Proceedings of the American Thoracic Society
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The Proceedings of the American Thoracic Society 3:465-466 (2006)
© 2006 The American Thoracic Society
doi: 10.1513/pats.200603-030MS
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Use of Proteomic Patterns of Serum Biomarkers in Patients with Chronic Obstructive Pulmonary Disease

Correlation with Clinical Parameters

Victor Pinto-Plata, John Toso, Kwan Lee, John Bilello, Hana Mullerova, Mary De Souza, Rupert Vessey and Bartolome Celli

Caritas St. Elizabeth's Medical Center, Tufts University, Boston, Massachusetts; Discovery Medicine and Worldwide Epidemiology, GlaxoSmithKline R&D, King of Prussia, Pennsylvania

Correspondence and requests for reprints should be addressed to Victor Pinto-Plata, M.D., Pulmonary-Critical Care Medicine, Caritas St. Elizabeth's Medical Center, 736 Cambridge Street, Boston, MA 02135. E-mail: vpinto{at}copdnet.org

Chronic obstructive pulmonary disease (COPD) is primarily a lung disease with important systemic consequences. These are associated with alterations in selected serum biomarkers (SM). A systematic analysis of multiple SM and their correlation with clinical parameters of COPD is possible with new protein microarray platform (PMP) technology.

We studied 47 patients (65% male) with COPD (FEV1 < 55%) and 48 matched control subjects. We measured anthropometrics, dyspnea (Medical Research Council [MRC] scale), pulmonary function tests, 6-min walk distance (6MWD), body mass index, obstruction, dyspnea, exercise (BODE) index, and number of exacerbations. We explored the association of these outcomes with the results of 143 SM, measured by rolling circle amplification using PMP. The SM were tested for significance by univariate analysis, and clustered (n = 30) by variable clustering. The clusters were ranked by computing the predictiveness of each cluster for COPD (partial least square discriminant analysis). From the eight "best predictive" clusters, we selected two to three SM based on their pathophysiologic profile (chemoattractants, inflammation, tissue destruction and repair) and by their statistical significance.

A panel of 25 SM had a significant correlation (p <= 0.01) with FEV1, DLCO, 6MWD, BODE index (Figure 1) and exacerbation frequency.


Figure 1
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  		Figure 1. Association of the selected biomarker panel with the FEV1. The size of the bar in the graph indicates the magnitude of the regression coefficients and the 95% confidence interval is also indicated for each bar. If the confidence interval includes zero, the associated biomarker is "not significant." The overall regression model was significant by a permutation test (p < 0.01).

 
We conclude that SM using PMP technology can be used in the diagnosis and characterization of COPD. It may also be useful to assess response to treatment and to develop novel drug targets.

FOOTNOTES

Conflict of Interest Statement: V.P.-P. does not have a financial relationship with a commercial entity that has an interest in the subject of this manuscript. J.T. is a full-time employee of GlaxoSmithKline (GSK). K.L. has been employed by GSK since 1993 to present. J.B. was a full-time employee of GSK during the course of this study. He left GSK in the beginning of 2006. While he is no longer an employee, he has circa 5,000 stock options remaining (granted at circa $47). H.M. is an employee of GSK Research & Development. M.D.S. is an employee of GSK and also received stock options from her employer. R.V. is an employee of GSK. B.C. received $3,000 in 2005 and $4,000 in 2004 and 2003 for speaking at conferences sponsored by GSK. He received $3,000 in 2005 and 2004 for serving on an advisory board for GSK.

(Received in original form March 15, 2006; accepted in final form March 20, 2006)




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[Abstract] [Full Text] [PDF]

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