Abstract

Background

Mutations in isocitrate dehydrogenase 1 ( IDH1 ) and associated CpG island hypermethylation represent early events in the development of low-grade gliomas and secondary glioblastomas. To identify candidate tumor suppressor genes whose promoter methylation may contribute to gliomagenesis, we compared methylation profiles of IDH1 mutant (MUT) and IDH1 wild-type (WT) tumors using massively parallel reduced representation bisulfite sequencing.

Methods

Reduced representation bisulfite sequencing was performed on ten pathologically matched WT and MUT glioma samples and compared with data from a methylation-sensitive restriction enzyme technique and data from The Cancer Genome Atlas (TCGA). Methylation in the gene retinol-binding protein 1 ( RBP1 ) was identified in IDH1 mutant tumors and further analyzed with primer-based bisulfite sequencing. Correlation between IDH1/IDH2 mutation status and RBP1 methylation was evaluated with Spearman correlation. Survival data were collected retrospectively and analyzed with Kaplan–Meier and Cox proportional hazards analysis. All statistical tests were two-sided.

Results

Methylome analysis identified coordinated CpG island hypermethylation in IDH1 MUT gliomas, consistent with previous reports. RBP1 , important in retinoic acid metabolism, was found to be hypermethylated in 76 of 79 IDH1 MUT, 3 of 3 IDH2 MUT, and 0 of 116 IDH1 / IDH2 WT tumors. IDH1/IDH2 mutation was highly correlated with RBP1 hypermethylation (n = 198; Spearman R = 0.94, 95% confidence interval = 0.92 to 0.95, P < .001). The Cancer Genome Atlas showed IDH1 MUT tumors (n = 23) to be RBP1 -hypermethylated with decreased RBP1 expression compared with WT tumors (n = 124). Among patients with primary glioblastoma, patients with RBP1- unmethylated tumors (n = 102) had decreased median overall survival compared with patients with RBP1 -methylated tumors (n = 22) (20.3 months vs 36.8 months, respectively; hazard ratio of death = 2.48, 95% confidence interval = 1.30 to 4.75, P = .006).

Conclusion

RBP1 promoter hypermethylation is found in nearly all IDH1 and IDH2 mutant gliomas and is associated with improved patient survival. Because RBP1 is involved in retinoic acid synthesis, our results suggest that dysregulation of retinoic acid metabolism may contribute to glioma formation along the IDH1 / IDH2 -mutant pathway.

Diffuse gliomas represent the most common type of adult primary brain cancer, affecting as many as 20 000 patients per year in the United States. A mutation in the enzyme isocitrate dehydrogenase 1 ( IDH1 ) was found in secondary glioblastomas (GBM) in the year 2008 and has since also been reported in acute myelogenous leukemia ( 1–3 ). IDH1 mutations are uniformly heterozygous at residue R132, and accumulating evidence indicates that it is likely an early event in the development of glioma ( 4 , 5 ). With lesser frequency, mutations in IDH2 have also been found in gliomas ( 6 ), but the role of IDH1 and IDH2 mutations in glioma formation has not been elucidated. Importantly, it was reported that the IDH1 mutant protein preferentially catalyzes the formation of 2-hydroxyglutarate (2-HG) ( 7 ), a metabolite potentially contributing to gliomagenesis. Also, a number of studies have confirmed that IDH1 mutant gliomas harbor a distinct CpG island hypermethylation profile ( 4 , 8–10 ). On the basis of its presence in low-grade gliomas, in addition to high-grade GBM, it has been hypothesized that the glioma-associated hypermethylation profile is an early event in gliomagenesis ( 4 ); however, it remains undetermined whether the hypermethylation profile contributes to the formation of IDH1 mutant gliomas via silencing of potential tumor suppressor genes.

The glioma-associated hypermethylation profile was initially identified and confirmed in previous studies using relatively low-resolution microarray-based methods ( 4 , 8 , 11 ). Although microarray-based methods have evolved to detect increasing numbers of CpG sites, genome-wide methylation profiling methods using massively parallel sequencing have the ability to provide vastly increased information at single-base resolution ( 12 , 13 ). Reduced representation bisulfite sequencing (RRBS) is a cost-effective technique for high-resolution methylome sequencing, which uses restriction enzymes that cleave genomic DNA into fragments enriched for CpG sites ( 14 , 15 ).

To characterize the CpG island methylation pattern in IDH1 mutant gliomas at high resolution, we performed genome-wide RRBS on five pairs of pathologically matched IDH1 wild-type (WT) and mutant (MUT) glioma tumor samples. Hypermethylated promoter-associated CpG islands were identified in IDH1 MUT samples, including the RBP1 promoter. RBP1 , located on 3q23, encodes the cytosolic retinol binding protein 1 (CRBP1) and is required for the efficient synthesis of all-trans retinoic acid (ATRA) ( 16–18 ). Given the role of ATRA as an important transcription regulator ( 19 ), and the possibility that decreased CRBP1 activity may lead to alterations in ATRA metabolism and consequent transcription dysregulation, we chose to further validate RBP1 promoter hypermethylation by targeted bisulfite sequencing (BiSEQ).

Methods

Patient Cohorts and Tumor Specimens

A total of 198 frozen or formalin-fixed paraffin embedded tissue specimens were obtained from the University of California, Los Angeles Brain Tumor Translational Resource (Los Angeles, CA). Remnant human brain tumor samples were collected from patients undergoing surgical resection and who provided written informed consent. The collection of human brain tumor samples was approved by the University of California, Los Angeles Institutional Review Board. Normal brain tissues were collected with University of California, Los Angeles Institutional Review Board approval from one patient undergoing non–tumor-related surgery and from four patients at the time of autopsy (the postmortem interval was less than 12 hours). IDH1 was sequenced on all samples, and IDH2 was sequenced on selected IDH1 WT samples. The clinical characteristics of the patient cohorts are listed in Tables 1 and 4 . A diagram showing the composition of the cohorts is shown in Figure 1 .

Table 1.

