Systematic analysis of somatic mutations impacting gene expression in 12 tumour types

Nature Communications

Volumn 6, Issue , 2015, Pages

  Ding, Jiarui ^a,b   McConechy, Melissa K ^a,b   Horlings, Hugo M ^a,b   Ha, Gavin ^a   Chun Chan, Fong ^a   Funnell, Tyler ^a   Mullaly, Sarah C ^a   Reimand, Jüri ^c   Bashashati, Ali ^a   Bader, Gary D ^c   Huntsman, David ^a,b   Aparicio, Samuel ^a,b   Condon, Anne ^b   Shah, Sohrab P ^a,b,d  

^a BRITISH COLUMBIA CANCER AGENCY   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^c UNIVERSITY OF TORONTO   (Canada)

^d CANADA'S MICHAEL SMITH GENOME SCIENCES CENTRE   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN ANALYSIS; BENCHMARKING; CANCER; GENE EXPRESSION; GENOME; MUTATION; THEORETICAL STUDY; TUMOR;

ARTICLE; CANCER GENETICS; COPY NUMBER VARIATION; GENE CONTROL; GENE EXPRESSION; GENE INTERACTION; GENE SEQUENCE; GENETIC TRANSCRIPTION; HUMAN; LOSS OF FUNCTION MUTATION; MISSENSE MUTATION; MUTATION RATE; ONCOGENE; POINT MUTATION; SOMATIC MUTATION; TUMOR GENE; TUMOR SUPPRESSOR GENE; BAYES THEOREM; BIOLOGICAL MODEL; GENE EXPRESSION REGULATION; GENETICS; MUTATION; NEOPLASM; STATISTICAL MODEL;

BAYES THEOREM; GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; MODELS, GENETIC; MODELS, STATISTICAL; MUTATION; NEOPLASMS;

EID: 84943279282     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms9554     Document Type: Article

References (63)

