1932

Abstract

Functional genomics holds great promise for the dissection of cancer biology. The elucidation of genetic cooperation and molecular details that govern oncogenesis, metastasis, and response to therapy is made possible by robust technologies for perturbing gene function coupled to quantitative analysis of cancer phenotypes resulting from genetic or epigenetic perturbations. Multiplexed genetic perturbations enable the dissection of cooperative genetic lesions as well as the identification of synthetic lethal gene pairs that hold particular promise for constructing innovative cancer therapies. Lastly, functional genomics strategies enable the highly multiplexed in vivo analysis of genes that govern tumorigenesis as well as of the complex multicellular biology of a tumor, such as immune response and metastasis phenotypes. In this review, we discuss both historical and emerging functional genomics approaches and their impact on the cancer research landscape.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-cancerbio-030518-055742
2019-03-04
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/cancerbio/3/1/annurev-cancerbio-030518-055742.html?itemId=/content/journals/10.1146/annurev-cancerbio-030518-055742&mimeType=html&fmt=ahah

Literature Cited

  1. Adamson B, Norman TM, Jost M, Cho MY, Nuñez JK et al. 2016. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167:71867–82.e21
    [Google Scholar]
  2. Aguirre AJ, Meyers RM, Weir BA, Vazquez F, Zhang CZ et al. 2016. Genomic copy number dictates a gene-independent cell response to CRISPR/Cas9 targeting. Cancer Discov 6:8914–29
    [Google Scholar]
  3. Anderson DJ, Le Moigne R, Djakovic S, Kumar B, Rice J et al. 2015. Targeting the AAA ATPase p97 as an approach to treat cancer through disruption of protein homeostasis. Cancer Cell 28:5653–65
    [Google Scholar]
  4. Ashworth A, Lord CJ, Reis-Filho JS 2011. Genetic interactions in cancer progression and treatment. Cell 145:130–38
    [Google Scholar]
  5. Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D et al. 2018. Comprehensive characterization of cancer driver genes and mutations. Cell 173:2371–85.e18
    [Google Scholar]
  6. Balling R. 2001. ENU mutagenesis: analyzing gene function in mice. Annu. Rev. Genomics Hum. Genet. 2:1463–92
    [Google Scholar]
  7. Baratta MG, Schinzel AC, Zwang Y, Bandopadhayay P, Bowman-Colin C et al. 2015. An in-tumor genetic screen reveals that the BET bromodomain protein, BRD4, is a potential therapeutic target in ovarian carcinoma. PNAS 112:1232–37
    [Google Scholar]
  8. Bassik MC, Kampmann M, Lebbink RJ, Wang S, Hein MY et al. 2013. A systematic mammalian genetic interaction map reveals pathways underlying ricin susceptibility. Cell 152:4909–22
    [Google Scholar]
  9. Beronja S, Janki P, Heller E, Lien WH, Keyes BE et al. 2013. RNAi screens in mice identify physiological regulators of oncogenic growth. Nature 501:7466185–90
    [Google Scholar]
  10. Beronja S, Livshits G, Williams S, Fuchs E 2010. Rapid functional dissection of genetic networks via tissue-specific transduction and RNAi in mouse embryos. Nat. Med. 16:7821–27
    [Google Scholar]
  11. Billon P, Bryant EE, Joseph SA, Nambiar TS, Hayward SB et al. 2017. CRISPR-mediated base editing enables efficient disruption of eukaryotic genes through induction of STOP codons. Mol. Cell 67:61068–79.e4
    [Google Scholar]
  12. Blomen VA, Májek P, Jae LT, Bigenzahn JW, Nieuwenhuis J et al. 2015. Gene essentiality and synthetic lethality in haploid human cells. Science 350:62641092–96
    [Google Scholar]
  13. Boettcher M, Tian R, Blau JA, Markegard E, Wagner RT et al. 2018. Dual gene activation and knockout screen reveals directional dependencies in genetic networks. Nat. Biotechnol. 36:2170–78
    [Google Scholar]
  14. Bossi D, Cicalese A, Dellino GI, Luzi L, Riva L et al. 2016. In vivo genetic screens of patient-derived tumors revealed unexpected frailty of the transformed phenotype. Cancer Discov 6:6650–63
