In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target (original) (raw)

References

1. Reck, M. et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N. Engl. J. Med. 375, 1823–1833 (2016)
   Article CAS PubMed Google Scholar
2. Wolchok, J. D. et al. Nivolumab plus ipilimumab in advanced melanoma. N. Engl. J. Med. 369, 122–133 (2013)
   Article CAS PubMed PubMed Central Google Scholar
3. 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
4. Dong, H. et al. Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat. Med. 8, 793–800 (2002)
   Article CAS PubMed Google Scholar
5. Holmgaard, R. B., Zamarin, D., Munn, D. H., Wolchok, J. D. & Allison, J. P. Indoleamine 2,3-dioxygenase is a critical resistance mechanism in antitumor T cell immunotherapy targeting CTLA-4. J. Exp. Med. 210, 1389–1402 (2013)
   Article CAS PubMed PubMed Central Google Scholar
6. Tseng, D. et al. Anti-CD47 antibody-mediated phagocytosis of cancer by macrophages primes an effective antitumor T-cell response. Proc. Natl Acad. Sci. USA 110, 11103–11108 (2013)
   Article ADS CAS PubMed PubMed Central Google Scholar
7. Sica, G. L. et al. B7-H4, a molecule of the B7 family, negatively regulates T cell immunity. Immunity 18, 849–861 (2003)
   Article CAS PubMed Google Scholar
8. Howard, T. P. et al. Functional genomic characterization of cancer genomes. Cold Spring Harb. Symp. Quant. Biol. 81, 237–246 (2016)
   Article PubMed Google Scholar
9. Cowley, G. S. et al. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci. Data 1, 140035 (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 ADS CAS PubMed 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. 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
13. Dranoff, G. GM-CSF-secreting melanoma vaccines. Oncogene 22, 3188–3192 (2003)
    Article CAS PubMed Google Scholar
14. Dranoff, G. et al. Vaccination with irradiated tumor cells engineered to secrete murine granulocyte-macrophage colony-stimulating factor stimulates potent, specific, and long-lasting anti-tumor immunity. Proc. Natl Acad. Sci. USA 90, 3539–3543 (1993)
    Article ADS CAS PubMed PubMed Central Google Scholar
15. Duraiswamy, J., Kaluza, K. M., Freeman, G. J. & Coukos, G. Dual blockade of PD-1 and CTLA-4 combined with tumor vaccine effectively restores T-cell rejection function in tumors. Cancer Res. 73, 3591–3603 (2013)
    Article CAS PubMed PubMed Central Google Scholar
16. Wada, H., Matsumoto, N. & Maenaka, K., Suzuki, K. & Yamamoto, K. The inhibitory NK cell receptor CD94/NKG2A and the activating receptor CD94/NKG2C bind the top of HLA-E through mostly shared but partly distinct sets of HLA-E residues. Eur. J. Immunol. 34, 81–90 (2004)
    Article CAS PubMed Google Scholar
17. Kleppe, M. et al. PTPN2 negatively regulates oncogenic JAK1 in T-cell acute lymphoblastic leukemia. Blood 117, 7090–7098 (2011)
    Article CAS PubMed Google Scholar
18. Kleppe, M. et al. Deletion of the protein tyrosine phosphatase gene PTPN2 in T-cell acute lymphoblastic leukemia. Nat. Genet. 42, 530–535 (2010)
    Article CAS PubMed PubMed Central Google Scholar
19. Pike, K. A. & Tremblay, M. L. TC-PTP and PTP1B: regulating JAK–STAT signaling, controlling lymphoid malignancies. Cytokine 82, 52–57 (2016)
    Article CAS PubMed Google Scholar
20. Todd, J. A. et al. Robust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes. Nat. Genet. 39, 857–864 (2007)
    Article CAS PubMed PubMed Central Google Scholar
21. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007)
22. Morandi, F. & Pistoia, V. Interactions between HLA-G and HLA-E in physiological and pathological conditions. Front. Immunol. 5, 394 (2014)
    Article PubMed PubMed Central Google Scholar
23. Benevolo, M. et al. High expression of HLA-E in colorectal carcinoma is associated with a favorable prognosis. J. Transl. Med. 9, 184 (2011)
    Article PubMed PubMed Central Google Scholar
24. Talebian Yazdi, M. et al. The positive prognostic effect of stromal CD8+ tumor-infiltrating T cells is restrained by the expression of HLA-E in non-small cell lung carcinoma. Oncotarget 7, 3477–3488 (2016)
    PubMed Google Scholar
25. Sáez-Borderías, A. et al. IL-12-dependent inducible expression of the CD94/NKG2A inhibitory receptor regulates CD94/NKG2C+ NK cell function. J. Immunol. 182, 829–836 (2009)
    Article PubMed Google Scholar
26. Moser, J. M., Gibbs, J., Jensen, P. E. & Lukacher, A. E. CD94-NKG2A receptors regulate antiviral CD8+ T cell responses. Nat. Immunol. 3, 189–195 (2002)
    Article CAS PubMed Google Scholar
27. Hu, D. et al. Analysis of regulatory CD8 T cells in Qa-1-deficient mice. Nat. Immunol. 5, 516–523 (2004)
    Article CAS PubMed Google Scholar
28. Ofengeim, D. & Yuan, J. Regulation of RIP1 kinase signalling at the crossroads of inflammation and cell death. Nat. Rev. Mol. Cell Biol. 14, 727–736 (2013)
    Article CAS PubMed Google Scholar
29. Ebert, B. L. et al. Identification of RPS14 as a 5q− syndrome gene by RNA interference screen. Nature 451, 335–339 (2008)
    Article ADS CAS PubMed PubMed Central Google Scholar
30. Broz, M. L. et al. Dissecting the tumor myeloid compartment reveals rare activating antigen-presenting cells critical for T cell immunity. Cancer Cell 26, 638–652 (2014)
    Article CAS PubMed PubMed Central Google Scholar
31. Doench, J. G. et al. Rational design of highly active sgRNAs for CRISPR–Cas9-mediated gene inactivation. Nat. Biotechnol. 32, 1262–1267 (2014)
    Article CAS PubMed PubMed Central Google Scholar
32. Liberzon, A. et al. The Molecular Signatures Database hallmark gene set collection. Cell Syst. 1, 417–425 (2015)
    Article CAS PubMed PubMed Central Google Scholar
33. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)
    Article ADS CAS PubMed PubMed Central Google Scholar
34. Olshen, A. B., Venkatraman, E. S., Lucito, R. & Wigler, M. Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5, 557–572 (2004)
    Article PubMed Google Scholar
35. Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015)
    Article ADS CAS PubMed PubMed Central Google Scholar

