Transfer of clinically relevant gene expression signatures in breast cancer: from Affymetrix microarray to Illumina RNA-Sequencing technology

BMC Genomics

Benjamin Haibe-Kains³  Christos Sotiriou⁷  Vincent Detours⁶  Martine Piccart⁵  Marion Maetens⁷  Michail Ignatiadis⁷  Denis Larsimont⁴  Roberto Salgado⁵  Samira Majjaj⁷  Françoise Rothé⁷  Stefan Michiels¹  David Gacquer⁶  Christine Desmedt⁷  David Brown⁷  Alexis Blanchet-Cohen²  Debora Fumagalli⁷ 

[1] Paris-Sud University, Paris, France;Bioinformatics Core Facility, Institut de Recherches cliniques de Montréal, Montreal, QC, Canada;Medical Biophysics Department, University of Toronto, Toronto, ON, Canada;Department of Pathology, Institut Jules Bordet, Brussels, Belgium;Breast International Group, Brussels, Belgium;IRIBHM, Université Libre de Bruxelles, Campus Erasme, Brussels, Belgium;Breast Cancer Translational Research Laboratory (BCTL), Institut Jules Bordet, Brussels, Belgium

关键词: HER2 receptor;    Progesterone receptor;    Estrogen receptor;    Immunohistochemistry;    RNA-Seq;    Illumina;    Microarray;    Affymetrix;    Gene expression signatures;    Breast cancer;   

Others  :  1091568
DOI  :  10.1186/1471-2164-15-1008

 received in 2014-08-05, accepted in 2014-11-10,  发布年份 2014

【 摘 要 】

Background

Microarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC’s subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated.

Results

16,097 genes common to the two platforms were retained for downstream analysis. Gene-wise comparison of microarray and RNA-Seq data revealed that 52% had a Spearman’s correlation coefficient greater than 0.7 with highly correlated genes displaying significantly higher expression levels. We found excellent correlation between microarray and RNA-Seq for the estrogen receptor (ER; r_s = 0.973; 95% CI: 0.971-0.975), progesterone receptor (PgR; r_s = 0.95; 0.947-0.954), and human epidermal growth factor receptor 2 (HER2; r_s = 0.918; 0.912-0.923), while a few discordances between ER and PgR quantified by immunohistochemistry and RNA-Seq/microarray were observed. All the subtype classifiers evaluated agreed well (Cohen’s kappa coefficients >0.8) and all the proliferation-based GES showed excellent Spearman correlations between microarray and RNA-Seq (all r_s >0.965). Immune-, stroma- and pathway-based GES showed a lower correlation relative to prognostic signatures (all r_s >0.6).

Conclusions

To our knowledge, this is the first study to report a systematic comparison of RNA-Seq to microarray for the evaluation of single genes and GES clinically relevant to BC. According to our results, the vast majority of single gene biomarkers and well-established GES can be reliably evaluated using the RNA-Seq technology.

