Identification of High-Impact cis-Regulatory Mutations Using Transcription Factor Specific Random Forest Models

PLoS Computational Biology

Volumn 11, Issue 11, 2015, Pages

  Svetlichnyy, Dmitry ^a   Imrichova, Hana ^a   Fiers, Mark ^b   Kalender Atak, Zeynep ^a   Aerts, Stein ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b DEPARTMENT OF PLANT SYSTEMS BIOLOGY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

CODES (SYMBOLS); DECISION TREES; DISEASES; LANTHANUM COMPOUNDS; LEARNING SYSTEMS; ONCOGENIC VIRUSES; TRANSCRIPTION;

CANCER GENOME; HIGH IMPACT; MACHINE LEARNING METHODS; ONCOGENESIS; PROTEIN-CODING GENES; RANDOM FOREST MODELING; REGULATORY DRIVERS; REGULATORY IMPACT; REGULATORY REGIONS; SOMATIC MUTATION;

TRANSCRIPTION FACTORS;

TRANSCRIPTION FACTOR; TRANSCRIPTION FACTOR TAL1;

ACUTE LYMPHOBLASTIC LEUKEMIA; ARTICLE; CARCINOGENESIS; CHROMATIN; GENE CONTROL; GENE EXPRESSION; GENE MUTATION; GENE REGULATORY NETWORK; GENE SEQUENCE; GENETIC ASSOCIATION; GENETIC CODE; GENETIC IDENTIFICATION; HUMAN; MACHINE LEARNING; ONCOGENE; RANDOM FOREST; SCORING SYSTEM; SOMATIC MUTATION; T CELL LEUKEMIA; TUMOR SUPPRESSOR GENE; ALGORITHM; BINDING SITE; BIOLOGY; GENETICS; GENOME; HELA CELL LINE; MUTATION; NEOPLASM; PROCEDURES; REGULATORY SEQUENCE; STATISTICAL MODEL;

ALGORITHMS; BINDING SITES; COMPUTATIONAL BIOLOGY; GENOME; HELA CELLS; HUMANS; MACHINE LEARNING; MODELS, STATISTICAL; MUTATION; NEOPLASMS; REGULATORY SEQUENCES, NUCLEIC ACID; TRANSCRIPTION FACTORS;

EID: 84949257173     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004590     Document Type: Article

References (76)

