Systematic functional interrogation of rare cancer variants identifies oncogenic alleles

Cancer Discovery

Volumn 6, Issue 7, 2016, Pages 714-726

  Kim, Eejung ^a,b   Ilic, Nina ^a,b   Shrestha, Yashaswi ^a   Zou, Lihua ^a,c   Kamburov, Atanas ^a,c   Zhu, Cong ^a   Yang, Xiaoping ^a   Lubonja, Rakela ^a   Tran, Nancy ^a   Nguyen, Cindy ^a   Lawrence, Michael S ^a   Piccioni, Federica ^a   Bagul, Mukta ^a   Doench, John G ^a   Chouinard, Candace R ^a   Wu, Xiaoyun ^a   Hogstrom, Larson ^a   Natoli, Ted ^a   Tamayo, Pablo ^a,d   Horn, Heiko ^a,c   Corsello, Steven M ^a,b   Lage, Kasper ^a,c   Root, David E ^a   Subramanian, Aravind ^a   Golub, Todd R ^a,b   Getz, Gad ^a,c   Boehm, Jesse S ^a   Hahn, William C ^a,b   more..

^a BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^b DANA FARBER CANCER INSTITUTE   (United States)

^c MASSACHUSETTS GENERAL HOSPITAL   (United States)

^d UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PHOSPHATIDYLINOSITOL 3,4,5 TRISPHOSPHATE 3 PHOSPHATASE;

ARTICLE; CARCINOGENICITY; CONTROLLED STUDY; CORRELATION ANALYSIS; DNA REPLICATION; GENE EXPRESSION; GENE FREQUENCY; HUMAN; IMMUNOBLOTTING; MAJOR CLINICAL STUDY; ONCOGENE; OPEN READING FRAME; POINT MUTATION; POLYMERASE CHAIN REACTION; PROTEIN EXPRESSION; VALIDATION PROCESS; ALLELE; ANIMAL; CELL TRANSFORMATION; DISEASE MODEL; GENE EXPRESSION PROFILING; GENETIC ASSOCIATION STUDY; GENETIC PREDISPOSITION; GENETIC VARIATION; GENETICS; HIGH THROUGHPUT SCREENING; MALE; MOUSE; NEOPLASMS; PROCEDURES; REPRODUCIBILITY; TUMOR CELL LINE; XENOGRAFT;

ALLELES; ANIMALS; CELL LINE, TUMOR; CELL TRANSFORMATION, NEOPLASTIC; DISEASE MODELS, ANIMAL; GENE EXPRESSION PROFILING; GENETIC ASSOCIATION STUDIES; GENETIC PREDISPOSITION TO DISEASE; GENETIC VARIATION; HETEROGRAFTS; HIGH-THROUGHPUT SCREENING ASSAYS; HUMANS; MALE; MICE; NEOPLASMS; ONCOGENES; REPRODUCIBILITY OF RESULTS;

EID: 84977497966     PISSN: 21598274     EISSN: 21598290     Source Type: Journal    
DOI: 10.1158/2159-8290.CD-16-0160     Document Type: Article

References (58)

