Skip to main content
See More

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:

A multilineage screen identifies actionable synthetic lethal interactions in human cancers

Nature Genetics volume 57, pages 154–164 (2025)Cite this article

Subjects

An Author Correction to this article was published on 20 January 2025

This article has been updated

Abstract

Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions. First, fundamental cellular processes are perturbed by systematic combinatorial knockouts across tumor lineages, identifying 1,805 synthetic lethal interactions (95% unreported). Interactions are then analyzed by hierarchical pooling, revealing that half segregate reliably by tissue type or biomarker status (51%) and a substantial minority are penetrant across lineages (34%). Interactions converge on 49 multigene systems, including MAPK signaling and BAF transcriptional regulatory complexes, which become essential on disruption of polymerases. Some 266 interactions translate to robust biomarkers of drug sensitivity, including frequent genetic alterations in the KDM5C/6A histone demethylases, which sensitize to inhibition of TIPARP (PARP7). SCHEMATIC offers a context-aware, data-driven approach to match genetic alterations to targeted therapies.

Access through your institution
Buy or subscribe

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

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Strategy for discovery of actionable synthetic lethal interactions.
Fig. 2: Multilineage and context-specific mapping of synthetic essentiality.
Fig. 3: Structural map of essential multigene systems.
Fig. 4: Prioritization of interactions that predict sensitivity to targeted therapeutic agents.
Fig. 5: Characterizing drug–mutation interactions that distinguish PARP family inhibitors.

Similar content being viewed by others

Data availability

The read counts for all screening data are provided as Supplementary Data 1 and 2. Raw sequencing data are publicly available via the Sequencing Read Archive (accession no. PRJNA701256). NeST (https://idekerlab.ucsd.edu/nest), Reactome (https://reactome.org/download-data), KEGG (https://www.genome.jp/kegg/pathway.html) and WikiPathways (https://www.wikipathways.org/index.php/Download_Pathways) were downloaded from their respective websites. We downloaded the GDSC drug-profiling data (http://www.cancerrxgene.org/downloads/bulk_download) and 2023 quarter 1 release of DepMap (http://depmap.org/portal/download/all).

Code availability

Customized code used to generate results in the present study is available via Github at https://github.com/samsonfong/SCHEMATIC (ref. 89).

Change history

References

  1. Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).

    Article  CAS  PubMed  Google Scholar 

  2. Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. 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 

  4. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Dixon, S. J. et al. Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes. Proc. Natl Acad. Sci. USA 105, 16653–16658 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Roguev, A. et al. Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322, 405–410 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 1385–1389 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Horn, T. et al. Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nat. Methods 8, 341–346 (2011).

    Article  CAS  PubMed  Google Scholar 

  9. Frost, A. et al. Functional repurposing revealed by comparing S. pombe and S. cerevisiae genetic interactions. Cell 149, 1339–1352 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Laufer, C., Fischer, B., Billmann, M., Huber, W. & Boutros, M. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nat. Methods 10, 427–431 (2013).

    Article  CAS  PubMed  Google Scholar 

  12. Mohr, S. E., Smith, J. A., Shamu, C. E., Neumüller, R. A. & Perrimon, N. RNAi screening comes of age: improved techniques and complementary approaches. Nat. Rev. Mol. Cell Biol. 15, 591–600 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wong, A. S. L. et al. Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM. Proc. Natl Acad. Sci. USA 113, 2544–2549 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Du, D. et al. Genetic interaction mapping in mammalian cells using CRISPR interference. Nat. Methods 14, 577–580 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Shen, J. P. et al. Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions. Nat. Methods 14, 573 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Han, K. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35, 463–474 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Horlbeck, M. A. et al. Mapping the genetic landscape of human cells. Cell 174, 953–967.e22 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zhao, D. et al. Combinatorial CRISPR-Cas9 metabolic screens reveal critical redox control points dependent on the KEAP1-NRF2 regulatory axis. Mol. Cell 69, 699–708.e7 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Najm, F. J. et al. Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens. Nat. Biotechnol. 36, 179–189 (2018).

