| Title | A Neural Network-Enabled, Enzymatic cfDNA Methylation Assay for Colorectal Cancer Early Detection. |
| Publication Type | Journal Article |
| Year of Publication | 2026 |
| Authors | Bacolod MD, Aguilera-Diaz A, Feinberg P, Fani S, Huang J, Barany F |
| Journal | Cancer Prev Res (Phila) |
| Volume | 19 |
| Issue | 9 |
| Pagination | 501-516 |
| Date Published | 2026 Sep 01 |
| ISSN | 1940-6215 |
| Keywords | Aged, 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. |
| DOI | 10.1158/1940-6207.CAPR-26-0072 |
| Alternate Journal | Cancer Prev Res (Phila) |
| PubMed ID | 42274209 |
Submitted by ljc4002 on September 8, 2026 - 10:19am
