Identification of essential genes for cancer immunotherapy (original) (raw)

References

1. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013)
   Article CAS PubMed PubMed Central ADS Google Scholar
2. Chan, T. A., Wolchok, J. D. & Snyder, A. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 373, 1984 (2015)
   Article CAS PubMed Google Scholar
3. 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 ADS Google Scholar
4. 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
5. 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 PubMed PubMed Central ADS Google Scholar
6. Tran, E. et al. Immunogenicity of somatic mutations in human gastrointestinal cancers. Science 350, 1387–1390 (2015)
   Article CAS PubMed ADS PubMed Central Google Scholar
7. Le, D. T . et al. Mismatch-repair deficiency predicts response of solid tumors to PD-1 blockade. Science aan6733 (2017)
8. Restifo, N. P. et al. Loss of functional β2-microglobulin in metastatic melanomas from five patients receiving immunotherapy. J. Natl Cancer Inst. 88, 100–108 (1996)
   Article CAS PubMed Google Scholar
9. Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016)
   Article CAS PubMed PubMed Central Google Scholar
10. Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015)
    Article CAS PubMed PubMed Central ADS Google Scholar
11. Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015)
    Article CAS PubMed Google Scholar
12. Shalem, O. et al. Genome-scale CRISPR–Cas9 knockout screening in human cells. Science 343, 84–87 (2014)
    Article CAS PubMed ADS Google Scholar
13. Wang, T., Wei, J. J., Sabatini, D. M. & Lander, E. S. Genetic screens in human cells using the CRISPR–Cas9 system. Science 343, 80–84 (2014)
    Article CAS PubMed ADS Google Scholar
14. Chen, S. et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–1260 (2015)
    Article CAS PubMed PubMed Central Google Scholar
15. Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015)
    Article CAS PubMed PubMed Central Google Scholar
16. Kvistborg, P. et al. Anti-CTLA-4 therapy broadens the melanoma-reactive CD8+ T cell response. Sci. Transl. Med. 6, 254ra128 (2014)
    Article PubMed CAS Google Scholar
17. Robbins, P. F. et al. Single and dual amino acid substitutions in TCR CDRs can enhance antigen-specific T cell functions. J. Immunol. 180, 6116–6131 (2008)
    Article CAS PubMed Google Scholar
18. Johnson, L. A. 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)
    Article CAS PubMed Google Scholar
19. Robbins, P. F. 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)
    Article CAS PubMed Google Scholar
20. Spiotto, M. T., Rowley, D. A. & Schreiber, H. Bystander elimination of antigen loss variants in established tumors. Nat. Med. 10, 294–298 (2004)
    Article CAS PubMed Google Scholar
21. 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
22. Luo, B. et al. Highly parallel identification of essential genes in cancer cells. Proc. Natl Acad. Sci. USA 105, 20380–20385 (2008)
    Article CAS PubMed ADS PubMed Central Google Scholar
23. Indraccolo, S. et al. Identification of genes selectively regulated by IFNs in endothelial cells. J. Immunol. 178, 1122–1135 (2007)
    Article CAS PubMed Google Scholar
24. Sanda, C. et al. Differential gene induction by type I and type II interferons and their combination. J. Interferon Cytokine Res. 26, 462–472 (2006)
    Article CAS PubMed Google Scholar
25. Viemann, D. et al. TNF induces distinct gene expression programs in microvascular and macrovascular human endothelial cells. J. Leukoc. Biol. 80, 174–185 (2006)
    Article CAS PubMed Google Scholar
26. Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306–312 (2015)
    Article CAS PubMed Google Scholar
27. Kan, Z. et al. Diverse somatic mutation patterns and pathway alterations in human cancers. Nature 466, 869–873 (2010)
    Article CAS PubMed ADS Google Scholar
28. Roh, W. 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)
    Article CAS Google Scholar
29. Nathanson, T. et al. Somatic mutations and neoepitope homology in melanomas treated with CTLA-4 blockade. Cancer Immunol. Res. 5, 84–91 (2017)
    Article CAS PubMed Google Scholar
30. O’Carroll, A.-M., Lolait, S. J., Harris, L. E. & Pope, G. R. The apelin receptor APJ: journey from an orphan to a multifaceted regulator of homeostasis. J. Endocrinol. 219, R13–R35 (2013)
    Article PubMed CAS Google Scholar
31. Stark, C. et al. BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34, D535–D539 (2006)
    Article CAS PubMed Google Scholar
32. Dunn, G. P., Koebel, C. M. & Schreiber, R. D. Interferons, immunity and cancer immunoediting. Nat. Rev. Immunol. 6, 836–848 (2006)
    Article CAS PubMed Google Scholar
33. Overwijk, W. W. 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)
    Article CAS PubMed PubMed Central Google Scholar
34. Wang, L.-X. 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)
    Article CAS PubMed PubMed Central ADS Google Scholar
35. Wrangle, J. et al. Alterations of immune response of non-small cell lung cancer with Azacytidine. Oncotarget 4, 2067–2079 (2013)
    Article PubMed PubMed Central Google Scholar
36. Marrero, M. B. et al. Direct stimulation of Jak/STAT pathway by the angiotensin II AT1 receptor. Nature 375, 247–250 (1995)
    Article CAS ADS PubMed Google Scholar
37. Kidoya, H. et al. The apelin/APJ system induces maturation of the tumor vasculature and improves the efficiency of immune therapy. Oncogene 31, 3254–3264 (2012)
    Article CAS PubMed Google Scholar
38. Kammertoens, T. et al. Tumour ischaemia by interferon-γ resembles physiological blood vessel regression. Nature 545, 98–102 (2017)
    Article CAS PubMed PubMed Central ADS Google Scholar
39. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011)
    Article Google Scholar
40. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009)
    Article PubMed PubMed Central Google Scholar
41. Wan, Y.-W., Allen, G. I. & Liu, Z. TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. Bioinformatics 32, 952–954 (2016)
    Article CAS PubMed Google Scholar
42. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013)
    Article PubMed PubMed Central CAS Google Scholar
43. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012)
    Article PubMed Google Scholar
44. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014)
    Article CAS PubMed PubMed Central Google Scholar
45. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012)
    Article CAS PubMed PubMed Central Google Scholar
46. Robbins, P. F. et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat. Med. 19, 747–752 (2013)
    Article CAS PubMed PubMed Central Google Scholar
47. Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013)
    Article PubMed PubMed Central CAS Google Scholar
48. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protocols 7, 562–578 (2012)
    Article CAS PubMed Google Scholar
49. Noonan, F. P. et al. Melanoma induction by ultraviolet A but not ultraviolet B radiation requires melanin pigment. Nat. Commun. 3, 884 (2012)
    Article PubMed ADS CAS Google Scholar

