Colorectal cancer (CRC) is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage CRC detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 CRC patients and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish CRC patients from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 CRC patients and 119 controls) and a prospective cohort of 242 participants (129 CRC patients and 113 controls). The ensemble stacked model showed remarkable discriminatory power between CRC patients and controls, outperforming all base models and achieving a high area under the ROC curve (AUC) of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting CRC in the validation cohort, with sensitivity increasing as cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early CRC detection and broad patient benefit.

中文翻译：

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多维片段组学实现结直肠癌的早期准确检测


结直肠癌 （CRC） 经常在晚期被诊断出来，这凸显了开发早期检测方法的必要性。使用游离 DNA （cfDNA） 片段组学进行液体活检是一种很有前途的方法，但临床应用受到复杂性和成本的阻碍。本研究旨在开发一种使用 cfDNA 片段组学的综合模型，用于准确、经济高效的早期 CRC 检测。从 360 名参与者的训练队列中提取血浆 cfDNA 并进行测序，其中包括 176 名 CRC 患者和 184 名健康对照者。采用由五个机器学习模型组成的集成堆叠模型，使用五个 cfDNA 片段组学特征将 CRC 患者与健康对照区分开来。该模型在 236 名参与者 （117 名 CRC 患者和 119 名对照） 的独立队列和 242 名参与者 （129 名 CRC 患者和 113 名对照） 的前瞻性队列中进行了验证。集成堆叠模型在 CRC 患者和对照组之间显示出显著的区分能力，优于所有基础模型，并在验证队列中实现了 0.986 的高 ROC 曲线下面积 （AUC）。在验证队列中检测 CRC 的灵敏度达到 94.88%，特异性达到 98%，灵敏度随着癌症的进展而增加。该模型在健康个体的运行内和运行间测试以及各种条件下也显示出始终如一的高精度。在前瞻性队列中，它实现了 91.47% 的敏感性和 95.58% 的特异性。该集成模型利用 cfDNA 片段组学的多重性质来实现高灵敏度和稳定性，为早期 CRC 检测和广泛的患者益处提供了重要前景。