An unbiased in vivo functional genomics screening approach in mice identifies novel tumor cell-based regulators of immune rejection

Abstract

The clinical successes of immune checkpoint therapies for cancer make it important to identify mechanisms of resistance to anti-tumor immune responses. Numerous resistance mechanisms have been identified employing studies of single genes or pathways, thereby parsing the tumor microenvironment complexity into tractable pieces. However, this limits the potential for novel gene discovery to in vivo immune attack. To address this challenge, we developed an unbiased in vivo genome-wide RNAi screening platform that leverages host immune selection in strains of immune-competent and immunodeficient mice to select for tumor cell-based genes that regulate in vivo sensitivity to immune attack. Utilizing this approach in a syngeneic triple-negative breast cancer (TNBC) model, we identified 709 genes that selectively regulated adaptive anti-tumor immunity and focused on five genes (CD47, TGFβ1, Sgpl1, Tex9 and Pex14) with the greatest impact. We validated the mechanisms that underlie the immune-related effects of expression of these genes in different TNBC lines, as well as tandem synergistic interactions. Furthermore, we demonstrate the impact of different genes with previously unknown immune functions (Tex9 and Pex14) on anti-tumor immunity. Thus, this innovative approach has utility in identifying unknown tumor-specific regulators of immune recognition in multiple settings to reveal novel targets for future immunotherapies.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Abbreviations

BCA:

Bicinchoninic acid assay

CaeCam2:

Carcinoembryonic antigen related cell adhesion molecule 2

CCK:

Cholecystokinin

CCL4:

C-C motif chemokine ligand 4

CO2:

Carbon dioxide

CRISPR:

Clustered regularly interspaced short palindromic repeats

ENPP2:

Ectonucleotide pyrophosphatase/phosphodiesterase 2

FDR:

False discovery rate

GAPDH:

Glyceraldehyde 3-phosphate dehydrogenase

HCL:

Hydrogen chloride

IDO:

Indoleamine-pyrrole 2,3 dioxygenase

IgG:

Immunoglobulin G

IL-4:

Interleukin 4

IL13Ra1:

Interleukin 13 receptor subunit alpha 1

KD:

Knocked down

LIMMA:

Linear models for microarray data

MOI:

Multiplicity of infection

NSG:

NOD-SCID-IL2 gamma chain knock-out

NaCl:

Sodium chloride

OE:

Overexpression

OX40L:

Tumor necrosis factor (ligand) superfamily, member 4

P53:

Tumor protein P53

Pex14:

Peroxisomal membrane protein 14

RNAi:

RNA interference

S1P:

Sphingosine-1-phosphate

SBI:

System biosciences

SEMA7a:

Semaphorin 7a

Sgpl1:

Sphingosine-1-phosphate ligase 1

shRNA:

Short harpin RNA

SMAD3:

SMAD family member 3

Spns2:

S1P transporter spinster homologue 2

Tex9:

Testis expressed gene 9

Tiam1:

T-Cell lymphoma invasion and metastasis 1

TME:

Tumor microenvironment

TNBC:

Triple-negative breast cancer

TSP1:

Thrombospondin 1

WT:

Wild-type

References

  1. 1.

    Wolchok JD, Kluger H, Callahan MK et al (2013) Nivolumab plus Ipilimumab in advanced melanoma. N Engl J Med 369:122–133. doi:10.1056/NEJMoa1302369

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Hodi FS, O’Day SJ, McDermott DF et al (2010) Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med 363:711–723. doi:10.1056/NEJMoa1003466

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Lynch TJ, Bondarenko I, Luft A et al (2012) Ipilimumab in combination with paclitaxel and carboplatin as first-line treatment in stage IIIB/IV non-small-cell lung cancer: results from a randomized, double-blind, multicenter phase II study. J Clin Oncol 30:2046–2054. doi:10.1200/JCO.2011.38.4032

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Topalian SL, Hodi FS, Brahmer JR et al (2012) Safety, activity, and immune correlates of anti–PD-1 antibody in cancer. N Engl J Med 366:2443–2454. doi:10.1056/NEJMoa1200690

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Porter DL, Levine BL, Kalos M et al (2011) Chimeric antigen receptor-modified T cells in chronic lymphoid leukemia. N Engl J Med 365:725–733. doi:10.1056/NEJMoa1103849

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Liedtke C, Mazouni C, Hess KR et al (2008) Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol 26:1275–1281. doi:10.1200/JCO.2007.14.4147

    Article  PubMed  Google Scholar 

  7. 7.

