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米氮平类药物作用于癌细胞线粒体代谢的机制。

Mechanisms of imipridones in targeting mitochondrial metabolism in cancer cells.

作者信息

Bonner Erin R, Waszak Sebastian M, Grotzer Michael A, Mueller Sabine, Nazarian Javad

机构信息

Center for Genetic Medicine, Children's National Health System, Washington, DC.

Institute for Biomedical Sciences, The George Washington University School of Medicine and Health Sciences, Washington, DC.

出版信息

Neuro Oncol. 2021 Apr 12;23(4):542-556. doi: 10.1093/neuonc/noaa283.

DOI:10.1093/neuonc/noaa283
PMID:33336683
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8041338/
Abstract

ONC201 is the first member of the imipridone family of anticancer drugs to enter the clinic for the treatment of diverse solid and hematologic cancers. A subset of pediatric and adult patients with highly aggressive brain tumors has shown remarkable clinical responses to ONC201, and recently, the more potent derivative ONC206 entered clinical trials as a single agent for the treatment of central nervous system (CNS) cancers. Despite the emerging clinical interest in the utility of imipridones, their exact molecular mechanisms are not fully described. In fact, the existing literature points to multiple pathways (e.g. tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) signaling, dopamine receptor antagonism, and mitochondrial metabolism) as putative drug targets. We have performed a comprehensive literature review and highlighted mitochondrial metabolism as the major target of imipridones. In support of this, we performed a meta-analysis of an ONC201 screen across 539 human cancer cell lines and showed that the mitochondrial caseinolytic protease proteolytic subunit (ClpP) is the most significant predictive biomarker of response to treatment. Herein, we summarize the main findings on the anticancer mechanisms of this potent class of drugs, provide clarity on their role, and identify clinically relevant predictive biomarkers of response.

摘要

ONC201是进入临床用于治疗多种实体癌和血液系统癌症的咪吡酮类抗癌药物家族的首个成员。一部分患有高度侵袭性脑肿瘤的儿科和成年患者对ONC201表现出显著的临床反应,最近,更强效的衍生物ONC206作为单一药物进入了治疗中枢神经系统(CNS)癌症的临床试验。尽管临床上对咪吡酮类药物的效用兴趣日增,但其确切的分子机制尚未完全阐明。事实上,现有文献指出多种途径(如肿瘤坏死因子(TNF)相关凋亡诱导配体(TRAIL)信号传导、多巴胺受体拮抗作用和线粒体代谢)可能是药物靶点。我们进行了全面的文献综述,并强调线粒体代谢是咪吡酮类药物的主要靶点。为此,我们对539个人类癌细胞系进行的ONC201筛选进行了荟萃分析,结果表明线粒体酪蛋白水解蛋白酶蛋白水解亚基(ClpP)是治疗反应最显著的预测生物标志物。在此,我们总结了这类强效药物抗癌机制的主要发现,明确了它们的作用,并确定了临床上相关的反应预测生物标志物。

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本文引用的文献

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Epidermal growth factor receptor as a molecular determinant of glioblastoma response to dopamine receptor D2 inhibitors.表皮生长因子受体作为胶质母细胞瘤对多巴胺受体 D2 抑制剂反应的分子决定因素。
Neuro Oncol. 2021 Mar 25;23(3):400-411. doi: 10.1093/neuonc/noaa188.
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Integrated pharmaco-proteogenomics defines two subgroups in isocitrate dehydrogenase wild-type glioblastoma with prognostic and therapeutic opportunities.整合药物基因组学和蛋白质组学定义了 IDH 野生型胶质母细胞瘤中的两个亚组,为其提供了预后和治疗机会。
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Discovering the anti-cancer potential of non-oncology drugs by systematic viability profiling.通过系统生存力分析发现非肿瘤药物的抗癌潜力。
Nat Cancer. 2020 Feb;1(2):235-248. doi: 10.1038/s43018-019-0018-6. Epub 2020 Jan 20.
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Distinct mitochondrial defects trigger the integrated stress response depending on the metabolic state of the cell.不同的线粒体缺陷会根据细胞的代谢状态触发整合应激反应。
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Mitochondrial stress is relayed to the cytosol by an OMA1-DELE1-HRI pathway.线粒体应激通过 OMA1-DELE1-HRI 途径传递到细胞质。
Nature. 2020 Mar;579(7799):427-432. doi: 10.1038/s41586-020-2078-2. Epub 2020 Mar 4.
6
Dual metabolic reprogramming by ONC201/TIC10 and 2-Deoxyglucose induces energy depletion and synergistic anti-cancer activity in glioblastoma.ONC201/TIC10 和 2-脱氧葡萄糖双重代谢重编程导致脑胶质瘤能量耗竭和协同抗癌活性。
Br J Cancer. 2020 Apr;122(8):1146-1157. doi: 10.1038/s41416-020-0759-0. Epub 2020 Mar 2.
7
Imipridone Anticancer Compounds Ectopically Activate the ClpP Protease and Represent a New Scaffold for Antibiotic Development.抑瘤素 M 抗癌化合物异位激活 ClpP 蛋白酶,代表了抗生素开发的新支架。
Genetics. 2020 Apr;214(4):1103-1120. doi: 10.1534/genetics.119.302851. Epub 2020 Feb 24.
8
Endoplasmic Reticulum Stress Pathway, the Unfolded Protein Response, Modulates Immune Function in the Tumor Microenvironment to Impact Tumor Progression and Therapeutic Response.内质网应激途径、未折叠蛋白反应调节肿瘤微环境中的免疫功能,影响肿瘤进展和治疗反应。
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A Bayesian machine learning approach for drug target identification using diverse data types.基于多种数据类型的药物靶点识别的贝叶斯机器学习方法。
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