Characteristics of samples used in the initial methylation screen

Characteristic  RRBS  MSRE*  TCGA 
No. of patients  10  31  147 
Mean age at diagnosis, y (range)  45.7 (33–63)  44.6 (21–75)  55.2 (19–86) 
Sex‡       
    No. of men   16  87 
    No. of women   15  52 
Tissue pathology, no. of patients       
    Astrocytoma grade II    
    Oligodendroglioma grade II    
    Oligoastrocytoma grade II    
    Astrocytoma grade III    
    Oligodendroglioma grade III    
    Oligoastrocytoma grade III    
    Glioblastoma grade IV   14  147 
IDH1 mutation status        
    WT   15  124 
    MUT   16  23 
Tissue treated with RT/chemotherapy    NA 
Tissue treated with isotretinoin    NA 
Pretreatment history  –  –   
    Yes  –  –  17 
    No  –  –  121 
    NA  –  –  
Characteristic  RRBS  MSRE*  TCGA 
No. of patients  10  31  147 
Mean age at diagnosis, y (range)  45.7 (33–63)  44.6 (21–75)  55.2 (19–86) 
Sex‡       
    No. of men   16  87 
    No. of women   15  52 
Tissue pathology, no. of patients       
    Astrocytoma grade II    
    Oligodendroglioma grade II    
    Oligoastrocytoma grade II    
    Astrocytoma grade III    
    Oligodendroglioma grade III    
    Oligoastrocytoma grade III    
    Glioblastoma grade IV   14  147 
IDH1 mutation status        
    WT   15  124 
    MUT   16  23 
Tissue treated with RT/chemotherapy    NA 
Tissue treated with isotretinoin    NA 
Pretreatment history  –  –   
    Yes  –  –  17 
    No  –  –  121 
    NA  –  –  

* The initial methylation-sensitive restriction enzyme (MSRE) screen included data from 64 patients; however, data on the retinol binding protein 1 was available for only 31 patients. The MSRE technique does not have coverage of every gene on every sample. Thus, only data from these 31 patients are presented here. IDH = isocitrate dehydrogenase; MUT = mutant; NA = not available; RRBS = reduced representation bisulfite sequencing; RT = radiotherapy; TCGA = The Cancer Genome Atlas; WT = wild type.

‡ Data on sex for TCGA were not available for eight patients.

Table 4.

Demographics and RBP1 methylation status* of grade II–IV glioma samples (n = 198)

Demographic  Total cohort † by targeted bisulfite sequencing  GBM survival cohort within the total cohort 
 Total  Methylated  Unmethylated  Total  Methylated  Unmethylated 
No. of patients  198  79  119  124  22  102 
Mean age at diagnosis, y (range)  48.5 (19–79)  39.5 (19–75)  54.4 (21–79)  53.0 (24–79)  43.7 (24–75)  55.0 (31–79) 
Sex             
    No. of males  122  51  71  70  14  56 
    No. of female  76  28  48  54   46 
Tissue pathology, no. of patients             
    Astrocytoma grade II       
    Oligodendroglioma grade II  16  16     
    Oligoastrocytoma grade II       
    Astrocytoma grade III  13      
    Oligodendroglioma grade III       
    Oligoastrocytoma grade III  10      
    Glioblastoma grade IV  137  29  108  124  22  102 
IDH1 / IDH2 WT   116   116  101   101 
IDH1 MUT   79  76  3‡  21  20  
IDH2 MUT        
    Tissue treated with RT/chemo  14  11     
    Tissue treated with isotretinoin       
    Tissue received no treatment  183  67  116  124  22  102 
Pretreatment data not available       
Demographic  Total cohort † by targeted bisulfite sequencing  GBM survival cohort within the total cohort 
 Total  Methylated  Unmethylated  Total  Methylated  Unmethylated 
No. of patients  198  79  119  124  22  102 
Mean age at diagnosis, y (range)  48.5 (19–79)  39.5 (19–75)  54.4 (21–79)  53.0 (24–79)  43.7 (24–75)  55.0 (31–79) 
Sex             
    No. of males  122  51  71  70  14  56 
    No. of female  76  28  48  54   46 
Tissue pathology, no. of patients             
    Astrocytoma grade II       
    Oligodendroglioma grade II  16  16     
    Oligoastrocytoma grade II       
    Astrocytoma grade III  13      
    Oligodendroglioma grade III       
    Oligoastrocytoma grade III  10      
    Glioblastoma grade IV  137  29  108  124  22  102 
IDH1 / IDH2 WT   116   116  101   101 
IDH1 MUT   79  76  3‡  21  20  
IDH2 MUT        
    Tissue treated with RT/chemo  14  11     
    Tissue treated with isotretinoin       
    Tissue received no treatment  183  67  116  124  22  102 
Pretreatment data not available       

* RBP1 methylation status was assessed by bisulfite sequencing in all 198 patients. If the average methylation level was greater than 50%, the sample was classified as methylated.

† The Total Cohort includes 41 patients from the initial screening cohort. Chemo = chemotherapy; IDH = isocitrate dehydrogenase; MUT = mutant; RBP1 = retinol binding protein 1; RT = radiotherapy; WT = wild type.

‡ All IDH1 MUT unmethylated patients had grade 4 gliomas.

Figure 1.

Flow diagram of patient cohorts included in the analysis. The different patient cohorts and number of patients in each cohort are given. BiSEQ = bisulfite sequencing; GBM = glioblastoma multiforme; MSRE = methylation-sensitive restriction enzyme; RBP1 = retinol binding protein 1; RRBS = reduced representation bisulfite sequencing; TCGA = The Cancer Genome Atlas.

Figure 1.

Flow diagram of patient cohorts included in the analysis. The different patient cohorts and number of patients in each cohort are given. BiSEQ = bisulfite sequencing; GBM = glioblastoma multiforme; MSRE = methylation-sensitive restriction enzyme; RBP1 = retinol binding protein 1; RRBS = reduced representation bisulfite sequencing; TCGA = The Cancer Genome Atlas.

Initial Methylation Screen

For RRBS, frozen tumor samples consisting of five pairs of pathologically matched IDH1 WT and IDH1 MUT World Health Organization grade III and IV glioma tumor samples were analyzed. Methylation data from 31 patients that had coverage of RBP1 were included for analysis by methylation-sensitive restriction enzyme assay (MSRE). Data from a total of 64 patients were used in the initial methylation screen. Thirty-three of the 64 samples did not have coverage at RBP1 and were not included in our results. MSRE data on 34 of 64 patients were previously reported ( 4 ). For The Cancer Genome Atlas (TCGA), IDH1 genotype, methylation, and gene-expression data were available on 147 grade IV tumor samples from the TCGA database ( 8 , 20 ).

Total Cohort to Assess Retinol Binding Protein 1 Methylation by Bisulfite Sequencing

RBP1 methylation was assessed in a total of 198 retrospectively identified frozen or formalin-fixed paraffin-embedded samples by BiSEQ. The Total Cohort included 41 patients who were included in the initial methylation screen (10 from RRBS, 31 from MSRE) and a validation set of 157 patients. RBP1 methylation status for all 198 patients was assessed by BiSEQ.

GBM Survival Cohort

Within the 198-patient Total Cohort, we retrospectively identified 124 samples that were obtained from treatment-naïve primary GBM patients with detailed clinical information. Patients were treated with a combination of radiation (RT) and temozolomide (TMZ) after surgical resection ( 21 ). Overall survival (OS) was defined as the date of diagnosis to the date of death from any cause. For patients lost to follow-up without obtainable date of death, censoring date was last clinic visit or contact. If the last clinic or contact was after the September 9, 2011 freeze date, the patient was censored at September 22, 2011. These patients were a part of two other studies examining clinical and molecular features of IDH1 MUT gliomas ( 4 ) and the prognostic values of O6-methylguanine-DNA methyl transferase promoter methylation on patient survival (data not shown).

