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• W2554421278 abstract "HomeCirculation: Cardiovascular GeneticsVol. 6, No. 2Blood-Based Gene Expression Tests Free AccessEditorialPDF/EPUBAboutView PDFSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBBlood-Based Gene Expression TestsPromises and Limitations Tanja Zeller, PhD and Stefan Blankenberg, MD Tanja ZellerTanja Zeller From the University Heart Center Hamburg, Hamburg, Germany. Search for more papers by this author and Stefan BlankenbergStefan Blankenberg From the University Heart Center Hamburg, Hamburg, Germany. Search for more papers by this author Originally published1 Apr 2013https://doi.org/10.1161/CIRCGENETICS.113.000149Circulation: Cardiovascular Genetics. 2013;6:139–140is corrected byCorrectionThe tremendous progress achieved in the omics field has successfully provided new insights into the pathogenesis of common diseases. Molecular phenotyping of the disease status has become feasible by novel, robust, and fast high-throughput analytic platforms, and global gene expression profiling offers a novel opportunity for molecular disease profiling and biomarker identification.Article see p 154A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention, and the overall expectation of a biomarker is to enhance the ability of the clinician to optimally manage the patient.1 To evaluate a biomarker, important measures are its sensitivity (ability to detect the disease, ie, identification of true-positives) and specificity (ability to the detect absence of disease, ie, identification of true-negatives), as well as its positive and negative predictive values.1Expression pattern of defined genes can also be viewed as a biomarker in which the expression levels of multiple genes are combined in a defined manner to provide an expression signature, an expression score, or a classifier. A precise gene expression signature or score has the promise to diagnose diseases, classify them further, and potentially guide personalized decisions for individual patients. Because of their easy accessibility, whole blood and peripheral blood mononuclear cells provide an optimal source for RNA and subsequent gene expression analyses. Gene expression profiling has already been shown to predict cardiomyopathy pathogenesis in heart failure2,3 and to be useful in monitoring clinically significant allograft rejection.4,5 Recent advances in the field of oncology have already translated gene expression signatures in medical applications, including the identification of breast cancer disease subgroups6 and the prognosis of breast cancer.7However, the use of gene expression data as a biomarker to improve diagnosis and prognosis of diseases is dependent on a number of factors, including an appropriate study design and adequate sample size, robust statistical derivation, and validation of initial results in independent studies to support the validity of the new assay.1,8,9 In addition, technical factors, such as the appropriate technology for gene expression analysis and preanalytical conditions (eg, time of the day of blood sampling, single- or multicenter sample processing, cellular composition of the blood, RNA quality, sample processing in batches), are further important contributors to the performance of gene expression as a biomarker.In this issue of Circulation: Cardiovascular Genetics, Thomas and colleagues10 report on a recently developed peripheral blood-based gene expression score (GES), which includes gene expression levels of 23 selected genes, age, and sex to assess obstructive coronary artery disease (CAD) likelihood in patients without diabetes mellitus. This report is an extension of this group's previous work in developing, validating, and evaluating outcome of the GES using a now commercially available assay (Corus CAD, CardioDx, Palo Alto, CA).11–14In this multicenter (19 sites involved) prospective study, Thomas and colleagues10 assessed the diagnostic accuracy of the GES to discriminate obstructive CAD in symptomatic patients without diabetes mellitus clinically referred for myocardial perfusion imaging. The investigators found that the GES was a significant predictor of obstructive CAD by receiver operator characteristic and net reclassification analyses, superior to clinical estimation scores, and showed proportional correlation to maximum percent stenosis. Furthermore, the GES at a predefined threshold resulted