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Recurrent SERPINB3 and SERPINB4 mutations in patients who respond to anti-CTLA4 immunotherapy

Nature Genetics volume 48, pages 1327–1329 (2016)Cite this article

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Abstract

Immune checkpoint blockade has shown significant promise as an anticancer treatment, yet the determinants of response are not completely understood. Here we show that somatic mutations in SERPINB3 and SERPINB4 are associated with survival after anti-CTLA4 immunotherapy in two independent cohorts of patients with melanoma (n = 174). Interestingly, serpins are homologs of the well-known ovalbumin antigen and are associated with autoimmunity. Our findings have implications for the personalization of immunotherapy.

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Figure 1: Somatic mutations of SERPINB3 and SERPINB4 predict improved survival from treatment with anti-CTLA4 therapy.
Figure 2: Characteristics of mutations in SERPINB3 and SERPINB4.

References

  1. Hodi, F.S. et al. N. Engl. J. Med. 363, 711–723 (2010).

    Article  CAS  Google Scholar 

  2. Robert, C. et al. N. Engl. J. Med. 364, 2517–2526 (2011).

    Article  CAS  Google Scholar 

  3. Brahmer, J. et al. N. Engl. J. Med. 373, 123–135 (2015).

    Article  CAS  Google Scholar 

  4. Motzer, R.J. et al. N. Engl. J. Med. 373, 1803–1813 (2015).

    Article  CAS  Google Scholar 

  5. Robert, C. et al. N. Engl. J. Med. 372, 320–330 (2015).

    Article  CAS  Google Scholar 

  6. Snyder, A. et al. N. Engl. J. Med. 371, 2189–2199 (2014).

    Article  Google Scholar 

  7. Rizvi, N.A. et al. Science 348, 124–128 (2015).

    Article  CAS  Google Scholar 

  8. Le, D.T. et al. N. Engl. J. Med. 372, 2509–2520 (2015).

    Article  CAS  Google Scholar 

  9. Van Allen, E.M. et al. Science 350, 207–211 (2015).

    Article  CAS  Google Scholar 

  10. McGranahan, N. et al. Science 351, 1463–1469 (2016).

    Article  CAS  Google Scholar 

  11. Cancer Genome Atlas Network. Cell 161, 1681–1696 (2015).

  12. Vidalino, L. et al. Autoimmun. Rev. 9, 108–112 (2009).

    Article  CAS  Google Scholar 

  13. Gatto, M. et al. Clin. Rev. Allergy Immunol. 45, 267–280 (2013).

    Article  CAS  Google Scholar 

  14. Sivaprasad, U. et al. J. Invest. Dermatol. 135, 160–169 (2015).

    Article  CAS  Google Scholar 

  15. Schneider, S.S. et al. Proc. Natl. Acad. Sci. USA 92, 3147–3151 (1995).

    Article  CAS  Google Scholar 

  16. Catanzaro, J.M. et al. Nat. Commun. 5, 3729 (2014).

    Article  CAS  Google Scholar 

  17. Guan, J., Gupta, R. & Filipp, F.V. Sci. Rep. 5, 7857 (2015).

    Article  CAS  Google Scholar 

  18. Katagiri, C., Nakanishi, J., Kadoya, K. & Hibino, T. J. Cell Biol. 172, 983–990 (2006).

    Article  CAS  Google Scholar 

  19. El-Rachkidy, R.G., Young, H.S., Griffiths, C.E. & Camp, R.D. J. Invest. Dermatol. 128, 2219–2224 (2008).

    Article  CAS  Google Scholar 

  20. Lysvand, H., Hagen, L., Klubicka, L., Slupphaug, G. & Iversen, O.J. Biochim. Biophys. Acta 1842, 734–738 (2014).

    Article  CAS  Google Scholar 

  21. Münz, C. Front. Immunol. 3, 9 (2012).

    Article  Google Scholar 

  22. Schumacher, T. et al. Nature 512, 324–327 (2014).

    Article  CAS  Google Scholar 

  23. Mine, Y. & Yang, M. J. Agric. Food Chem. 56, 4874–4900 (2008).

    Article  CAS  Google Scholar 

  24. Holen, E. & Elsayed, S. Clin. Exp. Allergy 26, 1080–1088 (1996).

    Article  CAS  Google Scholar 

  25. DePristo, M.A. et al. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  Google Scholar 

  26. Li, H. & Durbin, R. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  27. McKenna, A. et al. Genome Res. 20, 1297–1303 (2010).

    Article  CAS  Google Scholar 

  28. Cibulskis, K. et al. Nat. Biotechnol. 31, 213–219 (2013).

    Article  CAS  Google Scholar 

  29. Koboldt, D.C. et al. Genome Res. 22, 568–576 (2012).

    Article  CAS  Google Scholar 

  30. Larson, D.E. et al. Bioinformatics 28, 311–317 (2012).

    Article  CAS  Google Scholar 

  31. Saunders, C.T. et al. Bioinformatics 28, 1811–1817 (2012).

    Article  CAS  Google Scholar 

  32. Strona, G., Nappo, D., Boccacci, F., Fattorini, S. & San-Miguel-Ayanz, J. Nat. Commun. 5, 4114 (2014).

    Article  CAS  Google Scholar 

  33. Kim, J. et al. Nat. Genet. 48, 600–606 (2016).

    Article  CAS  Google Scholar 

  34. Schellens, I.M. et al. PLoS One 10, e0136417 (2015).

    Article  Google Scholar 

  35. Snyder, A. & Chan, T.A. Curr. Opin. Genet. Dev. 30, 7–16 (2015).

    Article  CAS  Google Scholar 

  36. Nielsen, M. et al. Protein Sci. 12, 1007–1017 (2003).

    Article  CAS  Google Scholar 

  37. Andreatta, M. et al. Immunogenetics 67, 641–650 (2015).

    Article  CAS  Google Scholar 

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Acknowledgements

We thank J. Wolchok, T. Merghoub, J. Yuan, P. Wong, and A. Snyder for collaborative interactions. We thank the Integrated Genomics Operation and the Ludwig Immune Monitoring Facility at Memorial Sloan Kettering for technical assistance. We thank A. Heguy and the Genome Technology Center at New York University and L. Mangarin for assistance with validation sequencing. This work was funded by a Pershing Square Sohn Cancer Research grant (T.A.C.), the Frederick Adler Chair (T.A.C.), Stand Up 2 Cancer (T.A.C.), the STARR Cancer Consortium (T.A.C.), and in part through NIH/NCI Cancer Center Support Grant P30 CA008748. Research supported by a Stand Up To Cancer – Cancer Research Institute Cancer Immunology Translational Cancer Research Grant. Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research.

Author information

Author notes

  1. Nadeem Riaz, Jonathan J Havel and Sviatoslav M Kendall: These authors contributed equally to this work.

Authors and Affiliations

  1. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, New York, USA

    Nadeem Riaz, Jonathan J Havel, Sviatoslav M Kendall, Vladimir Makarov, Logan A Walsh, Alexis Desrichard & Timothy A Chan

  2. Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA

    Nadeem Riaz, Nils Weinhold & Timothy A Chan

  3. Immunogenomics and Precision Oncology Platform, Memorial Sloan Kettering Cancer Center, New York, New York, USA

    Nadeem Riaz & Timothy A Chan

Authors
  1. Nadeem Riaz
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Contributions

T.A.C. and N.R. designed and conceived the study. Analysis of mutations in individual genes with outcome was performed by S.M.K., N.W., N.R., V.M., and J.J.H. Neoantigen analysis was performed by J.J.H., S.M.K., and V.M. Analysis of expression data was performed by L.A.W., A.D., and N.R. N.R., J.J.H., and T.A.C. prepared the manuscript. All authors participated in discussion of the final manuscript and interpretation of results.

Corresponding author

Correspondence to Timothy A Chan.

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Competing interests

T.A.C. is a co-founder of Gritstone Oncology.

Integrated supplementary information

Supplementary Figure 1 Example IGV plots of SERPINB3 mutations in cohort 1 (tumor–normal pairs).

Left, sequencing data from the tumor; right, sequencing data from the corresponding normal sample. (a) Patient CR1509 (Tcov = 119, TAF = 0.30). (b) Patient CR3665 (Tcov = 246; TAF = 0.20). (c) Patient NR2137 (Tcov = 191, TAF = 0.24).

Supplementary Figure 2 Alignment of human SERPINB3 and chicken ovalbumin proteins.

Residues in red represent SERPINB3 mutations identified in the presently studied patient cohorts. SERPINB3 regions highlighted in gray represent experimentally validated human HLA-binding peptides35. Ovalbumin regions highlighted in yellow represent functionally validated immunogenic epitopes of human T cells24. (An asterisk indicates an exact amino acid match, a colon indicates alignment of amino acid residues with strongly similar properties, and a period indicates alignment of amino acid residues with weakly similar properties, as described at http://www.ebi.ac.uk/Tools/msa/clustalo/help/faq.html#23 (accessed 28 March 2016).

Supplementary Figure 3 Expression of SERPINB3 and SERPINB4 in primary and metastatic melanomas.

(a) Expression of SERPINB3 in primary melanoma versus regional or distant metastatic samples (P = 1.16 × 10–13, Wilcoxon rank-sum test). (b) Expression of SERPINB4 in primary melanoma versus regional or distant metastatic samples (P = 2.99 × 10–15, Wilcoxon rank-sum test).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–3. (PDF 582 kb)

Supplementary Table 1

Recurrently mutated genes in melanoma as identified by InVex analysis performed by TCGA. (XLSX 8 kb)

Supplementary Table 2

Multivariate model of overall survival and SERPINB3 and SERPINB4 mutations. (XLSX 10 kb)

Supplementary Table 3

SERPINB3 and SERPINB4 mutations in both cohorts of patients. (XLSX 12 kb)

Supplementary Table 4

MHC class I predicted neoantigens from SERPINB3 and SERPINB4 mutations. (XLSX 11 kb)

Supplementary Table 5

MHC class II predicted neoantigens from SERPINB3 and SERPINB4 mutations. (XLSX 16 kb)

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Riaz, N., Havel, J., Kendall, S. et al. Recurrent SERPINB3 and SERPINB4 mutations in patients who respond to anti-CTLA4 immunotherapy. Nat Genet 48, 1327–1329 (2016). https://doi.org/10.1038/ng.3677

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