【 授权许可】

   
2014 Fumagalli et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Van Hal NL, Vorst O, van Houwelingen AM, Kok EJ, Peijnenburg A, Aharoni A, van Tunen AJ, Keijer J: The application of DNA microarrays in gene expression analysis. J Biotechnol 2000, 78:271-280.
• [2]Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lønning PE, Børresen-Dale AL, Brown PO, Botstein D: Molecular portraits of human breast tumours. Nature 2000, 406:747-752.
• [3]Peppercorn J, Perou CM, Carey LA: Molecular subtypes in breast cancer evaluation and management: divide and conquer. Cancer Invest 2008, 26:1-10.
• [4]Sørlie 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, Lønning PE, Børresen-Dale AL: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001, 98:10869-10874.
• [5]Sotiriou C, Neo S-Y, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET: Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A 2003, 100:10393-10398.
• [6]Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, Senn H-J, Panel members: Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International expert consensus on the primary therapy of early breast cancer 2013. Ann Oncol 2013, 24:2206-2223.
• [7]Desmedt C, Haibe-Kains B, Wirapati P, Buyse M, Larsimont D, Bontempi G, Delorenzi M, Piccart M, Sotiriou C: Biological processes associated with breast cancer clinical outcome depend on the molecular subtypes. Clin Cancer Res 2008, 14:5158-5165.
• [8]Wirapati P, Sotiriou C, Kunkel S, Farmer P, Pradervand S, Haibe-Kains B, Desmedt C, Ignatiadis M, Sengstag T, Schütz F, Goldstein DR, Piccart M, Delorenzi M: Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Res 2008, 10:R65. BioMed Central Full Text
• [9]Haibe-Kains B, Desmedt C, Loi S, Culhane AC, Bontempi G, Quackenbush J, Sotiriou C: A three-gene model to robustly identify breast cancer molecular subtypes. J Natl Cancer Inst 2012, 104:311-325.
• [10]Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lønning PE, Brown PO, Børresen-Dale A-L, Botstein D: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 2003, 100:8418-8423.
• [11]Hu Z, Fan C, Oh DS, Marron JS, He X, Qaqish BF, Livasy C, Carey LA, Reynolds E, Dressler L, Nobel A, Parker J, Ewend MG, Sawyer LR, Wu J, Liu Y, Nanda R, Tretiakova M, Ruiz Orrico A, Dreher D, Palazzo JP, Perreard L, Nelson E, Mone M, Hansen H, Mullins M, Quackenbush JF, Ellis MJ, Olopade OI, Bernard PS, et al.: The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006, 7:96. BioMed Central Full Text
• [12]Parker JS, Mullins M, Cheang MCU, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron JS, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS: Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 2009, 27:1160-1167.
• [13]Package Insert: Prosigna Breast Cancer Prognostic Gene Signature Assay; Version 01. 2013. REF LBL-C0223-01
• [14]Veer LJ V’t, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415:530-536.
• [15]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: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004, 351:2817-2826.
• [16]Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, Nordgren H, Farmer P, Praz V, Haibe-Kains B, Desmedt C, Larsimont D, Cardoso F, Peterse H, Nuyten D, Buyse M, Van de Vijver MJ, Bergh J, Piccart M, Delorenzi M: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006, 98:262-272.
• [17]Filipits M, Rudas M, Jakesz R, Dubsky P, Fitzal F, Singer CF, Dietze O, Greil R, Jelen A, Sevelda P, Freibauer C, Müller V, Jänicke F, Schmidt M, Kölbl H, Rody A, Kaufmann M, Schroth W, Brauch H, Schwab M, Fritz P, Weber KE, Feder IS, Hennig G, Kronenwett R, Gehrmann M, Gnant M, EP Investigators: A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clin Cancer Res 2011, 17:6012-6020.
• [18]Carter SL, Eklund AC, Kohane IS, Harris LN, Szallasi Z: A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nat Genet 2006, 38:1043-1048.