Cell Culture

The U87MG, U138MG, and U373MG glioma cell lines were obtained from Dr Paul Mischel (University of California, Los Angeles, Los Angeles, CA). Dr Carol Kruse (University of California, Los Angeles, CA) provided the D54MG cell line. Dr Glyn Dawson (University of Chicago, Chicago, IL) and Dr Anthony Campangoni (University of California, Los Angeles, CA) provided the HOG cell line. All glioma cell lines were cultured in Dulbecco’s modified Eagle medium/F12 medium (Invitrogen, Grand Island, NY) supplemented with 10% fetal bovine serum and 100U/mL penicillin/streptomycin. The human astrocytic progenitor cell line (APC) was obtained from Dr Ina Wanner (University of California, Los Angeles) and cultured in Delbecco’s modified Eagle medium/F12 medium with 10% fetal bovine serum ( 22 ).

Massively Parallel RRBS

RRBS was done using the protocol published by Meissner et al. ( 14 , 15 ). Details of the RRBS protocol, including generation of U87MG genomic DNA samples, RRBS quality control, and bioinformatics, are outlined in the Supplementary Methods and shown in Supplementary Figure 1 (available online). Briefly, DNA was isolated from U87MG glioma cells and frozen tumor tissues, digested with the restriction enzyme MspI to enrich for fragments containing CpG islands, and end-repaired using methylated cytosine. After adapter ligation, the DNA was size-fractionated by gel electrophoresis, and DNA fragments between 100 and 400 base pairs in length were isolated to minimize large fragments with poor sequencing coverage. Isolated DNA were then bisulfite treated, amplified, mixed with unmodified PhiX DNA (a bacterial genome inserted for quality control and to assess mapping), and sequenced on an Illumina Genome Analyzer IIx. The Novoalign software package ( www.novocraft.com ) was used to align the sequence data. Aligned sequence data were then sorted using the SAMTools software package ( 23 ) and stored in SAM format for further analysis.

Methylation status was determined at individual CpG sites, and the results were compiled to show the level of methylation at individual CpG islands. CpG islands were mapped by previously published definitions ( 24 ); and a strict quality control protocol (see Supplementary Methods, available online) was implemented to ensure the quality of the sequence base calls, alignment, and the final methylation data.

Methylation-Sensitive Restriction Enzyme Assay

MSRE, as described by Tran et al ( 25 ), was used to assess genome-wide methylation for 64 glioma samples. Briefly, genomic DNA was digested with the restriction enzyme BfaI and divided into two aliquots. One aliquot was digested with the methylation-sensitive restriction enzyme HpaII and labeled with Cy5 after polymerase chain reaction (PCR)-amplification ( 25 ). The second aliquot was digested with the methylation-insensitive enzyme MspI and labeled with Cy3 after PCR amplification. The PCR products were hybridized to an Agilent High-Density 2-color Human CpG Island Microarray (Agilent Technologies, Santa Clara, CA).

In silico digestion of the whole human genome (HG18) using enzyme BfaI was used to predict sensitive (containing CCGG) and insensitive (not containing CCGG) fragments of DNA from tumor samples. Each BfaI fragment was assigned a methylation score based on the median Loess corrected log ratios of the Cy-5/Cy-3 signal for all probes mapping to that fragment. Methylation scores were then standardized on the basis of the distribution of values for all insensitive fragments.

Analysis of Methylation and Gene Expression in The Cancer Genome Atlas Dataset

Methylation data for 147 GBM samples were obtained from the TCGA ( 8 ). Methylation was measured at approximately 27 000 CpG dinucleotides for 62 samples, using the Illumina Infinium Human Methylation 27 assay (Illumina, San Diego, CA) and at 1500 CpG dinucleotides for 85 samples, using the Illumina Goldengate Methylation Cancer Panel assay. Level 3 data, including normalized methylation signal per gene per sample, were downloaded directly from the TCGA data portal (http://tcga-data.nci.nih.gov/tcga/). Gene expression data were available for 142 of 147 TCGA samples. Gene expression was measured using the Affymetrix HT Human Genome U133A microarray (Affymetrix, Santa Clara, CA). A TCGA gene-expression probe (203423_at, chr3:140718973-140741180) covers transcript variant 1 of the RBP1 gene.

Statistical Analysis

Differentially methylated CpG islands located in gene promoter regions were identified by performing the student t test comparing IDH1 MUT and WT samples. To minimize the number of statistically significant but biologically nonsignificant changes in methylation, arbitrary thresholds of 0.4, 0.4, and 3 (for RRBS, TCGA, and MSRE, respectively) were set as the minimum difference between the mean levels of methylation between IDH1 WT and MUT samples that may be biologically significant to warrant further evaluation. Different cutoffs were chosen for the three different datasets because of their technical differences. Data for the MSRE technique are expressed as a log ratio, whereas data for the RRBS technique are expressed as a direct percentage of methy lation. Also, to control the false discovery rate, a Q of less than or equal to 0.05 was set as the threshold for statistical significance, as described by Storey et al. ( 26 ), for the three datasets. A false discovery rate of 0.02% was estimated via permutation testing of the RRBS data [252 permutations ( 28 ), http://biosun1.harvard.edu/complab/dchip ].

Correlation between IDH1 mutation and RBP1 methylation was determined using the Spearman correlation test. To examine the relationship between OS and RBP1 methylation, survival curves were estimated by the Kaplan–Meier method, and groups were compared using a log–rank test. Because of the tight correlation between IDH1 / IDH2 mutations and RBP1 methylation, multivariable analysis with Cox proportional hazards model was done, which included variables including age (years, continuous variable), sex (male or female), performance status (Karnofsky performance score 80–100 vs ≤ 70), extent of resection (gross-total vs subtotal/biopsy), and IDH1/IDH2 mutation status or RBP1 methylation status separately. Statistical analyses by race/ethnic group were not done. The assumption of proportionality was verified by the statistical test of correlation between Schoenfeld residuals and ranked survival time. Statistical analyses were performed using the open-source R statistical analysis package ( http://www.R-project.org ). All tests were two-sided, and a P value of less than .05 was considered statistically significant.

Targeted Bisulfite Sequencing of Retinol Binding Protein 1

The methylation status of the RBP1 promoter CpG island was assessed by standard bisulfite sequencing utilizing a nested PCR protocol with the primer sets: stage 1 (forward, 5ʹ–TTTATTGGGTATTGGAAGATGTTG–3ʹ and reverse, 5ʹ–TCCAATCTACAACCTAAAAACTACC–3ʹ) and stage 2 (forward, 5ʹ–GGTATTGGAAGATGTTGGTTAA–3ʹ and same reverse primer as stage 1). The sequence of each sample was determined using Chromas Lite 2.33 (Technelysium Pty Ltd, South Brisbane, QLD, Australia). The level of methylation was semiquantitatively scored in quartiles by the relative heights of the methylated and unmethylated peaks. For RBP1 methylation determination by BiSEQ, the mean methylation levels (of the 21 CpG sites sequenced) above 50% were classified as methylated.

