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

IF 2.6
Manny D Bacolod, Almudena Aguilera-Diaz, Philip Feinberg, Somayeh Fani, Jianmin Huang, Francis Barany
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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.

一种用于结直肠癌早期检测的神经网络激活的酶促cfDNA甲基化试验。
早期发现结直肠癌(CRC)对于降低疾病特异性死亡率至关重要,但目前的非侵入性筛查方法在敏感性、患者依从性和可扩展性方面存在局限性。我们开发并临床评估了一种基于循环无细胞DNA (cfDNA)甲基化谱的CRC检测的非下一代测序(non-NGS)液体活检方法。该方法通过对公共甲基组数据集的生物信息学分析选择了40个CpG区域,并使用TET2-APOBEC酶转化方法来保持cfDNA的完整性并提高扩增效率,从而实现快速且经济高效的qpcr工作流程。甲基化信号通过qPCR量化,并使用基于神经网络的预测模型与患者年龄相结合。该检测在216个血浆样本中进行了评估,其中包括86例结直肠癌病例和130例健康对照。在验证子集中,14个高性能模型的灵敏度从80.8%到92.3%不等,特异性从84.6%到97.4%不等。一个代表性模型的验证灵敏度为92.3% (95% CI, 75-99%),早期(I/II期)灵敏度为100% (95% CI, 72-100%),特异性为97.4% (95% CI, 87-100%)。这些发现支持了一种基于酶转化、机器学习指导的cfDNA甲基化检测作为一种实用、可扩展和微创的CRC检测方法的潜力。然而,本研究中相对有限的早期病例数量强调了需要更大的前瞻性收集队列来完善性能评估并确认临床应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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