• [19]Teschendorff AE, Miremadi A, Pinder SE, Ellis IO, Caldas C: An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 2007, 8:R157. BioMed Central Full Text
• [20]Farmer P, Bonnefoi H, Anderle P, Cameron D, Wirapati P, Wirapati P, Becette V, André S, Piccart M, Campone M, Brain E, Macgrogan G, Petit T, Jassem J, Bibeau F, Blot E, Bogaerts J, Aguet M, Bergh J, Iggo R, Delorenzi M: A stroma-related gene signature predicts resistance to neoadjuvant chemotherapy in breast cancer. Nat Med 2009, 15:68-74.
• [21]Desmedt C, Majjaj S, Kheddoumi N, Singhal SK, Haibe-Kains B, El Ouriaghli F, Chaboteaux C, Michiels S, Lallemand F, Journe F, Duvillier H, Loi S, Quackenbush J, Dekoninck S, Blanpain C, Lagneaux L, Houhou N, Delorenzi M, Larsimont D, Piccart M, Sotiriou C: Characterization and clinical evaluation of CD10+ stroma cells in the breast cancer microenvironment. Clin Cancer Res 2012, 18:1004-1014.
• [22]Loi S, Haibe-Kains B, Majjaj S, Lallemand F, Durbecq V, Larsimont D, Gonzalez-Angulo AM, Pusztai L, Symmans WF, Bardelli A, Ellis P, Tutt ANJ, Gillett CE, Hennessy BT, Mills GB, Phillips WA, Piccart MJ, Speed TP, McArthur GA, Sotiriou C: PIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor-positive breast cancer. Proc Natl Acad Sci U S A 2010, 107:10208-10213.
• [23]Saal LH, Johansson P, Holm K, Gruvberger-Saal SK, She Q-B, Maurer M, Koujak S, Ferrando AA, Malmström P, Memeo L, Isola J, Bendahl P-O, Rosen N, Hibshoosh H, Ringnér M, Borg A, Parsons R: Poor prognosis in carcinoma is associated with a gene expression signature of aberrant PTEN tumor suppressor pathway activity. Proc Natl Acad Sci U S A 2007, 104:7564-7569.
• [24]Creighton CJ, Casa A, Lazard Z, Huang S, Tsimelzon A, Hilsenbeck SG, Osborne CK, Lee AV: Insulin-like growth factor-I activates gene transcription programs strongly associated with poor breast cancer prognosis. J Clin Oncol 2008, 26:4078-4085.
• [25]Majumder PK, Febbo PG, Bikoff R, Berger R, Xue Q, McMahon LM, Manola J, Brugarolas J, McDonnell TJ, Golub TR, Loda M, Lane HA, Sellers WR: mTOR inhibition reverses Akt-dependent prostate intraepithelial neoplasia through regulation of apoptotic and HIF-1-dependent pathways. Nat Med 2004, 10:594-601.
• [26]Creighton CJ, Hilger AM, Murthy S, Rae JM, Chinnaiyan AM, El-Ashry D: Activation of mitogen-activated protein kinase in estrogen receptor alpha-positive breast cancer cells in vitro induces an in vivo molecular phenotype of estrogen receptor alpha-negative human breast tumors. Cancer Res 2006, 66:3903-3911.
• [27]Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D, Joshi M-B, Harpole D, Lancaster JM, Berchuck A, Olson JA Jr, Marks JR, Dressman HK, West M, Nevins JR: Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006, 439:353-357.
• [28]Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008, 5:621-628.
• [29]Sultan M, Schulz MH, Richard H, Magen A, Klingenhoff A, Scherf M, Seifert M, Borodina T, Soldatov A, Parkhomchuk D, Schmidt D, O’Keeffe S, Haas S, Vingron M, Lehrach H, Yaspo M-L: A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 2008, 321:956-960.
• [30]Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 2009, 10:57-63.
• [31]Ozsolak F, Milos PM: RNA sequencing: advances, challenges and opportunities. Nat Rev Genet 2011, 12:87-98.
• [32]Malone JH, Oliver B: Microarrays, deep sequencing and the true measure of the transcriptome. BMC Biol 2011, 9:34. BioMed Central Full Text
• [33]Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M: The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 2008, 320:1344-1349.
• [34]Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y: RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 2008, 18:1509-1517.
• [35]Fu X, Fu N, Guo S, Yan Z, Xu Y, Hu H, Menzel C, Chen W, Li Y, Zeng R, Khaitovich P: Estimating accuracy of RNA-Seq and microarrays with proteomics. BMC Genomics 2009, 10:161. BioMed Central Full Text
