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.

  • Review Article
  • Published:

Use of CRISPR-based screens to identify mechanisms of chemotherapy resistance

Cancer Gene Therapy volume 30, pages 1043–1050 (2023)Cite this article

Subjects

Abstract

Despite the development of new classes of targeted anti-cancer drugs, the curative treatment of metastatic solid tumors remains out of reach owing to the development of resistance to current chemotherapeutics. Although many mechanisms of drug resistance have been described, there is still a general lack of understanding of the many means by which cancer cells elude otherwise effective chemotherapy. The traditional strategy of isolating resistant clones in vitro, defining their mechanism of resistance, and testing to see whether these mechanisms play a role in clinical drug resistance is time-consuming and in many cases falls short of providing clinically relevant information. In this review, we summarize the use of CRISPR technology, including the promise and pitfalls, to generate libraries of cancer cells carrying sgRNAs that define novel mechanisms of resistance. The existing strategies using CRISPR knockout, activation, and inhibition screens, and combinations of these approaches are described. In addition, specialized approaches to identify more than one gene that may be contributing to resistance, as occurs in synthetic lethality, are described. Although these CRISPR-based approaches to cataloguing drug resistance genes in cancer cells are just beginning to be utilized, appropriately used they promise to accelerate understanding of drug resistance in cancer.

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: Catalytically active Cas9 and inactive dCas9 fusion proteins employed in knockout (CRISPRko), inhibition (CRISPRi), and activation (CRISPRa) screens.
Fig. 2: Workflow of a typical whole-genome pooled CRISPR screen to study drug resistance.

Similar content being viewed by others

References

  1. Hakala MT, Zakrzewski SF, Nichol CA. Relation of folic acid reductase to amethopterin resistance in cultured mammalian cells. J Biol Chem. 1961;236:952–8.

    Article  CAS  PubMed  Google Scholar 

  2. Nunberg JH, Kaufman RJ, Schimke RT, Urlaub G, Chasin LA. Amplified dihydrofolate reductase genes are localized to a homogeneously staining region of a single chromosome in a methotrexate-resistant Chinese hamster ovary cell line. Proc Natl Acad Sci USA. 1978;75:5553–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Dano K. Active outward transport of daunomycin in resistant Ehrlich ascites tumor cells. Biochim Biophys Acta. 1973;323:466–83.

    Article  CAS  PubMed  Google Scholar 

  4. Roninson IB, Chin JE, Choi KG, Gros P, Housman DE, Fojo A, et al. Isolation of human mdr DNA sequences amplified in multidrug-resistant KB carcinoma cells. Proc Natl Acad Sci USA. 1986;83:4538–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ueda K, Cornwell MM, Gottesman MM, Pastan I, Roninson IB, Ling V, et al. The mdr1 gene, responsible for multidrug-resistance, codes for P-glycoprotein. Biochem Biophys Res Commun. 1986;141:956–62.

    Article  CAS  PubMed  Google Scholar 

  6. Johannessen CM, Boehm JS, Kim SY, Thomas SR, Wardwell L, Johnson LA, et al. COT drives resistance to RAF inhibition through MAP kinase pathway reactivation. Nature. 2010;468:968–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Nazarian R, Shi H, Wang Q, Kong X, Koya RC, Lee H, et al. Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation. Nature. 2010;468:973–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sachse C, Echeverri CJ. Oncology studies using siRNA libraries: the dawn of RNAi-based genomics. Oncogene. 2004;23:8384–91.

    Article  CAS  PubMed  Google Scholar 

  9. Swanton C, Szallasi Z, Brenton JD, Downward J. Functional genomic analysis of drug sensitivity pathways to guide adjuvant strategies in breast cancer. Breast Cancer Res. 2008;10:214.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Yang H, Ren S, Yu S, Pan H, Li T, Ge S, et al. Methods favoring homology-directed repair choice in response to CRISPR/Cas9 induced-double strand breaks. Int J Mol Sci. 2020;21:6460.

    Google Scholar 

  11. Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelson T, et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science. 2014;343:84–7.

    Article  CAS  PubMed  Google Scholar 

  12. Qi LS, Larson MH, Gilbert LA, Doudna JA, Weissman JS, Arkin AP, et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell. 2013;152:1173–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Konermann S, Brigham MD, Trevino AE, Joung J, Abudayyeh OO, Barcena C, et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature. 2015;517:583–8.

