Cancer ‘genetic dependencies’ — genes whose products are essential for cancer cell fitness — are promising targets for therapeutic development. However, recent evidence has cast doubt on the validity of several putative dependencies that are currently being targeted in cancer clinical trials, underscoring the challenges inherent in correctly identifying cancer-essential genes. Here we review several common techniques and platforms for discovering and characterizing cancer dependencies. We discuss the strengths and drawbacks of different gene-perturbation approaches, and we highlight the use of poorly validated genetic and pharmacological agents as a common cause of target misidentification. A careful consideration of the limitations of current technologies and cancer models will improve our ability to correctly uncover cancer genetic dependencies and will facilitate the development of improved therapeutic agents.
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Luo, J., Solimini, N. L. & Elledge, S. J. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136, 823–837 (2009).
Ben-Neriah, Y., Daley, G. Q., Mes-Masson, A. M., Witte, O. N. & Baltimore, D. The chronic myelogenous leukemia-specific P210 protein is the product of the bcr/abl hybrid gene. Science 233, 212–214 (1986).
Nowell, P. C. & Hungerford, D. A. Chromosome studies on normal and leukemic human leukocytes. J. Natl Cancer Inst. 25, 85–109 (1960).
Lugo, T. G., Pendergast, A. M., Muller, A. J. & Witte, O. N. Tyrosine kinase activity and transformation potency of bcr-abl oncogene products. Science 247, 1079–1082 (1990).
Huettner, C. S., Zhang, P., Van Etten, R. A. & Tenen, D. G. Reversibility of acute B-cell leukaemia induced by BCR-ABL1. Nat. Genet. 24, 57–60 (2000).
Druker, B. J. & Lydon, N. B. Lessons learned from the development of an Abl tyrosine kinase inhibitor for chronic myelogenous leukemia. J. Clin. Invest. 105, 3–7 (2000).
Druker, B. J. et al. Efficacy and safety of a specific inhibitor of the BCR-ABL tyrosine kinase in chronic myeloid leukemia. N. Engl. J. Med. 344, 1031–1037 (2001).
Gorre, M. E. et al. Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification. Science 293, 876–880 (2001). This landmark article reports that patients with CML who relapsed following treatment with imatinib developed point mutations in BCR–ABL that restored kinase activity, thereby proving that the efficacy of imatinib resulted from an ‘on-target’ effect.
Roumiantsev, S. et al. Clinical resistance to the kinase inhibitor STI-571 in chronic myeloid leukemia by mutation of Tyr-253 in the Abl kinase domain P-loop. Proc. Natl Acad. Sci. USA 99, 10700–10705 (2002).
Azam, M., Latek, R. R. & Daley, G. Q. Mechanisms of autoinhibition and STI-571/Imatinib resistance revealed by mutagenesis of BCR-ABL. Cell 112, 831–843 (2003).
Druker, B. J. et al. Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N. Engl. J. Med. 355, 2408–2417 (2006).
Kantarjian, H. et al. Improved survival in chronic myeloid leukemia since the introduction of imatinib therapy: a single-institution historical experience. Blood 119, 1981–1987 (2012).
Luke, J. J. & Hodi, F. S. Vemurafenib and BRAF inhibition: a new class of treatment for metastatic melanoma. Clin. Cancer Res. 18, 9–14 (2012).
Seshacharyulu, P. et al. Targeting the EGFR signaling pathway in cancer therapy. Expert. Opin. Ther. Targets 16, 15–31 (2012).
Wong, C. H., Siah, K. W. & Lo, A. W. Estimation of clinical trial success rates and related parameters. Biostatistics 20, 273–286 (2019).
Hay, M., Thomas, D. W., Craighead, J. L., Economides, C. & Rosenthal, J. Clinical development success rates for investigational drugs. Nat. Biotechnol. 32, 40–51 (2014).
Waring, M. J. et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nat. Rev. Drug. Discov. 14, 475–486 (2015).
Settleman, J., Sawyers, C. L. & Hunter, T. Challenges in validating candidate therapeutic targets in cancer. eLife 7, e32402 (2018).
Giuliano, C. J., Lin, A., Smith, J. C., Palladino, A. C. & Sheltzer, J. M. MELK expression correlates with tumor mitotic activity but is not required for cancer growth. eLife 7, e32838 (2018).
Huang, H.-T. et al. MELK is not necessary for the proliferation of basal-like breast cancer cells. eLife 6, e26693 (2017).
