Biomarker discovery study design consistent with the receiver-operator characteristic

The field of early biomarker discovery is characterized by a lack of consensus on the choice of statistical methodology, which may impede later progress towards clinically useful biomarkers. The Receiver-Operator Characteristic (ROC) is a de facto standard for determining the performance of In Vitro Diagnostic (IVD) devices. In this study, we aimed to systematically identify and mitigate prevalent pitfalls in biomarker discovery efforts and propose a best-practice guideline based on a ROC analysis framework.

By maintaining a careful alignment to the study objectives through a sample procurement plan, study size determination and data analysis by the ROC framework, we formulated a biomarker discovery protocol. We performed Monte Carlo simulations to inform the investigator on the suitable number of study participants, the statistical power and sample bin allocation strategy. The main concept is illustrated using proteomic data of newly diagnosed cancer cases and concurrent external controls.

The work demonstrates a regulatory-adherent pipeline to achieve an effect superior to the current best biomarker used as a predicate medical device. In our proof-of-concept ROC-based analysis in samples from a publicly available dataset, we detected statistically significant composite biomarkers, of which we validated a subset in an independent dataset acquired using the same proteomic analysis method. Intriguingly, commonly used feature selection methods do not identify the same composite biomarkers from the same data, and their selections show limited overlap with the ROC-based analysis.

The proposed approach can facilitate translation of scientific discoveries into regulatory approved biomarker tests fit for use in clinical medicine.