in a high sensitivity and high negative predictive value. Finally, the authors present an outline of a clinical algorithm for the sequential use of the GES and myocardial perfusion imaging to stratify patients based on a defined threshold into a rule-out group where no further work-up is proposed, and a group that should undergo myocardial perfusion imaging subsequently—depending on myocardial perfusion imaging results—being referred to invasive coronary angiography.Although the concept to explore and to validate gene expression profiling for diagnosis and prognosis of cardiovascular disease is attractive, various limitations need to be overcome.To reach clinically meaningful results, adequate clinical phenotyping and handling of gene expression results are mandatory. The current study addresses the phenotype obstructive CAD. To clinically assess obstructive CAD, modern stress test techniques have to be applied to ensure the best possible clinical information. Stress-echocardiography or other modern state-of-the-art techniques might provide higher diagnostic sensitivity. Furthermore, inclusion of coronary stenosis <70% into the phenotype obstructive CAD might introduce a bias. Caution is also needed because a high percentage of the general CAD patient population is not addressed as the study is limited to patients with non–diabetes mellitus without known CAD, prior revascularization or myocardial infarction, and known chronic inflammatory or autoimmune disorders. Consequently, a GES as described by Thomas and Kawanishi10 must be rigorously tested against existing noninvasive standards and explored in a broader population to define its clinical utility. Scores and biomarkers tested in small selected populations often do not hold promises in adequate real-world sample sizes.Beyond these clinical limitations, biological and technical considerations need to be taken into account. In contrast to genomic data, where a specific variant is present or not, gene expression data will vary interindividually, temporally, and between different disease states.15,16 In addition, whole blood represents a heterogeneous pool of distinct cells with putatively variable patterns of mRNA expression. Therefore, cell composition and subsequently gene expression might be altered depending on the disease state. Furthermore, variation caused by preanalytical (technical) factors can substantially influence gene expression data and should be considered into the GES. As shown by Schurmann et al,17 factors such as RNA quality, storage time of whole blood, and the RNA processing and amplification batch have a high influence on gene expression data. This is in particular challenging in the clinical setting, as differences in sample collection, sample processing, and assay performance in the different clinical centers are expected and will influence the accuracy of the GES.18 Although Thomas et al10 included 19 centers in their multicenter study, automated RNA purification, RNA processing, and reverse transcriptase polymerase chain reaction analysis were performed in a central reference laboratory at CardioDx. Ideally, the GES will be validated in a multicenter real-world study, including decentralized processing of RNA and polymerase chain reaction analysis and optimization of (decentralized) clinical laboratory testing procedures.Another crucial consideration is the influence of cardiovascular risk factors, ethnicity, and medication on gene expression. Recent studies showed that age, sex, body mass index, inflammatory status, and smoking influence gene expression.19,20 Any such influence needs to be considered and thoroughly validated for each single gene within the GES. For clinical application, the question arises whether to use a GES differently, eg, in smokers versus nonsmokers or between men and women. Hence, as already discussed, an independent validation study in a real-world scenario should be performed. In addition, common gene variants (eg, single-nucleotide polymorphisms) and epigenetic pattern can influence gene expression and thereby may influence transcript abundance differently in certain individuals.In aggregate, despite its high potential for clinical purposes, the available evidence currently does not support the clinical benefit of GES for obstructive CAD. The GES should be tested more rigorously against state-of-the-art clinical tests. The impact of factors such as smoking, medication, and