Ji, W. et al. OTSSP167 abrogates mitotic checkpoint through inhibiting multiple mitotic kinases. PLoS One 11, e0153518 (2016).
Lin, A., Giuliano, C. J., Sayles, N. M. & Sheltzer, J. M. CRISPR/Cas9 mutagenesis invalidates a putative cancer dependency targeted in on-going clinical trials. eLife 6, e24179 (2017). To our knowledge, this study represents the first demonstration that a protein targeted in clinical trials is fully dispensable for cancer cell fitness, and that a small molecule studied in clinical trials kills specifically through an off-target mechanism.
Lin, A. et al. Off-target toxicity is a common mechanism of action of cancer drugs undergoing clinical trials. Sci. Transl Med. 11, eaaw8412 (2019). This study uses CRISPR-mediated gene editing to establish that many cancer drugs studied in clinical trials have been designed to inhibit non-essential targets and kill cancer cells only through off-target effects.
Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).
Bradley, C. A. Targeted therapies: understanding tumour drug addiction. Nat. Rev. Cancer 17, 634–635 (2017).
Feng, F. Y. & Gilbert, L. A. Lethal clues to cancer-cell vulnerability. Nature 568, 463–464 (2019).
Asghar, U., Witkiewicz, A. K., Turner, N. C. & Knudsen, E. S. The history and future of targeting cyclin-dependent kinases in cancer therapy. Nat. Rev. Drug. Discov. 14, 130–146 (2015).
Paplomata, E., Nahta, R. & O’Regan, R. M. Systemic therapy for early-stage HER2-positive breast cancers: time for a less-is-more approach? Cancer 121, 517–526 (2015).
Girdler, F. et al. Validating Aurora B as an anti-cancer drug target. J. Cell. Sci. 119, 3664–3675 (2006).
Settleman, J. Oncogene addiction. Curr. Biol. 22, R43–R44 (2012).
Felsher, D. W. & Bishop, J. M. Reversible tumorigenesis by MYC in hematopoietic lineages. Mol. Cell 4, 199–207 (1999).
Nagel, R., Semenova, E. A. & Berns, A. Drugging the addict: non-oncogene addiction as a target for cancer therapy. EMBO Rep. 17, 1516–1531 (2016).
Boutros, M. & Ahringer, J. The art and design of genetic screens: RNA interference. Nat. Rev. Genet. 9, 554–566 (2008).
Agrawal, N. et al. RNA interference: biology, mechanism, and applications. Microbiol. Mol. Biol. Rev. 67, 657–685 (2003).
Paddison, P. J., Caudy, A. A., Bernstein, E., Hannon, G. J. & Conklin, D. S. Short hairpin RNAs (shRNAs) induce sequence-specific silencing in mammalian cells. Genes Dev. 16, 948–958 (2002).
Paddison, P. J. et al. A resource for large-scale RNA-interference-based screens in mammals. Nature 428, 427–431 (2004).
Silva, J. M. et al. Profiling essential genes in human mammary cells by multiplex RNAi screening. Science 319, 617–620 (2008).
Dominguez, A. A., Lim, W. A. & Qi, L. S. Beyond editing: repurposing CRISPR–Cas9 for precision genome regulation and interrogation. Nat. Rev. Mol. Cell Biol. 17, 5–15 (2016).
Hsu, P. D., Lander, E. S. & Zhang, F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 1262–1278 (2014).
Adli, M. The CRISPR tool kit for genome editing and beyond. Nat. Commun. 9, 1–13 (2018).
Hart, T., Brown, K. R., Sircoulomb, F., Rottapel, R. & Moffat, J. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol. Syst. Biol. 10, 733 (2014).
Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015). This elegant study conducts multiple genome-wide CRISPR screens, introduces a Bayesian framework to analyse them and substantially expands the number of known essential genes in human cells.
Morgens, D. W., Deans, R. M., Li, A. & Bassik, M. C. Systematic comparison of CRISPR/Cas9 and RNAi screens for essential genes. Nat. Biotechnol. 34, 634–636 (2016).
Vu, V. et al. Natural variation in gene expression modulates the severity of mutant phenotypes. Cell 162, 391–402 (2015).
Chang, H. H. Y., Pannunzio, N. R., Adachi, N. & Lieber, M. R. Non-homologous DNA end joining and alternative pathways to double-strand break repair. Nat. Rev. Mol. Cell Biol. 18, 495–506 (2017).
