ENCAPP: Elastic-net-based prognosis prediction and biomarker discovery for human cancers

BMC Genomics

Volumn 16, Issue 1, 2015, Pages

  Das, Jishnu ^a,b   Gayvert, Kaitlyn M ^c   Bunea, Florentina ^d   Wegkamp, Marten H ^d   Yu, Haiyuan ^a,b  

^a Program on Infrastructure Policy   (United States)

^b School of Integrative Plant Sciences   (United States)

^c Tri Institutional Training Program in Computational Biology and Medicine   (United States)

^d NONE   (United States)

Author keywords

Cancer genomics; Elastic net; Gene expression; Prognosis prediction; Protein interaction network

Indexed keywords

ALGORITHM; ARTICLE; BREAST CANCER; CANCER PROGNOSIS; COLON CANCER; COMPUTER PREDICTION; FALSE POSITIVE RESULT; GENE EXPRESSION; MEASUREMENT ACCURACY; OVARY CANCER; PROTEIN INTERACTION; RECTUM CANCER; REGRESSION ANALYSIS; TUMOR GENE; BIOLOGY; COMPUTER PROGRAM; GENETICS; HUMAN; METABOLISM; NEOPLASMS; PROGNOSIS; PROTEIN PROTEIN INTERACTION;

TUMOR MARKER;

BIOMARKERS, TUMOR; COMPUTATIONAL BIOLOGY; GENE EXPRESSION; HUMANS; NEOPLASMS; PROGNOSIS; PROTEIN INTERACTION MAPS; SOFTWARE;

EID: 84927951046     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-015-1465-9     Document Type: Article

References (46)

1. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646-74.
2. 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(7484):495-501.
3. Chin L, Hahn WC, Getz G, Meyerson M. Making sense of cancer genomic data. Genes Dev. 2011;25(6):534-55.
4. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45(10):1113-20.
5. Hudson TJ, Anderson W, Artez A, Barker AD, Bell C, Bernabe RR, et al. International network of cancer genome projects. Nature. 2010;464(7291):993-8.
6. van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002;347(25):1999-2009.
7. Vidal M, Cusick ME, Barabasi AL. Interactome networks and human disease. Cell. 2011;144(6):986-98.
8. Wang X, Wei X, Thijssen B, Das J, Lipkin SM, Yu H. Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotechnol. 2012;30(2):159-64.
9. Guo Y, Wei X, Das J, Grimson A, Lipkin SM, Clark AG, et al. Dissecting disease inheritance modes in a three-dimensional protein network challenges the "guilt-by-association" principle. Am J Hum Genet. 2013;93(1):78-89.
10. Das J, Fragoza R, Lee HR, Cordero NA, Guo Y, Meyer MJ, et al. Exploring mechanisms of human disease through structurally resolved protein interactome networks. Mol Biosyst. 2014;10(1):9-17.
11. Das J, Lee HR, Sagar A, Fragoza R, Liang J, Wei X, et al. Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks. Hum Mutat. 2014;35(5):585-93.
12. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140.
13. Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009;27(2):199-204.
14. Wu G, Stein L. A network module-based method for identifying cancer prognostic signatures. Genome Biol. 2012;13(12):R112.
15. Hofree M, Shen JP, Carter H, Gross A, Ideker T. Network-based stratification of tumor mutations. Nat Methods. 2013;10(11):1108-15.
16. Das J, Yu H. HINT: High-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol. 2012;6(1):92.
17. Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12(1):55-67.
18. Tibshirani R. Regression shrinkage and selection via the Lasso. J Roy Stat Soc B Met. 1996;58(1):267-88.
19. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Stat Methodol). 2005;67(2):301-20.
20. Bunea F. Honest variable selection in linear and logistic regression models via l(1) and l(1)+l(2) penalization. Electron J Stat. 2008;2:1153-94.
21. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabasi AL. Hierarchical organization of modularity in metabolic networks. Science. 2002;297(5586):1551-5.
22. Ahn YY, Bagrow JP, Lehmann S. Link communities reveal multiscale complexity in networks. Nature. 2010;466(7307):761-4.
23. Ward JH. Hierarchical grouping to optimize an objective function. J Am Stat Assoc. 1963;58(301):236.
24. Frey BJ, Dueck D. Clustering by passing messages between data points. Science. 2007;315(5814):972-6.
25. Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods. 2012;9(5):471-2.
26. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015.
27. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365(9460):671-9.
28. Atlas TCG. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487(7407):330-7.
29. Atlas TCG. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609-15.
30. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50.
31. Zhou Y, Eppenberger-Castori S, Marx C, Yau C, Scott GK, Eppenberger U, et al. Activation of nuclear factor-kappaB (NFkappaB) identifies a high-risk subset of hormone-dependent breast cancers. Int J Biochem Cell Biol. 2005;37(5):1130-44.
32. Wakefield A, Piggott L, Croston D, Jiang WG, Clarkson R. Suppression of the NF-κB cofactor Bcl3 inhibits mammary epithelial cell apoptosis and, in breast tumours, correlates with poor prognosis. Breast Cancer Res. 2008;10 Suppl 2:O4.
33. Chen L, Jiang B, Wang Z, Liu M, Ma Y, Yang H, et al. Expression and prognostic significance of GATA-binding protein 2 in colorectal cancer. Med Oncol. 2013;30(2):498.
34. Ganoth D, Bornstein G, Ko TK, Larsen B, Tyers M, Pagano M, et al. The cell-cycle regulatory protein Cks1 is required for SCF(Skp2)-mediated ubiquitinylation of p27. Nat Cell Biol. 2001;3(3):321-4.
35. Dressler AC, Hudelist G, Fink-Retter A, Gschwantler-Kaulich D, Pfeiler G, Rosner M, et al. Tuberin and p27 expression in breast cancer patients with or without BRCA germline mutations. J Cancer Res Clin Oncol. 2013;139(8):1349-55.
36. Hashizume R, Fukuda M, Maeda I, Nishikawa H, Oyake D, Yabuki Y, et al. The RING heterodimer BRCA1-BARD1 is a ubiquitin ligase inactivated by a breast cancer-derived mutation. J Biol Chem. 2001;276(18):14537-40.
37. Brzovic PS, Keeffe JR, Nishikawa H, Miyamoto K, Fox 3rd D, Fukuda M, et al. Binding and recognition in the assembly of an active BRCA1/BARD1 ubiquitin-ligase complex. Proc Natl Acad Sci U S A. 2003;100(10):5646-51.
38. Irminger-Finger I. BARD1, a possible biomarker for breast and ovarian cancer. Gynecol Oncol. 2010;117(2):211-5.
39. Cooper EM, Cutcliffe C, Kristiansen TZ, Pandey A, Pickart CM, Cohen RE. K63-specific deubiquitination by two JAMM/MPN+ complexes: BRISC-associated Brcc36 and proteasomal Poh1. EMBO J. 2009;28(6):621-31.
40. Noordermeer SM, Sanders MA, Gilissen C, Tonnissen E, van der Heijden A, Dohner K, et al. High BRE expression predicts favorable outcome in adult acute myeloid leukemia, in particular among MLL-AF9-positive patients. Blood. 2011;118(20):5613-21.
41. Noordermeer SM, Monteferrario D, Sanders MA, Bullinger L, Jansen JH, van der Reijden BA. Improved classification of MLL-AF9-positive acute myeloid leukemia patients based on BRE and EVI1 expression. Blood. 2012;119(18):4335-7.
42. Das J, Mohammed J, Yu H. Genome-scale analysis of interaction dynamics reveals organization of biological networks. Bioinformatics. 2012;28(14):1873-8.
43. Das J, Vo TV, Wei X, Mellor JC, Tong V, Degatano AG, et al. Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways. Sci Signal. 2013;6(276):ra38.
44. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer; 2009.
45. 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(3):177-83.
46. 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(Database issue):D945-50.