Using high-throughput transcriptomic data for prognosis: A critical overview and perspectives

Cancer Research

Volumn 74, Issue 17, 2014, Pages 4612-4621

  Domany, Eytan ^a  

^a WEIZMANN INSTITUTE OF SCIENCE   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

TRANSCRIPTOME;

GENE EXPRESSION REGULATION; GENETICS; HUMAN; NEOPLASM; PATHOLOGY; PROGNOSIS;

GENE EXPRESSION REGULATION, NEOPLASTIC; HUMANS; NEOPLASMS; PROGNOSIS; TRANSCRIPTOME;

EID: 84907060514     PISSN: 00085472     EISSN: 15387445     Source Type: Journal    
DOI: 10.1158/0008-5472.CAN-13-3338     Document Type: Article

References (71)

1. Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med 2009;360:790-800.
2. van der Vegt B, de Bock GH, Hollema H, Wesseling J. Microarray methods to identify factors determining breast cancer progression: potentials, limitations, and challenges. Crit Rev Oncol Hematol 2009;70:1-11.
3. Bilal E, Dutkowski J, Guinney J, Jang IS, Logsdon BA, Pandey G, et al. Improving breast cancer survival analysis through competition-based multidimensional modeling. PLoS Comput Biol 2013;9:e1003047.
4. Geiger T, Madden SF, Gallagher WM, Cox J, Mann M. Proteomic portrait of human breast cancer progression identifies novel prognostic markers. Cancer Res 2012;72:2428-39.
5. Iorio MV, Croce CM. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med 2012;4:143-59.
6. Koscielny S. Why most gene expression signatures of tumors have not been useful in the clinic. Sci Transl Med 2010;2:14ps2.
7. Arpino G, Generali D, Sapino A, Lucia D, Frassoldati A, de Laurentis M, et al. Gene expression profiling in breast cancer: A clinical perspective. Breast 2013;22:109-20.
8. Allingham-Hawkins D, Lea A, Levine S. Decision Dx-GBM Gene Expression Assay for Prognostic Testing in Glioblastoma Multiform. PLoS Curr 2010;2:RRN1186.
9. Colman H, Zhang L, Sulman EP, McDonald JM, Shooshtari NL, Rivera A, et al.Amultigene predictor of outcome in glioblastoma. Neuro Oncol 2010;12:49-57.
10. van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415:530-6.
11. Cardoso F, Van't Veer L, Rutgers E, Loi S, MookS, Piccart-Gebhart MJ. Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol 2008;26:729-35.
12. Abu-Mostafa YS, Magdon-Ismail M, Lin H-T. Learning from data: a short course. S.I.; 2012. Available from: AMLBook.com.
13. Ideker T, Dutkowski J, Hood L. Boosting signal-to-noise in complex biology: prior knowledge is power. Cell 2011;144:860-3.
14. Nishimura D. BioCarta. Biotech Softw Internet Rep 2001;2:117-20.
15. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 2012;40:D109-14.
16. Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, et al. PID: the Pathway Interaction Database. Nucleic Acids Res 2009;37: D674-9.
17. Drier Y, Sheffer M, Domany E. Pathway-based personalized analysis of cancer. Proc Natl Acad Sci U S A 2013;110:6388-93.
18. Tamm IE. Yakov Il'ich Frenkel. Soviet Physics Uspekhi 1962;5: 173-194.
19. Dowsett M, Sestak I, Lopez-Knowles E, Sidhu K, Dunbier AK, Cowens JW, et al. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol 2013;31:2783-90.
20. Guyon I, Elisseeff A. An Introduction to Variable and Feature Selection. J Mach Learn Res 2003;3:1157-82.
21. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001;98:10869-74.
22. Ramaswamy S, Ross KN, Lander ES, Golub TR. A molecular signature of metastasis in primary solid tumors. Nat Genet 2003;33:49-54.
23. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al. Gene-expression profiles to predict distant metastasis of lymph-nodenegative primary breast cancer. Lancet 2005;365:671-9.
24. Tuma RS. Multiple gene signatures aim to qualify risk in breast cancer. J Natl Cancer Inst 2005;97:332.
25. Ein-Dor L, Kela I, Getz G, Givol D, Domany E. Outcomesignature genes in breast cancer: is there a unique set? Bioinformatics 2005;21:171-8.
26. Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet 2005;365: 488-92.
27. Efron B, Tibshirani R. An introduction to the bootstrap. New York: Chapman & Hall; 1993.
28. Ein-Dor L, Zuk O, Domany E. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci U S A 2006;103:5923-8.
29. van't Veer LJ, Bernards R. Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature 2008;452: 564-70.
30. Drier Y, Domany E. Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes? PLoS ONE 2011;6:e17795.
31. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. J Roy Stat Soc B Met 1995;57:289-300.
32. Tanay A. Computational Analysis of Transcriptional Programs: Function and Evolution [Dissertation]. Tel Aviv (Israel): Tel Aviv University; 2005.
33. Shen R, Chinnaiyan AM, Ghosh D. Pathway analysis reveals functional convergence of gene expression profiles in breast cancer. BMC Med Genomics 2008;1:28.
34. Aaltomaa S, Lipponen P, Eskelinen M, Kosma VM, Marin S, Alhava E, et al. Mitotic indexes as prognostic predictors in female breast cancer. J Cancer Res Clin Oncol 1992;118:75-81.
35. Penault-Llorca F, Abrial C, Raoelfils I, Chollet P, Cayre A, Mouret- Reynier MA, et al. Changes and predictive and prognostic value of the mitotic index, Ki-67, cyclin D1, and cyclo-oxygenase-2 in 710 operable breast cancer patients treated with neoadjuvant chemotherapy. Oncologist 2008;13:1235-45.
36. Mosley JD, Keri RA. Cell cycle correlated genes dictate the prognostic power of breast cancer gene lists. BMC Med Genomics 2008;1:11.
37. Baak JPA, Gudlaugsson E, Skaland I, Guo LHR, Klos J, LendeTH, et al. Proliferation is the strongest prognosticator in node-negative breast cancer: significance, error sources, alternatives and comparison with molecular prognostic markers. Breast Cancer Res Tr 2009;115: 241-54.
38. Haibe-Kains B, Desmedt C, Sotiriou C, Bontempi G. A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? Bioinformatics 2008;24:2200-8.
39. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol 2011;7:e1002240.
40. Beck AH, Knoblauch NW, Hefti MM, Kaplan J, Schnitt SJ, Culhane AC, et al. Significance analysis of prognostic signatures. PLoS Comput Biol 2013;9:e1002875.
41. Ransohoff DF. Gene-expression signatures in breast cancer. N Engl J Med 2003;348:1715-7.
42. Dupuy A, Simon RM. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 2007;99:147-57.
43. Goldhirsch A, Glick JH, Gelber RD, Coates AS, Thurlimann B, Senn HJ, et al. Meeting highlights: international expert consensus on the primary therapy of early breast cancer 2005. Ann Oncol 2005;16:1569-83.
44. Eifel P, Axelson JA, Costa J, Crowley J, Curran WJ Jr, Deshler A, et al. National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1-3, 2000. J Natl Cancer Inst 2001;93:979-89.
45. Galea MH, Blamey RW, Elston CE, Ellis IO. The NottinghamPrognostic Index in primary breast cancer. Breast Cancer Res Treat 1992;22: 207-19.
46. van de Vijver MJ, HeYD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, et al. Agene-expression signature as a predictor of survival in breast cancer. N Engl J Med 2002;347:1999-2009.
47. Buyse M, Loi S, van't Veer L, Viale G, Delorenzi M, Glas AM, et al. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 2006;98:1183-92.
48. Ravdin PM, Siminoff LA, Davis GJ, Mercer MB, Hewlett J, Gerson N, et al. Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. J Clin Oncol 2001;19: 980-91.
49. Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, Mori N, et al. Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. Lancet 2003;361:923-9.
50. Rosenwald A, Wright G, Chan WC, Connors JM, Campo E, Fisher RI, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med 2002;346: 1937-47.
51. Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 2002;8:816-24.
52. Bhattacharjee A, Richards WG, Staunton J, Li C, Monti S, Vasa P, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A 2001;98:13790-5.
53. Vaske CJ, Benz SC, Sanborn JZ, Earl D, Szeto C, Zhu J, et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 2010;26:i237-45.
54. Markert EK, Mizuno H, Vazquez A, Levine AJ. Molecular classification of prostate cancer using curated expression signatures. Proc Natl Acad Sci U S A 2011;108:21276-81.
55. Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006;439:353-7.
56. Cary MP, Bader GD, Sander C. Pathway information for systems biology. FEBS Lett 2005;579:1815-20.
57. Doniger SW, Salomonis N, Dahlquist KD, Vranizan K, Lawlor SC, Conklin BR. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol 2003;4:R7.
58. Efroni S, Schaefer CF, Buetow KH. Identification of key processes underlying cancer phenotypes using biologic pathway analysis. PLoS ONE 2007;2:e425.
59. Emmert-Streib F, Glazko GV. Pathway analysis of expression data: deciphering functional building blocks of complex diseases. PLoS Comput Biol 2011;7:e1002053.
60. Thomas DC, Conti DV, Baurley J, Nijhout F, Reed M, Ulrich CM. Use of pathway information in molecular epidemiology.HumGenomics 2009; 4:21-42.
61. Sheffer M, Bacolod MD, Zuk O, Giardina SF, Pincas H, Barany F, et al. Association of survival and disease progression with chromosomal instability: a genomic exploration of colorectal cancer. Proc Natl Acad Sci U S A 2009;106:7131-6.
62. Hastie T, Stuetzle W. Principal curves. J Am Stat Assoc 1989;84: 502-16.
63. Livshits A. Analysis of pathway deregulation in breast cancer. M. Sc. Thesis. Weizmann Institute of Science; 2013.
64. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012;486:346-52.
65. Network CGAR. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008;455: 1061-8.
66. Madhavan S, Zenklusen JC, Kotliarov Y, Sahni H, Fine HA, Buetow K. Rembrandt: helping personalized medicine become a reality through integrative translational research. Mol Cancer Res 2009;7:157-67.
67. Phillips HS, Kharbanda S, Chen RH, Forrest WF, Soriano RH, Wu TD, et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 2006;9:157-73.
68. Cuzick J, Dowsett M, Pineda S, Wale C, Salter J, Quinn E, et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer. J Clin Oncol 2011;29:4273-8.
69. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:2817-26.
70. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009;27:1160-7.
71. Weis JA, Miga MI, Arlinghaus LR, Li X, Chakravarthy AB, Abramson V, et al. A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy. Phys Med Biol 2013;58:5851-66.