Advertisement

Use of Expression Microarrays in Cancer Research

  • Jun Luo
  • Yidong Chen
Chapter
Part of the Applied Bioinformatics and Biostatistics in Cancer Research book series (ABB)

Abstract

Since its inception more than 15 years ago, the rapidly evolving array-based gene expression technology has been widely adopted and become an indispensable tool in cancer research. In this chapter, we will discuss the various platforms and the corresponding technical and analytical steps including study design, sample selection and processing, data generation and data analysis. We will identify and discuss key issues that may affect the reliability and precision of end-point array results, as well as common pitfalls that influence the interpretation of the analytical results.

Keywords

Target Transcript Class Comparison Array Platform Formalin Fixation Paraffin Embedding Affymetrix Platform 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Ahmed FE (2006) Microarray RNA transcriptional profiling: part I. Platforms, experimental design and standardization. Expert Rev Mol Diagn 6:535–550.PubMedCrossRefGoogle Scholar
  2. Attoor S, Dougherty ER, Chen Y, Bittner ML, Trent JM (2004) Which is better for cDNA-microarray-based classification: ratios or direct intensities. Bioinformatics 20:2513–2520.PubMedCrossRefGoogle Scholar
  3. Bammler T, Beyer RP, Bhattacharya S, Boorman GA, Boyles A, Bradford BU, Bumgarner RE, Bushel PR, Chaturvedi K, Choi D, Cunningham ML, Deng S, Dressman HK, Fannin RD, Farin FM, Freedman JH, Fry RC, Harper A, Humble MC, Hurban P, Kavanagh TJ, Kaufmann WK, Kerr KF, Jing L, Lapidus JA, Lasarev MR, Li J, Li YJ, Lobenhofer EK, Lu X, Malek RL, Milton S, Nagalla SR, O’Malley JP, Palmer VS, Pattee P, Paules RS, Perou CM, Phillips K, Qin LX, Qiu Y, Quigley SD, Rodland M, Rusyn I, Samson LD, Schwartz DA, Shi Y, Shin JL, Sieber SO, Slifer S, Speer MC, Spencer PS, Sproles DI, Swenberg JA, Suk WA, Sullivan RC, Tian R, Tennant RW, Todd SA, Tucker CJ, Van Houten B, Weis BK, Xuan S, Zarbl H (2005) Standardizing global gene expression analysis between laboratories and across platforms. Nat Methods 2:351–356.PubMedCrossRefGoogle Scholar
  4. Barrett T, Edgar R (2006) Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol 411:352–369.PubMedCrossRefGoogle Scholar
  5. Baugh LR, Hill AA, Brown EL, Hunter CP (2001) Quantitative analysis of mRNA amplification by in vitro transcription. Nucleic Acids Res 29:E29.PubMedCrossRefGoogle Scholar
  6. Bertone P, Stolc V, Royce TE, Rozowsky JS, Urban AE, Zhu X, Rinn JL, Tongprasit W, Samanta M, Weissman S, Gerstein M, Snyder M (2004) Global identification of human transcribed sequences with genome tiling arrays. Science 306:2242–2246.PubMedCrossRefGoogle Scholar
  7. Boelens MC, te Meerman GJ, Gibcus JH, Blokzijl T, Boezen HM, Timens W, Postma DS, Groen HJ, van den Berg A (2007) Microarray amplification bias: loss of 30% differentially expressed genes due to long probe – poly(A)-tail distances. BMC Genomics 8:277.PubMedCrossRefGoogle Scholar
  8. Bolstad BM, Irizarry RA, Astrand M, Speed TP (2003) A comparison of normalization methods for high density oligonucleotidde array data based on variance and bias. Bioinformatics 19:185–193.PubMedCrossRefGoogle Scholar