IDH1 and IDH2 Sequencing

Genomic DNA was isolated from formalin-fixed paraffin-embedded or frozen tissue using the Recoverall Total Nucleic Acid Isolation Kit (Invitrogen, Grand Island, NY). Sequencing of IDH1 at residue 132 and IDH2 at residue 172 was determined by Sanger sequencing with the following primers: IDH1 (forward, 5ʹ–gcgtcaaatgtgccactatc–3ʹ and reverse, 5ʹ–gcaaaatcacattattgccaac–3ʹ) and IDH2 (forward, 5ʹ–CTCACAGAGTTCAAGCTGAAG–3ʹ and reverse, 5ʹ–CTGTGGCCTTGTACTGCAGAG–3ʹ). Purified PCR products were sequenced using the BigDye Terminator v1.1 and analyzed on a 3730 sequencer (both from Applied Biosystems).

Analysis by Quantitative Real-Time Reverse Transcriptase PCR

Total RNA was extracted from culture cells or tumor tissues using Trizol (Invitrogen, Grand Island, NY) according to the manufacturer’s instructions. Purity of the total RNA was then determined by the 260/280nm ratio, and the integrity was checked by electrophoresis on 1% agarose gel.

One microgram of RNA from each sample was reverse-transcribed to cDNA with the Reverse Transcription System (Promega, San Luis Obispo, CA) using oligo-dT primers. Normal brain cDNA isolated from one frozen surgical tissue, four frozen autopsy tissues, and two commercially available cDNA libraries (Biochain, Hayward, CA; Invitrogen, Grand Island, NY) were used as controls. Reverse transcriptase PCR (RT-PCR) was performed using Platinum DNA polymerase (Invitrogen, Grand Island, NY). The PCR product was separated on a 3% agarose gel. Quantitative real-time RT-PCR (qRT-PCR) using FastStart Universal SYBRR-Green Master (Roche, Mannheim, Germany) using a LightCycler® 480 System (Roche, Mannheim, Germany) was done. The primers were designed with Primer3 ( 27 ) (version 0.4.0, http://frodo.wi.mit.edu ) software and were as follows: forward, 5ʹ–CAACTGGCTCCAGTCACTCC–3ʹ and reverse, 5ʹ–TGCACGATCTCTTTGTCTGG–3ʹ. The following conditions were used for amplification: 95°C for 3 minutes, 40 cycles at 95°C for 10 seconds, followed by 60°C for 30 seconds, and 72°C for 30 seconds. All samples were amplified in duplicate from the same RNA preparation, and the results are presented as the mean with standard error of the mean from three independent experiments. RBP1 mRNA levels were standardized to the levels of β-actin (internal control) and quantified by the relative Ct method (2 ∆∆Ct ).

Protein Expression by Western Blot

Total protein lysates were prepared using radio-immuno- precipitation assay buffer containing protease inhibitor to lyse the cells and tissues. The proteins (15 µg) were separated on a 4%–20% sodium dodecyl sulfate polyacrylamide gel and transferred to nitrocellulose membranes (0.45 µm, Bio-Rad, Hercules, CA). Western blot was performed with goat anti-CRBP1 (1:200) or rabbit anti-CRBP1 (1:200) polyclonal antibody (Sigma, St. Louis, MO), mouse anti-α-tubulin monoclonal antibody (1:4000, Sigma, St. Louis, MO), horse radish peroxidase-conjugated rabbit anti-goat (1:8000) or goat anti-rabbit (1:8000) IgG (Santa Cruz Biotech, Santa Cruz, CA), horse radish peroxidase-conjugated goat anti-mouse IgG (1:10 000, Jackson Immuno Research, West Grove, PA), and an enhanced chemiluminescence detection kit (Pierce, Rockford, IL). Densitometry was performed with Gel-Pro Analyzer 4.0 software (Media Cybernetics, Bethesda, MD).

Results

Development of RRBS for Methylome Profiling in Glioma

To examine potential tumor suppressor genes that may be hypermethylated in IDH1 MUT tumors, we applied the RRBS protocol ( 14 , 15 ) to profile the glioma methylome. As a part of the protocol validation, we initially performed RRBS on DNA isolated from U87MG cells, a widely used GBM cell line. U87MG DNA that had been treated with the CpG methyltransferase enzyme SssI was used as positive control. A negative control was obtained from PCR-amplified genomic DNA for which CpG methylation was not amplified. Data from U87MG DNA demonstrated no major gaps in our sequencing coverage ( Supplementary Figure 2, A , available online) and showed the expected methylation patterns in the positive and negative controls ( Table 2 and Supplementary Figure 2, B–D , available online). For reference, the high-resolution methylome of U87MG derived from RRBS is listed in Supplementary Table 1 (available online).

Table 2.

Reduced representation bisulfite sequencing (RRBS) in U87MG cells and frozen glioma tissues

Sample  U87MG  U87MG negative control  U87MG positive control  IDH1 WT mean (n=5)   IDH1 MUT mean (n=5)  
Total no. of reads  34 406 353  78 527 855*  121 683 196*  38 279 367  38 344 371 
No. of aligned reads  15 018 877  11 124 619  20 946 145  17 182 263  16 466 007 
No. of CpG sites covered  2 141 620  1 233 669  2 094 504  2 293 879  2 083 096 
Average reads per CpG site  19.01  17.81  24.11  20.16  20.93 
Total no. of CpG islands  26 567  26 567  26 567  26 567  26 567 
No. of CpG islands covered  23 540  22 102  23 254  23 985  23 923 
Coverage within island  0.552  0.423  0.512  0.589  0.592 
Total no. of unique refseq genes  21 338  21 338  21 338  21 338  21 338 
No. of unique refseq genes covered  17 854  18 114  17 933  18 112  17 467 
Total no. of unique promoters  21 338  21 338  21 338  21 338  21 338 
No. of unique promoters covered  14 280  13 687  14 011  14 588  14 621 
Mean CpG island methylation† (SEM)  30.14 (0.23)  1.12 (0.02)  97.29 (0.02)  28.26 (0.11)  32.80 (0.11) 
Methylated CpG sites, %  43.97  0.037  99.35  54.44  55.48 
Sample  U87MG  U87MG negative control  U87MG positive control  IDH1 WT mean (n=5)   IDH1 MUT mean (n=5)  
Total no. of reads  34 406 353  78 527 855*  121 683 196*  38 279 367  38 344 371 
No. of aligned reads  15 018 877  11 124 619  20 946 145  17 182 263  16 466 007 
No. of CpG sites covered  2 141 620  1 233 669  2 094 504  2 293 879  2 083 096 
Average reads per CpG site  19.01  17.81  24.11  20.16  20.93 
Total no. of CpG islands  26 567  26 567  26 567  26 567  26 567 
No. of CpG islands covered  23 540  22 102  23 254  23 985  23 923 
Coverage within island  0.552  0.423  0.512  0.589  0.592 
Total no. of unique refseq genes  21 338  21 338  21 338  21 338  21 338 
No. of unique refseq genes covered  17 854  18 114  17 933  18 112  17 467 
Total no. of unique promoters  21 338  21 338  21 338  21 338  21 338 
No. of unique promoters covered  14 280  13 687  14 011  14 588  14 621 
Mean CpG island methylation† (SEM)  30.14 (0.23)  1.12 (0.02)  97.29 (0.02)  28.26 (0.11)  32.80 (0.11) 
Methylated CpG sites, %  43.97  0.037  99.35  54.44  55.48 

* These samples were analyzed using an Illumina HiSeq 2000 system (Illumina Inc, San Diego, CA). IDH = isocitrate dehydrogenase; MUT = mutant; WT = wild type.