• [36]Montgomery SB, Sammeth M, Gutierrez-Arcelus M, Lach RP, Ingle C, Nisbett J, Guigo R, Dermitzakis ET: Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 2010, 464:773-777.
• [37]Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE, Nkadori E, Veyrieras J-B, Stephens M, Gilad Y, Pritchard JK: Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 2010, 464:768-772.
• [38]Bradford JR, Hey Y, Yates T, Li Y, Pepper SD, Miller CJ: A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling. BMC Genomics 2010, 11:282. BioMed Central Full Text
• [39]Liu S, Lin L, Jiang P, Wang D, Xing Y: A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. Nucleic Acids Res 2011, 39:578-588.
• [40]Nookaew I, Papini M, Pornputtapong N, Scalcinati G, Fagerberg L, Uhlén M, Nielsen J: A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae. Nucleic Acids Res 2012, 40:10084-10097.
• [41]Sekhon RS, Briskine R, Hirsch CN, Myers CL, Springer NM, Buell CR, de Leon N, Kaeppler SM: Maize gene atlas developed by RNA sequencing and comparative evaluation of transcriptomes based on RNA sequencing and microarrays. PLoS One 2013, 8:e61005.
• [42]Xu X, Zhang Y, Williams J, Antoniou E, McCombie WR, Wu S, Zhu W, Davidson NO, Denoya P, Li E: Parallel comparison of Illumina RNA-Seq and Affymetrix microarray platforms on transcriptomic profiles generated from 5-aza-deoxy-cytidine treated HT-29 colon cancer cells and simulated datasets. BMC Bioinformatics 2013, 14(Suppl 9):S1. BioMed Central Full Text
• [43]Sotiriou C, Pusztai L: Gene-expression signatures in breast cancer. N Engl J Med 2009, 360:790-800.
• [44]ArrayAnalysis.org http://www.arrayanalysis.org/ webcite
• [45]McCall MN, Bolstad BM, Irizarry RA: Frozen robust multiarray analysis (fRMA). Biostatistics 2010, 11:242-253.
• [46]R Development Core Team: R: A Language and Environment for Statistical Computing. 2011. ISBN 3-900051-07-0
• [47]Li Q, Birkbak NJ, Gyorffy B, Szallasi Z, Eklund AC: Jetset: selecting the optimal microarray probe set to represent a gene. BMC Bioinformatics 2011, 12:474. BioMed Central Full Text
• [48]Durinck S, Moreau Y, Kasprzyk A, Davis S, De Moor B, Brazma A, Huber W: BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 2005, 21:3439-3440.
• [49]European Genome-phenome Archive https://www.ebi.ac.uk/ega webcite
• [50]Andrews S: FastQC A Quality Control tool for High Throughput Sequence Data. 2014. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ webcite
• [51]Barnett DW, Garrison EK, Quinlan AR, Strömberg MP, Marth GT: BamTools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics 2011, 27:1691-1692.
• [52]Picard 2014. http://picard.sourceforge.net webcite
• [53]Garber M, Grabherr MG, Guttman M, Trapnell C: Computational methods for transcriptome annotation and quantification using RNA-seq. Nat Methods 2011, 8:469-477.
• [54]Bioconductor-genefu http://www.bioconductor.org/packages/release/bioc/html/genefu.html webcite
• [55]Ignatiadis M, Singhal SK, Desmedt C, Haibe-Kains B, Criscitiello C, Andre F, Loi S, Piccart M, Michiels S, Sotiriou C: Gene modules and response to neoadjuvant chemotherapy in breast cancer subtypes: a pooled analysis. J Clin Oncol 2012, 30:1996-2004.
• [56]Culhane AC, Schröder MS, Sultana R, Picard SC, Martinelli EN, Kelly C, Haibe-Kains B, Kapushesky M, St Pierre A-A, Flahive W, Picard KC, Gusenleitner D, Papenhausen G, O’Connor N, Correll M, Quackenbush J: GeneSigDB: a manually curated database and resource for analysis of gene expression signatures. Nucleic Acids Res 2012, 40(Database issue):D1060-D1066.
• [57]Gentleman R: Reproducible research: a bioinformatics case study. Stat Appl Genet Mol Biol 2005, 4:1544-6115.
• [58]Subramaniam S, Hsiao G: Gene-expression measurement: variance-modeling considerations for robust data analysis. Nat Immunol 2012, 13:199-203.
• [59]Harris L, Fritsche H, Mennel R, Norton L, Ravdin P, Taube S, Somerfield MR, Hayes DF, Bast RC Jr, American Society of Clinical Oncology: American society of clinical oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 2007, 25:5287-5312.