RNA quality on expression should be taken into account adequately. If these factors are considered and validated, the current concept is of high potential. Independent of these purely scientific/clinical aspects, research is also required to address other relevant questions concerning the translation of the GES into clinical practice, such as clinical applicability, and feasible time frame for expression analysis before patient treatment, ethical issues as well as optimal cost–benefit analysis.DisclosuresNone.FootnotesGuest Editor for this article was Craig T. Basson, MD.The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Correspondence to Tanja Zeller, PhD, University Heart Center Hamburg, Martinistraße 52, 20246 Hamburg, Germany. E-mail [email protected]References1. Vasan RS. Biomarkers of cardiovascular disease: molecular basis and practical considerations.Circulation. 2006; 113:2335–2362.LinkGoogle Scholar2. Kittleson MM, Ye SQ, Irizarry RA, Minhas KM, Edness G, Conte JV, et al.. Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy.Circulation. 2004; 110:3444–3451.LinkGoogle Scholar3. Kittleson MM, Minhas KM, Irizarry RA, Ye SQ, Edness G, Breton E, et al.. Gene expression analysis of ischemic and nonischemic cardiomyopathy: shared and distinct genes in the development of heart failure.Physiol Genomics. 2005; 21:299–307.CrossrefMedlineGoogle Scholar4. Horwitz PA, Tsai EJ, Putt ME, Gilmore JM, Lepore JJ, Parmacek MS, et al.. Detection of cardiac allograft rejection and response to immunosuppressive therapy with peripheral blood gene expression.Circulation. 2004; 110:3815–3821.LinkGoogle Scholar5. Deng MC, Eisen HJ, Mehra MR, Billingham M, Marboe CC, Berry G, et al..; CARGO Investigators. Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling.Am J Transplant. 2006; 6:150–160.CrossrefMedlineGoogle Scholar6. Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, et al.. Breast cancer classification and prognosis based on gene expression profiles from a population-based study.Proc Natl Acad Sci USA. 2003; 100:10393–10398.CrossrefMedlineGoogle Scholar7. van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, et al.. A gene-expression signature as a predictor of survival in breast cancer.N Engl J Med. 2002; 347:1999–2009.CrossrefMedlineGoogle Scholar8. Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers.J Clin Oncol. 2005; 23:7332–7341.CrossrefMedlineGoogle Scholar9. Pedrotty DM, Morley MP, Cappola TP. Transcriptomic biomarkers of cardiovascular disease.Prog Cardiovasc Dis. 2012; 55:64–69.CrossrefMedlineGoogle Scholar10. Thomas GS, Voros S, McPherson JA, Lansky AJ, Winn ME, Bateman TM, et al.. A Blood Based Gene Expression Test for Obstructive Coronary Artery Disease Tested in Symptomatic Non-Diabetic Patients Referred for Myocardial Perfusion Imaging: The COMPASS Study.Circ Cardiovasc Genet. 2013; 6:154–162.LinkGoogle Scholar11. Elashoff MR, Wingrove JA, Beineke P, Daniels SE, Tingley WG, Rosenberg S, et al.. Development of a blood-based gene expression algorithm for assessment of obstructive coronary artery disease in non-diabetic patients.BMC Med Genomics. 2011; 4:26.CrossrefMedlineGoogle Scholar12. Rosenberg S, Elashoff MR, Beineke P, Daniels SE, Wingrove JA, Tingley WG, et al..; PREDICT (Personalized Risk Evaluation and Diagnosis in the Coronary Tree) Investigators. Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients.Ann Intern Med. 2010; 153:425–434.CrossrefMedlineGoogle Scholar13. Elashoff MR, Nuttall R, Beineke P, Doctolero MH, Dickson M, Johnson AM, et al.. Identification of factors contributing to variability in a blood-based gene expression test.PLoS ONE. 2012; 7:e40068.CrossrefMedlineGoogle Scholar14. Rosenberg S, Elashoff MR, Lieu HD, Brown BO, Kraus WE, Schwartz RS, et al..; PREDICT Investigators. Whole blood gene expression testing for coronary artery disease in nondiabetic patients: major adverse cardiovascular events and interventions in the PREDICT trial.J Cardiovasc Transl Res. 2012; 5:366–374.CrossrefMedlineGoogle Scholar15. Tanriverdi K, Freedman JE. Blood and cardiovascular disease: the promise and limitations of gene expression analysis.Circ Cardiovasc Genet. 