Development of Commercial Gene-Expression-Based Signatures: Review of the Scientific Strategies

• Paik S , ShakS, TangG et al.: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.N. Engl. J. Med.351(27) , 2817–2826 (2004).
   PubMed Web of Science ®Google Scholar
• Simon R : Roadmap for developing and validating therapeutically relevant genomic classifiers.J. Clin. Oncol.23(29) , 7332–7341 (2005).
   PubMed Web of Science ®Google Scholar
• Pepe MS , EtzioniR, FengZ et al.: Phases of biomarker development for early detection of cancer.J. Natl Cancer Inst.93(14) , 1054–1061 (2001).
   PubMed Web of Science ®Google Scholar
• Ransohoff DF : How to improve reliability and efficiency of research about molecular markers: roles of phases, guidelines, and study design.J. Clin. Epidemiol.60(12) , 1205–1219 (2007).
   PubMed Web of Science ®Google Scholar
• Porta M , Hernandez-AguadoI, LumbrerasB, Crous-BouM: “Omics” research, monetization of intellectual property and fragmentation of knowledge: can clinical epidemiology strengthen integrative research?J. Clin. Epidemiol.60(12) , 1220–1225 (2007).
   PubMed Web of Science ®Google Scholar
• Robinson GK : Practical Strategies for Experimenting. Wiley, NY, USA (2000).
   Google Scholar
• Sis B , MengelM, HaasM et al.: Banff ‘09 meeting report: antibody mediated graft deterioration and implementation of Banff working groups.Am. J. Transplant.10(3) , 464–471 (2010).
   PubMed Web of Science ®Google Scholar
• Fransen J , Van Riel PL: The disease activity score and the EULAR response criteria. Clin. Exp. Rheumatol.23(5 Suppl. 39) , S93–S99 (2005).
   PubMed Web of Science ®Google Scholar
• Buyse M , LoiS, Van‘t Veer L et al.: Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J. Natl Cancer Inst.98(17) , 1183–1192 (2006).
   PubMed Web of Science ®Google Scholar
• Van De Vijver MJ , HeYD, Van‘t Veer LJ et al.: A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med.347(25) , 1999–2009 (2002).
   PubMed Web of Science ®Google Scholar
• Whitney AR , DiehnM, PopperSJ et al.: Individuality and variation in gene expression patterns in human blood.Proc. Natl Acad. Sci. USA100(4) , 1896–1901 (2003).
   PubMed Web of Science ®Google Scholar
• Deng MC , EisenHJ, MehraMR et al.: Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling.Am. J. Transplant.6(1) , 150–160 (2006).
   PubMed Web of Science ®Google Scholar
• Pham MX , TeutebergJJ, KfouryAG et al.: Gene-expression profiling for rejection surveillance after cardiac transplantation.N. Engl. J. Med.362(20) , 1890–1900 (2010).
   PubMed Web of Science ®Google Scholar
• Starling RC , DengMC, KobashigawaJ et al.: The influence of corticosteroids on the alloimmune molecular signature for cardiac allograft rejection.J. Heart Lung Transplant.24(Suppl.) , S65 (2006).
   Web of Science ®Google Scholar
• Martinez-Llordella M , LozanoJJ, Puig-PeyI et al.: Using transcriptional profiling to develop a diagnostic test of operational tolerance in liver transplant recipients.J. Clin. Invest.118(8) , 2845–2857 (2008).
   PubMed Web of Science ®Google Scholar
• Cronin M , PhoM, DuttaD et al.: Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay.Am. J. Pathol.164(1) , 35–42 (2004).
   PubMed Web of Science ®Google Scholar
• Van‘t Veer LJ , DaiH, Van De Vijver MJ et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature415(6871) , 530–536 (2002).
   PubMed Web of Science ®Google Scholar
• Baechler EC , BatliwallaFM, KarypisG et al.: Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation.Genes Immun.5(5) , 347–353 (2004).
