Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy

Michelle M. Kittleson, Shui Q. Ye, Rafael A. Irizarry, Khalid M. Minhas, Gina Edness, John V. Conte, Giovanni Parmigiani, Leslie W. Miller, Yingjie Chen, Jennifer L. Hall, Joe GN Garcia, Joshua M. Hare

Research output: Contribution to journalArticle

95 Citations (Scopus)

Abstract

Background - Gene expression profiling refines diagnostic and prognostic assessment in oncology but has not yet been applied to myocardial diseases. We hypothesized that gene expression differentiates ischemic and nonischemic cardiomyopathy, demonstrating that gene expression profiling by clinical parameters is feasible in cardiology. Methods and Results - Affymetrix U133 A microarrays of 48 myocardial samples from Johns Hopkins Hospital (JHH) and the University of Minnesota (UM) obtained (1) at transplantation or left ventricular assist device (LVAD) placement (end-stage; n = 25), (2) after LVAD support (post-LVAD; n=16), and (3) from newly diagnosed patients (biopsy; n=7) were analyzed with prediction analysis of microarrays. A training set was used to develop the profile and test sets to validate the accuracy of the profile. An etiology prediction profile developed in end-stage JHH samples was tested in independent samples from both JHH and UM with 100% sensitivity and 100% specificity in end-stage samples and 33% sensitivity and 100% specificity in both post-LVAD and biopsy samples. The overall sensitivity was 89% (95% CI 75% to 100%), and specificity was 89% (95% CI 60% to 100%) over 210 random partitions of end-stage samples into training and test sets. Age, gender, and hemodynamic differences did not affect the profile's accuracy in stratified analyses. Select gene expression was confirmed with quantitative polymerase chain reaction. Conclusions - Gene expression profiling accurately predicts cardiomyopathy etiology, is generalizable to samples from separate institutions, is specific to disease stage, and is unaffected by differences in clinical characteristics. This strongly supports ongoing efforts to incorporate expression profiling-based biomarkers in determining prognosis and response to therapy in heart failure.

Original languageEnglish (US)
Pages (from-to)3444-3451
Number of pages8
JournalCirculation
Volume110
Issue number22
DOIs
StatePublished - Nov 30 2004
Externally publishedYes

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Heart-Assist Devices
Cardiomyopathies
Transcriptome
Gene Expression Profiling
Biopsy
Gene Expression
Sensitivity and Specificity
Microarray Analysis
Cardiology
Heart Failure
Transplantation
Biomarkers
Hemodynamics
Polymerase Chain Reaction
Therapeutics

Keywords

  • Cardiomyopathy
  • Genetics
  • Heart failure

ASJC Scopus subject areas

  • Physiology
  • Cardiology and Cardiovascular Medicine

Cite this

Kittleson, M. M., Ye, S. Q., Irizarry, R. A., Minhas, K. M., Edness, G., Conte, J. V., ... Hare, J. M. (2004). Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy. Circulation, 110(22), 3444-3451. https://doi.org/10.1161/01.CIR.0000148178.19465.11

Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy. / Kittleson, Michelle M.; Ye, Shui Q.; Irizarry, Rafael A.; Minhas, Khalid M.; Edness, Gina; Conte, John V.; Parmigiani, Giovanni; Miller, Leslie W.; Chen, Yingjie; Hall, Jennifer L.; Garcia, Joe GN; Hare, Joshua M.

In: Circulation, Vol. 110, No. 22, 30.11.2004, p. 3444-3451.

Research output: Contribution to journalArticle

Kittleson, MM, Ye, SQ, Irizarry, RA, Minhas, KM, Edness, G, Conte, JV, Parmigiani, G, Miller, LW, Chen, Y, Hall, JL, Garcia, JGN & Hare, JM 2004, 'Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy', Circulation, vol. 110, no. 22, pp. 3444-3451. https://doi.org/10.1161/01.CIR.0000148178.19465.11
Kittleson, Michelle M. ; Ye, Shui Q. ; Irizarry, Rafael A. ; Minhas, Khalid M. ; Edness, Gina ; Conte, John V. ; Parmigiani, Giovanni ; Miller, Leslie W. ; Chen, Yingjie ; Hall, Jennifer L. ; Garcia, Joe GN ; Hare, Joshua M. / Identification of a gene expression profile that differentiates between ischemic and nonischemic cardiomyopathy. In: Circulation. 2004 ; Vol. 110, No. 22. pp. 3444-3451.
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AU - Edness, Gina

AU - Conte, John V.

AU - Parmigiani, Giovanni

AU - Miller, Leslie W.

AU - Chen, Yingjie

AU - Hall, Jennifer L.

AU - Garcia, Joe GN

AU - Hare, Joshua M.

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N2 - Background - Gene expression profiling refines diagnostic and prognostic assessment in oncology but has not yet been applied to myocardial diseases. We hypothesized that gene expression differentiates ischemic and nonischemic cardiomyopathy, demonstrating that gene expression profiling by clinical parameters is feasible in cardiology. Methods and Results - Affymetrix U133 A microarrays of 48 myocardial samples from Johns Hopkins Hospital (JHH) and the University of Minnesota (UM) obtained (1) at transplantation or left ventricular assist device (LVAD) placement (end-stage; n = 25), (2) after LVAD support (post-LVAD; n=16), and (3) from newly diagnosed patients (biopsy; n=7) were analyzed with prediction analysis of microarrays. A training set was used to develop the profile and test sets to validate the accuracy of the profile. An etiology prediction profile developed in end-stage JHH samples was tested in independent samples from both JHH and UM with 100% sensitivity and 100% specificity in end-stage samples and 33% sensitivity and 100% specificity in both post-LVAD and biopsy samples. The overall sensitivity was 89% (95% CI 75% to 100%), and specificity was 89% (95% CI 60% to 100%) over 210 random partitions of end-stage samples into training and test sets. Age, gender, and hemodynamic differences did not affect the profile's accuracy in stratified analyses. Select gene expression was confirmed with quantitative polymerase chain reaction. Conclusions - Gene expression profiling accurately predicts cardiomyopathy etiology, is generalizable to samples from separate institutions, is specific to disease stage, and is unaffected by differences in clinical characteristics. This strongly supports ongoing efforts to incorporate expression profiling-based biomarkers in determining prognosis and response to therapy in heart failure.

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