1. Stratton MR, Campbell PJ, Futreal PA. The cancer genome. Nature 2009; 458: 719–24.
2. Garraway Levi A, Lander Eric S. Lessons from the cancer genome. Cell 2013; 153: 17–37.
3. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr, Kinzler KW. Cancer genome landscapes. Science 2013; 339: 1546–58.
4. Cooper GM, Shendure J. Needles in stacks of needles: Finding disease-causal variants in a wealth of genomic data. Nat Rev Genet 2011; 12: 628–40.
5. Maemondo M, Inoue A, Kobayashi K, Sugawara S, Oizumi S, Isobe H, et al. Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR. N Engl J Med 2010; 362: 2380–8.
6. Van Allen EM, Wagle N, Levy MA. Clinical analysis and interpretation of cancer genome data. J Clin Oncol 2013; 31: 1825–33.
7. Paez JG, Janne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR mutations in lung cancer: Correlation with clinical response to gefitinib therapy. Science 2004; 304: 1497–500.
8. Douillard JY, Oliner KS, Siena S, Tabernero J, Burkes R, Barugel M, et al. Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N Engl J Med 2013; 369: 1023–34.
9. Garraway LA. Genomics-driven oncology: Framework for an emerging paradigm. J Clin Oncol 2013; 31: 1806–14.
10. Domchek SM, Greenberg RA. Breast cancer gene variants: Separating the harmful from the harmless. J Clin Invest 2009; 119: 2895–7.
11. Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 2015; 17: 405–24.
12. Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 2013; 499: 214–8.
13. Lawrence MS, Stojanov P, Mermel CH, Robinson JT, Garraway LA, Golub TR, et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 2014; 505: 495–501. doi: 10.1101/025445.
14. Yang X, Boehm JS, Yang X, Salehi-Ashtiani K, Hao T, Shen Y, et al. A public genome-scale lentiviral expression library of human ORFs. Nat Methods 2011; 8: 659–61.
15. Hahn WC, Counter CM, Lundberg AS, Beijersbergen RL, Brooks MW, Weinberg RA. Creation of human tumour cells with defined genetic elements. Nature 1999; 400: 464–8.
16. Boehm JS, Zhao JJ, Yao J, Kim SY, Firestein R, Dunn IF, et al. Integrative genomic approaches identify IKBKE as a breast cancer oncogene. Cell 2007; 129: 1065–79.
17. Dunn GP, Cheung HW, Agarwalla PK, Thomas S, Zektser Y, Karst AM, et al. In vivo multiplexed interrogation of amplified genes identifies GAB2 as an ovarian cancer oncogene. Proc Natl Acad Sci U S A 2014; 111: 1102–7.
18. Peck D, Crawford ED, Ross KN, Stegmaier K, Golub TR, Lamb J. A method for high-throughput gene expression signature analysis. Genome Biol 2006; 7: R61.
19. Ward PS, Patel J, Wise DR, Abdel-Wahab O, Bennett BD, Coller HA, et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting alpha- Ketoglutarate to 2-Hydroxyglutarate. Cancer Cell 2010; 17: 225–34.
20. Salmena L, Carracedo A, Pandolfi PP. Tenets of PTEN tumor suppression. Cell 2008; 133: 403–14.
21. Nguyen HN, Yang JM Jr, Rahdar M, Keniry M, Swaney KF, Parsons R, et al. A new class of cancer-associated PTEN mutations defined by membrane translocation defects. Oncogene 2015; 34: 3737–43.
22. Tilot AK, Gaugler MK, Yu Q, Romigh T, Yu W, Miller RH, et al. Germline disruption of Pten localization causes enhanced sex-dependent social motivation and increased glial production. Hum Mol Genet 2014; 23: 3212–27.
23. Han SY, Kato H, Kato S, Suzuki T, Shibata H, Ishii S, et al. Functional evaluation of PTEN missense mutations using in vitro phosphoinositide phosphatase assay. Cancer Res 2000; 60: 3147–51.
24. Xu J, Li Z, Wang J, Chen H, Fang J-Y. Combined PTEN mutation and protein expression associate with overall and disease-free survival of glioblastoma patients. Translat Oncol 2014; 7: 196–205.e1.
25. Papa A, Wan L, Bonora M, Salmena L, Song MS, Hobbs RM, et al. Cancer-associated PTEN mutants act in a dominant-negative manner to suppress PTEN protein function. Cell 2014; 157: 595–610.
26. Myers MP, Pass I, Batty IH, Van der Kaay J, Stolarov JP, Hemmings BA, et al. The lipid phosphatase activity of PTEN is critical for its tumor supressor function. Proc Natl Acad Sci U S A 1998; 95: 13513–8.
27. Barbieri CE, Baca SC, Lawrence MS, Demichelis F, Blattner M, Theurillat JP, et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat Genet 2012; 44: 685–9.
28. Le Gallo M, O’Hara AJ, Rudd ML, Urick ME, Hansen NF, O’Neil NJ, et al. Exome sequencing of serous endometrial tumors identifies recurrent somatic mutations in chromatin-remodeling and ubiquitin ligase complex genes. Nat Genet 2012; 44: 1310–5.
29. An J, Ren S, Murphy SJ, Dalangood S, Chang C, Pang X, et al. Truncated ERG oncoproteins from TMPRSS2-ERG fusions are resistant to SPOP-mediated proteasome degradation. Mol Cell 2015; 59: 904–16.
30. Gan W, Dai X, Lunardi A, Li Z, Inuzuka H, Liu P, et al. SPOP promotes ubiquitination and degradation of the ERG oncoprotein to suppress prostate cancer progression. Mol Cell 2015; 59: 917–30.
