A biomarker is an objectively measurable biological parameter that serves as an indicator of normal biological processes, disease processes, or the response to a therapeutic intervention. In clinical trials, biomarkers are used to characterize patients, assess efficacy at an early stage, or identify safety risks.
Types of biomarkers and typical areas of application
In practice, a distinction is made between different biomarker categories. This classification helps ensure that study design, endpoint strategy, and analytics are set up appropriately.
- Diagnostic biomarkers: support disease diagnosis (e.g. specific mutations or protein markers).
- Prognostic biomarkers: predict the natural course of the disease, regardless of treatment.
- Predictive biomarkers: indicate the likelihood of a treatment response and are central to personalized medicine.
- Pharmacodynamic biomarkers: show biological effects of a treatment, such as pathway inhibition.
- Safety biomarkers: provide warnings of toxicity, e.g. liver values as an indication of drug-induced injury.
In addition, monitoring biomarkers are used to track disease progression and treatment adherence, while response biomarkers serve as an early signal of efficacy. In oncology programs, biomarkers are often closely linked to the development of a companion diagnostic, which increases the requirements for standardization and documentation.
Biomarkers as endpoints, validation, and clinical relevance
A common pitfall is equating changes in biomarkers with clinical benefit. Not every biomarker is suitable as a surrogate endpoint. A surrogate endpoint replaces a clinically meaningful endpoint (e.g. morbidity or mortality) only when the relationship has been robustly established. In early clinical phases, biomarkers are often used exploratorily to optimize dose, mechanism of action, and patient selection.
For interpretation, it is important what role the biomarker plays in the clinical trial protocol (clinical-trial-protocol): Is it primary, secondary, or exploratory? Are multiplicity issues and statistical power taken into account? Particularly when using many exploratory biomarkers, clear hypotheses, a transparent approach to controlling error probabilities, and proper documentation in the data management plan are needed.
When biomarkers serve as inclusion/exclusion criteria, cut-offs must be justified in a comprehensible manner. In practice, cut-offs are sometimes derived from retrospective datasets; in such cases, sensitivity analyses and a plan for revalidation should be provided to ensure that results do not arise merely by chance in a subpopulation.
To ensure that the interpretation remains robust, potential sources of bias should be actively addressed: incomplete sample submission (selection bias), batch effects in the laboratory, or confounding due to concomitant medication and disease stage. Prespecified analysis populations and sensitivity analyses help make results more robust.
Operational implementation: sample chain, data flows, and quality
Biomarker data are only as good as the underlying analytics. Typical requirements concern preanalytics (sample collection, processing, and storage), the measurement method (e.g. immunological assays, PCR, sequencing), and evaluation. In GCP-compliant studies, processes must be documented in a traceable manner, including equipment calibration, reagent tracking, and the audit trail in the electronic system.
Different validation levels are required depending on the purpose. For biomarkers that serve as inclusion criteria or primary endpoints, robust performance data (accuracy, precision, limit of detection, and stability) are essential. Inter-laboratory comparisons and standardization also reduce variability. From a clinical data management perspective, biomarker variables should be integrated into the case report form structure at an early stage, including plausibility checks and a clear query process.
Another operationally relevant aspect is the chain of custody: from collection at the study site through transport conditions to measurement in the central laboratory. Documentation gaps or temperature deviations can invalidate analyses and frequently lead to findings in audits. Responsibilities, shipping windows, and escalation pathways should therefore be clearly defined in SOPs.
Relevance for clinical trials
Biomarkers influence numerous operational and regulatory aspects: They guide patient selection, define subgroup analyses, and can shape the benefit-risk assessment. For sponsors and CROs, a clear biomarker plan is relevant in order to plan sample logistics, central laboratory integration, data flows (electronic data capture), and statistical evaluation consistently. If biomarkers are to become relevant to the rationale for marketing authorization, it should be clarified at an early stage what evidence of clinical relevance is expected and how the biomarker strategy aligns with endpoints such as overall survival or progression-free survival.
In EU contexts, data protection and the use of samples and data also play a role: informed consent forms should clearly cover biospecimens, genetic analyses, secondary use, and retention periods.
Frequently Asked Questions (FAQ)
What is the difference between predictive and prognostic biomarkers?
Prognostic biomarkers describe the course of the disease regardless of treatment, whereas predictive biomarkers indicate whether a particular treatment is likely to work. Predictive biomarkers are therefore particularly important for treatment decisions and subgroups in clinical trials.
When is a biomarker considered a surrogate endpoint?
A biomarker is considered a surrogate endpoint only when scientific evidence has established that changes in the biomarker reliably reflect clinical benefit. This generally requires consistent evidence from multiple studies and a plausible biological mechanism of action.
What quality requirements apply to biomarker data in clinical trials?
Controlled preanalytics, validated measurement methods, traceable documentation, and consistent data flows are essential. In addition, plausibility checks, query management, and an audit trail should ensure that data are accurate, complete, and traceable.
Regulatory references
- ICH E6(R3) Good Clinical Practice: requirements for data integrity, traceability, and quality in clinical trials.
- EU Regulation 536/2014 (Clinical Trials Regulation): requirements for clinical trials in the EU, including documentation and safety requirements.
- ICH E9 (Statistical Principles for Clinical Trials): principles for planning and evaluation, relevant to biomarker analyses and subgroups.