• [60]Hammond MEH, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S, Fitzgibbons PL, Francis G, Goldstein NS, Hayes M, Hicks DG, Lester S, Love R, Mangu PB, McShane L, Miller K, Osborne CK, Paik S, Perlmutter J, Rhodes A, Sasano H, Schwartz JN, Sweep FCG, Taube S, Torlakovic EE, Valenstein P, Viale G, Visscher D, Wheeler T, Williams RB, et al.: American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer (unabridged version). Arch Pathol Lab Med 2010, 134:e48-e72.
• [61]Wolff AC, Hammond MEH, Hicks DG, Dowsett M, McShane LM, Allison KH, Allred DC, Bartlett JMS, Bilous M, Fitzgibbons P, Hanna W, Jenkins RB, Mangu PB, Paik S, Perez EA, Press MF, Spears PA, Vance GH, Viale G, Hayes DF: Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American society of clinical oncology/College of American Pathologists clinical practice guideline update. Arch Pathol Lab Med 2014, 138:241-256.
• [62]Cardoso F, Van’t Veer L, Rutgers E, Loi S, Mook S, Piccart-Gebhart MJ: Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol 2008, 26:729-735.
• [63]Sparano JA: TAILORx: trial assigning individualized options for treatment (Rx). Clin Breast Cancer 2006, 7:347-350.
• [64]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, MAQC Consortium, et al.: The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 2006, 24:1151-1161.
• [65]Guo Y, Sheng Q, Li J, Ye F, Samuels DC, Shyr Y: Large scale comparison of gene expression levels by microarrays and RNAseq using TCGA data. PLoS One 2013, 8:e71462.
• [66]Roepman P, Horlings HM, Krijgsman O, Kok M, Bueno-de-Mesquita JM, Bender R, Linn SC, Glas AM, van de Vijver MJ: Microarray-based determination of estrogen receptor, progesterone receptor, and HER2 receptor status in breast cancer. Clin Cancer Res 2009, 15:7003-7011.
• [67]Wesseling J, Tinterri C, Sapino A, Zanconati F, Lutke Holzik M, Nguyen B, Deck KB, Querzoli P, Perin T, Giardina C, Seitz G, Guinebretiere J-M, Barone J, Watanabe T, De Snoo F, Stork-Sloots L, Cusumano P: Central review of discordant samples for microarray-based ER, PR, and HER2 and local IHC/FISH assessment worldwide from 827 patients [abstract]. J Clin Oncol 2012., 30suppl 27; abstr 11
• [68]Li Q, Eklund AC, Juul N, Haibe-Kains B, Workman CT, Richardson AL, Szallasi Z, Swanton C: Minimising immunohistochemical false negative ER classification using a complementary 23 gene expression signature of ER status. PLoS One 2010, 5:e15031.
• [69]Kamalakaran S, Lezon-Geyda K, Varadan V, Banerjee N, Lannin D, Rizack T, Sikov W, Abu-Khalaf M, Janevski A, Harris L: Evaluation of ER/PR and HER2 status by RNA sequencing in tissue core biopsies from preoperative clinical trial specimens [abstract]. J Clin Oncol 2011., 29suppl 27; abstr 46
• [70]Lusa L, McShane LM, Reid JF, De Cecco L, Ambrogi F, Biganzoli E, Gariboldi M, Pierotti MA: Challenges in projecting clustering results across gene expression-profiling datasets. J Natl Cancer Inst 2007, 99:1715-1723.
• [71]Auer PL, Doerge RW: Statistical design and analysis of RNA sequencing data. Genetics 2010, 185:405-416.
• [72]Bullard JH, Purdom E, Hansen KD, Dudoit S: Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 2010, 11:94. BioMed Central Full Text
• [73]Zheng W, Chung LM, Zhao H: Bias detection and correction in RNA-Sequencing data. BMC Bioinformatics 2011, 12:290. BioMed Central Full Text
• [74]Dillies M-A, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, Guernec G, Jagla B, Jouneau L, Laloë D, Le Gall C, Schaëffer B, Le Crom S, Guedj M, Jaffrézic F, French StatOmique Consortium: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinformatics 2013, 14:671-683.
• [75]Goecks J, Nekrutenko A, Taylor J, Galaxy Team: Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. Genome Biol 2010, 11:R86. BioMed Central Full Text
• [76]MAQC Project http://www.fda.gov/ScienceResearch/BioinformaticsTools/MicroarrayQualityControlProject/ webcite