Application of Proteomics to Disease Diagnostics

An important aspect of the application of proteomics to the identification of disease biomarkers pertains to the substantial need for informatics resources. The proteomics field is now capturing the attention of computer scientists and bioinformaticians. New computational tools are being developed in several different areas, ranging from better algorithms for protein identification and measurements of statistical confidence in identification, to de novo peptide sequencing and whole genome databank searches. This chapter reviews the application of proteomics to disease diagnostics, particularly from the perspective of cancer. The emphasis is on the application of sound strategies rather than detailed description of methodologies and instrumentation. A useful repertoire of proteomics technologies is currently available for disease-related applications, though further technological innovations would be beneficial to increase sensitivity, reduce sample size requirements, increase throughput, and more effectively uncover various types of protein alterations, such as post-translational modifications. The application of high-throughput procedures to the discovery of biomarkers leads to large datasets of multi-dimensional data and a certain percentage of false discoveries. It becomes crucial to carefully design validation studies and characterize the sensitivity and specificity of each biomarker candidate in a representative population.