• 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
     

  • 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
     

  • Prakash, R., Zhang, Y., Feng, W. & Jasin, M. Homologous recombination and human health: the roles of BRCA1, BRCA2, and associated proteins. Cold Spring Harb. Perspect. Biol. 7, a016600 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pettitt, S. J. et al. Clinical BRCA1/2 reversion analysis identifies hotspot mutations and predicted neoantigens associated with therapy resistance. Cancer Discov. 10, 1475–1488 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tutt, A. N. J. et al. Adjuvant olaparib for patients with BRCA1- or BRCA2-mutated breast cancer. N. Engl. J. Med. 384, 2394–2405 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Langelier, M.-F., Lin, X., Zha, S. & Pascal, J. M. Clinical PARP inhibitors allosterically induce PARP2 retention on DNA. Sci. Adv. 9, eadf7175 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • GTEx Consortium. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Article 

    Google Scholar
     

  • Jiang, L. et al. A quantitative proteome map of the human body. Cell 183, 269–283.e19 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

    Article 

    Google Scholar
     

  • Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, S. et al. A genomic mutational constraint map using variation in 76,156 human genomes. Nature 625, 92–100 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Pacini, C. et al. A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization. Cancer Cell 42, 301–316.e9 (2024). This article provides a recent systematic analysis of genetic dependencies in 930 cancer cell lines, linking biomarkers with potential therapeutic targets and analysing the prevalence of these biomarkers in patient populations.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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
     

  • Garnett, M. J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chen, E. M. et al. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature 568, 551–556 (2019). References 16 and 17 both show for the first time using CRISPR–Cas9 screens that WRN is synthetically lethal in MSI cancers.

    Article 

    Google Scholar
     

  • Picco, G. et al. Werner helicase is a synthetic-lethal vulnerability in mismatch repair-deficient colorectal cancer refractory to targeted therapies, chemotherapy and immunotherapy. Cancer Discov. 11, 1923–1937 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • US National Library of Medicine. ClinicalTrials.gov https://classic.clinicaltrials.gov/ct2/show/NCT06004245.

  • Adams, D. & McDermott, U. Editorial overview: cancer genomics: kill it. Kill it dead. Curr. Opin. Genet. Dev. 24, v–vi (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • O’Neil, N. J., Bailey, M. L. & Hieter, P. Synthetic lethality and cancer. Nat. Rev. Genet. 18, 613–623 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • 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
     

  • Huang, A., Garraway, L. A., Ashworth, A. & Weber, B. Synthetic lethality as an engine for cancer drug target discovery. Nat. Rev. Drug Discov. 19, 23–38 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Setton, J. et al. Synthetic lethality in cancer therapeutics: the next generation. Cancer Discov. 11, 1626–1635 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Schäffer, A. A., Chung, Y., Kammula, A. V., Ruppin, E. & Lee, J. S. A systematic analysis of the landscape of synthetic lethality-driven precision oncology. Med 5, 73–89.e9 (2024). This article provides a systematic analysis of SL therapies in clinical trials, revealing that most of these therapies involve genes in the DDR and that success rates for SL trials are generally higher than for non-SL therapies.

    Article 
    PubMed 

    Google Scholar
     

  • Ryan, C. J., Mehta, I., Kebabci, N. & Adams, D. J. Targeting synthetic lethal paralogs in cancer. Trends Cancer Res. 9, 397–409 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Ryan, C. J., Devakumar, L. P. S., Pettitt, S. J. & Lord, C. J. Complex synthetic lethality in cancer. Nat. Genet. 55, 2039–2048 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ross, J. S. et al. The HER-2 receptor and breast cancer: ten years of targeted anti-HER-2 therapy and personalized medicine. Oncologist 14, 320–368 (2009).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chapman, P. B. et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N. Engl. J. Med. 364, 2507–2516 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Weinstein, I. B. & Joe, A. Oncogene addiction. Cancer Res. 68, 3077–3080 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dang, C. V., Reddy, E. P., Shokat, K. M. & Soucek, L. Drugging the ‘undruggable’ cancer targets. Nat. Rev. Cancer 17, 502–508 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, J. et al. SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery. Database 2022, baac030 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gökbağ, B. et al. SLKB: synthetic lethality knowledge base. Nucleic Acids Res. 52, D1418–D1428 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Zatreanu, D. et al. Polθ inhibitors elicit BRCA-gene synthetic lethality and target PARP inhibitor resistance. Nat. Commun. 12, 3636 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gravells, P., Grant, E., Smith, K. M., James, D. I. & Bryant, H. E. Specific killing of DNA damage-response deficient cells with inhibitors of poly(ADP-ribose) glycohydrolase. DNA Repair. 52, 81–91 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yap, T. A. et al. First-in-human phase I trial of the oral first-in-class ubiquitin specific peptidase 1 (USP1) inhibitor KSQ-4279 (KSQi), given as single agent (SA) and in combination with olaparib (OLA) or carboplatin (CARBO) in patients (pts) with advanced solid tumors, enriched for deleterious homologous recombination repair (HRR) mutations. J. Clin. Oncol. 42(16_Suppl.), 3005 (2024).