1. Aerts S, Cools J, Cancer: Mutations close in on gene regulation. Nature. 2013;499: 35–36. doi: 10.1038/499035a 23823789
2. Horn S, Figl A, Rachakonda PS, Fischer C, Sucker A, Gast A, et al. TERT promoter mutations in familial and sporadic melanoma. Science. 2013;339: 959–961. doi: 10.1126/science.1230062 23348503
3. Huang FW, Hodis E, Xu MJ, Kryukov GV, Chin L, Garraway LA, Highly Recurrent TERT Promoter Mutations in Human Melanoma. Science. 2013;339: 957–959. doi: 10.1126/science.1229259 23348506
4. Vinagre J, Almeida A, Pópulo H, Batista R, Lyra J, Pinto V, et al. Frequency of TERT promoter mutations in human cancers. Nat Commun. 2013;4: 2185. doi: 10.1038/ncomms3185 23887589
5. Heidenreich B, Rachakonda PS, Hemminki K, Kumar R, TERT promoter mutations in cancer development. Curr Opin Genet Dev. 2014;24: 30–37. doi: 10.1016/j.gde.2013.11.005 24657534
6. Mansour MR, Abraham BJ, Anders L, Berezovskaya A, Gutierrez A, Durbin AD, et al. An oncogenic super-enhancer formed through somatic mutation of a noncoding intergenic element. Science. 2014;346: 1373–1377. doi: 10.1126/science.1259037 25394790
7. Weinhold N, Jacobsen A, Schultz N, Sander C, Lee W, Genome-wide analysis of noncoding regulatory mutations in cancer. Nat Genet. 2014;46: 1160–1165. doi: 10.1038/ng.3101 25261935
8. Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM, Lappalainen T, et al. Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics. Science. 2013;342: 1235587. doi: 10.1126/science.1235587 24092746
9. Perera D, Chacon D, Thoms JA, Poulos RC, Shlien A, Beck D, et al. OncoCis: annotation of cis-regulatory mutations in cancer. Genome Biol. 2014;15: 485. doi: 10.1186/s13059-014-0485-0 25298093
10. Fu Y, Liu Z, Lou S, Bedford J, Mu XJ, Yip KY, et al. FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer. Genome Biol. 2014;15: 480. doi: 10.1186/s13059-014-0480-5 25273974
11. Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22: 1790–1797. doi: 10.1101/gr.137323.112 22955989
12. Consortium TEP, An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489: 57–74. doi: 10.1038/nature11247 22955616
13. Degner JF, Pai AA, Pique-Regi R, Veyrieras J-B, Gaffney DJ, Pickrell JK, et al. DNase I sensitivity QTLs are a major determinant of human expression variation. Nature. 2012;482: 390–394. doi: 10.1038/nature10808 22307276
14. Gaffney DJ, Veyrieras J-B, Degner JF, Pique-Regi R, Pai AA, Crawford GE, et al. Dissecting the regulatory architecture of gene expression QTLs. Genome Biol. 2012;13: R7. doi: 10.1186/gb-2012-13-1-r7 22293038
15. Ongen H, Andersen CL, Bramsen JB, Oster B, Rasmussen MH, Ferreira PG, et al. Putative cis-regulatory drivers in colorectal cancer. Nature. 2014;advance online publication. doi: 10.1038/nature13602
16. Cowper-Sal lari R, Zhang X, Wright JB, Bailey SD, Cole MD, Eeckhoute J, et al. Breast cancer risk-associated SNPs modulate the affinity of chromatin for FOXA1 and alter gene expression. Nat Genet. 2012;44: 1191–1198. doi: 10.1038/ng.2416 23001124
17. Huang D, Ovcharenko I, Identifying causal regulatory SNPs in ChIP-seq enhancers. Nucleic Acids Res. 2015;43: 225–236. doi: 10.1093/nar/gku1318 25520196
18. Frith MC, Li MC, Weng Z, Cluster-Buster: finding dense clusters of motifs in DNA sequences. Nucleic Acids Res. 2003;31: 3666–3668. 12824389
19. Berman BP, Nibu Y, Pfeiffer BD, Tomancak P, Celniker SE, Levine M, et al. Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome. Proc Natl Acad Sci U S A. 2002;99: 757–762. doi: 10.1073/pnas.231608898 11805330
20. Rajewsky N, Vergassola M, Gaul U, Siggia ED, Computational detection of genomic cis-regulatory modules applied to body patterning in the early Drosophila embryo. BMC Bioinformatics. 2002;3: 30. 12398796
