Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

The Concept of Synthetic Lethality in the Context of Anticancer Therapy

Key Points

  • Many chemicals kill cancer cells but their toxicity to normal cells limits their usefulness as anticancer drugs.

  • Epigenetic and genetic alterations within cancer cells, as well as changes in their microenvironment, might increase their requirement for a particular molecular target (or targets) relative to normal cells, creating an opportunity for selectivity.

  • Two genes are synthetic lethal if mutation of either gene alone is compatible with viability but mutation of both leads to death. Inhibiting the products of genes that are synthetic lethal to cancer-causing mutations should, by definition, kill cells that harbour such mutations, while sparing normal cells.

  • Most drugs induce a loss-of-function phenotype. High-throughput screens using matched cell-line pairs and chemical libraries allow the identification of chemicals that inhibit or kill cells in a genotype-specific manner. The challenge in this setting is to identify the relevant target (or targets) of compounds that score positively.

  • Genome-wide RNA-interference screens can now be used to identify synthetic lethal interactions in cells that are derived from higher eukaryotes, including humans.

  • Gene–gene interactions, including synthetic lethal interactions that are discovered in cell-culture experiments, will ultimately need to be validated in vivo. It seems likely that some gene–gene interactions will be highly robust, whereas others might be valid only in specific cells or under specific experimental conditions.

Abstract

Two genes are synthetic lethal if mutation of either alone is compatible with viability but mutation of both leads to death. So, targeting a gene that is synthetic lethal to a cancer-relevant mutation should kill only cancer cells and spare normal cells. Synthetic lethality therefore provides a conceptual framework for the development of cancer-specific cytotoxic agents. This paradigm has not been exploited in the past because there were no robust methods for systematically identifying synthetic lethal genes. This is changing as a result of the increased availability of chemical and genetic tools for perturbing gene function in somatic cells.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: Framework for developing anticancer drugs with a high therapeutic index.
Figure 2: Gene–gene interactions: synthetic lethal and suppressive interactions for two genes.
Figure 3: Theoretical fitness curves for wild-type and A−/− cells in response to a drug that inhibits the B gene product.
Figure 4: Models of oncogene addiction.
Figure 5: Synthetic lethal screening with chemical or interfering RNA libraries.
Figure 6: Fluorescence-based mammalian synthetic lethal assay.

Similar content being viewed by others

References

  1. Kaelin, W. G. Jr. Choosing anticancer drug targets in the postgenomic era. J. Clin. Invest. 104, 1503–1506 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Reddy, A. & Kaelin, W. G. Jr. Using cancer genetics to guide the selection of anticancer drug targets. Curr. Opin. Pharmacol. 2, 366–373 (2002).

    Article  CAS  PubMed  Google Scholar 

  3. Kaelin, W. G. Jr. Gleevec: prototype or outlier? Sci. STKE 2004, PE12 (2004). References 1–3 provide counter-arguments to naysayers who suggest that genetically complex cancers will never be successfully treated with drugs.

  4. Hartman, J. T., Garvik, B. & Hartwell, L. Principles for the buffering of genetic variation. Science 291, 1001–1004 (2001).

    Article  CAS  PubMed  Google Scholar 

  5. Guarente, L. Synthetic enhancement in gene interaction: a genetic tool come of age. Trends Genet. 9, 362–366 (1993).

    Article  CAS  PubMed  Google Scholar 

  6. Kamb, A. Mutation load, functional overlap, and synthetic lethality in the evolution and treatment of cancer. J. Theor. Biol. 223, 205–213 (2003). This paper and reference 58 are thoughtful essays on maladaptive genetic changes in cancer cells that might render them vunerable to pharmacological attack.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sharom, J. R., Bellows, D. S. & Tyers, M. From large networks to small molecules. Curr. Opin. Chem. Biol. 8, 81–90 (2004). Excellent introduction to systems biology as applied to cancer and cancer pharmacology.

    Article  CAS  PubMed  Google Scholar 

  11. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Measday, V. & Hieter, P. Synthetic dosage lethality. Methods Enzymol. 350, 316–326 (2002).

    Article  CAS  PubMed  Google Scholar 

  13. Li, J. J. & Herskowitz, I. Isolation of ORC6, a component of the yeast origin recognition complex by a one-hybrid system. Science 262, 1870–1874 (1993).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  15. Tong, A. H. et al. Global mapping of the yeast genetic interaction network. Science 303, 808–813 (2004). References 14 and 15 provide a glimpse into the complexity of synthetic lethal networks in yeast.

    Article  CAS  PubMed  Google Scholar 

  16. Hartwell, L., Szankasi, P., Roberts, C., Murray, A. & Friend, S. Integrating genetic approaches into the discovery of anticancer drugs. Science 278, 1064–1068 (1997). This seminal paper argues that synthetic lethal interactions be exploited to arrive at safer, more efficacious cancer drugs.

    Article  CAS  PubMed  Google Scholar 

  17. Sellers, W. R. & Kaelin, W. G. Jr. Role of the retinoblastoma protein in the pathogenesis of human cancer. J. Clin. Oncol. 15, 3301–3312 (1997).

    Article  CAS  PubMed  Google Scholar 

  18. Nip, J. et al. E2F-1 cooperates with topoisomerase II inhibition and DNA damage to selectively augment p53-independent apoptosis. Mol. Cell. Biol. 17, 1049–1056 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Almasan, A. et al. Deficiency of retinoblastoma protein leads to inappropriate S-phase entry, activation of E2F-responsive genes, and apoptosis. Proc. Natl Acad. Sci. USA 92, 5436–5440 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Banerjee, D. et al. Role of E2F-1 in chemosensitivity. Cancer Res. 58, 4292–4296 (1998).

    CAS  PubMed  Google Scholar 

  21. Dolma, S., Lessnick, S. L., Hahn, W. C. & Stockwell, B. R. Identification of genotype-selective antitumor agents using synthetic lethal chemical screening in engineered human tumor cells. Cancer Cell 3, 285–296 (2003).

    Article  CAS  PubMed  Google Scholar 

  22. Evan, G. I. & Vousden, K. H. Proliferation, cell cycle and apoptosis in cancer. Nature 411, 342–348 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Zaika, A., Irwin, M., Sansome, C. & Moll, U. M. Oncogenes induce and activate endogenous p73 protein. J. Biol. Chem. 276, 11310–11316 (2001).

    Article  CAS  PubMed  Google Scholar 

  24. Meng, R., Phillips, P. & El-Deiry, W. p53-independent increase in E2F-1 expression enhances the cytoxic effects of etoposide and of adriamycin. Intl J. Oncol. 14, 5–14 (1999).

    CAS  Google Scholar 

  25. Irwin, M. S. et al. Chemosensitivity linked to p73 function. Cancer Cell 3, 403–410 (2003).

    Article  CAS  PubMed  Google Scholar 

  26. Rutherford, S. L. & Lindquist, S. HSP90 as a capacitor for morphological evolution. Nature 396, 336–342 (1998).

    Article  CAS  PubMed  Google Scholar 

  27. Isaacs, J. S., Xu, W. & Neckers, L. Heat shock protein 90 as a molecular target for cancer therapeutics. Cancer Cell 3, 213–217 (2003).

    Article  CAS  PubMed  Google Scholar 

  28. Workman, P. Altered states: selectively drugging the HSP90 cancer chaperone. Trends Mol. Med. 10, 47–51 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Neckers, L. & Neckers, K. Heat-shock protein 90 inhibitors as novel cancer chemotherapeutics – an update. Expert Opin. Emerg. Drugs 10, 137–149 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Goldberg, A. L. Protein degradation and protection against misfolded or damaged proteins. Nature 426, 895–899 (2003).

    Article  CAS  PubMed  Google Scholar 

  31. Rajkumar, S. V., Richardson, P. G., Hideshima, T. & Anderson, K. C. Proteasome inhibition as a novel therapeutic target in human cancer. J. Clin. Oncol. 23, 630–639 (2005).

    Article  CAS  PubMed  Google Scholar 

  32. Krek, W., Xu, G., & Livingston, D. M. Cyclin A-kinase regulation of E2F1 DNA binding function underlies suppression of an S phase checkpoint. Cell 83, 1149–1158 (1995).

    Article  CAS  PubMed  Google Scholar 

  33. Dynlacht, B. D., Flores, O., Lees, J. A. & Harlow, E. Differential regulation of E2F transactivation by cyclin/CDK complexes. Genes Dev. 8, 1772–1786 (1994).

    Article  CAS  PubMed  Google Scholar 

  34. Krek, W. et al. Negative regulation of the growth-promoting transcription factor E2F-1 by a stably bound cyclin A-dependent protein kinase. Cell 78, 161–172 (1994).

    Article  CAS  PubMed  Google Scholar 

  35. Xu, M., Sheppard, K. A., Peng, C-Y., Yee, A. S. & Piwnica-Worms, H. Cyclin A/CDK2 binds directly to E2F1 and inhibits the DNA-binding activity of E2F1/DP1 by phosphorylation. Mol. Cell. Biol. 14, 8420–8431 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Parr, M. J. et al. Tumor-selective transgene expression in vivo mediated by an E2F-responsive adenoviral vector. Nature Med. 3, 1145–1149 (1997).

    Article  CAS  PubMed  Google Scholar 

  37. Chen, Y. et al. Selective killing of transformed cells by cyclin/cyclin-dependent kinase 2 antagonists. Proc. Natl Acad. Sci. USA 96, 4325–4329 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Chen, W., Lee, J., Cho, S. Y. & Fine, H. A. Proteasome-mediated destruction of the cyclin A/cyclin-dependent kinase 2 complex suppresses tumor cell growth in vitro and in vivo. Cancer Res. 64, 3949–3957 (2004).

    Article  CAS  PubMed  Google Scholar 

  39. Mendoza, N. et al. Selective cyclin-dependent kinase 2/cyclin A antagonists that differ from ATP site inhibitors block tumor growth. Cancer Res. 63, 1020–1024 (2003).

    CAS  PubMed  Google Scholar 

  40. Tetsu, O. & McCormick, F. Proliferation of cancer cells despite CDK2 inhibition. Cancer Cell 3, 233–245 (2003).

    Article  CAS  PubMed  Google Scholar 

  41. Berthet, C., Aleem, E., Coppola, V., Tessarollo, L. & Kaldis, P. CDK2 knockout mice are viable. Curr. Biol. 13, 1775–1785 (2003).

    Article  CAS  PubMed  Google Scholar 

  42. Ortega, S. et al. Cyclin-dependent kinase 2 is essential for meiosis but not for mitotic cell division in mice. Nature Genet. 35, 25–31 (2003).

    Article  CAS  PubMed  Google Scholar 

  43. Schlegel, R. & Pardee, A. B. Caffeine-induced uncoupling of mitosis from the completion of DNA replication in mammalian cells. Science 232, 1264–1266 (1986).

    Article  CAS  PubMed  Google Scholar 

  44. Nishimoto, T., Ishida, R., Ajiro, K., Yamamoto, S. & Takahashi, T. The synthesis of protein(s) for chromosome condensation may be regulated by a post-transcriptional mechanism. J. Cell. Physiol. 109, 299–308 (1981).

    Article  CAS  PubMed  Google Scholar 

  45. Hall-Jackson, C. A., Cross, D. A., Morrice, N. & Smythe, C. ATR is a caffeine-sensitive, DNA-activated protein kinase with a substrate specificity distinct from DNA-PK. Oncogene 18, 6707–6713 (1999).

    Article  CAS  PubMed  Google Scholar 

  46. Sarkaria, J. N. et al. Inhibition of ATM and ATR kinase activities by the radiosensitizing agent, caffeine. Cancer Res. 59, 4375–4382 (1999).

    CAS  PubMed  Google Scholar 

  47. Nghiem, P., Park, P., Kim, Y., Vaziri, C. & Schreiber, S. ATR inhibition selectively sensitizes G1 checkpoint-deficient cells to lethal premature chromatin condensation. Proc. Natl Acad. Sci. USA 98, 9092–9097 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Sordella, R., Bell, D. W., Haber, D. A. & Settleman, J. Gefitinib-sensitizing EGFR mutations in lung cancer activate anti-apoptotic pathways. Science 305, 1163–1167 (2004).

    Article  CAS  PubMed  Google Scholar 

  49. Lynch, T. J. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 (2004).

    Article  CAS  PubMed  Google Scholar 

  50. Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004).

    Article  CAS  PubMed  Google Scholar 

  51. Pao, W. et al. EGF receptor gene mutations are common in lung cancers from 'never smokers' and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc. Natl Acad. Sci. USA 101, 13306–13311 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Weinstein, I. B. et al. Disorders in cell circuitry associated with multistage carcinogenesis: exploitable targets for cancer prevention and therapy. Clin. Cancer Res. 3, 2696–2702 (1997).

    CAS  PubMed  Google Scholar 

  53. Weinstein, I. B. Disorders in cell circuitry during multistage carcinogenesis: the role of homeostasis. Carcinogenesis 21, 857–864 (2000).

    Article  CAS  PubMed  Google Scholar 

  54. Weinstein, I. B. Cancer. Addiction to oncogenes — the Achilles heal of cancer. Science 297, 63–64 (2002). References 52–54, introduced the term 'oncogene addiction'.

    Article  CAS  PubMed  Google Scholar 

  55. Adams, P. & Kaelin, W. J. Jr. The cellular effects of E2F overexpression. Curr. Top. Microbiol. Immunol. 208, 79–93 (1996).

    CAS  PubMed  Google Scholar 

  56. Sherr, C. The Pezcoller lecture: cancer cell cycles revisited. Cancer Res. 60, 3689–3695 (2000).

    CAS  PubMed  Google Scholar 

  57. Mills, G., Lu, Y. & Kohn, E. Linking molecular therapeutics to molecular diagnostics: inhibition of the FRAP/RAFT/TOR component of the PI3K pathway preferentially blocks PTEN mutant cells in vitro and in vivo. Proc. Natl Acad. Sci. USA 98, 10031–10033 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Kamb, A. Consequences of nonadaptive alterations in cancer. Mol. Biol. Cell 14, 2201–2205 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Neshat, M. et al. Enhanced sensitivity of PTEN-deficient tumors to inhibition of FRAP/mTOR. Proc. Natl Acad. Sci. USA 98, 10314–10319 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Frei, E. D. Gene deletion: a new target for cancer chemotherapy. Lancet 342, 662–664 (1993).

    Article  PubMed  Google Scholar 

  61. Cairns, P. et al. Frequency of homozygous deletion at p16/CDKN2 in primary human tumours. Nature Genet. 11, 210–212 (1995).

    Article  CAS  PubMed  Google Scholar 

  62. Li, W. et al. Status of methylthioadenosine phosphorylase and its impact on cellular response to L-alanosine and methylmercaptopurine riboside in human soft tissue sarcoma cells. Oncol. Res. 14, 373–379 (2004).

    Article  CAS  PubMed  Google Scholar 

  63. 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 

  64. Simon, J. A. et al. Differential toxicities of anticancer agents among DNA repair and checkpoint mutants of Saccharomyces cerevisiae. Cancer Res. 60, 328–333 (2000).

    CAS  PubMed  Google Scholar 

  65. Stockwell, B., Haggarty, S. & Schreiber, S. High-throughput screening of small molecules in miniaturized mammalian cell-based assays involving post-translational modifications. Chem. Biol. 6, 71–83 (1999). References 64 and 65 are two early examples of using isogenic cell lines to isolate compounds that kill cells in a genotype-specific manner.

    Article  CAS  PubMed  Google Scholar 

  66. Torrance, C., Agrawal, V., Vogelstein, B. & Kinzler, K. Use of isogenic human cancer cells for high-throughput screening and drug discovery. Nature Biotechnol. 19, 940–945 (2001).

    Article  CAS  Google Scholar 

  67. 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 

  68. Simons, A., Dafni, N., Dotan, I., Oron, Y. & Canaani, D. Establishment of a chemical synthetic lethality screen in cultured human cells. Genome Res. 11, 266–273 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Simons, A., Dafni, N., Dotan, I., Oron, Y. & Canaani, D. Genetic synthetic lethality screen at the single gene level in cultured human cells. Nucleic Acids Res. 29, E100 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Fantin, V. R. & Leder, P. F16, a mitochondriotoxic compound, triggers apoptosis or necrosis depending on the genetic background of the target carcinoma cell. Cancer Res. 64, 329–336 (2004).

    Article  CAS  PubMed  Google Scholar 

  71. Fantin, V. R., Berardi, M. J., Scorrano, L., Korsmeyer, S. J. & Leder, P. A novel mitochondriotoxic small molecule that selectively inhibits tumor cell growth. Cancer Cell 2, 29–42 (2002).

    Article  CAS  PubMed  Google Scholar 

  72. Wang, Y. et al. Synthetic lethal targeting of MYC by activation of the DR5 death receptor pathway. Cancer Cell 5, 501–512 (2004).

    Article  CAS  PubMed  Google Scholar 

  73. Haggarty, S. J., Clemons, P. A. & Schreiber, S. L. Chemical genomic profiling of biological networks using graph theory and combinations of small molecule perturbations. J. Am. Chem. Soc. 125, 10543–10545 (2003).

    Article  CAS  PubMed  Google Scholar 

  74. Borisy, A. A. et al. Systematic discovery of multicomponent therapeutics. Proc. Natl Acad. Sci. USA 100, 7977–7982 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Lipinski, C. A., Lombardo, F., Dominy, B. W. & Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 46, 3–26 (2001).

    Article  CAS  PubMed  Google Scholar 

  76. Lipinski, C. A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235–249 (2000).

    Article  CAS  PubMed  Google Scholar 

  77. Knockaert, M. et al. Intracellular targets of cyclin-dependent kinase inhibitors: identification by affinity chromatography using immobilised inhibitors. Chem. Biol. 7, 411–422 (2000).

    Article  CAS  PubMed  Google Scholar 

  78. Hultsch, T., Albers, M. W., Schreiber, S. L. & Hohman, R. J. Immunophilin ligands demonstrate common features of signal transduction leading to exocytosis or transcription. Proc. Natl Acad. Sci. USA 88, 6229–6233 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Baetz, K. et al. Yeast genome-wide drug-induced haploinsufficiency screen to determine drug mode of action. Proc. Natl Acad. Sci. USA 101, 4525–4230 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Giaever, G. et al. Genomic profiling of drug sensitivities via induced haploinsufficiency. Nature Genet. 21, 278–283 (1999).

    Article  CAS  PubMed  Google Scholar 

  81. Marton, M. et al. Drug target validation and identification of secondary drug target effects using DNA microarrays. Nature Med. 4, 1293–1301 (1998).

    Article  CAS  PubMed  Google Scholar 

  82. Lu, X. & Horvitz, H. R. lin-35 and lin-53, two genes that antagonize a C. elegans Ras pathway, encode proteins similar to RB and its binding protein RBAp48. Cell 95, 981–991 (1998).

    Article  CAS  PubMed  Google Scholar 

  83. Fay, D. S., Large, E., Han, M. & Darland, M. lin-35/Rb and ubc-18, an E2 ubiquitin-conjugating enzyme, function redundantly to control pharyngeal morphogenesis in C. elegans. Development 130, 3319–3330 (2003).

    Article  CAS  PubMed  Google Scholar 

  84. Fay, D. S., Keenan, S. & Han, M. fzr-1 and lin-35/Rb function redundantly to control cell proliferation in C. elegans as revealed by a nonbiased synthetic screen. Genes Dev. 16, 503–517 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Edgar, K. A. et al. Synthetic lethality of retinoblastoma mutant cells in the Drosophila eye by mutation of a novel peptidyl prolyl isomerase gene. Genetics 170, 161–171 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Kamath, R. S. et al. Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature 421, 231–237 (2003).

    Article  CAS  PubMed  Google Scholar 

  87. Ashrafi, K. et al. Genome-wide RNAi analysis of Caenorhabditis elegans fat regulatory genes. Nature 421, 268–272 (2003).

    Article  CAS  PubMed  Google Scholar 

  88. Cherry, S. et al. Genome-wide RNAi screen reveals a specific sensitivity of IRES-containing RNA viruses to host translation inhibition. Genes Dev. 19, 445–452 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Rual, J. F. et al. Toward improving Caenorhabditis elegans phenome mapping with an ORFeome-based RNAi library. Genome Res. 14, 2162–2168 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Willingham, A. T., Deveraux, Q. L., Hampton, G. M. & Aza-Blanc, P. RNAi and HTS: exploring cancer by systematic loss-of-function. Oncogene 23, 8392–8400 (2004).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  92. Brummelkamp, T. R., Bernards, R. & Agami, R. Stable suppression of tumorigenicity by virus-mediated RNA interference. Cancer Cell 2, 243–247 (2002).

    Article  CAS  PubMed  Google Scholar 

  93. Brummelkamp, T. R., Bernards, R. & Agami, R. A system for stable expression of short interfering RNAs in mammalian cells. Science 296, 550–553 (2002).

    Article  CAS  PubMed  Google Scholar 

  94. Lee, N. S. et al. Expression of small interfering RNAs targeted against HIV-1 rev transcripts in human cells. Nature Biotechnol. 20, 500–505 (2002).

    Article  CAS  Google Scholar 

  95. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Sui, G. et al. A DNA vector-based RNAi technology to suppress gene expression in mammalian cells. Proc. Natl Acad. Sci. USA 99, 5515–5520 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  98. Paddison, P. J. et al. A resource for large-scale RNA-interference-based screens in mammals. Nature 428, 427–431 (2004). References 97 and 98 suggest that it should eventually be possible to carry out synthetic lethal screens in isogenic human cell-line pairs using bar-coded shRNA libraries.

    Article  CAS  PubMed  Google Scholar 

  99. Shirane, D. et al. Enzymatic production of RNAi libraries from cDNAs. Nature Genet. 36, 190–196 (2004).

    Article  CAS  PubMed  Google Scholar 

  100. Aza-Blanc, P. et al. Identification of modulators of TRAIL-induced apoptosis via RNAi-based phenotypic screening. Mol. Cell 12, 627–637 (2003).

    Article  CAS  PubMed  Google Scholar 

  101. Gorre, M. et al. Clinical resistance to STI-571 cancer therapy caused by BCRABL gene mutation or amplification. Science 293, 876–880 (2001).

    Article  CAS  PubMed  Google Scholar 

  102. Shah, N. P. et al. L. Multiple BCR–ABL kinase domain mutations confer polyclonal resistance to the tyrosine kinase inhibitor imatinib (STI571) in chronic phase and blast crisis chronic myeloid leukemia. Cancer Cell 2, 117–125 (2002).

    Article  CAS  PubMed  Google Scholar 

  103. Jonkers, J. & Berns, A. Oncogene addiction: sometimes a temporary slavery. Cancer Cell 6, 535–538 (2004).

    CAS  PubMed  Google Scholar 

  104. Bailey, S. N., Sabatini, D. M. & Stockwell, B. R. Microarrays of small molecules embedded in biodegradable polymers for use in mammalian cell-based screens. Proc. Natl Acad. Sci. USA 101, 16144–16149 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Wheeler, D. B. et al. RNAi living-cell microarrays for loss-of-function screens in Drosophila melanogaster cells. Nature Methods 1, 127–132 (2004).

    Article  CAS  PubMed  Google Scholar 

  106. Ooi, S. L., Shoemaker, D. D. & Boeke, J. D. DNA helicase gene interaction network defined using synthetic lethality analyzed by microarray. Nature Genet. 35, 277–286 (2003). Describes the use of DNA bar codes coupled with oligonucleotide microarrays to conduct synthetic lethal assays in yeast.

    Article  CAS  PubMed  Google Scholar 

  107. Shoemaker, D. D., Lashkari, D. A., Morris, D., Mittmann, M. & Davis, R. W. Quantitative phenotypic analysis of yeast deletion mutants using a highly parallel molecular bar-coding strategy. Nature Genet. 14, 450–456 (1996).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  109. Eason, R. G. et al. Characterization of synthetic DNA bar codes in Saccharomyces cerevisiae gene-deletion strains. Proc. Natl Acad. Sci. USA 101, 11046–11051 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Hensel, M. et al. Simultaneous identification of bacterial virulence genes by negative selection. Science 269, 400–403 (1995).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

I would like to thank S. Elledge, A. Reddy, M. Tyers, P. Silver and M. Vidal for their critical reading of this manuscript and/or helpful comments. I apologize to colleagues whose work was not cited due to space limitations or my ignorance. Dedicated to the memory of Nancy P. Kaelin.

Author information

Authors and Affiliations

Authors

Ethics declarations

Competing interests

The author declares no competing financial interests.

Related links

Related links

DATABASES

Entrez Gene

ABL

BCR

CDK2

E2F1

EGFR

ERBB2

HSP90

KRAS

MYC

p53

pRB

PTEN

National Cancer Institute

chronic myelogenous leukaemia

FURTHER INFORMATION

Author's homepage

Glossary

THERAPEUTIC INDEX

The therapeutic index for a drug is defined as the dose (concentration) required for toxic effects divided by the dose (concentration) required for therapeutic effects.

THERAPEUTIC WINDOW

The therapeutic window for a drug refers to the concentration range over which therapeutic effects can be expected.

PARALOGUES

Paralogues are genes that share significant homology within a particular species. Such genes are paralogous to each other.

BROMODEOXYURIDINE (BRDU) INCORPORATION

Incorporation of the thymidine nucleotide analogue bromodeoxyuridine into DNA can be used to measure the rate of DNA synthesis.

ISOGENIC

Two cell lines are isogenic if they are derived from the same parental cell line, or from one another such that they are genetically identical.

LIPINSKIS RULES

Lipinski noted that the following properties predict that a chemical will have poor bioavailability after oral administration: molecular mass greater than 500 Da, high lipophilicity (calculated LogP greater than 5, where LogP indicates solubility in octanol relative to water), more than 5 hydrogen-bond donors and more than 10 hydrogen-bond acceptors.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kaelin, W. The Concept of Synthetic Lethality in the Context of Anticancer Therapy. Nat Rev Cancer 5, 689–698 (2005). https://doi.org/10.1038/nrc1691

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc1691

This article is cited by

Search

Quick links

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