The Biomarker Toolkit — an evidence-based guideline to predict cancer biomarker success and guide development

Abstract

Background

An increased number of resources are allocated on cancer biomarker discovery, but very few of these biomarkers are clinically adopted. To bridge the gap between Biomarker discovery and clinical use, we aim to generate the Biomarker Toolkit, a tool designed to identify clinically promising biomarkers and promote successful biomarker translation.

Methods

All features associated with a clinically useful biomarker were identified using mixed-methodology, including systematic literature search, semi-structured interviews, and an online two-stage Delphi-Survey. Validation of the checklist was achieved by independent systematic literature searches using keywords/subheadings related to clinically and non-clinically utilised breast and colorectal cancer biomarkers. Composite aggregated scores were generated for each selected publication based on the presence/absence of an attribute listed in the Biomarker Toolkit checklist.

Results

Systematic literature search identified 129 attributes associated with a clinically useful biomarker. These were grouped in four main categories including: rationale, clinical utility, analytical validity, and clinical validity. This checklist was subsequently developed using semi-structured interviews with biomarker experts (n=34); and 88.23% agreement was achieved regarding the identified attributes, via the Delphi survey (consensus level:75%, n=51). Quantitative validation was completed using clinically and non-clinically implemented breast and colorectal cancer biomarkers. Cox-regression analysis suggested that total score is a significant driver of biomarker success in both cancer types (BC: p>0.0001, 95.0% CI: 0.869–0.935, CRC: p>0.0001, 95.0% CI: 0.918–0.954).

Conclusions

This novel study generated a validated checklist with literature-reported attributes linked with successful biomarker implementation. Ultimately, the application of this toolkit can be used to detect biomarkers with the highest clinical potential and shape how biomarker studies are designed/performed.