31. Cancer Genome Atlas Research N, Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, et al. Integrated genomic characterization of endometrial carcinoma. Nature 2013; 497: 67–73.
32. Zhang P, Gao K, Jin X, Ma J, Peng J, Wumaier R, et al. Endometrial cancer-associated mutants of SPOP are defective in regulating estrogen receptor-alpha protein turnover. Cell Death Dis 2015; 6: e1687.
33. Geng C, Rajapakshe K, Shah SS, Shou J, Eedunuri VK, Foley C, et al. Androgen receptor is the key transcriptional mediator of the tumor suppressor SPOP in prostate cancer. Cancer Res 2014; 74: 5631–43.
34. Dang CV. MYC on the path to cancer. Cell 2012; 149: 22–35.
35. Delmore JE, Issa GC, Lemieux ME, Rahl PB, Shi J, Jacobs HM, et al. BET bromodomain inhibition as a therapeutic strategy to target c-Myc. Cell 2011; 146: 904–17.
36. Davis RJ, Welcker M, Clurman BE. Tumor suppression by the Fbw7 ubiquitin ligase: Mechanisms and opportunities. Cancer Cell 2014; 26: 455–64.
37. Akhoondi S, Sun D, von der Lehr N, Apostolidou S, Klotz K, Maljukova A, et al. FBXW7/hCDC4 is a general tumor suppressor in human cancer. Cancer Res 2007; 67: 9006–12.
38. O’Neil J, Grim J, Strack P, Rao S, Tibbitts D, Winter C, et al. FBW7 mutations in leukemic cells mediate NOTCH pathway activation and resistance to gamma-secretase inhibitors. J Exp Med 2007; 204: 1813–24.
39. Sun Q, Burke JP, Phan J, Burns MC, Olejniczak ET, Waterson AG, et al. Discovery of small molecules that bind to K-Ras and inhibit Sos-mediated activation. Angewandte Chemie 2012; 51: 6140–3.
40. Kang S, Denley A, Vanhaesebroeck B, Vogt PK. Oncogenic transformation induced by the p110beta, -gamma, and -delta isoforms of class I phosphoinositide 3-kinase. Proc Natl Acad Sci U S A 2006; 103: 1289–94.
41. Robles-Espinoza CD, Harland M, Ramsay AJ, Aoude LG, Quesada V, Ding Z, et al. POT1 loss-of-function variants predispose to familial melanoma. Nat Genet 2014; 46: 478–81.
42. Shi J, Yang XR, Ballew B, Rotunno M, Calista D, Fargnoli MC, et al. Rare missense variants in POT1 predispose to familial cutaneous malignant melanoma. Nat Genet 2014; 46: 482–6.
43. Ramsay AJ, Quesada V, Foronda M, Conde L, Martinez-Trillos A, Villamor N, et al. POT1 mutations cause telomere dysfunction in chronic lymphocytic leukemia. Nat Genet 2013; 45: 526–30.
44. Bainbridge MN, Armstrong GN, Gramatges MM, Bertuch AA, Jhangiani SN, Doddapaneni H, et al. Germline mutations in shelterin complex genes are associated with familial glioma. J Natl Cancer Inst 2015; 107: 384.
45. Calvete O, Martinez P, Garcia-Pavia P, Benitez-Buelga C, Paumard- Hernandez B, Fernandez V, et al. A mutation in the POT1 gene is responsible for cardiac angiosarcoma in TP53-negative Li-Fraumenilike families. Nat Commun 2015; 6: 8383.
46. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, et al. A method and server for predicting damaging missense mutations. Nat Methods 2010; 7: 248–9.
47. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: Application to cancer genomics. Nucleic Acids Res 2011; 39: e118.
48. Carter H, Chen S, Isik L, Tyekucheva S, Velculescu VE, Kinzler KW, et al. Cancer-specifi c high-throughput annotation of somatic mutations: Computational prediction of driver missense mutations. Cancer Res 2009; 69: 6660–7.
49. Carter H, Douville C, Stenson PD, Cooper DN, Karchin R. Identifying Mendelian disease genes with the variant effect scoring tool. BMC Genomics 2013; 14(Suppl 3): S3.
50. Gonzalez-Perez A, Mustonen V, Reva B, Ritchie GR, Creixell P, Karchin R, et al. Computational approaches to identify functional genetic variants in cancer genomes. Nat Methods 2013; 10: 723–9.
51. Stephen AG, Esposito D, Bagni RK, McCormick F. Dragging ras back in the ring. Cancer Cell 2014; 25: 272–81.
52. Robinson D, Van Allen EM, Wu YM, Schultz N, Lonigro RJ, Mosquera JM, et al. Integrative clinical genomics of advanced prostate cancer. Cell 2015; 161: 1215–28.
53. Horn H, Lawrence MS, Hu JX, Worstell E, Ilic N, Shrestha Y, et al. A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes. bioRxiv 2015.
54. Kamburov A, Lawrence MS, Polak P, Leshchiner I, Lage K, Golub TR, et al. Comprehensive assessment of cancer missense mutation clustering in protein structures. Proc Natl Acad Sci U S A 2015; 112: E5486–E5495.
55. Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res 2010; 20: 110–21.
56. Suzuki Y, Kagawa N, Fujino T, Sumiya T, Andoh T, Ishikawa K, et al. A novel high-throughput (HTP) cloning strategy for sitedirected designed chimeragenesis and mutation using the Gateway cloning system. Nucleic Acids Res 2005; 33: e109.
57. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014.
58. Oliveros JC. Venny: An interactive tool for comparing lists with Venn’s diagrams. [Internet]; 2007–2015; Available from: http://bioinfogp.cnb.csic.es/tools/venny/index.html. Date accessed: March 29, 2016.