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IMPRESS:利用限制酶和 smMIP 测序进行改良的甲基化分析,结合新的生物标志物面板,创建一种多癌症检测分析方法。

IMPRESS: Improved methylation profiling using restriction enzymes and smMIP sequencing, combined with a new biomarker panel, creating a multi-cancer detection assay.

机构信息

Centre of Medical Genetics, University of Antwerp and Antwerp University Hospital, Edegem, Belgium.

Centre for Oncological Research Antwerp (CORE), University of Antwerp and Antwerp University Hospital, Wilrijk, Belgium.

出版信息

Br J Cancer. 2024 Oct;131(7):1224-1236. doi: 10.1038/s41416-024-02809-1. Epub 2024 Aug 24.

DOI:10.1038/s41416-024-02809-1
PMID:39181941
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11442765/
Abstract

BACKGROUND

Despite the worldwide progress in cancer diagnostics, more sensitive diagnostic biomarkers are needed. The methylome has been extensively investigated in the last decades, but a low-cost, bisulfite-free detection method for multiplex analysis is still lacking.

METHODS

We developed a methylation detection technique called IMPRESS, which combines methylation-sensitive restriction enzymes and single-molecule Molecular Inversion Probes. We used this technique for the development of a multi-cancer detection assay for eight of the most lethal cancer types worldwide. We selected 1791 CpG sites that can distinguish tumor from normal tissue based on DNA methylation. These sites were analysed with IMPRESS in 35 blood, 111 tumor and 114 normal samples. Finally, a classifier model was built.

RESULTS

We present the successful development of IMPRESS and validated it with ddPCR. The final classifier model discriminating tumor from normal samples was built with 358 CpG target sites and reached a sensitivity of 0.95 and a specificity of 0.91. Moreover, we provide data that highlight IMPRESS's potential for liquid biopsies.

CONCLUSIONS

We successfully created an innovative DNA methylation detection technique. By combining this method with a new multi-cancer biomarker panel, we developed a sensitive and specific multi-cancer assay, with potential use in liquid biopsies.

摘要

背景

尽管癌症诊断在全球范围内取得了进展,但仍需要更敏感的诊断生物标志物。甲基组在过去几十年中得到了广泛的研究,但仍然缺乏一种低成本、无亚硫酸氢盐的多重分析检测方法。

方法

我们开发了一种称为 IMPRESS 的甲基化检测技术,该技术结合了甲基化敏感的限制性内切酶和单分子分子反转探针。我们使用该技术开发了一种用于检测全球八种最致命癌症类型的多癌检测方法。我们选择了 1791 个 CpG 位点,这些位点可基于 DNA 甲基化区分肿瘤与正常组织。在 35 份血液、111 份肿瘤和 114 份正常样本中,使用 IMPRESS 对这些位点进行了分析。最后,构建了一个分类器模型。

结果

我们成功开发了 IMPRESS 并通过 ddPCR 进行了验证。最终的分类器模型区分肿瘤与正常样本的模型使用 358 个 CpG 靶位点构建,达到了 0.95 的灵敏度和 0.91 的特异性。此外,我们还提供了数据,突出了 IMPRESS 在液体活检中的潜力。

结论

我们成功创建了一种创新的 DNA 甲基化检测技术。通过将这种方法与新的多癌生物标志物面板相结合,我们开发了一种敏感且特异性的多癌检测方法,具有在液体活检中的潜在应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/cb588e1fbe8f/41416_2024_2809_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/4e65c3e681e4/41416_2024_2809_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/66648116e5cb/41416_2024_2809_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/a345c4ac55c2/41416_2024_2809_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/ced039fddaee/41416_2024_2809_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/a21b9187ef08/41416_2024_2809_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/9515dec47d55/41416_2024_2809_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/cb588e1fbe8f/41416_2024_2809_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/4e65c3e681e4/41416_2024_2809_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/66648116e5cb/41416_2024_2809_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/a345c4ac55c2/41416_2024_2809_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/ced039fddaee/41416_2024_2809_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/a21b9187ef08/41416_2024_2809_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/9515dec47d55/41416_2024_2809_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1a06/11442765/cb588e1fbe8f/41416_2024_2809_Fig7_HTML.jpg

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Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set.
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