Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers.
Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers.
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从拷贝数畸变推断染色体不稳定性作为人类癌症染色体不稳定性的衡量标准。
DOI：
10.1101/2023.05.24.542174
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Lasseigne,BrittanyN
中科院分区：
文献类型：
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作者：
Taluri,Sasha;Oza,VishalH;Soelter,TabeaM;Fisher,JenniferL;Lasseigne,BrittanyN
背景癌症是一种复杂的疾病，是美国第二大死亡原因。尽管进行了研究努力，但控制癌症并为每位患者选择最佳治疗反应的能力仍然难以捉摸。染色体不稳定性（CIN）主要是分离错误的产物，其中一条或多条染色体（部分或全部）的数量发生变化。 CIN 是癌症的一个有利特征，导致肿瘤细胞异质性，并在多步骤肿瘤发生过程中发挥着至关重要的作用，特别是在肿瘤生长和起始以及对治疗的反应中。目的多项研究报道了不同的指标，用于分析拷贝数畸变，作为 DNA 拷贝数变异数据中 CIN 的替代指标。然而，这些指标在变化类型、变化幅度和断点包含方面的计算方式有所不同。在这里，我们比较了癌症基因组图谱 (TCGA) 33 个癌症数据集中捕获 CIN 的指标，包括数值畸变、结构畸变或两者的组合。方法和结果使用 CINmetrics R 包中的方法推断出的 CIN，我们通过评估每个肿瘤类型以及它们与肿瘤分期的关系，评估了 6 个拷贝数 CIN 替代物在 TCGA 队列中的比较情况。 转移、淋巴结受累以及患者性别。结论我们发现肿瘤类型会影响任何两个给定 CIN 指标的相关性。虽然我们还发现了与临床特征和患者性别相关的指标之间的重叠，但指标之间并不完全一致。我们发现了几个病例，其中只有一个 CIN 指标与给定肿瘤类型的临床特征或患者性别显着相关。因此，在根据给定指标描述 CIN 或将其与其他研究进行比较时应谨慎。
BackgroundCancer is a complex disease that is the second leading cause of death in the United States. Despite research efforts, the ability to manage cancer and select optimal therapeutic responses for each patient remains elusive. Chromosomal instability (CIN) is primarily a product of segregation errors wherein one or many chromosomes, in part or whole, vary in number. CIN is an enabling characteristic of cancer, contributes to tumor‐cell heterogeneity, and plays a crucial role in the multistep tumorigenesis process, especially in tumor growth and initiation and in response to treatment.AimsMultiple studies have reported different metrics for analyzing copy number aberrations as surrogates of CIN from DNA copy number variation data. However, these metrics differ in how they are calculated with respect to the type of variation, the magnitude of change, and the inclusion of breakpoints. Here we compared metrics capturing CIN as either numerical aberrations, structural aberrations, or a combination of the two across 33 cancer data sets from The Cancer Genome Atlas (TCGA).Methods and ResultsUsing CIN inferred by methods in the CINmetrics R package, we evaluated how six copy number CIN surrogates compared across TCGA cohorts by assessing each across tumor types, as well as how they associate with tumor stage, metastasis, and nodal involvement, and with respect to patient sex.ConclusionsWe found that the tumor type impacts how well any two given CIN metrics correlate. While we also identified overlap between metrics regarding their association with clinical characteristics and patient sex, there was not complete agreement between metrics. We identified several cases where only one CIN metric was significantly associated with a clinical characteristic or patient sex for a given tumor type. Therefore, caution should be used when describing CIN based on a given metric or comparing it to other studies.