    Creelan BC (2014) Update on immune checkpoint inhibitors in lung cancer. Cancer Control 21:80–89

    Article  PubMed  Google Scholar 

  8. 8.

    Leto SM, Trusolino L (2014) Primary and acquired resistance to EGFR-targeted therapies in colorectal cancer: impact on future treatment strategies. J Mol Med 92:709–722. doi:10.1007/s00109-014-1161-2

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Napolitano S, Martini G, Rinaldi B et al (2015) Primary and acquired resistance of colorectal cancer to anti-EGFR monoclonal antibody can be overcome by combined treatment of regorafenib with cetuximab. Clin Cancer Res 21(13):2975–2983. doi:10.1158/1078-0432.CCR-15-0020

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Zahreddine H, Borden KL (2013) Mechanisms and insights into drug resistance in cancer. Front Pharmacol 4:28. doi:10.3389/fphar.2013.00028

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Dunn GP, Bruce AT, Ikeda H et al (2002) Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol 3:991–998. doi:10.1038/ni1102-991

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Driessens G, Kline J, Gajewski TF (2009) Costimulatory and coinhibitory receptors in anti-tumor immunity. Immunol Rev 229:126–144. doi:10.1111/j.1600-065X.2009.00771.x

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Pardoll DM (2012) The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 12:252–264. doi:doi:10.1038/nrc3239

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Shuptrine CW, Surana R, Weiner LM (2012) Monoclonal antibodies for the treatment of cancer. Sem Cancer Biol 22:3–13. doi:10.1016/j.semcancer.2011.12.009

    CAS  Article  Google Scholar 

  15. 15.

    Surana R, Wang S, Xu W et al (2014) IL4 limits the efficacy of tumor-targeted antibody therapy in a murine model. Cancer Immunol Res 2:1103–1112. doi:10.1158/2326-6066.CIR-14-0103

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Zhou P, Shaffer DR, Alvarez Arias DA et al (2014) In vivo discovery of immunotherapy targets in the tumour microenvironment. Nature 506:52–57. doi:10.1038/nature12988

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Zender L, Xue W, Zuber J et al (2008) An oncogenomics-based In Vivo RNAi screen identifies tumor suppressors in liver cancer. Cell 135:852–864. doi:10.1016/j.cell.2008.09.061

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Chen S, Sanjana NE, Zheng K et al (2015) Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160:1246–1260. doi:10.1016/j.cell.2015.02.038

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Wolf J, Müller-Decker K, Flechtenmacher C et al (2013) An in vivo RNAi screen identifies SALL1 as a tumor suppressor in human breast cancer with a role in CDH1 regulation. Oncogene 33:4273–4278. doi:10.1038/onc.2013.515

    Article  PubMed  Google Scholar 

  20. 20.

    Andrysik Z, Kim J, Tan AC, Espinosa JM (2013) A genetic screen identifies TCF3/E2A and TRIAP1 as pathway-specific regulators of the cellular response to p53 activation. Cell Rep 3:1346–1354. doi:10.1016/j.celrep.2013.04.014

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Yeung ML, Houzet L, Yedavalli VSRK, Jeang KT (2009) A genome-wide short hairpin RNA screening of jurkat T-cells for human proteins contributing to productive HIV-1 replication. J Biol Chem 284:19463–19473. doi:10.1074/jbc.M109.010033

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Hattori H, Zhang X, Jia Y et al (2007) RNAi screen identifies UBE2D3 as a mediator of all-trans retinoic acid-induced cell growth arrest in human acute promyelocytic NB4 cells. Blood 110:640–650. doi:10.1182/blood-2006-11-059048

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Berens EB, Sharif GM, Schmidt MO et al (2016) Keratin-associated protein 5-5 controls cytoskeletal function and cancer cell vascular invasion. Oncogene 36:593–605. doi:10.1038/onc.2016.234

    Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Seyhan AA, Varadarajan U, Choe S et al (2012) A genome-wide RNAi screen identifies novel targets of neratinib resistance leading to identification of potential drug resistant genetic markers. Mol BioSyst 8:1553. doi:10.1039/c2mb05512k

    CAS  Article  PubMed  Google Scholar 

  25. 25.

    Berns K, Horlings HM, Hennessy BT et al (2007) A functional genetic approach identifies the PI3 K pathway as a major determinant of trastuzumab resistance in breast cancer. Cancer Cell 12:395–402. doi:10.1016/j.ccr.2007.08.030

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Brummelkamp TR, Fabius AWM, Mullenders J et al (2006) An shRNA barcode screen provides insight into cancer cell vulnerability to MDM2 inhibitors. Nat Chem Biol 2:202–206. doi:10.1038/nchembio774

    CAS  Article  PubMed  Google Scholar 

  27. 27.

