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本文引用的文献

1
Transcriptional characterization of immunological infiltrates and their relation with glioblastoma patients overall survival.免疫浸润的转录特征及其与胶质母细胞瘤患者总生存期的关系。
Oncoimmunology. 2018 Feb 12;7(6):e1431083. doi: 10.1080/2162402X.2018.1431083. eCollection 2018.
2
DNA methylation-based classification of central nervous system tumours.基于 DNA 甲基化的中枢神经系统肿瘤分类。
Nature. 2018 Mar 22;555(7697):469-474. doi: 10.1038/nature26000. Epub 2018 Mar 14.
3
xCell: digitally portraying the tissue cellular heterogeneity landscape.xCell:数字化描绘组织细胞异质性景观。
Genome Biol. 2017 Nov 15;18(1):220. doi: 10.1186/s13059-017-1349-1.
4
Comprehensive Analysis of Hypermutation in Human Cancer.人类癌症中高突变的综合分析。
Cell. 2017 Nov 16;171(5):1042-1056.e10. doi: 10.1016/j.cell.2017.09.048. Epub 2017 Oct 19.
5
Clonal expansion and epigenetic reprogramming following deletion or amplification of mutant .突变. 缺失或扩增后的克隆扩张和表观遗传重编程。
Proc Natl Acad Sci U S A. 2017 Oct 3;114(40):10743-10748. doi: 10.1073/pnas.1708914114. Epub 2017 Sep 15.
6
Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment.胶质瘤内在基因表达亚型的肿瘤进化与微环境中的免疫变化相关。
Cancer Cell. 2017 Jul 10;32(1):42-56.e6. doi: 10.1016/j.ccell.2017.06.003.
7
Biomarkers of response to PD-1/PD-L1 inhibition.PD-1/PD-L1抑制反应的生物标志物。
Crit Rev Oncol Hematol. 2017 Aug;116:116-124. doi: 10.1016/j.critrevonc.2017.06.001. Epub 2017 Jun 9.
8
Immune Checkpoint in Glioblastoma: Promising and Challenging.胶质母细胞瘤中的免疫检查点:充满希望与挑战
Front Pharmacol. 2017 May 9;8:242. doi: 10.3389/fphar.2017.00242. eCollection 2017.
9
Spatiotemporal genomic architecture informs precision oncology in glioblastoma.时空基因组结构为胶质母细胞瘤的精准肿瘤学提供信息。
Nat Genet. 2017 Apr;49(4):594-599. doi: 10.1038/ng.3806. Epub 2017 Mar 6.
10
Suppression of T cells by myeloid-derived suppressor cells in cancer.癌症中髓源性抑制细胞对T细胞的抑制作用。
Hum Immunol. 2017 Feb;78(2):113-119. doi: 10.1016/j.humimm.2016.12.001. Epub 2016 Dec 7.

对胶质母细胞瘤肿瘤、BTIC 和异种移植物进行全面基因组分析,揭示了其在生长环境中的稳定性和适应性。

Comprehensive genomic profiling of glioblastoma tumors, BTICs, and xenografts reveals stability and adaptation to growth environments.

机构信息

Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, BC, Canada V5Z 4S6.

Center for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC, Canada V5Z 4H4.

出版信息

Proc Natl Acad Sci U S A. 2019 Sep 17;116(38):19098-19108. doi: 10.1073/pnas.1813495116. Epub 2019 Aug 30.

DOI:10.1073/pnas.1813495116
PMID:31471491
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6754609/
Abstract

Glioblastoma multiforme (GBM) is the most deadly brain tumor, and currently lacks effective treatment options. Brain tumor-initiating cells (BTICs) and orthotopic xenografts are widely used in investigating GBM biology and new therapies for this aggressive disease. However, the genomic characteristics and molecular resemblance of these models to GBM tumors remain undetermined. We used massively parallel sequencing technology to decode the genomes and transcriptomes of BTICs and xenografts and their matched tumors in order to delineate the potential impacts of the distinct growth environments. Using data generated from whole-genome sequencing of 201 samples and RNA sequencing of 118 samples, we show that BTICs and xenografts resemble their parental tumor at the genomic level but differ at the mRNA expression and epigenomic levels, likely due to the different growth environment for each sample type. These findings suggest that a comprehensive genomic understanding of in vitro and in vivo GBM model systems is crucial for interpreting data from drug screens, and can help control for biases introduced by cell-culture conditions and the microenvironment in mouse models. We also found that lack of expression in pretreated GBM is linked to hypermutation, which in turn contributes to increased genomic heterogeneity and requires new strategies for GBM treatment.

摘要

多形性胶质母细胞瘤(GBM)是最致命的脑肿瘤,目前缺乏有效的治疗选择。脑肿瘤起始细胞(BTICs)和原位异种移植广泛用于研究 GBM 的生物学和这种侵袭性疾病的新疗法。然而,这些模型与 GBM 肿瘤的基因组特征和分子相似性仍未确定。我们使用大规模平行测序技术来解码 BTICs 和异种移植物及其匹配肿瘤的基因组和转录组,以描绘不同生长环境的潜在影响。使用来自 201 个样本的全基因组测序和 118 个样本的 RNA 测序生成的数据,我们表明 BTICs 和异种移植物在基因组水平上与其亲本肿瘤相似,但在 mRNA 表达和表观基因组水平上存在差异,这可能是由于每种样本类型的生长环境不同。这些发现表明,对体外和体内 GBM 模型系统的全面基因组理解对于解释药物筛选数据至关重要,并有助于控制细胞培养条件和小鼠模型微环境带来的偏差。我们还发现,预处理的 GBM 中 的缺失与超突变有关,超突变反过来又导致基因组异质性增加,需要针对 GBM 治疗的新策略。