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利用DNA甲基化特征预测脑转移瘤的发展

Prediction of brain metastasis development with DNA methylation signatures.

作者信息

Zuccato Jeffrey A, Mamatjan Yasin, Nassiri Farshad, Ajisebutu Andrew, Liu Jeffrey C, Muazzam Ammara, Singh Olivia, Zhang Wen, Voisin Mathew, Mirhadi Shideh, Suppiah Suganth, Wybenga-Groot Leanne, Tajik Alireza, Simpson Craig, Saarela Olli, Tsao Ming S, Kislinger Thomas, Aldape Kenneth D, Moran Michael F, Patil Vikas, Zadeh Gelareh

机构信息

MacFeeters Hamilton Neuro-Oncology Program, Princess Margaret Cancer Centre, University Health Network and University of Toronto, Toronto, Ontario, Canada.

Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada.

出版信息

Nat Med. 2025 Jan;31(1):116-125. doi: 10.1038/s41591-024-03286-y. Epub 2024 Oct 8.

DOI:10.1038/s41591-024-03286-y
PMID:39379704
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11750707/
Abstract

Brain metastases (BMs) are the most common and among the deadliest brain tumors. Currently, there are no reliable predictors of BM development from primary cancer, which limits early intervention. Lung adenocarcinoma (LUAD) is the most common BM source and here we obtained 402 tumor and plasma samples from a large cohort of patients with LUAD with or without BM (n = 346). LUAD DNA methylation signatures were evaluated to build and validate an accurate model predicting BM development from LUAD, which was integrated with clinical factors to provide comprehensive patient-specific BM risk probabilities in a nomogram. Additionally, immune and cell interaction gene sets were differentially methylated at promoters in BM versus paired primary LUAD and had aligning dysregulation in the proteome. Immune cells were differentially abundant in BM versus LUAD. Finally, liquid biomarkers identified from methylated cell-free DNA sequenced in plasma were used to generate and validate accurate classifiers for early BM detection. Overall, LUAD methylomes can be leveraged to predict and noninvasively identify BM, moving toward improved patient outcomes with personalized treatment.

摘要

脑转移瘤(BMs)是最常见且最致命的脑肿瘤之一。目前,尚无可靠的指标可预测原发性癌症发生脑转移,这限制了早期干预。肺腺癌(LUAD)是脑转移最常见的来源,我们在此从一大群有或无脑转移的肺腺癌患者(n = 346)中获取了402份肿瘤和血浆样本。对肺腺癌DNA甲基化特征进行评估,以建立并验证一个准确的模型来预测肺腺癌发生脑转移,该模型与临床因素相结合,通过列线图为患者提供个性化的脑转移风险概率。此外,免疫和细胞相互作用基因集在脑转移瘤与配对的原发性肺腺癌启动子处存在差异甲基化,并且在蛋白质组中存在相应的失调。脑转移瘤与肺腺癌中的免疫细胞丰度存在差异。最后,从血浆中测序的甲基化游离DNA鉴定出的液体生物标志物用于生成并验证用于早期脑转移检测的准确分类器。总体而言,肺腺癌甲基化组可用于预测和非侵入性识别脑转移瘤,朝着通过个性化治疗改善患者预后的方向发展。

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