Purpose: The detection of circulating tumor DNA, which allows non-invasive tumor molecular profiling and disease follow-up, promises optimal and individualized management of patients with cancer. However, detecting small fractions of tumor DNA released when the tumor burden is reduced remains a challenge. Experimental Design: We implemented a new highly sensitive strategy to detect base-pair resolution methylation patterns from plasma DNA and assessed the potential of hypomethylation of LINE-1 retrotransposons as a non-invasive multi-cancer detection biomarker. The DIAMOND (Detection of Long Interspersed Nuclear Element Altered Methylation ON plasma DNA) method targets 30-40,000 young L1 scattered throughout the genome, covering about 100,000 CpG sites and is based on a reference-free analysis pipeline. Results: Resulting machine learning-based classifiers showed powerful correct classification rates discriminating healthy and tumor plasmas from 6 types of cancers (colorectal, breast, lung, ovarian, gastric cancers and uveal melanoma including localized stages) in two independent cohorts (AUC = 88% to 100%, N = 747). DIAMOND can also be used to perform copy number alterations (CNA) analysis which improves cancer detection. Conclusions: This should lead to the development of more efficient non-invasive diagnostic tests adapted to all cancer patients, based on the universality of these factors.

中文翻译：

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使用 LINE-1 反转录转座子的 DNA 低甲基化进行无创多癌检测


目的：检测循环肿瘤 DNA，允许非侵入性肿瘤分子分析和疾病随访，有望对癌症患者进行最佳和个体化管理。然而，检测肿瘤负荷减少时释放的一小部分肿瘤 DNA 仍然是一个挑战。实验设计： 我们实施了一种新的高灵敏度策略来检测血浆 DNA 中的碱基对分辨率甲基化模式，并评估了 LINE-1 反转录转座子低甲基化作为非侵入性多癌检测生物标志物的潜力。DIAMOND（检测血浆 DNA 上长散布核元件改变甲基化）方法靶向散布在整个基因组中的 30-40,000 个年轻 L1，覆盖约 100,000 个 CpG 位点，基于无参考分析管道。结果： 由此产生的基于机器学习的分类器显示出强大的正确分类率，可在两个独立的队列中区分健康和肿瘤血浆与 6 种癌症 （结直肠癌、乳腺癌、肺癌、卵巢癌、胃癌和葡萄膜黑色素瘤，包括局部分期） （AUC = 88% 至 100%，N = 747）。DIAMOND 还可用于进行拷贝数改变 （CNA） 分析，从而提高癌症检测能力。结论：基于这些因素的普遍性，这应该导致开发适用于所有癌症患者的更有效的无创诊断测试。