  20. Zamanighomi, M. et al. GEMINI: a variational Bayesian approach to identify genetic interactions from combinatorial CRISPR screens. Genome Biol. 20, 137 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Kelly, M. R. et al. Combined proteomic and genetic interaction mapping reveals new RAS effector pathways and susceptibilities. Cancer Discov. 10, 1950–1967 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Ito, T. et al. Paralog knockout profiling identifies DUSP4 and DUSP6 as a digenic dependence in MAPK pathway-driven cancers. Nat. Genet. 53, 1664–1672 (2021).

    Article  CAS  PubMed  Google Scholar 

  23. Ward, H. N. et al. Analysis of combinatorial CRISPR screens with the Orthrus scoring pipeline. Nat. Protoc. 16, 4766–4798 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Bakerlee, C. W., Ba, A. N. N., Shulgina, Y., Echenique, J. I. R. & Desai, M. M. Idiosyncratic epistasis leads to global fitness-correlated trends. Science 376, 630–635 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Friend, S. H. & Oliff, A. Emerging uses for genomic information in drug discovery. N. Engl. J. Med. 338, 125–126 (1998).

    Article  CAS  PubMed  Google Scholar 

  26. Zhao, D. & DePinho, R. A. Synthetic essentiality: targeting tumor suppressor deficiencies in cancer. Bioessays https://doi.org/10.1002/bies.201700076 (2017).

  27. Hartwell, L. H., Szankasi, P., Roberts, C. J., Murray, A. W. & Friend, S. H. Integrating genetic approaches into the discovery of anticancer drugs. Science 278, 1064–1068 (1997).

    Article  CAS  PubMed  Google Scholar 

  28. Reinhardt, H. C., Jiang, H., Hemann, M. T. & Yaffe, M. B. Exploiting synthetic lethal interactions for targeted cancer therapy. Cell Cycle 8, 3112–3119 (2009).

    Article  CAS  PubMed  Google Scholar 

  29. Ashworth, A. & Lord, C. J. Synthetic lethal therapies for cancer: what’s next after PARP inhibitors? Nat. Rev. Clin. Oncol. 15, 564–576 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Bland, P. et al. SF3B1 hotspot mutations confer sensitivity to PARP inhibition by eliciting a defective replication stress response. Nat. Genet. 55, 1311–1323 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Martin, T. D. et al. A role for mitochondrial translation in promotion of viability in K-Ras mutant cells. Cell Rep. 20, 427–438 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ryan, C. J., Bajrami, I. & Lord, C. J. Synthetic lethality and cancer—penetrance as the major barrier. Trends Cancer Res. 4, 671–683 (2018).

    Article  CAS  Google Scholar 

  33. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zheng, F. et al. Interpretation of cancer mutations using a multiscale map of protein systems. Science 374, eabf3067 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  36. 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 

  37. Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 174, 1034–1035 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Tate, J. G. et al. COSMIC: the catalogue of somatic mutations In cancer. Nucleic Acids Res. 47, D941–D947 (2019).

    Article  CAS  PubMed  Google Scholar 

  40. Mitsopoulos, C. et al. canSAR: update to the cancer translational research and drug discovery knowledgebase. Nucleic Acids Res. 49, D1074–D1082 (2021).

  41. Kim, E. & Hart, T. Improved analysis of CRISPR fitness screens and reduced off-target effects with the BAGEL2 gene essentiality classifier. Genome Med. https://doi.org/10.1186/s13073-020-00809-3 (2021).

  42. Oughtred, R. et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 47, D529–D541 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Stott, F. J. The alternative product from the human CDKN2A locus, p14ARF, participates in a regulatory feedback loop with p53 and MDM2. EMBO J. 17, 5001–5014 (1998).

  44. O’Brien, V. & Brown, R. Signalling cell cycle arrest and cell death through the MMR system. Carcinogenesis 27, 682–692 (2006).

    Article  PubMed  Google Scholar 

  45. Mantovani, F., Drost, J., Voorhoeve, P. M., Del Sal, G. & Agami, R. Gene regulation and tumor suppression by the bromodomain-containing protein BRD7. Cell Cycle 9, 2777–2781 (2010).

    Article  CAS  PubMed  Google Scholar 

  46. Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).

    Article  CAS  PubMed  Google Scholar 

  47. Lim, K. S. et al. USP1 is required for replication fork protection in BRCA1-deficient tumors. Mol. Cell 72, 925–941.e4 (2018).

