Longitudinal DNA methylation analysis of adult-type IDH-mutant gliomas

Catalytically distinct IDH1 mutants tune phenotype severity in tumor models

Mutations in isocitrate dehydrogenase 1 (IDH1) impart a neomorphic reaction that produces the oncometabolite D-2-hydroxyglutarate (D2HG), which can inhibit DNA and histone demethylases to drive tumorigenesis via epigenetic changes. Though heterozygous point mutations in patients primarily affect residue R132, there are myriad D2HG-producing mutants that display unique catalytic efficiency of D2HG production. Here, we show that catalytic efficiency of D2HG production is greater in IDH1 R132Q than R132H mutants, and expression of IDH1 R132Q in cellular and mouse xenograft models leads to higher D2HG concentrations in cells, tumors, and sera compared to R132H-expressing models. Reduced representation bisulfite sequencing (RRBS) analysis of xenograft tumors shows expression of IDH1 R132Q relative to R132H leads to hypermethylation patterns in pathways associated with DNA damage. Transcriptome analysis indicates that the IDH1 R132Q mutation has a more aggressive pro-tumor phenotype, with members of EGFR, Wnt, and PI3K signaling pathways differentially expressed, perhaps through non-epigenetic routes. Together, these data suggest that the catalytic efficiency of IDH1 mutants modulate D2HG levels in cellular and in vivo models, resulting in unique epigenetic and transcriptomic consequences where higher D2HG levels appear to be associated with more aggressive tumors.

Decoding the DNA methylome of central nervous system tumors: An emerging modality for integrated diagnosis

The definitive diagnosis and classification of individual cancers are crucial for patient care and cancer research. To achieve a robust diagnosis of central nervous system (CNS) tumors, a genotype-phenotype integrated diagnostic approach was introduced in recent versions of the World Health Organization classification, followed by the incorporation of a genome-wide DNA methylome-based classification. Microarray-based platforms are widely used to obtain DNA methylome data, and the German Cancer Research Center (Deutsches Krebsforschungszentrum [DKFZ]) has a webtool for a DNA methylation-based classifier (DKFZ classifier). Integration of DNA methylome will further enhance the precision of CNS tumor classification, especially in diagnostically challenging cases. However, in the clinical application of DNA methylome-based classification, challenges related to data interpretation persist, in addition to technical caveats, regulations, and limited accessibility. Dimensionality reduction (DMR) can complement integrated diagnosis by visualizing a profile and comparing it with other known samples. Therefore, DNA methylome-based classification is a highly useful research tool for auxiliary analysis in challenging diagnostic and rare disease cases, and for establishing novel tumor concepts. Decoding the DNA methylome, especially by DMR in addition to DKFZ classifier, emphasizes the capability of grasping the fundamental biological principles that provide new perspectives on CNS tumors.

Brain Plasticity Profiling as a Key Support to Therapeutic Decision-Making in Low-Grade Glioma Oncological Strategies

The ability of neural circuits to compensate for damage to the central nervous system is called postlesional plasticity. In diffuse low-grade gliomas (LGGs), a crosstalk between the brain and the tumor activates modulations of plasticity, as well as tumor proliferation and migration, by means of paracrine and electrical intercommunications. Such adaptative mechanisms have a major impact on the benefits and risks of oncological treatments but are still disregarded by current neuro-oncological guidelines. In this review, the authors first aimed to highlight clinical, radiological, and oncological markers that robustly reflect the plasticity potentials and limitations in LGG patients, including the location of the tumor and the degree of critical white matter tract infiltration, the velocity of tumor expansion, and the reactional changes of neuropsychological performances over time. Second, the interactions between the potential/limitations of cerebral plasticity and the efficacy/tolerance of treatment options (i.e., surgery, chemotherapy, and radiotherapy) are reviewed. Finally, a longitudinal and multimodal treatment approach accounting for the evolutive profiles of brain plasticity is proposed. Such an approach integrates personalized predictive models of plasticity potentials with a step-by-step therapeutic decision making and supports onco-functional balanced strategies in patients with LGG, with the ultimate aim of optimizing overall survival and quality of life.