Yonsei Med J.  2015 Sep;56(5):1186-1198. 10.3349/ymj.2015.56.5.1186.

Cancer Cell Line Panels Empower Genomics-Based Discovery of Precision Cancer Medicine

• Kim HS
• Sung YJ
• Paik S

• KMID: 2163608
• DOI: http://doi.org/10.3349/ymj.2015.56.5.1186

Abstract

Since the first human cancer cell line, HeLa, was established in the early 1950s, there has been a steady increase in the number and tumor type of available cancer cell line models. Cancer cell lines have made significant contributions to the development of various chemotherapeutic agents. Recent advances in multi-omics technologies have facilitated detailed characterizations of the genomic, transcriptomic, proteomic, and epigenomic profiles of these cancer cell lines. An increasing number of studies employ the power of a cancer cell line panel to provide predictive biomarkers for targeted and cytotoxic agents, including those that are already used in clinical practice. Different types of statistical and machine learning algorithms have been developed to analyze the large-scale data sets that have been produced. However, much work remains to address the discrepancies in drug assay results from different platforms and the frequent failures to translate discoveries from cell line models to the clinic. Nevertheless, continuous expansion of cancer cell line panels should provide unprecedented opportunities to identify new candidate targeted therapies, particularly for the so-called "dark matter" group of cancers, for which pharmacologically tractable driver mutations have not been identified.

Keyword

Targeted therapy; cancer genomics; biomarker; personalized medicine

MeSH Terms

Algorithms
Antineoplastic Agents/*therapeutic use
Biomarkers, Tumor/*analysis/blood
Cell Line, Tumor/*drug effects
Genomics
Humans
Neoplasms/*drug therapy
*Precision Medicine
Proteomics
Antineoplastic Agents
Biomarkers, Tumor

Figure

• 

  Fig. 1 A compendium of established cancer cell line models and the number of associated multi-omics data sets are represented as heatmaps for each of the 12 major tumor types. See Table 1 for the list of relevant studies that were used to generate this figure.

Reference

1. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000; 100:57–70.

2. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004; 351:2817–2826.

3. Dawson JC, Carragher NO. Quantitative phenotypic and pathway profiling guides rational drug combination strategies. Front Pharmacol. 2014; 5:118.

4. Gazdar AF, Girard L, Lockwood WW, Lam WL, Minna JD. Lung cancer cell lines as tools for biomedical discovery and research. J Natl Cancer Inst. 2010; 102:1310–1321.

5. Sharma SV, Haber DA, Settleman J. Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents. Nat Rev Cancer. 2010; 10:241–253.

6. McDermott U, Sharma SV, Dowell L, Greninger P, Montagut C, Lamb J, et al. Identification of genotype-correlated sensitivity to selective kinase inhibitors by using high-throughput tumor cell line profiling. Proc Natl Acad Sci U S A. 2007; 104:19936–19941.

7. Luo J, Solimini NL, Elledge SJ. Principles of cancer therapy: oncogene and non-oncogene addiction. Cell. 2009; 136:823–837.

8. Masters JR. HeLa cells 50 years on: the good, the bad and the ugly. Nat Rev Cancer. 2002; 2:315–319.

9. Gazdar AF, Gao B, Minna JD. Lung cancer cell lines: Useless artifacts or invaluable tools for medical science? Lung Cancer. 2010; 68:309–318.

10. Gazdar AF, Kurvari V, Virmani A, Gollahon L, Sakaguchi M, Westerfield M, et al. Characterization of paired tumor and non-tumor cell lines established from patients with breast cancer. Int J Cancer. 1998; 78:766–774.

11. Beaufort CM, Helmijr JC, Piskorz AM, Hoogstraat M, Ruigrok-Ritstier K, Besselink N, et al. Ovarian cancer cell line panel (OCCP): clinical importance of in vitro morphological subtypes. PLoS One. 2014; 9:e103988.

12. Behren A, Anaka M, Lo PH, Vella LJ, Davis ID, Catimel J, et al. The Ludwig institute for cancer research Melbourne melanoma cell line panel. Pigment Cell Melanoma Res. 2013; 26:597–600.

13. Ahmed D, Eide PW, Eilertsen IA, Danielsen SA, Eknæs M, Hektoen M, et al. Epigenetic and genetic features of 24 colon cancer cell lines. Oncogenesis. 2013; 2:e71.