    Article  CAS  PubMed  Google Scholar 

  14. Li W, Xu H, Xiao T, Cong L, Love MI, Zhang F, et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 2014;15:554.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016;34:184–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Spahn PN, Bath T, Weiss RJ, Kim J, Esko JD, Lewis NE, et al. PinAPL-Py: a comprehensive web-application for the analysis of CRISPR/Cas9 screens. Sci Rep. 2017;7:15854.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Kurata M, Rathe SK, Bailey NJ, Aumann NK, Jones JM, Veldhuijzen GW, et al. Using genome-wide CRISPR library screening with library resistant DCK to find new sources of Ara-C drug resistance in AML. Sci Rep. 2016;6:36199.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Barghout SH, Aman A, Nouri K, Blatman Z, Arevalo K, Thomas GE, et al. A genome-wide CRISPR/Cas9 screen in acute myeloid leukemia cells identifies regulators of TAK-243 sensitivity. JCI Insight. 2021;6:e141518.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Sun X, Vilar S, Tatonetti NP. High-throughput methods for combinatorial drug discovery. Sci Transl Med. 2013;5:205rv1.

    Article  PubMed  Google Scholar 

  20. Cong L, Ran FA, Cox D, Lin S, Barretto R, Habib N, et al. Multiplex genome engineering using CRISPR/Cas systems. Science. 2013;339:819–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Tsai SQ, Wyvekens N, Khayter C, Foden JA, Thapar V, Reyon D, et al. Dimeric CRISPR RNA-guided FokI nucleases for highly specific genome editing. Nat Biotechnol. 2014;32:569–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wong AS, Choi GC, Cui CH, Pregernig G, Milani P, Adam M, et al. Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM. Proc Natl Acad Sci USA. 2016;113:2544–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Xie K, Minkenberg B, Yang Y. Boosting CRISPR/Cas9 multiplex editing capability with the endogenous tRNA-processing system. Proc Natl Acad Sci USA. 2015;112:3570–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Han K, Jeng EE, Hess GT, Morgens DW, Li A, Bassik MC. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat Biotechnol. 2017;35:463–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Qian S, Liang S, Yu H. Leveraging genetic interactions for adverse drug-drug interaction prediction. PLoS Comput Biol. 2019;15:e1007068.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. le Sage C, Lawo S, Panicker P, Scales TME, Rahman SA, Little AS, et al. Dual direction CRISPR transcriptional regulation screening uncovers gene networks driving drug resistance. Sci Rep. 2017;7:17693.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Jost M, Chen Y, Gilbert LA, Horlbeck MA, Krenning L, Menchon G, et al. Combined CRISPRi/a-based chemical genetic screens reveal that Rigosertib is a microtubule-destabilizing agent. Mol Cell. 2017;68:210–23.e6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Palmer AC, Chidley C, Sorger PK. A curative combination cancer therapy achieves high fractional cell killing through low cross-resistance and drug additivity. Elife. 2019;8:e50036.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Fu Y, Foden JA, Khayter C, Maeder ML, Reyon D, Joung JK, et al. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat Biotechnol. 2013;31:822–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013;31:827–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang XH, Tee LY, Wang XG, Huang QS, Yang SH. Off-target effects in CRISPR/Cas9-mediated genome engineering. Mol Ther Nucleic Acids. 2015;4:e264.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Barrangou R, Fremaux C, Deveau H, Richards M, Boyaval P, Moineau S, et al. CRISPR provides acquired resistance against viruses in prokaryotes. Science. 2007;315:1709–12.

    Article  CAS  PubMed  Google Scholar 

  33. Lin S, Larrue C, Scheidegger NK, Seong BKA, Dharia NV, Kuljanin M, et al. An in vivo CRISPR screening platform for prioritizing therapeutic targets in AML. Cancer Discov. 2022;12:432–49.

    Article  CAS  PubMed  Google Scholar 

  34. Merino D, Whittle JR, Vaillant F, Serrano A, Gong JN, Giner G, et al. Synergistic action of the MCL-1 inhibitor S63845 with current therapies in preclinical models of triple-negative and HER2-amplified breast cancer. Sci Transl Med. 2017;9:eaam7049.

