What scRNA sequencing taught us about MGMT expression in glioblastoma
Alnahhas, I.; Khan, M.; Shi, W.
Show abstract
IntroductionThe promoter methylation status of O-6-methylguanine-DNA methyltransferase (MGMTp) is an established predictive and prognostic marker in GBM. Previous studies showed that the expression of MGMT based on immunohistochemistry was variable and lacked association with survival. This in part is because non-tumor cells including endothelial cells and macrophages express MGMT. Advanced technologies such as single-cell RNA (scRNA) sequencing have helped to elucidate the cellular composition of cancer and its microenvironment. scRNA sequencing allows to assess gene expression level in tumor cells specifically. MethodsWe used publicly available data from two recent GBM scRNA studies that included MGMTp methylation status data for patients to explore and uncover details about MGMT expression at the single-cell level: CPTAC (13 primary samples) and Neftel (20 primary samples). ResultsIn the CPTAC study, MGMT expression ranged from 0.19%-1.43% in the MGMTp methylated group (median 0.82%), and from 2.17%-28.36% in the MGMTp unmethylated group (median 5.7%). It therefore appears that 2% is a reasonable expression cutoff to predict the MGMTp methylation status based on scRNA data. In the Neftel study, MGMT expression ranged from 0-1.26% in the MGMTp methylated group (median 0.59%), and from 0.3-27.67% in the MGMTp unmethylated group (median 12.44%). Three unmethylated samples (out of 16) did not follow the 2% rule. It remains unclear if this is due to technical inaccuracies as the Neftel paper did not specify the method used to detect MGMTp methylation or even mere typos. Alternatively, could it be that truly MGMTp unmethylated samples can have low MGMT expression? Could this explain why some unmethylated MGMTp GBM patients surpass the expected survival? Interestingly, gene set enrichment analysis shows that MGMT expressing cells are enriched with mesenchymal genes, whereas MGMT negative cells are enriched with proneural genes. ConclusionFewer than 2% of GBM cells express MGMT when MGMTp is methylated.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Detection of local microvascular proliferation in IDH wild-type Glioblastoma using relative Cerebral Blood Volume 91%
- An integrative pan-cancer investigation reveals common genetic and transcriptional alterations of AMPK pathway genes as important predictors of clinical outcomes across major cancer types 91%
- Connecting Patterns of Tumor Growth with Sex Differences in Extreme Survivorship for Primary Glioblastoma Patients 91%
Similar papers in this journal
- Genomic profile in TGCT Mexican patients reveals a potential biomarker of sensitivity to platinum-based therapy 93%
- Tyrosyl-DNA phosphodiesterase 1 and topoisomerase I activities as predictive indicators for Glioblastoma susceptibility to genotoxic agents 93%
- LNX1 Modulates Notch1 Signaling to Promote Expansion of the Glioma Stem Cell Population During Temozolomide Therapy in Glioblastoma 93%
Similar papers in this journal
- The impact of germline variants in DNA repair pathways on survival and temozolomide toxicity in adults with glioma 92%
- A subset of pediatric thalamic gliomas share a distinct DNA methylation profile, H3K27me3 loss and frequent alteration of EGFR 92%
- SIOP Ependymoma I: Final results, long term follow-up and molecular analysis of the trial cohort: A BIOMECA Consortium Study 91%
Similar papers in this journal
- DNA methylation-based age acceleration observed in IDH wild-type glioblastoma is associated with better outcome - including in elderly patients 93%
- Integrative analysis of DNA methylation suggests down-regulation of oncogenic pathways and reduced de-novo mutation in survival outliers of glioblastoma 93%
- MiR-212-3p functions as a tumor suppressor gene in group 3 medulloblastoma via targeting Nuclear Factor I/B (NFIB) 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.