Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications

Malta, Tathiane M.; Souza, Cláudia; Sabedot, Thaís; Silva, Tiago C.; Mosella, Maritza; Kalkanis, Steven N.; Snyder, James M.; Castro, Ana Valéria; Noushmehr, Houtan

doi:10.1093/neuonc/nox183

Cited by 208 publications

(161 citation statements)

References 100 publications

Supporting

Mentioning

143

Contrasting

Unclassified

Order By: Relevance

“…Survival analysis in [52] shows a striking difference between the G-CIMP-high subtype and their other molecular subtypes, similar to the difference seen in our survival analysis. A recent review [53], reinforces the connection between G-CIMP-high tumours and mutations in IDH1 and the favourable prognoses associated with this subtype. This further supports the importance of our identified subtypes as they may be predictive of patient outcomes.…”

Section: Real Data Resultsmentioning

confidence: 85%

Uncovering the subtype-specific temporal order of cancer pathway dysregulation

et al. 2019

View full text Add to dashboard Cite

Cancer is driven by genetic mutations that dysregulate pathways important for proper cell function. Therefore, discovering these cancer pathways and their dysregulation order is key to understanding and treating cancer. However, the heterogeneity of mutations between different individuals makes this challenging and requires that cancer progression is studied in a subtype-specific way. To address this challenge, we provide a mathematical model, called Subtype-specific Pathway Linear Progression Model (SPM), that simultaneously captures cancer subtypes and pathways and order of dysregulation of the pathways within each subtype. Experiments with synthetic data indicate the robustness of SPM to problem specifics including noise compared to an existing method. Moreover, experimental results on glioblastoma multiforme and colorectal adenocarcinoma show the consistency of SPM’s results with the existing knowledge and its superiority to an existing method in certain cases. The implementation of our method is available at https://github.com/Dalton386/SPM.

show abstract

Section: Real Data Resultsmentioning

confidence: 85%

Uncovering the subtype-specific temporal order of cancer pathway dysregulation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Survival analysis by Brennan et al [9], shows a striking difference between the G-CIMP-high subtype and their other molecular subtypes, similar to the difference seen in our survival analysis. A recent review by Malta et al [34], reinforces the connection between G-CIMP-high tumours and mutations in IDH1 and the favourable prognoses associated with this subtype. This further supports the importance of our identified subtypes as they may be predictive of patient outcomes.…”

Section: Glioblastoma Multiformementioning

confidence: 84%

Uncovering the subtype-specific temporal order of cancer pathway dysregulation

Khakabmamaghani

Ding

Snow

et al. 2019

Preprint

View full text Add to dashboard Cite

Cancer is driven by genetic mutations that dysregulate pathways important for proper cell function. Therefore, discovering these cancer pathways and their dysregulation order is key to understanding and treating cancer. However, the heterogeneity of mutations between different individuals makes this challenging and requires that cancer progression is studied in a subtypespecific way. To address this challenge, we provide a mathematical model, called Subtype-specific Pathway Linear Progression Model (SPM), that simultaneously captures cancer subtypes and pathways and order of dysregulation of the pathways within each subtype. Experiments with synthetic data indicate the robustness of SPM to problem specifics including noise compared to an existing method. Moreover, experimental results on glioblastoma multiforme and colorectal adenocarcinoma show the consistency of SPM's results with the existing knowledge and its superiority to an existing method in certain cases. The implementation of our method is available at https://github.com/Dalton386/SPM.

show abstract

“…However, in glioma in particularly, low sensitivity and reliability of extracting any glioma-specific ctDNA from plasma remains a challenge because of the low frequency in somatic mutation and the targeted approach 36 . Since epigenetic reflect the cell-of-origin 54 and in glioma, somatic DNA methylation aberrations are widespread 1,3,39,41 , we focused on profiling the DNA methylation of the released DNA to detect glioma status (Glioma-eLB) and associated prognostic subtypes ( IDH -eLB) by blood (summarized in Fig. 4A).…”

Section: Discussionmentioning

confidence: 99%

“…First, we stratified the IDH -eLB CpG probes into distinct genomic regions 41,54 defined as “OpenSeas” (N=51 IDH mut, N=33 IDH wt), “Shores” (N=26 IDH mut, N=44 IDH wt), “Shelves” (N=10 IDH mut, N=4 IDH wt) and “CpG Islands” (N=27 IDH mut, N=43 IDH wt) (Extended Data Fig. S3E).…”