    Article  PubMed  Google Scholar 

  35. Bester AC, Lee JD, Chavez A, Lee YR, Nachmani D, Vora S, et al. An integrated genome-wide CRISPRa approach to functionalize lncRNAs in drug resistance. Cell. 2018;173:649–64.e20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wei L, Lee D, Law CT, Zhang MS, Shen J, Chin DW, et al. Genome-wide CRISPR/Cas9 library screening identified PHGDH as a critical driver for Sorafenib resistance in HCC. Nat Commun. 2019;10:4681.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Chen J, Huang Y, Tang Z, Li M, Ling X, Liao J, et al. Genome-scale CRISPR-Cas9 transcriptional activation screening in metformin resistance related gene of prostate cancer. Front Cell Dev Biol. 2020;8:616332.

    Article  PubMed  Google Scholar 

  38. Lau MT, Ghazanfar S, Parkin A, Chou A, Rouaen JR, Littleboy JB, et al. Systematic functional identification of cancer multi-drug resistance genes. Genome Biol. 2020;21:27.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lin JF, Hu PS, Wang YY, Tan YT, Yu K, Liao K, et al. Phosphorylated NFS1 weakens oxaliplatin-based chemosensitivity of colorectal cancer by preventing PANoptosis. Signal Transduct Target Ther. 2022;7:54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Chen Y, Li L, Lan J, Cui Y, Rao X, Zhao J, et al. CRISPR screens uncover protective effect of PSTK as a regulator of chemotherapy-induced ferroptosis in hepatocellular carcinoma. Mol Cancer. 2022;21:11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. MacLeod G, Bozek DA, Rajakulendran N, Monteiro V, Ahmadi M, Steinhart Z, et al. Genome-wide CRISPR-Cas9 screens expose genetic vulnerabilities and mechanisms of temozolomide sensitivity in glioblastoma stem cells. Cell Rep. 2019;27:971–86.e9.

    Article  CAS  PubMed  Google Scholar 

  42. Szlachta K, Kuscu C, Tufan T, Adair SJ, Shang S, Michaels AD, et al. CRISPR knockout screening identifies combinatorial drug targets in pancreatic cancer and models cellular drug response. Nat Commun. 2018;9:4275.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Addgene. CRISPR Libraries. 2022. https://www.addgene.org/pooled-library/#crispr.

  44. Agilent. CRISPR Libraries. 2022. https://www.agilent.com/en/product/crispr-cas/crispr-libraries.

  45. GenScript. CRISPR gRNA Libraries. 2022. https://www.genscript.com/CRISPR-gRNA-library.html.

  46. Colic M, Hart T. Common computational tools for analyzing CRISPR screens. Emerg Top Life Sci. 2021;5:779–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. String Consortium. Protein-protein interaction networks, functional enrichment analysis. 2022. https://string-db.org/.

  48. Enrichr. Analyze, Input data. 2022. https://maayanlab.cloud/Enrichr/.

  49. GSEA. Molecular Signatures Database. 2022. https://www.gsea-msigdb.org/gsea/msigdb/.

  50. Kaplan-Meier Plotter. What is the KM plotter? 2022. https://kmplot.com/analysis/.

Download references

Acknowledgements

We thank G. Leiman in the Laboratory of Cell Biology for editorial assistance.

Funding

This research was supported by the Intramural Research Program of the National Institutes of Health, the National Cancer Institute.

Author information

Authors and Affiliations

  1. Laboratory of Cell Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA

    George Alyateem, Heidi M. Wade, Aaron A. Bickert, Crystal C. Lipsey, Priya Mondal, MacKinzie D. Smith, Robert W. Robey & Michael M. Gottesman

  2. Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA

    Rania M. Labib & Beverly A. Mock

Authors
  1. George Alyateem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heidi M. Wade
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aaron A. Bickert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Crystal C. Lipsey
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Priya Mondal
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. MacKinzie D. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rania M. Labib
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Beverly A. Mock
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Robert W. Robey
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Michael M. Gottesman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conception and design of the study: GA, HMW and AAB. Drafting the article: GA, HMW, AAB, CCL, MDS, RML, BAM, RWR and MMG. Revision and editing: GA, PM, RWR and MMG. All authors reviewed the final version before submission.

Corresponding author

Correspondence to Michael M. Gottesman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alyateem, G., Wade, H.M., Bickert, A.A. et al. Use of CRISPR-based screens to identify mechanisms of chemotherapy resistance. Cancer Gene Ther 30, 1043–1050 (2023). https://doi.org/10.1038/s41417-023-00608-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41417-023-00608-z

This article is cited by

Search

Advanced search

Quick links