    [Google Scholar]
  15. Braun CJ, Bruno PM, Horlbeck MA, Gilbert LA, Weissman JS, Hemann MT 2016. Versatile in vivo regulation of tumor phenotypes by dCas9-mediated transcriptional perturbation. PNAS 113:27E3892–900
    [Google Scholar]
  16. Bric A, Miething C, Bialucha CU, Scuoppo C, Zender L et al. 2009. Functional identification of tumor-suppressor genes through an in vivo RNA interference screen in a mouse lymphoma model. Cancer Cell 16:4324–35
    [Google Scholar]
  17. Carette JE, Guimaraes CP, Varadarajan M, Park AS, Wuethrich I et al. 2009. Haploid genetic screens in human cells identify host factors used by pathogens. Science 326:59571231–35
    [Google Scholar]
  18. Chavez A, Scheiman J, Vora S, Pruitt BW, Tuttle M et al. 2015. Highly efficient Cas9-mediated transcriptional programming. Nat. Methods 12:4326–28
    [Google Scholar]
  19. Chen S, Sanjana NE, Zheng K, Shalem O, Lee K et al. 2015. Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis. Cell 160:61246–60
    [Google Scholar]
  20. Chow RD, Guzman CD, Wang G, Schmidt F, Youngblood MW et al. 2017. AAV-mediated direct in vivo CRISPR screen identifies functional suppressors in glioblastoma. Nat. Neurosci. 20:101329–41
    [Google Scholar]
  21. Costanzo M, Baryshnikova A, Bellay J, Kim Y, Spear ED et al. 2010. The genetic landscape of a cell. Science 327:5964425–31
    [Google Scholar]
  22. Costanzo M, VanderSluis B, Koch EN, Baryshnikova A, Pons C et al. 2016. A global genetic interaction network maps a wiring diagram of cellular function. Science 353:6306aaf1420
    [Google Scholar]
  23. Cowley GS, Weir BA, Vazquez F, Tamayo P, Scott JA et al. 2014. Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies. Sci. Data 1:140035
    [Google Scholar]
  24. Cox DBT, Gootenberg JS, Abudayyeh OO, Franklin B, Kellner MJ et al. 2017. RNA editing with CRISPR-Cas13. Science 358:63661019–27
    [Google Scholar]
  25. Dail M, Li Q, McDaniel A, Wong J, Akagi K et al. 2010. Mutant Ikzf1, KrasG12D, and Notch1 cooperate in T lineage leukemogenesis and modulate responses to targeted agents. PNAS 107:115106–11
    [Google Scholar]
  26. Dail M, Wong J, Lawrence J, O'Connor D, Nakitandwe J et al. 2014. Loss of oncogenic Notch1 with resistance to a PI3K inhibitor in T-cell leukaemia. Nature 513:7519512–16
    [Google Scholar]
  27. Datlinger P, Rendeiro AF, Schmidl C, Krausgruber T, Traxler P et al. 2017. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14:3297–301
    [Google Scholar]
  28. Diehl P, Tedesco D, Chenchik A 2014. Use of RNAi screens to uncover resistance mechanisms in cancer cells and identify synthetic lethal interactions. Drug Discov. Today Technol. 11:111–18
    [Google Scholar]
  29. Ding L, Bailey MH, Porta-Pardo E, Thorsson V, Colaprico A et al. 2018. Perspective on oncogenic processes at the end of the beginning of cancer genomics. Cell 173:2305–20.e10
    [Google Scholar]
  30. Dixit A, Parnas O, Li B, Chen J, Fulco CP et al. 2016. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167:71853–66.e17
    [Google Scholar]
  31. Doench JG. 2018. Am I ready for CRISPR? A user's guide to genetic screens. Nat. Rev. Genet. 19:267–80
    [Google Scholar]
  32. Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW et al. 2016. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34:2184–91
    [Google Scholar]
  33. Doudna JA, Charpentier E 2014. The new frontier of genome engineering with CRISPR-Cas9. Science 346:62131258096
    [Google Scholar]
  34. Dow LE, Lowe SW 2012. Life in the fast lane: mammalian disease models in the genomics era. Cell 148:61099–109
    [Google Scholar]
  35. Du D, Roguev A, Gordon DE, Chen M, Chen SH et al. 2017. Genetic interaction mapping in mammalian cells using CRISPR interference. Nat. Methods 14:6577–80
    [Google Scholar]
  36. Dupuy AJ, Akagi K, Largaespada DA, Copeland NG, Jenkins NA 2005. Mammalian mutagenesis using a highly mobile somatic Sleeping Beauty transposon system. Nature 436:7048221–26
    [Google Scholar]
