多维片段组学可实现结直肠癌的早期准确检测

IF 12.5 1区 医学 Q1 ONCOLOGY Cancer research Pub Date : 2024-10-01 DOI:10.1158/0008-5472.CAN-23-3486
Yuepeng Cao, Nannan Wang, Xuxiaochen Wu, Wanxiangfu Tang, Hua Bao, Chengshuai Si, Peng Shao, Dongzheng Li, Xin Zhou, Dongqin Zhu, Shanshan Yang, Fufeng Wang, Guoqing Su, Ke Wang, Qifan Wang, Yao Zhang, Qiangcheng Wang, Dongsheng Yu, Qian Jiang, Jun Bao, Liu Yang
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引用次数: 0

摘要

结肠直肠癌(CRC)经常在晚期才被诊断出来,因此需要开发早期检测方法。利用无细胞DNA(cfDNA)片段组学进行液体活检是一种很有前景的方法,但其临床应用却受到复杂性和成本的阻碍。本研究旨在利用cfDNA片段组学开发一种综合模型,用于准确、经济高效地检测早期CRC。研究人员从 360 人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。该研究采用了一个由五个机器学习模型组成的集合堆叠模型,利用五个cfDNA片段组学特征来区分CRC患者和健康对照组。该模型在一个由 236 名参与者(117 名 CRC 患者和 119 名对照组)组成的独立队列和一个由 242 名参与者(129 名 CRC 患者和 113 名对照组)组成的前瞻性队列中进行了验证。集合堆叠模型在 CRC 患者和对照组之间显示出显著的鉴别力,优于所有基础模型,在验证队列中的 ROC 曲线下面积(AUC)高达 0.986。在验证队列中,该模型检测 CRC 的灵敏度和特异度分别达到 94.88% 和 98%,灵敏度随癌症进展而增加。该模型在运行内和运行间检测以及健康人的各种情况下也始终保持着较高的准确性。在前瞻性队列中,该模型的灵敏度达到 91.47%,特异性达到 95.58%。该综合模型利用了 cfDNA 片段组学的多重特性,实现了高灵敏度和稳健性,为早期 CRC 检测和广泛造福患者带来了巨大希望。
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Multidimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer 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 colorectal cancer detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 patients with colorectal cancer and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish patients with colorectal cancer from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 patients with colorectal cancer and 119 controls) and a prospective cohort of 242 participants (129 patients with colorectal cancer and 113 controls). The ensemble stacked model showed remarkable discriminatory power between patients with colorectal cancer and controls, outperforming all base models and achieving a high area under the receiver operating characteristic curve of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting colorectal cancer in the validation cohort, with sensitivity increasing as the 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 colorectal cancer detection and broad patient benefit.  Significance: The development of a minimally invasive, efficient approach for early colorectal cancer detection using advanced machine learning to analyze cfDNA fragment patterns could expedite diagnosis and improve treatment outcomes for patients. See related commentary by Rolfo and Russo, p. 3128.

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来源期刊
Cancer research
Cancer research 医学-肿瘤学
CiteScore
16.10
自引率
0.90%
发文量
7677
审稿时长
2.5 months
期刊介绍: Cancer Research, published by the American Association for Cancer Research (AACR), is a journal that focuses on impactful original studies, reviews, and opinion pieces relevant to the broad cancer research community. Manuscripts that present conceptual or technological advances leading to insights into cancer biology are particularly sought after. The journal also places emphasis on convergence science, which involves bridging multiple distinct areas of cancer research. With primary subsections including Cancer Biology, Cancer Immunology, Cancer Metabolism and Molecular Mechanisms, Translational Cancer Biology, Cancer Landscapes, and Convergence Science, Cancer Research has a comprehensive scope. It is published twice a month and has one volume per year, with a print ISSN of 0008-5472 and an online ISSN of 1538-7445. Cancer Research is abstracted and/or indexed in various databases and platforms, including BIOSIS Previews (R) Database, MEDLINE, Current Contents/Life Sciences, Current Contents/Clinical Medicine, Science Citation Index, Scopus, and Web of Science.
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