Pathway and network analysis of cancer genomes

Nature Methods

Volumn 12, Issue 7, 2015, Pages 615-621

  Creixell, Pau ^a,q   Reimand, Jüri ^b   Haider, Syed ^c   Wu, Guanming ^c,d   Shibata, Tatsuhiro ^e   Vazquez, Miguel ^f   Mustonen, Ville ^g   Gonzalez Perez, Abel ^h   Pearson, John ⁱ   Sander, Chris ^j   Raphael, Benjamin J ^k   Marks, Debora S ^l   Ouellette, B F Francis ^c,m   Valencia, Alfonso ^f   Bader, Gary D ^b   Boutros, Paul C ^b,c,m   Stuart, Joshua M ⁿ   Linding, Rune ^a,o   Lopez Bigas, Nuria ^h,p   Stein, Lincoln D ^b,c  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b UNIVERSITY OF TORONTO   (Canada)

^c ONTARIO INSTITUTE FOR CANCER RESEARCH   (Canada)

^d OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^e NATIONAL CANCER CENTER   (Japan)

^f Centro Nacional de Investigaciones Oncológicas   (Spain)

^g WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^h UNIVERSITAT POMPEU FABRA   (Spain)

ⁱ UNIVERSITY OF QUEENSLAND   (Australia)

^j MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^k Brown Univerity   (United States)

^l HARVARD MEDICAL SCHOOL   (United States)

^m UNIVERSITY OF TORONTO   (Canada)

ⁿ University of California Santa Cruz   (United States)

^o UNIVERSITY OF COPENHAGEN   (Denmark)

^p INSTITUCIÓ CATALANA DE RECERCA I ESTUDIS AVANÇATS ICREA   (Spain)

^q MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

APC PROTEIN; EPIDERMAL GROWTH FACTOR; STAT3 PROTEIN; VON HIPPEL LINDAU PROTEIN;

APOPTOSIS; CANCER GENETICS; CELLULAR DISTRIBUTION; COMPLEX FORMATION; CONFORMATIONAL TRANSITION; FUZZY LOGIC; GAIN OF FUNCTION MUTATION; GENE ACTIVITY; GENE EXPRESSION; GENE INTERACTION; GENE MUTATION; GENE ONTOLOGY; GENE REGULATORY NETWORK; GENETIC VARIABILITY; GLIOBLASTOMA; HUMAN; MELANOMA; MOLECULAR INTERACTION; MULTIPLE CANCER; OVARY CANCER; PRIORITY JOURNAL; PROTEIN INTERACTION; PROTEIN PHOSPHORYLATION; REVIEW; SIGNAL TRANSDUCTION; SINGLE NUCLEOTIDE VARIANT; SOMATIC MUTATION; GENETICS; GENOME; NEOPLASM; PHYSIOLOGY;

GENE REGULATORY NETWORKS; GENOME; HUMANS; NEOPLASMS; SIGNAL TRANSDUCTION;

EID: 84934268985     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.3440     Document Type: Review

References (74)

1. Newman, W.G. & Black, G.C. Delivery of a clinical genomics service. Genes (Basel) 5, 1001-1017 (2014).
2. Lawrence, M.S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495-501 (2014).
3. Biankin, A.V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399-405 (2012).
4. Imielinski, M. et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 150, 1107-1120 (2012).
5. Banerji, S. et al. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature 486, 405-409 (2012).
6. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061-1068 (2008).
7. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609-615 (2011).
8. Garraway, L.A. & Lander, E.S. Lessons from the cancer genome. Cell 153, 17-37 (2013).
9. Zack, T.I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134-1140 (2013).
10. Mack, S.C. et al. Epigenomic alterations define lethal CIMP-positive ependymomas of infancy. Nature 506, 445-450 (2014).
11. Gonzalez-Perez, A. et al. Computational approaches to identify functional genetic variants in cancer genomes. Nat. Methods 10, 723-729 (2013).
12. Leiserson, M.D.M., Blokh, D., Sharan, R. & Raphael, B.J. Simultaneous identification of multiple driver pathways in cancer. PLoS Comput. Biol. 9, e1003054 (2013).
13. Pe'er, D. & Hacohen, N. Principles and strategies for developing network models in cancer. Cell 144, 864-873 (2011).
14. Califano, A., Butte, A.J., Friend, S., Ideker, T. & Schadt, E. Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nat. Genet. 44, 841-847 (2012).
15. Chi, Y.Y., Gribbin, M.J., Johnson, J.L. & Muller, K.E. Power calculation for overall hypothesis testing with high-dimensional commensurate outcomes. Stat. Med. 33, 812-827 (2014).
16. Akavia, U.D. et al. An integrated approach to uncover drivers of cancer. Cell 143, 1005-1017 (2010).
17. Danussi, C. et al. RHPN2 drives mesenchymal transformation in malignant glioma by triggering RhoA activation. Cancer Res. 73, 5140-5150 (2013).
18. 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).
19. Sonabend, A.M. et al. The transcriptional regulatory network of proneural glioma determines the genetic alterations selected during tumor progression. Cancer Res. 74, 1440-1451 (2014).
20. Carro, M.S. et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463, 318-325 (2010).
21. International Cancer Genome Consortium. International network of cancer genome projects. Nature 464, 993-998 (2010).
22. Bader, G.D., Cary, M.P. & Sander, C. Pathguide: a pathway resource list. Nucleic Acids Res. 34, D504-D506 (2006).
23. The Gene Ontology Consortium. The Gene Ontology in 2010: extensions and refinements. Nucleic Acids Res. 38, D331-D335 (2010).
24. Huang, D.W. et al. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35, W169-W175 (2007).
25. Reimand, J., Kull, M., Peterson, H., Hansen, J. & Vilo, J. g:Profiler- A web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35, W193-W200 (2007).
26. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545-15550 (2005).
27. Gundem, G. & Lopez-Bigas, N. Sample-level enrichment analysis unravels shared stress phenotypes among multiple cancer types. Genome Med. 4, 28 (2012).
28. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
29. Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G.D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5, e13984 (2010).
30. Cline, M.S. et al. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366-2382 (2007).
31. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109-D114 (2012).
32. Croft, D. et al. Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res. 39, D691-D697 (2011).
33. Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 42, D459-D471 (2014).
34. Wu, D. & Smyth, G.K. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 40, e133 (2012).
35. Razick, S., Magklaras, G. & Donaldson, I.M. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9, 405 (2008).
36. Chatr-Aryamontri, A. et al. The BioGRID interaction database: 2013 update. Nucleic Acids Res. 41, D816-D823 (2013).
37. Kerrien, S. et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res. 40, D841-D846 (2012).
38. Franceschini, A. et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808-D815 (2013).
39. Warde-Farley, D. et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38, W214-W220 (2010).
40. Wu, G., Dawson, E., Duong, A., Haw, R. & Stein, L. ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis. F1000Res. 3, 146 (2014).
41. Lan, A. et al. ResponseNet: revealing signaling and regulatory networks linking genetic and transcriptomic screening data. Nucleic Acids Res. 39, W424-W429 (2011).