    Article  CAS  PubMed  Google Scholar 

  48. Álvarez-Quilón, A. et al. Endogenous DNA 3′ blocks are vulnerabilities for BRCA1 and BRCA2 deficiency and are reversed by the APE2 nuclease. Mol. Cell 78, 1152–1165.e8 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Martens, M. et al. WikiPathways: connecting communities. Nucleic Acids Res. 49, D613–D621 (2021).

    Article  CAS  PubMed  Google Scholar 

  50. Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 48, D498–D503 (2020).

    CAS  PubMed  Google Scholar 

  51. Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. https://doi.org/10.1093/nar/gkac963 (2022).

    Article  PubMed Central  Google Scholar 

  52. Yang, W. et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41, D955–D961 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Cliby, W. A. et al. Overexpression of a kinase-inactive ATR protein causes sensitivity to DNA-damaging agents and defects in cell cycle checkpoints. EMBO J. 17, 159–169 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Turner, N. C. et al. Capivasertib in hormone receptor-positive advanced breast cancer. N. Engl. J. Med. 388, 2058–2070 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Luo, X. & Kraus, W. L. On PAR with PARP: cellular stress signaling through poly(ADP-ribose) and PARP-1. Genes Dev. 26, 417–432 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Do, K. & Chen, A. P. Molecular pathways: targeting PARP in cancer treatment. Clin. Cancer Res. 19, 977–984 (2013).

    Article  CAS  PubMed  Google Scholar 

  58. Morales, J. et al. Review of poly(ADP-ribose) polymerase (PARP) mechanisms of action and rationale for targeting in cancer and other diseases. Crit. Rev. Eukaryot. Gene Expr. 24, 15–28 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  59. MacPherson, L. et al. 2,3,7,8-Tetrachlorodibenzo-p-dioxin poly(ADP-ribose) polymerase (TiPARP, ARTD14) is a mono-ADP-ribosyltransferase and repressor of aryl hydrocarbon receptor transactivation. Nucleic Acids Res. 41, 1604–1621 (2013).

    Article  CAS  PubMed  Google Scholar 

  60. Gozgit, J. M. et al. PARP7 negatively regulates the type I interferon response in cancer cells and its inhibition triggers antitumor immunity. Cancer Cell 39, 1214–1226.e10 (2021).

    Article  CAS  PubMed  Google Scholar 

  61. Harrision, D., Gravells, P., Thompson, R. & Bryant, H. E. Poly(ADP-ribose) glycohydrolase (PARG) vs. poly(ADP-ribose) polymerase (PARP)—function in genome maintenance and relevance of inhibitors for anti-cancer therapy. Front. Mol. Biosci. 7, 191 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. 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 

  64. Corsello, S. M. et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat. Cancer 1, 235–248 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hopfner, K.-P. & Hornung, V. Molecular mechanisms and cellular functions of cGAS–STING signalling. Nat. Rev. Mol. Cell Biol. 21, 501–521 (2020).

    Article  CAS  PubMed  Google Scholar 

  66. Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).

    Article  CAS  PubMed  Google Scholar 

  67. Chan, E. M. et al. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature 568, 551–556 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. El Tekle, G. et al. Co-occurrence and mutual exclusivity: what cross-cancer mutation patterns can tell us. Trends Cancer Res. 7, 823–836 (2021).

    Article  Google Scholar 

  69. Haar, J. v.d. et al. Identifying epistasis in cancer genomes: a delicate affair. Cell 177, 1375–1383 (2019).

  70. Cadzow, L. et al. Development of KSQ-4279 as a first-in-class USP1 inhibitor for the treatment of BRCA-deficient cancers. Eur. J. Cancer 138, S52 (2020).

    Article  Google Scholar 

  71. Tischkowitz, M. & Xia, B. PALB2/FANCN: recombining cancer and Fanconi anemia. Cancer Res. 70, 7353–7359 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Carbone, M. et al. BAP1 and cancer. Nat. Rev. Cancer 13, 153–159 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lord, C. J. & Ashworth, A. BRCAness revisited. Nat. Rev. Cancer 16, 110–120 (2016).

    Article  CAS  PubMed  Google Scholar 

  74. Rasmussen, M. et al. Loss of PARP7 increases type I interferon signaling in EO771 breast cancer cells and prevents mammary tumor growth by increasing antitumor immunity. Cancers 15, 3689 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Sanderson, D. J. et al. Structurally distinct PARP7 inhibitors provide new insights into the function of PARP7 in regulating nucleic acid-sensing and IFN-β signaling. Cell Chem. Biol. 30, 43–54.e8 (2023).

    Article  CAS  PubMed  Google Scholar 

  76. Shen, H.-F. et al. The dual function of KDM5C in both gene transcriptional activation and repression promotes breast cancer cell growth and tumorigenesis. Adv. Sci. 8, 2004635 (2021).

    Article  CAS  Google Scholar 

  77. Wu, L. et al. KDM5 histone demethylases repress immune response via suppression of STING. PLoS Biol. 16, e2006134 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Sangfelt, O., Erickson, S. & Grander, D. Mechanisms of interferon-induced cell cycle arrest. Front. Biosci. 5, D479–D487 (2000).

    Article  CAS  PubMed  Google Scholar 

  79. Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).

    Article  CAS  PubMed  Google Scholar 

  80. Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274.e22 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Calandrini, C. et al. Organoid-based drug screening reveals neddylation as therapeutic target for malignant rhabdoid tumors. Cell Rep. 36, 109568 (2021).

  82. André, F. et al. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).

    Article  Google Scholar 

  83. Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    Article  PubMed Central  Google Scholar 

  84. Langmead, B., Wilks, C., Antonescu, V. & Charles, R. Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics 35, 421–432 (2019).

    Article  CAS  PubMed  Google Scholar 

  85. Ford, K. et al. Multimodal perturbation analyses of cyclin-dependent kinases reveal a network of synthetic lethalities associated with cell-cycle regulation and transcriptional regulation. Sci. Rep. 13, 7678 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 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 

  87. Collins, S. R., Roguev, A. & Krogan, N. J. Quantitative genetic interaction mapping using the E-MAP approach. Methods Enzymol. 470, 205–231 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 76, 7.20.1–7.20.41 (2013).

    Google Scholar 

  89. Fong, S. et al. Code for ‘A multi-lineage screen identifies actionable synthetic-lethal interactions in human cancers’. Zenodo https://doi.org/10.5281/zenodo.13864661 (2024).

Download references

Acknowledgements

We thank W. Hahn of the Broad Institute and Dana-Farber Cancer Institute for sharing selected cell lines used in this project. We also thank M. Kelley for his input and fruitful discussions. We would also like to thank the PRISM Lab, at The Broad Institute, for running the PRISM assay. This work was supported by the National Institutes of Health (NIH) under grant nos. U54 CA274502 (the Cancer Cell Map Initiative, previously U54 CA209891), R50 CA243885, K00 CA212456, K00 CA274649, and R01 HG012351, as well as by a contract to UCSD from Ideaya Biosciences. Ideaya Biosciences provided feedback for the study design but did not influence data collection and analysis. Other funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Samson H. Fong, Brent M. Kuenzi, Nicole M. Mattson, John Lee, Kyle Sanchez, Ana Bojorquez-Gomez, Brenton P. Munson, Katherine Licon, Sarah Bergendahl, John Paul Shen, Jason F. Kreisberg & Trey Ideker

  2. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

    Samson H. Fong, Kyle Ford, Brenton P. Munson, Prashant Mali & Trey Ideker

  3. IDEAYA Biosciences, San Francisco, CA, USA

    Jeffrey H. Hager & Michael A. White

  4. Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA

    Trey Ideker

Authors
  1. Samson H. Fong
    View author publications

    Search author on:PubMed Google Scholar

  2. Brent M. Kuenzi
    View author publications

    Search author on:PubMed Google Scholar

  3. Nicole M. Mattson
    View author publications

    Search author on:PubMed Google Scholar

  4. John Lee
    View author publications

    Search author on:PubMed Google Scholar

  5. Kyle Sanchez
    View author publications

    Search author on:PubMed Google Scholar

  6. Ana Bojorquez-Gomez
    View author publications

    Search author on:PubMed Google Scholar

  7. Kyle Ford
    View author publications

    Search author on:PubMed Google Scholar

  8. Brenton P. Munson
    View author publications

    Search author on:PubMed Google Scholar

  9. Katherine Licon
    View author publications

    Search author on:PubMed Google Scholar

  10. Sarah Bergendahl
    View author publications

    Search author on:PubMed Google Scholar

  11. John Paul Shen
    View author publications

    Search author on:PubMed Google Scholar

  12. Jason F. Kreisberg
    View author publications

    Search author on:PubMed Google Scholar

  13. Prashant Mali
    View author publications

    Search author on:PubMed Google Scholar

  14. Jeffrey H. Hager
    View author publications

    Search author on:PubMed Google Scholar

  15. Michael A. White
    View author publications

    Search author on:PubMed Google Scholar

  16. Trey Ideker
    View author publications

    Search author on:PubMed Google Scholar

Contributions

S.H.F., B.M.K., J.P.S., P.M., J.H.H. and T.I. conceived the combinatorial CRISPR experiments. S.H.F., K.S., A.B.-G., K.F. and K.L. constructed the combinatorial CRISPR library. S.H.F., B.M.K., J.L. and K.S. performed the combinatorial CRISPR experiments. S.H.F. and B.P.M. analyzed the next-generation sequencing data. N.M.M., S.B. and K.L. performed the experiments related to the PARP7 inhibitor. S.H.F. performed all computation analysis with advice from B.M.K. and T.I. J.F.K., M.A.W., P.M. and J.H.H. provided technical advice. S.H.F., B.M.K., J.F.K. and T.I. wrote the paper.

Corresponding author

Correspondence to Trey Ideker.

Ethics declarations

Competing interests

J.H.H. and M.A.W. are former or current employees of Ideaya Biosciences and have an equity interest. T.I. is a member of the Ideaya Biosciences scientific advisory board and has an equity interest. T.I. is also a co-founder and member of the advisory board and has an equity interest in Data4Cure and Serinus Biosciences. The terms of these arrangements have been reviewed and approved by the UCSD in accordance with its conflict-of-interest policies. B.M.K. is an employee of Vividion Therapeutics and has equity interests in Pfizer and Vividion Therapeutics. The other authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Stephen Friend, Rachael Natrajan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Combinatorial gRNA library design.

(a) The dual CRISPR library targets all pairs of 67 by 176 genes across 7 cell lines. Each gene is targeted by 3 guide RNAs resulting in 9 guide pairs for each gene pair assayed in 2 replicates. (b) Design of the custom 130 base pair oligonucleotide pool used to construct the combinatorial CRISPR library. sgRNA1 and sgRNA2 can target the same gene or two different genes. hU6, human U6 promoter; sgRNA, single-guide RNA; BsmBI, BsmBI restriction enzyme recognition site. (c) Two-step cloning strategy to package the oligonucleotides in panel B into a functional combinatorial CRISPR library. mU6, murine U6 promoter.

Extended Data Fig. 2 Reproducibility and validation of fitness measurements.

(a–c) Scatter plots showing reproducibility across replicates of fitness measurements at the level of (a) individual guide pairs; (b) gene pairs, median over all relevant guide pairs; and (c) genes, integrating over all relevant pairwise fitnesses involving each gene. Contour lines are drawn at 5, 10, 25, 50, 75, 99 percentiles. All measurements beyond the 99 percentile are represented explicitly as black points. Note progressive increases in reproducibility (Pearson correlation r) with increasing integration of data. (d) Bar plot of the Pearson correlation between replicate guide pair fitness measurements (teal) or replicate gene pair fitness measurements (blue) in each of the 7 cell lines. (e) Bar plot of Pearson correlation between replicate single-gene fitness measurements from the human U6 position (hU6, blue) or the murine U6 (mU6, red) position. (f) Bar plot of the Pearson correlation between the single-gene fitness measurements from this study (hU6 position, blue; mU6 position, red) and the single-gene fitness measurements from the DepMap project. (g) Recovery of common-essential genes annotated by DepMap (area under the receiver operating characteristic curve, auROC) when scoring essential genes de novo based on sgRNAs expressed by the human U6 (hU6, blue) or murine U6 (mU6, red) promoters. (h) Scatterplot of single-gene knockout fitness measurements scored in this study versus those measured by DepMap, including data from all seven cell-line contexts. (i) Contingency table of essential genes classified by DepMap versus this study. OR, odds ratio. P-value by Fisher’s exact test. (j) Distributions of single-gene fitness measured in this study, split by DepMap status (DepMap common-essential genes in orange, non-essential genes in blue). Distributions are shown for 6/7 cell lines, as the seventh line (MCF10A) is not characterized by DepMap.

Extended Data Fig. 3 Reproducibility and validation of genetic interaction measurements.

(a) Decrease in fitness due to single-gene disruptions to BRCA1 or PARP1, alongside double-gene disruption to BRCA1 and PARP1. Fitnesses tracked over the course of 21 days. Error bars denote the standard error of the mean (n = 9 sgRNA pairs). (b) Volcano plot showing false discovery rate versus genetic interaction score for all gene pairs characterized in CAL27 cell line. The confidence interval contains 95% of all scores where at one sgRNA targets the AAVS1 safe-harbor locus (adeno-associated virus integration site 1). Point color shows the absolute fitness score of each gene pair. (c) Distribution of coefficients of variation (CV) of top 1000 synthetic-essential gene pairs in individual cell lines (blue) versus the corresponding distribution where the guide-pair to gene-pair mapping is randomized (orange). Dotted line shows the threshold CV that best separates the two distributions, used in the Methods to triage interactions into single cell line versus multi-lineage classifications. (d) Bar plot of the Pearson correlation between replicate genetic interaction measurements in each of the 7 cell lines. Pearson correlations are also shown after pooling measurements within each of the 3 tissues or across all tissues (multi-lineage). All measurements shown in teal, significant interactions (FDR < 30%) shown in blue. FDR, False Discovery Rate. (e) Number of significant genetic interactions in each of the 7 cell lines, in each of the 3 tissue pools, or when pooling all contexts as multi-lineage. FDR < 1% in teal, FDR < 10% in blue.

Extended Data Fig. 4 Heatmap of essential genes identified in this study.

Blue color indicates a human gene (columns) scoring as essential in a tumor cell line (bottom-most rows), tissue or subtype context (middle rows), or multi-lineage (top row).

Extended Data Fig. 5 Mapping essential systems with combinatorial CRISPR data.

(a) Same map of multi-gene systems as Fig. 3a, visualized as a vertical hierarchy. Nodes represent systems, arrows represent containment of one system within a larger one. System size (number of proteins) shown by node size. Individual proteins not shown. (b) Tests for identifying essential systems by independent gene lethality (IE), synthetic lethality within systems (SEinternal), or synthetic lethality with an external gene (SEexternal). Circle nodes represent systems; diamond nodes represent genes; arrows linking one circle to another indicate hierarchical containment of the first system (child) by the second (parent). Color represents viable (gray) versus lethal (red) status of the corresponding gene or pairwise gene knockout. (c) Fraction of systems (y-axis) scoring as essential in each of four databases of subcellular systems (x-axis) revealed by the different essentiality rubrics (bar colors). Error bars, 95% confidence intervals of the sampling proportion.

Extended Data Fig. 6 Heatmap of all systems identified by across-system essentiality.

Each colored box indicates a system (rows) that scored as essential conditioned on knockout of an independent gene outside the system (columns). Colors denote the relevant context (tissue type or multi-lineage). Abbreviated version shown in Fig. 3b.

Extended Data Fig. 7 Example synthetic-essential interactions prioritized by GDSC or DepMap.

(a–d) Dose response curves of AKT1 inhibitors GSK690693 (a), afuresertib (b), uprosertib (c), and ipatasertib (d). Solid black curve shows median dose response of all cell lines not disrupted in CHEK1 (GSK690693 n = 892, afuresertib n = 936, uprosertib n = 943, ipatasertib n = 936). For each drug, three example dose response curves of CHEK1-mutant cell lines are shown in red. (e) Genetic interaction scores measured in experiments disrupting MAP2K1, MRE11, or both genes in combination. Relevant interaction measurements for all cell lines, replicates, and time points are shown (points, n = 47). Interaction measurements for single genes are measured in the combinatorial gRNA format (see fig. S1) by targeting the AAVS safe-harbor locus in addition to the gene. Box plots depict the median and the bounds of the box depict the first and third quartiles. The whiskers of the box plot mark the minima and maxima, but no further than 1.5 times the interquartile range. Significance of difference in means tested by Mann-Whitney’s U test (*, P = 8.3 × 10−9 and P = 4.1 × 10−13 for comparing MAP2K1-AAVS and AAVS-MRE11 to MRE11-MAP2K1 respectively). (f) Violin plots showing the distribution of fitness changes in tumor cell lines due to knockout of a homologous recombination gene (x-axis). n represents the number of MAP2K1-disrupted or MAP2K1 wild-type cell lines profiled with CRISPR knockouts in DepMap. Blue and yellow distributions group cell lines by MAP2K1 mutation status. Box plots and asterisks follow convention of panel e.

