Pathology Department, Foundation Medicine, Morrisville, NC, USA.
Pathology Department, Foundation Medicine, Cambridge, MA, USA.
Oncologist. 2024 Jan 5;29(1):e47-e58. doi: 10.1093/oncolo/oyad224.
The authors present a cohort of 661 young adult glioblastomas diagnosed using 2016 WHO World Health Organization Classification of Tumors of the Central Nervous System, utilizing comprehensive genomic profiling (CGP) to explore their genomic landscape and assess their relationship to currently defined disease entities. This analysis explored variants with evidence of pathogenic function, common copy number variants (CNVs), and several novel fusion events not described in literature. Tumor mutational burden (TMB) mutational signatures, anatomic location, and tumor recurrence are further explored. Using data collected from CGP, unsupervised machine-learning techniques were leveraged to identify 10 genomic classes in previously assigned young adult glioblastomas. The authors relate these molecular classes to current World Health Organization guidelines and reference current literature to give therapeutic and prognostic descriptions where possible.
作者报告了一组使用 2016 年世界卫生组织中枢神经系统肿瘤分类诊断的 661 例年轻成人胶质母细胞瘤，利用全面基因组分析 (CGP) 来探索其基因组图谱，并评估其与目前定义的疾病实体的关系。该分析探讨了具有致病性功能证据的变异、常见拷贝数变异 (CNV) 和一些文献中未描述的新融合事件。肿瘤突变负担 (TMB) 突变特征、解剖位置和肿瘤复发也进一步进行了研究。利用从 CGP 收集的数据，使用无监督机器学习技术在先前分配的年轻成人胶质母细胞瘤中识别出 10 个基因组类别。作者将这些分子类别与当前的世界卫生组织指南相关联，并参考当前的文献，尽可能给出治疗和预后描述。
