Radiogenomic profiling of global DNA methylation associated with molecular phenotypes and immune features in glioma.

in BMC medicine by Zhuokai Zhuang, Jinxin Lin, Zixiao Wan, Jingrong Weng, Ze Yuan, Yumo Xie, Zongchao Liu, Peiyi Xie, Siyue Mao, Zongming Wang, Xiaolin Wang, Meijin Huang, Yanxin Luo, Huichuan Yu

TLDR

  • The study used machine learning to find patterns in brain scans (MRI) that are related to the genetic makeup of gliomas. The study found that global DNA methylation, which is a type of genetic change, could be reflected by the texture and shape of certain areas in the brain scans. The study also found that global DNA methylation was associated with specific genetic and immune features of gliomas, which could help doctors diagnose and treat these tumors more effectively.

Abstract

The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA). The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.

Overview

  • The study aims to identify magnetic resonance imaging (MRI) features associated with molecular profiles in 146 patients with gliomas using machine learning approaches. The study develops and validates fitting models for each molecular feature (MoRad) and applies two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA) to provide radiological annotations for the molecular profiles. The study demonstrates the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. The study highlights the potential of radiogenomic global methylation as an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.

Comparative Analysis & Findings

  • The study identifies MRI features associated with molecular profiles in 146 patients with gliomas using machine learning approaches. The study demonstrates the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. The study highlights the potential of radiogenomic global methylation as an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.

Implications and Future Directions

  • The study's findings highlight the potential of radiogenomic global methylation as an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features. The study's findings could assist the molecular diagnosis and tumor treatment of gliomas. Future research could focus on identifying additional MRI features associated with molecular profiles in gliomas and developing more accurate fitting models for each molecular feature. Future research could also explore the potential of radiogenomic global methylation as a prognostic biomarker for gliomas.
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