† Data represent the mean and standard of the mean (SEM).

Figure 2.

Characterization of genome-wide differential methylation. A ) Hierarchical clustering of differentially methylated CpG islands ( P < .05, unpaired t test) identified for isocitrate dehydrogenase 1 ( IDH1 ) mutant (MUT) and wild-type (WT) tumors using reduced representation bisulfite sequencing (RRBS) is shown. B ) A list of statistically significantly enriched annotation terms (enrichment score > 1.30) associated with differentially methylated genes by DAVID annotation analysis is given. Terms with multiple enrichment scores denote multiple statistically significant clusters of genes with the specified annotation term. C ) Candidate hypermethylated genes in IDH1 MUT tumors are shown. Statistically significantly methylated genes in a given dataset denote an adjusted Q value of less than 0.05 (calculated by Storey multiple comparison adjustment, unpaired t test). MSRE = methylation-sensitive restriction enzyme; NA = not available; RBP1 = retinol binding protein 1; TCGA = The Cancer Genome Atlas.

Figure 2.

Characterization of genome-wide differential methylation. A ) Hierarchical clustering of differentially methylated CpG islands ( P < .05, unpaired t test) identified for isocitrate dehydrogenase 1 ( IDH1 ) mutant (MUT) and wild-type (WT) tumors using reduced representation bisulfite sequencing (RRBS) is shown. B ) A list of statistically significantly enriched annotation terms (enrichment score > 1.30) associated with differentially methylated genes by DAVID annotation analysis is given. Terms with multiple enrichment scores denote multiple statistically significant clusters of genes with the specified annotation term. C ) Candidate hypermethylated genes in IDH1 MUT tumors are shown. Statistically significantly methylated genes in a given dataset denote an adjusted Q value of less than 0.05 (calculated by Storey multiple comparison adjustment, unpaired t test). MSRE = methylation-sensitive restriction enzyme; NA = not available; RBP1 = retinol binding protein 1; TCGA = The Cancer Genome Atlas.

Methylated Genes in IDH1 Wild-Type and Mutant Gliomas

RRBS was then applied to characterize the methylome of five pairs of pathologically matched IDH1 WT and MUT frozen glioma samples. The clinical characteristics of the ten tumors are listed in Table 1 . Similar to results with U87MG DNA, approximately 19 million aligned reads were generated per sample, yielding methylation information on approximately 2 million CpG sites within 23 900 CpG islands, with an average of 59% coverage per island ( Table 2 ). Approximately 14 000 covered CpG islands were associated with unique promoters.

The mean methylation levels of each CpG island were compared between the five IDH1 WT and five IDH1 MUT tumors. Using an absolute difference in methylation greater than 40% and a cutoff of P value of less than or equal to .05, we found 346 CpG islands that were statistically significantly differentially methylated between IDH1 WT and MUT tumors; 125 of the CpG islands were associated with promoters ( Supplementary Table 2 , available online). Given the small sample size, only 81 CpG islands, including RBP1 , passed multiple testing correction with Q less than or equal to 0.05. The false discovery rate in this cohort, using the above statistical significance criteria with correction, was estimated at less than 0.02% by permutation testing.

Whole genome characterization revealed that methylation of IDH1 MUT tumors was statistically significantly increased compared with IDH1 WT tumors ( Table 2 and Figure 2A ). This finding is consistent with a glioma CpG island methylator phenotype observed by Noushmehr et al. and Christensen et al. ( 8 , 9 ). Compared with publicly available data, 106 of the 346 genes identified in our study were among the 1550 hypermethylated genes found by Noushmehr et al. ( 8 ). Annotation analysis of hypermethylated genes using the software package DAVID ( 28 ) revealed enrichment of several annotation terms including transcriptional regulation and apoptosis ( Figure 2B ).

RBP1 Hypermethylation in IDH1 and IDH2 Mutant Gliomas

To screen for candidate tumor suppressor genes that were hypermethylated in IDH1 MUT tumors, we employed the criteria that the hypermethylated CpG island had to be in the 5ʹ promoter region, show statistically significant hypermethylation within all three screening datasets (RRBS, MSRE, and TCGA), and correlate with decreased gene expression from the TCGA dataset. Several genes met these screening criteria, and a portion of the complete list (346 hypermethylated CpG islands, Supplementary Table 2 , available online) is provided in Figure 2C . One of the identified genes was RBP1 ( Figure 3A and Table 3 ). Analysis of data obtained from the TCGA database showed increased RBP1 methylation in 23 IDH1 MUT tumors compared with 124 WT tumors ( Figure 3B , and Table 3 ), and RBP1 promoter methylation was associated with decreased expression of CRBP1 in 141 GBM samples from the TCGA ( R2 = .625, P < .001) ( Figure 3B ).

Table 3.

RBP1 methylation by reduced representation bisulfite sequencing (RRBS), methylation-sensitive restriction enzyme (MSRE), and The Cancer Genome Atlas (TCGA) analysis*

  IDH1 WT  IDH1 MUT      
Assay No. of patients No. of patients Mean methylation (SD) No. of patients Mean methylation (SD) Possible range Actual range P P 
RRBS  10 0.05 (0.02) 0.68 (0.06)  0–1.0 0.01–0.81 <.001 .008 
MSRE  31  15 4.05 (0.57) 16  7.82 (0.49) −9.19–21.75  1.68–10.95 <.001 <.001 
TCGA 147 124 0.14 (0.01) 23 0.69 (0.08)  0–1.0  0–0.98 <.001 <.001 
  IDH1 WT  IDH1 MUT      
Assay No. of patients No. of patients Mean methylation (SD) No. of patients Mean methylation (SD) Possible range Actual range P P 
RRBS  10 0.05 (0.02) 0.68 (0.06)  0–1.0 0.01–0.81 <.001 .008 
MSRE  31  15 4.05 (0.57) 16  7.82 (0.49) −9.19–21.75  1.68–10.95 <.001 <.001 
TCGA 147 124 0.14 (0.01) 23 0.69 (0.08)  0–1.0  0–0.98 <.001 <.001 

* For RRBS and TCGA, the mean methylation represents the percentage of methylation in the region of interest. For MSRE, mean methylation represents standard deviations above known baseline (unmethylated) signal. IDH = isocitrate dehydrogenase; MUT = mutant; RBP1 = retinol binding protein 1; WT = wild type.

