肿瘤中的非整倍性：83,862个肿瘤的单细胞数据
Aneuploidy in neoplasia: Single-cell data on 83,862 tumors
影响因子:4.70000
分区:医学2区 / 肿瘤学2区
发表日期:2025 Jan 01
作者:
Fredrik Mertens, Jakob Hofvander, Nils Mandahl, Felix Mitelman
摘要
染色体非整倍性，即数值染色体畸变，是癌症的分子标志之一。但是，当使用基于测序和阵列的方法研究肿瘤时，通常从大量DNA数据推断出染色体数和倍增状态。此外，发表的肿瘤相关性非整倍性的分子估计值通常还包括由各种类型的结构重排引起的基因组失衡，这可能是由于数值染色体畸变以外的其他机制而引起的。因此，我们使用来自83,862个肿瘤的单细胞细胞遗传学数据分析了染色体数，并表明良性和恶性肿瘤在与正常二倍体状态的偏差方面都是高度异质的。专注于112种特定肿瘤类型中的染色体数，这是由精确的形态学诊断和器官位置定义的，并且从中可以从≥50例病例的数据中进行数据，我们发现了两个主要簇：一种主要簇：一种由近二倍性肿瘤主导，另一种由近二倍体肿瘤占主导地位。前簇包括最良性的实体瘤，髓样肿瘤和恶性基因融合相关的实体瘤，而后者则由恶性实体瘤和淋巴瘤主导。对于16种恶性肿瘤类型，可以将染色体数量的分布与TCGA倍性水平数据进行比较。细胞遗传学和分子数据很好地相关，但前者表明克隆异质性水平较高。此处介绍的结果表明，在某些肿瘤类型中具有共同的致病机制，并为分子分析提供了参考。
Abstract
Chromosomal aneuploidy, that is, numerical chromosome aberrations, is one of the molecular hallmarks of cancer. However, when neoplasms are studied with sequencing- and array-based approaches, chromosome numbers and ploidy states are typically inferred from bulk DNA data. Furthermore, published molecular estimates of neoplasia-associated aneuploidy often also include genomic imbalances resulting from various types of structural rearrangement, which likely result from other mechanisms than numerical chromosome aberrations. We thus analyzed chromosome numbers using single-cell cytogenetic data from 83,862 tumors, and show that both benign and malignant tumors are highly heterogeneous with regard to deviations from the normal, diploid state. Focusing on the chromosome numbers in 112 specific tumor types, defined by both exact morphologic diagnosis and organ location and from which data from ≥50 cases were available, we found two major clusters: one predominated by near-diploid neoplasms and one by neoplasms with extensive aneuploidy and one or more whole genome doublings. The former cluster included most benign solid tumors, myeloid neoplasms, and malignant gene fusion-associated solid tumors, whereas the latter was predominated by malignant solid tumors and lymphomas. For 16 malignant tumor types, the distribution of chromosome numbers could be compared to TCGA ploidy level data. Cytogenetic and molecular data correlated well, but the former indicates a higher level of clonal heterogeneity. The results presented here suggest shared pathogenetic mechanisms in certain tumor types and provide a reference for molecular analyses.