Ferreira da Silva, J. et al. Genome-scale CRISPR screens are efficient in non-homologous end-joining deficient cells. Sci. Rep. 9, 1–10 (2019).
Kass, E. M. & Jasin, M. Collaboration and competition between DNA double-strand break repair pathways. FEBS Lett. 584, 3703–3708 (2010).
Aguirre, A. J. et al. Genomic copy number dictates a gene-independent cell response to CRISPR-Cas9 targeting. Cancer Discov. 6, 914–929 (2016).
Munoz, D. M. et al. CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Discov. 6, 900–913 (2016).
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).
Haapaniemi, E., Botla, S., Persson, J., Schmierer, B. & Taipale, J. CRISPR–Cas9 genome editing induces a p53-mediated DNA damage response. Nat. Med. 24, 927 (2018).
Enache, O. M. et al. Cas9 activates the p53 pathway and selects for p53-inactivating mutations. Nat. Genet. https://doi.org/10.1038/s41588-020-0623-4 (2020).
Brown, K. R., Mair, B., Soste, M. & Moffat, J. CRISPR screens are feasible in TP53 wild-type cells. Mol. Syst. Biol. 15, e8679 (2019).
Haapaniemi, E., Botla, S., Persson, J., Schmierer, B. & Taipale, J. Reply to “CRISPR screens are feasible in TP53 wild-type cells”. Mol Syst Biol 15, e9059 (2019).
Jackson, A. L. & Linsley, P. S. Recognizing and avoiding siRNA off-target effects for target identification and therapeutic application. Nat. Rev. Drug Discov. 9, 57–67 (2010).
Krueger, U. et al. Insights into effective RNAi gained from large-scale siRNA validation screening. Oligonucleotides 17, 237–250 (2007).
Sigoillot, F. D. et al. A bioinformatics method identifies prominent off-targeted transcripts in RNAi screens. Nat. Methods 9, 363–366 (2012).
Aagaard, L. & Rossi, J. J. RNAi therapeutics: principles, prospects and challenges. Adv. Drug Deliv. Rev. 59, 75–86 (2007).
Bartel, D. P. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233 (2009).
Kamola, P. J., Nakano, Y., Takahashi, T., Wilson, P. A. & Ui-Tei, K. The siRNA non-seed region and its target sequences are auxiliary determinants of off-target effects. PLoS Comput. Biol. 11, e1004656 (2015).
Putzbach, W. et al. Many si/shRNAs can kill cancer cells by targeting multiple survival genes through an off-target mechanism. eLife 6, e29702 (2017).
Putzbach, W. et al. CD95/Fas ligand mRNA is toxic to cells. eLife 7, e38621 (2018).
Grimm, D. et al. Fatality in mice due to oversaturation of cellular microRNA/short hairpin RNA pathways. Nature 441, 537–541 (2006).
Khan, A. A. et al. Transfection of small RNAs globally perturbs gene regulation by endogenous microRNAs. Nat. Biotechnol. 27, 549–555 (2009).
Scholl, C. et al. Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells. Cell 137, 821–834 (2009).
Luo, T. et al. STK33 kinase inhibitor BRD-8899 has no effect on KRAS-dependent cancer cell viability. Proc. Natl Acad. Sci. USA 109, 2860–2865 (2012).
Weïwer, M. et al. A potent and selective quinoxalinone-based stk33 inhibitor does not show synthetic lethality in KRAS-dependent cells. ACS Med. Chem. Lett. 3, 1034–1038 (2012).
Babij, C. et al. STK33 kinase activity is nonessential in KRAS-dependent cancer cells. Cancer Res. 71, 5818–5826 (2011).
Fu, Y. et al. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat. Biotechnol. 31, 822–826 (2013).
Pattanayak, V. et al. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat. Biotechnol. 31, 839–843 (2013).
Boettcher, M. & McManus, M. T. Choosing the right tool for the job: RNAi, TALEN or CRISPR. Mol. Cell 58, 575–585 (2015).
Shalem, O., Sanjana, N. E. & Zhang, F. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 16, 299–311 (2015).
Smith, I. et al. Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the connectivity map. PLoS Biol. 15, e2003213 (2017).
Kleinstiver, B. P. et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016).
Popp, M. W. & Maquat, L. E. Leveraging rules of nonsense-mediated mRNA decay for genome engineering and personalized medicine. Cell 165, 1319–1322 (2016).