  9. Bovelstad HM, Nygard S, Storvold HL, Aldrin M, Borgan O, Frigessi A, Lingjaerde OC (2007) Predicting survival from microarray data–a comparative study. Bioinformatics 23:2080–2087.PubMedCrossRefGoogle Scholar
  10. Carbone M, Pass HI (2004) Multistep and multifactorial carcinogenesis: when does a contributing factor become a carcinogen? Semin Cancer Biol 14:399–405.PubMedCrossRefGoogle Scholar
  11. Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, Maeda N, Oyama R, Ravasi T, Lenhard B, Wells C, Kodzius R, Shimokawa K, Bajic VB, Brenner SE, Batalov S, Forrest AR, Zavolan M, Davis MJ, Wilming LG, Aidinis V, Allen JE, Ambesi-Impiombato A, Apweiler R, Aturaliya RN, Bailey TL, Bansal M, Baxter L, Beisel KW, Bersano T, Bono H, Chalk AM, Chiu KP, Choudhary V, Christoffels A, Clutterbuck DR, Crowe ML, Dalla E, Dalrymple BP, de Bono B, Della Gatta G, di Bernardo D, Down T, Engstrom P, Fagiolini M, Faulkner G, Fletcher CF, Fukushima T, Furuno M, Futaki S, Gariboldi M, Georgii-Hemming P, Gingeras TR, Gojobori T, Green RE, Gustincich S, Harbers M, Hayashi Y, Hensch TK, Hirokawa N, Hill D, Huminiecki L, Iacono M, Ikeo K, Iwama A, Ishikawa T, Jakt M, Kanapin A, Katoh M, Kawasawa Y, Kelso J, Kitamura H, Kitano H, Kollias G, Krishnan SP, Kruger A, Kummerfeld SK, Kurochkin IV, Lareau LF, Lazarevic D, Lipovich L, Liu J, Liuni S, McWilliam S, Madan Babu M, Madera M, Marchionni L, Matsuda H, Matsuzawa S, Miki H, Mignone F, Miyake S, Morris K, Mottagui-Tabar S, Mulder N, Nakano N, Nakauchi H, Ng P, Nilsson R, Nishiguchi S, Nishikawa S, Nori F, Ohara O, Okazaki Y, Orlando V, Pang KC, Pavan WJ, Pavesi G, Pesole G, Petrovsky N, Piazza S, Reed J, Reid JF, Ring BZ, Ringwald M, Rost B, Ruan Y, Salzberg SL, Sandelin A, Schneider C, Schonbach C, Sekiguchi K, Semple CA, Seno S, Sessa L, Sheng Y, Shibata Y, Shimada H, Shimada K, Silva D, Sinclair B, Sperling S, Stupka E, Sugiura K, Sultana R, Takenaka Y, Taki K, Tammoja K, Tan SL, Tang S, Taylor MS, Tegner J, Teichmann SA, Ueda HR, van Nimwegen E, Verardo R, Wei CL, Yagi K, Yamanishi H, Zabarovsky E, Zhu S, Zimmer A, Hide W, Bult C, Grimmond SM, Teasdale RD, Liu ET, Brusic V, Quackenbush J, Wahlestedt C, Mattick JS, Hume DA, Kai C, Sasaki D, Tomaru Y, Fukuda S, Kanamori-Katayama M, Suzuki M, Aoki J, Arakawa T, Iida J, Imamura K, Itoh M, Kato T, Kawaji H, Kawagashira N, Kawashima T, Kojima M, Kondo S, Konno H, Nakano K, Ninomiya N, Nishio T, Okada M, Plessy C, Shibata K, Shiraki T, Suzuki S, Tagami M, Waki K, Watahiki A, Okamura-Oho Y, Suzuki H, Kawai J, Hayashizaki Y (2005) The transcriptional landscape of the mammalian genome. Science 309:1559–1563.PubMedCrossRefGoogle Scholar
  12. Chen JJ, Wang SJ, Tsai CA, Lin CJ (2007) Selection of differentially expressed genes in microarray data analysis. Pharmacogenomics J 7:212–220.PubMedCrossRefGoogle Scholar
  13. Cheng J, Kapranov P, Drenkow J, Dike S, Brubaker S, Patel S, Long J, Stern D, Tammana H, Helt G, Sementchenko V, Piccolboni A, Bekiranov S, Bailey DK, Ganesh M, Ghosh S, Bell I, Gerhard DS, Gingeras TR (2005) Transcriptional maps of 10 human chromosomes at 5-nucleotide resolution. Science 308:1149–1154.PubMedCrossRefGoogle Scholar
  14. Cobleigh MA, Tabesh B, Bitterman P, Baker J, Cronin M, Liu ML, Borchik R, Mosquera JM, Walker MG, Shak S (2005) Tumor gene expression and prognosis in breast cancer patients with 10 or more positive lymph nodes. Clin Cancer Res 11:8623–8631.PubMedCrossRefGoogle Scholar
