de Mendonça Mónica Leiria, Coletti Roberta, Gonçalves Céline S, Martins Eduarda P, Costa Bruno M, Vinga Susana, Lopes Marta B
INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, R. Alves Redol 9, 1000-029, Lisbon, Portugal.
Center for Mathematics and Applications (NOVA Math), NOVA FCT, 2829-516, Caparica, Portugal.
Sci Data. 2025 Jun 4;12(1):935. doi: 10.1038/s41597-025-05117-2.
The understanding of glioma disease has significantly advanced through the application of genetic and molecular profiling techniques on brain tumour tissue. Molecular biomarkers have gained a crucial role in glioma diagnosis, driving groundbreaking changes in the disease classification as standardised by the 2016 and 2021 World Health Organisation (WHO) Classification of Tumours of the Central Nervous System. Recent insights from large-scale multi-omics databases, such as The Cancer Genome Atlas (TCGA), have enriched our comprehension of this cancer type. However, given the evolution of glioma classification, retrospective databases may contain outdated annotations, suboptimal for research. To address this issue, we propose two methods for updating the tumor classification of TCGA glioma samples according to the 2016 and 2021 WHO guidelines, through the integration of open-access curated molecular profiling data. Respectively, our Method-2016 and Method-2021 allowed for the diagnostic update of 98% and 87% of cases. The proposed reclassification pipelines, provided in R scripts, enable straightforward reproduction or customisation upon new WHO guideline releases.
通过对脑肿瘤组织应用基因和分子分析技术，对胶质瘤疾病的认识有了显著进展。分子生物标志物在胶质瘤诊断中发挥了关键作用，推动了疾病分类的突破性变化，这一分类由2016年和2021年世界卫生组织（WHO）中枢神经系统肿瘤分类进行标准化。来自大规模多组学数据库（如癌症基因组图谱（TCGA））的最新见解丰富了我们对这种癌症类型的理解。然而，鉴于胶质瘤分类的演变，回顾性数据库可能包含过时的注释，不利于研究。为了解决这个问题，我们提出了两种方法，通过整合开放获取的经过整理的分子分析数据，根据2016年和2021年WHO指南更新TCGA胶质瘤样本的肿瘤分类。我们的2016方法和2021方法分别使98%和87%的病例诊断得以更新。R脚本中提供的拟议重新分类管道，在新的WHO指南发布时能够直接重现或定制。
