Abstract

Somatic gene mutations can alter the vulnerability of cancer cells to T-cell-based immunotherapies. Here we perturbed genes in human melanoma cells to mimic loss-of-function mutations involved in resistance to these therapies, by using a genome-scale CRISPR–Cas9 library that consisted of around 123,000 single-guide RNAs, and profiled genes whose loss in tumour cells impaired the effector function of CD8+ T cells. The genes that were most enriched in the screen have key roles in antigen presentation and interferon-γ signalling, and correlate with cytolytic activity in patient tumours from The Cancer Genome Atlas. Among the genes validated using different cancer cell lines and antigens, we identified multiple loss-of-function mutations in APLNR, encoding the apelin receptor, in patient tumours that were refractory to immunotherapy. We show that APLNR interacts with JAK1, modulating interferon-γ responses in tumours, and that its functional loss reduces the efficacy of adoptive cell transfer and checkpoint blockade immunotherapies in mouse models. Our results link the loss of essential genes for the effector function of CD8+ T cells with the resistance or non-responsiveness of cancer to immunotherapies.

  • Subscribe to Nature for full access:

    $199

    Subscribe

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

References

  1. 1.

    et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013)

  2. 2.

    , & Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 373, 1984 (2015)

  3. 3.

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

  4. 4.

    et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165, 35–44 (2016)

  5. 5.

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

  6. 6.

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

  7. 7.

    . et al. Mismatch-repair deficiency predicts response of solid tumors to PD-1 blockade. Science aan6733 (2017)

  8. 8.

    et al. Loss of functional β2-microglobulin in metastatic melanomas from five patients receiving immunotherapy. J. Natl Cancer Inst. 88, 100–108 (1996)

  9. 9.

    et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016)

  10. 10.

    et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015)

  11. 11.

    et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015)

  12. 12.

    et al. Genome-scale CRISPR–Cas9 knockout screening in human cells. Science 343, 84–87 (2014)

  13. 13.

    , , & Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014)

  14. 14.

    et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–1260 (2015)

  15. 15.

    , , , & Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015)

  16. 16.

    et al. Anti-CTLA-4 therapy broadens the melanoma-reactive CD8+ T cell response. Sci. Transl. Med. 6, 254ra128 (2014)

  17. 17.

    et al. Single and dual amino acid substitutions in TCR CDRs can enhance antigen-specific T cell functions. J. Immunol. 180, 6116–6131 (2008)

  18. 18.

    et al. Gene transfer of tumor-reactive TCR confers both high avidity and tumor reactivity to nonreactive peripheral blood mononuclear cells and tumor-infiltrating lymphocytes. J. Immunol. 177, 6548–6559 (2006)

  19. 19.

    et al. A pilot trial using lymphocytes genetically engineered with an NY-ESO-1-reactive T-cell receptor: long-term follow-up and correlates with response. Clin. Cancer Res. 21, 1019–1027 (2015)

  20. 20.

    , & Bystander elimination of antigen loss variants in established tumors. Nat. Med. 10, 294–298 (2004)

  21. 21.

    , & Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014)

  22. 22.

    et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl Acad. Sci. USA 105, 20380–20385 (2008)

  23. 23.

    et al. Identification of genes selectively regulated by IFNs in endothelial cells. J. Immunol. 178, 1122–1135 (2007)

  24. 24.

    et al. Differential gene induction by type I and type II interferons and their combination. J. Interferon Cytokine Res. 26, 462–472 (2006)

  25. 25.

    et al. TNF induces distinct gene expression programs in microvascular and macrovascular human endothelial cells. J. Leukoc. Biol. 80, 174–185 (2006)

  26. 26.

    et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306–312 (2015)

  27. 27.

    et al. Diverse somatic mutation patterns and pathway alterations in human cancers. Nature 466, 869–873 (2010)

  28. 28.

    et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci. Transl. Med. 9, aah3560 (2017)

  29. 29.

    et al. Somatic mutations and neoepitope homology in melanomas treated with CTLA-4 blockade. Cancer Immunol. Res. 5, 84–91 (2017)

  30. 30.

    , , & The apelin receptor APJ: journey from an orphan to a multifaceted regulator of homeostasis. J. Endocrinol. 219, R13–R35 (2013)

  31. 31.

    et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006)

  32. 32.

    , & Interferons, immunity and cancer immunoediting. Nat. Rev. Immunol. 6, 836–848 (2006)

  33. 33.

    et al. Tumor regression and autoimmunity after reversal of a functionally tolerant state of self-reactive CD8+ T cells. J. Exp. Med. 198, 569–580 (2003)

  34. 34.

    et al. Low dose decitabine treatment induces CD80 expression in cancer cells and stimulates tumor specific cytotoxic T lymphocyte responses. PLoS One 8, e62924 (2013)

  35. 35.

    et al. Alterations of immune response of non-small cell lung cancer with Azacytidine. Oncotarget 4, 2067–2079 (2013)

  36. 36.

    et al. Direct stimulation of Jak/STAT pathway by the angiotensin II AT1 receptor. Nature 375, 247–250 (1995)

  37. 37.

    et al. The apelin/APJ system induces maturation of the tumor vasculature and improves the efficiency of immune therapy. Oncogene 31, 3254–3264 (2012)

  38. 38.

    et al. Tumour ischaemia by interferon-γ resembles physiological blood vessel regression. Nature 545, 98–102 (2017)

  39. 39.

    Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011)

  40. 40.

    , , & Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)

  41. 41.

    , & TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. Bioinformatics 32, 952–954 (2016)

  42. 42.

    et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013)

  43. 43.

    et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012)

  44. 44.

    , & Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014)

  45. 45.

    & Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012)

  46. 46.

    et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat. Med. 19, 747–752 (2013)

  47. 47.

    et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013)

  48. 48.

    et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protocols 7, 562–578 (2012)

  49. 49.

    et al. Melanoma induction by ultraviolet A but not ultraviolet B radiation requires melanin pigment. Nat. Commun. 3, 884 (2012)

Download references

Acknowledgements

The research was supported by the Intramural Research Program of the NCI, and by the Cancer Moonshot program for the Center for Cell-based Therapy at the NCI, NIH. The work was also supported by the Milstein Family Foundation. We thank S. A. Rosenberg, K. Hanada, A. Wellstein, C. Hurley and L. M. Weiner for their valuable discussions and intellectual input, M. Kruhlak, Z. Yu, C. Subramaniam, C. Kariya, A. J. Leonardi, N. Ha, H. Xu, M. A. Black and H. Chinnasamy for technical assistance in this project. This work used the computational resources of the NIH HPC Biowulf cluster (http://hpc.nih.gov). The results here are in part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. This study was done in partial fulfilment of a PhD in Tumor Biology to S.J.P. N.E.S. is supported by the NIH through NHGRI (R00-HG008171) and a Sidney Kimmel Scholar Award.

Author information

Author notes

    • Shashank J. Patel
    •  & Neville E. Sanjana

    These authors contributed equally to this work.

Affiliations

  1. National Cancer Institute, National Institutes of Health (NIH), Bethesda, Maryland 20892, USA

    • Shashank J. Patel
    • , Rigel J. Kishton
    • , Arash Eidizadeh
    • , Suman K. Vodnala
    • , Maggie Cam
    • , Jared J. Gartner
    • , Li Jia
    • , Seth M. Steinberg
    • , Tori N. Yamamoto
    • , Anand S. Merchant
    • , Gautam U. Mehta
    • , Anna Chichura
    • , Eric Tran
    • , Robert Eil
    • , Madhusudhanan Sukumar
    • , Eva Perez Guijarro
    • , Chi-Ping Day
    • , Paul Robbins
    • , Steve Feldman
    • , Glenn Merlino
    •  & Nicholas P. Restifo
  2. NIH-Georgetown University Graduate Partnership Program, Georgetown University Medical School, Washington DC 20057, USA

    • Shashank J. Patel
  3. New York Genome Center, New York, New York 10013, USA

    • Neville E. Sanjana
  4. Department of Biology, New York University, New York, New York 10012, USA

    • Neville E. Sanjana
  5. Immunology Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Tori N. Yamamoto
  6. Children’s Hospital of Philadelphia and Department of Genetics, University of Pennsylvania, Pennsylvania 19104, USA

    • Ophir Shalem
  7. Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA

    • Feng Zhang
  8. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • Feng Zhang
  9. Center for Cell-based Therapy, Center for Cancer Research, National Institutes of Health (NIH), Bethesda, Maryland 20892, USA

    • Nicholas P. Restifo

Authors

  1. Search for Shashank J. Patel in:

  2. Search for Neville E. Sanjana in:

  3. Search for Rigel J. Kishton in:

  4. Search for Arash Eidizadeh in:

  5. Search for Suman K. Vodnala in:

  6. Search for Maggie Cam in:

  7. Search for Jared J. Gartner in:

  8. Search for Li Jia in:

  9. Search for Seth M. Steinberg in:

  10. Search for Tori N. Yamamoto in:

  11. Search for Anand S. Merchant in:

  12. Search for Gautam U. Mehta in:

  13. Search for Anna Chichura in:

  14. Search for Ophir Shalem in:

  15. Search for Eric Tran in:

  16. Search for Robert Eil in:

  17. Search for Madhusudhanan Sukumar in:

  18. Search for Eva Perez Guijarro in:

  19. Search for Chi-Ping Day in:

  20. Search for Paul Robbins in:

  21. Search for Steve Feldman in:

  22. Search for Glenn Merlino in:

  23. Search for Feng Zhang in:

  24. Search for Nicholas P. Restifo in:

Contributions

S.J.P., N.E.S., and N.P.R. designed the study and wrote the manuscript. S.J.P. carried out CRISPR screens and validation experiments. N.E.S., O.S. and S.J.P. analysed CRISPR screen data. S.J.P. and N.E.S. analysed human mutation datasets from immunotherapy cohorts. T.N.Y., G.U.M., A.C., M.S. and S.F. assisted in generation of TCR-engineered T cells and CRISPR-edited cells. R.E., A.E., T.N.Y., S.K.V., G.U.M., A.C. and M.S. edited the manuscript. S.J.P., A.E. and S.K.V. carried out mouse experiments. G.M., E.P.G. and C.-P.D. developed B2905 mouse model for anti-CTLA4 experiments. S.K.V. and L.J. analysed RNA-seq data. M.C. and A.S.M. analysed TCGA datasets. J.J.G. performed indel analyses. S.M.S. analysed clinical data. R.J.K. performed western blots and immunoprecipitation experiments. F.Z., E.T. and P.R. contributed reagents. N.P.R. supervised the study.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Shashank J. Patel or Neville E. Sanjana or Nicholas P. Restifo.

Reviewer Information Nature thanks R. Levine and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains a list of figure legends for Supplementary Tables 1-8, a Supplementary Discussion and Supplementary Figures 1-9.

  2. 2.

    Reporting Summary

Zip files

  1. 1.

    Supplementary Tables

    This file contains Supplementary Tables 1-8. See Supplementary Document for Supplementary Table legends.

Excel files

  1. 1.

    Supplementary Data

    This file contains source data for Supplementary Figure 4 RNAseq Analysis concerning Gene Expression log10 RPKM.

  2. 2.

    Supplementary Data

    This file contains source data for Supplementary Figure 7.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nature23477

Rights and permissions

To obtain permission to re-use content from this article visit RightsLink.

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.