Chen, D. et al. CRISPR/Cas9-mediated genome editing induces exon skipping by complete or stochastic altering splicing in the migratory locust. BMC Biotechnol. 18, 60 (2018).
Tang, J.-X. et al. CRISPR/Cas9-mediated genome editing induces gene knockdown by altering the pre-mRNA splicing in mice. BMC Biotechnol. https://doi.org/10.1186/s12896-018-0472-8 (2018).
Tuladhar, R. et al. CRISPR/Cas9-based mutagenesis frequently provokes on-target mRNA misregulation. bioRxiv https://doi.org/10.1101/583138 (2019).
Smits, A. H. et al. Biological plasticity rescues target activity in CRISPR knock outs. Nat. Methods 16, 1087–1093 (2019). Tuladhar et al. (2020) and Smits et al. (2019) reveal that some CRISPR manipulations fail to generate true gene knockouts, as target expression is rescued through alternative transcriptional regulation.
El-Brolosy, M. A. et al. Genetic compensation triggered by mutant mRNA degradation. Nature 568, 193–197 (2019).
Ajina, R. et al. SpCas9-expression by tumor cells can cause T cell-dependent tumor rejection in immunocompetent mice. Oncoimmunology 8, e1577127 (2019).
Crudele, J. M. & Chamberlain, J. S. Cas9 immunity creates challenges for CRISPR gene editing therapies. Nat. Commun. 9, 3497 (2018).
Mullenders, J. & Bernards, R. Loss-of-function genetic screens as a tool to improve the diagnosis and treatment of cancer. Oncogene 28, 4409–4420 (2009).
Gupta, S., Schoer, R. A., Egan, J. E., Hannon, G. J. & Mittal, V. Inducible, reversible, and stable RNA interference in mammalian cells. Proc. Natl Acad. Sci. USA 101, 1927–1932 (2004).
Giuliano, C. J., Lin, A., Girish, V. & Sheltzer, J. M. Generating single cell–derived knockout clones in mammalian cells with CRISPR/Cas9. Curr. Protoc. Mol. Biol. 128, e100 (2019).
Zou, X. et al. Validating the concept of mutational signatures with isogenic cell models. Nat. Commun. 9, 1744 (2018).
Depetter, Y. et al. Selective pharmacological inhibitors of HDAC6 reveal biochemical activity but functional tolerance in cancer models. Int. J. Cancer 145, 735–747 (2019).
Kasap, C., Elemento, O. & Kapoor, T. M. DrugTargetSeqR: a genomics- and CRISPR-Cas9-based method to analyze drug targets. Nat. Chem. Biol. 10, 626–628 (2014).
Evers, B. et al. CRISPR knockout screening outperforms shRNA and CRISPRi in identifying essential genes. Nat. Biotechnol. 34, 631–633 (2016).
Knott, S. R. V. et al. A computational algorithm to predict shRNA potency. Mol. Cell 56, 796–807 (2014).
Sanson, K. R. et al. Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).
Shi, J. et al. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat. Biotechnol. 33, 661–667 (2015). This article demonstrates how domain-specific CRISPR screening can be used to identify druggable cancer dependencies.
Stegmeier, F., Hu, G., Rickles, R. J., Hannon, G. J. & Elledge, S. J. A lentiviral microRNA-based system for single-copy polymerase II-regulated RNA interference in mammalian cells. Proc. Natl Acad. Sci. USA 102, 13212–13217 (2005).
Ma, H. T., On, K. F., Tsang, Y. H. & Poon, R. Y. C. An inducible system for expression and validation of the specificity of short hairpin RNA in mammalian cells. Nucleic Acids Res. 35, e22 (2007).
Buehler, E., Chen, Y.-C. & Martin, S. C911: a bench-level control for sequence specific siRNA off-target effects. PLoS One 7, e51942 (2012).
Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).
Larson, M. H. et al. CRISPR interference (CRISPRi) for sequence-specific control of gene expression. Nat. Protoc. 8, 2180–2196 (2013).
Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).
Liu, S. J. et al. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 355, eaah7111 (2017).
Zetsche, B. et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell 163, 759–771 (2015).
Sanson, K. R. et al. Optimization of AsCas12a for combinatorial genetic screens in human cells. bioRxiv https://doi.org/10.1101/747170 (2019).
Zetsche, B. et al. Multiplex gene editing by CRISPR–Cpf1 using a single crRNA array. Nat. Biotechnol. 35, 31–34 (2017).