    Smyth GK, Michaud J, Scott HS (2005) Use of within-array replicate spots for assessing differential expression in microarray experiments. Bioinformatics 21:2067–2075. doi:10.1093/bioinformatics/bti270

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Du P, Zhang X, Huang C-C et al (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11:587. doi:10.1186/1471-2105-11-587

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Lin SM, Du P, Huber W, Kibbe WA (2008) Model-based variance-stabilizing transformation for Illumina microarray data. Nucleic Acids Res 36:e11. doi:10.1093/nar/gkm1075

    Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Du P, Kibbe WA, Lin SM (2007) nuID: a universal naming scheme of oligonucleotides for Illumina, Affymetrix, and other microarrays. Biol Direct 2:16. doi:10.1186/1745-6150-2-16

    Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Leek JT, Johnson WE, Parker HS et al (2012) The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28:882–883. doi:10.1093/bioinformatics/bts034

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Nikitin A, Egorov S, Daraselia N, Mazo I (2003) Pathway studio–the analysis and navigation of molecular networks. Bioinformatics 19:2155–2157. doi:10.1093/bioinformatics/btg290

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Sugiura K, Stock CC (1952) Studies in a tumor spectrum. I. Comparison of the action of methylbis(2-chloroethyl)amine and 3-bis(2-chloroethyl)aminomethyl-4-methoxymethyl-5-hydroxy-6-methylpyridine on the growth of a variety of mouse and rat tumors. Cancer 5:382–402. doi:10.1002/1097-0142(195203)5:2<382:AID-CNCR2820050229>3.0.CO;2-3

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Sugiura K, Hitchings GH, Cavalieri LF, Stock CC (1950) The effect of 8-azaguanine on the growth of carcinoma, sarcoma, osteogenic sarcoma, lymphosarcoma and melanoma in animals. Cancer Res 10:178–185

    CAS  PubMed  Google Scholar 

  35. 35.

    Snell GD, Cloudman AM (2016) The effect of rate of freezing on the survival of fourteen transplantable tumors of mice. Cancer Res. http://cancerres.aacrjournals.org/content/canres/3/6/396.full.pdf. Accessed 22 Jul 2016

  36. 36.

    Johnstone CN, Smith YE, Cao Y et al (2015) Functional and molecular characterisation of EO771.LMB tumours, a new C57BL/6-mouse-derived model of spontaneously metastatic mammary cancer. Dis Model Mech 8:237–251. doi:10.1242/dmm.017830

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Purrington KS, Slager S, Eccles D et al (2014) Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer. Carcinogenesis 35:1012–1019. doi:10.1093/carcin/bgt404

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Willingham SB, Volkmer J-P, Gentles AJ et al (2012) The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. Proc Natl Acad Sci USA 109:6662–6667. doi:10.1073/pnas.1121623109

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Liu X, Pu Y, Cron K et al (2015) CD47 blockade triggers T cell–mediated destruction of immunogenic tumors. Nat Med 21:1209–1215. doi:10.1038/nm.3931

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Massagué J (2008) TGFβ in Cancer. Cell 134:215–230. doi:10.1016/j.cell.2008.07.001

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Spiegel S, Milstien S (2011) The outs and the ins of sphingosine-1-phosphate in immunity. Nat Rev Immunol 11:403–415. doi:10.1038/nri2974

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Neuzillet C, Tijeras-Raballand A, Cohen R et al (2015) Targeting the TGFβ pathway for cancer therapy. Pharmacol Ther 147:22–31. doi:10.1016/j.pharmthera.2014.11.001

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Murphy-Ullrich JE, Poczatek M (2000) Activation of latent TGF-β by thrombospondin-1: mechanisms and physiology. Cytokine Growth Factor Rev 11:59–69. doi:10.1016/S1359-6101(99)00029-5

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Shimada K, Nakajima A, Ikeda K et al (2011) CD47 regulates the TGF-β signaling pathway in osteoblasts and is distributed in Meckel’s cartilage. J Oral Sci 53:169–175. doi:10.2334/josnusd.53.169

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Rogers NM, Yao M, Novelli EM et al (2012) Activated CD47 regulates multiple vascular and stress responses: implications for acute kidney injury and its management. AJP Renal Physiol 303:F1117–F1125. doi:10.1152/ajprenal.00359.2012

    CAS  Article  Google Scholar 

  46. 46.

