1932

Abstract

The great majority of targeted anticancer drugs inhibit mutated oncogenes that display increased activity. Yet many tumors do not contain such actionable aberrations, such as those harboring loss-of-function mutations. The notion of targeting synthetic lethal vulnerabilities in cancer cells has provided an alternative approach to exploiting more of the genetic and epigenetic changes acquired during tumorigenesis. Here, we review synthetic lethality as a therapeutic concept that exploits the inherent differences between normal cells and cancer cells. Furthermore, we provide an overview of the screening approaches that can be used to identify synthetic lethal interactions in human cells and present several recently identified interactions that may be pharmacologically exploited. Finally, we indicate some of the challenges of translating synthetic lethal interactions into the clinic and how these may be overcome.

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2015-01-06
2024-06-20
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Literature Cited

  1. Hanahan D, Weinberg RA. 1.  2011. Hallmarks of cancer: the next generation. Cell 144:646–74 [Google Scholar]
  2. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, Kinzler KW. 2.  2013. Cancer genome landscapes. Science 339:1546–58 [Google Scholar]
  3. Levitzki A. 3.  2013. Tyrosine kinase inhibitors: views of selectivity, sensitivity, and clinical performance. Annu. Rev. Pharmacol. Toxicol. 53:161–85 [Google Scholar]
  4. Tebbutt N, Pedersen MW, Johns TG. 4.  2013. Targeting the ERBB family in cancer: couples therapy. Nat. Rev. Cancer 13:663–73 [Google Scholar]
  5. Weinstein IB. 5.  2002. Addiction to oncogenes–the Achilles heal of cancer. Science 297:63–64 [Google Scholar]
  6. Smida M, Nijman SM. 6.  2012. Functional drug-gene interactions in lung cancer. Expert Rev. Mol. Diagn. 12:291–302 [Google Scholar]
  7. Kandoth C, McLellan MD, Vandin F, Ye K, Niu B. 7.  et al. 2013. Mutational landscape and significance across 12 major cancer types. Nature 502:333–39 [Google Scholar]
  8. Garraway LA, Janne PA. 8.  2012. Circumventing cancer drug resistance in the era of personalized medicine. Cancer Discov. 2:214–26 [Google Scholar]
  9. Hartwell LH, Szankasi P, Roberts CJ, Murray AW, Friend SH. 9.  1997. Integrating genetic approaches into the discovery of anticancer drugs. Science 278:1064–68 [Google Scholar]
  10. Bridges CB. 10.  1922. The origin of variations in sexual and sex-limited characters. Am. Nat. 56:51–63 [Google Scholar]
  11. Dobzhansky T. 11.  1946. Genetics of natural populations. Xiii. Recombination and variability in populations of Drosophila pseudoobscura. Genetics 31:269–90 [Google Scholar]
  12. Lucchesi JC. 12.  1968. Synthetic lethality and semi-lethality among functionally related mutants of Drosophila melanogaster. Genetics 59:37–44 [Google Scholar]
  13. Kaelin WG Jr. 13.  2005. The concept of synthetic lethality in the context of anticancer therapy. Nat. Rev. Cancer 5:689–98 [Google Scholar]
  14. Kaelin WG Jr. 14.  2009. Synthetic lethality: a framework for the development of wiser cancer therapeutics. Genome Med. 1:99 [Google Scholar]
  15. Nijman SM. 15.  2011. Synthetic lethality: general principles, utility and detection using genetic screens in human cells. FEBS Lett. 585:1–6 [Google Scholar]
  16. Ashworth A, Lord CJ, Reis-Filho JS. 16.  2011. Genetic interactions in cancer progression and treatment. Cell 145:30–38 [Google Scholar]
  17. Tischler J, Lehner B, Fraser AG. 17.  2008. Evolutionary plasticity of genetic interaction networks. Nat. Genet. 40:390–91 [Google Scholar]