14. Arumugam T, Ramachandran V, Fournier KF, Wang H, Marquis L, Abbruzzese JL, et al. Epithelial to mesenchymal transition contributes to drug resistance in pancreatic cancer. Cancer Res. 2009; 69:5820–5828.

15. Sobel RE, Sadar MD. Cell lines used in prostate cancer research: a compendium of old and new lines--part 1. J Urol. 2005; 173:342–359.

16. Sobel RE, Sadar MD. Cell lines used in prostate cancer research: a compendium of old and new lines--part 2. J Urol. 2005; 173:360–372.

17. Gazdar AF, Minna JD. NCI series of cell lines: an historical perspective. J Cell Biochem Suppl. 1996; 24:1–11.

18. Suggitt M, Bibby MC. 50 years of preclinical anticancer drug screening: empirical to target-driven approaches. Clin Cancer Res. 2005; 11:971–981.

19. Kong D, Yamori T. JFCR39, a panel of 39 human cancer cell lines, and its application in the discovery and development of anticancer drugs. Bioorg Med Chem. 2012; 20:1947–1951.

20. Dan S, Okamura M, Seki M, Yamazaki K, Sugita H, Okui M, et al. Correlating phosphatidylinositol 3-kinase inhibitor efficacy with signaling pathway status: in silico and biological evaluations. Cancer Res. 2010; 70:4982–4994.

21. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012; 483:603–607.

22. Garnett MJ, Edelman EJ, Heidorn SJ, Greenman CD, Dastur A, Lau KW, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012; 483:570–575.

23. Basu A, Bodycombe NE, Cheah JH, Price EV, Liu K, Schaefer GI, et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell. 2013; 154:1151–1161.

24. Shoemaker RH. The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer. 2006; 6:813–823.

25. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013; 41:D955–D961.

26. Cancer Target Discovery and Development Network. Schreiber SL, Shamji AF, Clemons PA, Hon C, Koehler AN, et al. Towards patient-based cancer therapeutics. Nat Biotechnol. 2010; 28:904–906.

27. Greshock J, Bachman KE, Degenhardt YY, Jing J, Wen YH, Eastman S, et al. Molecular target class is predictive of in vitro response profile. Cancer Res. 2010; 70:3677–3686.

28. Kim HS, Mendiratta S, Kim J, Pecot CV, Larsen JE, Zubovych I, et al. Systematic identification of molecular subtype-selective vulnerabilities in non-small-cell lung cancer. Cell. 2013; 155:552–566.

29. Gillet JP, Calcagno AM, Varma S, Marino M, Green LJ, Vora MI, et al. Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance. Proc Natl Acad Sci U S A. 2011; 108:18708–18713.

30. Domcke S, Sinha R, Levine DA, Sander C, Schultz N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nat Commun. 2013; 4:2126.

31. Burdall SE, Hanby AM, Lansdown MR, Speirs V. Breast cancer cell lines: friend or foe? Breast Cancer Res. 2003; 5:89–95.

32. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011; 144:646–674.

33. Haibe-Kains B, El-Hachem N, Birkbak NJ, Jin AC, Beck AH, Aerts HJ, et al. Inconsistency in large pharmacogenomic studies. Nature. 2013; 504:389–393.

34. Pao W, Miller VA, Politi KA, Riely GJ, Somwar R, Zakowski MF, et al. Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med. 2005; 2:e73.

35. Cross DA, Ashton SE, Ghiorghiu S, Eberlein C, Nebhan CA, Spitzler PJ, et al. AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer. Cancer Discov. 2014; 4:1046–1061.

36. Nazarian R, Shi H, Wang Q, Kong X, Koya RC, Lee H, et al. Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation. Nature. 2010; 468:973–977.

37. Oie HK, Russell EK, Carney DN, Gazdar AF. Cell culture methods for the establishment of the NCI series of lung cancer cell lines. J Cell Biochem Suppl. 1996; 24:24–31.

38. Zheng C, Sun YH, Ye XL, Chen HQ, Ji HB. Establishment and characterization of primary lung cancer cell lines from Chinese population. Acta Pharmacol Sin. 2011; 32:385–392.

