利用 DNA 甲基化特征预测脑转移发展。
Prediction of brain metastasis development with DNA methylation signatures.
发表日期:2024 Oct 08
作者:
Jeffrey A Zuccato, Yasin Mamatjan, Farshad Nassiri, Andrew Ajisebutu, Jeffrey C Liu, Ammara Muazzam, Olivia Singh, Wen Zhang, Mathew Voisin, Shideh Mirhadi, Suganth Suppiah, Leanne Wybenga-Groot, Alireza Tajik, Craig Simpson, Olli Saarela, Ming S Tsao, Thomas Kislinger, Kenneth D Aldape, Michael F Moran, Vikas Patil, Gelareh Zadeh
来源:
NATURE MEDICINE
摘要:
脑转移瘤 (BM) 是最常见且最致命的脑肿瘤之一。目前,尚无原发性癌症发生骨髓转移的可靠预测因素,这限制了早期干预。肺腺癌 (LUAD) 是最常见的 BM 来源,在这里,我们从一大群伴有或不伴有 BM 的 LUAD 患者中获得了 402 个肿瘤和血浆样本 (n = 346)。对 LUAD DNA 甲基化特征进行评估,以建立和验证预测 LUAD BM 发展的准确模型,该模型与临床因素相结合,在列线图中提供全面的患者特异性 BM 风险概率。此外,与配对的初级 LUAD 相比,BM 中的免疫和细胞相互作用基因集在启动子处存在差异甲基化,并且在蛋白质组中存在对齐失调。 BM 与 LUAD 中的免疫细胞丰度存在差异。最后,使用从血浆中甲基化无细胞 DNA 测序中鉴定出的液体生物标志物来生成和验证用于早期 BM 检测的准确分类器。总体而言,LUAD 甲基化组可用于预测和无创识别 BM,通过个性化治疗改善患者预后。© 2024。作者。
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.© 2024. The Author(s).