Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges

Abstract

Tremendous efforts have been made over the past few decades to discover novel cancer biomarkers for use in clinical practice. However, a striking discrepancy exists between the effort directed toward biomarker discovery and the number of markers that make it into clinical practice. One of the confounding issues in translating a novel discovery into clinical practice is that quite often the scientists working on biomarker discovery have limited knowledge of the analytical, diagnostic, and regulatory requirements for a clinical assay. This review provides an introduction to such considerations with the aim of generating more extensive discussion for study design, assay performance, and regulatory approval in the process of translating new proteomic biomarkers from discovery into cancer diagnostics. We first describe the analytical requirements for a robust clinical biomarker assay, including concepts of precision, trueness, specificity and analytical interference, and carryover. We next introduce the clinical considerations of diagnostic accuracy, receiver operating characteristic analysis, positive and negative predictive values, and clinical utility. We finish the review by describing components of the FDA approval process for protein-based biomarkers, including classification of biomarker assays as medical devices, analytical and clinical performance requirements, and the approval process workflow. While we recognize that the road from biomarker discovery, validation, and regulatory approval to the translation into the clinical setting could be long and difficult, the reward for patients, clinicians and scientists could be rather significant.

Figure 1

Illustration of the hook effect. This phenomenon arises because high concentrations of analyte saturate all antigen binding sites on the capture and label reagent antibodies and thereby interfere with sandwich-formation. A subsequent wash step removes all species not bound to the capture antibody (including analyte-label antibody complexes) and leads to a lower-than-expected signal during detection. (A) The analyte concentration is low relative to the number of available antibody binding sites. A hook effect does not occur. (B) The analyte concentration is high relative to the number of available antibody binding sites. The hook effect leads to falsely low signal.

Figure 2

Hypothetical ROC curves. (A) A hypothetical non-parametric ROC plot. Each open square corresponds to the sensitivity and (1 minus specificity) values obtained for a particular decision threshold. The dashed diagonal line corresponds to the random chance line. The hashed region corresponds to the PAUC for the range of specificities between 68% and 100%. (B) Two hypothetical ROC curves with identical AUCs but different performances over the range of thresholds.