Distinct epigenetic shift in a subset of Glioma CpG island methylator phenotype (G-CIMP) during tumor recurrence

Souza, Cláudia; Sabedot, Thaís; Malta, Tathiane M.; Morozova, Olena; Sokolov, Artem; Laird, Peter W.; Wiznerowicz, Maciej; Iavarone, Antonio; Snyder, James M.; deCarvalho, Ana; Sanborn, Zachary; McDonald, Kerrie L.; Friedman, William A.; Tirapelli, Daniela Pretti da Cunha; Poisson, Laila; Mikkelsen, Tom; Carlotti, Carlos Gilberto; Kalkanis, Steven N.; Zenklusen, Jean C.; Salama, Sofie R.; Barnholtz‐Sloan, Jill S.; Noushmehr, Houtan

doi:10.1101/156646

Cited by 10 publications

(32 citation statements)

References 43 publications

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“…Remarkably, the analysis of a small cohort of primary and recurrent matched tumor specimens, composed of LGG and GBM, revealed that some G-CIMP-high tumors exhibited a demethylated pattern after relapse that was similar to those observed in the G-CIMP-low gliomas, suggesting a progression from the G-CIMP-high to the G-CIMP-low subset 7 . Unpublished work from our own laboratory may provide further refinement of the epigenetic shift from G-CIMP-high to G-CIMP-low upon tumor recurrence in TCGA and non-TCGA specimens 73 . Interestingly, in that cohort, G-CIMP-low at recurrence appeared in 12% of all gliomas and shared epigenomic features resembling IDH-wildtype primary GBM 73 .…”

Section: G-cimp+ Subsets and Glioma Progression/recurrencementioning

confidence: 99%

“…Unpublished work from our own laboratory may provide further refinement of the epigenetic shift from G-CIMP-high to G-CIMP-low upon tumor recurrence in TCGA and non-TCGA specimens 73 . Interestingly, in that cohort, G-CIMP-low at recurrence appeared in 12% of all gliomas and shared epigenomic features resembling IDH-wildtype primary GBM 73 . Genome-wide decreases in DNA methylation levels associated with progression have also been reported by other studies 74,75 .…”

Section: G-cimp+ Subsets and Glioma Progression/recurrencementioning

confidence: 99%

“…Accordingly, G-CIMP-low tumors displayed abnormalities in cell cycle pathway genes, such as CDK4 and CDKN2A, supporting the association between stem cell signaling pathways and tumor proliferation in gliomas. Therefore, loss of CpG methylation at these functional genomic elements, known to be associated with normal development and pluripotency, defines a possible mechanism of glioma progression 7,73 . Figure 4).…”

Section: G-cimp+ Subsets and Glioma Progression/recurrencementioning

confidence: 99%

“…In summary, the detection of G-CIMP+ and its subsets (-high and -low) builds on the 2016 WHO molecular effort to refine glioma classification ( Figure 5) and provides insight into disease course and treatment opportunity 73 . The prognostic significance of G-CIMP+ across all glioma types has been confirmed in many studies 7,18,19,[45][46][47] .…”

Section: Final Remarks and Perspectivesmentioning

confidence: 99%

“…Although preclinical studies using these drugs in gliomas seem promising, a corresponding clinical trial using decitabine on adult glioma has not been reported. Considering the potential adverse impact of loss of methylation in the progression or recurrence of gliomas, reported previously 72,73 , it seems reasonable to take G-CIMP subsets into account during clinical trial design. Targeting IDH-mutant tumors, including G-CIMP+ gliomas, with IDH-mutant enzyme inhibiting agents is currently under investigation in clinical trials 52,66,80 .…”

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confidence: 99%

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Glioma CpG Island Methylator Phenotype (G-CIMP): Biological and Clinical Implications

Malta

Souza

Sabedot

et al. 2017

Preprint

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Gliomas are a heterogenous group of brain tumors with distinct biological and clinical properties. Despite advances in surgical techniques and clinical regimens, treatment of high-grade glioma remains challenging and carries dismal rates of therapeutic success and overall survival. Challenges include the molecular complexity of gliomas as well as inconsistencies in histopathological grading, resulting in an inaccurate prediction of disease progression and failure of standard therapy. The updated 2016 World Health Organization (WHO) classification of tumors of the central nervous system reflects a refinement of tumor diagnostics by integrating genotypic and phenotypic features, thereby narrowing defined subgroups. The new classification recommends the molecular diagnosis of IDH mutational status in gliomas. IDH-mutant gliomas are prompt to manifest the CpG Island Methylator Phenotype (G-CIMP).Notably, the recent identification of clinically relevant subsets of G-CIMP tumors (G-CIMP-high and G-CIMP-low) provided a further refinement in glioma classification that is independent of grade and histology. This scheme is useful for predicting patient outcome and may be translated into effective therapeutic strategies tailored to each patient. In the present review, we highlight the evolution of our understanding of G-CIMP subsets and how recent advances in characterizing gliomas' genome and epigenome may influence future basic and translational research.

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Section: G-cimp+ Subsets and Glioma Progression/recurrencementioning

confidence: 99%

Section: G-cimp+ Subsets and Glioma Progression/recurrencementioning

confidence: 99%

Section: G-cimp+ Subsets and Glioma Progression/recurrencementioning

confidence: 99%

Section: Final Remarks and Perspectivesmentioning

confidence: 99%

mentioning

confidence: 99%

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Glioma CpG Island Methylator Phenotype (G-CIMP): Biological and Clinical Implications

Malta

Souza

Sabedot

et al. 2017

Preprint

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Electrotherapies for Glioblastoma

et al. 2021

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Non-thermal, intermediate frequency (100-500 kHz) electrotherapies present a unique therapeutic strategy to treat malignant neoplasms. Here, pulsed electric fields (PEFs) which induce reversible or irreversible electroporation (IRE) and tumour-treating fields (TTFs) are reviewed highlighting the foundations, advances, and considerations of each method when applied to glioblastoma (GBM). Several biological aspects of GBM that contribute to treatment complexity (heterogeneity, recurrence, resistance, and blood-brain barrier(BBB)) and electrophysiological traits which are suggested to promote glioma progression are described. Particularly, the biological responses at the cellular and molecular level to specific parameters of the electrical stimuli are discussed offering ways to compare these parameters despite the lack of a universally adopted physical description. Reviewing the literature, a disconnect is found between electrotherapy techniques and how they target the biological complexities of GBM that make treatment difficult in the first place. An attempt is made to bridge the interdisciplinary gap by mapping biological characteristics to different methods of electrotherapy, suggesting important future research topics and directions in both understanding and treating GBM. To the authors' knowledge, this is the first paper that attempts an in-tandem assessment of the biological effects of different aspects of intermediate frequency electrotherapy methods, thus offering possible strategies toward GBM treatment.

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Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Malta

Sokolov

Gentles

et al. 2018

Cell

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1,576

1,659

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Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.

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Distinct epigenetic shift in a subset of Glioma CpG island methylator phenotype (G-CIMP) during tumor recurrence

Cited by 10 publications

References 43 publications

Glioma CpG Island Methylator Phenotype (G-CIMP): Biological and Clinical Implications

Glioma CpG Island Methylator Phenotype (G-CIMP): Biological and Clinical Implications

Electrotherapies for Glioblastoma

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

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