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Clinical Chemistry 51: 695-707, 2005. First published February 3, 2005; 10.1373/clinchem.2004.041087
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(Clinical Chemistry. 2005;51:695-707.)
© 2005 American Association for Clinical Chemistry, Inc.


Proteomics and Protein Markers

Identification of Serum Amyloid A as a Biomarker to Distinguish Prostate Cancer Patients with Bone Lesions

Lyly Le1, Kim Chi1, Scott Tyldesley1, Stephane Flibotte1, Deborah L. Diamond2, Michael A. Kuzyk1 and Marianne D. Sadar1,a

1 British Columbia Cancer Agency, Vancouver, British Columbia, Canada.
2 Ciphergen Biosystems, Inc., Fremont, CA.

aAddress correspondence to this author at: Genomics Sciences Centre, British Columbia Cancer Agency, 600 West 10th Ave., Vancouver, British Columbia, V5Z 4E6 Canada. Fax 604-877-6011; e-mail msadar{at}bccrc.ca.

Background: Prostate cancer has a propensity to metastasize to the bone. Currently, there are no curative treatments for this stage of the disease. Sensitive biomarkers that can be monitored in the blood to indicate the presence or development of bone metastases and/or response to therapies are lacking. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is an affinity-based approach that allows sensitive and high-throughput protein profiling and screening of biological samples.

Methods: We used SELDI-TOF MS for protein profiling of sera from prostate cancer patients (n = 38) with and without bone metastases in our effort to identify individual or multiple serum markers that may be of added benefit to those in current use. Serum was applied to ProteinChip® surfaces (H4 and IMAC) to quickly screen samples and detect peaks predominating in the samples obtained from patients with bone metastases. Unique proteins in the bone metastasis cohort observed by SELDI-TOF MS were identified by two-dimensional gel electrophoresis, in-gel trypsin digestion, and tandem MS. The identities of the proteins were confirmed by ELISA and immunodepletion assays.

Results: The cluster of unique proteins in the sera of patients with bone metastases was identified as isoforms of serum amyloid A. Machine-learning algorithms were also used to identify patients with bone metastases with a sensitivity and specificity of 89.5%.

Conclusions: SELDI-TOF MS protein profiling in combination with other proteomic approaches may provide diagnostic tools with potential clinical applications and serve as tools to aid in the discovery of biomarkers associated with various diseases.




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