A Novel DNA Methylation-Based Signature Can Predict the Responses of MGMT Promoter Unmethylated Glioblastomas to Temozolomide

Abstract

Glioblastoma (GBM) is the most malignant glioma, with a median overall survival (OS) of 14-16 months. Temozolomide (TMZ) is the first-line chemotherapy drug for glioma, but whether TMZ should be withheld from patients with GBMs that lack O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is still under debate. DNA methylation profiling holds great promise for further stratifying the responses of MGMT promoter unmethylated GBMs to TMZ. In this study, we studied 147 TMZ-treated MGMT promoter unmethylated GBM, whose methylation information was obtained from the HumanMethylation27 (HM-27K) BeadChips (n = 107) and the HumanMethylation450 (HM-450K) BeadChips (n = 40) for training and validation, respectively. In the training set, we performed univariate Cox regression and identified that 3,565 CpGs were significantly associated with the OS of the TMZ-treated MGMT promoter unmethylated GBMs. Functional analysis indicated that the genes corresponding to these CpGs were enriched in the biological processes or pathways of mitochondrial translation, cell cycle, and DNA repair. Based on these CpGs, we developed a 31-CpGs methylation signature utilizing the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. In both training and validation datasets, the signature identified the TMZ-sensitive GBMs in the MGMT promoter unmethylated GBMs, and only the patients in the low-risk group appear to benefit from the TMZ treatment. Furthermore, these identified TMZ-sensitive MGMT promoter unmethylated GBMs have a similar OS when compared with the MGMT promoter methylated GBMs after TMZ treatment in both two datasets. Multivariate Cox regression demonstrated the independent prognostic value of the signature in TMZ-treated MGMT promoter unmethylated GBMs. Moreover, we also noticed that the hallmark of epithelial-mesenchymal transition, ECM related biological processes and pathways were highly enriched in the MGMT unmethylated GBMs with the high-risk score, indicating that enhanced ECM activities could be involved in the TMZ-resistance of GBM. In conclusion, our findings promote our understanding of the roles of DNA methylation in MGMT umethylated GBMs and offer a very promising TMZ-sensitivity predictive signature for these GBMs that could be tested prospectively.

Keywords: DNA methylation; MGMT; glioblastoma; signature; temozolomide.

Figure 1

The workflow for this study. The workflow for the selection of TMZ therapeutic prognosis related CpGs, development and validation of a TMZ therapeutic prognostic risk signature, and the functional analysis of genes that are correlated with the signature risk score.

Figure 2

TMZ therapeutic prognosis associated CpGs’ methylation profile in MGMT unmethylated GBMs. (A) Heatmap showing the methylation levels of the 3,565 GpGs associated with the overall survival of TMZ treated patients with MGMT unmethylated GBMs. The MGMT unmethylated GBMs could be clustered into 3 clusters (Cluster A–C) according to the CpGs methylation levels. (B) Kaplan–Meier overall survival (OS) curves of TMZ treated MGMT unmethylated GBM patients (stratified by Cluster A–C) and TMZ treated MGMT methylated GBM patients. (C, D) GO biological process terms (C) and KEGG pathways (D) enriched among the genes positively and negatively corresponding to the 3,565 GpGs.

Figure 3

Identification of the risk signature could stratify the TMZ therapeutic prognosis of the MGMT unmethylated GBM. (A) Ten-fold cross validation for tuning parameter selection in the LASSO model. The minimum criterion was indicated by the dashed vertical line (left). (B) Heatmap shows the association of risk scores and clinicopathological features based on the methylation profile of the 31 CpGs in the signature. The coefficients were calculated by multivariate Cox regression analysis using LASSO. (C–D) Kaplan–Meier overall survival (OS) curves for TMZ treated patients with MGMT methylated GBMs, TMZ treated patients with MGMT unmethylated GBMs with low- or high-risk significance scores in the training set (C) and validation set (D), respectively.

Figure 4

Clinical outcomes prediction of the signature in patients with stratified GBMs. (A–B) Kaplan–Meier overall survival (OS) curves for MGMT unmethylated GBM patients with or without TMZ treatment in the low-risk group (A) and high-risk groups (B) of the training set. (C–E) Kaplan–Meier overall survival (OS) curves for stratified GBM patients (C) MGMT unmethylated GBM without TMZ; (D) MGMT methylated GBM with TMZ; (E) MGMT methylated GBM without TMZ) with low- or high-risk scores in the training set. (F–J) Kaplan–Meier overall survival (OS) curves for stratified GBM patients in the validation set.

Figure 5

Functional annotation for genes differentially expressed between low- and high-risk groups. (A) The differential genes between low- and high-risk groups are shown by green (enriched in the low-risk group) and red (enriched in the high-risk group) dots. (B–C) Go analysis (B) and KEGG analysis (C) are used to evaluate differential genes between low-and high-risk groups. (D and E) GSEA analysis reveals the biological processes (D) and cancer hallmarks (E) enriched in the high-risk groups.