† Two-sided P was calculated by Student t test.

‡ Two-sided P was calculated by Wilcoxon test.

Figure 3.

Retinol binding protein 1 ( RBP1 ) methylation and gene expression. A ) The CpG island methylation pattern for the RBP1 promoter is shown. Sixty-two CpG sites were included in the analysis. CpG methylation for isocitrate dehydrogenase 1 ( IDH1 ) mutant (MUT) and wild-type (WT) glioma samples were determined by reduced representation bisulfite sequencing. Solid arrows flank the location sequenced by bisulfite sequencing with the sequencing primer pair 1. B ) Data from The Cancer Genome Atlas were analyzed to determine a relationship between RBP1 promoter methylation and gene expression using the Pearson correlation coefficient ( R2 ) and the slope of the regression line ( β ). The two-sided P value was based on the T statistic with 140 df . The blue circles represent IDH1 WT samples, and red squares represent IDH1 MUT samples. All tumors from the The Cancer Genome Atlas were glioblastoma multiforme (GBM, grade IV). MSRE = methylation-sensitive restriction enzyme; A3 = anaplastic astrocytoma (grade III); O3 = anaplastic oligodendroglioma (grade III).

Figure 3.

Retinol binding protein 1 ( RBP1 ) methylation and gene expression. A ) The CpG island methylation pattern for the RBP1 promoter is shown. Sixty-two CpG sites were included in the analysis. CpG methylation for isocitrate dehydrogenase 1 ( IDH1 ) mutant (MUT) and wild-type (WT) glioma samples were determined by reduced representation bisulfite sequencing. Solid arrows flank the location sequenced by bisulfite sequencing with the sequencing primer pair 1. B ) Data from The Cancer Genome Atlas were analyzed to determine a relationship between RBP1 promoter methylation and gene expression using the Pearson correlation coefficient ( R2 ) and the slope of the regression line ( β ). The two-sided P value was based on the T statistic with 140 df . The blue circles represent IDH1 WT samples, and red squares represent IDH1 MUT samples. All tumors from the The Cancer Genome Atlas were glioblastoma multiforme (GBM, grade IV). MSRE = methylation-sensitive restriction enzyme; A3 = anaplastic astrocytoma (grade III); O3 = anaplastic oligodendroglioma (grade III).

Given that RBP1 was found to be hypermethylated in the three datasets, correlated with decreased gene expression, and has been reported in the literature to be involved in the synthesis of the transcription regulator ATRA ( 17 , 29 , 30 ), we sought to evaluate RBP1 methylation by gene-specific BiSEQ in the Total Cohort of 198 glioma patients with available frozen or paraffin-embedded tissues. All 198 patients in the Total Cohort had RBP1 methylation determined by BiSEQ and included the 41 patients in the initial methylation screen (10 by RRBS and 31 by MSRE). RBP1 was hypermethylated in 76 of 79 IDH1 MUT tumors, 3 of 3 IDH2 MUT tumors, and 0 of 116 IDH1/IDH2 WT tumors ( Table 4 ). The RBP1 promoter was found by BiSEQ to be either essentially fully methylated or fully unmethylated across the 21 CpG sites evaluated ( Supplementary Figure 3 , available online). Also, in samples with BiSEQ and RRBS/MSRE data, the results from the techniques were concordant, supporting the overall accuracy of our RRBS/MSRE dataset ( Supplementary Figure 4 , available online). These data show that both IDH1 and IDH2 mutations are highly correlated with RBP1 hypermethylation in the 198 gliomas we analyzed (Spearman R2 = 0.94, 95% confidence interval = 0.92 to 0.95, P < .001).

Figure 4.

Retinol binding protein 1 ( RBP1 ) gene expression in human cell lines and glioma tumor samples. A ) RBP1 mRNA levels in the astrocyte progenitor cells (APC), human oligodendroglioma (HOG), U373, U138, and D54 cell lines were determined by quantitative real-time reverse transcriptase polymerase chain reaction using β-actin as an internal control and standardized to the mRNA level for APC cells, which was set as 100%. Data represent the mean and 95% confidence interval ( whisker bars ) from three independent experiments done in triplicate. B ) RBP1 mRNA levels in 56 samples from the Total Cohort were measured by quantitative real-time reverse transcriptase polymerase chain reaction using β−actin as an internal control and standardized to the expression observed for normal brain cDNA (n = 7). Data represent the mean from three independent experiments done in duplicate. C ) Representative western blots for cellular retinol binding protein 1 (CRBP1) protein expression in six cell lines, five normal brain samples, and sixteen tumor samples are shown. Isocitrate dehydrogenase 1 ( IDH1 ) /IDH2 wild-type (WT) tumors (T1–T8) and IDH1/IDH2 mutant (MUT) tumors (T9–T16) were analyzed, and α-tubulin was used as a loading control. Please note that the goat anti-CRBP1 antibody used for the cell lines produced two nonspecific bands that are shown in the top panel of some samples. These nonspecific bands were not observed with the rabbit anti-CRBP1 antibody used in the normal brain and tumor tissues.

Figure 4.

Retinol binding protein 1 ( RBP1 ) gene expression in human cell lines and glioma tumor samples. A ) RBP1 mRNA levels in the astrocyte progenitor cells (APC), human oligodendroglioma (HOG), U373, U138, and D54 cell lines were determined by quantitative real-time reverse transcriptase polymerase chain reaction using β-actin as an internal control and standardized to the mRNA level for APC cells, which was set as 100%. Data represent the mean and 95% confidence interval ( whisker bars ) from three independent experiments done in triplicate. B ) RBP1 mRNA levels in 56 samples from the Total Cohort were measured by quantitative real-time reverse transcriptase polymerase chain reaction using β−actin as an internal control and standardized to the expression observed for normal brain cDNA (n = 7). Data represent the mean from three independent experiments done in duplicate. C ) Representative western blots for cellular retinol binding protein 1 (CRBP1) protein expression in six cell lines, five normal brain samples, and sixteen tumor samples are shown. Isocitrate dehydrogenase 1 ( IDH1 ) /IDH2 wild-type (WT) tumors (T1–T8) and IDH1/IDH2 mutant (MUT) tumors (T9–T16) were analyzed, and α-tubulin was used as a loading control. Please note that the goat anti-CRBP1 antibody used for the cell lines produced two nonspecific bands that are shown in the top panel of some samples. These nonspecific bands were not observed with the rabbit anti-CRBP1 antibody used in the normal brain and tumor tissues.

