Manny D Bacolod, Almudena Aguilera-Diaz, Philip Feinberg, Somayeh Fani, Jianmin Huang, Francis Barany
{"title":"A Neural Network-Enabled, Enzymatic cfDNA Methylation Assay for Colorectal Cancer Early Detection.","authors":"Manny D Bacolod, Almudena Aguilera-Diaz, Philip Feinberg, Somayeh Fani, Jianmin Huang, Francis Barany","doi":"10.1158/1940-6207.CAPR-26-0072","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p><p><strong>Prevention relevance: </strong>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.</p>","PeriodicalId":72514,"journal":{"name":"Cancer prevention research (Philadelphia, Pa.)","volume":" ","pages":"501-516"},"PeriodicalIF":2.6000,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer prevention research (Philadelphia, Pa.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1158/1940-6207.CAPR-26-0072","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.