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Disease Markers
Volume 20, Issue 6, Pages 295-307

Measurement Reproducibility in the Early Stages of Biomarker Development

Walter S. Liggett,1 Peter E. Barker,1 O. John Semmes,2 and Lisa H. Cazares2

1Statistical Engineering Division and Biotechnology Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
2Center for Biomedical Proteomics, Eastern Virginia Medical School, Norfolk, VA 23507, USA

Received 18 January 2005; Accepted 18 January 2005

Copyright © 2004 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Biomarker discovery and development requires measurement reproducibility studies in addition to case-control studies. Parallel pursuit of reproducibility studies is especially important for emerging technologies such as protein biomarkers based on time-of-flight mass spectrometry, the case considered in this paper. For parallel studies, a way to improve reproducibility prior to identification of protein species is necessary. One approach is use of functional principal components analysis (PCA) as the basis for assessing measurement reproducibility. Reproducibility studies involve repeated measurement of a reference material such as a human serum standard. Measurement in our example is by SELDI-TOF (surface-enhanced laser desorption and ionization time-of-flight) mass spectrometry. Reproducibility is defined in reference to a source of variation, which in our example is associated with a type of commercially available protein biochip. We obtained spectra for 8 spots on each 11 chips. Two spectra are generally more alike when obtained from the same chip rather than different chips. Thus, our experiment indicates potential improvements from reducing variation in chip manufacture and chip handling during measurement. Our analysis involves careful registration of the spectra and characterization of the spectral differences. As shown by our example, a metrological analysis may enhance case-control studies by guiding optimization of the measurements underlying the biomarker.