Extended Data Fig. 8 Translation rate of synthetic-lethal interactions based on context.

The y-axis shows the fraction of interactions that translate to predictions of cell-line sensitivity in the DepMap screen of genome-wide CRISPR gene knockouts × cell lines4,55. The x-axis considers this fraction for each of the three tissue-specific networks and the pan-essential network. A gene-gene interaction is tested in DepMap if at least 25 cell lines have an alteration in one of the two genes. Suggestive interactions refer to those at a threshold of P < 0.05 by Student’s two-sided t-test without multiple hypothesis correction. Stringent interactions refer to a threshold of FDR < 30%. NS = No sample interactions met the threshold.

Extended Data Fig. 9 Supplemental exploration of PARP7 inhibition.

(a) Violin plots of PARP7 expression (left) and fitness effects of PARP7 CRISPR knockout (right) as measured in DepMap. Both show significant differences between KDM6A loss and wildtype status (P = 0.034 and 1.4 × 10−4 respectively) by two-sided Mann-Whitney U test. Above each plot are values of n providing the number of KDM6A-disrupted or KDM6A wild-type cell lines. Box plots depict the median, and the bounds of the box depict the first and third quartiles. Distributions truncated to show middle 90% of data. (b) Differences in mRNA expression associated with KDM5C loss, shown for interferon stimulated genes (left, ISGs) or PARPs (right). ISGs are significantly upregulated as a set (P = 5.4 × 10−7 by Mann-Whitney U test versus a background of all human genes). (c) As for panel a, but stratifying cell lines on the genetic alteration status of the genome-integrity cluster (loss of any genes in this cluster). Both PARP7 expression and fitness of PARP7 knockout are significantly affected by loss of genes in this cluster (P = 6.4 × 10−5 and 1.6 × 10−6 respectively). (d) Violin plots showing PARP7-inhibitor dose response (area under curve), which is significantly stratified by KDM6A (left, P = 1.5 × 10−3) or genome-integrity cluster (right, P = 8.2 × 10−4) alteration status. Significance determined by two-sided Mann-Whitney U test. Values of n represent the number of cell lines with and without alterations in the KDM6A (left) or genome-integrity cluster (right). Boxplots follow the same convention as panel A. (e–g) Dose-response curves from our PARP7 inhibitor PRISM screen, providing specific examples from tumor lines derived from pancreas (e), urinary tract (f), or colon (g). Median dose response curves for cell lines without alterations in the genome integrity cluster appear in solid black (error bars, standard errors of the mean; Pancreas n = 16, Urinary tract n = 22, Colorectal n = 30). Dose response is represented by the log2 fold change in cell abundances between PARP7 inhibitor treatment to DMSO control. Two example dose response curves from cell lines with alterations in this cluster are depicted in red. h, PARP7i dose responses for KDM5C unaltered (blue) or KDM5 loss (red) tumor cell lines, including all colorectal, urinary tract, or pancreas cell lines on the PRISM panel. Area under the dose response curve measurements were normalized to the median of those measured in unaltered cells. *, significant difference determined by two-sided Mann-Whitney U test. Box plots depict the median and the bounds of the box depict the first and third quartiles. The whiskers of the box plot mark the minima and maxima, but no further than 1.5 times the interquartile range. Measurements beyond the whiskers are not shown.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–12.

Supplementary Data 1

Read counts for CRISPR screens.

Supplementary Data 2

Interaction scores for CRISPR screens.

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

Fong, S.H., Kuenzi, B.M., Mattson, N.M. et al. A multilineage screen identifies actionable synthetic lethal interactions in human cancers. Nat Genet 57, 154–164 (2025). https://doi.org/10.1038/s41588-024-01971-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41588-024-01971-9

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer