Reclassification of TCGA Diffuse Glioma Profiles Linked to Transcriptomic, Epigenetic, Genomic and Clinical Data, According to the 2021 WHO CNS Tumor Classification.
Galina ZakharovaVictor EfimovMikhail RaevskiyPavel RumiantsevAlexander GudkovOksana Yu Belogurova-OvchinnikovaMaxim I SorokinAnton BuzdinPublished in: International journal of molecular sciences (2022)
In 2021, the fifth edition of the WHO classification of tumors of the central nervous system (WHO CNS5) was published. Molecular features of tumors were directly incorporated into the diagnostic decision tree, thus affecting both the typing and staging of the tumor. It has changed the traditional approach, based solely on histopathological classification. The Cancer Genome Atlas project (TCGA) is one of the main sources of molecular information about gliomas, including clinically annotated transcriptomic and genomic profiles. Although TCGA itself has played a pivotal role in developing the WHO CNS5 classification, its proprietary databases still retain outdated diagnoses which frequently appear incorrect and misleading according to the WHO CNS5 standards. We aimed to define the up-to-date annotations for gliomas from TCGA's database that other scientists can use in their research. Based on WHO CNS5 guidelines, we developed an algorithm for the reclassification of TCGA glioma samples by molecular features. We updated tumor type and diagnosis for 828 out of a total of 1122 TCGA glioma cases, after which available transcriptomic and methylation data showed clustering features more consistent with the updated grouping. We also observed better stratification by overall survival for the updated diagnoses, yet WHO grade 3 IDH -mutant oligodendrogliomas and astrocytomas are still indistinguishable. We also detected altered performance in the previous diagnostic transcriptomic molecular biomarkers (expression of SPRY1 , CRNDE and FREM2 genes and FREM2 molecular pathway) and prognostic gene signature ( FN1 , ITGA5 , OSMR , and NGFR ) after reclassification. Thus, we conclude that further efforts are needed to reconsider glioma molecular biomarkers.
Keyphrases
- machine learning
- deep learning
- single cell
- blood brain barrier
- genome wide
- gene expression
- dna methylation
- big data
- copy number
- single molecule
- high grade
- poor prognosis
- emergency department
- lymph node
- low grade
- young adults
- drinking water
- clinical practice
- papillary thyroid
- genome wide identification
- transcription factor
- genetic diversity
- childhood cancer