  15. Cox WG, Beaudet MP, Agnew JY, Ruth JL (2004) Possible sources of dye-related signal correlation bias in two-color DNA microarray assays. Anal Biochem 331:243–254.PubMedCrossRefGoogle Scholar
  16. Dobbin K, Simon R (2005) Sample size determination in microarray experiments for class comparison and prognostic classification. Biostatistics 6:27–38.PubMedCrossRefGoogle Scholar
  17. Dobbin KK, Beer DG, Meyerson M, Yeatman TJ, Gerald WL, Jacobson JW, Conley B, Buetow KH, Heiskanen M, Simon RM, Minna JD, Girard L, Misek DE, Taylor JM, Hanash S, Naoki K, Hayes DN, Ladd-Acosta C, Enkemann SA, Viale A, Giordano TJ (2005a) Interlaboratory comparability study of cancer gene expression analysis using oligonucleotide microarrays. Clin Cancer Res 11:565–572.PubMedGoogle Scholar
  18. Dobbin KK, Kawasaki ES, Petersen DW, Simon RM (2005b) Characterizing dye bias in microarray experiments. Bioinformatics 21:2430–2437.PubMedCrossRefGoogle Scholar
  19. Dougherty ER, Barrera J, Brun M, Kim S, Cesar RM, Chen Y, Bittner M, Trent JM (2002) Inference from clustering with application to gene-expression microarrays. J Comput Biol 9:105–126.PubMedCrossRefGoogle Scholar
  20. Edgar R, Domrachev M, Lash AE (2002) Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–210.PubMedCrossRefGoogle Scholar
  21. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95:14863–14868.PubMedCrossRefGoogle Scholar
  22. Elvidge G (2006) Microarray expression technology: from start to finish. Pharmacogenomics 7:123–134.PubMedCrossRefGoogle Scholar
  23. Fan W, Pritchard JI, Olson JM, Khalid N, Zhao LP (2005) A class of models for analyzing GeneChip gene expression analysis array data. BMC Genomics 6:16.PubMedCrossRefGoogle Scholar
  24. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531–537.PubMedCrossRefGoogle Scholar
  25. Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100:57–70.PubMedCrossRefGoogle Scholar
  26. Hardiman G (2004) Microarray platforms – comparisons and contrasts. Pharmacogenomics 5:487–502.PubMedCrossRefGoogle Scholar
  27. Harr B, Schlotterer C (2006) Comparison of algorithms for the analysis of Affymetrix microarray data as evaluated by co-expression of genes in known operons. Nucleic Acids Res 34:e8.PubMedCrossRefGoogle Scholar
  28. Hughes TR, Mao M, Jones AR, Burchard J, Marton MJ, Shannon KW, Lefkowitz SM, Ziman M, Schelter JM, Meyer MR, Kobayashi S, Davis C, Dai H, He YD, Stephaniants SB, Cavet G, Walker WL, West A, Coffey E, Shoemaker DD, Stoughton R, Blanchard AP, Friend SH, Linsley PS (2001) Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer. Nat Biotechnol 19:342–347.PubMedCrossRefGoogle Scholar
  29. Irizarry RA, Warren D, Spencer F, Kim IF, Biswal S, Frank BC, Gabrielson E, Garcia JG, Geoghegan J, Germino G, Griffin C, Hilmer SC, Hoffman E, Jedlicka AE, Kawasaki E, Martinez-Murillo F, Morsberger L, Lee H, Petersen D, Quackenbush J, Scott A, Wilson M, Yang Y, Ye SQ, Yu W (2005) Multiple-laboratory comparison of microarray platforms. Nat Methods 2:345–350.PubMedCrossRefGoogle Scholar
  30. Jeffery IB, Higgins DG, Culhane AC (2006) Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinformatics 7:359.PubMedCrossRefGoogle Scholar
  31. Jiang Z, Gentleman R (2007) Extensions to gene set enrichment. Bioinformatics 23:306–313.PubMedCrossRefGoogle Scholar
