Updating TCGA glioma classification through integration of molecular data following the latest WHO guidelines.

in Scientific data by Mónica Leiria de Mendonça, Roberta Coletti, Céline S Gonçalves, Eduarda P Martins, Bruno M Costa, Susana Vinga, Marta B Lopes

TLDR

  • The study proposes two methods to update glioma classifications from The Cancer Genome Atlas (TCGA) according to 2016 and 2021 WHO guidelines, with 98% and 87% accuracy, respectively.
  • The findings highlight the need for periodic reclassification of glioma samples to ensure precise diagnosis and classification.
  • The study's outcome has significant implications for the diagnosis and treatment of glioma, enabling more accurate and personalized patient care.

Abstract

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 However, given the evolution of glioma classification, retrospective databases may contain outdated annotations, suboptimal for research.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.

Overview

  • The study focuses on proposing two methods to update the tumor classification of glioma samples from The Cancer Genome Atlas (TCGA) according to the 2016 and 2021 World Health Organisation (WHO) Classification of Tumours of the Central Nervous System.
  • The study aims to address the issue of outdated annotations in retrospective databases by integrating open-access curated molecular profiling data to enable precise diagnosis and classification.
  • The primary objective is to provide a straightforward method for reproducing or customizing the reclassification pipelines upon new WHO guideline releases.

Comparative Analysis & Findings

  • The proposed Method-2016 and Method-2021 allowed for the diagnostic update of 98% and 87% of glioma cases, respectively, from the TCGA dataset.
  • The study highlights the importance of integrating open-access curated molecular profiling data to ensure precise diagnosis and classification.
  • The findings demonstrate the need for periodic reclassification of glioma samples to update diagnostic annotations in accordance with new WHO guidelines.

Implications and Future Directions

  • The study's findings have significant implications for the diagnosis and treatment of glioma, enabling more accurate and personalized patient care.
  • Future research directions include the development of machine learning algorithms to improve the reclassification pipelines and integrate new molecular profiling data.
  • The study's outcome also emphasizes the importance of standardising and curating molecular profiling data to ensure uniform diagnostic annotations.
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