
Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).
Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).
Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).
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).
Roguev, A. et al. Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322, 405–410 (2008).
Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 1385–1389 (2010).
Horn, T. et al. Mapping of signaling networks through synthetic genetic interaction analysis by RNAi. Nat. Methods 8, 341–346 (2011).
Frost, A. et al. Functional repurposing revealed by comparing S. pombe and S. cerevisiae genetic interactions. Cell 149, 1339–1352 (2012).
Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016).
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).
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).
Wong, A. S. L. et al. Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM. Proc. Natl Acad. Sci. USA 113, 2544–2549 (2016).
Du, D. et al. Genetic interaction mapping in mammalian cells using CRISPR interference. Nat. Methods 14, 577–580 (2017).
Shen, J. P. et al. Combinatorial CRISPR–Cas9 screens for de novo mapping of genetic interactions. Nat. Methods 14, 573 (2017).
Han, K. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35, 463–474 (2017).
Horlbeck, M. A. et al. Mapping the genetic landscape of human cells. Cell 174, 953–967.e22 (2018).
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).
Najm, F. J. et al. Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens. Nat. Biotechnol. 36, 179–189 (2018).
Zamanighomi, M. et al. GEMINI: a variational Bayesian approach to identify genetic interactions from combinatorial CRISPR screens. Genome Biol. 20, 137 (2019).
Kelly, M. R. et al. Combined proteomic and genetic interaction mapping reveals new RAS effector pathways and susceptibilities. Cancer Discov. 10, 1950–1967 (2020).
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).
Ward, H. N. et al. Analysis of combinatorial CRISPR screens with the Orthrus scoring pipeline. Nat. Protoc. 16, 4766–4798 (2021).
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).
Friend, S. H. & Oliff, A. Emerging uses for genomic information in drug discovery. N. Engl. J. Med. 338, 125–126 (1998).
Zhao, D. & DePinho, R. A. Synthetic essentiality: targeting tumor suppressor deficiencies in cancer. Bioessays https://doi.org/10.1002/bies.201700076 (2017).
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).
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).
Ashworth, A. & Lord, C. J. Synthetic lethal therapies for cancer: what’s next after PARP inhibitors? Nat. Rev. Clin. Oncol. 15, 564–576 (2018).
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).
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).
Ryan, C. J., Bajrami, I. & Lord, C. J. Synthetic lethality and cancer—penetrance as the major barrier. Trends Cancer Res. 4, 671–683 (2018).
Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).
Zheng, F. et al. Interpretation of cancer mutations using a multiscale map of protein systems. Science 374, eabf3067 (2021).
Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).
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).
Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).
Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 174, 1034–1035 (2018).
Tate, J. G. et al. COSMIC: the catalogue of somatic mutations In cancer. Nucleic Acids Res. 47, D941–D947 (2019).
Mitsopoulos, C. et al. canSAR: update to the cancer translational research and drug discovery knowledgebase. Nucleic Acids Res. 49, D1074–D1082 (2021).
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).
Oughtred, R. et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 47, D529–D541 (2019).
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).
O’Brien, V. & Brown, R. Signalling cell cycle arrest and cell death through the MMR system. Carcinogenesis 27, 682–692 (2006).
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).
Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005).
Lim, K. S. et al. USP1 is required for replication fork protection in BRCA1-deficient tumors. Mol. Cell 72, 925–941.e4 (2018).
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).
Martens, M. et al. WikiPathways: connecting communities. Nucleic Acids Res. 49, D613–D621 (2021).
Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 48, D498–D503 (2020).
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).
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).
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).
Turner, N. C. et al. Capivasertib in hormone receptor-positive advanced breast cancer. N. Engl. J. Med. 388, 2058–2070 (2023).
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).
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).
Do, K. & Chen, A. P. Molecular pathways: targeting PARP in cancer treatment. Clin. Cancer Res. 19, 977–984 (2013).
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).
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).
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).
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).
Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).
Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).
Corsello, S. M. et al. Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling. Nat. Cancer 1, 235–248 (2020).
Hopfner, K.-P. & Hornung, V. Molecular mechanisms and cellular functions of cGAS–STING signalling. Nat. Rev. Mol. Cell Biol. 21, 501–521 (2020).
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).
Chan, E. M. et al. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature 568, 551–556 (2019).
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).
Haar, J. v.d. et al. Identifying epistasis in cancer genomes: a delicate affair. Cell 177, 1375–1383 (2019).
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).
Tischkowitz, M. & Xia, B. PALB2/FANCN: recombining cancer and Fanconi anemia. Cancer Res. 70, 7353–7359 (2010).
Carbone, M. et al. BAP1 and cancer. Nat. Rev. Cancer 13, 153–159 (2013).
Lord, C. J. & Ashworth, A. BRCAness revisited. Nat. Rev. Cancer 16, 110–120 (2016).
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).
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).
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).
Wu, L. et al. KDM5 histone demethylases repress immune response via suppression of STING. PLoS Biol. 16, e2006134 (2018).
Sangfelt, O., Erickson, S. & Grander, D. Mechanisms of interferon-induced cell cycle arrest. Front. Biosci. 5, D479–D487 (2000).
Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).
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).
Calandrini, C. et al. Organoid-based drug screening reveals neddylation as therapeutic target for malignant rhabdoid tumors. Cell Rep. 36, 109568 (2021).
André, F. et al. AACR Project GENIE: powering precision medicine through an international consortium. Cancer Discov. 7, 818–831 (2017).
Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).
Langmead, B., Wilks, C., Antonescu, V. & Charles, R. Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics 35, 421–432 (2019).
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).
Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).
Collins, S. R., Roguev, A. & Krogan, N. J. Quantitative genetic interaction mapping using the E-MAP approach. Methods Enzymol. 470, 205–231 (2010).
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).
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).
- SEO Powered Content & PR Distribution. Get Amplified Today.
- PlatoData.Network Vertical Generative Ai. Empower Yourself. Access Here.
- PlatoAiStream. Web3 Intelligence. Knowledge Amplified. Access Here.
- PlatoESG. Carbon, CleanTech, Energy, Environment, Solar, Waste Management. Access Here.
- PlatoHealth. Biotech and Clinical Trials Intelligence. Access Here.
- Source: https://www.nature.com/articles/s41588-024-01971-9