21. Aerts S, van Helden J, Sand O, Hassan BA, Fine-tuning enhancer models to predict transcriptional targets across multiple genomes. PloS One. 2007;2: e1115. doi: 10.1371/journal.pone.0001115 17973026
22. Narlikar L, Sakabe NJ, Blanski AA, Arimura FE, Westlund JM, Nobrega MA, et al. Genome-wide discovery of human heart enhancers. Genome Res. 2010;20: 381–392. doi: 10.1101/gr.098657.109 20075146
23. Lee D, Karchin R, Beer MA, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; gr.121905.111. doi: 10.1101/gr.121905.111
24. Kazemian M, Zhu Q, Halfon MS, Sinha S, Improved accuracy of supervised CRM discovery with interpolated Markov models and cross-species comparison. Nucleic Acids Res. 2011;39: 9463–9472. doi: 10.1093/nar/gkr621 21821659
25. Won K-J, Ren B, Wang W, Genome-wide prediction of transcription factor binding sites using an integrated model. Genome Biol. 2010;11: R7. doi: 10.1186/gb-2010-11-1-r7 20096096
26. Ernst J, Kellis M, ChromHMM: automating chromatin-state discovery and characterization. Nat Methods. 2012;9: 215–216. doi: 10.1038/nmeth.1906 22373907
27. Cusanovich DA, Pavlovic B, Pritchard JK, Gilad Y, The Functional Consequences of Variation in Transcription Factor Binding. PLoS Genet. 2014;10: e1004226. doi: 10.1371/journal.pgen.1004226 24603674
28. Imrichová H, Hulselmans G, Kalender Atak Z, Potier D, Aerts S, i-cisTarget 2015 update: generalized cis-regulatory enrichment analysis in human, mouse and fly. Nucleic Acids Res. 2015; gkv395. doi: 10.1093/nar/gkv395
29. Gorkin DU, Lee D, Reed X, Fletez-Brant C, Bessling SL, Loftus SK, et al. Integration of ChIP-seq and machine learning reveals enhancers and a predictive regulatory sequence vocabulary in melanocytes. Genome Res. 2012;22: 2290–2301. doi: 10.1101/gr.139360.112 23019145
30. He H, Garcia EA, Learning from Imbalanced Data. IEEE Trans Knowl Data Eng. 2009;21: 1263–1284. doi: 10.1109/TKDE.2008.239
31. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW, Cancer Genome Landscapes. Science. 2013;339: 1546–1558. doi: 10.1126/science.1235122 23539594
32. Futreal PA, Coin L, Marshall M, Down T, Hubbard T, Wooster R, et al. A census of human cancer genes. Nat Rev Cancer. 2004;4: 177–183. doi: 10.1038/nrc1299 14993899
33. Ciriello G, Miller ML, Aksoy BA, Senbabaoglu Y, Schultz N, Sander C, Emerging landscape of oncogenic signatures across human cancers. Nat Genet. 2013;45: 1127–1133. doi: 10.1038/ng.2762 24071851
34. Tamborero D, Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, Kandoth C, Reimand J, et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci Rep. 2013;3: 2650. doi: 10.1038/srep02650 24084849
35. Cancer Genome Atlas Research NetworkGenomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013;368: 2059–2074. doi: 10.1056/NEJMoa1301689 23634996
36. Loo PV, Marynen P, Computational methods for the detection of cis-regulatory modules. Brief Bioinform. 2009;10: 509–524. doi: 10.1093/bib/bbp025 19498042
37. Nik-Zainal S, Van Loo P, Wedge DC, Alexandrov LB, Greenman CD, Lau KW, et al. The Life History of 21 Breast Cancers. Cell. 2012;149: 994–1007. doi: 10.1016/j.cell.2012.04.023 22608083
38. Berger MF, Hodis E, Heffernan TP, Deribe YL, Lawrence MS, Protopopov A, et al. Melanoma genome sequencing reveals frequent PREX2 mutations. Nature. 2012;485: 502–506. doi: 10.1038/nature11071 22622578
39. Bell RJA, Rube HT, Kreig A, Mancini A, Fouse SD, Nagarajan RP, et al. Cancer. The transcription factor GABP selectively binds and activates the mutant TERT promoter in cancer. Science. 2015;348: 1036–1039. doi: 10.1126/science.aab0015 25977370
40. Verfaillie A, Imrichova H, Atak ZK, Dewaele M, Rambow F, Hulselmans G, et al. Decoding the regulatory landscape of melanoma reveals TEADS as regulators of the invasive cell state. Nat Commun. 2015;6. doi: 10.1038/ncomms7683
