Synthetic lethality and cancer

Key Points

  • Synthetic lethal genetic interactions with tumour-specific mutations may be exploited to develop anticancer therapeutics.

  • Synthetic dosage lethality and conditional synthetic lethality can expand the scope of conventional synthetic lethal studies.

  • Genetic interaction networks in model organisms provide a framework for screening cancer-relevant candidate synthetic lethal interactions in human cells.

  • Large-scale screening for cancer gene-specific synthetic lethal candidates in human cells has progressed through advances in RNA interference and the CRISPR–Cas9 system.

  • The CRISPR–Cas9 technology is a versatile platform for exploring genetic networks and synthetic lethal interaction phenotypes.

  • The search for synthetic lethality-based therapeutic strategies could be enhanced by integrating synthetic lethal interactions from three distinct sources: model organism genetic networks, human high-throughput screening and synthetic lethal predictions from statistical genetics.

Abstract

A synthetic lethal interaction occurs between two genes when the perturbation of either gene alone is viable but the perturbation of both genes simultaneously results in the loss of viability. Key to exploiting synthetic lethality in cancer treatment are the identification and the mechanistic characterization of robust synthetic lethal genetic interactions. Advances in next-generation sequencing technologies are enabling the identification of hundreds of tumour-specific mutations and alterations in gene expression that could be targeted by a synthetic lethality approach. The translation of synthetic lethality to therapy will be assisted by the synthesis of genetic interaction data from model organisms, tumour genomes and human cell lines.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: The concept of synthetic lethality.
Figure 2: The concept of conditional synthetic lethality.
Figure 3: A cross-platform approach for discovering clinically relevant synthetic lethal interactions.
Figure 4: Strategy for large-scale synthetic lethality screens for a gene of interest in human cells.

References

  1. 1

    Hyman, D. M., Taylor, B. S. & Baselga, J. Implementing genome-driven oncology. Cell 168, 584–599 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Laskin, J. et al. Lessons learned from the application of whole-genome analysis to the treatment of patients with advanced cancers. Cold Spring Harb. Mol. Case Stud. 1, a000570 (2015).

    PubMed  PubMed Central  Google Scholar 

  3. 3

    Stockley, T. L. et al. Molecular profiling of advanced solid tumors and patient outcomes with genotype-matched clinical trials: the Princess Margaret IMPACT/COMPACT trial. Genome Med. 8, 109 (2016).

    PubMed  PubMed Central  Google Scholar 

  4. 4

    Swanton, C. et al. Consensus on precision medicine for metastatic cancers: a report from the MAP conference. Ann. Oncol. 27, 1443–1448 (2016).

    CAS  PubMed  Google Scholar 

  5. 5

    Pagliarini, R., Shao, W. & Sellers, W. R. Oncogene addiction: pathways of therapeutic response, resistance, and road maps toward a cure. EMBO Rep. 16, 280–296 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Dobzhansky, T. Genetics of natural populations; recombination and variability in populations of Drosophila pseudoobscura. Genetics 31, 269–290 (1946).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Lucchesi, J. C. Synthetic lethality and semi-lethality among functionally related mutants of Drosophila melanogaster. Genetics 59, 37–44 (1968).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

    Kaiser, C. A. & Schekman, R. Distinct sets of SEC genes govern transport vesicle formation and fusion early in the secretory pathway. Cell 61, 723–733 (1990).

    CAS  PubMed  Google Scholar 

  9. 9

    Hennessy, K. M., Lee, A., Chen, E. & Botstein, D. A group of interacting yeast DNA replication genes. Genes Dev. 5, 958–969 (1991).

    CAS  PubMed  Google Scholar 

  10. 10

    Bender, A. & Pringle, J. R. Use of a screen for synthetic lethal and multicopy suppressee mutants to identify two new genes involved in morphogenesis in Saccharomyces cerevisiae. Mol. Cell. Biol. 11, 1295–1305 (1991).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    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). This study suggests that model organism genetics can be used to identify drug targets in human cancer and is the first to propose synthetic lethality screening as a strategy to develop anticancer therapeutics.

