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.

  • Article
  • Published:

Genome-scale screens identify factors regulating tumor cell responses to natural killer cells

Nature Genetics volume 53, pages 1196–1206 (2021)Cite this article

Subjects

Abstract

To systematically define molecular features in human tumor cells that determine their degree of sensitivity to human allogeneic natural killer (NK) cells, we quantified the NK cell responsiveness of hundreds of molecularly annotated ‘DNA-barcoded’ solid tumor cell lines in multiplexed format and applied genome-scale CRISPR-based gene-editing screens in several solid tumor cell lines, to functionally interrogate which genes in tumor cells regulate the response to NK cells. In these orthogonal studies, NK cell–sensitive tumor cells tend to exhibit ‘mesenchymal-like’ transcriptional programs; high transcriptional signature for chromatin remodeling complexes; high levels of B7-H6 (NCR3LG1); and low levels of HLA-E/antigen presentation genes. Importantly, transcriptional signatures of NK cell–sensitive tumor cells correlate with immune checkpoint inhibitor (ICI) resistance in clinical samples. This study provides a comprehensive map of mechanisms regulating tumor cell responses to NK cells, with implications for future biomarker-driven applications of NK cell immunotherapies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the PRISM study using NK cells.
Fig. 2: Overview of the genome-scale CRISPR studies of NK cell treatment.
Fig. 3: Mesenchymal-like tumor cells are, on average, more sensitive to NK cell cytotoxicity in vitro.
Fig. 4: Key regulators of tumor cell responses to NK cells in PRISM and CRISPR studies.
Fig. 5: HLA-E as a major regulator of tumor cell responses to NK cells.
Fig. 6: Surface protein levels reveal functional relationship of B7-H6 and MHC class I molecules.
Fig. 7: Transcriptional signatures associated with tumor cell responses to NK cells.
Fig. 8: GSEA for NK sensitivity and NK resistance gene sets in patient tumor samples.

Similar content being viewed by others

Data availability

TCGA data were acquired from the Broad Firehose web site, v.2016_07_15. Read counts from the CRISPR and PRISM screens are available in Supplementary Table 4. Raw data can be available upon request. Molecular profiling data of tumor cells treated with broad-spectrum HDAC inhibitors were accessed from Gene Expression Omnibus (GEO: GSE108191, GSE96649, GSE101708 and GSE37376). RNA-seq data of tumors from patients treated with ICI were accessed from GEO: GSE115978 (Jerby-Arnon et al.25), GSE91061 (Riaz et al.26) and GSE78220 (Hugo et al.24). The Van-Allen dataset was acquired from dbGaP (accession phs000452.v2.p1 (ref. 28)). Source data are provided with this paper.

Code availability

The study did not develop new software of custom code, but applied in sequence different previously available codes for which details are provided in the Methods and Supplementary Information. All code used in this study can be available upon request.

References

  1. Liu, E. et al. Use of CAR-transduced natural killer cells in CD19-positive lymphoid tumors. N. Engl. J. Med. 382, 545–553 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Guillerey, C., Huntington, N. D. & Smyth, M. J. Targeting natural killer cells in cancer immunotherapy. Nat. Immunol. 17, 1025–1036 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Nayyar, G., Chu, Y. & Cairo, M. S. Overcoming resistance to natural killer cell based immunotherapies for solid tumors. Front. Oncol. 9, 51 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Shimasaki, N., Jain, A. & Campana, D. NK cells for cancer immunotherapy. Nat. Rev. Drug Discov. 19, 200–218 (2020).

    Article  CAS  PubMed  Google Scholar 

  5. Gurjao, C. et al. Intrinsic resistance to immune checkpoint blockade in a mismatch repair-deficient colorectal cancer. Cancer Immunol. Res. 7, 1230–1236 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sade-Feldman, M. et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nat. Commun. 8, 1136 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Yu, C. et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34, 419–423 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  11. Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Brandt, C. S. et al. The B7 family member B7-H6 is a tumor cell ligand for the activating natural killer cell receptor NKp30 in humans. J. Exp. Med. 206, 1495–1503 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Kaifu, T., Escaliere, B., Gastinel, L. N., Vivier, E. & Baratin, M. B7-H6/NKp30 interaction: a mechanism of alerting NK cells against tumors. Cell. Mol. Life Sci. 68, 3531–3539 (2011).

