, M. Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32, 511–517 (2016).

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    The Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

    Article  CAS  PubMed Central  Google Scholar 

Download references

Acknowledgements

We to acknowledge J. Goldberg for the initial design of the clinical trial, K. Pilones for assistance with DNA preparation, L. Chriboga for help with immunohistochemistry, D. Morrison for blood processing, and the NYULH Genome Technology Center (GTC) technical personnel for sequencing. We thank S. Chandraseckhar for data management, M. Fenton-Kerimian for patient care, and G. Inghirami for providing the PDX mice. We thank Bristol Meyer Squibb, New York, NY, USA, for providing ipilimumab for this research study. We are indebted to G. Koretzky for insightful discussion and review of the manuscript. The immunological studies were funded by NCI grants no. R01CA198533 and no. R01CA201246 (to S.D.). The NYU Experimental Pathology Immunohistochemistry Core Laboratory and the GTC are partially supported by the Cancer Center support grant P30CA016087 at the Laura and Isaac Perlmutter Cancer Center, NYULH. E.W. is supported by a DOD W81XWH-17-1-0029 post-doctoral fellowship. S.G. acknowledges the Human Immune Monitoring Center at Mount Sinai and Cancer Center support grant P30CA196521. L.F.A. was funded by a Friends for Life Neuroblastoma Fellowship and K.W.W. was supported by National Cancer Institute (NCI) grant no. R01 CA173750.

Author information

Affiliations

Authors

Contributions

S.C.F., A.C., and S.D. conceived and designed the clinical protocol. S.C.F. and S.D. designed the correlative studies. E.G., B.C., and K.F. contributed to patients enrollment and treatment and/or response evaluation. N.-P.R. developed the neoantigen prediction pipeline and analyzed the TCR repertoire. A.H. and T.Z. helped with WES and RNA-seq. E.W., C.L., N.I., and S.G. performed flow cytometry and functional T cell studies. C.V.-B. contributed to cytokine measurements and the patient-derived tumor xenograft experiment. L.F.d.A. and K.W.W. evaluated sMICA and antibodies. R.O.E. helped with TCR repertoire analysis. X.K.Z. performed the statistical evaluations. S.D., S.C.F., and N.-P.R. wrote the manuscript. All authors had final responsibility for the decision to submit this report as written for publication.

Corresponding authors

Correspondence to Silvia C. Formenti or Sandra Demaria.

Ethics declarations

Competing interests

Bristol Meyer Squibb did not have any role in the design, data collection and analysis, interpretation of results and preparation of the manuscript. Potential conflicts of interest: Full-time employment and equity ownership at Adaptive Biotechnologies Corporation (R.O.E.). Prior honorarium for consulting from Third Rock Ventures/Neon Therapeutics, B4CC, OncoMed, Merck, and research funding from Agenus, Bristol Meyer Squibb, Genentech, Pfizer, Janssen R&D, Immune Design (S.G.). Service on Scientific Advisory Board of Lytix Biopharma, prior honorarium for consulting/speaker from AstraZeneca, AbbVie Inc., Cytune Pharma, EMD Serono, Eisai Inc., Regeneron, Ventana Medical Systems, Inc. Research grants from Nanobiotix, and Lytix Biopharma (S.D.). Prior honorarium for consulting/speaker from Sanofi, AstraZeneca, Merck, Regeneron, Bayer, Serono/Merck, and research funding from Janssen R&D, Varian, Merck, Bristol Meyer Squibb (for a different study) (S.C.F.). Service on Scientific Advisory Board of Nextech, T-scan and TCR2, consultancy for Novartis, research funding from Astellas, Bristol-Myers Squibb and Novartis (not related to the topic of this mansucript) (K.W.W.).

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Tables 1–9

Reporting Summary

Supplementary Data Set 1

Raw data for all soluble markers and circulating lymphocytes analyzed

Supplementary Data Set 2

Summary tables of all soluble markers and circulating immune cell subsets showing significant differences at baseline in responding and nonresponding patients and/or showing significant changes during treatment

Supplementary Data Set 3

Summary tables showing the results of CTLA-4 expression analysis on circulating conventional and regulatory CD4 T cells and CD8 T cells at baseline and during treatment

Supplementary Data Set 4

List of variants identified by WES in the tumours of patients #4, 32, 36, and 38

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Formenti, S.C., Rudqvist, NP., Golden, E. et al. Radiotherapy induces responses of lung cancer to CTLA-4 blockade. Nat Med 24, 1845–1851 (2018). https://doi.org/10.1038/s41591-018-0232-2

Download citation

Further reading

Search

Quick links

Nature Briefing

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

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