Molecular diagnosis and prognosis with DNA microarrays

References

• Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al.. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999;286(5439):531–7.
   PubMed Web of Science ®Google Scholar
• Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al.. Molecular portraits of human breast tumours. Nature 2000;406(6797):747–52.
   PubMed Web of Science ®Google Scholar
• Forus A, Sørlie T, Børresen-Dale AL, Myklebost O. Microarray technology–potential in cancer research. Tidsskr Nor Laegeforen 2001;121(21):2498–503.
   PubMedGoogle Scholar
• Shen Y, Wu BL. Microarray-based genomic DNA profiling technologies in clinical molecular diagnostics. Clin Chem 2009;55(4):659–69.
   PubMed Web of Science ®Google Scholar
• Bejjani BA, Theisen AP, Ballif BC, Shaffer LG. Array-based comparative genomic hybridization in clinical diagnosis. Expert Rev Mol Diagn 2005;5(3):421–9.
   PubMed Web of Science ®Google Scholar
• Geyer FC, Decker T, Reis-Filho JS. Genome-wide expression profiling as a clinical tool: are we there yet? Pathologe 2009;30(2):141–6.
   PubMed Web of Science ®Google Scholar
• Hoek KS. Melanoma progression, gene expression and DNA microarrays. G Ital Dermatol Venereol 2009;144(1):39–49.
   PubMed Web of Science ®Google Scholar
• Chun H, Lee DS, Kim HC. Bio-cell chip fabrication and applications. Methods Mol Biol 2009;509:145–58.
   PubMedGoogle Scholar
• Dufva M. Microchips for cell-based assays. Methods Mol Biol 2009;509:135–44.
   PubMedGoogle Scholar
• Liotta L, Petricoin E. Molecular profiling of human cancer. Nat Rev Genet 2000;1:48–56.
   PubMed Web of Science ®Google Scholar
• Schena M, Shalon D, Davis RW, Brown PO. Quantitave monitoring of gene expression patterns with a complementary DNA microarray. Science 1995;270(5235):467–70.
   PubMed Web of Science ®Google Scholar
• DeRisi JL, Iyer VR, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997;278(5338):680–6.
   PubMed Web of Science ®Google Scholar
• Lockhart DJ, Winzeler EA. Genomics, gene expression and DNA arrays. Nature 2000;405(6788):827–36.
   PubMed Web of Science ®Google Scholar
• Young RA. Biomedical discovery with DNA arrays. Cell 2000;102(1):9–15.
   PubMed Web of Science ®Google Scholar
• Zhang MQ. Large-scale gene expression data analysis: a new challenge to computational biologists. Genome Res 1999;9(8):681–8.
   PubMed Web of Science ®Google Scholar
• Schimek MG, Wiltgen M. Microarray gene expression analysis basics for biometricians. Colloquium Biometryczne 2004;34a:141–59.
   Google Scholar
• Sherlock G, Hernandez-Boussard T, Kasarskis A, Binkley G, Matese JC, Dwight SS, et al.. The Stanford Microarray database. Nucleic Acids Res 2001;29:152–5.
   PubMed Web of Science ®Google Scholar
• Tseng GC, Oh M, Rohlin L, Liao JC, Wong WH. Issues in cDNA microarray filtering: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res 2001;29:2549–57.
   PubMed Web of Science ®Google Scholar
• Churchill GA. Fundamentals of experimental design for cDNA microarrays. Nat Genet 2002;32:490–5.
   PubMed Web of Science ®Google Scholar
• Yang YH, Speed T. Design issues for cDNA microarray experiments. Nat Rev Genet 2002;3:579–88.
   PubMed Web of Science ®Google Scholar
• Kooperberg C, Fazzio TG, Delrow JJ, Tsukiyama T. Background correction for spotted DNA microarrays. J Comput Biol 2002;9:55–66.
   PubMed Web of Science ®Google Scholar
• Quackenbush J. Microarray data normalization and transformation. Nat Genet 2002;32:496–501.
   PubMed Web of Science ®Google Scholar
• Clarke PA, te Poele R, Wooster R, Workman P. Gene expression microarray analysis in cancer biology, pharmacology and drug development: progress and potential. Biochem Pharmacol 2001;62:1311–36.
   PubMed Web of Science ®Google Scholar
• Wiltgen M, Tilz GP. DNA microarray analysis: principles and clinical impact. Hematology 2007;12(4):271–87.
