研究动态
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从纵向测序中识别神经胶质瘤进化的预测因子。

Identifying predictors of glioma evolution from longitudinal sequencing.

发表日期:2023 Oct 04
作者: Quanhua Mu, Ruichao Chai, Bo Pang, Yingxi Yang, Hanjie Liu, Zheng Zhao, Zhaoshi Bao, Dong Song, Zhihan Zhu, Mengli Yan, Biaobin Jiang, Zongchao Mo, Jihong Tang, Jason K Sa, Hee Jin Cho, Yuzhou Chang, Kaitlin Hao Yi Chan, Danson Shek Chun Loi, Sindy Sing Ting Tam, Aden Ka Yin Chan, Angela Ruohao Wu, Zhaoqi Liu, Wai Sang Poon, Ho Keung Ng, Danny Tat Ming Chan, Antonio Iavarone, Do-Hyun Nam, Tao Jiang, Jiguang Wang
来源: Science Translational Medicine

摘要:

克隆进化驱动癌症进展和治疗耐药性。最近的研究揭示了神经胶质瘤的不同纵向轨迹,但指导治疗后癌症进化的早期分子特征仍不清楚。在这里,我们收集了 544 例成人弥漫性神经胶质瘤的初始-复发肿瘤对的测序和临床数据,并进行多变量分析,以确定三种弥漫性神经胶质瘤亚型中肿瘤进化的早期分子预测因子。我们发现,初始诊断时 CDKN2A 缺失先于 IDH 突变神经胶质瘤后期发生的肿瘤坏死和微血管增殖。 IDH 野生型神经胶质瘤诊断时的 Ki67 表达与复发时获得的超突变呈正相关。在所有神经胶质瘤亚型中,诊断时的 MYC 增益或 MYC 靶标激活与复发时治疗诱导的超突变相关。为了预测神经胶质瘤的进化,我们构建了 CELLO2(纵向数据癌症进化第 2 版),这是一种机器学习模型,集成了诊断时的特征,以预测治疗后的超突变和进展。 CELLO2 成功地将患者分为具有不同预后的亚组,并从低级别 IDH 突变非编码亚型中识别出一个以 MYC 增益为特征且进展后生存率较差的高风险患者组。然后,我们在神经胶质瘤细胞系和同基因患者来源的神经胶质瘤球中进行了慢性替莫唑胺诱导实验,并证明 MYC 通过促进超突变来驱动替莫唑胺耐药性。从机制上讲,我们证明,通过与开放染色质和转录活性基因组区域结合,c-MYC 增加了关键错配修复基因对治疗诱导突变的脆弱性,从而引发超突变。这项研究揭示了治疗下癌症演变的早期预测因素,并为针对弥漫性神经胶质瘤的癌症动态的精准肿瘤学提供了资源。
Clonal evolution drives cancer progression and therapeutic resistance. Recent studies have revealed divergent longitudinal trajectories in gliomas, but early molecular features steering posttreatment cancer evolution remain unclear. Here, we collected sequencing and clinical data of initial-recurrent tumor pairs from 544 adult diffuse gliomas and performed multivariate analysis to identify early molecular predictors of tumor evolution in three diffuse glioma subtypes. We found that CDKN2A deletion at initial diagnosis preceded tumor necrosis and microvascular proliferation that occur at later stages of IDH-mutant glioma. Ki67 expression at diagnosis was positively correlated with acquiring hypermutation at recurrence in the IDH-wild-type glioma. In all glioma subtypes, MYC gain or MYC-target activation at diagnosis was associated with treatment-induced hypermutation at recurrence. To predict glioma evolution, we constructed CELLO2 (Cancer EvoLution for LOngitudinal data version 2), a machine learning model integrating features at diagnosis to forecast hypermutation and progression after treatment. CELLO2 successfully stratified patients into subgroups with distinct prognoses and identified a high-risk patient group featured by MYC gain with worse post-progression survival, from the low-grade IDH-mutant-noncodel subtype. We then performed chronic temozolomide-induction experiments in glioma cell lines and isogenic patient-derived gliomaspheres and demonstrated that MYC drives temozolomide resistance by promoting hypermutation. Mechanistically, we demonstrated that, by binding to open chromatin and transcriptionally active genomic regions, c-MYC increases the vulnerability of key mismatch repair genes to treatment-induced mutagenesis, thus triggering hypermutation. This study reveals early predictors of cancer evolution under therapy and provides a resource for precision oncology targeting cancer dynamics in diffuse gliomas.