Stefan Holdenrieder

 

Holdenrieder

Prof. Stefan Holdenrieder

Holdenrieder@
dhm.mhn.de

Title of Presentation

Validation strategies for quality control of new diagnostic biomarker approaches in precision medicine

 

Date and Place

Session D5

 

Speaker Biography

Prof. Stefan Holdenrieder is Director of the Institute of Laboratory Medicine at the German Heart Center (DHM) of the Technical University Munich (Germany). Currently he establishes at the DHM a Biomarker Center and a Biofluid Biobank which he had coordinated until recently at the University of Bonn. Being a specialist in laboratory medicine his research interests are the development and comprehensive validation of new biomarkers and technologies for diagnostic applications in cardiology, oncology, immunology, neurology and pediatrics. His special focus is spot on circulating cell-free nucleic acids, their genetic and epigenetic modifications, (exosomal) miRNA, protein and metabolomics markers as well as the development of multiparametric algorithms (together with QuoData Statistics). His laboratory has been part of many international multicentric biomarker evaluation studies and serves as diagnostic center for the Central European Society of Anticancer Drug Research (CESAR). Prof. Holdenrieder is secretary of the International Society of Oncological BioMarkers (ISOBM), EQA consultant for tumor marker ring trials at Instand e.V. and associate editor of several international journals.

At the European Biobank Week conference in Vienna, Prof. Holdenrieder will outline validation strategies for quality control of new diagnostic biomarker approaches on the methodical, preanalytical and analytical level that should be considered prior to their use in the clinical application and will point out the role of modern biobanking in this process.

 

Abstract

Precision medicine is considered a new approach in diverse areas of medicine that aims to treat patients not only according to their phenotypic but also to their genotypic and biochemical profile. For instance in cancer diseases, specific molecular changes in tumor tissue are the precondition to stratify patients for so called “targeted therapies” that address these defined changes. While this concept of “companion diagnostics” currently requires genetic tissue testing, new approaches include tumor-specific profiling on cell-free DNA or tumor cells circulating in the blood and open the door for individualized disease monitoring and patient guidance. In addition to genetic testing, profiling on the epigenetic, protein or metabolic levels provides meaningful diagnostic, predictive and prognostic insights that can be used for patient characterisation and therapy stratification.

The rise of new diagnostic technologies, parameters and algorithms poses a considerable challenge for laboratories dedicated to patient care to assure their methodical quality, control inter- and intraindividual variations, respect potentially influencing factors and define clearly their diagnostic and predictive power. Thereby it’s important to compare the new with already established approaches to see whether and how they improve or add to diagnostic sensitivity and specificity, or the positive or negative predictive value depending on the clinical question.

When evaluating new technologies and biomarkers in our biomarker center, we first test them thoroughfully on their methodical quality and robustness. Subsequently, we investigate all factors in the preanalytical phase from blood drawing until analysis that could influence the marker values. Finally, we do a comprehensive evaluation of diagnostic, predictive and prognostic power including not only the disease at focus and healthy controls but also all other clinical conditions that also could lead to non-intentional marker changes that are relevant for the interpretation of the results. For the latter approach, we use large numbers of serum or plasma samples collected in a high-quality biobank that were collected in diverse diagnostic areas of the University hospital. Ideally, we compare several biomarker classes on the same sample set in order to identify the most meaningful pattern for specific clinical indications.