Dede, M., Kim, E. & Hart, T. Biases and blind-spots in genome-wide CRISPR knockout screens. bioRxiv https://doi.org/10.1101/2020.01.16.909606 (2020).
Konermann, S. et al. Transcriptome engineering with RNA-targeting type VI-D CRISPR effectors. Cell 173, 665–676.e14 (2018). This article describes a new CRISPR system — CasRx/Cas13d — that can be used to eliminate target mRNA molecules with greater specificity than RNAi-mediated approaches.
Wessels, H.-H. et al. Massively parallel Cas13 screens reveal principles for guide RNA design. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0456-9 (2020).
Bondeson, D. P. et al. Catalytic in vivo protein knockdown by small-molecule PROTACs. Nat. Chem. Biol. 11, 611–617 (2015).
Zhang, X. et al. Protein targeting chimeric molecules specific for bromodomain and extra-terminal motif family proteins are active against pre-clinical models of multiple myeloma. Leukemia 32, 2224–2239 (2018).
Pei, H., Peng, Y., Zhao, Q. & Chen, Y. Small molecule PROTACs: an emerging technology for targeted therapy in drug discovery. RSC Adv. 9, 16967–16976 (2019).
Wilmington, S. R. & Matouschek, A. An Inducible system for rapid degradation of specific cellular proteins using proteasome adaptors. PLoS One 11, e0152679 (2016).
Gillet, J.-P. et al. Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. Proc. Natl Acad. Sci. USA 108, 18708–18713 (2011).
Hidalgo, M. et al. Patient derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 4, 998–1013 (2014).
Lampreht Tratar, U., Horvat, S. & Cemazar, M. Transgenic mouse models in cancer research. Front. Oncol. 8, 268 (2018).
Ertel, A., Verghese, A., Byers, S. W., Ochs, M. & Tozeren, A. Pathway-specific differences between tumor cell lines and normal and tumor tissue cells. Mol. Cancer 5, 55 (2006).
Goodspeed, A., Heiser, L. M., Gray, J. W. & Costello, J. C. Tumor-derived cell lines as molecular models of cancer pharmacogenomics. Mol. Cancer Res. 14, 3–13 (2016).
Friberg, S. & Mattson, S. On the growth rates of human malignant tumors: implications for medical decision making. J. Surg. Oncol. 65, 284–297 (1997).
Tubiana, M. Tumor cell proliferation kinetics and tumor growth rate. Acta Oncol. 28, 113–121 (1989).
Ben-David, U. et al. Genetic and transcriptional evolution alters cancer cell line drug response. Nature 560, 325–330 (2018).
Imamura, Y. et al. Comparison of 2D- and 3D-culture models as drug-testing platforms in breast cancer. Oncol. Rep. 33, 1837–1843 (2015).
Unger, C. et al. Modeling human carcinomas: physiologically relevant 3D models to improve anti-cancer drug development. Adv. Drug. Deliv. Rev. 79–80, 50–67 (2014).
Yamada, K. M. & Cukierman, E. Modeling tissue morphogenesis and cancer in 3D. Cell 130, 601–610 (2007).
Han, K. et al. CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities. Nature 580, 136–141 (2020).
Birgersdotter, A., Sandberg, R. & Ernberg, I. Gene expression perturbation in vitro—a growing case for three-dimensional (3D) culture systems. Semin. Cancer Biol. 15, 405–412 (2005).
Li, C. et al. Cell type and culture condition-dependent alternative splicing in human breast cancer cells revealed by splicing-sensitive microarrays. Cancer Res. 66, 1990–1999 (2006).
Amann, A. et al. Development of a 3D angiogenesis model to study tumour – endothelial cell interactions and the effects of anti-angiogenic drugs. Sci. Rep. 7, 2963 (2017).
Day, C.-P., Merlino, G. & Van Dyke, T. Preclinical mouse cancer models: a maze of opportunities and challenges. Cell 163, 39–53 (2015).
Cooper, L. A. D. et al. The tumor microenvironment strongly impacts master transcriptional regulators and gene expression class of glioblastoma. Am. J. Pathol. 180, 2108–2119 (2012).
Gerashchenko, G. V. et al. Expression pattern of genes associated with tumor microenvironment in prostate cancer. Exp. Oncol. 40, 315–322 (2018).
Kondou, R. et al. Classification of tumor microenvironment immune types based on immune response-associated gene expression. Int. J. Oncol. 54, 219–228 (2019).
Wang, M. et al. Humanized mice in studying efficacy and mechanisms of PD-1-targeted cancer immunotherapy. FASEB J. 32, 1537–1549 (2018).
Ben-David, U. et al. Patient-derived xenografts undergo mouse-specific tumor evolution. Nat. Genet. 49, 1567–1575 (2017). Although PDXs have sometimes been described as the gold standard model for faithfully capturing human cancer biology, this article demonstrates that passaging PDXs in mice can cause significant evolutionary divergence from their initial state.
Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386.e10 (2018).
Boj, S. F. et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).
Yang, H., Sun, L., Liu, M. & Mao, Y. Patient-derived organoids: a promising model for personalized cancer treatment. Gastroenterol. Rep. 6, 243–245 (2018).
Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).
Verissimo, C. S. et al. Targeting mutant RAS in patient-derived colorectal cancer organoids by combinatorial drug screening. eLife 5, e18489 (2016).
Driehuis, E. & Clevers, H. CRISPR/Cas 9 genome editing and its applications in organoids. Am. J. Physiol. Gastrointest. Liver Physiol. 312, G257–G265 (2017).
Kondo, J. & Inoue, M. Application of cancer organoid model for drug screening and personalized therapy. Cells 8, 470 (2019).
Holderfield, M., Deuker, M. M., McCormick, F. & McMahon, M. Targeting RAF kinases for cancer therapy: BRAF mutated melanoma and beyond. Nat. Rev. Cancer 14, 455–467 (2014).
Jorda, R. et al. How selective are pharmacological inhibitors of cell-cycle-regulating cyclin-dependent kinases? J. Med. Chem. 61, 9105–9120 (2018).
Cho, Y.-S., Kang, Y., Kim, K., Cha, Y.-J. & Cho, H.-S. The crystal structure of MPK38 in complex with OTSSP167, an orally administrative MELK selective inhibitor. Biochem. Biophys. Res. Commun. 447, 7–11 (2014).
Gad, H. et al. MTH1 inhibition eradicates cancer by preventing sanitation of the dNTP pool. Nature 508, 215–221 (2014).
Kettle, J. G. et al. Potent and selective inhibitors of MTH1 probe its role in cancer cell survival. J. Med. Chem. 59, 2346–2361 (2016).
Kawamura, T. et al. Proteomic profiling of small-molecule inhibitors reveals dispensability of MTH1 for cancer cell survival. Sci. Rep. 6, 26521 (2016).
Patterson, J. C. et al. VISAGE reveals a targetable mitotic spindle vulnerability in cancer cells. Cell Syst. 9, 74–92.e8 (2019).
Wacker, S. A., Houghtaling, B. R., Elemento, O. & Kapoor, T. M. Using transcriptome sequencing to identify mechanisms of drug action and resistance. Nat. Chem. Biol. 8, 235–237 (2012). This article describes a powerful approach that takes advantage of microsatellite-unstable cancer cells to identify mutations that confer resistance to drugs with unknown targets.
Szlachta, K. et al. CRISPR knockout screening identifies combinatorial drug targets in pancreatic cancer and models cellular drug response. Nat. Commun. 9, 4275 (2018).
Hess, G. T. et al. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods 13, 1036–1042 (2016).
Neggers, J. E. et al. Target identification of small molecules using large-scale CRISPR-Cas mutagenesis scanning of essential genes. Nat. Commun. 9, 502 (2018).
Ipsaro, J. J. et al. Rapid generation of drug-resistance alleles at endogenous loci using CRISPR-Cas9 indel mutagenesis. PLoS One 12, e0172177 (2017).
Gurden, M. D. et al. Naturally occurring mutations in the mps1 gene predispose cells to kinase inhibitor drug resistance. Cancer Res. 75, 3340–3354 (2015).
Murai, J. et al. Trapping of PARP1 and PARP2 by clinical PARP inhibitors. Cancer Res. 72, 5588–5599 (2012).
Antolin, A. A. et al. Objective, quantitative, data-driven assessment of chemical probes. Cell Chem. Biol. 25, 194–205.e5 (2018).
Arrowsmith, C. H. et al. The promise and peril of chemical probes. Nat. Chem. Biol. 11, 536–541 (2015).