42. Cerami, E., Demir, E., Schultz, N., Taylor, B.S. & Sander, C. Automated network analysis identifies core pathways in glioblastoma. PLoS ONE 5, e8918 (2010).
43. Ciriello, G., Cera, E., Sander, C. & Schultz, N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 22, 398-406 (2012).
44. Glaab, E., Baudot, A., Krasnogor, N., Schneider, R. & Valencia, A. EnrichNet: network-based gene set enrichment analysis. Bioinformatics 28, i451-i457 (2012).
45. Wu, G. & Stein, L. A network module-based method for identifying cancer prognostic signatures. Genome Biol. 13, R112 (2012).
46. Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D. & Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007).
47. Leung, A., Bader, G.D. & Reimand, J. HyperModules: identifying clinically and phenotypically significant network modules with disease mutations for biomarker discovery. Bioinformatics 30, 2230-2232 (2014).
48. Reimand, J. & Bader, G.D. Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol. Syst. Biol. 9, 637 (2013).
49. Krallinger, M. et al. The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text. BMC Bioinformatics 12, S3 (2011).
50. Kwong, L.N. et al. Oncogenic NRAS signaling differentially regulates survival and proliferation in melanoma. Nat. Med. 18, 1503-1510 (2012).
51. Schadt, E.E. et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat. Genet. 37, 710-717 (2005).
52. Aytes, A. et al. Cross-species analysis of genome-wide regulatory networks identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell 25, 638-651 (2014).
53. Piovan, E. et al. Direct reversal of glucocorticoid resistance by AKT inhibition in acute lymphoblastic leukemia. Cancer Cell 24, 766-776 (2013).
54. Bandyopadhyay, S. et al. A human MAP kinase interactome. Nat. Methods 7, 801-805 (2010).
55. Vandin, F., Upfal, E. & Raphael, B.J. Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol. 18, 507-522 (2011).
56. Paull, E.O. et al. Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events (TieDIE). Bioinformatics 29, 2757-2764 (2013).
57. Drier, Y., Sheffer, M. & Domany, E. Pathway-based personalized analysis of cancer. Proc. Natl. Acad. Sci. USA 110, 6388-6393 (2013).
58. Tarca, A.L. et al. A novel signaling pathway impact analysis. Bioinformatics 25, 75-82 (2009).
59. Margolin, A.A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7 (suppl. 1), S7 (2006).
60. Morris, M.K., Saez-Rodriguez, J., Sorger, P.K. & Lauffenburger, D.A. Logic-based models for the analysis of cell signaling networks. Biochemistry 49, 3216-3224 (2010).
61. Morris, M.K., Saez-Rodriguez, J., Clarke, D.C., Sorger, P.K. & Lauffenburger, D.A. Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS Comput. Biol. 7, e1001099 (2011).
62. Janes, K.A. et al. A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310, 1646-1653 (2005).
63. Lee, M.J. et al. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149, 780-794 (2012).
64. Saez-Rodriguez, J. et al. Flexible informatics for linking experimental data to mathematical models via DataRail. Bioinformatics 24, 840-847 (2008).
65. Greenblum, S.I., Efroni, S., Schaefer, C.F. & Buetow, K.H. The PathOlogist: an automated tool for pathway-centric analysis. BMC Bioinformatics 12, 133 (2011).
66. Brubaker, D. et al. Drug Intervention Response Predictions with PARADIGM (DIRPP) identifies drug resistant cancer cell lines and pathway mechanisms of resistance. Pac. Symp. Biocomput. doi:10.1142/9789814583220-0013 (2014).
67. Vaske, C.J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237-i245 (2010).
68. Ng, S. et al. PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis. Bioinformatics 28, i640-i646 (2012).
69. Hill, S.M. et al. Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics 28, 2804-2810 (2012).
70. Sanghvi, J.C. et al. Accelerated discovery via a whole-cell model. Nat. Methods 10, 1192-1195 (2013).
71. Dittrich, M.T., Klau, G.W., Rosenwald, A., Dandekar, T. & Müller, T. Identifying functional modules in protein-protein interaction networks: an integrated exact approach. Bioinformatics 24, i223-i231 (2008).
72. Wu, M., Pastor-Pareja, J.C. & Xu, T. Interaction between RasV12 and scribbled clones induces tumour growth and invasion. Nature 463, 545-548 (2010).
73. Berry, D.A. Adaptive clinical trials: the promise and the caution. J. Clin. Oncol. 29, 606-609 (2011).
74. Green, R.C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565-574 (2013).