  37. Egeblad M, Nakasone ES, Werb Z 2010. Tumors as organs: complex tissues that interface with the entire organism. Dev. Cell 18:6884–901
    [Google Scholar]
  38. Fellmann C, Hoffmann T, Sridhar V, Hopfgartner B, Muhar M et al. 2013. An optimized microRNA backbone for effective single-copy RNAi. Cell Rep 5:61704–13
    [Google Scholar]
  39. Fellmann C, Zuber J, McJunkin K, Chang K, Malone CD et al. 2011. Functional identification of optimized RNAi triggers using a massively parallel sensor assay. Mol. Cell 41:6733–46
    [Google Scholar]
  40. Findlay GM, Daza RM, Martin B, Zhang MD, Leith AP et al. 2018. Accurate classification of BRCA1 variants with saturation genome editing. Nature 562:217–22
    [Google Scholar]
  41. Fulco CP, Munschauer M, Anyoha R, Munson G, Grossman SR et al. 2016. Systematic mapping of functional enhancer-promoter connections with CRISPR interference. Science 354:6313769–73
    [Google Scholar]
  42. Gargiulo G, Cesaroni M, Serresi M, DeVries N, Hulsman D et al. 2013. In vivo RNAi screen for BMI1 targets identifies TGF-β/BMP-ER stress pathways as key regulators of neural- and malignant glioma-stem cell homeostasis. Cancer Cell 23:5660–76
    [Google Scholar]
  43. Gaudelli NM, Komor AC, Rees HA, Packer MS, Badran AH et al. 2017. Programmable base editing of A·T to G·C in genomic DNA without DNA cleavage. Nature 551:7681464–71
    [Google Scholar]
  44. Gerhards NM, Rottenberg S 2018. New tools for old drugs: functional genetic screens to optimize current chemotherapy. Drug Resist. Updates 36:30–46
    [Google Scholar]
  45. Gilbert LA, Larson MH, Morsut L, Liu Z, Brar GA et al. 2013. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154:2442–51
    [Google Scholar]
  46. Gilbert LA, Horlbeck MA, Adamson B, Villalta JE, Chen Y et al. 2014. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159:3647–61
    [Google Scholar]
  47. Grimm S. 2004. The art and design of genetic screens: mammalian culture cells. Nat. Rev. Genet. 5:3179–89
    [Google Scholar]
  48. Gutmann DH, Hunter-Schaedle K, Shannon KM 2006. Harnessing preclinical mouse models to inform human clinical cancer trials. J. Clin. Investig. 116:4847–52
    [Google Scholar]
  49. Han K, Jeng EE, Hess GT, Morgens DW, Li A, Bassik MC 2017. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat. Biotechnol. 35:5463–74
    [Google Scholar]
  50. Hart T, Brown KR, Sircoulomb F, Rottapel R, Moffat J 2014. Measuring error rates in genomic perturbation screens: gold standards for human functional genomics. Mol. Syst. Biol. 10:7733
    [Google Scholar]
  51. Hart T, Chandrashekhar M, Aregger M, Steinhart Z, Brown KR et al. 2015. High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities. Cell 163:61515–26
    [Google Scholar]
  52. Hartman JLI V, Garvik B, Hartwell L 2001. Principles for the buffering of genetic variation. Science 291:55061001–4
    [Google Scholar]
  53. Hess GT, Frésard L, Han K, Lee CH, Li A et al. 2016. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods 13:121036–42
    [Google Scholar]
  54. Hidalgo M, Amant F, Biankin AV, Budinská E, Byrne AT et al. 2014. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov 4:9998–1013
    [Google Scholar]
  55. Hilton IB, D'Ippolito AM, Vockley CM, Thakore PI, Crawford GE et al. 2015. Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers. Nat. Biotechnol. 33:5510–17
    [Google Scholar]
  56. Horlbeck MA, Xu A, Wang M, Bennett NK, Park CY et al. 2018. Mapping the genetic landscape of human cells. Cell 174:4953–67.e22
    [Google Scholar]
  57. Horlbeck MA, Gilbert LA, Villalta JE, Adamson B, Pak RA et al. 2016. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation. eLife 5:e19760
    [Google Scholar]
  58. Hu JH, Miller SM, Geurts MH, Tang W, Chen L et al. 2018. Evolved Cas9 variants with broad PAM compatibility and high DNA specificity. Nature 556:769957–63
    [Google Scholar]
  59. Jaitin DA, Weiner A, Yofe I, Lara-Astiaso D, Keren-Shaul H et al. 2016. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167:71883–96.e15
