Mass Spectrometry and Biomarker DevelopmentView this Special Issue
Richard C. Zangar, Susan M. Varnum, Chandice Y. Covington, Richard D. Smith, "A Rational Approach for Discovering and Validating Cancer Markers in Very Small Samples Using Mass Spectrometry and ELISA Microarrays", Disease Markers, vol. 20, Article ID 754640, 14 pages, 2004. https://doi.org/10.1155/2004/754640
A Rational Approach for Discovering and Validating Cancer Markers in Very Small Samples Using Mass Spectrometry and ELISA Microarrays
Identifying useful markers of cancer can be problematic due to limited amounts of sample. Some samples such as nipple aspirate fluid (NAF) or early-stage tumors are inherently small. Other samples such as serum are collected in larger volumes but archives of these samples are very valuable and only small amounts of each sample may be available for a single study. Also, given the diverse nature of cancer and the inherent variability in individual protein levels, it seems likely that the best approach to screen for cancer will be to determine the profile of a battery of proteins. As a result, a major challenge in identifying protein markers of disease is the ability to screen many proteins using very small amounts of sample. In this review, we outline some technological advances in proteomics that greatly advance this capability. Specifically, we propose a strategy for identifying markers of breast cancer in NAF that utilizes mass spectrometry (MS) to simultaneously screen hundreds or thousands of proteins in each sample. The best potential markers identified by the MS analysis can then be extensively characterized using an ELISA microarray assay. Because the microarray analysis is quantitative and large numbers of samples can be efficiently analyzed, this approach offers the ability to rapidly assess a battery of selected proteins in a manner that is directly relevant to traditional clinical assays.
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.