    Article 

    Google Scholar
     

  • Simoneau, A. Abstract B054: TNG348, a selective USP1 inhibitor, shows strong preclinical combination activity with PARP inhibitors and other agents targeting DNA repair. Mol. Cancer Ther. 22, B054 (2023).

    Article 

    Google Scholar
     

  • 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
     

  • Downward, J. RAS synthetic lethal screens revisited: still seeking the elusive prize? Clin. Cancer Res. 21, 1802–1809 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ku, A. A. et al. Integration of multiple biological contexts reveals principles of synthetic lethality that affect reproducibility. Nat. Commun. 11, 2375 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, Y. et al. Critical role for transcriptional repressor Snail2 in transformation by oncogenic RAS in colorectal carcinoma cells. Oncogene 29, 4658–4670 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Luo, J. et al. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137, 835–848 (2009).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lord, C. J., Quinn, N. & Ryan, C. J. Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions. eLife 9, e58925 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Thompson, N. A. et al. Combinatorial CRISPR screen identifies fitness effects of gene paralogues. Nat. Commun. 12, 1302 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

  • Adam, S. et al. The CIP2A-TOPBP1 axis safeguards chromosome stability and is a synthetic lethal target for BRCA-mutated cancer. Nat. Cancer 2, 1357–1371 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Noordermeer, S. M. et al. The shieldin complex mediates 53BP1-dependent DNA repair. Nature 560, 117–121 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tsujino, T. et al. CRISPR screens reveal genetic determinants of PARP inhibitor sensitivity and resistance in prostate cancer. Nat. Commun. 14, 252 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bouwman, P. et al. 53BP1 loss rescues BRCA1 deficiency and is associated with triple-negative and BRCA-mutated breast cancers. Nat. Struct. Mol. Biol. 17, 688–695 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Harvey-Jones, E. et al. Longitudinal profiling identifies co-occurring BRCA1/2 reversions, TP53BP1, RIF1 and PAXIP1 mutations in PARP inhibitor-resistant advanced breast cancer. Ann. Oncol. 35, 364–380 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, Y. et al. The BRCA1-Δ11q alternative splice isoform bypasses germline mutations and promotes therapeutic resistance to PARP inhibition and cisplatin. Cancer Res. 76, 2778–2790 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kim, H. S. et al. Systematic identification of molecular subtype-selective vulnerabilities in non-small-cell lung cancer. Cell 155, 552–566 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Herken, B. W., Wong, G. T., Norman, T. M. & Gilbert, L. A. Environmental challenge rewires functional connections among human genes. Preprint at bioRxiv https://doi.org/10.1101/2023.08.09.552346 (2023).

  • Cheung, H. W. et al. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc. Natl Acad. Sci. USA 108, 12372–12377 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

  • Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017). Seminal paper introducing the concept of a Cancer Dependency Map.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

  • Spradlin, J. N., Zhang, E. & Nomura, D. K. Reimagining druggability using chemoproteomic platforms. Acc. Chem. Res. 54, 1801–1813 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ochoa, D. et al. The next-generation open targets platform: reimagined, redesigned, rebuilt. Nucleic Acids Res. 51, D1353–D1359 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. 9, eaag1166 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Helming, K. C. et al. ARID1B is a specific vulnerability in ARID1A-mutant cancers. Nat. Med. 20, 251–254 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chory, E. J. et al. Chemical inhibitors of a selective SWI/SNF function synergize with ATR inhibition in cancer cell killing. ACS Chem. Biol. 15, 1685–1696 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Békés, M., Langley, D. R. & Crews, C. M. PROTAC targeted protein degraders: the past is prologue. Nat. Rev. Drug Discov. 21, 181–200 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sasso, J. M. et al. Molecular glues: the adhesive connecting targeted protein degradation to the clinic. Biochemistry 62, 601–623 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Schneider, M. et al. The PROTACtable genome. Nat. Rev. Drug Discov. 20, 789–797 (2021). This Review provides a systematic effort to identify proteins that might be druggable using PROTAC approaches, identifying 1,336 promising candidates.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cantley, J. et al. Selective PROTAC-mediated degradation of SMARCA2 is efficacious in SMARCA4 mutant cancers. Nat. Commun. 13, 6814 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kofink, C. et al. A selective and orally bioavailable VHL-recruiting PROTAC achieves SMARCA2 degradation in vivo. Nat. Commun. 13, 5969 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tejwani, V. et al. PROTAC-mediated conditional degradation of the WRN helicase as a potential strategy for selective killing of cancer cells with microsatellite instability. Sci. Rep. 14, 20824 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chang, Y. et al. Development of an orally bioavailable CDK12/13 degrader and induction of synthetic lethality with AKT pathway inhibition. Cell Rep. Med. 5, 101752 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Muller, F. L. et al. Passenger deletions generate therapeutic vulnerabilities in cancer. Nature 488, 337–342 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yoshihama, Y. et al. Potent and selective PTDSS1 inhibitors induce collateral lethality in cancers with PTDSS2 deletion. Cancer Res. 82, 4031–4043 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • van der Lelij, P. et al. Synthetic lethality between the cohesin subunits STAG1 and STAG2 in diverse cancer contexts. eLife 6, e26980 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Benedetti, L., Cereda, M., Monteverde, L., Desai, N. & Ciccarelli, F. D. Synthetic lethal interaction between the tumour suppressor STAG2 and its paralog STAG1. Oncotarget 8, 37619–37632 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leonard, P. G. et al. SF2312 is a natural phosphonate inhibitor of enolase. Nat. Chem. Biol. 12, 1053–1058 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lin, Y.-H. et al. An enolase inhibitor for the targeted treatment of ENO1-deleted cancers. Nat. Metab. 2, 1413–1426 (2020). This article describes the development of a paralogue-specific inhibitor for ENO2 that exploits a collateral lethality associated with ENO1 deletion.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Herencia-Ropero, A. et al. The PARP1 selective inhibitor saruparib (AZD5305) elicits potent and durable antitumor activity in patient-derived BRCA1/2-associated cancer models. Genome Med. 16, 107 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Johannes, J. W. et al. Discovery of 5-{4-[(7-Ethyl-6-oxo-5,6-dihydro-1,5-naphthyridin-3-yl)methyl]piperazin-1-yl}-N-methylpyridine-2-carboxamide (AZD5305): a PARP1-DNA trapper with high selectivity for PARP1 over PARP2 and other PARPs. J. Med. Chem. 64, 14498–14512 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dvorak, V. et al. Paralog-dependent isogenic cell assay cascade generates highly selective SLC16A3 inhibitors. Cell Chem. Biol. 30, 953–964.e9 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gonçalves, E. et al. Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens. Mol. Syst. Biol. 16, e9405 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Vangamudi, B. et al. The SMARCA2/4 ATPase domain surpasses the bromodomain as a drug target in SWI/SNF-mutant cancers: insights from cDNA rescue and PFI-3 inhibitor studies. Cancer Res. 75, 3865–3878 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Murai, J. et al. Trapping of PARP1 and PARP2 by clinical PARP inhibitors. Cancer Res. 72, 5588–5599 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Krill-Burger, J. M. et al. Partial gene suppression improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal. Genome Biol. 24, 192 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Steckel, M. et al. Determination of synthetic lethal interactions in KRAS oncogene-dependent cancer cells reveals novel therapeutic targeting strategies. Cell Res. 22, 1227–1245 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Marjon, K. et al. MTAP deletions in cancer create vulnerability to targeting of the MAT2A/PRMT5/RIOK1 axis. Cell Rep. 15, 574–587 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Mavrakis, K. J. et al. Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5. Science 351, 1208–1213 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kryukov, G. V. et al. MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science 351, 1214–1218 (2016). References 84, 85 and 86 present a series of studies all demonstrating, using multiple models and gene perturbation approaches, that homozygous deletion of MTAP is associated with increased sensitivity to PRMT5.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cottrell, K. M. et al. MTA-cooperative PRMT5 inhibitors: mechanism switching through structure-based design. J. Med. Chem. 68, 4217–4236 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Smith, C. R. et al. Fragment-based discovery of MRTX1719, a synthetic lethal inhibitor of the PRMT5•MTA complex for the treatment of MTAP-deleted cancers. J. Med. Chem. 65, 1749–1766 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cottrell, K. M. et al. Discovery of TNG908: a selective, brain penetrant, MTA-cooperative PRMT5 inhibitor that is synthetically lethal with MTAP-deleted cancers. J. Med. Chem. 67, 6064–6080 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rodon, J. et al. First-in-human study of AMG 193, an MTA-cooperative PRMT5 inhibitor, in patients with MTAP-deleted solid tumors: results from phase I dose exploration. Ann. Oncol. 35, 1138–1147 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Engstrom, L. D. et al. MRTX1719 is an MTA-cooperative PRMT5 inhibitor that exhibits synthetic lethality in preclinical models and patients with MTAP-deleted cancer. Cancer Discov. 13, 2412–2431 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chang, L., Ruiz, P., Ito, T. & Sellers, W. R. Targeting pan-essential genes in cancer: challenges and opportunities. Cancer Cell 39, 466–479 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cacheiro, P. et al. Human and mouse essentiality screens as a resource for disease gene discovery. Nat. Commun. 11, 655 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Groza, T. et al. The International mouse phenotyping consortium: comprehensive knockout phenotyping underpinning the study of human disease. Nucleic Acids Res. 51, D1038–D1045 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Drost, J. & Clevers, H. Organoids in cancer research. Nat. Rev. Cancer 18, 407–418 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ringel, T. et al. Genome-scale CRISPR screening in human intestinal organoids identifies drivers of TGF-β resistance. Cell Stem Cell 26, 431–440.e8 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • LaFargue, C. J., Dal Molin, G. Z., Sood, A. K. & Coleman, R. L. Exploring and comparing adverse events between PARP inhibitors. Lancet Oncol. 20, e15–e28 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Simoneau, A. et al. Characterization of TNG348: a selective, allosteric USP1 inhibitor that synergizes with PARP inhibitors in tumors with homologous recombination deficiency. Mol. Cancer Ther. 24, 678–691 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Burris, H. A. et al. A phase I study of ATR inhibitor gartisertib (M4344) as a single agent and in combination with carboplatin in patients with advanced solid tumours. Br. J. Cancer 130, 1131–1140 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ashburn, T. T. & Thor, K. B. Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3, 673–683 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

  • Lappin, K. M. et al. Cancer-associated SF3B1 mutations confer a BRCA-like cellular phenotype and synthetic lethality to PARP inhibitors. Cancer Res. 82, 819–830 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Brenner, J. C. et al. PARP-1 inhibition as a targeted strategy to treat Ewing’s sarcoma. Cancer Res. 72, 1608–1613 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bajrami, I. et al. E-Cadherin/ROS1 inhibitor synthetic lethality in breast cancer. Cancer Discov. 8, 498–515 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shaw, A. T. et al. Crizotinib in ROS1-rearranged non-small-cell lung cancer. N. Engl. J. Med. 371, 1963–1971 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Han, K. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35, 463–474 (2017). This article provides an early example of rational selection of gene pairs for screening in a combinatorial CRISPR screen, focusing on known drug targets.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Najm, F. J. et al. Orthologous CRISPR-Cas9 enzymes for combinatorial genetic screens. Nat. Biotechnol. 36, 179–189 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dede, M., McLaughlin, M., Kim, E. & Hart, T. Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens. Genome Biol. 21, 262 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Esmaeili Anvar, N. et al. Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform. Nat. Commun. 15, 3577 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yan, W. X. et al. Cas13d is a compact RNA-targeting type VI CRISPR effector positively modulated by a WYL-domain-containing accessory protein. Mol. Cell 70, 327–339.e5 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Aregger, M. et al. Systematic mapping of genetic interactions for de novo fatty acid synthesis identifies C12orf49 as a regulator of lipid metabolism. Nat. Metab. 2, 499–513 (2020). This article reports the results from a CRISPR screen, using Cas12a, to identify synthetic lethality between human paralogue pairs across three cell lines.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • DeWeirdt, P. C. et al. Optimization of AsCas12a for combinatorial genetic screens in human cells. Nat. Biotechnol. 39, 94–104 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hsiung, C. C.-S. et al. Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations. Nat. Biotechnol. 43, 369–383 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Boettcher, M. et al. Dual gene activation and knockout screen reveals directional dependencies in genetic networks. Nat. Biotechnol. 36, 170–178 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

  • 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
     

  • Ryan, C. J. et al. Hierarchical modularity and the evolution of genetic interactomes across species. Mol. Cell 46, 691–704 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • De Kegel, B., Quinn, N., Thompson, N. A., Adams, D. J. & Ryan, C. J. Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines. Cell Syst. 12, 1144–1159.e6 (2021). This study describes the development of a machine learning approach to predict synthetic lethality between all human paralogue pairs and demonstrates that this model can predict the results of combinatorial CRISPR screens.

    Article 
    PubMed 

    Google Scholar
     

  • Schuldiner, M. et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507–519 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Collins, S. R. et al. Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature 446, 806–810 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Simpson, D., Ling, J., Jing, Y. & Adamson, B. Mapping the genetic interaction network of PARP inhibitor response. Preprint at bioRxiv https://doi.org/10.1101/2023.08.19.553986 (2023).

  • Fielden, J. et al. Comprehensive interrogation of synthetic lethality in the DNA damage response. Nature 640, 1093–1102 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wong, S. L. et al. Combining biological networks to predict genetic interactions. Proc. Natl Acad. Sci. USA 101, 15682–15687 (2004).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Picco, G. et al. Novel WRN helicase inhibitors selectively target microsatellite-unstable cancer cells. Cancer Discov. 14, 1457–1475 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Anzalone, A. V. et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576, 149–157 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dace, P. & Findlay, G. M. Reducing uncertainty in genetic testing with saturation genome editing. Med. Genet. 34, 297–304 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Waters, A. J. et al. Saturation genome editing of BAP1 functionally classifies somatic and germline variants. Nat. Genet. 56, 1434–1445 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Olvera-León, R. et al. High-resolution functional mapping of RAD51C by saturation genome editing. Cell 187, 5719–5734.e19 (2024).

    Article 
    PubMed 

    Google Scholar
     

  • Coelho, M. A. et al. Base editing screens define the genetic landscape of cancer drug resistance mechanisms. Nat. Genet. 56, 2479–2492 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cagiada, M. et al. Discovering functionally important sites in proteins. Nat. Commun. 14, 4175 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kelley, R. & Ideker, T. Systematic interpretation of genetic interactions using protein networks. Nat. Biotechnol. 23, 561–566 (2005).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tischler, J., Lehner, B. & Fraser, A. G. Evolutionary plasticity of genetic interaction networks. Nat. Genet. 40, 390–391 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • 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
     

  • Troyanskaya, O. G., Dolinski, K., Owen, A. B., Altman, R. B. & Botstein, D. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Natl Acad. Sci. USA 100, 8348–8353 (2003).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Srivas, R. et al. A network of conserved synthetic lethal interactions for exploration of precision cancer therapy. Mol. Cell 63, 514–525 (2016). This article provides a systematic analysis of SL interactions conserved from yeast to humans, demonstrating that this conservation is, at least to some extent, predictable using a machine learning approach.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nilsson, A. & Nielsen, J. Genome scale metabolic modeling of cancer. Metab. Eng. 43, 103–112 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zomorrodi, A. R. & Maranas, C. D. Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. BMC Syst. Biol. 4, 178 (2010).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Szappanos, B. et al. An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat. Genet. 43, 656–662 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • van Pel, D. M. et al. An evolutionarily conserved synthetic lethal interaction network identifies FEN1 as a broad-spectrum target for anticancer therapeutic development. PLoS Genet. 9, e1003254 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tosti, E. et al. Evolutionarily conserved genetic interactions with budding and fission yeast MutS identify orthologous relationships in mismatch repair-deficient cancer cells. Genome Med. 6, 68 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mengwasser, K. E. et al. Genetic screens reveal FEN1 and APEX2 as BRCA2 synthetic lethal targets. Mol. Cell 73, 885–899.e6 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Guo, E. et al. FEN1 endonuclease as a therapeutic target for human cancers with defects in homologous recombination. Proc. Natl Acad. Sci. USA 117, 19415–19424 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, L. et al. Chemical-genetic profiling of imidazo[1,2-a]pyridines and -pyrimidines reveals target pathways conserved between yeast and human cells. PLoS Genet. 4, e1000284 (2008).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shoemaker, R. H. The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 6, 813–823 (2006).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Trastulla, L., Noorbakhsh, J., Vazquez, F., McFarland, J. & Iorio, F. Computational estimation of quality and clinical relevance of cancer cell lines. Mol. Syst. Biol. 18, e11017 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Boehm, J. S. et al. Cancer research needs a better map. Nature 589, 514–516 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ben-David, U. et al. Genetic and transcriptional evolution alters cancer cell line drug response. Nature 560, 325–330 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jerby-Arnon, L. et al. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell 158, 1199–1209 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sinha, S. et al. Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data. Nat. Commun. 8, 15580 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, J. S. et al. Harnessing synthetic lethality to predict the response to cancer treatment. Nat. Commun. 9, 2546 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, J. S. et al. Synthetic lethality-mediated precision oncology via the tumor transcriptome. Cell 184, 2487–2502.e13 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Barrena, N., Valcárcel, L. V., Olaverri-Mendizabal, D., Apaolaza, I. & Planes, F. J. Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells. NPJ Syst. Biol. Appl. 9, 32 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, C. & Hua, Q. Applications of genome-scale metabolic models in biotechnology and systems medicine. Front. Physiol. 6, 413 (2015).

    PubMed 

    Google Scholar
     

  • Folger, O. et al. Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol. 7, 501 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Frezza, C. et al. Haem oxygenase is synthetically lethal with the tumour suppressor fumarate hydratase. Nature 477, 225–228 (2011). Reference 157 demonstrates the use of metabolic flux balance modelling to predict new SL targets in cancer, predicting haem oxygenase to be SL with the tumour suppressor fumarate hydratase. These results are subsequently confirmed in reference 158.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Robinson, J. L. et al. An atlas of human metabolism. Sci. Signal. 13, eaaz1482 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alzoubi, D., Desouki, A. A. & Lercher, M. J. Flux balance analysis with or without molecular crowding fails to predict two thirds of experimentally observed epistasis in yeast. Sci. Rep. 9, 11837 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kryazhimskiy, S. Emergence and propagation of epistasis in metabolic networks. eLife 10, e60200 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Seale, C., Tepeli, Y. & Gonçalves, J. P. Overcoming selection bias in synthetic lethality prediction. Bioinformatics 38, 4360–4368 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Feng, Y. et al. Benchmarking machine learning methods for synthetic lethality prediction in cancer. Nat. Commun. 15, 9058 (2024). This article describes a systematic effort to benchmark machine learning methods to predict synthetic lethality, demonstrating that performance is highly dependent on the source of ‘true-positive’ SLs.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

  • Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K. & Moult, J. Critical assessment of methods of protein structure prediction (CASP)-round XIV. Proteins 89, 1607–1617 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Guo, J., Liu, H. & Zheng, J. SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets. Nucleic Acids Res. 44, D1011–D1017 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bray, M.-A. et al. Cell painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Norman, T. M. et al. Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365, 786–793 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Way, G. P. et al. Predicting cell health phenotypes using image-based morphology profiling. Mol. Biol. Cell 32, 995–1005 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lotfollahi, M. et al. Predicting cellular responses to complex perturbations in high-throughput screens. Mol. Syst. Biol. 19, e11517 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ji, Y. et al. Scalable and universal prediction of cellular phenotypes. Preprint at bioRxiv https://doi.org/10.1101/2024.08.12.607533 (2024).

  • Regev, A. et al. The human cell atlas. eLife 6, 121202 (2017).

    Article 

    Google Scholar
     

  • Zhang, J. et al. Tahoe-100M: a Giga-scale single-cell perturbation atlas for context-dependent gene function and cellular modeling. Preprint at bioRxiv https://doi.org/10.1101/2025.02.20.639398 (2025).

  • Replogle, J. M. et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell 185, 2559–2575.e28 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang, A. C. et al. X-atlas/Orion: genome-wide Perturb-seq datasets via a scalable fix-cryopreserve platform for training dose-dependent biological foundation models. Preprint at bioRxiv https://doi.org/10.1101/2025.06.11.659105 (2025).

  • Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Caicedo, J. C. et al. Data-analysis strategies for image-based cell profiling. Nat. Methods 14, 849–863 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bock, C. et al. High-content CRISPR screening. Nat. Rev. Methods Prim. 2, 9 (2022).

    Article 

    Google Scholar
     

  • Heigwer, F. et al. A global genetic interaction network by single-cell imaging and machine learning. Cell Syst. 14, 346–362.e6 (2023). This article demonstrates in fruit fly cells how high-content imaging can be combined with genetic interaction screening to reveal interactions that impact phenotypes beyond growth.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tunyasuvunakool, K. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590–596 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Abramson, J. et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630, 493–500 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baek, M. et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373, 871–876 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baek, M. et al. Accurate prediction of protein-nucleic acid complexes using RoseTTAFoldNA. Nat. Methods 21, 117–121 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Cagiada, M., Thomasen, F. E., Ovchinnikov, S., Deane, C. M. & Lindorff-Larsen, K. AF2χ: predicting protein side-chain rotamer distributions with AlphaFold2. Preprint at bioRxiv https://doi.org/10.1101/2025.04.16.649219 (2025).

  • Borkakoti, N. & Thornton, J. M. AlphaFold2 protein structure prediction: implications for drug discovery. Curr. Opin. Struct. Biol. 78, 102526 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Terwilliger, T. C. et al. AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nat. Methods 21, 110–116 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038–1040 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Munson, B. P. et al. De novo generation of multi-target compounds using deep generative chemistry. Nat. Commun. 15, 3636 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tang, X. et al. A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation. Brief. Bioinform. 25, bbae338 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gangwal, A. & Lavecchia, A. Unleashing the power of generative AI in drug discovery. Drug Discov. Today 29, 103992 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Olivieri, M. et al. A genetic map of the response to DNA damage in human cells. Cell 182, 481–496.e21 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Davoodi, S., Daryaee, F., Chang, A., Walker, S. G. & Tonge, P. J. Correlating drug-target residence time and post-antibiotic effect: insight into target vulnerability. ACS Infect. Dis. 6, 629–636 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Price, S. et al. A suspension technique for efficient large-scale cancer organoid culturing and perturbation screens. Sci. Rep. 12, 5571 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Okines, A. et al. Abstract P2-07-24: results from the phase II study of ROS1 targeting with crizotinib in advanced E-cadherin negative lobular breast cancer (ROLo). Clin. Cancer Res. 31(12_Suppl.), P2-07-24 (2025).

    Article 

    Google Scholar
     

  • Ahn, D. H. et al. Onvansertib in combination with chemotherapy and bevacizumab in second-line treatment of KRAS-mutant metastatic colorectal cancer: a single-arm, phase II trial. J. Clin. Oncol. 43, 840–851 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wessels, H.-H. et al. Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq. Nat. Methods 20, 86–94 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chen, P. J. & Liu, D. R. Prime editing for precise and highly versatile genome manipulation. Nat. Rev. Genet. 24, 161–177 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ross, D. T. et al. Systematic variation in gene expression patterns in human cancer cell lines. Nat. Genet. 24, 227–235 (2000).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bairoch, A. The cellosaurus, a cell-line knowledge resource. J. Biomol. Tech. 29, 25–38 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Holbeck, S. L., Collins, J. M. & Doroshow, J. H. Analysis of food and drug administration-approved anticancer agents in the NCI60 panel of human tumor cell lines. Mol. Cancer Ther. 9, 1451–1460 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

  • Garcia-Alonso, L. et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer Res. 78, 769–780 (2018).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Li, J. et al. Characterization of human cancer cell lines reverse-phase protein arrays. Cancer Cell 31, 225–239 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Roumeliotis, T. I. et al. Genomic determinants of protein abundance variation in colorectal cancer cells. Cell Rep. 20, 2201–2214 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nusinow, D. P. et al. Quantitative proteomics of the cancer cell line encyclopedia. Cell 180, 387–402.e16 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, H. et al. The landscape of cancer cell line metabolism. Nat. Med. 25, 850–860 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Shorthouse, D., Bradley, J., Critchlow, S. E., Bendtsen, C. & Hall, B. A. Heterogeneity of the cancer cell line metabolic landscape. Mol. Syst. Biol. 18, e11006 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cherkaoui, S. et al. A functional analysis of 180 cancer cell lines reveals conserved intrinsic metabolic programs. Mol. Syst. Biol. 18, e11033 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Seashore-Ludlow, B. et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 5, 1210–1223 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rees, M. G. et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat. Chem. Biol. 12, 109–116 (2016).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Jaaks, P. et al. Effective drug combinations in breast, colon and pancreatic cancer cells. Nature 603, 166–173 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bashi, A. C. et al. Large-scale pan-cancer cell line screening identifies actionable and effective drug combinations. Cancer Discov. 14, 846–865 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Whitehurst, A. W. et al. Synthetic lethal screen identification of chemosensitizer loci in cancer cells. Nature 446, 815–819 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Turner, N. C. et al. A synthetic lethal siRNA screen identifying genes mediating sensitivity to a PARP inhibitor. EMBO J. 27, 1368–1377 (2008).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lord, C. J., McDonald, S., Swift, S., Turner, N. C. & Ashworth, A. A high-throughput RNA interference screen for DNA repair determinants of PARP inhibitor sensitivity. DNA Repair 7, 2010–2019 (2008).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Vizeacoumar, F. J. et al. A negative genetic interaction map in isogenic cancer cell lines reveals cancer cell vulnerabilities. Mol. Syst. Biol. 9, 696 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • McDonald, E. R. 3rd et al. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell 170, 577–592.e10 (2017).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

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

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Shen, J. P. et al. Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nat. Methods 14, 573–576 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903.e15 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 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
     

  • De Kegel, B. & Ryan, C. J. Paralog buffering contributes to the variable essentiality of genes in cancer cell lines. PLoS Genet. 15, e1008466 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sharma, S., Dincer, C., Weidemüller, P., Wright, G. J. & Petsalaki, E. CEN-tools: an integrative platform to identify the contexts of essential genes. Mol. Syst. Biol. 16, e9698 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Madhukar, N. S., Elemento, O. & Pandey, G. Prediction of genetic interactions using machine learning and network properties. Front. Bioeng. Biotechnol. 3, 172 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Conde-Pueyo, N., Munteanu, A., Solé, R. V. & Rodríguez-Caso, C. Human synthetic lethal inference as potential anti-cancer target gene detection. BMC Syst. Biol. 3, 116 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lu, X., Kensche, P. R., Huynen, M. A. & Notebaart, R. A. Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets. Nat. Commun. 4, 2124 (2013).

    Article 
    PubMed 

    Google Scholar
     

  • Kim, J. W. et al. Characterizing genomic alterations in cancer by complementary functional associations. Nat. Biotechnol. 34, 539–546 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Apaolaza, I. et al. An in-silico approach to predict and exploit synthetic lethality in cancer metabolism. Nat. Commun. 8, 459 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Park, S. & Lehner, B. Cancer type-dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types. Mol. Syst. Biol. 11, 824 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liany, H., Jeyasekharan, A. & Rajan, V. Predicting synthetic lethal interactions using heterogeneous data sources. Bioinformatics 36, 2209–2216 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Liu, X. et al. PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers. Bioinformatics 38, ii106–ii112 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Srivatsa, S. et al. Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens. Nat. Commun. 13, 7748 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Menden, M. P. et al. Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS ONE 8, e61318 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dempster, J. M. et al. Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets. Nat. Commun. 10, 5817 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cancer Cell Line Encyclopedia Consortium & Genomics of Drug Sensitivity in Cancer Consortium.Pharmacogenomic agreement between two cancer cell line data sets. Nature 528, 84–87 (2015).

    Article 

    Google Scholar
     

  • Cai, Z. et al. Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning. Nat. Commun. 15, 10390 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Najgebauer, H. et al. CELLector: genomics-guided selection of cancer in vitro models. Cell Syst. 10, 424–432.e6 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dharia, N. V. et al. A first-generation pediatric cancer dependency map. Nat. Genet. 53, 529–538 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bonneville, R. et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis. Oncol. 2017, PO.17.00073 (2017).

    PubMed 

    Google Scholar
     

  • Lieb, S. et al. Werner syndrome helicase is a selective vulnerability of microsatellite instability-high tumor cells. eLife 8, e43333 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kategaya, L., Perumal, S. K., Hager, J. H. & Belmont, L. D. Werner syndrome helicase is required for the survival of cancer cells with microsatellite instability. iScience 13, 488–497 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • van Wietmarschen, N. et al. Repeat expansions confer WRN dependence in microsatellite-unstable cancers. Nature 586, 292–298 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stover, E. H., Fuh, K., Konstantinopoulos, P. A., Matulonis, U. A. & Liu, J. F. Clinical assays for assessment of homologous recombination DNA repair deficiency. Gynecol. Oncol. 159, 887–898 (2020).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Ferretti, S. et al. Discovery of WRN inhibitor HRO761 with synthetic lethality in MSI cancers. Nature 629, 443–449 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kikuchi, S. et al. Abstract ND11: chemoproteomic-enabled discovery of VVD-214, a synthetic lethal allosteric inhibitor of WRN helicase. Cancer Res. 84(7_Suppl.), ND11 (2024).

    Article 

    Google Scholar