Blagg, J. & Workman, P. Choose and use your chemical probe wisely to explore cancer biology. Cancer Cell 32, 9–25 (2017).
Workman, P. & Collins, I. Probing the probes: fitness factors for small molecule tools. Chem. Biol. 17, 561–577 (2010).
Frye, S. V. The art of the chemical probe. Nat. Chem. Biol. 6, 159–161 (2010).
Hunter, P. The reproducibility “crisis”. EMBO Rep. 18, 1493–1496 (2017).
Kaelin, W. G. Common pitfalls in preclinical cancer target validation. Nat. Rev. Cancer 17, 441–450 (2017). This is a highly instructive review that describes how flaws in assay design and experimental interpretation can affect the reproducibility of preclinical research.
Plesser, H. E. Reproducibility vs. replicability: a brief history of a confused terminology. Front. Neuroinform. 11, 76 (2018).
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).
Ghandi, M. et al. Next-generation characterization of the cancer cell line encyclopedia. Nature 569, 503–508 (2019).
McDonald, E. R. 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).
Dempster, J. M. et al. Agreement between two large pan-cancer CRISPR-Cas9 gene dependency datasets. bioRxiv https://doi.org/10.1101/604447 (2019).
Rosenbluh, J. et al. Complementary information derived from CRISPR Cas9 mediated gene deletion and suppression. Nat. Commun. 8, 15403 (2017).
El-Brolosy, M. A. & Stainier, D. Y. R. Genetic compensation: a phenomenon in search of mechanisms. PLoS Genet. 13, e1006780 (2017).
Hoelder, S., Clarke, P. A. & Workman, P. Discovery of small molecule cancer drugs: Successes, challenges and opportunities. Mol. Oncol. 6, 155–176 (2012).
von Bubnoff, N., Peschel, C. & Duyster, J. Resistance of Philadelphia-chromosome positive leukemia towards the kinase inhibitor imatinib (STI571, Glivec): a targeted oncoprotein strikes back. Leukemia 17, 829–838 (2003).
Chandrasekhar, C., Kumar, P. S. & Sarma, P. V. G. K. Novel mutations in the kinase domain of BCR-ABL gene causing imatinib resistance in chronic myeloid leukemia patients. Sci. Rep. 9, 2412 (2019).
Yamamoto, M., Kurosu, T., Kakihana, K., Mizuchi, D. & Miura, O. The two major imatinib resistance mutations E255K and T315I enhance the activity of BCR/ABL fusion kinase. Biochem. Biophys. Res. Commun. 319, 1272–1275 (2004).
Gorre, M. E., Ellwood-Yen, K., Chiosis, G., Rosen, N. & Sawyers, C. L. BCR-ABL point mutants isolated from patients with imatinib mesylate–resistant chronic myeloid leukemia remain sensitive to inhibitors of the BCR-ABL chaperone heat shock protein 90. Blood 100, 3041–3044 (2002).
Gottesman, M. M. Mechanisms of cancer drug resistance. Annu. Rev. Med. 53, 615–627 (2002).
Phillips, R. E. et al. Target identification reveals lanosterol synthase as a vulnerability in glioma. Proc. Natl Acad. Sci. USA 116, 7957–7962 (2019).
Thomenius, M. J. et al. Small molecule inhibitors and CRISPR/Cas9 mutagenesis demonstrate that SMYD2 and SMYD3 activity are dispensable for autonomous cancer cell proliferation. PLoS One 13, e0197372 (2018).
The authors thank the members of the Sheltzer laboratory for their helpful comments on the manuscript. Research in the Sheltzer laboratory is supported by an NIH Early Independence award (1DP5OD021385), a Breast Cancer Alliance Young Investigator award, a Damon Runyon-Rachleff Innovation award, a Gates Foundation Innovative Technology Solutions grant and a CSHL–Northwell Health Translational Cancer Research grant. A.L. is supported by an NSF Graduate Research Fellowship and a Gabilan Stanford Graduate Fellowship. The figures in this Review were made with Biorender.
A.L. and J.M.S. are co-founders of Meliora Therapeutics. J.M.S. has received consulting fees from Ono Pharmaceutical Co. and is a member of the advisory board of Tyra Biosciences.
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Lin, A., Sheltzer, J.M. Discovering and validating cancer genetic dependencies: approaches and pitfalls. Nat Rev Genet 21, 671–682 (2020). https://doi.org/10.1038/s41576-020-0247-7
Nature Reviews Cancer (2021)