41. Network TCGAComprehensive molecular portraits of human breast tumours. Nature. 2012;490: 61–70. doi: 10.1038/nature11412 23000897
42. Adey A, Burton JN, Kitzman JO, Hiatt JB, Lewis AP, Martin BK, et al. The haplotype-resolved genome and epigenome of the aneuploid HeLa cancer cell line. Nature. 2013;500: 207–211. doi: 10.1038/nature12064 23925245
43. Landry JJM, Pyl PT, Rausch T, Zichner T, Tekkedil MM, Stütz AM, et al. The Genomic and Transcriptomic Landscape of a HeLa Cell Line. G3 GenesGenomesGenetics. 2013;3: 1213–1224. doi: 10.1534/g3.113.005777
44. Stephens PJ, Tarpey PS, Davies H, Van Loo P, Greenman C, Wedge DC, et al. The landscape of cancer genes and mutational processes in breast cancer. Nature. 2012;486: 400–404. doi: 10.1038/nature11017 22722201
45. Fredriksson NJ, Ny L, Nilsson JA, Larsson E, Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types. Nat Genet. 2014;46: 1258–1263. doi: 10.1038/ng.3141 25383969
46. Borah S, Xi L, Zaug AJ, Powell NM, Dancik GM, Cohen SB, et al. TERT promoter mutations and telomerase reactivation in urothelial cancer. Science. 2015; doi: 10.1126/science.1260200
47. Aerts S, Computational strategies for the genome-wide identification of cis-regulatory elements and transcriptional targets. Curr Top Dev Biol. 2012;98: 121–145. doi: 10.1016/B978-0-12-386499-4.00005-7 22305161
48. Shlyueva D, Stampfel G, Stark A, Transcriptional enhancers: from properties to genome-wide predictions. Nat Rev Genet. 2014;15: 272–286. doi: 10.1038/nrg3682 24614317
49. Arvey A, Agius P, Noble WS, Leslie C, Sequence and chromatin determinants of cell-type-specific transcription factor binding. Genome Res. 2012;22: 1723–1734. doi: 10.1101/gr.127712.111 22955984
50. Yáñez-Cuna JO, Dinh HQ, Kvon EZ, Shlyueva D, Stark A, Uncovering cis-regulatory sequence requirements for context-specific transcription factor binding. Genome Res. 2012;22: 2018–2030. doi: 10.1101/gr.132811.111 22534400
51. Ghandi M, Lee D, Mohammad-Noori M, Beer MA, Enhanced regulatory sequence prediction using gapped k-mer features. PLoS Comput Biol. 2014;10: e1003711. doi: 10.1371/journal.pcbi.1003711 25033408
52. Weirauch MT, Yang A, Albu M, Cote AG, Montenegro-Montero A, Drewe P, et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell. 2014;158: 1431–1443. doi: 10.1016/j.cell.2014.08.009 25215497
53. Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome Res. 2012;22: 1798–1812. doi: 10.1101/gr.139105.112 22955990
54. Rajagopal N, Xie W, Li Y, Wagner U, Wang W, Stamatoyannopoulos J, et al. RFECS: a random-forest based algorithm for enhancer identification from chromatin state. PLoS Comput Biol. 2013;9: e1002968. doi: 10.1371/journal.pcbi.1002968 23526891
55. Gotea V, Visel A, Westlund JM, Nobrega MA, Pennacchio LA, Ovcharenko I, Homotypic clusters of transcription factor binding sites are a key component of human promoters and enhancers. Genome Res. 2010;20: 565–577. doi: 10.1101/gr.104471.109 20363979
56. Lee D, Gorkin DU, Baker M, Strober BJ, Asoni AL, McCallion AS, et al. A method to predict the impact of regulatory variants from DNA sequence. Nat Genet. 2015;47: 955–961. doi: 10.1038/ng.3331 26075791
57. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP, Molecular signatures database (MSigDB) 3.0. Bioinforma Oxf Engl. 2011;27: 1739–1740. doi: 10.1093/bioinformatics/btr260
58. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P, Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE. 2010;5: e12776. doi: 10.1371/journal.pone.0012776 20927193
59. Riker AI, Enkemann SA, Fodstad O, Liu S, Ren S, Morris C, et al. The gene expression profiles of primary and metastatic melanoma yields a transition point of tumor progression and metastasis. BMC Med Genomics. 2008;1: 13. doi: 10.1186/1755-8794-1-13 18442402