Section: Subjects and Methodologymentioning

confidence: 99%

“…Thus, DNA methylome profiling is an attractive approach for the identification of diagnostic, prognostic and predictive markers in LB 34,37 In the tumoral tissue, genome-wide DNA methylation profiling has shown that epigenetic abnormalities play important biological and clinical roles in CNS tumors, particularly in gliomas 3,38–41 . For instance, G-CIMP is a subset of glioma with extensive epigenomic alterations that confers a stronger prognostic value than age, grade and histology 1,3,41 . However, the relevance of comprehensive DNA methylation profiling in the blood-based circulating free tumor DNA of patients with gliomas has not been explored.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of glioma and prognostic subtypes by non-invasive circulating cell-free DNA methylation markers

Noushmehr

Sabedot

Malta

et al. 2019

Preprint

View full text Add to dashboard Cite

24 25 26 2 SUMMARY 27Genome-wide DNA methylation profiling has shown that epigenetic abnormalities are 28 biologically important in glioma and can be used to classify these tumors into distinct prognostic 29 groups. Thus far, DNA profiling has required surgically resected glioma tissue; however, 30 gliomas release tumoral material into biofluids, such as blood and cerebrospinal fluid, providing 31 an opportunity for a minimally invasive testing. While prior studies have shown that genetic and 32 epigenetic markers can be detected in blood or cerebrospinal fluid (e.g., liquid biopsy [LB]), 33 there has been low sensitivity for tumor-specific markers. We hypothesize that the low 34 sensitivity is due to the targeted assay methods. Therefore, we profiled the genome-wide CpG 35 methylation levels in DNA of tumor tissue and cell-free DNA in serum of glioma patients, to 36 identify non-invasive epigenetic LB (eLB) markers in the serum that reflect the characteristics of 37 the tumor tissue. From the epigenetic profiles of serum from patients diagnosed with glioma 38 (N=15 IDH mutant and N=7 IDH wildtype) and with epilepsy (N=3), we defined glioma-specific 39 and IDH-specific eLB signatures (Glioma-eLB and IDH-eLB, respectively). The epigenetic 40 profiles of the matched tissue demonstrate that these eLB signatures reflected the signature of the 41 tumor. Through cross-validation we show that Glioma-eLB can accurately predict a patient's 42 glioma from those with other neoplasias (N=6 Colon; N=14 Pituitary; N=3 Breast; N=4 Lung), 43 non-neoplastic immunological conditions (N=22 sepsis; N=9 pancreatic islet transplantation), 44 and from healthy individuals (sensitivity: 98%; specificity: 99%). Finally, IDH-eLB includes 45 promoter methylated markers associated with genes known to be involved in glioma 46 tumorigenesis (PVT1 and CXCR6). The application of the non-invasive eLB signature discovered 47 109 with glioma. We showed that the eLB could differentiate glioma from non-tumoral brain tissue 110 and stratify gliomas based on prognostic class (e.g., IDH mutation status). We further observed 111 that the specificity of the eLB allowed accurate discrimination of patients with glioma from 112 patients with tumors of other origins and from patients with immune-related disease states 113 (pancreatic islet transplantation and sepsis). The IDH-eLB signature includes promoter 114 methylated markers associated with genes known to be involved in glioma tumorigenesis (e.g., 115PVT1 and CXCR6). Finally, we propose a novel clinical approach to apply the eLB panels to 116 complement the standard of care in the diagnosis and follow-up. The ability to monitor patients 117 6 by eLB has the potential to improve the pre-and post-surgical quality of care for patients 118 harboring gliomas. 119 RESULTS 120Glioma cell-free DNA methylome 121 In this study, we selected 22 matching pairs of primary glioma tissue and serum, stored at the 122 Hermelin Brain Tumor Center (HBTC) bank from patients who underwent neurosurgery at the 123 Henr...

show abstract

Glioma CpG island methylator phenotype (G-CIMP): biological and clinical implications

Cited by 208 publications

References 100 publications

Uncovering the subtype-specific temporal order of cancer pathway dysregulation

Uncovering the subtype-specific temporal order of cancer pathway dysregulation

Uncovering the subtype-specific temporal order of cancer pathway dysregulation

Detection of glioma and prognostic subtypes by non-invasive circulating cell-free DNA methylation markers

Contact Info

Product

Resources

About