   PubMed Web of Science ®Google Scholar
• Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D, et al.. Broad patterens of gene expression revealed by clustering analysis of tumour and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 1999;96:6745–50.
   PubMed Web of Science ®Google Scholar
• Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci USA 1998;95:14863–8.
   PubMed Web of Science ®Google Scholar
• Wen X, Fuhrman S, Michaels GS, Carr DB, Smith S, Barker JL, et al. Large-scale temporal gene expression mapping of CNS development. Proc Natl Acad Sci USA 1998;95:334–9.
   PubMed Web of Science ®Google Scholar
• Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM. Systematic determination of genetic network architecture. Nat Genet 2000;22:281–5.
   Web of Science ®Google Scholar
• Herwig R, Poustka A, Müller C, Bull C, Lehrach H, O’Brien J. Large-scale clustering of genetic fingerprinting data. Genome Res 1999;9:1093–105.
   PubMed Web of Science ®Google Scholar
• Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, et al.. Interpreting patterns of gene expression with self-organizing maps: methods and applications to hematopoietic differentiation. Proc Natl Acad Sci USA 1999;96:2907–12.
   PubMed Web of Science ®Google Scholar
• Törönen P, Kolehmainen M, Wong G, Castren E. Analysis of gene expression data using self-organizing maps. FEBS Lett 1999;451:142–6.
   PubMed Web of Science ®Google Scholar
• Brown M, Grundy W, Lin D, Christianini N, Sugnet C, Furey TS, et al.. Knowledge-based analysis of microarray gene expression data using support vector machines. Proc Natl Acad Sci USA 1999;97:262–7.
   Web of Science ®Google Scholar
• Cristianini N, Shawe-Taylor J. An introduction to support vector machines. Cambridge: Cambridge University Press, 2000.
   Google Scholar
• Furey TS, Christianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000;16:906–14.
   PubMed Web of Science ®Google Scholar
• Brown MPS, Grundy WN, Lin D, Christianini N, Sugnet C, Ares M, et al.. Support vector machine classification of microarray gene expression data. UCSC-CRL-99-9, Department of Computer Science, University of California Santa Cruz, 1999.
   Google Scholar
• Gaasterland T, Bekiranov S. Making the most of microarray data. Nat Genet 2000;24(3):204–6.
   PubMed Web of Science ®Google Scholar
• Minsky M, Papert S. Perceptrons. Cambridge, MA: MIT Press; 1969.
   Google Scholar
• Shawe-Taylor J, Cristianini N. Kernel methods for pattern analysis. Cambridge: Cambridge University Press; 2004.
   Google Scholar
• Cortes C, Vapnik V. Support vector networks. Machine Learning 1995;20(3):273–97.
   Web of Science ®Google Scholar
• Wallentine JC, Perkins SL, Tripp SR, Bruggman RD, Bayerl MG. Diffuse large B-cell lymphoma with coexpression of CD3 in a pediatric patient: a case report, review of the literature, and tissue microarray study. J Pediatr Hematol Oncol 2009;31(2):124–7.
   PubMed Web of Science ®Google Scholar
• Jabłońska J, Jesionek-Kupnicka D. Usefulness of immunohistochemistry in identification of prognostically important subgroups (GCB and ABC) in a heterogeneous group of diffuse large B-cell lymphomas-a review article. Pol J Pathol 2008;59(3):121–7.
   PubMed Web of Science ®Google Scholar
• de Jong D, Xie W, Rosenwald A, Chhanabhai M, Gaulard P, Klapper W, et al.. Immunohistochemical prognostic markers in diffuse large B-cell lymphoma: validation of tissue microarray as a prerequisite for broad clinical applications (a study from the Lunenburg Lymphoma Biomarker Consortium). J Clin Pathol 2009;62(2):128–38.
   PubMed Web of Science ®Google Scholar
• Jehan Z, Siraj AK, Abubaker J, Ruiz C, Simon R, Sultana M, et al.. Distinct gene expression profiles: nodal versus extranodal diffuse large B-cell lymphoma. Oncology 2008;75(1–2):71–80.
   PubMed Web of Science ®Google Scholar
• Jang KY, Hwang SH, Kwon KS, Kim KR, Choi HN, Lee NR, et al.. SIRT1 expression is associated with poor prognosis of diffuse large B-cell lymphoma. Am J Surg Pathol 2008;32(10):1523–31.
