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拷贝数变异异质性揭示了癌症分级分类中的生物学不一致性。

Copy number variation heterogeneity reveals biological inconsistency in hierarchical cancer classifications.

作者信息

Yang Ziying, Carrio-Cordo Paula, Baudis Michael

机构信息

Department of Molecular Life Sciences, University of Zurich, Winterthurerstr. 190, 8057, Zurich, Switzerland.

Swiss Institute of Bioinformatics, Zurich, Switzerland.

出版信息

Mol Cytogenet. 2024 Nov 6;17(1):26. doi: 10.1186/s13039-024-00692-2.

DOI:10.1186/s13039-024-00692-2
PMID:39506842
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11542350/
Abstract

Cancers are heterogeneous diseases with unifying features of abnormal and consuming cell growth, where the deregulation of normal cellular functions is initiated by the accumulation of genomic mutations in cells of - potentially - any organ. At diagnosis malignancies typically present with patterns of somatic genome variants on diverse levels of heterogeneity. Among the different types of genomic alterations, copy number variants (CNV) represent a distinct, near-ubiquitous class of structural variants. Cancer classifications are foundational for patient care and oncology research. Terminologies such as the National Cancer Institute Thesaurus provide large sets of hierarchical cancer classification vocabularies and promote data interoperability and ontology-driven computational analysis. To find out how categorical classifications correspond to genomic observations, we conducted a meta-analysis of inter-sample genomic heterogeneity for classification hierarchies on CNV profiles from 97,142 individual samples across 512 cancer entities, and evaluated recurring CNV signatures across diagnostic subsets. Our results highlight specific biological mechanisms across cancer entities with the potential for improvement of patient stratification and future enhancement of cancer classification systems and provide some indications for cooperative genomic events across distinct clinical entities.

摘要

癌症是异质性疾病,具有异常且消耗性细胞生长的统一特征,其中正常细胞功能的失调是由潜在任何器官的细胞中基因组突变的积累引发的。在诊断时,恶性肿瘤通常表现出不同异质性水平上的体细胞基因组变异模式。在不同类型的基因组改变中,拷贝数变异(CNV)代表了一类独特的、几乎普遍存在的结构变异。癌症分类是患者护理和肿瘤学研究的基础。诸如美国国立癌症研究所术语表之类的术语提供了大量分层的癌症分类词汇表,并促进了数据的互操作性和本体驱动的计算分析。为了弄清楚分类分类如何与基因组观察结果相对应,我们对来自512个癌症实体的97142个个体样本的CNV谱上的分类层次进行了样本间基因组异质性的荟萃分析,并评估了诊断亚组中反复出现的CNV特征。我们的结果突出了癌症实体间的特定生物学机制,这些机制有可能改善患者分层并在未来增强癌症分类系统,并为不同临床实体间的协同基因组事件提供了一些线索。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/ab0609685314/13039_2024_692_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/5f3073c46260/13039_2024_692_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/a4afdbcedebb/13039_2024_692_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/075ba46cf724/13039_2024_692_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/8a8cb83794a6/13039_2024_692_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/f7000e9844bf/13039_2024_692_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/ab0609685314/13039_2024_692_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/5f3073c46260/13039_2024_692_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/33c8ce76f6f7/13039_2024_692_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/4edbbe045e75/13039_2024_692_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/a4afdbcedebb/13039_2024_692_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/075ba46cf724/13039_2024_692_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/8a8cb83794a6/13039_2024_692_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/f7000e9844bf/13039_2024_692_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce78/11542350/ab0609685314/13039_2024_692_Fig8_HTML.jpg

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本文引用的文献

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IDH-wild type glioblastomas featuring at least 30% giant cells are characterized by frequent RB1 and NF1 alterations and hypermutation.伴有至少 30%巨细胞的 IDH 野生型胶质母细胞瘤的特征是频繁的 RB1 和 NF1 改变和高度突变。
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CaSpER identifies and visualizes CNV events by integrative analysis of single-cell or bulk RNA-sequencing data.CaSpER 通过对单细胞或批量 RNA 测序数据的综合分析来识别和可视化 CNV 事件。
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