Herausforderungen an die Planung und Durchführung von Diagnosestudien mit molekularen Biomarkern

Zusammenfassung

Die Bedeutung von Biomarkern für die personalisierte Medizin wächst stetig, und Biomarker finden ihre Anwendung im Bereich der Diagnose, Prognose sowie der Auswahl zielgerichteter Therapien. In vielen Biomarkerstudien werden inzwischen molekulare Biomarker verwendet, die sich bei -omics Experimenten gegen eine Vielzahl anderer Kandidaten durchgesetzt haben. Die Intensitäten, z. B. Proteinkonzentrationen, werden typischerweise zwischen zwei oder mehr Gruppen verglichen, um die diagnostische Wertigkeit eines molekularen Biomarkers zu bestimmen. Verschiedene prospektive oder retrospektive Studiendesigns können für molekulare Biomarkerstudien gewählt werden, und der Biomarker kann entweder durch eine einzelne Messung oder eine Kombination verschiedener Messungen, also ein Biomarkerprofil, gemessen werden. In dieser Arbeit werden die methodischen Herausforderungen an die Planung und Durchführung von diagnostischen Studien mit molekularen Biomarkern betrachtet. Zunächst werden die Grade der Evidenz von Diagnosestudien betrachtet. Anschließend werden die damit eng verbundenen verschiedenen Phasen von Diagnosestudien für Biomarker skizziert und die unterschiedlichen Studiendesigns diskutiert. Insbesondere unterscheidet sich die Auswahl der Personen je nach Phase der molekularen Biomarkerstudie wesentlich. Anhand von Beispielen sowie zweier systematischer Übersichten aus der Literatur werden die typischen Verzerrungsquellen molekularer Diagnosestudien illustriert und ihre Relevanz für Anwendungen diskutiert. Insbesondere werden die extreme Auswahl von Patienten und Kontrollen sowie die Verifikationsverzerrung betrachtet. Bei der Validierung molekularer Biomarker spielt die Variabilität der Biomarker-Messung, üblicherweise ausgedrückt als Variationskoeffizient, eine große Rolle. Es wird abschließend aufgezeigt, dass die erforderliche Fallzahl zur Validierung von Biomarkern quadratisch mit dem Variationskoeffizienten, also der Variabilität der Biomarkermessung, steigt. Die Konsequenz dieser Eigenschaft wird anhand von Realdaten verschiedener Labortechniken erläutert.

Abstract

Biomarkers are of increasing importance for personalized medicine in many areas of application, such as diagnosis, prognosis, or the selection of targeted therapies. In many molecular biomarker studies, intensity values are obtained from large scale ‑omics experiments. These intensity values, such as protein concentrations, are often compared between at least two groups of subjects to determine the diagnostic ability of the molecular biomarker. Various prospective or retrospective study designs are available for molecular biomarker studies, and the biomarker used may be univariate or may even consist in a multimarker rule. In this work, several challenges are discussed for the planning and conduct of biomarker studies. The phases of diagnostic biomarker studies are closely related to levels of evidence in diagnosis, and they are therefore discussed upfront. Different study designs for molecular biomarker studies are discussed, and they primarily differ in the way subjects are selected. Using two systematic reviews from the literature, common sources of bias of molecular diagnostic studies are illustrated. The extreme selection of patients and controls and verification bias are specifically discussed. The pre-analytical and technical variability of biomarker measurements is usually expressed in terms of the coefficient of variation, and is of great importance for subsequent validation studies for molecular biomarkers. It is finally shown that the required sample size for biomarker validation quadratically increases with the coefficient of variation, and the effect is illustrated using real data from different laboratory technologies.

Schlüsselwörter

Biomarker - Diagnosestudie - Molekulare Diagnostik - Stichprobenverzerrung - Validierung - Variationskoeffizient - Verifikationsverzerrung

Keywords

biomarker - coefficient of variation - diagnosis study - molecular diagnostics - sample selection bias - validation - verification bias

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