   PubMed Web of Science ®Google Scholar
• Burczynski ME , PetersonRL, TwineNC et al.: Molecular classification of Crohn‘s disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells.J. Mol. Diagn.8(1) , 51–61 (2006).
   PubMed Web of Science ®Google Scholar
• Wingrove JA , DanielsSE, SehnertAJ et al.: Correlation of peripheral-blood gene expression with the extent of coronary artery stenosis.Circ. Cardiovasc. Genet.1(1) , 31–38 (2008).
   PubMedGoogle Scholar
• Min JL , BarrettA, WattsT et al.: Variability of gene expression profiles in human blood and lymphoblastoid cell lines.BMC Genomics11 , 96 (2010).
   PubMed Web of Science ®Google Scholar
• Vartanian K , SlottkeR, JohnstoneT et al.: Gene expression profiling of whole blood: Comparison of target preparation methods for accurate and reproducible microarray analysis.BMC Genomics10 , 2 (2009).
   PubMed Web of Science ®Google Scholar
• Shen-Orr SS , TibshiraniR, KhatriP et al.: Cell type-specific gene expression differences in complex tissues.Nat. Methods7(4) , 287–289 (2010).
   PubMed Web of Science ®Google Scholar
• Livak KJ , SchmittgenTD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Δ Δ c(t)) method.Methods25(4) , 402–408 (2001).
   PubMed Web of Science ®Google Scholar
• Leushner J , ChiuNH: Automated mass spectrometry: a revolutionary technology for clinical diagnostics.Mol. Diagn.5(4) , 341–348 (2000).
   PubMedGoogle Scholar
• Garcia EP , DowdingLA, StantonLW, SlepnevVI: Scalable transcriptional analysis routine – multiplexed quantitative real-time polymerase chain reaction platform for gene expression analysis and molecular diagnostics.J. Mol. Diagn.7(4) , 444–454 (2005).
   PubMed Web of Science ®Google Scholar
• Sotiriou C , WirapatiP, LoiS et al.: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.J. Natl Cancer Inst.98(4) , 262–272 (2006).
   PubMed Web of Science ®Google Scholar
• Tibshirani R : A simple method for assessing sample sizes in microarray experiments.BMC Bioinformatics7 , 106 (2006).
   PubMed Web of Science ®Google Scholar
• Tong T , ZhaoH: Practical guidelines for assessing power and false discovery rate for a fixed sample size in microarray experiments.Stat. Med.27(11) , 1960–1972 (2008).
   PubMed Web of Science ®Google Scholar
• Ramasamy A , MondryA, HolmesCC, AltmanDG: Key issues in conducting a meta-analysis of gene expression microarray datasets.PLoS Med.5(9) , e184 (2008).
   PubMed Web of Science ®Google Scholar
• Buyse M , SargentDJ, GrotheyA, MathesonA, De Gramont A: Biomarkers and surrogate end points – the challenge of statistical validation. Nat. Rev. Clin. Oncol.7(6) , 309–317 (2010).
   PubMed Web of Science ®Google Scholar
• De Reynies A , GerominD, CayuelaJM et al.: Comparison of the latest commercial short and long oligonucleotide microarray technologies.BMC Genomics7 , 51 (2006).
   PubMed Web of Science ®Google Scholar
• Shi L , ReidLH, JonesWD et al.: The microarray quality control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.Nat. Biotechnol.24(9) , 1151–1161 (2006).
   PubMed Web of Science ®Google Scholar
• Patterson TA , LobenhoferEK, Fulmer-SmentekSB et al.: Performance comparison of one-color and two-color platforms within the microarray quality control (MAQC) project.Nat. Biotechnol.24(9) , 1140–1150 (2006).
   PubMed Web of Science ®Google Scholar
• Wang Y , BarbacioruC, HylandF et al.: Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays.BMC Genomics7 , 59 (2006).
