Identifying a protein biomarker in blood applying an unbiased data-driven approach

regulation profiles. To start with, we used GENEVESTIGATOR(1) to identify genes strongly and specifically expressed in chosen cancer types (as compared to normal tissues and over 1,000 other cancer types). The search was performed across 40'000 curated Affymetrix expression arrays covering a wide variety of well-described experiments and resulted in up to 20 marker genes highly specific for a particular cancer type. The corresponding proteins were then filtered by molecular properties, leading to a few testable candidates expected to be abundant, cancer specific, and secreted into blood. This method was successfully applied to a chosen cancer type as demonstrated by experimental validation. Out of four candidate proteins tested with ELISA assays from blood samples of cancer patients, one showed significant discriminative power.

Our method can similarly be used to identify drug targets by applying protein filters for druggability, cellular localization or molecular function. The power of this approach is the very large number of experimental conditions that can be simultaneously screened to find genes with very specific profiles, combined with protein property data. It offers an unbiased, data-driven and global context-based approach for selecting testable candidates.