    Daniel C, Wiede J, Krutzsch HC et al (2004) Thrombospondin-1 is a major activator of TGF-β in fibrotic renal disease in the rat in vivo. Kidney Int 65:459–468. doi:10.1111/j.1523-1755.2004.00395.x

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Tseng D, Volkmer J-P, Willingham SB et al (2013) Anti-CD47 antibody-mediated phagocytosis of cancer by macrophages primes an effective antitumor T-cell response. Proc Natl Acad Sci USA 110(27):11103–11108. doi:10.1073/pnas.1305569110

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Kumar A, Saba JD (2009) Lyase to live by: sphingosine phosphate lyase as a therapeutic target. Expert Opin. Ther. Targets 13:1013–1025. doi:10.1517/14728220903039722

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Alvarez SE, Milstien S, Spiegel S (2007) Autocrine and paracrine roles of sphingosine-1-phosphate. Trends Endocrinol Metab 18:300–307. doi:10.1016/j.tem.2007.07.005

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Weyden LVD, Arends MJ, Campbell AD et al (2017) Genome-wide in vivo screen identifies novel host regulators of metastatic colonization. Nature 541:233–236. doi:10.1038/nature20792

    Article  PubMed  Google Scholar 

  51. 51.

    Zhao J, Liu J, Lee J-F et al (2016) TGF-β/SMAD3 pathway stimulates sphingosine-1 phosphate receptor 3 expression. J Biol Chem 291:27343–27353. doi:10.1074/jbc.M116.740084

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Radeke HH, von Wenckstern H, Stoidtner K et al (2005) Overlapping signaling pathways of sphingosine 1-phosphate and TGF- in the Murine Langerhans Cell Line XS52. J Immunol 174:2778–2786. doi:10.4049/jimmunol.174.5.2778

    CAS  Article  PubMed  Google Scholar 

  53. 53.

    Xin C, Ren S, Kleuser B et al (2004) Sphingosine 1-phosphate cross-activates the smad signaling cascade and mimics transforming growth factor-β-induced cell responses. J Biol Chem 279:35255–35262. doi:10.1074/jbc.M312091200

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Miller AV, Alvarez SE, Spiegel S, Lebman DA (2008) Sphingosine kinases and sphingosine-1-phosphate are critical for transforming growth factor β-induced extracellular signal-regulated kinase 1 and 2 activation and promotion of migration and invasion of esophageal cancer cells. Mol Cell Biol 28:4142–4151. doi:10.1128/MCB.01465-07

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    The MGC Project Team (2004) The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene collection (MGC). Genome Res 14:2121–2127. doi:10.1101/gr.2596504

    Article  Google Scholar 

  56. 56.

    Dammai V, Subramani S (2001) The Human peroxisomal targeting signal receptor, Pex5p, is translocated into the peroxisomal matrix and recycled to the cytosol. Cell 105:187–196. doi:10.1016/S0092-8674(01)00310-5

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Albertini M, Rehling P, Erdmann R et al (1997) Pex14p, a peroxisomal membrane protein binding both receptors of the two PTS-dependent import pathways. Cell 89:83–92. doi:10.1016/S0092-8674(00)80185-3

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

This research was supported by the following Shared Resources at Lombardi Comprehensive Cancer Center: The Genomics and Epigenomics Shared Resource, the Flow Cytometry and Cell Sorting Shared Resource, and the Tissue Culture Shared Resource. All Lombardi Comprehensive Cancer Center Shared Resources are partially supported by National Institutes of Health (NIH)/National Cancer Institute (NCI) Grant P30-CA051008. An antibody to CD47 was kindly provided by Dr. Robert Karr, Tioma Therapeutics, Inc (St. Louis, MO).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Louis M. Weiner.

Ethics declarations

Funding sources

This manuscript was supported by NIH Grants CA50633 (Louis M Weiner) and CA51880 (Louis M Weiner), and Susan G. Komen Career Catalyst Research Grant CCR14299200 (Zachary C Hartman).

Conflict of interest

The authors have no conflicts of interest to report or disclose.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 7621 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shuptrine, C.W., Ajina, R., Fertig, E.J. et al. An unbiased in vivo functional genomics screening approach in mice identifies novel tumor cell-based regulators of immune rejection. Cancer Immunol Immunother 66, 1529–1544 (2017). https://doi.org/10.1007/s00262-017-2047-2

Download citation

Keywords

  • In Vivo Genome-wide RNAi Screen
  • Triple-negative breast cancer
  • Cancer-derived resistance to immunotherapy
  • Functional genomics