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.

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Author notes

1. Shashank J. Patel and Neville E. Sanjana: These authors contributed equally to this work.

Authors and Affiliations

1. National Cancer Institute, National Institutes of Health (NIH), Bethesda, 20892, Maryland, 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, 20057, DC, USA
   Shashank J. Patel
3. New York Genome Center, New York, 10013, New York, USA
   Neville E. Sanjana
4. Department of Biology, New York University, New York, 10012, New York, USA
   Neville E. Sanjana
5. Immunology Graduate Group, University of Pennsylvania, Philadelphia, 19104, Pennsylvania, 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, 02142, Massachusetts, USA
   Feng Zhang
8. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, 02139, Massachusetts, USA
   Feng Zhang
9. Center for Cell-based Therapy, Center for Cancer Research, National Institutes of Health (NIH), Bethesda, 20892, Maryland, USA
   Nicholas P. Restifo

Authors

1. Shashank J. Patel
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2. Neville E. Sanjana
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3. Rigel J. Kishton
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4. Arash Eidizadeh
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5. Suman K. Vodnala
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6. Maggie Cam
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7. Jared J. Gartner
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8. Li Jia
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9. Seth M. Steinberg
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10. Tori N. Yamamoto
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11. Anand S. Merchant
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12. Gautam U. Mehta
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13. Anna Chichura
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14. Ophir Shalem
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15. Eric Tran
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16. Robert Eil
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17. Madhusudhanan Sukumar
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18. Eva Perez Guijarro
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19. Chi-Ping Day
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20. Paul Robbins
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21. Steve Feldman
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22. Glenn Merlino
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23. Feng Zhang
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24. Nicholas P. Restifo
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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.

Corresponding authors

Correspondence toShashank J. Patel, Neville E. Sanjana or Nicholas P. Restifo.

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The authors declare no competing financial interests.

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

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Extended data figures and tables

Extended Data Figure 1 Intratumoral expression of antigen presentation genes, B2M and TAP1 informs long-term survival of patients with melanoma treated with anti-CTLA4 (ipilimumab) immunotherapy.

a, Pearson’s correlation matrix of intratumoral cytolytic activity (CYT, expression of perforin and granzyme A15) with tumour-infiltrating effector cell markers for natural killer (NK, expression of NCAM1 and NCR1), regulatory T (Treg, expression of FOXP3 and IL2RA), CD4+ T (expression of CD3E and CD4) and CD8+ T cells (expression of CD3E and CD8A). b, Pearson’s correlation matrix of CYT with the expression of MHC class I antigen presentation genes. c, Pearson’s correlation matrix of CYT with the expression of IFNγ signalling genes. d–g, Kaplan–Meier survival plots of patient overall survival with the expression of antigen presentation genes after ipilimumab immunotherapy (Van Allen et al. cohort3). Data were divided into quartiles based on RPKM values of each individual gene and the four groups were evaluated for their association with survival. The global P values shown indicate the overall association of the quartiles of gene expression levels with survival. n = 42 patients (a–g).

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Extended Data Figure 2 Optimization of selection pressure and duration of co-culture for 2CT-CRISPR assay system.

a, FACS plots showing percentages of CD4+ and CD8+ T cells in three different patient PBMCs after transduction with a retroviral plasmid encoding NY-ESO-1 TCR and expansion for 7 days. b, Transduction efficiency of T cells transduced with a retroviral plasmid encoding NY-ESO-1 TCR as determined by FACS. T cells were obtained from the peripheral blood of patients with metastatic melanoma. c, Transduction efficiency of T cells transduced with a retroviral plasmid encoding NY-ESO-1 TCR as determined by FACS. T cells were obtained from the peripheral blood of healthy donors. d, Transduction efficiency of T cells transduced with a retroviral plasmid encoding MART-1 TCR as determined by FACS. T cells were obtained from the peripheral blood of healthy donors. e, Representative plots of FACS-based determination of live, PI− (propidium iodine) CD3− tumour cell counts after co-culture of patient ESO T cells with Mel624 cells at an E:T ratio of 100 for 24 h. f, Bar plot quantifies the cytolytic efficiency of T cells for data shown in e. n = 3 biological replicates. g, Optimization of selection pressure exerted by ESO T cells on Mel624 cells at variable timings of co-culture and E:T ratios. Numbers in the grid represent average tumour cell survival (%) after co-culture. Data pooled from 3 independent experiments. n = 3 culture replicates. h, Upregulation of β2M expression at 0, 6 or 12 h after co-culture of Mel624 cells with ESO T cells at an E:T ratio of 0.5. Left, representative FACS plot showing distribution of β2M-expressing tumour cells. Right, bar plot depicts mean fluorescence intensities of n = 3 co-culture replicates. i, Specific reactivity of ESO T cells against NY-ESO-1 antigen assessed in tumour lines by IFNγ secretion (pg ml−1) after overnight co-culture. n = 3 co-culture replicates. Values in f and h are mean ± s.e.m. ***P < 0.001 as determined by two-tailed Student’s _t-_test.

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Extended Data Figure 3 Optimization of 2CT-CRISPR assay system for genome-scale screening.

a, Representative FACS plot of β2M expression in Mel624 cells on day 5 after transduction with lentiCRISPRv2 lentivirus containing a pool of three sgRNAs targeting B2M. b, c, Cas9 disruption of MHC class I antigen presentation/processing pathway genes reduces efficacy of T-cell-mediated cytolysis. Timeline shows 12 h of co-culture of ESO T cells with individual gene edited Mel624 cells at E:T ratio of 0.5. Live cell survival (%) was calculated from control cells unexposed to T cell selection. Each dot in the plot represents independent gene-specific CRISPR lentivirus infection replicate (n = 3). Improvement in CRISPR-edited cell yields at 60 h time point compared to 36 h after 2CT assay as shown in c. All values are mean ± s.e.m. Data are representative of two independent experiments.

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Extended Data Figure 4 Genome-scale 2CT-CRISPR mutagenesis identifies genes in tumour cells essential for the effector function of T cells.

a, Scatterplot of sgRNA representation in the plasmid pool and Mel624 cells at Day 7 after transduction with the GeCKOv.2 library for 2CT-CRISPR screens with E:T of 0.5 and 0.3. b, Scatterplot showing the effect of T cell selection pressure on the global distribution of sgRNAs after co-culture at E:T of 0.5 and 0.3. c, Agreement between top ranked genes enriched via two different metrics: the second-most-sgRNA and RIGER P value analyses in 2CT-CRISPR screens performed at E:T of 0.5. d, Scatterplot showing the enrichment of the most versus the second-most-enriched sgRNAs for top 100 genes after T-cell-based selection at E:T 0.3. Data pooled from two independent screens with libraries A and B. e, Overlap of genes and microRNAs (miRs) enriched after T-cell-based selection at E:T of 0.5 (high selection) and 0.3 (low selection). Venn diagrams depicts shared and unique most-enriched candidates in top 5% of the second-most-enriched sgRNA. f, Common enriched genes across all screens within the top 500 genes ranked by the second-most-enriched sgRNA.

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Extended Data Figure 5 Association of candidate essential genes with cytolytic activity and mutation spectrum.

a, Top candidate genes are categorized based on their inducibility by effector cytokines IFNγ (light blue) or TNFα (orange), using publicly available gene expression profiles GSE3920, GSE5542, GSE2638. b, Genes whose expression are positively correlated (P < 0.05) with cytolytic activity (defined as the geometric mean of _PRF1_ and _GZMA_ expression) in TCGA datasets for 36 human cancers. **c**, Overlap (Jaccard coefficient) between genes correlated with cytolytic activity (from **b**) with top 2.5% of CRISPR screen gene hits (with second best sgRNA enrichment >0.5). d, Bubble plot depicting the number of overlapping genes from b correlated across multiple cancers. Previously known genes B2M, CASP7 and CASP8, and novel validated genes from CRISPR screen are highlighted (in bold) according to their correlation to the cytolytic activity in the number of different cancer types. The size of each bubble represents the number of genes in each dataset. e, f, Pan-cancer mutational heterogeneity of top candidate genes from CRISPR screens with T cell based selection at E:T of 0.5 (e) and 0.3 (f). Patient tumour data containing genetic aberrations including missense, nonsense, non-start, frameshift, truncation or splice-site mutations, or homozygous deletions was retrieved from TCGA database.

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Extended Data Figure 6 Validation of top ranked candidate genes using Mel624 cells and two different T cell receptors.

a, b, Survival of Mel624 cells edited with individual sgRNAs (2–4 per gene) after co-culture with ESO T cells (a) and MART-1 T cells (b) at E:T ratio of 0.5 in 2CT assay. P value calculated for positively enriched gene-targeting sgRNAs compared to control sgRNA by Student’s _t_-test. Data representative of at least two independent experiments. n = 3 replicates per sgRNA. c, Representative histogram of deep sequencing analysis of on-target insertion–deletion (indel) mutations by individual lentiCRISPR. d, e, Deep sequencing analysis of indels generated by CRISPR–Cas9 at each exonic target site for the genes validated in Mel624 cells at day 20 after transduction.