RBP1 Hypermethylation and CRBP1 Regulation

To assess if RBP1 hypermethylation is associated with decreased CRBP1 expression, mRNA and protein expression were measured in various cell lines and glioma tumor samples by quantitative RT-PCR and western blot. Nontransformed APC cells were unmethylated for RBP1 , and CRBP1 expression in APC cells was used as the reference. All of the tested glioma cell lines demonstrated RBP1 promoter methylation and decreased CRBP1 mRNA, despite being IDH1 wild-type ( Figure 4A ). Western blot results were consistent with decreased CRBP1 protein expression in glioma cell lines ( Figure 4C ). Also, we performed quantitative RT-PCR and western blot on 12 IDH1 WT, 43 IDH1 MUT, and 1 IDH2 MUT glioma samples using available frozen tissue. In all 56 samples, RBP1 was unmethylated in IDH1/IDH2 WT and methylated in IDH1/IDH2 MUT (data not shown). RBP1 mRNA levels from seven normal brain samples were used as controls. Figure 4B shows that IDH1/IDH2 MUT tumors (n = 44) had statistically significantly decreased mRNA levels compared with WT tumors (n = 12) ( IDH1/IDH2 MUT tumors vs WT tumors: mean = 17.93, SD =39.3 vs mean = 208.9, SD =337.5, respectively; P < .001). Western blot showed high CRBP1 protein expression in WT tumors (n = 12), but essentially undetectable levels in mutant tumors (n = 44) ( Figure 4C ), which was confirmed by densitometric analysis (relative CRBP1 in IDH1/IDH2 WT vs MUT tumors: mean = 193.3, SD = 224.2 vs mean = 0.02, SD = 0.15, respectively; P < .001) (data not shown).

Relationship Between Overall Survival and RBP1 Methylation Status

We identified 124 primary GBM patients in our cohort who had treatment-naïve tumor samples and who had received standard chemoradiation postoperatively ( 21 ). In our cohort, patients with WT GBM tumors (n = 101) had a statistically significant decrease in median OS compared with patients with IDH1 or IDH2 MUT GBM tumors (n = 23) (20.3 months vs 36.9 months, respectively; hazard ratio of death = 2.91, 95% confidence interval = 1.50 to 5.66, P = .002) ( Figure 5A ). As expected from the close association between RBP1 promoter methylation and IDH1 or IDH2 mutation, RBP1 -unmethylated patients (n = 102) had a statistically significantly decreased median OS of 20.3 months vs 36.8 months for RBP1 -methylated patients (n = 22; hazard ratio of death = 2.48, 95% confidence interval= 1.30 to 4.75, P = .006) ( Figure 5B ). Multivariable analysis showed that either IDH1/IDH2 mutation or RBP1 methylation status is an independent predictor of OS when IDH1/IDH2 mutation or RBP1 methylation status was analyzed separately with the clinical variables age, sex, performance status, and extent of resection ( Supplementary Tables 3 and 4 , available online). When patients were stratified by whether or not they received adjuvant isotretinoin with temozolomide, RBP1 -unmethylated patients who were treated (n = 19) and those who were not (n = 83) had a similar median OS (22.0 months vs 19.8 months, respectively; P = .25) ( Supplementary Figure 5, A , available online). The median OS for RBP1 -methylated patients who had received isotretinion (n = 7) and untreated patients (n = 15) was also similar (2.5 months vs 36.7 months, respectively; P = .53) ( Supplementary Figure 5, B , available online).

Figure 5.

Kaplan–Meier analysis of overall survival (OS) in a cohort of 124 primary glioblastoma multiforme patients using Cox proportional hazards analysis. A ) Survival among patients with isocitrate dehydrogenase 1 ( IDH1 ) /IDH2 MUT (solid line, n = 23) and IDH1/IDH2 WT (dashed, n = 101) tumors shown. B ) Survival among retinol binding protein 1 ( RBP1) -methylated (solid line, n = 22) and RBP1 -unmethylated (dashed line, n = 102) patients is shown. The number of patients at risk is given below each Kaplan–Meier curve. M = methylated; MUT = mutant; U = unmethylated; WT = wild-type.

Figure 5.

Kaplan–Meier analysis of overall survival (OS) in a cohort of 124 primary glioblastoma multiforme patients using Cox proportional hazards analysis. A ) Survival among patients with isocitrate dehydrogenase 1 ( IDH1 ) /IDH2 MUT (solid line, n = 23) and IDH1/IDH2 WT (dashed, n = 101) tumors shown. B ) Survival among retinol binding protein 1 ( RBP1) -methylated (solid line, n = 22) and RBP1 -unmethylated (dashed line, n = 102) patients is shown. The number of patients at risk is given below each Kaplan–Meier curve. M = methylated; MUT = mutant; U = unmethylated; WT = wild-type.

Discussion

With the clinically significant utility of MGMT promoter methylation as a predictor of response to standard-of-care temozolomide treatment in gliomas ( 31–36 ), and the recent discovery of a CpG island hypermethylation profile associated with IDH1 MUT gliomas ( 4 , 8–10 ), detailed characterization of DNA CpG island methylation has become important in the understanding of gliomas. Epigenetic gene regulation plays an important role in tumorigenesis by regulating cellular processes such as DNA repair and cellular differentiation ( 37–40 ), and methylation changes at multiple genes have been observed in gliomas ( 8 , 37 , 41 ). Although other epigenetic regulatory mechanisms exist, genome-wide methylation sequencing can provide epigenetic information to complement DNA sequence and gene-expression information in understanding the molecular basis of glioma pathogenesis. RRBS is an innovative yet cost-effective technique for high-resolution methylome sequencing using restriction enzymes that cleave genomic DNA into fragments enriched for CpG sites ( 15 ). Through RRBS, we were able to identify multiple promoters that were hypermethylated in IDH1 mutant gliomas. In particular, the promoter for RBP1 , a gene important in retinoic acid metabolism, is nearly always hypermethylated in IDH1/IDH2 MUT tumors and may contribute to their pathogenesis.

In an overall cohort of 198 patients, RBP1 was found to be consistently hypermethylated in IDH1 or IDH2 MUT glioma tumors but not in WT tumors. Data from the current study and from the TCGA showed that RBP1 methylation is associated with decreased expression of CRBP1. In addition, although our sample size for the rare IDH2 mutation [2%–3% of gliomas ( 6 , 42 , 43 )] is small, all three IDH2 mutants were RBP1 -hypermethylated. RBP1 hypermethylation was observed in both IDH1 and IDH2 MUT tumors, suggesting that decreased expression of RBP1 may be a common mechanism for the formation of both IDH1 and IDH2 MUT gliomas. Diagnostically, RBP1 methylation may have utility as a single biomarker for detecting both IDH1 and IDH2 mutations in gliomas. In acute myelogenous leukemia cells, IDH1 or IDH2 mutation produces 2-HG ( 2 , 3 ) and has been associated with a hypermethylation profile ( 44 ). It is unclear at present whether RBP1 is hypermethylated in acute myelogenous leukemia cells with IDH1 or IDH2 mutation, although RBP1 has been observed to be methylated with decreased gene expression in diffuse large B-cell lymphoma ( 45 ).

