UCSF

Abstract

BACKGROUND

Epigenetic inhibition of the O6-methylguanine-DNA-methyltransferase (MGMT) gene has emerged as a clinically relevant prognostic marker in glioblastoma (GBM). Methylation of the MGMT promoter has been shown to increase chemotherapy efficacy. While traditionally reported as a binary marker, recent methodological advancements have led to quantitative approaches that measure methylation, providing clearer insights into methylation’s functional relationship with survival.

METHODS

A CLIA assay and bisulfite sequencing was utilized to develop a quantitative, 17-point MGMT promoter methylation index derived from the number of methylated CpG sites. Retrospective review of 240 newly diagnosed GBM patients was performed in order to discern how risk for mortality transforms as promoter methylation increases. Non-linearities were captured by fitting splines to Cox proportional hazard models, plotting smoothed residuals, and creating survival plots. Covariates included age, KPS, IDH1 mutation, and extent of resection.

RESULTS

Median follow-up time and progression free survival were 16 and 9 months, respectively. 176 subjects experienced death. A one-unit increase in CpG methylation on a scale of 1-17 resulted in a 4% reduction in hazard (95% CI 0.93–0.99, P< 0.005). Moreover, GBM patients with low-levels of methylation (1-6 CpG sites) fared markedly worse (HR=1.62, 95% CI 1.03-2.54, P< 0.036) than individuals who were unmethylated (reference group). Subjects with medium-levels of methylation (7-12 CpG sites) had the greatest reduction in hazard (HR=0.48, 95% CI 0.29-0.80, P< 0.004), followed by individuals in the highest methylation tertile (HR=0.62, 95% CI 0.40-0.97, P< 0.035).

CONCLUSION

This novel approach offers greater bisulfite conversion efficiency when compared to alternative methods, reducing the likelihood of false positives. Analysis of the resulting methylation index scores demonstrates a non-linear relationship between MGMT methylation and survival, suggesting conformation of the marker’s protective effect. These findings challenge the current understanding of MGMT’s functional form and underline why implementing an “optimal cutoff point” may be disadvantageous.