  32. Jin W, Riley RM, Wolfinger RD, White KP, Passador-Gurgel G, Gibson G (2001) The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet 29:389–395.PubMedCrossRefGoogle Scholar
  33. Kong SW, Pu WT, Park PJ (2006) A multivariate approach for integrating genome-wide ­expression data and biological knowledge. Bioinformatics 22:2373–2380.PubMedCrossRefGoogle Scholar
  34. Kuo WP, Liu F, Trimarchi J, Punzo C, Lombardi M, Sarang J, Whipple ME, Maysuria M, Serikawa K, Lee SY, McCrann D, Kang J, Shearstone JR, Burke J, Park DJ, Wang X, Rector TL, Ricciardi-Castagnoli P, Perrin S, Choi S, Bumgarner R, Kim JH, Short GF 3rd, Freeman MW, Seed B, Jensen R, Church GM, Hovig E, Cepko CL, Park P, Ohno-Machado L, Jenssen TK (2006) A sequence-oriented comparison of gene expression measurements across different hybridization-based technologies. Nat Biotechnol 24:832–840.PubMedCrossRefGoogle Scholar
  35. Lee JC, Stiles D, Lu J, Cam MC (2007) A detailed transcript-level probe annotation reveals alternative splicing based microarray platform differences. BMC Genomics 8:284.PubMedCrossRefGoogle Scholar
  36. Lloyd MD, Darley DJ, Wierzbicki AS, Threadgill MD (2008) Alpha-methylacyl-CoA racemase – an ‘obscure’ metabolic enzyme takes centre stage. FEBS J 275:1089–1102.PubMedCrossRefGoogle Scholar
  37. Luo J, Duggan DJ, Chen Y, Sauvageot J, Ewing CM, Bittner ML, Trent JM, Isaacs WB (2001) Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Res 61:4683–4688.PubMedGoogle Scholar
  38. Luo J, Zha S, Gage WR, Dunn TA, Hicks JL, Bennett CJ, Ewing CM, Platz EA, Ferdinandusse S, Wanders RJ, Trent JM, Isaacs WB, De Marzo AM (2002) Alpha-methylacyl-CoA racemase: a new molecular marker for prostate cancer. Cancer Res 62:2220–2226.PubMedGoogle Scholar
  39. Luo J, Isaacs WB, Trent JM, Duggan DJ (2003) Looking beyond morphology: cancer gene expression profiling using DNA microarrays. Cancer Invest 21:937–949.PubMedCrossRefGoogle Scholar
  40. McShane LM, Radmacher MD, Freidlin B, Yu R, Li MC, Simon R (2002) Methods for assessing reproducibility of clustering patterns observed in analyses of microarray data. Bioinformatics 18:1462–1469.PubMedCrossRefGoogle Scholar
  41. Molinaro AM, Simon R, Pfeiffer RM (2005) Prediction error estimation: a comparison of resampling methods. Bioinformatics 21:3301–3307.PubMedCrossRefGoogle Scholar
  42. Nelson WG, De Marzo AM, Isaacs WB (2003) Prostate cancer. N Engl J Med 349:366–381.PubMedCrossRefGoogle Scholar
  43. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826.PubMedCrossRefGoogle Scholar
  44. Patterson TA, Lobenhofer EK, Fulmer-Smentek SB, Collins PJ, Chu TM, Bao W, Fang H, Kawasaki ES, Hager J, Tikhonova IR, Walker SJ, Zhang L, Hurban P, de Longueville F, Fuscoe JC, Tong W, Shi L, Wolfinger RD (2006) Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat Biotechnol 24:1140–1150.PubMedCrossRefGoogle Scholar
  45. Pease AC, Solas D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SP (1994) Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci U S A 91:5022–5026.PubMedCrossRefGoogle Scholar
  46. Quackenbush J (2002) Microarray data normalization and transformation. Nat Genet 32(Suppl):496–501.PubMedCrossRefGoogle Scholar
  47. Radmacher MD, McShane LM, Simon R (2002) A paradigm for class prediction using gene expression profiles. J Comput Biol 9:505–511.PubMedCrossRefGoogle Scholar
