Exposing the criminal record of every blood sample: Use of SOMAmer technology and sample mapping vectors to mitigate false biomarker discoveries in lung cancer.

Abstract

e21012 Background: Biomarker discovery studies may fail to translate to the clinic because the study population does not match the intended clinical use or because hidden preanalytic variability in the discovery samples contaminates the apparent disease specific information in the biomarkers. This can arise from differences in blood sample processing between study sites or in samples collected differently at the same study site. Methods: To better understand the effect of different blood sample processing procedures, we evaluated protein measurement bias in a large multi-center lung cancer study using the >1000 protein SOMAscan™ assay. These analyses revealed that perturbations in serum collection and processing result in changes to families of proteins from known biological pathways. We subsequently developed protein biomarker signatures of cell lysis, platelet activation and complement activation and assembled these preanalytic signatures into quantitative multi-dimensional Sample Mapping Vector (SMV) scores. Results: The SMV score provides critical evaluation of the quality of every blood-based sample used in discovery and also enables the evaluation of candidate protein biomarkers for resistance to preanalytic variability. Despite uniform processing protocols for each clinic, the SMV analysis revealed unexpected case/control bias arising from collecting case and control serum from different clinics at the same academic centers, an effect that created false or bias-contaminated disease markers. We therefore used the SMV score to remove bias-susceptible analytes and to define a well-collected, unbiased training set. An improved classifier was developed, resistant to common artifacts in serum processing. Conclusions: . The performance of this classifier to detect lung cancer in a high-risk population is more likely to represent real-world diagnostic results. We believe this approach is generally applicable to clinical investigations in all fields of biomarker discovery and translational medicine.