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

TitleA Neural Network-Enabled, Enzymatic cfDNA Methylation Assay for Colorectal Cancer Early Detection.
Publication TypeJournal Article
Year of Publication2026
AuthorsBacolod MD, Aguilera-Diaz A, Feinberg P, Fani S, Huang J, Barany F
JournalCancer Prev Res (Phila)
Volume19
Issue9
Pagination501-516
Date Published2026 Sep 01
ISSN1940-6215
KeywordsAged, Biomarkers, Tumor, Case-Control Studies, Cell-Free Nucleic Acids, Colorectal Neoplasms, CpG Islands, DNA Methylation, Early Detection of Cancer, Female, Humans, Liquid Biopsy, Male, Middle Aged, Neural Networks, Computer, Sensitivity and Specificity
Abstract

UNLABELLED: Early detection of colorectal cancer remains critical for reducing disease-specific mortality, yet current noninvasive screening approaches have limitations in sensitivity (Sens), patient adherence, and scalability. We developed and clinically evaluated a non-next-generation sequencing (non-NGS) liquid biopsy assay for colorectal cancer detection based on methylation profiling of circulating cell-free DNA (cfDNA). The assay focuses on 40 CpG regions selected via bioinformatics analysis of public methylome datasets and uses a ten-eleven translocation methylcytosine dioxygenase 2-apolipoprotein B mRNA editing enzyme, catalytic polypeptide enzymatic conversion method to maintain cfDNA integrity and enhance amplification efficiency, enabling a rapid and cost-effective quantitative PCR (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 colorectal cancer 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 Sens of 92.3% [95% confidence interval (CI), 75%-99%], with early-stage (stage I/II) Sens 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 colorectal cancer 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.

PREVENTION RELEVANCE: We present a noninvasive cfDNA methylation assay for early colorectal cancer detection using a non-NGS platform. Improved Sens for early-stage disease may enhance screening uptake and enable timely intervention, supporting colorectal cancer prevention.

DOI10.1158/1940-6207.CAPR-26-0072
Alternate JournalCancer Prev Res (Phila)
PubMed ID42274209

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