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胶质母细胞瘤细胞的治疗耐药性和转录组特征分析为联合放化疗的敏感性脆弱性提供了新靶点。

Integrative analysis of therapy resistance and transcriptomic profiling data in glioblastoma cells identifies sensitization vulnerabilities for combined modality radiochemotherapy.

机构信息

Department of Radiation Oncology, University Hospital, LMU München, Marchioninistrasse 15, 81377, Munich, Germany.

German Cancer Consortium (DKTK), Munich, Germany.

出版信息

Radiat Oncol. 2022 Apr 19;17(1):79. doi: 10.1186/s13014-022-02052-z.

DOI:10.1186/s13014-022-02052-z
PMID:35440003
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9020080/
Abstract

BACKGROUND

Inherent resistance to radio/chemotherapy is one of the major reasons for early recurrence, treatment failure, and dismal prognosis of glioblastoma. Thus, the identification of resistance driving regulators as prognostic and/or predictive markers as well as potential vulnerabilities for combined modality treatment approaches is of pivotal importance.

METHODS

We performed an integrative analysis of treatment resistance and DNA damage response regulator expression in a panel of human glioblastoma cell lines. mRNA expression levels of 38 DNA damage response regulators were analyzed by qRT-PCR. Inherent resistance to radiotherapy (single-shot and fractionated mode) and/or temozolomide treatment was assessed by clonogenic survival assays. Resistance scores were extracted by dimensionality reduction and subjected to correlation analyses with the mRNA expression data. Top-hit candidates with positive correlation coefficients were validated by pharmacological inhibition in clonogenic survival assays and DNA repair analyses via residual γH2AX/53BP1-foci staining.

RESULTS

Inherent resistance to single-shot and similarly also to fractionated radiotherapy showed strong positive correlations with mRNA expression levels of known vulnerabilities of GBM, including PARP1, NBN, and BLM, as well as ATR and LIG4-two so far underestimated targets. Inhibition of ATR by AZD-6738 resulted in robust and dose-dependent radiosensitization of glioblastoma cells, whereas LIG4 inhibition by L189 had no noticeable impact. Resistance against temozolomide showed strong positive correlation with mRNA expression levels of MGMT as to be expected. Interestingly, it also correlated with mRNA expression levels of ATM, suggesting a potential role of ATM in the context of temozolomide resistance in glioblastoma cells. ATM inhibition exhibited slight sensitization effects towards temozolomide treatment in MGMT low expressing glioblastoma cells, thus encouraging further characterization.

CONCLUSIONS

Here, we describe a systematic approach integrating clonogenic survival data with mRNA expression data of DNA damage response regulators in human glioblastoma cell lines to identify markers of inherent therapy resistance and potential vulnerabilities for targeted sensitization. Our results provide proof-of-concept for the feasibility of this approach, including its limitations. We consider this strategy to be adaptable to other cancer entities as well as other molecular data qualities, and its upscaling potential in terms of model systems and observational data levels deserves further investigation.

摘要

背景

对放化疗的固有抗性是胶质母细胞瘤早期复发、治疗失败和预后不良的主要原因之一。因此,鉴定作为预后和/或预测标志物的耐药驱动调节剂以及联合治疗方法的潜在弱点至关重要。

方法

我们对一组人胶质母细胞瘤细胞系中的治疗耐药性和 DNA 损伤反应调节剂表达进行了综合分析。通过 qRT-PCR 分析 38 种 DNA 损伤反应调节剂的 mRNA 表达水平。通过集落形成存活测定评估对放射治疗(单次和分割模式)和/或替莫唑胺治疗的固有抗性。通过降维提取耐药评分,并与 mRNA 表达数据进行相关分析。与相关系数呈正相关的高命中候选物通过集落形成存活测定中的药理学抑制以及残留 γH2AX/53BP1 焦点染色的 DNA 修复分析进行验证。

结果

单次和类似的分割放疗的固有抗性与已知 GBM 易损性的 mRNA 表达水平呈强正相关,包括 PARP1、NBN 和 BLM,以及 ATR 和 LIG4——两个迄今为止被低估的靶点。AZD-6738 抑制 ATR 导致胶质母细胞瘤细胞的放射增敏作用显著且呈剂量依赖性,而 L189 抑制 LIG4 则没有明显影响。对替莫唑胺的耐药性与 MGMT 的 mRNA 表达水平呈强正相关,这是意料之中的。有趣的是,它还与 ATM 的 mRNA 表达水平相关,提示 ATM 在胶质母细胞瘤细胞中替莫唑胺耐药的背景下可能发挥作用。在 MGMT 低表达的胶质母细胞瘤细胞中,ATM 抑制对替莫唑胺治疗表现出轻微的增敏作用,因此鼓励进一步研究。

结论

在这里,我们描述了一种系统方法,将集落形成存活数据与人类胶质母细胞瘤细胞系中 DNA 损伤反应调节剂的 mRNA 表达数据相结合,以鉴定固有治疗耐药性的标志物和潜在的靶向增敏弱点。我们的结果为这种方法的可行性提供了概念验证,包括其局限性。我们认为这种策略适用于其他癌症实体以及其他分子数据质量,并且在模型系统和观测数据水平上的扩展潜力值得进一步研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6159/9020080/5196ba3e837a/13014_2022_2052_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6159/9020080/5a89ef303ea2/13014_2022_2052_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6159/9020080/9f631e64aad4/13014_2022_2052_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6159/9020080/2cb3bc245eaf/13014_2022_2052_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6159/9020080/5196ba3e837a/13014_2022_2052_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6159/9020080/5a89ef303ea2/13014_2022_2052_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6159/9020080/9f631e64aad4/13014_2022_2052_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6159/9020080/2cb3bc245eaf/13014_2022_2052_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6159/9020080/5196ba3e837a/13014_2022_2052_Fig4_HTML.jpg

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