39. Dangles-Marie V, Pocard M, Richon S, Weiswald LB, Assayag F, Saulnier P, et al. Establishment of human colon cancer cell lines from fresh tumors versus xenografts: comparison of success rate and cell line features. Cancer Res. 2007; 67:398–407.

40. Rheinwald JG, Green H. Serial cultivation of strains of human epidermal keratinocytes: the formation of keratinizing colonies from single cells. Cell. 1975; 6:331–343.

41. Liu X, Ory V, Chapman S, Yuan H, Albanese C, Kallakury B, et al. ROCK inhibitor and feeder cells induce the conditional reprogramming of epithelial cells. Am J Pathol. 2012; 180:599–607.

42. Crystal AS, Shaw AT, Sequist LV, Friboulet L, Niederst MJ, Lockerman EL, et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science. 2014; 346:1480–1486.

43. Sato T, Vries RG, Snippert HJ, van de Wetering M, Barker N, Stange DE, et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature. 2009; 459:262–265.

44. Boj SF, Hwang CI, Baker LA, Chio II, Engle DD, Corbo V, et al. Organoid models of human and mouse ductal pancreatic cancer. Cell. 2015; 160:324–338.

45. Blanco R, Iwakawa R, Tang M, Kohno T, Angulo B, Pio R, et al. A gene-alteration profile of human lung cancer cell lines. Hum Mutat. 2009; 30:1199–1206.

46. Hollestelle A, Nagel JH, Smid M, Lam S, Elstrodt F, Wasielewski M, et al. Distinct gene mutation profiles among luminal-type and basal-type breast cancer cell lines. Breast Cancer Res Treat. 2010; 121:53–64.

47. Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2011; 39:D945–D950.

48. Ross DT, Scherf U, Eisen MB, Perou CM, Rees C, Spellman P, et al. Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet. 2000; 24:227–235.

49. Bussey KJ, Chin K, Lababidi S, Reimers M, Reinhold WC, Kuo WL, et al. Integrating data on DNA copy number with gene expression levels and drug sensitivities in the NCI-60 cell line panel. Mol Cancer Ther. 2006; 5:853–867.

50. Shen L, Kondo Y, Ahmed S, Boumber Y, Konishi K, Guo Y, et al. Drug sensitivity prediction by CpG island methylation profile in the NCI-60 cancer cell line panel. Cancer Res. 2007; 67:11335–11343.

51. Moghaddas Gholami A, Hahne H, Wu Z, Auer FJ, Meng C, Wilhelm M, et al. Global proteome analysis of the NCI-60 cell line panel. Cell Rep. 2013; 4:609–620.

52. Neve RM, Chin K, Fridlyand J, Yeh J, Baehner FL, Fevr T, et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell. 2006; 10:515–527.

53. Zaman N, Li L, Jaramillo ML, Sun Z, Tibiche C, Banville M, et al. Signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets. Cell Rep. 2013; 5:216–223.

54. Kao J, Salari K, Bocanegra M, Choi YL, Girard L, Gandhi J, et al. Molecular profiling of breast cancer cell lines defines relevant tumor models and provides a resource for cancer gene discovery. PLoS One. 2009; 4:e6146.

55. Liu J, Lee W, Jiang Z, Chen Z, Jhunjhunwala S, Haverty PM, et al. Genome and transcriptome sequencing of lung cancers reveal diverse mutational and splicing events. Genome Res. 2012; 22:2315–2327.

56. Jia P, Jin H, Meador CB, Xia J, Ohashi K, Liu L, et al. Next-generation sequencing of paired tyrosine kinase inhibitor-sensitive and -resistant EGFR mutant lung cancer cell lines identifies spectrum of DNA changes associated with drug resistance. Genome Res. 2013; 23:1434–1445.

57. Byers LA, Diao L, Wang J, Saintigny P, Girard L, Peyton M, et al. An epithelial-mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin Cancer Res. 2013; 19:279–290.

58. Liu J, McCleland M, Stawiski EW, Gnad F, Mayba O, Haverty PM, et al. Integrated exome and transcriptome sequencing reveals ZAK isoform usage in gastric cancer. Nat Commun. 2014; 5:3830.

59. Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008; 321:1801–1806.

60. Liu Y, Bodmer WF. Analysis of P53 mutations and their expression in 56 colorectal cancer cell lines. Proc Natl Acad Sci U S A. 2006; 103:976–981.

61. Demuth T, Rennert JL, Hoelzinger DB, Reavie LB, Nakada M, Beaudry C, et al. Glioma cells on the run - the migratory transcriptome of 10 human glioma cell lines. BMC Genomics. 2008; 9:54.

62. Abaan OD, Polley EC, Davis SR, Zhu YJ, Bilke S, Walker RL, et al. The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology. Cancer Res. 2013; 73:4372–4382.

63. Garraway LA, Lander ES. Lessons from the cancer genome. Cell. 2013; 153:17–37.

64. Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013; 6:pl1.

65. Prahallad A, Sun C, Huang S, Di Nicolantonio F, Salazar R, Zecchin D, et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature. 2012; 483:100–103.

66. Ashraf SQ, Nicholls AM, Wilding JL, Ntouroupi TG, Mortensen NJ, Bodmer WF. Direct and immune mediated antibody targeting of ERBB receptors in a colorectal cancer cell-line panel. Proc Natl Acad Sci U S A. 2012; 109:21046–21051.

67. Sos ML, Michel K, Zander T, Weiss J, Frommolt P, Peifer M, et al. Predicting drug susceptibility of non-small cell lung cancers based on genetic lesions. J Clin Invest. 2009; 119:1727–1740.

68. Epstein RJ. The unpluggable in pursuit of the undruggable: tackling the dark matter of the cancer therapeutics universe. Front Oncol. 2013; 3:304.

69. Silva JM, Marran K, Parker JS, Silva J, Golding M, Schlabach MR, et al. Profiling essential genes in human mammary cells by multiplex RNAi screening. Science. 2008; 319:617–620.

70. Schlabach MR, Luo J, Solimini NL, Hu G, Xu Q, Li MZ, et al. Cancer proliferation gene discovery through functional genomics. Science. 2008; 319:620–624.

71. Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelsen TS, et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science. 2014; 343:84–87.

72. Wang T, Wei JJ, Sabatini DM, Lander ES. Genetic screens in human cells using the CRISPR-Cas9 system. Science. 2014; 343:80–84.

73. Luo B, Cheung HW, Subramanian A, Sharifnia T, Okamoto M, Yang X, et al. Highly parallel identification of essential genes in cancer cells. Proc Natl Acad Sci U S A. 2008; 105:20380–20385.

74. Silva JM, Li MZ, Chang K, Ge W, Golding MC, Rickles RJ, et al. Second-generation shRNA libraries covering the mouse and human genomes. Nat Genet. 2005; 37:1281–1288.

75. Marcotte R, Brown KR, Suarez F, Sayad A, Karamboulas K, Krzyzanowski PM, et al. Essential gene profiles in breast, pancreatic, and ovarian cancer cells. Cancer Discov. 2012; 2:172–189.

76. Cheung HW, Cowley GS, Weir BA, Boehm JS, Rusin S, Scott JA, et al. Systematic investigation of genetic vulnerabilities across cancer cell lines reveals lineage-specific dependencies in ovarian cancer. Proc Natl Acad Sci U S A. 2011; 108:12372–12377.

77. Barbie DA, Tamayo P, Boehm JS, Kim SY, Moody SE, Dunn IF, et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature. 2009; 462:108–112.

78. Corcoran RB, Cheng KA, Hata AN, Faber AC, Ebi H, Coffee EM, et al. Synthetic lethal interaction of combined BCL-XL and MEK inhibition promotes tumor regressions in KRAS mutant cancer models. Cancer Cell. 2013; 23:121–128.

79. Kessler JD, Kahle KT, Sun T, Meerbrey KL, Schlabach MR, Schmitt EM, et al. A SUMOylation-dependent transcriptional subprogram is required for Myc-driven tumorigenesis. Science. 2012; 335:348–353.

80. Luo J, Emanuele MJ, Li D, Creighton CJ, Schlabach MR, Westbrook TF, et al. A genome-wide RNAi screen identifies multiple synthetic lethal interactions with the Ras oncogene. Cell. 2009; 137:835–848.

81. Scholl C, Fröhling S, Dunn IF, Schinzel AC, Barbie DA, Kim SY, et al. Synthetic lethal interaction between oncogenic KRAS dependency and STK33 suppression in human cancer cells. Cell. 2009; 137:821–834.

82. Sims D, Mendes-Pereira AM, Frankum J, Burgess D, Cerone MA, Lombardelli C, et al. High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing. Genome Biol. 2011; 12:R104.

83. Mendes-Pereira AM, Sims D, Dexter T, Fenwick K, Assiotis I, Kozarewa I, et al. Genome-wide functional screen identifies a compendium of genes affecting sensitivity to tamoxifen. Proc Natl Acad Sci U S A. 2012; 109:2730–2735.

84. Chen R, Bélanger S, Frederick MA, Li B, Johnston RJ, Xiao N, et al. In vivo RNA interference screens identify regulators of antiviral CD4(+) and CD8(+) T cell differentiation. Immunity. 2014; 41:325–338.

85. Martens-de Kemp SR, Nagel R, Stigter-van Walsum M, van der Meulen IH, van Beusechem VW, Braakhuis BJ, et al. Functional genetic screens identify genes essential for tumor cell survival in head and neck and lung cancer. Clin Cancer Res. 2013; 19:1994–2003.

86. Petrocca F, Altschuler G, Tan SM, Mendillo ML, Yan H, Jerry DJ, et al. A genome-wide siRNA screen identifies proteasome addiction as a vulnerability of basal-like triple-negative breast cancer cells. Cancer Cell. 2013; 24:182–196.

87. Toyoshima M, Howie HL, Imakura M, Walsh RM, Annis JE, Chang AN, et al. Functional genomics identifies therapeutic targets for MYC-driven cancer. Proc Natl Acad Sci U S A. 2012; 109:9545–9550.

88. Whitehurst AW, Bodemann BO, Cardenas J, Ferguson D, Girard L, Peyton M, et al. Synthetic lethal screen identification of chemosensitizer loci in cancer cells. Nature. 2007; 446:815–819.

89. de Bruin EC, Cowell C, Warne PH, Jiang M, Saunders RE, Melnick MA, et al. Reduced NF1 expression confers resistance to EGFR inhibition in lung cancer. Cancer Discov. 2014; 4:606–619.

90. Turner NC, Lord CJ, Iorns E, Brough R, Swift S, Elliott R, et al. A synthetic lethal siRNA screen identifying genes mediating sensitivity to a PARP inhibitor. EMBO J. 2008; 27:1368–1377.

91. Lamba S, Russo M, Sun C, Lazzari L, Cancelliere C, Grernrum W, et al. RAF suppression synergizes with MEK inhibition in KRAS mutant cancer cells. Cell Rep. 2014; 8:1475–1483.

92. Kranz D, Boutros M. A synthetic lethal screen identifies FAT1 as an antagonist of caspase-8 in extrinsic apoptosis. EMBO J. 2014; 33:181–197.

93. Hsu PD, Lander ES, Zhang F. Development and applications of CRISPR-Cas9 for genome engineering. Cell. 2014; 157:1262–1278.

94. Sánchez-Rivera FJ, Papagiannakopoulos T, Romero R, Tammela T, Bauer MR, Bhutkar A, et al. Rapid modelling of cooperating genetic events in cancer through somatic genome editing. Nature. 2014; 516:428–431.

95. Zhang JH, Chung TD, Oldenburg KR. A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays. J Biomol Screen. 1999; 4:67–73.

96. Singh NK, Seo BY, Vidyasagar M, White MA, Kim HS. siMacro: A Fast and Easy Data Processing Tool for Cell-Based Genomewide siRNA Screens. Genomics Inform. 2013; 11:55–57.

97. Paull KD, Shoemaker RH, Hodes L, Monks A, Scudiero DA, Rubinstein L, et al. Display and analysis of patterns of differential activity of drugs against human tumor cell lines: development of mean graph and COMPARE algorithm. J Natl Cancer Inst. 1989; 81:1088–1092.

98. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Statist Soc B. 2005; 67:301–320.

99. Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014; 32:1202–1212.

100. Sadanandam A, Lyssiotis CA, Homicsko K, Collisson EA, Gibb WJ, Wullschleger S, et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat Med. 2013; 19:619–625.