1. Vogelstein, B., et al. Cancer genome landscapes. Science 339, 1546-1558 (2013
2. Reva, B., Antipin, Y., & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39 doi: 10.1093/nar/gkr407 (2011
3. Lawrence, M. S., et al. Mutational heterogeneity in cancer and the search for new cancer-Associated genes. Nature 499, 214-218 (2013
4. Nagy, E., & Maquat, L. E A rule for termination-codon position within introncontaining genes: when nonsense affects RNA abundance. Trends Biochem. Sci. 23, 198-199 (1998
5. Curtis, C., et al. The genomic and transcriptomic architecture of 2, 000 breast tumours reveals novel subgroups. Nature 486, 346-352 (2012
6. Kandoth, C., et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333-339 (2013
7. Zack, T. I., et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134-1140 (2013
8. Ciriello, G., et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127-1133 (2013
9. Leiserson, M. D., et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47, 106-114 (2015
10. Hoadley, K. A., et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929-944 (2014
11. Raphael, B. J., Dobson, J. R., Oesper, L., & Vandin, F. Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine. Genome Med. 6 doi: 10.1186/gm524 (2014
12. Akavia, U., et al. An integrated approach to uncover drivers of cancer. Cell 143, 1005-1017 (2010
13. Jörnsten, R., et al. Network modeling of the transcriptional effects of copy number aberrations in glioblastoma. Mol. Syst. Biol. 7 doi: 10.1038/msb.2011.17 (2011
14. Masica, D. L., & Karchin, R. Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival. Cancer Res. 71, 4550-4561 (2011
15. Vaske, C. J., et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237-i245 (2010
16. Bashashati, A., et al. DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biol. 13, R124 (2012
17. Weinstein, J. N., et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45, 1113-1120 (2013
18. Mermel, C. H., et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011
19. Tang, Y.-C., & Amon, A. Gene copy-number alterations: a cost-benefit analysis. Cell 152, 394-405 (2013
20. Vandin, F., Upfal, E., & Raphael, B. J. Algorithms for detecting significantly mutated pathways in cancer. J. Comp. Biol. 18, 507-522 (2011
21. Pearl, J. in Proceedings of the Second National Conference on Artificial Intelligence. 133-136 (Pittsburgh, PA, USA, 1982
22. Dempster, A. P., Laird, N. M., & Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B Stat. Methodol. 39, 1-38 (1977
23. The Cancer Genome Atlas Research Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059-2074 (2013
24. Ha, G., & Shah, S. in Array Comparative Genomic Hybridization Vol. 973, 355-372 (Humana Press (2013
25. Futreal, P., et al A census of human cancer genes. Nat. Rev. Cancer 4, 177-183 (2004
26. Lawrence, M. S., et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495-501 (2014
27. Dees, N. D., et al. MuSiC: identifying mutational significance in cancer genomes. Genome Res. 22, 1589-1598 (2012
28. Tripathi, S., et al. Gene ontology annotation of sequence-specific DNA binding transcription factors: setting the stage for a large-scale curation effort. Database 2013, bat062 (2013
29. Reimand, J., & Bader, G. D. Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol. Syst. Biol. 9, 637 (2013
30. Davoli, T., et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948-962 (2013
31. Shah, P., et al. Ubiquilin1 represses migration and epithelial-To-mesenchymal transition of human non-small cell lung cancer cells. Oncogene 34, 1709-1717 (2015
32. Shi, J., Han, Q., Zhao, H., Zhong, C., & Yao, F. Downregulation of med23 promoted the tumorigenecity of esophageal squamous cell carcinoma. Mol. Carcinog. 53, 833-840 (2014
33. Rolland, T., et al A proteome-scale map of the human interactome network. Cell 159, 1212-1226 (2014
34. Human interactome database. Available at http://interactome.dfci.harvard.edu/H-sapiens/index.php?page=download
35. Reimand, J., Wagih, O., & Bader, G. D. The mutational landscape of phosphorylation signaling in cancer. Sci. Rep. 3 doi: 10.1038/srep02651 (2013
36. Smyth, G. K. in Bioinformatics and Computational Biology Solutions Using R and Bioconductor 397-420 (Springer, 2005
37. Benjamini, Y., & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57, 289-3000 (1995
38. The Cancer Genome Atlas Research Network. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67-73 (2013
39. Prahallad, A., et al. Unresponsiveness of colon cancer to BRAF (V600E) inhibition through feedback activation of EGFR. Nature 483, 100-103 (2012
40. Kobayashi, S., et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 352, 786-792 (2005
41. Patel, A. P., et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396-1401 (2014
42. Murphy, K. Machine Learning: a Probabilistic Perspective (MIT press, 2012
43. Kim, J. H., & Pearl, J. in Proceedings of the Eighth International Joint Conference on Artificial Intelligence, 190-193. Vol. 1 (Karlsruhe, Germany, 1983
44. Franceschini, A., et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808-D815 (2013
45. Kanehisa, M., & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27-30 (2000
46. Pico, A. R., et al. WikiPathways: pathway editing for the people. PLoS Biol. 6, e184 (2008
47. Karp, P. D., et al. Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res. 33, 6083-6089 (2005
48. Zhou, H., et al. IntPath-An integrated pathway gene relationship database for model organisms and important pathogens. BMC Syst. Biol. 6 doi: 10.1186/1752-0509-6-S2-S2 (2012
49. Yusuf, D., et al. The transcription factor encyclopedia. Genome Biol. 13 doi: 10.1186/gb-2012-13-3-r24 (2012
50. Gerstein, M. B., et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91-100 (2012
51. Architecture of the human regulatory network derived from ENCODE data. Available at http://encodenets.gersteinlab.org/(2012
52. The R project for statistical computing. Available at https://www.r-project.org/
53. xseq: Assessing functional impact on gene expression of mutations in cancer. Available at https://cran.r-project.org/web/packages/xseq/(2015
54. xseq: Assessing functional impact on gene expression of mutations in cancer. Available at http://compbio.bccrc.ca/software/xseq/(2015
55. Weinberg, R. The Biology of Cancer 2 edn (Garland Science, 2013
56. Wynes, M. W., et al. FGFR1 mRNA and protein expression, not gene copy number, predict FGFR TKI sensitivity across all lung cancer histologies. Clin. Cancer Res. 20, 3299-3309 (2014
57. Forbes, S. A., et al. COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res. 39, D945-D950 (2011
58. D'Antonio, M., & Ciccarelli, F. D. Integrated analysis of recurrent properties of cancer genes to identify novel drivers. Genome Biol. 14 doi: 10.1186/gb-2013-14-5-r52 (2013
59. Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15 doi: 10.1186/gb-2014-15-2-r29 (2014
60. Gevaert, O., Villalobos, V., Sikic, B. I., & Plevritis, S. K. Identification of ovarian cancer driver genes by using module network integration of multi-omics data. Interface Focus 3 doi: 10.1098/rsfs.2013.0013 (2013
61. Mo, Q., et al. Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc. Natl Acad. Sci. USA 110, 4245-4250 (2013
62. Aure, M. R., et al. Identifying in-Trans process associated genes in breast cancer by integrated analysis of copy number and expression data. PloS ONE 8 doi: 10.1371/journal.pone.0053014 (2013
63. Huang, N., Shah, P. K., & Li, C. Lessons from a decade of integrating cancer copy number alterations with gene expression profiles. Brief. Bioinform. 13, 305-316 (2012