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Extended Data Figure 7 Gene perturbation efficiency and indel mutations after CRISPR–Cas9 targeted disruption in A375 cells.

Deep sequencing analysis of indels generated by CRISPR–Cas9 at the exonic target site of each gene validated in A375 cells at day 5 after transduction. Average values are mean. Error bars denotes s.e.m.

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Extended Data Figure 8 Characterization of non-synonymous mutations in APLNR identified in patient tumours resistant to immunotherapy.

a, List of all somatic mutations in APLNR from four published immunotherapy studies3,5,28,29 and one unpublished patient tumour from NCI Surgery Branch. b, Schematic of the re-introduction of wild-type or mutated APLNR in _APLNR-_edited cells to functionally verify the point mutations from the NCI Surgery Branch and Van Allen et al.3 cohorts. Blasticidin selects for cells that received the wild-type/mutated APLNR rescue construct.

Extended Data Figure 9 APLNR modulates IFNγ signalling via physical interaction with JAK1.

a, Pull-down of JAK1 and APLNR in the extracts from HEK293T cells transiently transfected with _APLNR_-Flag plasmid. b, Immunoblot showing the upregulation of JAK1 protein expression in APLNR overexpressing A375 cells (APLNR OE). EV: empty vector control. c, Effect of overexpression of APLNR in tumour cells on T-cell-mediated cytolysis. n = 4 biological replicates. d, Immunoblot showing that addition of 100 μM apelin ligand does not induce phosphorylation of JAK1 in tumour cells. e, Immunoblot showing the phosphorylation levels of JAK1 at Tyr1022/1023 residues and STAT1 at Tyr701 residue upon 100 ng ml−1 IFNγ treatment for 30 min in _APLNR_-edited cells versus cells receiving a control sgRNA. f, Quantitative reverse-transcription PCR analysis of JAK1–STAT1 pathway-induced genes in _APLNR_-edited cells after 4, 8 and 24 h of treatment with 1 μg ml−1 IFNγ. n = 3 biological replicates. g, Induction of surface expression of β2M on _APLNR-_edited cells upon co-culture with ESO T cells for 6 h as measured by FACS. h, Intracellular staining assay performed on CD8+ T cells to measure IFNγ production after co-culture with A375 cells as target for 5–6 h. n = 3 biological replicates. All data are representative of at least two independent experiments. Data represent mean ± s.e.m. of replicate measurements. ****P < 0.0001, ***P < 0.001, **P < 0.01, *P < 0.05.

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Extended Data Figure 10 APLNR knock-down decreases the efficiency of in vivo adoptive cell transfer immunotherapy.

Subcutaneous tumour growth in mice receiving ACT of Pmel T cells. a, b, Tumour area (a) and overall survival (b) are shown. Significance for tumour growth kinetics were calculated by Wilcoxon rank-sum test. Survival significance was assessed by a log-rank Mantel–Cox test. n = 5 mice per ‘untreated’ groups. n = 10 mice per ‘Pmel ACT treated’ groups. All values are mean ± s.e.m. ****P < 0.0001, **P < 0.01, *P < 0.05. Data are representative of two independent experiments.

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Supplementary information

Supplementary Information

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

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Supplementary Tables

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

Supplementary Data

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

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Patel, S., Sanjana, N., Kishton, R. et al. Identification of essential genes for cancer immunotherapy.Nature 548, 537–542 (2017). https://doi.org/10.1038/nature23477

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• Received: 16 December 2016
• Accepted: 13 July 2017
• Published: 07 August 2017
• Issue Date: 31 August 2017
• DOI: https://doi.org/10.1038/nature23477