Tools and techniques that enable earlier detection of cancer have the potential to improve survival rates as treatment options are more likely to be successful. Liquid biopsy-based early screening tools, however, pose both biological and analytical challenges due to low tumor DNA shedding and the low analytical sensitivity of many existing approaches. Hybrid capture-based enrichment of differentially methylated regions, combined with computational biology and machine learning approaches, allows researchers to overcome these challenges, delivering highly efficient and uniform analysis of the targets of interest, leading to early and accurate identification of cancer signals.

CeGaT GmbH is a provider of genetic diagnostics and NGS services for research and pharma customers. One big part of our diagnostic offerings is tumor diagnostics. In recent years, analyses starting from formalin-fixed or fresh frozen tumor tissue samples were complemented by analyses starting from cell-free DNA extracted from patient blood plasma obtained in "liquid biopsies". These samples have several benefits for non-invasive monitoring. But they pose significant technical challenges as the amount of material is typically low and the percentage of tumor DNA in the total cell-free DNA fraction very low (<10%). Special wet-lab protocols and bioinformatics algorithms are required to provide sensitivity at these low amounts and allele frequencies. End-to-end validation of these processes is essential to ascertain performance characteristics such as specificity and sensitivity. However, such validations require well-defined control samples. This talk will cover the difficulties labs face when trying to establish such standards on their own, we will share our experience with a publicly available standard, present validation results and discuss some caveats.