   PubMed Web of Science ®Google Scholar
• Haferlach T, Kohlmann A, Wieczorek L, Basso G, Kronnie GT, Béné MC, et al.. Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group. J Clin Oncol 2010;28(15):2529–37.
   PubMed Web of Science ®Google Scholar
• Berg T, Fliegauf M, Burger J, Staege MS, Liu S, Martinez N, et al.. Transcriptional upregulation of p21/WAF/Cip1 in myeloid leukemic blasts expressing AML1-ETO. Haematologica 2008;93(11):1728–33.
   PubMed Web of Science ®Google Scholar
• Baldus CD, Bullinger L. Gene expression with prognostic implications in cytogenetically normal acute myeloid leukemia. Semin Oncol 2008;35(4):356–64.
   PubMed Web of Science ®Google Scholar
• Suzuki T, Ishigami Y, Okada N, Kaneko A, Fukutomi R, Isemura M. Differentiation- associated alteration in gene expression of importins and exportins in human leukemia HL-60 cells. Biomed Res 2008;29(3):141–5.
   PubMed Web of Science ®Google Scholar
• Marcucci G, Radmacher MD, Maharry K, Mrózek K, Ruppert AS, Paschka P, et al.. MicroRNA expression in cytogenetically normal acute myeloid leukemia. J Clin Oncol 2008;26(31):5078–87.
   PubMedGoogle Scholar
• Cowan-Jacob SW, Fendrich G, Floersheimer A, Furet P, Liebetanz J, Rummel G, et al.. Structural biology contributions to the discovery of drugs to treat chronic myelogenous leukaemia. N Engl J Med 2008;358(18):1919–28.
   PubMedGoogle Scholar
• Rahmatpanah FB, Carstens S, Hooshmand SI, Welsh EC, Sjahputera O, Taylor KH, et al.. Large-scale analysis of DNA methylation in chronic lymphocytic leukemia. Epigenomics 2009;1(1):39–61.
   PubMed Web of Science ®Google Scholar
• Marstrand TT, Borup R, Willer A, Borregaard N, Sandelin A, Porse BT, et al.. A conceptual framework for the identification of candidate drugs and drug targets in acute promyelocytic leukemia. Leukemia 2010;24(7):1265–75.
   PubMed Web of Science ®Google Scholar
• Ferrando AA, Look AT. Gene expression profiling in T-cell acute lymphoblastic leukemia. Semin Hematol 2003;40(4):274–80.
   PubMed Web of Science ®Google Scholar
• Okamoto R, Ogawa S, Nowak D, Kawamata N, Akagi T, Kato M, et al.. Genomic profiling of adult acute lymphoblastic leukemia by single nucleotide polymorphism oligonucleotide microarray and comparison to pediatric acute lymphoblastic leukemia. Haematologica 2010;95(9):1481–8.
   PubMed Web of Science ®Google Scholar
• Haouas H, Haouas S, Uzan G, Hafsia A. Identification of new markers discriminating between myeloid and lymphoid acute leukemia. Hematology 2010;15(4):193–203.
   PubMed Web of Science ®Google Scholar
• Hömme C, Krug U, Tidow N, Schulte B, Kühler G, Serve H, et al.. Low SMC1A protein expression predicts poor survival in acute myeloid leukemia. Oncol Rep 2010;24(1):47–56.
   PubMed Web of Science ®Google Scholar
• Verhaak RG, Valk PJ. Genes predictive of outcome and novel molecular classification schemes in adult acute myeloid leukemia. Cancer Treat Res 2010;145:67–83.
   PubMedGoogle Scholar
• Leuenberger M, Frigerio S, Wild PJ, Noetzli F, Korol D, Zimmermann DR, et al.. AID protein expression in chronic lymphocytic leukemia/small lymphocytic lymphoma is associated with poor prognosis and complex genetic alterations. Mod Pathol 2010;23(2):177–86.
   PubMed Web of Science ®Google Scholar
• Burguillo FJ, Martin J, Barrera I, Bardsley WG. Meta-analysis of microarray data: the case of imatinib resistance in chronic myelogenous leukemia. Comput Biol Chem 2010;34(3):184–92.
   PubMed Web of Science ®Google Scholar