   PubMed Web of Science ®Google Scholar
• Dudoit S , FridlyandJ, SpeedP: Comparison of discrimination methods for classification of tumors using gene expression data.J. Amer. Statist. Assoc.97 , 77–87 (2002).
   Web of Science ®Google Scholar
• Golub TR , SlonimDK, TamayoP et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.Science286(5439) , 531–537 (1999).
   PubMed Web of Science ®Google Scholar
• Huang X , PanW: Linear regression and two-class classification with gene expression data.Bioinformatics19(16) , 2072–2078 (2003).
   PubMed Web of Science ®Google Scholar
• Cheng F , ChoSH, LeeJK: Multi-gene expression-based statistical approaches to predicting patients‘ clinical outcomes and responses.Methods Mol. Biol.620 , 471–484 (2010).
   PubMedGoogle Scholar
• Stamova BS , AppersonM, WalkerWL et al.: Identification and validation of suitable endogenous reference genes for gene expression studies in human peripheral blood.BMC Med. Genomics2 , 49 (2009).
   PubMed Web of Science ®Google Scholar
• Kato K : Algorithm for in vitro diagnostic multivariate index assay.Breast Cancer16(4) , 248–251 (2009).
   PubMed Web of Science ®Google Scholar
• Esteva FJ , SahinAA, CristofanilliM et al.: Prognostic role of a multigene reverse transcriptase-PCR assay in patients with node-negative breast cancer not receiving adjuvant systemic therapy.Clin. Cancer Res.11(9) , 3315–3319 (2005).
   PubMed Web of Science ®Google Scholar
• Habel LA , ShakS, JacobsMK et al.: A population-based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients.Breast Cancer Res.8(3) , R25 (2006).
   PubMed Web of Science ®Google Scholar
• Goldhirsch A , GlickJH, GelberRD, CoatesAS, SennHJ: Meeting highlights: International consensus panel on the treatment of primary breast cancer. Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer.J. Clin. Oncol.19(18) , 3817–3827 (2001).
   PubMed Web of Science ®Google Scholar
• Eifel P , AxelsonJA, CostaJ et al.: National Institutes of Health consensus development conference statement: adjuvant therapy for breast cancer, November 1–3, 2000.J. Natl Cancer Inst.93(13) , 979–989 (2001).
   PubMed Web of Science ®Google Scholar
• Elston CW , EllisIO: Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up.Histopathology19(5) , 403–410 (1991).
   PubMed Web of Science ®Google Scholar
• Loi S , Haibe-KainsB, DesmedtC et al.: Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade.J. Clin. Oncol.25(10) , 1239–1246 (2007).
   PubMed Web of Science ®Google Scholar

▪ Websites

• GenomeWeb daily news: FDA warns DTC genomics firms that genetic tests are not LDTs, may need clearance or approval. 11 June 2010 www.genomeweb.com/dxpgx/fda-warns-dtc-genomics-firms-genetic-tests-are-not-ldts-may-need-clearance-or-ap
   Google Scholar
• AlloMap, molecular expression testing http://allomap.com/documents/XDx_LabServicesGuideFINAL3electronicversion.pdf
   Google Scholar
• PreAnalytiX www.preanalytix.com
   Google Scholar
• LightCycler® Systems for Real-Time PCR www.roche-applied-science.com/lightcycler-online
   Google Scholar
• Gene Expression Omnibus www.ncbi.nlm.nih.gov/geo
   Google Scholar
• ArrayExpress archive www.ebi.ac.uk/microarray-as/ae
   Google Scholar
• BioGPS: gene annotation portal http://biogps.gnf.org
   Google Scholar
• TcLand expression www.genopole.fr/IMG/pdf/Cervino.pdf
   Google Scholar
• Personalized Medicine Coalition www.personalizedmedicinecoalition.org
   Google Scholar
• Association of European Personalised Medicine www.epemed.org
   Google Scholar
• US NIH Clinical Trials www.clinicaltrials.gov
   Google Scholar