    [Google Scholar]
  60. Jiang H, Pritchard JR, Williams RT, Lauffenburger DA, Hemann MT 2011. A mammalian functional-genetic approach to characterizing cancer therapeutics. Nat. Chem. Biol. 7:292–100
    [Google Scholar]
  61. Johnson JI, Decker S, Zaharevitz D, Rubinstein LV, Venditti JM et al. 2001. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br. J. Cancer 84:101424–31
    [Google Scholar]
  62. Jost M, Chen Y, Gilbert LA, Horlbeck MA, Krenning L et al. 2017. Combined CRISPRi/a-based chemical genetic screens reveal that rigosertib is a microtubule-destabilizing agent. Mol. Cell 68:1210–23.e6
    [Google Scholar]
  63. Jost M, Weissman JS 2017. CRISPR approaches to small molecule target identification. ACS Chem. Biol. 13:2366–75
    [Google Scholar]
  64. Kampmann M, Horlbeck MA, Chen Y, Tsai JC, Bassik MC et al. 2015. Next-generation libraries for robust RNA interference-based genome-wide screens. PNAS 112:26E3384–91
    [Google Scholar]
  65. Kerbel RS. 2003. Human tumor xenografts as predictive preclinical models for anticancer drug activity in humans: better than commonly perceived—but they can be improved. Cancer Biol. Ther. 2:Suppl. 1S134–39
    [Google Scholar]
  66. Kersten K, de Visser KE, van Miltenburg MH, Jonkers J 2017. Genetically engineered mouse models in oncology research and cancer medicine. EMBO Mol. Med. 9:2137–53
    [Google Scholar]
  67. Kobayashi T, Yamaguchi T, Hamanaka S, Kato-Itoh M, Yamazaki Y et al. 2010. Generation of rat pancreas in mouse by interspecific blastocyst injection of pluripotent stem cells. Cell 142:5787–99
    [Google Scholar]
  68. Koike-Yusa H, Li Y, Tan EP, Velasco-Herrera MDC, Yusa K 2014. Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library. Nat. Biotechnol. 32:3267–73
    [Google Scholar]
  69. Komor AC, Kim YB, Packer MS, Zuris JA, Liu DR 2016. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533:7603420–24
    [Google Scholar]
  70. Konermann S, Brigham MD, Trevino AE, Joung J, Abudayyeh OO et al. 2015. Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex. Nature 517:7536583–88
    [Google Scholar]
  71. Konermann S, Lotfy P, Brideau NJ, Oki J, Shokhirev MN, Hsu PD 2018. Transcriptome engineering with RNA-targeting type VI-D CRISPR effectors. Cell 173:3665–76.e14
    [Google Scholar]
  72. Korkmaz G, Lopes R, Ugalde AP, Nevedomskaya E, Han R et al. 2016. Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9. Nat. Biotechnol. 34:2192–98
    [Google Scholar]
  73. Kryukov GV, Wilson FH, Ruth JR, Paulk J, Tsherniak A et al. 2016. MTAP deletion confers enhanced dependency on the PRMT5 arginine methyltransferase in cancer cells. Science 351:62781214–18
    [Google Scholar]
  74. Kuscu C, Parlak M, Tufan T, Yang J, Szlachta K et al. 2017. CRISPR-STOP: gene silencing through base-editing-induced nonsense mutations. Nat. Methods 14:7710–12
    [Google Scholar]
  75. Kuzmin E, VanderSluis B, Wang W, Tan G, Deshpande R et al. 2018. Systematic analysis of complex genetic interactions. Science 360:6386eaao1729
    [Google Scholar]
  76. Lauchle JO, Kim D, Le DT, Akagi K, Crone M et al. 2009. Response and resistance to MEK inhibition in leukaemias initiated by hyperactive Ras. Nature 461:7262411–14
    [Google Scholar]
  77. Le Sage C, Lawo S, Panicker P, Scales TME, Rahman SA et al. 2017. Dual direction CRISPR transcriptional regulation screening uncovers gene networks driving drug resistance. Sci. Rep. 7:117693
    [Google Scholar]
  78. Lee MJ, Ye AS, Gardino AK, Heijink AM, Sorger PK et al. 2012. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149:4780–94
    [Google Scholar]
  79. Liu SJ, Horlbeck MA, Cho SW, Birk HS, Malatesta M et al. 2017. CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells. Science 355:6320eaah7111
    [Google Scholar]
  80. Liu XS, Wu H, Ji X, Stelzer Y, Wu X et al. 2016. Editing DNA methylation in the mammalian genome. Cell 167:1233–47.e17
    [Google Scholar]