  48. Ransohoff DF (2004) Rules of evidence for cancer molecular-marker discovery and validation. Nat Rev Cancer 4:309–314.PubMedCrossRefGoogle Scholar
  49. Reiner A, Yekutieli D, Benjamini Y (2003) Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19:368–375.PubMedCrossRefGoogle Scholar
  50. Rosenzweig BA, Pine PS, Domon OE, Morris SM, Chen JJ, Sistare FD (2004) Dye bias correction in dual-labeled cDNA microarray gene expression measurements. Environ Health Perspect 112:480–487.PubMedCrossRefGoogle Scholar
  51. Rubin MA, Zhou M, Dhanasekaran SM, Varambally S, Barrette TR, Sanda MG, Pienta KJ, Ghosh D, Chinnaiyan AM (2002) alpha-Methylacyl coenzyme A racemase as a tissue biomarker for prostate cancer. JAMA 287:1662–1670.PubMedCrossRefGoogle Scholar
  52. Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–470.PubMedCrossRefGoogle Scholar
  53. Shannon W, Culverhouse R, Duncan J (2003) Analyzing microarray data using cluster analysis. Pharmacogenomics 4:41–52.PubMedCrossRefGoogle Scholar
  54. Shi L, Reid LH, Jones WD, Shippy R, Warrington JA, Baker SC, Collins PJ, de Longueville F, Kawasaki ES, Lee KY, Luo Y, Sun YA, Willey JC, Setterquist RA, Fischer GM, Tong W, Dragan YP, Dix DJ, Frueh FW, Goodsaid FM, Herman D, Jensen RV, Johnson CD, Lobenhofer EK, Puri RK, Schrf U, Thierry-Mieg J, Wang C, Wilson M, Wolber PK, Zhang L, Amur S, Bao W, Barbacioru CC, Lucas AB, Bertholet V, Boysen C, Bromley B, Brown D, Brunner A, Canales R, Cao XM, Cebula TA, Chen JJ, Cheng J, Chu TM, Chudin E, Corson J, Corton JC, Croner LJ, Davies C, Davison TS, Delenstarr G, Deng X, Dorris D, Eklund AC, Fan XH, Fang H, Fulmer-Smentek S, Fuscoe JC, Gallagher K, Ge W, Guo L, Guo X, Hager J, Haje PK, Han J, Han T, Harbottle HC, Harris SC, Hatchwell E, Hauser CA, Hester S, Hong H, Hurban P, Jackson SA, Ji H, Knight CR, Kuo WP, LeClerc JE, Levy S, Li QZ, Liu C, Liu Y, Lombardi MJ, Ma Y, Magnuson SR, Maqsodi B, McDaniel T, Mei N, Myklebost O, Ning B, Novoradovskaya N, Orr MS, Osborn TW, Papallo A, Patterson TA, Perkins RG, Peters EH, Peterson R, Philips KL, Pine PS, Pusztai L, Qian F, Ren H, Rosen M, Rosenzweig BA, Samaha RR, Schena M, Schroth GP, Shchegrova S, Smith DD, Staedtler F, Su Z, Sun H, Szallasi Z, Tezak Z, Thierry-Mieg D, Thompson KL, Tikhonova I, Turpaz Y, Vallanat B, Van C, Walker SJ, Wang SJ, Wang Y, Wolfinger R, Wong A, Wu J, Xiao C, Xie Q, Xu J, Yang W, Zhang L, Zhong S, Zong Y, Slikker W Jr (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24:1151–1161.PubMedCrossRefGoogle Scholar
  55. Simon R, Radmacher MD, Dobbin K, McShane LM (2003) Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst 95:14–18.PubMedCrossRefGoogle Scholar
  56. Smyth GK, Yang YH, Speed T (2003) Statistical issues in cDNA microarray data analysis. Methods Mol Biol 224:111–136.PubMedGoogle Scholar
  57. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Eystein Lonning P, Borresen-Dale AL (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98:10869–10874.PubMedCrossRefGoogle Scholar
  58. Southern EM, Maskos U, Elder JK (1992) Analyzing and comparing nucleic acid sequences by hybridization to arrays of oligonucleotides: evaluation using experimental models. Genomics 13:1008–1017.PubMedCrossRefGoogle Scholar
