Bioinformatics Methods for Prioritizing Serum Biomarker Candidates (Abstract of Oak Ridge Posters‪)‬

A major research objective of NIH is to discover novel biomarkers that can improve cancer detection assays. Many biomarker candidates with apparent differential expression identified by high throughput genomic and proteomic experiments have been reported as candidates for novel cancer detection assays. Despite these many discoveries, few biomarkers have been validated and successfully translated into clinical tests, a situation that may be attributable to the extensive and costly experimental evaluation required to fully develop a candidate biomarker into a clinically useful assay. The use of information in addition to disease-state expression data may help to focus biomarker assay development projects more selectively and facilitate successful assay development and translation. We describe a bioinformatics and data mining method for evaluating diagnostic serum biomarker candidates by selecting genes and gene products that possess intrinsic protein localization and tissue expression properties. The bioinformatics methods we describe are based on the assumption that candidate markers with diagnostic value have been identified previously. We defined diagnostic value as differential up-regulation or high expression of the biomarker in cancer tissue compared with benign tissue. Our experience (unpublished) has shown that detection of differentially down-regulated serum biomarker candidates is problematic because the serum biomarker candidates are usually present in low concentrations at which it is difficult to detect the absence of signal under the normal tissue background. Regardless of the definition of diagnostic value, the bioinformatics methods we describe are not contingent on discovering or confirming the informative value of the candidate biomarkers for differentiating healthy vs diseased states. These methods are designed to select candidate biomarkers with properties that make them more readily detectable in the biologically complex serum environment, in which secreted or extracellular proteins would be present at higher concentrations than proteins generally found inside of cells. Candidate biomarker sets are rapidly screened with in silico algorithms to predict which genes encode proteins that are secreted from the cell and thus are likely to be detectable in serum. Tissue-specific profiles of individual genes systematically generated from transcriptomic databases are used to select markers with expression patterns specific for the target diagnostic tissue type or to exclude markers without such expression patterns. Thus candidate markers with high signal-to-background expression distinguishable in serum are identified and provide a focused list for experimental validation.