  81. Liu Y, Chen C, Xu Z, Scuoppo C, Rillahan CD et al. 2016. Deletions linked to TP53 loss drive cancer through p53-independent mechanisms. Nature 531:7595471–75
    [Google Scholar]
  82. Lord CJ, Ashworth A 2017. PARP inhibitors: synthetic lethality in the clinic. Science 355:63301152–58
    [Google Scholar]
  83. Lund AH, Turner G, Trubetskoy A, Verhoeven E, Wientjens E et al. 2002. Genome-wide retroviral insertional tagging of genes involved in cancer in Cdkn2a-deficient mice. Nat. Genet. 32:1160–65
    [Google Scholar]
  84. Luo J, Emanuele MJ, Li D, Creighton CJ, Schlabach MR et al. 2009.a A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell 137:5835–48
    [Google Scholar]
  85. Luo J, Solimini NL, Elledge SJ 2009.b Principles of cancer therapy: oncogene and non-oncogene addiction. Cell 136:5823–37
    [Google Scholar]
  86. Ma Y, Zhang J, Yin W, Zhang Z, Song Y, Chang X 2016. Targeted AID-mediated mutagenesis (TAM) enables efficient genomic diversification in mammalian cells. Nat. Methods 13:121029–35
    [Google Scholar]
  87. Maddalo D, Manchado E, Concepcion CP, Bonetti C, Vidigal JA et al. 2014. In vivo engineering of oncogenic chromosomal rearrangements with the CRISPR/Cas9 system. Nature 516:7531423–28
    [Google Scholar]
  88. Manguso RT, Pope HW, Zimmer MD, Brown FD, Yates KB et al. 2017. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547:7664413–18
    [Google Scholar]
  89. Maresch R, Mueller S, Veltkamp C, Öllinger R, Friedrich M et al. 2016. Multiplexed pancreatic genome engineering and cancer induction by transfection-based CRISPR/Cas9 delivery in mice. Nat. Commun. 7:10770
    [Google Scholar]
  90. Matheny CJ, Wei MC, Bassik MC, Donnelly AJ, Kampmann M et al. 2013. Next-generation NAMPT inhibitors identified by sequential high-throughput phenotypic chemical and functional genomic screens. Chem. Biol. 20:111352–63
    [Google Scholar]
  91. Mavrakis KJ, McDonald RE, Schlabach MR, Billy E, Hoffman GR et al. 2016. Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5. Science 351:62781208–13
    [Google Scholar]
  92. McDonald ER, de Weck A, Schlabach MR, Billy E, Mavrakis KJ et al. 2017. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep RNAi screening. Cell 170:3577–92.e10
    [Google Scholar]
  93. McDonald JI, Celik H, Rois LE, Fishberger G, Fowler T et al. 2016. Reprogrammable CRISPR/Cas9-based system for inducing site-specific DNA methylation. Biol. Open 5:6866–74
    [Google Scholar]
  94. McManus MT, Sharp PA 2002. Gene silencing in mammals by small interfering RNAs. Nat. Rev. Genet. 3:10737–47
    [Google Scholar]
  95. Meacham CE, Ho EE, Dubrovsky E, Gertler FB, Hemann MT 2009. In vivo RNAi screening identifies regulators of actin dynamics as key determinants of lymphoma progression. Nat. Genet. 41:101133–37
    [Google Scholar]
  96. Meacham CE, Lawton LN, Soto-Feliciano YM, Pritchard JR, Joughin BA et al. 2015. A genome-scale in vivo loss-of-function screen identifies phf6 as a lineage-specific regulator of leukemia cell growth. Genes Dev 29:5483–88
    [Google Scholar]
  97. Meyers RM, Bryan JG, McFarland JM, Weir BA, Sizemore AE et al. 2017. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49:121779–84
    [Google Scholar]
  98. Mikkers H, Allen J, Knipscheer P, Romeyn L, Hart A et al. 2002. High-throughput retroviral tagging to identify components of specific signaling pathways in cancer. Nat. Genet. 32:1153–59
    [Google Scholar]
  99. Miller TE, Liau BB, Wallace LC, Morton AR, Xie Q et al. 2017. Transcription elongation factors represent in vivo cancer dependencies in glioblastoma. Nature 547:7663355–59
    [Google Scholar]
  100. Morita S, Noguchi H, Horii T, Nakabayashi K, Kimura M et al. 2016. Targeted DNA demethylation in vivo using dCas9-peptide repeat and scFv-TET1 catalytic domain fusions. Nat. Biotechnol. 34:101060–65
    [Google Scholar]
  101. Munoz DM, Cassiani PJ, Li L, Billy E, Korn JM et al. 2016. CRISPR screens provide a comprehensive assessment of cancer vulnerabilities but generate false-positive hits for highly amplified genomic regions. Cancer Discov 6:8900–13
    [Google Scholar]
  102. Najm FJ, Strand C, Donovan KF, Hegde M, Sanson KR et al. 2018. Orthologous CRISPR-Cas9 enzymes for combinatorial genetic screens. Nat. Biotechnol. 36:2179–89
    [Google Scholar]
  103. Ngo VN, Davis RE, Lamy L, Yu X, Zhao H et al. 2006. A loss-of-function RNA interference screen for molecular targets in cancer. Nature 441:1106–10
    [Google Scholar]
  104. Nishida K, Arazoe T, Yachie N, Banno S, Kakimoto M et al. 2016. Targeted nucleotide editing using hybrid prokaryotic and vertebrate adaptive immune systems. Science 353:6305aaf8729
    [Google Scholar]
  105. Pritchard JR, Bruno PM, Gilbert LA, Capron KL, Lauffenburger DA, Hemann MT 2013. Defining principles of combination drug mechanisms of action. PNAS 110:2E170–79
    [Google Scholar]
  106. Pritchard JR, Gilbert LA, Meacham CE, Ricks JL, Jiang H et al. 2011. Bcl-2 family genetic profiling reveals microenvironment-specific determinants of chemotherapeutic response. Cancer Res 71:175850–58
    [Google Scholar]
  107. Qi LS, Larson MH, Gilbert LA, Doudna JA, Weissman JS et al. 2013. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152:51173–83
    [Google Scholar]
  108. Richmond A, Su Y 2008. Mouse xenograft models versus GEM models for human cancer therapeutics. Dis. Model. Mech. 1:2–378–82
    [Google Scholar]
  109. Ritchie MD, Hahn LW, Roodi N, Bailey LR, Dupont WD et al. 2001. Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am. J. Hum. Genet. 69:1138–47
    [Google Scholar]
  110. Rogers ZN, McFarland CD, Winters IP, Naranjo S, Chuang CH et al. 2017. A quantitative and multiplexed approach to uncover the fitness landscape of tumor suppression in vivo. Nat. Methods 14:7737–42
    [Google Scholar]
  111. Rogers ZN, McFarland CD, Winters IP, Seoane JA, Brady JJ et al. 2018. Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice. Nat. Genet. 50:4483
    [Google Scholar]
  112. Roguev A, Talbot D, Negri GL, Shales M, Cagney G et al. 2013. Quantitative genetic-interaction mapping in mammalian cells. Nat. Methods 10:5432–37
    [Google Scholar]
  113. Rosenbluh J, Mercer J, Shrestha Y, Oliver R, Tamayo P et al. 2016. Genetic and proteomic interrogation of lower confidence candidate genes reveals signaling networks in β-catenin-active cancers. Cell Syst 3:3302–16.e4
    [Google Scholar]
  114. Rual J-F, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A et al. 2005. Towards a proteome-scale map of the human protein–protein interaction network. Nature 437:70621173–78
    [Google Scholar]
  115. Rudalska R, Dauch D, Longerich T, McJunkin K, Wuestefeld T et al. 2014. In vivo RNAi screening identifies a mechanism of sorafenib resistance in liver cancer. Nat. Med. 20:101138–46
    [Google Scholar]
  116. Sa JK, Yoon Y, Kim M, Kim Y, Cho HJ et al. 2015. In vivo RNAi screen identifies NLK as a negative regulator of mesenchymal activity in glioblastoma. Oncotarget 6:2420145–59
    [Google Scholar]
  117. Sack LM, Davoli T, Li MZ, Li Y, Xu Q et al. 2018. Profound tissue specificity in proliferation control underlies cancer drivers and aneuploidy patterns. Cell 173:2499–514.e23
    [Google Scholar]
  118. Sánchez-Rivera FJ, Jacks T 2015. Applications of the CRISPR-Cas9 system in cancer biology. Nat. Rev. Cancer 15:7387–95
    [Google Scholar]
  119. Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A et al. 2018. Oncogenic signaling pathways in The Cancer Genome Atlas. Cell 173:2321–37.e10
    [Google Scholar]
  120. Sanjana NE, Wright J, Zheng K, Shalem O, Fontanillas P et al. 2016. High-resolution interrogation of functional elements in the noncoding genome. Science 353:63071545–49
    [Google Scholar]
  121. Schlabach MR, Luo J, Solimini NL, Hu G, Xu Q et al. 2008. Cancer proliferation gene discovery through functional genomics. Science 319:5863620–24
    [Google Scholar]