Despite the large number of gliomas carrying IDH1 mutations, the mechanisms by which IDH1 mutations contribute to gliomagenesis remain unclear. One possible mechanism is through the production of 2-HG ( 7 ) and inhibition of α-ketoglutarate-dependent dioxygenases such as the TET-family of 5-methylcytosine hydroxylases ( 46 ). By inhibiting α-ketoglutarate-dependent cellular enzymes important in regulating DNA methylation, it is possible that one consequence of IDH1 mutation is aberrant DNA hypermethylation and inactivation of tumor suppressor genes ( 10 ). IDH2 mutation occurs less frequently than IDH1 mutation, but it also produces 2-HG ( 3 , 6 ). We suspect that given the similar production of 2-HG, IDH2 mutation would also induce a global methylator phenotype. It is possible that RBP1 is one of the driver genes and may serve as a reporter for the observed global methylator phenotype.

ATRA is an important transcriptional regulator acting through the retinoic acid receptor (RAR and RXR) families of nuclear ligand-dependent transcription factors ( 17 ). Retinoic acid receptors are expressed in various brain regions ( 47–48 ), and ATRA is involved in nervous system development and regulation of cellular processes such as differentiation, apoptosis, and cell cycle progression ( 19 , 49 ). Dysregulation of retinoic acid metabolism has been implicated in several cancers ( 19 , 50–53 ), and although few studies have examined CRBP1 in gliomas, CRBP1 knockdown increased anchorage-independent growth in telomerase-immortalized nontransformed human breast epithelial HME1 cells ( 50 ). In addition, CRBP1 is an important transporter in the synthesis of ATRA ( 17 , 29 , 30 ). Given that decreased CRBP1 expression is associated with decreased levels of ATRA ( 17 , 29 , 30 , 54 ), we propose that decreased CRBP1 expression may lead to decreased ATRA levels in RBP1 -methylated tumors, and further studies are needed to assess ATRA levels in RBP1 -methylated gliomas.

In our RRBS screen, other genes involved in ATRA metabolism were not differentially methylated in the five IDH1 MUT samples examined (RAR-α, -β, -γ, RXR-α, -β, -γ, and cellular retinoic acid binding proteins CRABP1 and CRABP2, data not shown). Interestingly, all-trans, 13-cis, and 9-cis retinoic acid levels have been noted to be lower in grade II gliomas vs grade IV gliomas in a recent study ( 54 ). IDH1/IDH2 mutation status was not stated in the above-mentioned study, and it is unclear if these observations were related to IDH1/IDH2 mutation ( 54 ). However, given the high prevalence of IDH1 mutation in grade II gliomas (70%–80%), it is possible that the observed lower retinoic acid levels are related to IDH1 mutation, increased synthesis of retinoic acid in higher grade tumors, or a combination of both ( 54 ). Further studies are also needed to examine if retinoic acid levels are different between IDH1 MUT and WT tumors of similar grades, if increased expression of CRBP1 can occur in IDH1 MUT gliomas, and if RBP1 hypermethylation contributes to the improved survival observed in IDH1 MUT patients.

We observed that patients with RBP1 -methylated tumors had increased OS. This observation is consistent with a recent study showing decreased RBP1 expression in long-term–surviving glioma patients ( 55 ). However, retinoic acid supplementation has been used with mixed success in glioma patients. It remains controversial whether treatment with the retinoic acid analog isotretinoin prolongs survival of GBM patients, although several studies have suggested that isotretinoin delays tumor progression in GBM patients ( 56–58 ). In our study, there was a nonstatistically significant increase in median OS for RBP1 -methylated patients who received adjuvant isotretinoin compared with RBP1 -methylated patients who did not receive adjuvant isotretinoin. Although we do not routinely treat patients with isotretinoin in our institution because of its limited efficacy and substantial side effects, our data raise the question of whether isotretinoin would be more efficacious in patients with RBP1 -methylated or IDH1/IDH2 MUT tumors. Although not proven by our data, we propose the hypothesis that RBP1 methylation or IDH1/IDH2 mutational status may predict response to isotretinoin therapy in glioma patients, and further studies are needed to examine if therapy with retinoids is particularly effective in patients with RBP1 promoter hypermethylation or IDH1/IDH2 mutation.

Our study is not without limitations. First, the link between IDH1/IDH2 mutation and RBP1 methylation is correlative and does not imply causation. Second, the exact functional consequence of RBP1 hypermethylation in gliomas has yet to be elucidated. Third, our observation on the effects of isotretinoin treatment on the survival of patients with RBP1- methylated (or IDH1/IDH2 mutant) tumors is preliminary and is limited by the small sample size of our cohort, inconsistent isotretinoin treatment regimen, and a lack of a proper control for other prognostic factors such as MGMT status. Lastly, although successfully employed in the current study, the RRBS technique has high reagent and bioinformatics cost and is limited by incomplete coverage of the whole genome.

In conclusion, our results demonstrate the utility of RRBS for genome-scale methylation sequencing in gliomas by identifying several potential tumor suppressor genes that are hypermethylated in IDH1 MUT tumors. RBP1 promoter hypermethylation is found in nearly all IDH1/IDH2 MUT gliomas and is associated with decreased CRBP1 expression. Because CRBP1 is involved in retinoic acid synthesis, our results raise the possibility that dysregulation of retinoic acid metabolism may contribute to glioma formation and affect the response to retinoid-based therapies. The high correlation between RBP1 methylation and IDH1/IDH2 mutations in glioma may also serve as a biomarker for IDH mutations and a “reporter” for further mechanistic studies on IDH mutations and DNA methylation. Further studies are needed to examine the mechanism by which IDH mutations contribute to RBP1 hypermethylation, if dysregulation of retinoic acid metabolism is involved in gliomagenesis, and if therapy with retinoids is particularly effective in patients with RBP1 promoter hypermethylation.

Funding

This work was supported by the National Cancer Institute at the National Institutes of Health (K08CA124479 to AL); University of California/Cancer Research Coordinating Committee (to AL); American Association of Neurological Surgeons Neurosurgery Research and Education Foundation Fellowship sponsored by the Section on Tumors (to APC); Congress of Neurological Surgeons Wilder Penfield Fellowship (to APC); and American Brain Tumor Association Basic Research Fellowship (to SLi).

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Notes

The sponsors played no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; and the decision to submit the manuscript for publication. The Regents of the University of California have filed a provisional patent application based on the work in this manuscript (inventors A. P. Chou, A. Lai, R. Chowdhury, W. Chen). A. Lai has been a consultant to and receives unrelated grant support from Genentech/Roche (Basel, Switzerland). T. Cloughesy has been paid for lectures and served on a speakers’ bureau for Merck (Whitehouse Station, NJ) and has been a consultant to Astrazeneca (London, UK), Amgen (Thousand Oaks, CA), Roche (Basel, Switzerland), Novartis (Basel, Switzerland), Celgene (Summit, NJ), Merck, and Merck Serono (Geneva, Switzerland). H. I. Kornblum, R. M. Prins, and L. M. Liau received unrelated grant support from Agios Pharmaceuticals (Cambridge, MA). L. M. Liau also received unrelated grant support from Northwest Biotherapeutics (Bethesda, MD).