小柯机器人

美国圣杯公司Arash Jamshidi和Oliver Venn共同合作，近期取得重要工作进展，他们研究开发了多癌早期检测的细胞游离DNA方法评价。相关研究成果2022年11月17在线发表于《癌细胞》杂志上。

在循环细胞游离基因组图谱（NCT02889978）子研究1中，研究人员通过定义基于循环肿瘤等位基因分数（cTAF）的临床检测限度（LOD），对基于循环细胞游离DNA（cfDNA）多癌早期检测（MCED）的几种方法进行评估，以便进行性能的比较。在对相同样本进行训练并独立验证的10个机器学习分类器中，当以98%的特异性进行评估时，使用全基因组（WG）甲基化、具有配对白细胞背景去除的单核苷酸变体，以及本研究评估的分类器综合评分显示出最高的癌症信号检测灵敏度。

与临床分期和肿瘤类型相比，cTAF是分类器性能的一个更重要的预测因素，可能更密切地反映肿瘤生物学。临床LOD反映所有方法的相对敏感性。WG甲基化特征最能预测癌症信号来源，是MCED最有前途的技术，为靶向甲基化MCED测试的发展提供信息。

附：英文原文

Title: Evaluation of cell-free DNA approaches for multi-cancer early detection

Author: Arash Jamshidi, Minetta C. Liu, Eric A. Klein, Oliver Venn, Earl Hubbell, John F. Beausang, Samuel Gross, Collin Melton, Alexander P. Fields, Qinwen Liu, Nan Zhang, Eric T. Fung, Kathryn N. Kurtzman, Hamed Amini, Craig Betts, Daniel Civello, Peter Freese, Robert Calef, Konstantin Davydov, Saniya Fayzullina, Chenlu Hou, Roger Jiang, Byoungsok Jung, Susan Tang, Vasiliki Demas, Joshua Newman, Onur Sakarya, Eric Scott, Archana Shenoy, Seyedmehdi Shojaee, Kristan K. Steffen, Virgil Nicula, Tom C. Chien, Siddhartha Bagaria, Nathan Hunkapiller, Mohini Desai, Zhao Dong, Donald A. Richards, Timothy J. Yeatman, Allen L. Cohn, David D. Thiel, Donald A. Berry, Mohan K. Tummala, Kristi McIntyre, Mikkael A. Sekeres, Alan Bryce, Alexander M. Aravanis, Michael V. Seiden, Charles Swanton

Issue&Volume: 2022-11-17

Abstract: In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology. Clinical LODs mirror relative sensitivities for all approaches. The WG methylation feature best predicts cancer signal origin. WG methylation is the most promising technology for MCED and informs development of a targeted methylation MCED test.

DOI: 10.1016/j.ccell.2022.10.022

Source: https://www.cell.com/cancer-cell/fulltext/S1535-6108(22)00513-X

Cancer Cell：《癌细胞》，创刊于2002年。隶属于细胞出版社，最新IF：38.585
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