  122. Scholz CC, Berger DP, Winterhalter BR, Henß H, Fiebig HH 1990. Correlation of drug response in patients and in the clonogenic assay with solid human tumour xenografts. Eur. J. Cancer Clin. Oncol. 26:8901–5
    [Google Scholar]
  123. Schramek D, Sendoel A, Segal JP, Beronja S, Heller E et al. 2014. Direct in vivo RNAi screen unveils myosin IIa as a tumor suppressor of squamous cell carcinomas. Science 343:6168309–13
    [Google Scholar]
  124. Schuldiner M, Collins SR, Thompson NJ, Denic V, Bhamidipati A et al. 2005. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123:3507–19
    [Google Scholar]
  125. Schuster A, Erasimus H, Fritah S, Nazarov PV, van Dyck E et al. 2018. RNAi/CRISPR screens: from a pool to a valid hit. Trends Biotechnol In press
  126. Scuoppo C, Miething C, Lindqvist L, Reyes J, Ruse C et al. 2012. A tumour suppressor network relying on the polyamine-hypusine axis. Nature 487:7406244–48
    [Google Scholar]
  127. Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA et al. 2014. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343:616684–87
    [Google Scholar]
  128. Shalem O, Sanjana NE, Zhang F 2015. High-throughput functional genomics using CRISPR-Cas9. Nat. Rev. Genet. 16:5299–311
    [Google Scholar]
  129. Sharpless NE, DePinho RA 2006. The mighty mouse: genetically engineered mouse models in cancer drug development. Nat. Rev. Drug Discov. 5:9741–54
    [Google Scholar]
  130. Shen JP, Zhao D, Sasik R, Luebeck J, Birmingham A et al. 2017. Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nat. Methods 14:6573–76
    [Google Scholar]
  131. Shi J, Wang E, Milazzo JP, Wang Z, Kinney JB, Vakoc CR 2015. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat. Biotechnol. 33:6661–67
    [Google Scholar]
  132. Shorthouse AJ, Smyth JF, Steel GG, Ellison M, Mills J, Peckham MJ 1980. The human tumour xenograft—A valid model in experimental chemotherapy?. Br. J. Surg. 67:10715–22
    [Google Scholar]
  133. Silva JM, Marran K, Parker JS, Silva J, Golding M et al. 2008. Profiling essential genes in human mammary cells by multiplex RNAi screening. Science 319:5863617–20
    [Google Scholar]
  134. Stanford WL, Cohn JB, Cordes SP 2001. Gene-trap mutagenesis: past, present and beyond. Nat. Rev. Genet. 2:10756–68
    [Google Scholar]
  135. Suzuki T, Shen H, Akagi K, Morse HC, Malley JD et al. 2002. New genes involved in cancer identified by retroviral tagging. Nat. Genet. 32:1166–74
    [Google Scholar]
  136. Takahashi K, Yamanaka S 2006. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 126:4663–76
    [Google Scholar]
  137. Tanenbaum ME, Gilbert LA, Qi LS, Weissman JS, Vale RD 2014. A protein-tagging system for signal amplification in gene expression and fluorescence imaging. Cell 159:3635–46
    [Google Scholar]
  138. Tian X, Azpurua J, Hine C, Vaidya A, Myakishev-Rempel M et al. 2013. High-molecular-mass hyaluronan mediates the cancer resistance of the naked mole rat. Nature 499:7458346–49
    [Google Scholar]
  139. Tong AHY, Evangelista M, Parsons AB, Xu H, Bader GD et al. 2001. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294:55502364–68
    [Google Scholar]
  140. Tong AHY, Lesage G, Bader GD, Ding H, Xu H et al. 2004. Global mapping of the yeast genetic interaction network. Science 303:5659808–13
    [Google Scholar]
  141. Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G et al. 2017. Defining a cancer dependency map. Cell 170:3564–76.e16
    [Google Scholar]
  142. Tzelepis K, Koike-Yusa H, De Braekeleer E, Li Y, Metzakopian E et al. 2016. A CRISPR dropout screen identifies genetic vulnerabilities and therapeutic targets in acute myeloid leukemia. Cell Rep 17:41193–205
    [Google Scholar]
  143. Vanoli F, Tomishima M, Feng W, Lamribet K, Babin L et al. 2017. CRISPR-Cas9–guided oncogenic chromosomal translocations with conditional fusion protein expression in human mesenchymal cells. PNAS 114:143696–701
    [Google Scholar]
  144. Vojta A, Dobrinić P, Tadić V, Bočkor L, Korać P et al. 2016. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res 44:125615–28
