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.