    Article  CAS  PubMed  Google Scholar 

  14. Braud, V. M. et al. HLA-E binds to natural killer cell receptors CD94/NKG2A, B and C. Nature 391, 795–799 (1998).

    Article  CAS  PubMed  Google Scholar 

  15. Andre, P. et al. Anti-NKG2A mAb is a checkpoint inhibitor that promotes anti-tumor immunity by unleashing both T and NK cells. Cell 175, 1731–1743.e13 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Kamiya, T., Seow, S. V., Wong, D., Robinson, M. & Campana, D. Blocking expression of inhibitory receptor NKG2A overcomes tumor resistance to NK cells. J. Clin. Invest. 129, 2094–2106 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zhou, F. Molecular mechanisms of IFN-γ to up-regulate MHC class I antigen processing and presentation. Int. Rev. Immunol. 28, 239–260 (2009).

    Article  CAS  PubMed  Google Scholar 

  18. Dendoncker, K. et al. The nature of the GRE influences the screening for GR-activity enhancing modulators. PLoS ONE 12, e0181101 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Rafehi, H. et al. Vascular histone deacetylation by pharmacological HDAC inhibition. Genome Res. 24, 1271–1284 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Sanchez, G. J. et al. Genome-wide dose-dependent inhibition of histone deacetylases studies reveal their roles in enhancer remodeling and suppression of oncogenic super-enhancers. Nucleic Acids Res. 46, 1756–1776 (2018).

    Article  CAS  PubMed  Google Scholar 

  21. Pech, M. F. et al. Systematic identification of cancer cell vulnerabilities to natural killer cell-mediated immune surveillance. eLife 8, e47362 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Tawbi, H. A. et al. Pembrolizumab in advanced soft-tissue sarcoma and bone sarcoma (SARC028): a multicentre, two-cohort, single-arm, open-label, phase 2 trial. Lancet Oncol. 18, 1493–1501 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Burgess, M. A. et al. Clinical activity of pembrolizumab (P) in undifferentiated pleomorphic sarcoma (UPS) and dedifferentiated/pleomorphic liposarcoma (LPS): final results of SARC028 expansion cohorts. J. Clin. Oncol. 37, 11015–11015 (2019).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Jerby-Arnon, L. et al. A cancer cell program promotes T cell exclusion and resistance to checkpoint blockade. Cell 175, 984–997.e24 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949.e16 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Schwartzkopff, S. et al. Tumor-associated E-cadherin mutations affect binding to the killer cell lectin-like receptor G1 in humans. J. Immunol. 179, 1022–1029 (2007).

    Article  CAS  PubMed  Google Scholar 

  30. Freeman, A. J. et al. Natural killer cells suppress T cell-associated tumor immune evasion. Cell Rep. 28, 2784–2794.e5 (2019).

    Article  CAS  PubMed  Google Scholar 

  31. Zhuang, X., Veltri, D. P. & Long, E. O. Genome-wide CRISPR screen reveals cancer cell resistance to NK cells induced by NK-derived IFN-γ. Front. Immunol. 10, 2879 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Leung, W. H. et al. Modulation of NKG2D ligand expression and metastasis in tumors by spironolactone via RXRγ activation. J. Exp. Med. 210, 2675–2692 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mou, X. et al. The regulatory effect of UL-16 binding protein-3 expression on the cytotoxicity of NK cells in cancer patients. Sci. Rep. 4, 6138 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Xu, X., Rao, G. & Li, Y. Xanthine oxidoreductase is required for genotoxic stress-induced NKG2D ligand expression and gemcitabine-mediated antitumor activity. Oncotarget 7, 59220–59235 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Ferrari de Andrade, L. et al. Antibody-mediated inhibition of MICA and MICB shedding promotes NK cell-driven tumor immunity. Science 359, 1537–1542 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Woods, D. M. et al. HDAC inhibition upregulates PD-1 ligands in melanoma and augments immunotherapy with PD-1 blockade. Cancer Immunol. Res. 3, 1375–1385 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Zhu, S. et al. The narrow-spectrum HDAC inhibitor entinostat enhances NKG2D expression without NK cell toxicity, leading to enhanced recognition of cancer cells. Pharm. Res. 32, 779–792 (2015).