    [Google Scholar]
  145. Walrath JC, Hawes JJ, Van Dyke T, Reilly KM 2010. Genetically engineered mouse models in cancer research. Adv. Cancer Res. 106:113–64
    [Google Scholar]
  146. Wang T, Birsoy K, Hughes NW, Krupczak KM, Post Y et al. 2015. Identification and characterization of essential genes in the human genome. Science 350:62641096–101
    [Google Scholar]
  147. Wang T, Wei JJ, Sabatini DM, Lander ES 2014. Genetic screens in human cells using the CRISPR-Cas9 system. Science 343:616680–84
    [Google Scholar]
  148. Wang T, Yu H, Hughes NW, Liu B, Kendirli A et al. 2017. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168:5890–903.e15
    [Google Scholar]
  149. Weber J, Öllinger R, Friedrich M, Ehmer U, Barenboim M et al. 2015. CRISPR/Cas9 somatic multiplex-mutagenesis for high-throughput functional cancer genomics in mice. PNAS 112:4513982–87
    [Google Scholar]
  150. Wilson FH, Johannessen CM, Piccioni F, Tamayo P, Kim JW et al. 2015. A functional landscape of resistance to ALK inhibition in lung cancer. Cancer Cell 27:3397–408
    [Google Scholar]
  151. Winters IP, Chiou SH, Paulk NK, McFarland CD, Lalgudi PV et al. 2017. Multiplexed in vivo homology-directed repair and tumor barcoding enables parallel quantification of Kras variant oncogenicity. Nat. Commun. 8:12053
    [Google Scholar]
  152. Wolf J, Müller-Decker K, Flechtenmacher C, Zhang F, Shahmoradgoli M et al. 2014. An in vivo RNAi screen identifies SALL1 as a tumor suppressor in human breast cancer with a role in CDH1 regulation. Oncogene 33:334273–78
    [Google Scholar]
  153. Wong ASL, Choi GCG, Cui CH, Pregernig G, Milani P et al. 2016. Multiplexed barcoded CRISPR-Cas9 screening enabled by CombiGEM. PNAS 113:92544–49
    [Google Scholar]
  154. Wu J, Platero-Luengo A, Sakurai M, Sugawara A, Gil MA et al. 2017. Interspecies chimerism with mammalian pluripotent stem cells. Cell 168:3473–86.e15
    [Google Scholar]
  155. Wuestefeld T, Pesic M, Rudalska R, Dauch D, Longerich T et al. 2013. A direct in vivo RNAi screen identifies MKK4 as a key regulator of liver regeneration. Cell 153:2389–401
    [Google Scholar]
  156. Xu C, Qi X, Du X, Zou H, Gao F et al. 2017. piggyBac mediates efficient in vivo CRISPR library screening for tumorigenesis in mice. PNAS 114:4722–27
    [Google Scholar]
  157. Xu X, Tao Y, Gao X, Zhang L, Li X et al. 2016. A CRISPR-based approach for targeted DNA demethylation. Cell Discov 2:116009
    [Google Scholar]
  158. Yau EH, Kummetha IR, Lichinchi G, Tang R, Zhang Y, Rana TM 2017. Genome-wide CRISPR screen for essential cell growth mediators in mutant KRAS colorectal cancers. Cancer Res 77:226330–39
    [Google Scholar]
  159. Zender L, Spector MS, Xue W, Flemming P, Cordon-Cardo C et al. 2006. Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach. Cell 125:71253–67
    [Google Scholar]
  160. Zender L, Xue W, Zuber J, Semighini CP, Krasnitz A et al. 2008. An oncogenomics-based in vivo RNAi screen identifies tumor suppressors in liver cancer. Cell 135:5852–64
    [Google Scholar]
  161. Zhao B, Pritchard JR, Lauffenburger DA, Hemann MT 2014. Addressing genetic tumor heterogeneity through computationally predictive combination therapy. Cancer Discov 4:2166–74
    [Google Scholar]
  162. Zhao B, Sedlak JC, Srinivas R, Creixell P, Pritchard JR et al. 2016. Exploiting temporal collateral sensitivity in tumor clonal evolution. Cell 165:1234–46
    [Google Scholar]
  163. Zhou P, Shaffer DR, Alvarez Arias DA, Nakazaki Y, Pos W et al. 2014. In vivo discovery of immunotherapy targets in the tumour microenvironment. Nature 506:748652–57
    [Google Scholar]
  164. Zhu S, Li W, Liu J, Chen CH, Liao Q et al. 2016. Genome-scale deletion screening of human long non-coding RNAs using a paired-guide RNA CRISPR-Cas9 library. Nat. Biotechnol. 34:121279–86
    [Google Scholar]
/content/journals/10.1146/annurev-cancerbio-030518-055742
Loading
  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error