  59. Storey JD, Xiao W, Leek JT, Tompkins RG, Davis RW (2005) Significance analysis of time course microarray experiments. Proc Natl Acad Sci U S A 102:12837–12842.PubMedCrossRefGoogle Scholar
  60. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550.PubMedCrossRefGoogle Scholar
  61. Taylor J, Tibshirani R, Efron B (2005) The ‘miss rate’ for the analysis of gene expression data. Biostatistics 6:111–117.PubMedCrossRefGoogle Scholar
  62. Thompson KL, Pine PS, Rosenzweig BA, Turpaz Y, Retief J (2007) Characterization of the effect of sample quality on high density oligonucleotide microarray data using progressively degraded rat liver RNA. BMC Biotechnol 7:57.PubMedCrossRefGoogle Scholar
  63. Tian L, Greenberg SA, Kong SW, Altschuler J, Kohane IS, Park PJ (2005) Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci U S A 102:13544–13549.PubMedCrossRefGoogle Scholar
  64. Tinker AV, Boussioutas A, Bowtell DD (2006) The challenges of gene expression microarrays for the study of human cancer. Cancer Cell 9:333–339.PubMedCrossRefGoogle Scholar
  65. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A 98:5116–5121.PubMedCrossRefGoogle Scholar
  66. van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009.PubMedCrossRefGoogle Scholar
  67. Van Gelder RN, von Zastrow ME, Yool A, Dement WC, Barchas JD, Eberwine JH (1990) Amplified RNA synthesized from limited quantities of heterogeneous cDNA. Proc Natl Acad Sci U S A 87:1663–1667.PubMedCrossRefGoogle Scholar
  68. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536.CrossRefGoogle Scholar
  69. von Ahlfen S, Missel A, Bendrat K, Schlumpberger M (2007) Determinants of RNA quality from FFPE samples. PLoS ONE 2:e1261.CrossRefGoogle Scholar
  70. Walt DR (2000) Techview: molecular biology. Bead-based fiber-optic arrays. Science 287:451–452.PubMedCrossRefGoogle Scholar
  71. Wang E, Miller LD, Ohnmacht GA, Liu ET, Marincola FM (2000) High-fidelity mRNA amplification for gene profiling. Nat Biotechnol 18:457–459.PubMedCrossRefGoogle Scholar
  72. Weis S, Llenos IC, Dulay JR, Elashoff M, Martinez-Murillo F, Miller CL (2007) Quality control for microarray analysis of human brain samples: the impact of postmortem factors, RNA characteristics, and histopathology. J Neurosci Methods 165:198–209.PubMedCrossRefGoogle Scholar
  73. Went PT, Sauter G, Oberholzer M, Bubendorf L (2006) Abundant expression of AMACR in many distinct tumour types. Pathology 38:426–432.PubMedCrossRefGoogle Scholar
  74. Wolber PK, Collins PJ, Lucas AB, De Witte A, Shannon KW (2006) The Agilent in situ-synthesized microarray platform. Methods Enzymol 410:28–57.PubMedCrossRefGoogle Scholar
  75. Wu Z, Irizarry RA (2004) Preprocessing of oligonucleotide array data. Nat Biotechnol 22:656–658; author reply 658.PubMedCrossRefGoogle Scholar
  76. Xu J, Stolk JA, Zhang X, Silva SJ, Houghton RL, Matsumura M, Vedvick TS, Leslie KB, Badaro R, Reed SG (2000) Identification of differentially expressed genes in human prostate cancer using subtraction and microarray. Cancer Res 60:1677–1682.PubMedGoogle Scholar
  77. Yang MC, Yang JJ, McIndoe RA, She JX (2003) Microarray experimental design: power and sample size considerations. Physiol Genomics 16:24–28.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of UrologyJohns Hopkins University School of MedicineBaltimoreUSA

Personalised recommendations