A Neural Network-Enabled, Enzymatic cfDNA Methylation Assay for Colorectal Cancer Early Detection.

Early detection of colorectal cancer (CRC) remains critical for reducing disease-specific mortality, yet current noninvasive screening approaches have limitations in sensitivity, patient adherence, and scalability. We developed and clinically evaluated a non-next-generation sequencing (non-NGS) liquid biopsy assay for CRC detection based on methylation profiling of circulating cell-free DNA (cfDNA). The assay focuses on 40 CpG regions selected via bioinformatic analysis of public methylome datasets and uses a TET2-APOBEC enzymatic conversion method to maintain cfDNA integrity and enhance amplification efficiency, enabling a rapid and cost-effective qPCR-based workflow. Methylation signals were quantified by qPCR and integrated with patient age using neural network-based predictive models. The assay was evaluated in a cohort of 216 plasma samples, including 86 CRC cases and 130 healthy controls. In the validation subset, 14 high-performing models demonstrated sensitivities ranging from 80.8% to 92.3% and specificities from 84.6% to 97.4%. A representative model achieved a validation sensitivity of 92.3% (95% CI, 75-99%), with early-stage (Stage I/II) sensitivity of 100 % (95% CI, 72-100%) at a specificity of 97.4% (95% CI, 87-100%). These findings support the potential of an enzymatic conversion-based, machine learning-guided cfDNA methylation assay as a practical, scalable, and minimally invasive approach for CRC detection. However, the relatively limited number of early-stage cases in this study highlights the need for larger, prospectively collected cohorts to refine performance estimates and confirm clinical utility.