60. Nuutila K, Siltanen A, Peura M, Bizik J, Kaartinen I, Kuokkanen H, et al. Human skin transcriptome during superficial cutaneous wound healing. Wound Repair Regen Off Publ Wound Heal Soc Eur Tissue Repair Soc. 2012;20: 830–839. doi: 10.1111/j.1524-475X.2012.00831.x
61. Nair RP, Duffin KC, Helms C, Ding J, Stuart PE, Goldgar D, et al. Genome-wide scan reveals association of psoriasis with IL-23 and NF-kappaB pathways. Nat Genet. 2009;41: 199–204. doi: 10.1038/ng.311 19169254
62. Swindell WR, Johnston A, Carbajal S, Han G, Wohn C, Lu J, et al. Genome-wide expression profiling of five mouse models identifies similarities and differences with human psoriasis. PloS One. 2011;6: e18266. doi: 10.1371/journal.pone.0018266 21483750
63. Augustine CK, Jung S-H, Sohn I, Yoo JS, Yoshimoto Y, Olson JA, et al. Gene expression signatures as a guide to treatment strategies for in-transit metastatic melanoma. Mol Cancer Ther. 2010;9: 779–790. doi: 10.1158/1535-7163.MCT-09-0764 20371714
64. Beasley GM, Riboh JC, Augustine CK, Zager JS, Hochwald SN, Grobmyer SR, et al. Prospective multicenter phase II trial of systemic ADH-1 in combination with melphalan via isolated limb infusion in patients with advanced extremity melanoma. J Clin Oncol Off J Am Soc Clin Oncol. 2011;29: 1210–1215. doi: 10.1200/JCO.2010.32.1224
65. Books: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics) (Hardcover) by Trevor Hastie, Robert Tibshirani, Jerome Friedman [Internet]. Available: http://www.tower.com/elements-statistical-learning-data-mining-inference-prediction-second-jerome-friedman-hardcover/wapi/113059096
66. Quinlan AR, BEDTools: The Swiss-Army Tool for Genome Feature Analysis. Curr Protoc Bioinforma Ed Board Andreas Baxevanis Al. 2014;47: 1–34. doi: 10.1002/0471250953.bi1112s47
67. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12: 2825–2830.
68. Aerts S, Loo PV, Thijs G, Mayer H, de Martin R, Moreau Y, et al. TOUCAN 2: the all-inclusive open source workbench for regulatory sequence analysis. Nucleic Acids Res. 2005;33: W393–W396. doi: 10.1093/nar/gki354 15980497
69. Siepel A, Bejerano G, Pedersen JS, Hinrichs AS, Hou M, Rosenbloom K, et al. Evolutionarily conserved elements in vertebrate, insect, worm, and yeast genomes. Genome Res. 2005;15: 1034–1050. doi: 10.1101/gr.3715005 16024819
70. Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D, BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics. 2010;26: 2204–2207. doi: 10.1093/bioinformatics/btq351 20639541
71. Rosenbloom KR, Sloan CA, Malladi VS, Dreszer TR, Learned K, Kirkup VM, et al. ENCODE Data in the UCSC Genome Browser: year 5 update. Nucleic Acids Res. 2013;41: D56–D63. doi: 10.1093/nar/gks1172 23193274
72. Heger A, Webber C, Goodson M, Ponting CP, Lunter G, GAT: a simulation framework for testing the association of genomic intervals. Bioinforma Oxf Engl. 2013;29: 2046–2048. doi: 10.1093/bioinformatics/btt343
73. Adey A, Burton JN, Kitzman JO, Hiatt JB, Lewis AP, Martin BK, et al. The haplotype-resolved genome and epigenome of the aneuploid HeLa cancer cell line. Nature. 2013;500: 207–211. doi: 10.1038/nature12064 23925245
74. Adams D, Altucci L, Antonarakis SE, Ballesteros J, Beck S, Bird A, et al. BLUEPRINT to decode the epigenetic signature written in blood. Nat Biotechnol. 2012;30: 224–226. doi: 10.1038/nbt.2153 22398613
75. Martens JHA, Stunnenberg HG, BLUEPRINT: mapping human blood cell epigenomes. Haematologica. 2013;98: 1487–1489. doi: 10.3324/haematol.2013.094243 24091925
76. Kleftogiannis D, Kalnis P, Bajic VB, DEEP: a general computational framework for predicting enhancers. Nucleic Acids Res. 2014; gku1058. doi: 10.1093/nar/gku1058