Download references

Acknowledgements

This work was supported by BroadIgnite and BroadNext10 awards from the Broad Institute of Harvard and MIT. S.A.W. was supported by award number T32GM007753 from the NIGMS. We thank members of the Haining laboratory for their spirited discussions.

Author information

Authors and Affiliations

1. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, 02115, Massachusetts, USA
   Robert T. Manguso, Hans W. Pope, Margaret D. Zimmer, Flavian D. Brown, Kathleen B. Yates, Brian C. Miller, Natalie B. Collins, Kevin Bi, Martin W. LaFleur, Sarah A. Weiss & W. Nicholas Haining
2. Division of Medical Sciences, Harvard Medical School, Boston, 02115, Massachusetts, USA
   Robert T. Manguso, Flavian D. Brown, Martin W. LaFleur, Diana Miao & Eliezer Van Allen
3. Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, 02142, Massachusetts, USA
   Robert T. Manguso, Hans W. Pope, Margaret D. Zimmer, Kathleen B. Yates, Brian C. Miller, Natalie B. Collins, Kevin Bi, Diana Miao, Eliezer Van Allen, David E. Root, John G. Doench & W. Nicholas Haining
4. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, 02115, Massachusetts, USA
   Brian C. Miller
5. Division of Pediatric Hematology and Oncology, Children’s Hospital, Boston, 02115, Massachusetts, USA
   Natalie B. Collins, Arlene H. Sharpe & W. Nicholas Haining
6. Department of Microbiology and Immunology, Harvard Medical School, Boston, 02115, Massachusetts, USA
   Vikram R. Juneja
7. Cutaneous Biology Research Center, Massachusetts General Hospital and Harvard Medical School, Building 149, 13th Street, Charlestown, 02129, Massachusetts, USA
   Jennifer Lo & David E. Fisher
8. Evergrande Center for Immunologic Diseases, Harvard Medical School, Boston, 02115, Massachusetts, USA
   Arlene H. Sharpe