    CAS  PubMed  Google Scholar 

  13. 13

    Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Yan, H., Gibson, S. & Tye, B. K. Mcm2 and Mcm3, two proteins important for ARS activity, are related in structure and function. Genes Dev. 5, 944–957 (1991).

    CAS  PubMed  Google Scholar 

  15. 15

    Kroll, E. S., Hyland, K. M., Hieter, P. & Li, J. J. Establishing genetic interactions by a synthetic dosage lethality phenotype. Genetics 143, 95–102 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Bian, Y. et al. Synthetic genetic array screen identifies PP2A as a therapeutic target in Mad2-overexpressing tumors. Proc. Natl Acad. Sci. USA 111, 1628–1633 (2014).

    CAS  PubMed  Google Scholar 

  17. 17

    Reid, R. J. et al. A synthetic dosage lethal genetic interaction between CKS1B and PLK1 is conserved in yeast and human cancer cells. Genetics 204, 807–819 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Duffy, S. et al. Overexpression screens identify conserved dosage chromosome instability genes in yeast and human cancer. Proc. Natl Acad. Sci. USA 113, 9967–9976 (2016).

    CAS  PubMed  Google Scholar 

  19. 19

    Tong, A. H. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004).

    CAS  PubMed  Google Scholar 

  20. 20

    Haber, J. E. et al. Systematic triple-mutant analysis uncovers functional connectivity between pathways involved in chromosome regulation. Cell Rep. 3, 2168–2178 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Bunting, S. F. et al. 53BP1 inhibits homologous recombination in Brca1-deficient cells by blocking resection of DNA breaks. Cell 141, 243–254 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Jaspers, J. E. et al. Loss of 53BP1 causes PARP inhibitor resistance in Brca1-mutated mouse mammary tumors. Cancer Discov. 3, 68–81 (2013).

    CAS  PubMed  Google Scholar 

  23. 23

    Bindra, R. S. et al. Down-regulation of Rad51 and decreased homologous recombination in hypoxic cancer cells. Mol. Cell. Biol. 24, 8504–8518 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Chan, N. et al. Contextual synthetic lethality of cancer cell kill based on the tumor microenvironment. Cancer Res. 70, 8045–8054 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Bandyopadhyay, S. et al. Rewiring of genetic networks in response to DNA damage. Science 330, 1385–1389 (2010). This study describes the creation of condition- dependent genetic interaction maps in yeast and demonstrates that genetic interactions can change in response to DNA damage.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Guenole, A. et al. Dissection of DNA damage responses using multiconditional genetic interaction maps. Mol. Cell 49, 346–358 (2013).

    CAS  PubMed  Google Scholar 

  27. 27

    Li, X., O'Neil, N. J., Moshgabadi, N. & Hieter, P. Synthetic cytotoxicity: digenic interactions with TEL1/ATM mutations reveal sensitivity to low doses of camptothecin. Genetics 197, 611–623 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Rouleau, M., Patel, A., Hendzel, M. J., Kaufmann, S. H. & Poirier, G. G. PARP inhibition: PARP1 and beyond. Nat. Rev. Cancer 10, 293–301 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Bailey, M. L. et al. Glioblastoma cells containing mutations in the cohesin component STAG2 are sensitive to PARP inhibition. Mol. Cancer Ther. 13, 724–732 (2014).

    CAS  PubMed  Google Scholar 

  30. 30

    Hartwell, L. H. & Kastan, M. B. Cell cycle control and cancer. Science 266, 1821–1828 (1994).

    CAS  PubMed  Google Scholar 

  31. 31

    Rudrapatna, V. A., Cagan, R. L. & Das, T. K. Drosophila cancer models. Dev. Dyn. 241, 107–118 (2012).

    CAS  PubMed  Google Scholar 

  32. 32

    Kirienko, N. V., Mani, K. & Fay, D. S. Cancer models in Caenorhabditis elegans. Dev. Dyn. 239, 1413–1448 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Tarailo, M., Tarailo, S. & Rose, A. M. Synthetic lethal interactions identify phenotypic “interologs” of the spindle assembly checkpoint components. Genetics 177, 2525–2530 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Dixon, S. J., Andrews, B. J. & Boone, C. Exploring the conservation of synthetic lethal genetic interaction networks. Commun. Integr. Biol. 2, 78–81 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Boucher, B. & Jenna, S. Genetic interaction networks: better understand to better predict. Front. Genet. 4, 290 (2013).

    PubMed  PubMed Central  Google Scholar 

  36. 36

    Baryshnikova, A., Costanzo, M., Myers, C. L., Andrews, B. & Boone, C. Genetic interaction networks: toward an understanding of heritability. Annu. Rev. Genomics Hum. Genet. 14, 111–133 (2013).

    CAS  PubMed  Google Scholar 

  37. 37

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

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

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

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    McLellan, J. L. et al. Synthetic lethality of cohesins with PARPs and replication fork mediators. PLoS Genet. 8, e1002574 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Tong, A. H. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001).

    CAS  PubMed  Google Scholar 

  41. 41

    Pan, X. et al. A robust toolkit for functional profiling of the yeast genome. Mol. Cell 16, 487–496 (2004).

    CAS  PubMed  Google Scholar 

  42. 42

    Costanzo, M. et al. A global genetic interaction network maps a wiring diagram of cellular function. Science 353, aaf1420 (2016). This study describes a global genetic interaction network derived from the analysis of pairwise interactions of nearly all the 6,000 genes in S. cerevisiae.

    PubMed  PubMed Central  Google Scholar 

  43. 43

    McManus, K. J., Barrett, I. J., Nouhi, Y. & Hieter, P. Specific synthetic lethal killing of RAD54B-deficient human colorectal cancer cells by FEN1 silencing. Proc. Natl Acad. Sci. USA 106, 3276–3281 (2009).

    CAS  PubMed  Google Scholar 

  44. 44

    Srivas, R. et al. A network of conserved synthetic lethal interactions for exploration of precision cancer therapy. Mol. Cell 63, 514–525 (2016). This study describes a large-scale cross-species approach to identify clinically relevant genetic interactions with genes mutated in cancer.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Fire, A. et al. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391, 806–811 (1998).

    CAS  PubMed  Google Scholar 

  46. 46

    Elbashir, S. M. et al. Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells. Nature 411, 494–498 (2001).

    CAS  PubMed  Google Scholar 

  47. 47

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Berns, K. et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428, 431–437 (2004).

    CAS  PubMed  Google Scholar 

  49. 49

    Paddison, P. J. et al. A resource for large-scale RNA-interference-based screens in mammals. Nature 428, 427–431 (2004).

    CAS  PubMed  Google Scholar 

  50. 50

    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).

    CAS  PubMed  Google Scholar 

  51. 51

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Bhinder, B. & Djaballah, H. Systematic analysis of RNAi reports identifies dismal commonality at gene-level and reveals an unprecedented enrichment in pooled shRNA screens. Comb. Chem. High Throughput Screen. 16, 665–681 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Reynolds, A. et al. Rational siRNA design for RNA interference. Nat. Biotechnol. 22, 326–330 (2004).

    CAS  PubMed  Google Scholar 

  54. 54

    Fellmann, C. et al. Functional identification of optimized RNAi triggers using a massively parallel sensor assay. Mol. Cell 41, 733–746 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Jackson, A. L. et al. Expression profiling reveals off-target gene regulation by RNAi. Nat. Biotechnol. 21, 635–637 (2003).

    CAS  PubMed  Google Scholar 

  56. 56

    Birmingham, A. et al. 3′ UTR seed matches, but not overall identity, are associated with RNAi off-targets. Nat. Methods 3, 199–204 (2006).

    CAS  PubMed  Google Scholar 

  57. 57

    Sigoillot, F. D. et al. A bioinformatics method identifies prominent off-targeted transcripts in RNAi screens. Nat. Methods 9, 363–366 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Buehler, E. et al. siRNA off-target effects in genome-wide screens identify signaling pathway members. Sci. Rep. 2, 428 (2012).

    PubMed  PubMed Central  Google Scholar 

  59. 59

    Hoffman, G. R. et al. Functional epigenetics approach identifies BRM/SMARCA2 as a critical synthetic lethal target in BRG1-deficient cancers. Proc. Natl Acad. Sci. USA 111, 3128–3133 (2014).

    CAS  PubMed  Google Scholar 

  60. 60

    Bassik, M. C. et al. Rapid creation and quantitative monitoring of high coverage shRNA libraries. Nat. Methods 6, 443–445 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

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

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Haibe-Kains, B. et al. Inconsistency in large pharmacogenomic studies. Nature 504, 389–393 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Haverty, P. M. et al. Reproducible pharmacogenomic profiling of cancer cell line panels. Nature 533, 333–337 (2016).

    CAS  PubMed  Google Scholar 

  64. 64

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

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    Jinek, M. et al. RNA-programmed genome editing in human cells. eLife 2, e00471 (2013).

    PubMed  PubMed Central  Google Scholar 

  67. 67

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

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

    Shalem, O., Sanjana, N. E. & Zhang, F. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 16, 299–311 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

    Aguirre, A. J. et al. Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting. Cancer Discov. 6, 914–929 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    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).

    CAS  PubMed  Google Scholar 

  71. 71

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

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72

    Hart, T. et al. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163, 1515–1526 (2015).

    CAS  PubMed  Google Scholar 

  73. 73

    Horlbeck, M. A. et al. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation. eLife 5, e19760 (2016). This study introduces the second generation of genome-wide CRISPRi and CRISPRa libraries.

    PubMed  PubMed Central  Google Scholar 

  74. 74

    Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903 (2017). This study is one of several recent papers that use a new generation of genome-wide CRISPR knockout libraries to search for cell line-dependent genetic vulnerabilities and potential synthetic lethality candidates.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75

    Blomen, V. A. et al. Gene essentiality and synthetic lethality in haploid human cells. Science 350, 1092–1096 (2015).

    CAS  PubMed  Google Scholar 

  76. 76

    Horlbeck, M. A. et al. Nucleosomes impede Cas9 access to DNA in vivo and in vitro. eLife 5, e12677 (2016).

    PubMed  PubMed Central  Google Scholar 

  77. 77

    Kuscu, C., Arslan, S., Singh, R., Thorpe, J. & Adli, M. Genome-wide analysis reveals characteristics of off-target sites bound by the Cas9 endonuclease. Nat. Biotechnol. 32, 677–683 (2014).

    CAS  PubMed  Google Scholar 

  78. 78

    Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79

    Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

    Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81

    Konermann, S. et al. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517, 583–588 (2015).

    CAS  PubMed  Google Scholar 

  82. 82

    Wong, A. S. et al. Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM. Proc. Natl Acad. Sci. USA 113, 2544–2549 (2016). This study combines the method CombiGEM with the CRISPR–Cas9 system to enable multiplexed synthetic lethality screening strategies.

    CAS  PubMed  Google Scholar 

  83. 83

    Kabadi, A. M., Ousterout, D. G., Hilton, I. B. & Gersbach, C. A. Multiplex CRISPR/Cas9-based genome engineering from a single lentiviral vector. Nucleic Acids Res. 42, e147 (2014).

    PubMed  PubMed Central  Google Scholar 

  84. 84

    Sakuma, T., Nishikawa, A., Kume, S., Chayama, K. & Yamamoto, T. Multiplex genome engineering in human cells using all-in-one CRISPR/Cas9 vector system. Sci. Rep. 4, 5400 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85

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

    CAS  PubMed  PubMed Central  Google Scholar 

  86. 86

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

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87

    Wong, A. S., Choi, G. C., Cheng, A. A., Purcell, O. & Lu, T. K. Massively parallel high-order combinatorial genetics in human cells. Nat. Biotechnol. 33, 952–961 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89

    Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882 (2016). References 88 and 89 introduce the method of Perturb-seq, which combines CRISPR-based screening with single-cell transcriptional readout for a highly integrated phenotypic analysis of genetic perturbations.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896 (2016).

    CAS  PubMed  Google Scholar 

  91. 91

    Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92

    Leiserson, M. D., Wu, H. T., Vandin, F. & Raphael, B. J. CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer. Genome Biol. 16, 160 (2015).

    PubMed  PubMed Central  Google Scholar 

  93. 93

    Babur, O. et al. Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations. Genome Biol. 16, 45 (2015).

    PubMed  PubMed Central  Google Scholar 

  94. 94

    Zhang, F. et al. Predicting essential genes and synthetic lethality via influence propagation in signaling pathways of cancer cell fates. J. Bioinform. Comput. Biol. 13, 1541002 (2015).

    CAS  PubMed  Google Scholar 

  95. 95

    Wappett, M. et al. Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs. BMC Genomics 17, 65 (2016).

    PubMed  PubMed Central  Google Scholar 

  96. 96

    Jackson, R. A. & Chen, E. S. Synthetic lethal approaches for assessing combinatorial efficacy of chemotherapeutic drugs. Pharmacol. Ther. 162, 69–85 (2016).

    CAS  PubMed  Google Scholar 

  97. 97

    Jerby-Arnon, L. et al. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell 158, 1199–1209 (2014). The authors developed and applied the statistical model DAISY, which analyses and integrates cancer genomic data and experimental genetic screens to predict synthetic lethal candidates.

    CAS  PubMed  Google Scholar 

  98. 98

    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).

    CAS  PubMed  Google Scholar 

  99. 99

    Thoma, C. R. et al. A high-throughput-compatible 3D microtissue co-culture system for phenotypic RNAi screening applications. J. Biomol. Screen. 18, 1330–1337 (2013).

    PubMed  Google Scholar 

  100. 100

    Matano, M. et al. Modeling colorectal cancer using CRISPR-Cas9-mediated engineering of human intestinal organoids. Nat. Med. 21, 256–262 (2015).

    CAS  PubMed  Google Scholar 

  101. 101

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

    CAS  PubMed  PubMed Central  Google Scholar 

  102. 102

    Braun, C. J. et al. Versatile in vivo regulation of tumor phenotypes by dCas9-mediated transcriptional perturbation. Proc. Natl Acad. Sci. USA 113, E3892–E3900 (2016).

    CAS  PubMed  Google Scholar 

  103. 103

    Chen, S. et al. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160, 1246–1260 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104

    Walcott, F. L., Patel, J., Lubet, R., Rodriguez, L. & Calzone, K. A. Hereditary cancer syndromes as model systems for chemopreventive agent development. Semin. Oncol. 43, 134–145 (2016).

    CAS  PubMed  Google Scholar 

  105. 105

    Wu, X. & Lippman, S. M. An intermittent approach for cancer chemoprevention. Nat. Rev. Cancer 11, 879–885 (2011).

    CAS  PubMed  Google Scholar 

  106. 106

    Bryant, H. E. et al. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature 434, 913–917 (2005).

    CAS  PubMed  Google Scholar 

  107. 107

    Farmer, H. et al. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434, 917–921 (2005). References 106 and 107 show the first evidence of a synthetic lethality-based therapeutic strategy between the BRCA genes, which are commonly mutated in breast and other cancers, and PARP1.

    CAS  PubMed  Google Scholar 

  108. 108

    Fong, P. C. et al. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 361, 123–134 (2009).

    CAS  PubMed  Google Scholar 

  109. 109

    Gelmon, K. A. et al. Olaparib in patients with recurrent high-grade serous or poorly differentiated ovarian carcinoma or triple-negative breast cancer: a phase 2, multicentre, open-label, non-randomised study. Lancet Oncol. 12, 852–861 (2011).

    CAS  PubMed  Google Scholar 

  110. 110

    Mateo, J. et al. DNA-repair defects and olaparib in metastatic prostate cancer. N. Engl. J. Med. 373, 1697–1708 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111

    McCabe, N. et al. Deficiency in the repair of DNA damage by homologous recombination and sensitivity to poly(ADP-ribose) polymerase inhibition. Cancer Res. 66, 8109–8115 (2006).

    CAS  PubMed  Google Scholar 

  112. 112

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

    CAS  PubMed  Google Scholar 

  113. 113

    Barber, L. J. et al. Secondary mutations in BRCA2 associated with clinical resistance to a PARP inhibitor. J. Pathol. 229, 422–429 (2013).

    CAS  PubMed  Google Scholar 

  114. 114

    Ter Brugge, P. et al. Mechanisms of therapy resistance in patient-derived xenograft models of BRCA1-deficient breast cancer. J. Natl Cancer Inst. 108, djw148 (2016).

    Google Scholar 

  115. 115

    Xu, G. et al. REV7 counteracts DNA double-strand break resection and affects PARP inhibition. Nature 521, 541–544 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116

    Rottenberg, S. et al. High sensitivity of BRCA1-deficient mammary tumors to the PARP inhibitor AZD2281 alone and in combination with platinum drugs. Proc. Natl Acad. Sci. USA 105, 17079–17084 (2008).

    CAS  PubMed  Google Scholar 

  117. 117

    Knezevic, C. E. et al. Proteome-wide profiling of clinical PARP inhibitors reveals compound-specific secondary targets. Cell Chem. Biol. 23, 1490–1503 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  118. 118

    Schiewer, M. J. & Knudsen, K. E. Transcriptional roles of PARP1 in cancer. Mol. Cancer Res. 12, 1069–1080 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. 119

    Burkle, A. & Virag, L. Poly(ADP-ribose): PARadigms and PARadoxes. Mol. Aspects Med. 34, 1046–1065 (2013).

    PubMed  Google Scholar 

  120. 120

    Murai, J. et al. Stereospecific PARP trapping by BMN 673 and comparison with olaparib and rucaparib. Mol. Cancer Ther. 13, 433–443 (2014).

    CAS  PubMed  Google Scholar 

  121. 121

    Hopkins, T. A. et al. Mechanistic dissection of PARP1 trapping and the impact on in vivo tolerability and efficacy of PARP inhibitors. Mol. Cancer Res. 13, 1465–1477 (2015).

    CAS  PubMed  Google Scholar 

  122. 122

    Helleday, T. The underlying mechanism for the PARP and BRCA synthetic lethality: clearing up the misunderstandings. Mol. Oncol. 5, 387–393 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

P.H. is a senior fellow in the Genetic Networks program at the Canadian Institute for Advanced Research.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Philip Hieter.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Glossary

Synthetic lethality

A synthetic lethal interaction occurs between two genes when a perturbation (a mutation, RNA interference knockdown or inhibition) that affects either gene alone is viable but the perturbation of both genes simultaneously is lethal.

Non-homologous end-joining

(NHEJ). The repair of double-strand DNA breaks by direct ligation without the use of a homologous template.

Homologous recombination

The exchange of nucleotide sequences between identical (or near identical) DNA molecules. Homologous recombination is the most common form of homology-directed repair of double-strand DNA breaks.

Genetic interaction network

A genetic interaction occurs when the perturbation of one or more genes affects the phenotype of another gene alteration. A genetic interaction network defines the functional relationship between many genes.

Orthologue

Genes in different species that are originated from a single gene of the last common ancestor.

Modules

Groups of genes or proteins that act together in a common cellular function.

Synthetic sickness

A synthetic sickness interaction occurs between two genes when a perturbation (a mutation, RNA interference knockdown or inhibition) that affects either gene alone is viable but the disruption of both genes simultaneously results in a reduction of viability.

Driver mutations

Mutations that confer a selective growth advantage to a cancer or a pre-cancerous cell.

Isogenic

Organisms or cell lines that contain identical or nearly identical genotypes.

Heterogeneity

In a cell line or tumour, the diversity of genotypes within the population.

Gene essentiality profiles

Sets of genes required for proliferation or viability in the context of a single cell line or tumour type.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

O'Neil, N., Bailey, M. & Hieter, P. Synthetic lethality and cancer. Nat Rev Genet 18, 613–623 (2017). https://doi.org/10.1038/nrg.2017.47

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing