Challenges and solutions in the development of genomic biomarker panels: A systematic phased approach

Current Genomics

Volumn 13, Issue 4, 2012, Pages 334-341

  Shahzad, K ^a   Fatima, A ^b   Cadeiras, M ^b   Wisniewski, N ^b   Bondar, G ^b   Cheng, R ^b   Reed, E ^b   Deng, M C ^b  

^a EAST CAROLINA UNIVERSITY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Gene expression transplantation; Genomics; Multiorgan dysfunction; Personalized medicine; Post genome era

Indexed keywords

BIOLOGICAL MARKER; MESSENGER RNA;

ARTICLE; BLOOD SAMPLING; CARDIAC ALLOGRAFT VASCULOPATHY; CARDIAC GRAFT REJECTION; CLINICAL DECISION MAKING; COPY NUMBER VARIATION; GENE DELETION; GENE DUPLICATION; GENE EXPRESSION PROFILING; GENE MUTATION; GENETIC SCREENING; GENOMIC BIOMARKER PANEL; HEART TRANSPLANTATION; HUMAN; MARKER GENE; MICROARRAY ANALYSIS; MULTIPLE ORGAN FAILURE; NOISE; PERIPHERAL BLOOD MONONUCLEAR CELL; PERSONALIZED MEDICINE; PHENOTYPE; PHENOTYPIC VARIATION; REAL TIME POLYMERASE CHAIN REACTION; RNA EXTRACTION; SINGLE NUCLEOTIDE POLYMORPHISM; STANDARDIZATION;

EID: 84862627907     PISSN: 13892029     EISSN: 18755488     Source Type: Journal    
DOI: 10.2174/138920212800793339     Document Type: Article

References (70)

1. Collins, F. S.; Morgan, M.; Patrinos, A. The Human GenomeProject: lessons from large-scale biology. Science, 2003, 300(5617), 286-290.
2. Cobb, J. P.; Ognibene, F. P.; Ingbar, D. H.; Mann, H. J.; Hoyt, D.B.; Angus, D. C.; Thomas, A. V. Jr.; Danner, R. L.; Suffredini, A.F. Forging a critical alliance: Addressing the research needs of the United States critical illness and injury community. Crit. Care Med., 2009, 37(12), 3158-3160.
3. Schena, M.; Shalon, D.; Heller, R.; Chai, A.; Brown, P. O.; Davis, R. W. Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc. Natl. Acad. Sci. U S A, 1996, 93(20), 10614-10619.
4. Eisen, M. B.; Spellman, P. T.; Brown, P. O.; Botstein, D. Clusteranalysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U S A, 1998, 95(25), 14863-14868.
5. Tusher, V. G.; Tibshirani, R.; Chu, G. Significance analysis ofmicroarrays applied to the ionizing radiation response. Proc. Nat. Acad. Sci. USA, 2001, 98(9), 5116-5121.
6. Tibshirani, R.; Hastie, T.; Narasimhan, B.; Chu, G. Diagnosis ofmultiple cancer types by shrunken centroids of gene expression. Proc. Natl. Acad. Sci. U S A, 2002, 99(10), 6567-6572.
7. Sarwal, M.; Chua, M. S.; Kambham, N.; Hsieh, S. C.; Satterwhite, T.; Masek, M.; Salvatierra, O. Jr. Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N. Engl. J. Med., 2003, 349(2), 125-138.
8. Calvano, S. E.; Xiao, W.; Richards, D. R.; Felciano, R. M.; Baker, H. V.; Cho, R. J.; Chen, R. O.; Brownstein, B. H.; Cobb, J. P.; Tschoeke, S. K.; Miller-Graziano, C.; Moldawer, L. L.; Mindrinos, M. N.; Davis, R. W.; Tompkins, R. G.; Lowry, S. F. Inflamm and Host Response to Injury Large Scale Collab. Res. Program. A network-based analysis of systemic inflammation in humans. Nature, 2005, 437(7061), 1032-1037.
9. Laudanski, K.; Miller-Graziano, C.; Xiao, W.; Mindrinos, M. N.; Richards, D. R.; De, A.; Moldawer, L. L.; Maier, R. V.; Bankey, P.; Baker, H. V.; Brownstein, B. H.; Cobb, J. P.; Calvano, S. E.; Davis, R. W.; Tompkins, R. G. Cell-specific expression and pathway analyses reveal alterations in trauma-related human T cell and monocyte pathways. Proc. Natl. Acad. Sci. U S A, 2006, 103(42), 15564-15569.
10. Tang, B. M.; McLean, A. S.; Dawes, I. W.; Huang, S. J.; Lin, R. C.Gene-expression profiling of peripheral blood mononuclear cells in sepsis. Crit. Care Med, 2009, 37(3), 882-888.
11. Sinha, A.; Shahzad, K.; Latif, F.; Cadeiras, M.; Von Bayern, M. P.; Oz, S.; Naka, Y.; Deng, M. C. Leukocyte Expression Signatures Early After Mechanical Circulatory Support Device Implantation. Hum. Immunol., 2010, 71(2), 164-169.
12. Van't Veer, L. J.; Dai, H.; van de Vijver, M. J.; He, Y. D.; Hart, A.A.; Mao, M.; Peterse, H. L.; van der Kooy, K.; Marton, M. J.; Witteveen, A. T.; Schreiber, G. J.; Kerkhoven, R. M.; Roberts, C.; Linsley, P. S.; Bernards, R.; Friend, S. H. Gene expression profiling predicts clinical outcome of breast cancer. Nature, 2002, 415(6871), 530-536.
13. Paik, S.; Shak, S.; Tang, G.; Kim, C.; Baker, J.; Cronin, M.; Baehner, F. L.; Walker, M. G.; Watson, D.; Park, T.; Hiller, W.; Fisher, E. R.; Wickerham, D. L.; Bryant, J.; Wolmark, N. A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer. N. Engl. J. Med., 2004, 351(27), 2817-2826.
14. Deng, M. C.; Eisen, H. J.; Mehra, M. R.; Billingham, M.; Marboe, C. C.; Berry, G.; Kobashigawa, J.; Johnson, F. L.; Starling, R. C.; Murali, S.; Pauly, D. F.; Baron, H.; Wohlgemuth, J. G.; Woodward, R. N.; Klingler, T. M.; Walther, D.; Lal, P. G.; Rosenberg, S.; Hunt, S. CARGO Investigators. Non-invasive detection of rejection in cardiac allograft recipients using gene expression profiling. Am. J. Transplant., 2006, 6(1), 150-160.
15. Wingrove, J. A.; Daniels, S. E.; Sehnert, A. J.; Tingley, W.; Elashoff, M. R.; Rosenberg, S.; Buellesfeld, L.; Grube, E.; Newby, L. K.; Ginsburg, G. S.; Kraus, W. E. Correlation of peripheral- blood gene expression with the extent of coronary artery stenosis. Circ. Cardiovasc. Genet., 2008, 1(1), 31-38.
16. Sinnaeve, P. R.; Donahue, M. P.; Grass, P.; Seo, D.; Vonderscher, J.; Chibout, S. D.; Kraus, W. E.; Sketch, M. Jr.; Nelson, C.; Ginsburg, G. S.; Goldschmidt-Clermont, P. J.; Granger, C. B. Gene expression patterns in peripheral blood correlate with the extent of coronary artery disease. PLoS One, 2009, 4(9), e7037.
17. Allison, D. B.; Cu, X.; Page, G. P.; Sabripour, M. Microarray dataanalysis:from disarray to consolidation and Consensus. Nat. Rev. Genet., 2006, 7(1), 55-65.
18. Aderem, A. Systems biology: its practice and challenges. Cell,2005, 121(4), 511-513
19. von Bertalanffy, L. General System Theory: foundations,development, applications. G Braziler Inc., New York, 1978, 297.
20. Bhalla, U. S.; Iyengar, R. Emergent properties of networks ofbiological signaling pathways. Science, 1999, 283(5400), 381-387.
21. Weng, G.; Bhalla, U. S.; Iyengar, R. Complexity in biologicalsignaling systems. Science, 1999, 284(5411), 92-96.
22. Germain, R. N. The art of the probable: system control in theadaptive immune system. Science, 2001, 293(5528), 240-245.
23. Ideker, T.; Thorsson, V.; Ranish, J. A. Integrated genomic andproteomic analyses of a systematically perturbed metabolic network. Science, 2001, 292(5518), 929-934.
24. Stoll, M.; Cowley, A. W. Jr.; Tonellato, P. J.; Greene, A. S.; Kaldunski, M. L.; Roman, R. J.; Dumas, P.; Schork, N. J.; Wang, Z.; Jacob, H. J. A genomic-systems biology map for cardiovascular function. Science, 2001, 294(5547), 1723-1726.
25. Yao, T. Bioinformatics for the genomic sciences and towardssystems biology. Japanese activities in the post-genomeera. Prog. Biophys. Mol. Biol., 2002, 80(1-2), 23-42.
26. Alm, E.; Arkin, P. Biological networks. Curr. Opin. Struct. Biol.,2003, 13(2), 193-202.
27. Germain, R. N. An innately interesting decade of research inimmunology. Nat. Med., 2004, 10, 1307-1320.
28. Hood, L. Systems biology: integrating technology, biology, andcomputation. Mech. Ageing Dev., 2003, 124(1), 9-16.
29. Hood, L.; Heath, J. R.; Phelps, M. E.; Lin, B. Systems biology andnew technologies enable predictive and preventative medicine. Science, 2004, 306(5696), 640-643.
30. Cowley, A. W. Jr. The elusive field of systems biology. Physiol.Genomics, 2004, 16(3), 285-286.
31. Liang, M.; Cowley, A. W. Jr.; Hessner, M. J.; Lazar, J.; Basile, D.P.; Pietrusz, J. L. Transcriptome analysis and kidney research: Toward systems biology. Kidney Int., 2005, 67(6), 2114-2122.
32. Pittman, J.; Huang, E.; Dressman, H.; Horng, C. F.; Cheng, S. H.; Tsou, M. H.; Chen, C. M.; Bild, A.; Iversen, E. S.; Huang, A. T.; Nevins, J. R.; West, M. Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc. Natl. Acad. Sci. U S A, 2004, 101(22), 8431-8436.
33. Moore, J. H.; Williams, S. M. Traversing the conceptual dividebetween biological and statistical epistasis: systems biology and a more modern synthesis. Bioessays, 2005, 27(6), 637-646.
34. Son, C. G.; Bilke, S.; Davis, S.; Greer, B. T.; Wei, J. S.; Whiteford, C. C.; Chen, Q. R.; Cenacchi, N.; Khan, J. Database of mRNA gene expression profiles of multiple human organs. Genome Res., 2005, 15(3), 443-450.
35. Basso, K.; Margolin, A.; Stolovitzky, G.; Klein, U.; Dalla-Favera, R.; Califano, A. Reverse engineering of regulatory networks in human B cells. Nat. Genet., 2005, 37(4), 382-390.
36. Kirschner, M. W. The meaning of systems biology. Cell, 2005,121(4), 503-504.
37. Raia, F. Students' Understanding of complex dynamic systems. J.Geoscience Res., 2005, 53, 297-308.
38. Simon, R. Roadmap for Developing and Validating TherapeuticallyRelevant Genomic Classifiers. J. Clin. Oncol., 2005, 23(29), 7332-7341.
39. Liu, E. T. Systems biology, integrative biology, predictive biology.Cell, 2005, 121(4), 505-506.
40. Zerhouni, E. The NIH roadmap. Science, 2003, 302(5642), 63-72.
41. Rothman, K. J. Modern Epidemiology, Boston. 1986.
42. Michiels, S.; Koscielny, S.; Hill, C. Prediction of cancer outcomewith microarrays: a multiple random validation strategy. Lancet, 2005, 365(9458), 488-492.
43. Ioannidis, J. P. Microarrays and molecular research: noisediscovery? Lancet, 2005, 365 (9458), 454-455.
44. Halloran, P. F.; Reeve, J.; Kaplan, B. Lies, damn lies, and statistics:the perils of the P value. Am. J. Transplant., 2006, 6(1), 10-11.
45. Simon, R. Development and Validation of Biomarker Classifiersfor Treatment Selection. J. Stat. Plan. Inference, 2008, 138(2), 308-320.
46. Furness, P. N.; Taub, N.; Assmann, K. J.; Banfi, G.; Cosyns, J. P.; Dorman, A. M.; Hill, C. M.; Kapper, S. K.; Waldherr, R.; Laurinavicius, A.; Marcussen, N.; Martins, A. P.; Nogueira, M.; Regele, H.; Seron, D.; Carrera, M.; Sund, S.; Taskinen, E. I.; Paavonen, T.; Tihomirova, T.; Rosenthal, R. International variation in histologic grading is large, and persistent feedback does not improve reproducibility. Am. J. Surg. Pathol., 2003, 27(6), 805-810.
47. Marboe, C. C.; Billingham, M.; Eisen, H.; Deng, M. C.; Baron, H.; Mehra, M.; Hunt, S.; Wohlgemuth, J.; Mahmood, I.; Prentice, J.; Berry, G. Nodular endocardial infiltrates (Quilty Lesions) cause significant variability in the diagnosis of ISHLT rejection grades 2 and 3A in endomyocardial biopsies from cardiac allograft recipients. J. Heart Lung Transplant., 2005, 24(7), S219-S226.
48. Knaus, W. A.; Zimmerman, J. E.; Wagner, D. P.; Draper, E. A.; Lawrence, D. E. APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Crit. Care Med., 1981, 9(8), 591-597.
49. Knaus, W. A.; Draper, E. A.; Wagner, D. P.; Zimmerman, J. E.APACHE II: a severity of disease classification system. Crit. Care Med., 1985, 13(10), 818-829.
50. Zimmerman, J. E.; Kramer, A. A.; McNair, D. S.; Malila, F. M.Acute Physiology and Chronic Health Evaluation (APACHE) IV: hospital mortality assessment for today's critically ill patients. Crit. Care Med., 2006, 34(5), 1297-1310.
51. Marshall, J. C.; Cook, D. J.; Christou, N. V.; Bernard, G. R.; Sprung, C. L.; Sibbald, W. J. Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome. Crit. Care Med., 1995, 23(10), 1638-1652.
52. Vincent, J. L.; Moreno, R.; Takala, J.; Willatts, S.; De Mendonça, A.; Bruining, H.; Reinhart, C. K.; Suter, P. M.; Thijs, L. G. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med., 1996, 22(7), 707-710.
53. Vincent, J. L.; de Mendonça, A.; Cantraine, F.; Moreno, R.; Takala, J.; Suter, P. M.; Sprung, C. L.; Colardyn, F.; Blecher, S. Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on sepsis-related problems of the European Society of Intensive Care Medicine. Crit. Care Med., 1998, 26(11), 1793-1800.
54. Shahzad, K.; Sinha, A.; Latif, F.; Deng, M. C. Standardizedoperational procedures in clinical gene expression biomarker panel development. In: Batch Effects and Experimental Noise in Microarray Analysis: Sources and Solutions (Editor: Scherer A). Chichester, West Sussex: John Wiley & Sons Ltd; 2009, p. 203-213.
55. Liu, J.; Walter, E.; Stenger, D.; Thach, D. Effects of globin mRNAreduction methods on gene expression profiles from whole blood.J. Mol. Diagn., 2006, 8(5), 551-558.
56. Sarup, P.; Sørensen, J. G.; Kristensen, T. N.; Hoffmann, A. A.;Loeschcke, V.; Paige, K. N.; Sørensen, P. Candidate genes detected in transcriptome studies are strongly dependent on genetic background. PLoS One, 2011, 6(1), e15644.
57. Palka-Santini, M.; Cleven, B. E.; Eichinger, L.; Krönke, M.; Krut, O. Large scale multiplex PCR improves pathogen detection by DNA microarrays. BMC Microbiol., 2009, 9:1.
58. Geiss, G. K.; Bumgarner, R. E.; Birditt, B.; Dahl, T.; Dowidar, N.; Dunaway, D. L.; Fell, H. P.; Ferree, S.; George, R. D.; Grogan, T.; James, J. J.; Maysuria, M.; Mitton, J. D.; Oliveri, P.; Osborn, J. L.; Peng, T.; Ratcliffe, A. L.; Webster, P. J.; Davidson, E. H.; Hood, L.; Dimitrov, K. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol., 2008, 26(3), 317-325.
59. Ozsolak, F.; Milos, P. M. RNA sequencing: advances, challengesand opportunities. Nat. Rev. Genet., 2011, 12(2), 87-98.
60. Malone, J. H.; Oliver, B. Microarrays, deep sequencing and the truemeasure of the transcriptome. BMC Biol., 2011, 9, 34.
61. Zhang, B.; Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol., 2005, 4, Article17.
62. Basso, K.; Margolin, A. A.; Stolovitzky, G.; Klein, U.; la-Favera,R.; Califano, A. Reverse engineering of regulatory networks in human B cells. Nat. Genet., 2005, 37, 382-390.
63. Reshef, D. N.; Reshef, Y. A.; Finucane, H. K.; Grossman, S. R.; McVean, G.; Turnbaugh, P. J.; Lander, E. S.; Mitzenmacher, M.; Sabeti, P. C. Detecting novel associations in large data sets. Science, 2011, 334(6062), 1518-1524.
64. Brunet, J. P.; Tamayo, P.; Golub, T. R.; Mesirov, J. P. Metagenesand molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. U S A, 2004, 101(12), 4164-4169.
65. Starling, R. C.; Pham, M.; Valantine, H.; Miller, L.; Eisen, H.; Rodriguez, E. R.; Taylor, D. O.; Yamani, M. H.; Kobashigawa, J.; McCurry, K.; Marboe, C.; Mehra, M. R.; Zuckerman, A.; Deng, M.C. Working Group on Molecular Testing in Cardiac Transplantation. Molecular testing in the management of cardiac transplant recipients: initial clinical experience. J. Heart Lung Transplant., 2006, 25(12), 1389-1395.
66. MacLellan, W. R.; Wang, Y.; Lusis, A. J. Systems-basedapproaches to cardiovascular disease. Nat. Rev. Cardiol., 2012, 9(3), 172-184.
67. Pham, M. X.; Deng, M. C.; Kfoury, A. G.; Teuteberg, J. J.; Starling, R. C.; Valantine, H. Molecular testing for long-term rejection surveillance in heart transplant recipients: design of the Invasive Monitoring Attenuation Through Gene Expression (IMAGE) trial. J. Heart Lung Transplant., 2007, 26(8), 808-814.
68. Pham, M. X.; Teuteberg, J. J.; Kfoury, A. G.; Starling, R. C.; Deng, M. C.; Cappola, T. P.; Kao, A.; Anderson, A. S.; Cotts, W. G.; Ewald, G. A.; Baran, D. A.; Bogaev, R. C.; Elashoff, B.; Baron, H.; Yee, J.; Valantine, H. A. IMAGE Study Group. Gene-expression profiling for rejection surveillance after cardiac transplantation. N. Engl. J. Med., 2010, 362(20), 1890-1900.
69. Deng, M. C.; Alexander, G.; Wolters, H.; Shahzad, K.; Cadeiras, M.; Hicks, A.; Rowe, T.; Klingler, T. M.; Eisen, H. J. Low Variability of Intraindividual Longitudinal Leukocyte Gene Expression Profiling Cardiac Allograft Rejection Scores. Transplantation, 2010, 90(4), 459-461.
70. Shahzad, K.; Cadeiras, M.; Arai, K.; Abramov, D.; Burke, E.; Deng, M. C. Unexplained Graft Dysfunction after Heart Transplantation-Role of Novel Molecular Expression Test Score and QTc-Interval: A Case Report. Cardiol. Res. Pract., 2010, 2010, 230810.