Authors

1. Robert T. Manguso
   You can also search for this author inPubMed Google Scholar
2. Hans W. Pope
   You can also search for this author inPubMed Google Scholar
3. Margaret D. Zimmer
   You can also search for this author inPubMed Google Scholar
4. Flavian D. Brown
   You can also search for this author inPubMed Google Scholar
5. Kathleen B. Yates
   You can also search for this author inPubMed Google Scholar
6. Brian C. Miller
   You can also search for this author inPubMed Google Scholar
7. Natalie B. Collins
   You can also search for this author inPubMed Google Scholar
8. Kevin Bi
   You can also search for this author inPubMed Google Scholar
9. Martin W. LaFleur
   You can also search for this author inPubMed Google Scholar
10. Vikram R. Juneja
    You can also search for this author inPubMed Google Scholar
11. Sarah A. Weiss
    You can also search for this author inPubMed Google Scholar
12. Jennifer Lo
    You can also search for this author inPubMed Google Scholar
13. David E. Fisher
    You can also search for this author inPubMed Google Scholar
14. Diana Miao
    You can also search for this author inPubMed Google Scholar
15. Eliezer Van Allen
    You can also search for this author inPubMed Google Scholar
16. David E. Root
    You can also search for this author inPubMed Google Scholar
17. Arlene H. Sharpe
    You can also search for this author inPubMed Google Scholar
18. John G. Doench
    You can also search for this author inPubMed Google Scholar
19. W. Nicholas Haining
    You can also search for this author inPubMed Google Scholar

Contributions

W.N.H. and R.T.M. conceived the study and wrote the manuscript. W.N.H., R.T.M. and N.B.C. contributed to study design. R.T.M., H.W.P., M.D.Z., K.B.Y., B.C.M., F.D.B., S.A.W. and K.B. conducted experiments with J.G.D., D.E.R. and M.W.L. J.L. and D.E.F. provided the Braf/Pten line. V.R.J. and A.H.S. provided RNA-sequencing data of tumour cells. D.M. and E.V.A. analysed exome-sequencing data.

Corresponding author

Correspondence toW. Nicholas Haining.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

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 figures and tables

Extended Data Figure 1 Analysis of screen performance.

a, Western blot of for Cas9 and β-actin B16 cell lysate of uninfected cells or after transduction with a lentiviral vector encoding Cas9. For western blot source data, see Supplementary Fig. 1. b, Pie chart showing the fraction of genes targeted in the screen in each of the GO term categories indicated. c, Tumour volume for individual animals (dots) on the day of euthanasia in the conditions indicated. Data are mean ± s.d. d, 2D frequency histograms showing the pair wise distribution of sgRNAs abundance (averaged for each condition). e, Saturation analysis of animal replicates from the three in vivo screening conditions. Pearson’s correlations are calculated for the library distribution in one animal versus any other animal, then for two animals averaged versus any other two averaged, and so on. Saturation approaches r = 0.95. f, A matrix of the Pearson’s correlations of the library distribution from one animal compared to any other animal for B16 pool 1. g, Expression of PD-L1 (top) or CD47 (bottom) after infection of B16-Cas9 cells with four different sgRNAs targeting the indicated gene. h, Enrichment analysis (hypergeometric test) of functional classes of genes (GO) targeted by sgRNAs that were enriched or depleted in tumours in animals treated with GVAX and anti-PD-1 compared to _Tcra_−/− animals. **P < 0.01; ***P < 0.001; ****P < 0.0001.

Source data

Extended Data Figure 2 Additional validation data for screen hits.

a, Expression of CD47 by B16 cells transfected with either CD47-targeting (red) or control (grey) sgRNA. b, Tumour volume over time for CD47-null or control tumours growing in mice treated with GVAX and PD-1 blockade. Data are mean ± s.e.m.; n = 10 animals per group; representative of two independent experiments. c, Tumour growth in immunotherapy-treated animals challenged with B16 cells lacking the indicated genes. Each line represents one animal; n = 5 animals per group. d, Tumour volume averaged for each group at each time point (left) and survival (right) for Braf/Pten melanoma cells lacking the indicated gene in mice treated with PD-1 blockade. Data are mean ± s.e.m.; n = 5 animals per group. e, Change in the ratios (log2(normalized fold change)) of B16 cells lacking the indicated genes:control cells after in vitro culture with the combinations of cytokines indicated below. Data are mean ± s.e.m.; n = 3 replicates per group. f, Expression of MHC-I (H2K(b)/H2D(b)) on B16 cells lacking the indicated genes with or without IFNγ treatment. g, Change in the ratios (log2(normalized fold change)) of OVA-expressing B16 cells lacking the indicated genes:control cells after in vitro co-culture with OVA-specific T cells at the indicated ratio of T cells:tumour cells. Data are mean ± s.d.; n = 3 replicates per group. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Source data

Extended Data Figure 3 Additional validation data for Ptpn2.

a, Western blot of B16 cell lysate for PTPN2 and β-actin after transfection with either control or _Ptpn2_-targeting sgRNAs. For western blot source data, see Supplementary Fig. 1. b, Ptpn2 transcript abundance for the same conditions as in a measured by qPCR. Data are mean ± s.d. c, Change in the ratios (log2(normalized fold change)) of B16 cells lacking Ptpn2:control cells in immunotherapy-treated mice relative to _Tcra_−/− mice with three different sgRNAs. Data are mean ± s.d.; n = 5 animals per group; representative of two independent experiments. d, Survival analysis for _Ptpn2_-null (red) or control (grey) B16 cells growing in mice treated with GVAX and PD-1 blockade. n = 20 animals per group; representative of two independent experiments. **P < 0.01; ****P < 0.0001.

Source data

Extended Data Figure 4 Deletion of screen hits from tumour cells.

a, Western blot of B16 cell lysate for STAT1 (left) and JAK1 (right) after transfection with the indicated sgRNA and Cas9. β-Actin serves as a loading control for each. The numbers below the lane indicate the per cent expression of the target relative to control sgRNA cells. b, The same as in a, but for Braf/Pten melanoma cells. c, Expression of Ifngr1 in B16 cells transfected with the indicated _Ifngr1_-targeting (red) or control (grey) sgRNA and Cas9 (left) and quantification of mean fluorescence intensity (right). d, The same as in c, but for Braf/Pten melanoma cells. e, Expression of Ifnar2 in B16 cells transfected with the indicated _Ifnar2_-targeting or control sgRNA and Cas9 (left) and quantification of mean fluorescence intensity (right). f, Western blot of Braf/Pten melanoma cell lysate for PTPN2 and β-actin after transfection with either control or _Ptpn2_-targeting sgRNAs (left) and Ptpn2 transcript abundance for the same conditions measured by qPCR (right). Data are mean ± s.d. g, Expression of H2-T23 by B16-Cas9 cells infected with the indicated _H2-T23_-targeting (red) and control (grey) sgRNA. For western blot source data, see Supplementary Fig. 1. **P < 0.01.

Source data

Extended Data Figure 5 _Ptpn2_-null tumour cells do not have a growth disadvantage in vivo in the absence of T cells or immunotherapy and MC38 colon carcinoma cells are sensitive to Ptpn2 deletion in vivo.

a, Tumour volume averaged for each group at each time point for _Ptpn2_-null (red) or control (grey) B16 cells growing in wild-type (WT) mice with no treatment. Data are mean ± s.e.m.; n = 10 animals per group. b, Survival analysis for the groups shown in a. c, Tumour volume averaged for each group at each time point for _Ptpn2_-null or control B16 cells growing in _Tcra_−/− mice with no treatment. Data are mean ± s.e.m.; n = 10 animals per group. d, Survival analysis for the groups shown in c. Both groups reached the end point on the same day. e, Tumour volume averaged for each group at each time point for _Ptpn2_-null or control Braf/Pten melanoma cells growing in _Tcra_−/− mice with no treatment. Data are mean ± s.e.m.; n = 5 animals per group. f, Survival analysis for the groups shown in e. g, Tumour volume averaged for each group at each time point for _Ptpn2_-null or control MC38 cells growing in wild-type mice with no treatment. Data are mean ± s.e.m.; n = 10 animals per group. h, Survival analysis for the groups shown in g. i, Tumour volume averaged for each group at each time point for _Ptpn2_-null or control MC38 cells growing in _Tcra_−/− mice with no treatment. Data are mean ± s.e.m.; n = 5 animals per group. j, Survival analysis for the groups shown in i. Both groups reached the end point on the same day. *P < 0.05.

Source data

Extended Data Figure 6 Analysis of Ptpn2 amplifications in human cancer using exome sequencing.

a, Patients treated with anti-PD-1, anti-PD-L1 or anti-CTLA-4 therapies in four cancer types: bladder cancer, head and neck squamous cell carcinoma (HNSCC), lung cancer and melanoma with copy-number amplifications (CNA) of PTPN2 were grouped according to whether or not they received clinical benefit from immunotherapy. The mutational burden is indicated on the y axis and the type of amplification event (focal, arm level or chromosome level) is indicated for each patient.

Source data

Extended Data Figure 7 _Ptpn2_-null tumours have increased effector T cell populations, but no other significant differences in immune infiltrates compared with control tumours were found.

a, Representative plots showing the gating strategy for the data shown in Fig. 4a. Populations of interest are gated in blue. b, The number of CD45+ cells per mg of tumour for _Ptpn2_-null (red) and control (grey) B16 tumours. Data are mean ± s.d. c, Representative plots showing the gating strategy for the quantification of T cell populations from _Ptpn2_-null and control B16 tumours shown in d. Populations of interest are gated in blue. d, Quantification of effector T cell populations in either _Ptpn2_-null or control B16 tumours. Data mean ± s.d. Data pooled from two independent experiments with a minimum of eight mice per group.

Source data

Extended Data Figure 8 _Ptpn2_-null tumours show no significant differences in myeloid cell infiltration compared to control tumours.

a, Representative plots showing the gating strategy for the myeloid cell populations quantified in b30. Populations of interest are gated in blue. b, Numbers of myeloid cells per mg of tumour for _Ptpn2_-null (red) and control (grey) B16 tumours. Data are mean s.d. Data pooled from two independent experiments with a minimum of eight mice per group.

Source data

Extended Data Figure 9 _Ptpn2_-null tumour cells have increased MHC-I expression after IFNγ stimulation and have increased sensitivity to costimulation with TNF and IFNγ.

a, Expression of MHC-I (H2K(b)/H2D(b)) on either _Ptpn2_-null (red) or control (grey) B16 cells after stimulation with IFNγ for 72 h (left) and quantification of mean fluorescence intensity (right). Data are mean ± s.d. b, Gene set enrichment analysis showing enrichment of signatures for IFNγ, IFNα and TNFα signalling via NF-κB in _Ptpn2_-null B16 cells after costimulation with TNF and IFNγ relative to _Ptpn2_-null cells stimulated with IFNγ alone.

Supplementary information

Supplementary information

This file contains uncropped western blot scans with size marker indications.

Supplementary Table 1

STARS analysis of CRISPR screening data. The STARS algorithm was used to identify statistically enriched and depleted sets of sgRNAs from the raw screening data. This file contains the output of the STARS analysis for all the comparisons of the screening conditions. Each tab of the spreadsheet contains a different comparison and the name of the tab indicates which comparison was analyzed. For example, tab 1: “GVAX+PD-1 v TCRaKO_depleted” contains the genes targeted by sgRNAs that are significantly depleted from GVAX + PD-1 blockade-treated mice relative to TCRα KO mice. STARS reports the gene symbol for the target gene, the ranks of the sgRNAs targeting that gene out of the total 9,998 sgRNAs in the library, a cumulative score for the enrichment of the set of sgRNAs, an average enrichment score for each sgRNA, and a p-value, FDR and q-value for the enrichment of that set of sgRNAs.