  18. Luo J, Solimini NL, Elledge SJ. 18.  2009. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136:823–37 [Google Scholar]
  19. Dobbelstein M, Moll U. 19.  2014. Targeting tumour-supportive cellular machineries in anticancer drug development. Nat. Rev. Drug Discov. 13:179–96 [Google Scholar]
  20. Whitesell L, Lindquist SL. 20.  2005. HSP90 and the chaperoning of cancer. Nat. Rev. Cancer 5:761–72 [Google Scholar]
  21. Mair B, Kubicek S, Nijman SM. 21.  2014. Exploiting epigenetic vulnerabilities for cancer therapeutics. Trends Pharmacol. Sci. 35:136–45 [Google Scholar]
  22. Hartman JL IV, Garvik B, Hartwell L. 22.  2001. Principles for the buffering of genetic variation. Science 291:1001–4 [Google Scholar]
  23. Giaever G, Chu AM, Ni L, Connelly C, Riles L. 23.  et al. 2002. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418:387–91 [Google Scholar]
  24. Winzeler EA, Shoemaker DD, Astromoff A, Liang H, Anderson K. 24.  et al. 1999. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285:901–6 [Google Scholar]
  25. Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED. 25.  et al. 2010. The genetic landscape of a cell. Science 327:425–31 [Google Scholar]
  26. Hillenmeyer ME, Fung E, Wildenhain J, Pierce SE, Hoon S. 26.  et al. 2008. The chemical genomic portrait of yeast: uncovering a phenotype for all genes. Science 320:362–65 [Google Scholar]
  27. Dixon SJ, Costanzo M, Baryshnikova A, Andrews B, Boone C. 27.  2009. Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43:601–25 [Google Scholar]
  28. Laufer C, Fischer B, Billmann M, Huber W, Boutros M. 28.  2013. Mapping genetic interactions in human cancer cells with RNAi and multiparametric phenotyping. Nat. Methods 10:427–31 [Google Scholar]
  29. Queitsch C, Sangster TA, Lindquist S. 29.  2002. Hsp90 as a capacitor of phenotypic variation. Nature 417:618–24 [Google Scholar]
  30. Kirschner M, Gerhart J. 30.  1998. Evolvability. Proc. Natl. Acad. Sci. USA 95:8420–27 [Google Scholar]
  31. Masel J, Siegal ML. 31.  2009. Robustness: mechanisms and consequences. Trends Genet. 25:395–403 [Google Scholar]
  32. Lum PY, Armour CD, Stepaniants SB, Cavet G, Wolf MK. 32.  et al. 2004. Discovering modes of action for therapeutic compounds using a genome-wide screen of yeast heterozygotes. Cell 116:121–37 [Google Scholar]
  33. Lehar J, Stockwell BR, Giaever G, Nislow C. 33.  2008. Combination chemical genetics. Nat. Chem. Biol. 4:674–81 [Google Scholar]
  34. Giaever G, Flaherty P, Kumm J, Proctor M, Nislow C. 34.  et al. 2004. Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. Proc. Natl. Acad. Sci. USA 101:793–98 [Google Scholar]
  35. Parsons AB, Brost RL, Ding H, Li Z, Zhang C. 35.  et al. 2004. Integration of chemical-genetic and genetic interaction data links bioactive compounds to cellular target pathways. Nat. Biotechnol. 22:62–69 [Google Scholar]
  36. Parsons AB, Lopez A, Givoni IE, Williams DE, Gray CA. 36.  et al. 2006. Exploring the mode-of-action of bioactive compounds by chemical-genetic profiling in yeast. Cell 126:611–25 [Google Scholar]
  37. Hoon S, Smith AM, Wallace IM, Suresh S, Miranda M. 37.  et al. 2008. An integrated platform of genomic assays reveals small-molecule bioactivities. Nat. Chem. Biol. 4:498–506 [Google Scholar]
  38. Jarosz DF, Taipale M, Lindquist S. 38.  2010. Protein homeostasis and the phenotypic manifestation of genetic diversity: principles and mechanisms. Annu. Rev. Genet. 44:189–216 [Google Scholar]
  39. McLellan J, O'Neil N, Tarailo S, Stoepel J, Bryan J. 39.  et al. 2009. Synthetic lethal genetic interactions that decrease somatic cell proliferation in Caenorhabditis elegans identify the alternative RFC CTF18 as a candidate cancer drug target. Mol. Biol. Cell 20:5306–13 [Google Scholar]
  40. Sajesh BV, Bailey M, Lichtensztejn Z, Hieter P, McManus KJ. 40.  2013. Synthetic lethal targeting of superoxide dismutase 1 selectively kills RAD54B-deficient colorectal cancer cells. Genetics 195:757–67 [Google Scholar]
  41. van Pel DM, Barrett IJ, Shimizu Y, Sajesh BV, Guppy BJ. 41.  et al. 2013. An evolutionarily conserved synthetic lethal interaction network identifies FEN1 as a broad-spectrum target for anticancer therapeutic development. PLOS Genet. 9:e1003254 [Google Scholar]
  42. Roguev A, Bandyopadhyay S, Zofall M, Zhang K, Fischer T. 42.  et al. 2008. Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322:405–10 [Google Scholar]
  43. Dixon SJ, Fedyshyn Y, Koh JL, Prasad TS, Chahwan C. 43.  et al. 2008. Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes. Proc. Natl. Acad. Sci. USA 105:16653–58 [Google Scholar]
  44. Boone C, Bussey H, Andrews BJ. 44.  2007. Exploring genetic interactions and networks with yeast. Nat. Rev. Genet. 8:437–49 [Google Scholar]
  45. Luo J, Emanuele MJ, Li D, Creighton CJ, Schlabach MR. 45.  et al. 2009. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137:835–48 [Google Scholar]
  46. Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE. 46.  et al. 2009. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462:108–12 [Google Scholar]
  47. Scholl C, Frohling S, Dunn IF, Schinzel AC, Barbie DA. 47.  et al. 2009. Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells. Cell 137:821–34 [Google Scholar]
  48. Babij C, Zhang Y, Kurzeja RJ, Munzli A, Shehabeldin A. 48.  et al. 2011. STK33 kinase activity is nonessential in KRAS-dependent cancer cells. Cancer Res. 71:5818–26 [Google Scholar]
  49. Kumar MS, Hancock DC, Molina-Arcas M, Steckel M, East P. 49.  et al. 2012. The GATA2 transcriptional network is requisite for RAS oncogene-driven non-small cell lung cancer. Cell 149:642–55 [Google Scholar]
  50. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA. 50.  et al. 2012. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–7 [Google Scholar]
  51. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A. 51.  et al. 2012. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483:570–75 [Google Scholar]
  52. Muellner MK, Uras IZ, Gapp BV, Kerzendorfer C, Smida M. 52.  et al. 2011. A chemical-genetic screen reveals a mechanism of resistance to PI3K inhibitors in cancer. Nat. Chem. Biol. 7:787–93 [Google Scholar]
  53. Torrance CJ, Agrawal V, Vogelstein B, Kinzler KW. 53.  2001. Use of isogenic human cancer cells for high-throughput screening and drug discovery. Nat. Biotechnol. 19:940–45 [Google Scholar]
  54. Mohr S, Bakal C, Perrimon N. 54.  2010. Genomic screening with RNAi: results and challenges. Annu. Rev. Biochem. 79:37–64 [Google Scholar]
  55. Brummelkamp TR, Bernards R. 55.  2003. New tools for functional mammalian cancer genetics. Nat. Rev. Cancer 3:781–89 [Google Scholar]
  56. Ngo VN, Davis RE, Lamy L, Yu X, Zhao H. 56.  et al. 2006. A loss-of-function RNA interference screen for molecular targets in cancer. Nature 441:106–10 [Google Scholar]
  57. Schlabach MR, Luo J, Solimini NL, Hu G, Xu Q. 57.  et al. 2008. Cancer proliferation gene discovery through functional genomics. Science 319:620–24 [Google Scholar]
  58. Silva JM, Marran K, Parker JS, Silva J, Golding M. 58.  et al. 2008. Profiling essential genes in human mammary cells by multiplex RNAi screening. Science 319:617–20 [Google Scholar]
  59. Schnorrer F, Schonbauer C, Langer CC, Dietzl G, Novatchkova M. 59.  et al. 2010. Systematic genetic analysis of muscle morphogenesis and function in Drosophila. Nature 464:287–91 [Google Scholar]
  60. Pospisilik JA, Schramek D, Schnidar H, Cronin SJF, Nehme NT. 60.  et al. 2010. Drosophila genome-wide obesity screen reveals hedgehog as a determinant of brown versus white adipose cell fate. Cell 140:148–60 [Google Scholar]
  61. Neumuller RA, Richter C, Fischer A, Novatchkova M, Neumuller KG, Knoblich JA. 61.  2011. Genome-wide analysis of self-renewal in Drosophila neural stem cells by transgenic RNAi. Cell Stem Cell 8:580–93 [Google Scholar]
  62. Zender L, Xue W, Zuber J, Semighini CP, Krasnitz A. 62.  et al. 2008. An oncogenomics-based in vivo RNAi screen identifies tumor suppressors in liver cancer. Cell 135:852–64 [Google Scholar]
  63. Bric A, Miething C, Bialucha CU, Scuoppo C, Zender L. 63.  et al. 2009. Functional identification of tumor-suppressor genes through an in vivo RNA interference screen in a mouse lymphoma model. Cancer Cell 16:324–35 [Google Scholar]
  64. Beronja S, Janki P, Heller E, Lien WH, Keyes BE. 64.  et al. 2013. RNAi screens in mice identify physiological regulators of oncogenic growth. Nature 501:185–90 [Google Scholar]
  65. Schramek D, Sendoel A, Segal JP, Beronja S, Heller E. 65.  et al. 2014. Direct in vivo RNAi screen unveils myosin IIa as a tumor suppressor of squamous cell carcinomas. Science 343:309–13 [Google Scholar]
  66. Shoemaker RH. 66.  2006. The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 6:813–23 [Google Scholar]
  67. Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K. 67.  et al. 2013. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154:1151–61 [Google Scholar]
  68. Haibe-Kains B, El-Hachem N, Birkbak NJ, Jin AC, Beck AH. 68.  et al. 2013. Inconsistency in large pharmacogenomic studies. Nature 504:389–93 [Google Scholar]
  69. Hopkins AL. 69.  2008. Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4:682–90 [Google Scholar]
  70. Schenone M, Dancik V, Wagner BK, Clemons PA. 70.  2013. Target identification and mechanism of action in chemical biology and drug discovery. Nat. Chem. Biol. 9:232–40 [Google Scholar]
  71. Kim HS, Mendiratta S, Kim J, Pecot CV, Larsen JE. 71.  et al. 2013. Systematic identification of molecular subtype-selective vulnerabilities in non-small-cell lung cancer. Cell 155:552–66 [Google Scholar]
  72. Ostrem JM, Peters U, Sos ML, Wells JA, Shokat KM. 72.  2013. K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions. Nature 503:548–51 [Google Scholar]
  73. Luo T, Masson K, Jaffe JD, Silkworth W, Ross NT. 73.  et al. 2012. STK33 kinase inhibitor BRD-8899 has no effect on KRAS-dependent cancer cell viability. Proc. Natl. Acad. Sci. USA 109:2860–65 [Google Scholar]
  74. Zhu Z, Aref AR, Cohoon TJ, Barbie TU, Imamura Y. 74.  et al. 2014. Inhibition of KRAS-driven tumorigenicity by interruption of an autocrine cytokine circuit. Cancer Discov. 4:452–65 [Google Scholar]
  75. Puyol M, Martin A, Dubus P, Mulero F, Pizcueta P. 75.  et al. 2010. A synthetic lethal interaction between K-Ras oncogenes and Cdk4 unveils a therapeutic strategy for non-small cell lung carcinoma. Cancer Cell 18:63–73 [Google Scholar]
  76. Liu G, Gandara DR, Lara PN Jr, Raghavan D, Doroshow JH. 76.  et al. 2004. A Phase II trial of flavopiridol (NSC #649890) in patients with previously untreated metastatic androgen-independent prostate cancer. Clin. Cancer Res. 10:924–28 [Google Scholar]
  77. Guha M. 77.  2012. Cyclin-dependent kinase inhibitors move into Phase III. Nat. Rev. Drug Discov. 11:892–94 [Google Scholar]
  78. Vicent S, Chen R, Sayles LC, Lin C, Walker RG. 78.  et al. 2010. Wilms tumor 1 (WT1) regulates KRAS-driven oncogenesis and senescence in mouse and human models. J. Clin. Investig. 120:3940–52 [Google Scholar]
  79. Corcoran RB, Cheng KA, Hata AN, Faber AC, Ebi H. 79.  et al. 2013. Synthetic lethal interaction of combined BCL-XL and MEK inhibition promotes tumor regressions in KRAS mutant cancer models. Cancer Cell 23:121–28 [Google Scholar]
  80. Dolma S, Lessnick SL, Hahn WC, Stockwell BR. 80.  2003. Identification of genotype-selective antitumor agents using synthetic lethal chemical screening in engineered human tumor cells. Cancer Cell 3:285–96 [Google Scholar]
  81. Yagoda N, von Rechenberg M, Zaganjor E, Bauer AJ, Yang WS. 81.  et al. 2007. RAS-RAF-MEK-dependent oxidative cell death involving voltage-dependent anion channels. Nature 447:864–68 [Google Scholar]
  82. Dixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM. 82.  et al. 2012. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell 149:1060–72 [Google Scholar]
  83. Shaw AT, Winslow MM, Magendantz M, Ouyang C, Dowdle J. 83.  et al. 2011. Selective killing of K-ras mutant cancer cells by small molecule inducers of oxidative stress. Proc. Natl. Acad. Sci. USA 108:8773–78 [Google Scholar]
  84. Dang CV. 84.  2012. MYC on the path to cancer. Cell 149:22–35 [Google Scholar]
  85. Wang Y, Engels IH, Knee DA, Nasoff M, Deveraux QL, Quon KC. 85.  2004. Synthetic lethal targeting of MYC by activation of the DR5 death receptor pathway. Cancer Cell 5:501–12 [Google Scholar]
  86. Zaman S, Wang R, Gandhi V. 86.  2014. Targeting the apoptosis pathway in hematologic malignancies. Leuk. Lymphoma 55:1980–92 [Google Scholar]
  87. Maris JM. 87.  2010. Recent advances in neuroblastoma. N. Engl. J. Med. 362:2202–11 [Google Scholar]
  88. Molenaar JJ, Ebus ME, Geerts D, Koster J, Lamers F. 88.  et al. 2009. Inactivation of CDK2 is synthetically lethal to MYCN over-expressing cancer cells. Proc. Natl. Acad. Sci. USA 106:12968–73 [Google Scholar]
  89. Goga A, Yang D, Tward AD, Morgan DO, Bishop JM. 89.  2007. Inhibition of CDK1 as a potential therapy for tumors over-expressing MYC. Nat. Med. 13:820–27 [Google Scholar]
  90. Horiuchi D, Kusdra L, Huskey NE, Chandriani S, Lenburg ME. 90.  et al. 2012. MYC pathway activation in triple-negative breast cancer is synthetic lethal with CDK inhibition. J. Exp. Med. 209:679–96 [Google Scholar]
  91. Kang J, Sergio CM, Sutherland RL, Musgrove EA. 91.  2014. Targeting cyclin-dependent kinase 1 (CDK1) but not CDK4/6 or CDK2 is selectively lethal to MYC-dependent human breast cancer cells. BMC Cancer 14:32 [Google Scholar]
  92. den Hollander J, Rimpi S, Doherty JR, Rudelius M, Buck A. 92.  et al. 2010. Aurora kinases A and B are up-regulated by Myc and are essential for maintenance of the malignant state. Blood 116:1498–505 [Google Scholar]
  93. Yang D, Liu H, Goga A, Kim S, Yuneva M, Bishop JM. 93.  2010. Therapeutic potential of a synthetic lethal interaction between the MYC proto-oncogene and inhibition of aurora-B kinase. Proc. Natl. Acad. Sci. USA 107:13836–41 [Google Scholar]
  94. Brockmann M, Poon E, Berry T, Carstensen A, Deubzer HE. 94.  et al. 2013. Small molecule inhibitors of Aurora-A induce proteasomal degradation of N-myc in childhood neuroblastoma. Cancer Cell 24:75–89 [Google Scholar]
  95. Kessler JD, Kahle KT, Sun T, Meerbrey KL, Schlabach MR. 95.  et al. 2012. A SUMOylation-dependent transcriptional subprogram is required for Myc-driven tumorigenesis. Science 335:348–53 [Google Scholar]
  96. Zuber J, Shi J, Wang E, Rappaport AR, Herrmann H. 96.  et al. 2011. RNAi screen identifies Brd4 as a therapeutic target in acute myeloid leukaemia. Nature 478:524–28 [Google Scholar]
  97. Mertz JA, Conery AR, Bryant BM, Sandy P, Balasubramanian S. 97.  et al. 2011. Targeting MYC dependence in cancer by inhibiting BET bromodomains. Proc. Natl. Acad. Sci. USA 108:16669–74 [Google Scholar]
  98. Delmore JE, Issa GC, Lemieux ME, Rahl PB, Shi J. 98.  et al. 2011. BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell 146:904–17 [Google Scholar]
  99. Ott CJ, Kopp N, Bird L, Paranal RM, Qi J. 99.  et al. 2012. BET bromodomain inhibition targets both c-Myc and IL7R in high-risk acute lymphoblastic leukemia. Blood 120:2843–52 [Google Scholar]
  100. Puissant A, Frumm SM, Alexe G, Bassil CF, Qi J. 100.  et al. 2013. Targeting MYCN in neuroblastoma by BET bromodomain inhibition. Cancer Discov. 3:308–23 [Google Scholar]
  101. Bandopadhayay P, Bergthold G, Nguyen B, Schubert S, Gholamin S. 101.  et al. 2014. BET bromodomain inhibition of MYC-amplified medulloblastoma. Clin. Cancer Res. 20:912–25 [Google Scholar]
  102. Jackson SP, Bartek J. 102.  2009. The DNA-damage response in human biology and disease. Nature 461:1071–78 [Google Scholar]
  103. Bryant HE, Schultz N, Thomas HD, Parker KM, Flower D. 103.  et al. 2005. Specific killing of BRCA2-deficient tumours with inhibitors of poly(ADP-ribose) polymerase. Nature 434:913–17 [Google Scholar]
  104. Farmer H, McCabe N, Lord CJ, Tutt AN, Johnson DA. 104.  et al. 2005. Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy. Nature 434:917–21 [Google Scholar]
  105. Turner N, Tutt A, Ashworth A. 105.  2004. Hallmarks of ‘BRCAness’ in sporadic cancers. Nat. Rev. Cancer 4:814–19 [Google Scholar]
  106. Fong PC, Boss DS, Yap TA, Tutt A, Wu P. 106.  et al. 2009. Inhibition of poly(ADP-ribose) polymerase in tumors from BRCA mutation carriers. N. Engl. J. Med. 361:123–34 [Google Scholar]
  107. Ledermann J, Harter P, Gourley C, Friedlander M, Vergote I. 107.  et al. 2012. Olaparib maintenance therapy in platinum-sensitive relapsed ovarian cancer. N. Engl. J. Med. 366:1382–92 [Google Scholar]
  108. Mateo J, Ong M, Tan DSP, Gonzalez MA, de Bono JS. 108.  2013. Appraising iniparib, the PARP inhibitor that never was—what must we learn?. Nat. Rev. Clin. Oncol. 10:688–96 [Google Scholar]
  109. O'Shaughnessy J, Osborne C, Pippen JE, Yoffe M, Patt D. 109.  et al. 2011. Iniparib plus chemotherapy in metastatic triple-negative breast cancer. N. Engl. J. Med. 364:205–14 [Google Scholar]
  110. Mendes-Pereira AM, Martin SA, Brough R, McCarthy A, Taylor JR. 110.  et al. 2009. Synthetic lethal targeting of PTEN mutant cells with PARP inhibitors. EMBO Mol. Med. 1:315–22 [Google Scholar]
  111. Weston VJ, Oldreive CE, Skowronska A, Oscier DG, Pratt G. 111.  et al. 2010. The PARP inhibitor olaparib induces significant killing of ATM-deficient lymphoid tumor cells in vitro and in vivo. Blood 116:4578–87 [Google Scholar]
  112. Reaper PM, Griffiths MR, Long JM, Charrier JD, MacCormick S. 112.  et al. 2011. Selective killing of ATM- or p53-deficient cancer cells through inhibition of ATR. Nat. Chem. Biol. 7:428–30 [Google Scholar]
  113. Nijman SM, Friend SH. 113.  2013. Potential of the synthetic lethality principle. Science 342:809–11 [Google Scholar]
  114. Arrowsmith J, Miller P. 114.  2013. Trial watch: phase II and phase III attrition rates 2011–2012. Nat. Rev. Drug Discov. 12:569 [Google Scholar]
  115. Carette JE, Guimaraes CP, Varadarajan M, Park AS, Wuethrich I. 115.  et al. 2009. Haploid genetic screens in human cells identify host factors used by pathogens. Science 326:1231–35 [Google Scholar]
  116. Carette JE, Guimaraes CP, Wuethrich I, Blomen VA, Varadarajan M. 116.  et al. 2011. Global gene disruption in human cells to assign genes to phenotypes by deep sequencing. Nat. Biotechnol. 29:542–46 [Google Scholar]
  117. Elling U, Taubenschmid J, Wirnsberger G, O'Malley R, Demers SP. 117.  et al. 2011. Forward and reverse genetics through derivation of haploid mouse embryonic stem cells. Cell Stem Cell 9:563–74 [Google Scholar]
  118. Pettitt SJ, Rehman FL, Bajrami I, Brough R, Wallberg F. 118.  et al. 2013. A genetic screen using the PiggyBac transposon in haploid cells identifies Parp1 as a mediator of olaparib toxicity. PLOS ONE 8e61520 [Google Scholar]
  119. Burckstummer T, Banning C, Hainzl P, Schobesberger R, Kerzendorfer C. 119.  et al. 2013. A reversible gene trap collection empowers haploid genetics in human cells. Nat. Methods 10:965–71 [Google Scholar]
  120. Cong L, Ran FA, Cox D, Lin S, Barretto R. 120.  et al. 2013. Multiplex genome engineering using CRISPR/Cas systems. Science 339:819–23 [Google Scholar]
  121. Mali P, Yang L, Esvelt KM, Aach J, Guell M. 121.  et al. 2013. RNA-guided human genome engineering via Cas9. Science 339:823–26 [Google Scholar]
  122. Wang T, Wei JJ, Sabatini DM, Lander ES. 122.  2014. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343:80–84 [Google Scholar]
  123. Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA. 123.  et al. 2014. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343:84–87 [Google Scholar]
  124. Toyoshima M, Howie HL, Imakura M, Walsh RM, Annis JE. 124.  et al. 2012. Functional genomics identifies therapeutic targets for MYC-driven cancer. Proc. Natl. Acad. Sci. USA 109:9545–50 [Google Scholar]
  125. Pourdehnad M, Truitt ML, Siddiqi IN, Ducker GS, Shokat KM, Ruggero D. 125.  2013. Myc and mTOR converge on a common node in protein synthesis control that confers synthetic lethality in Myc-driven cancers. Proc. Natl. Acad. Sci. USA 110:11988–93 [Google Scholar]
  126. Martin SA, McCabe N, Mullarkey M, Cummins R, Burgess DJ. 126.  et al. 2010. DNA polymerases as potential therapeutic targets for cancers deficient in the DNA mismatch repair proteins MSH2 or MLH1. Cancer Cell 17:235–48 [Google Scholar]
  127. Brenner JC, Ateeq B, Li Y, Yocum AK, Cao Q. 127.  et al. 2011. Mechanistic rationale for inhibition of poly(ADP-ribose) polymerase in ETS gene fusion-positive prostate cancer. Cancer Cell 19:664–78 [Google Scholar]
  128. Sullivan KD, Padilla-Just N, Henry RE, Porter CC, Kim J. 128.  et al. 2012. ATM and MET kinases are synthetic lethal with nongenotoxic activation of p53. Nat. Chem. Biol. 8:646–54 [Google Scholar]
  129. Riabinska A, Daheim M, Herter-Sprie GS, Winkler J, Fritz C. 129.  et al. 2013. Therapeutic targeting of a robust non-oncogene addiction to PRKDC in ATM-defective tumors. Sci. Translational Med. 5:189ra78 [Google Scholar]
  130. Chan DA, Sutphin PD, Nguyen P, Turcotte S, Lai EW. 130.  et al. 2011. Targeting GLUT1 and the Warburg effect in renal cell carcinoma by chemical synthetic lethality. Sci. Translational Med. 3:94ra70 [Google Scholar]
  131. Shackelford DB, Abt E, Gerken L, Vasquez DS, Seki A. 131.  et al. 2013. LKB1 inactivation dictates therapeutic response of non-small cell lung cancer to the metabolism drug phenformin. Cancer Cell 23:143–58 [Google Scholar]
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