    Article  CAS  PubMed  Google Scholar 

  38. McMillin, D. W. et al. Tumor cell-specific bioluminescence platform to identify stroma-induced changes to anticancer drug activity. Nat. Med. 16, 483–489 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. McMillin, D. W. et al. Compartment-specific bioluminescence imaging platform for the high-throughput evaluation of antitumor immune function. Blood 119, e131–e138 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Tsafrir, D. et al. Sorting points into neighborhoods (SPIN): data analysis and visualization by ordering distance matrices. Bioinformatics 21, 2301–2308 (2005).

    Article  CAS  PubMed  Google Scholar 

  41. Shirasaki, R. et al. Functional genomics identify distinct and overlapping genes mediating resistance to different classes of heterobifunctional degraders of oncoproteins. Cell Rep. 34, 108532 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Lozupone, F. et al. Effect of human natural killer and γδ T cells on the growth of human autologous melanoma xenografts in SCID mice. Cancer Res. 64, 378–385 (2004).

    Article  CAS  PubMed  Google Scholar 

  44. Bobisse, S. et al. Reprogramming T lymphocytes for melanoma adoptive immunotherapy by T-cell receptor gene transfer with lentiviral vectors. Cancer Res. 69, 9385–9394 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Eisenberg, V. et al. Targeting multiple tumors using T-cells engineered to express a natural cytotoxicity receptor 2-based chimeric receptor. Front. Immunol. 8, 1212 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Gunnarsdottir, F. B., Kiessling, R. & Pico de Coana, Y. Establishment of melanoma tumor xenograft using single cell line suspension and co-injection of patient-derived T cells in immune-deficient NSG mice. Methods Mol. Biol. 1913, 207–215 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Jakka, G. et al. Antigen-specific in vitro expansion of functional redirected NY-ESO-1-specific human CD8+ T-cells in a cell-free system. Anticancer Res. 33, 4189–4201 (2013).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Stand Up To Cancer (SU2C) Convergence 2.0 Grant (M.S., A.C.C., C.S.M.), a collaboration by SU2C and the Society for Immunotherapy of Cancer; the SU2C Phillip A. Sharp Award for Innovation in Collaboration (grant number: SU2C-AACR-PS-22) (M.S., C.S.M.); the Claudia Adams Barr Program in Innovative Basic Cancer Research at Dana–Farber Cancer Institute (M.S., E.D., C.S.M., E.L.); the Human Frontier Science Program (HFSP) Fellowship (LT000834/2014-L, M.S.); the Leukemia and Lymphoma Society (LLS) Scholar Award (C.S.M.); and the Ludwig Center at Harvard (C.S.M.). The collaborating laboratories have also been partially supported by grants NIH R01 CA050947 (C.S.M.), CA196664 (C.S.M.) and U01 CA225730 (C.S.M.); de Gunzburg Myeloma Research Fund (C.S.M.); International Myeloma Society (C.S.M.), the Shawna Ashlee Corman Investigatorship in Multiple Myeloma Research (C.S.M.); Cobb Family Myeloma Research Fund (C.S.M.); the Department of Defense grants W81XWH-15-1-0012 (C.S.M.) and W81XWH-15-1-0013 (A.C.); the Finnish Cancer Organizations, Sigrid Juselius Foundation, Academy of Finland (S.M.); the Health Foundation Limburg, Nijbakker-Morra Stichting (N.B.); and the Dutch Cancer Association (KWF Kanker Bestrijding, UM2012-5375) (L. Wieten). We thank M. Sade-Feldman for technical advice and discussion, A. Rotem for the gift of Malme-3M cell line and Y. Maruvka for assistance with MSI profiling.

Author information

Author notes

  1. Yiguo Hu

    Present address: Sichuan University, Chengdu, China

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Michal Sheffer, Emily Lowry, Minasri Borah, Olga Dashevsky, Sara Gandolfi, Ricardo De Matos Simoes, Eugen Dhimolea, Channing Yu, Yiguo Hu, Marios Giannakis, Todd Golub, Rizwan Romee & Constantine S. Mitsiades

  2. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Michal Sheffer, Olga Dashevsky, Sara Gandolfi, Ricardo De Matos Simoes, Eugen Dhimolea, Channing Yu, Marios Giannakis, Rizwan Romee & Constantine S. Mitsiades

  3. Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, USA

    Michal Sheffer, Chris C. Mader, Jennifer A. Roth, Aviad Tsherniak, Samuel S. Freeman, Olga Dashevsky, Sara Gandolfi, Samantha Bender, Jordan G. Bryan, Cong Zhu, Li Wang, Ifrah Tariq, Ricardo De Matos Simoes, Eugen Dhimolea, Channing Yu, Marios Giannakis, Ernest Fraenkel, Todd Golub & Constantine S. Mitsiades

  4. Ludwig Center, Harvard Medical School, Boston, MA, USA

    Michal Sheffer, Olga Dashevsky, Sara Gandolfi, Ricardo De Matos Simoes, Eugen Dhimolea & Constantine S. Mitsiades

  5. Department of Transplantation Immunology, Maastricht University Medical Center+, Maastricht, the Netherlands

    Nicky Beelen & Lotte Wieten

  6. School for Oncology and Developmental Biology, Maastricht University Medical Center+ GROW, Maastricht, the Netherlands

    Nicky Beelen & Lotte Wieten

  7. Department of Cell Biology, Harvard Medical School, Boston, MA, USA

    Suha Naffar-Abu Amara

  8. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ifrah Tariq & Ernest Fraenkel

  9. Microsoft Research, Cambridge, MA, USA

    Govinda M. Kamath & Vasilis Syrgkanis

  10. Hematology Research Unit Helsinki, Helsinki University Hospital Comprehensive Cancer Center, Helsinki, Finland

    Olli Dufva & Satu Mustjoki

  11. Translational Immunology Research Program and Department of Clinical Chemistry and Hematology, University of Helsinki, Helsinki, Finland

    Olli Dufva & Satu Mustjoki

  12. iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland

    Olli Dufva & Satu Mustjoki

  13. Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA

    Aedin C. Culhane

  14. Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA

    Aedin C. Culhane

Authors
  1. Michal Sheffer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emily Lowry
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicky Beelen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Minasri Borah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Suha Naffar-Abu Amara
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chris C. Mader
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jennifer A. Roth
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Aviad Tsherniak
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Samuel S. Freeman
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Olga Dashevsky
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Sara Gandolfi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Samantha Bender
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Jordan G. Bryan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Cong Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Li Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Ifrah Tariq
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Govinda M. Kamath
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Ricardo De Matos Simoes
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Eugen Dhimolea
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Channing Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Yiguo Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Olli Dufva
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Marios Giannakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Vasilis Syrgkanis
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Ernest Fraenkel
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Todd Golub
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Rizwan Romee
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Satu Mustjoki
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Aedin C. Culhane
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Lotte Wieten
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Constantine S. Mitsiades
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.S. and C.S.M. conceived and designed the study overall. M.S., E.L., N.B., S.N.-A.A, M.B., C.C.M, J.A.R., O. Dashevsky, R.D.M.S., E.D., C.Y., Y.H., S.G., T.G., C.Z., L. Wang, L. Wieten and C.S.M. contributed to design of individual experiments. C.C.M., J.A.R., C.Y. and T.G. provided critical reagents. M.S., E.L., N.B., S.N.-A.A., M.B., O. Dashevsky, S.B., C.Z. and L. Wang performed experiments. M.S., E.L., N.B., J.G.B., S.S.F., A.T., R.D.M.S. and A.C.C. performed data analyses. M.S., J.A.R., A.T., J.G.B., O. Dufva, M.G., I.T., G.M.K., V.S., E.F., T.G., R.R., S.M., A.C.C., L. Wieten and C.S.M. contributed to data interpretation. M.S. and C.S.M. wrote the manuscript with input from all co-authors.

Corresponding authors

Correspondence to Michal Sheffer or Constantine S. Mitsiades.

Ethics declarations

Competing interests

C.C.M., J.A.R., C.Y., T.G., L. Wieten, M.S. and C.S.M. are authors of a patent application related to antitumor activity of NK cells. C.S.M. also discloses consultant/honoraria from Fate Therapeutics, Ionis Pharmaceuticals and FIMECS; employment of a relative with Takeda; and research funding from Janssen/Johnson & Johnson, TEVA, EMD Serono, Abbvie, Arch Oncology, Karyopharm, Sanofi, Nurix and H3 Biomedicine. M.G. receives research funding from Bristol-Myers Squibb and Merck. A.T. is a consultant for Tango Therapeutics. S.M. has received honoraria and research funding from Novartis, Pfizer and Bristol-Myers Squibb (not related to this study). The remaining authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Joseph Sun, Sidi Chen 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

Extended Data Fig. 1 Genes associated with tumour cell responses to NK cells in PRISM studies.

a Scatter plots for AUC derived from PRISM study per time point (x-axis) and from cytotoxic assays of individual cell lines (y-axis). The Spearman correlation coefficients are specified for each time point. b Volcano plot for the -log10(p-value) of the Spearman correlation between AUC and gene expression (y-axis) vs log2 fold changes of expression for the respective genes between NK cell-resistant (upper third of AUC distribution) and NK cell-sensitive (lower third of AUC distribution) cell lines (see Methods). Results are shown for 24hr-AUC as representative of results for other time points. Genes with sgRNA enrichment are labeled red and sgRNA depletion are labeled blue. Genes in bold were significant based on MaGECK and STARS analyses in at least one CRISPR screen.

Extended Data Fig. 2 Key regulators of tumor cell responses to NK cells in CRISPR studies.

Plots for sgRNA enrichment (upper panels) or depletion (lower panels) for each one of the genome-scale CRISPR gene-editing screens. Highlighted genes were identified as significant based on MaGECK and STARS in at least one screen.

Extended Data Fig. 3 Stratified PRISM-based analyses for global (all cell lines), epithelial-like or mesenchymal-like groups of lines.

Normalized transcript levels for genes which have significant correlation (Spearman correlation coefficient, adjusted p-values<0.05, see Methods) with AUC values in ≥2 time points for the entire set of PRISM cell lines. A gene was considered to correlate with AUC in the epithelial-like or the mesenchymal-like subset of lines if p-value ≤0.05 in ≥2 time points in the respective subgroup analysis. Horizontal black lines represent genes which exhibited significant sgRNA enrichment or depletion in at least one of the CRISPR studies (based on MAGECK and STARS) and in a manner concordant with the results of the PRISM analyses. Group A was enriched for these genes, compared with groups B-D (p = 0.022, χ2 test, df = 1).

Extended Data Fig. 4 Integration of results for PRISM and CRISPR-based identification of regulators of tumor cell response to NK cells.

Functional clusters of genes that were associated with tumour cell responses to NK cells (based on genes that were significant for both MaGECK and STARS in at least one screen). Gene highlighted in bold were also significant for PRISM.

Extended Data Fig. 5 B7-H6 and HLA-E as key regulators of NK cell responses.

a, In vivo testing of NK cell activity against SW620 tumour cells with vs. without B7-H6 CRISPR knockout: Bioluminescence intensity (photons/sec/cm2) measured at week 2. P-values are calculated using two- sided Mann-Whitney tests between B7-H6– vs. B7-H6+ tumours in each group. Data are represented as mean ± SD, n = 10 per group. b, B7-H6 log2-fold changes of tumour vs. normal samples in RNA-seq data of the TCGA dataset. Data are represented as box/dot plots, demarcating the first and third quartiles of the distribution, with the median shown in the center and whiskers covering data within 1.5x the interquartile range of the box (significant FDR-adjusted p-values for upregulation are shown in red; two-sided Mann-Whitney tests). c, Heatmap for z-scores of transcript levels for antigen presentation genes, showing co-expression in the different cancer types of the TCGA. The gene expression levels were normalized across the samples of each cancer type. d, Western blot analyses for STAT1 protein levels in STAT1 knockouts vs. OR10A2 as control knockouts in HT29 and SW620 cell lines. Staining for GAPDH on the same uncropped membrane serves as loading control. n = 2 distinct experiments. e, Flow cytometry analysis for surface HLA-E levels in HT29 cells with sgRNA for HLA-E vs. control sgRNA with vs. without IFNγ (5ng/ml) overnight incubation, n = 2 experiments.

Source data

Extended Data Fig. 6 Functional relationship of B7-H6 and MHC class I molecules.

a, Comparison of transcript levels between Wild-Type (WT) and B2M-mutated cell lines of the PRISM collection (coding mutations), Mann-Whitney two-sided test. b, Spearman correlation coefficients between B7-H6 and B2M (upper panel) or HLA-E (lower panel) in TCGA and CCLE datasets. c, Heatmap of the normalized expression levels of B7-H6 and HLA-E in PRISM cell lines sorted based on difference (normalized log2 fold) of B7-H6 compared with HLA-E. d, Percentages of MSI cell lines (brown) vs. MSS cell lines (orange) in the group of HLA-low (n = 35) cell lines compared with all other lines (n = 348) of the PRISM-based surface proteomic analysis. e, HLA-A/B/C surface protein scores or B2M transcript levels between the groups of MSS vs. MSI cell lines; two-sided Mann-Whitney test. f, Left: AUC for MSI lines with low-HLA-A/B/C (n = 13) vs. all other lines (n = 370) of the PRISM-based surface proteomic analysis. Right: AUC for MSI lines with low B2M expression (n = 37) vs. all other lines (n = 490); two-sided Mann-Whitney test. Results are shown for 24hr-AUC and are representative of those for other time points. g, HLA-E and B2M log2-fold changes of tumour vs. normal samples in the RNA-seq data of the TCGA dataset (significant FDR-adjusted p-values for downregulation are shown in red; two-sided Mann-Whitney). Data in a,e,f,g, are represented as box/dot plots, demarcating the first and third quartiles of the distribution, with the median shown in the center and whiskers covering data within 1.5x the interquartile range of the box.

Extended Data Fig. 7 Transcriptional signatures in tumor cells associated with responses to NK cells.

a, Left: GSEA NES scores for selected gene sets that had FDR adjusted p<0.05 in at least two time points in analyses that included all PRISM cell lines: NES are also shown for GSEA of only epithelial-like and only mesenchymal-like lines (see Methods). Right: GSEA NES scores for same gene sets as in left panel in tumors with ICI resistance (see Methods). Red and blue represent enrichment and suppression, respectively, of gene sets in the corresponding analyses. Lack of significant changes (p>0.05) was colored white. b,c, Normalized expression levels of core genes from pathways correlating with AUC (see Methods) in b, epithelial-like or mesenchymal-like cell lines and c, TCGA tumours, sorted by the expression of CDH1 and ZEB2 using SPIN40. Tumours with low vs. high CDH1 are separated with a dotted line. d, SW620 cells were pre-treated for 12 hrs with 50nM of the HDAC inhibitor panobinostat, 5 ng/ml IFNγ or DMSO. The cells were stained with anti-HLA-E (left) and anti-B7-H6 (right) antibodies. Unlabeled controls are colored grey. n = 1 experiments. e, Flow cytometry assays to quantify NK cell cytotoxicity against tumour cells (see Methods) pre-treated with HDAC inhibitor or DMSO. Cells were treated with 12.5nM panobinostat and cocultured with NK cells in two E:T ratios. n = 4 distinct experiments. f, GSEA enrichment plots for select gene sets from a, in all cell lines of PRISM (upper) and in ICI nonresponders vs. responders of the Riaz et al. dataset (lower). g, Gene scores for NK cell sensitivity in ICI nonresponders vs. responders of the Riaz et al. dataset (see Methods, two-sided Mann-Whitney test).

Supplementary information

Supplementary Information

Supplementary Note and Tables 2, 6 and 7

Reporting Summary

Supplementary Tables

Supplementary Tables 1 and 3–5

Source data

Source Data Extended Data Fig. 5

Unprocessed western blot.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheffer, M., Lowry, E., Beelen, N. et al. Genome-scale screens identify factors regulating tumor cell responses to natural killer cells. Nat Genet 53, 1196–1206 (2021). https://doi.org/10.1038/s41588-021-00889-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-021-00889-w

This article is cited by

Search

Advanced 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