2008; 1:7–9.LinkGoogle Scholar16. Whitney AR, Diehn M, Popper SJ, Alizadeh AA, Boldrick JC, Relman DA, et al.. Individuality and variation in gene expression patterns in human blood.Proc Natl Acad Sci USA. 2003; 100:1896–1901.CrossrefMedlineGoogle Scholar17. Schurmann C, Heim K, Schillert A, Blankenberg S, Carstensen M, Dörr M, et al.. Analyzing illumina gene expression microarray data from different tissues: methodological aspects of data analysis in the metaxpress consortium.PLoS ONE. 2012; 7:e50938.CrossrefMedlineGoogle Scholar18. Luo J, Schumacher M, Scherer A, Sanoudou D, Megherbi D, Davison T, et al.. A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data.Pharmacogenomics J. 2010; 10:278–291.CrossrefMedlineGoogle Scholar19. Beineke P, Fitch K, Tao H, Elashoff MR, Rosenberg S, Kraus WE, et al..; PREDICT Investigators. A whole blood gene expression-based signature for smoking status.BMC Med Genomics. 2012; 5:58.CrossrefMedlineGoogle Scholar20. Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, Castagne R, et al.. Genetics and beyond–the transcriptome of human monocytes and disease susceptibility.PLoS ONE. 2010; 5:e10693.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Zhang Y, Pan X, Zeng T, Chen L, Huang T and Cai Y (2020) Identifying the RNA signatures of coronary artery disease from combined lncRNA and mRNA expression profiles, Genomics, 10.1016/j.ygeno.2020.09.016, 112:6, (4945-4958), Online publication date: 1-Nov-2020. Szabó E, Parajkó Z, Opincariu D, Chițu M, Raț N and Benedek I (2019) A Genomic Approach to Characterize the Vulnerable Patient – a Clinical Update, Journal of Interdisciplinary Medicine, 10.2478/jim-2019-0023, 4:3, (141-144), Online publication date: 1-Sep-2019., Online publication date: 1-Sep-2019. Menche J, Guney E, Sharma A, Branigan P, Loza M, Baribaud F, Dobrin R and Barabási A (2017) Integrating personalized gene expression profiles into predictive disease-associated gene pools, npj Systems Biology and Applications, 10.1038/s41540-017-0009-0, 3:1, Online publication date: 1-Dec-2017. Cecelja M, Jiang B, Mangino M, Spector T and Chowienczyk P (2015) Association of Cross-Sectional and Longitudinal Change in Arterial Stiffness With Gene Expression in the Twins UK Cohort, Hypertension, 67:1, (70-76), Online publication date: 1-Jan-2016. Martin F, Talikka M, Ivanov N, Haziza C, Hoeng J and Peitsch M (2016) Evaluation of the tobacco heating system 2.2. Part 9: Application of systems pharmacology to identify exposure response markers in peripheral blood of smokers switching to THS2.2, Regulatory Toxicology and Pharmacology, 10.1016/j.yrtph.2016.11.011, 81, (S151-S157), Online publication date: 1-Nov-2016. Martin F, Talikka M, Hoeng J and Peitsch M (2015) Identification of gene expression signature for cigarette smoke exposure response—from man to mouse, Human & Experimental Toxicology, 10.1177/0960327115600364, 34:12, (1200-1211), Online publication date: 1-Dec-2015. Zeller T, Hughes M, Tuovinen T, Schillert A, Conrads-Frank A, Ruijter H, Schnabel R, Kee F, Salomaa V, Siebert U, Thorand B, Ziegler A, Breek H, Pasterkamp G, Kuulasmaa K, Koenig W and Blankenberg S (2014) BiomarCaRE: rationale and design of the European BiomarCaRE project including 300,000 participants from 13 European countries, European Journal of Epidemiology, 10.1007/s10654-014-9952-x, 29:10, (777-790), Online publication date: 1-Oct-2014. Siemelink M and Zeller T (2014) Biomarkers of Coronary Artery Disease: The Promise of the Transcriptome, Current Cardiology Reports, 10.1007/s11886-014-0513-4, 16:8, Online publication date: 1-Aug-2014. Daniels S, Beineke P, Rhees B, McPherson J, Kraus W, Thomas G and Rosenberg S (2014) Biological and Analytical Stability of a Peripheral Blood Gene Expression Score for Obstructive Coronary Artery Disease in the PREDICT and COMPASS Studies, Journal of Cardiovascular Translational Research, 10.1007/s12265-014-9583-3, 7:7, (615-622), Online publication date: 1-Oct-2014. Fernandes M, Patel A, Husi H and Gomes A (2018) C/VDdb: A multi-omics expression profiling database for a knowledge-driven approach in cardiovascular disease (CVD), PLOS ONE, 10.1371/journal.pone.0207371, 13:11, (e0207371) Related articlesCorrectionCirculation: Cardiovascular Genetics. 2013;6:e9-e9 April 2013Vol 6, Issue 2 Advertisement Article InformationMetrics © 2013 American Heart Association, Inc.https://doi.org/10.1161/CIRCGENETICS.113.000149 Originally publishedApril 1, 2013 Keywordsblood-based gene expressionEditorialsgene expression analysisgene expression scorecoronary artery diseasebiomarkerPDF download Advertisement SubjectsGene Expression and Regulation" @default.
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