Supplementary Table 2

Differentially expressed genes between control and Ptpn2 null B16 melanoma cells treated with IFNγ. DESeq2 analysis package was used to identify genes that were differentially expressed between control and Ptpn2 null B16 cells treated with IFNg. This file contains all the genes with a Benjamini–Hochberg (BH) adjusted p-value <0.05. The first column lists the mouse gene name, the second column is the mean normalized count for that gene across all the samples included in the differential analysis. The third column lists the log2 fold change between control and Ptpn2 null samples. The fourth column lists the standard error of the log2 fold change for each gene between the two conditions. The fifth column lists the Wald test statistic for each gene. The sixth and seventh columns list the p-value and BH adjusted p-value, respectively, for each gene.

Supplementary Table 3

Heatmap statistics for Fig 5b. This table contains the Log2 fold change values and Benjamini–Hochberg (BH) adjusted p-values for the IFNγ signature genes shown in the heatmap in Fig 5b that are differentially expressed between Ptpn2 null and control B16 melanoma cells. The first two columns show these values for unstimulated Ptpn2 null vs control B16 cells, followed by stimulation with TNFα, then IFNγ, and finally TNFα + IFNγ.

Supplementary Table 4

Differentially expressed genes between control and Ptpn2 null A375 melanoma cells treated with IFNγ. DESeq2 analysis package was used to identify genes that were differentially expressed between control and Ptpn2 null A375 cells treated with IFNg. This file contains all the genes with a Benjamini–Hochberg (BH) adjusted p-value <0.05. The first column lists the human gene name, the second column is the mean normalized count for that gene across all the samples included in the differential analysis. The third column lists the log2 fold change between control and Ptpn2 null samples. The fourth column lists the standard error of the log2 fold change for each gene between the two conditions. The fifth column lists the Wald test statistic for each gene. The sixth and seventh columns list the p-value and BH adjusted p-value, respectively, for each gene.

Supplementary Table 5

Differentially expressed genes between control and Ptpn2 null HT-29 colon carcinoma cells treated with IFNγ. DESeq2 analysis package was used to identify genes that were differentially expressed between control and Ptpn2 null HT-29 cells treated with IFNg. This file contains all the genes with a Benjamini–Hochberg (BH) adjusted p-value <0.05. The first column lists the human gene name, the second column is the mean normalized count for that gene across all the samples included in the differential analysis. The third column lists the log2 fold change between control and Ptpn2 null samples. The fourth column lists the standard error of the log2 fold change for each gene between the two conditions. The fifth column lists the Wald test statistic for each gene. The sixth and seventh columns list the p-value and BH adjusted p-value, respectively, for each gene.

Supplementary Table 6

Differentially expressed genes between control and Ptpn2 null MelJuSo melanoma cells treated with IFNγ. DESeq2 analysis package was used to identify genes that were differentially expressed between control and Ptpn2 null MelJuSo cells treated with IFNg. This file contains all the genes with a Benjamini–Hochberg (BH) adjusted p-value <0.05. The first column lists the human gene name, the second column is the mean normalized count for that gene across all the samples included in the differential analysis. The third column lists the log2 fold change between control and Ptpn2 null samples. The fourth column lists the standard error of the log2 fold change for each gene between the two conditions. The fifth column lists the Wald test statistic for each gene. The sixth and seventh columns list the p-value and BH adjusted p-value, respectively, for each gene.

Supplementary Table 7

Differentially expressed genes between control and Ptpn2 null A549 lung carcinoma cells treated with IFNγ. DESeq2 analysis package was used to identify genes that were differentially expressed between control and Ptpn2 null A549 cells treated with IFNg. This file contains all the genes with a Benjamini–Hochberg (BH) adjusted p-value <0.05. The first column lists the human gene name, the second column is the mean normalized count for that gene across all the samples included in the differential analysis. The third column lists the log2 fold change between control and Ptpn2 null samples. The fourth column lists the standard error of the log2 fold change for each gene between the two conditions. The fifth column lists the Wald test statistic for each gene. The sixth and seventh columns list the p-value and BH adjusted p-value, respectively, for each gene.

PowerPoint slides

Source data

Rights and permissions

About this article

Cite this article

Manguso, R., Pope, H., Zimmer, M. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target.Nature 547, 413–418 (2017). https://doi.org/10.1038/nature23270

Download citation

• Received: 08 April 2017
• Accepted: 06 June 2017
• Published: 19 July 2017
• Issue Date: 27 July 2017
• DOI: https://doi.org/10.1038/nature23270