The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space
Supplementary Website
Abstract
Glioblastoma is characterized by widespread genetic and transcriptional heterogeneity, yet little is known about the role of the epigenome in glioblastoma disease progression. Here, we present genome-scale maps of DNA methylation in matched primary and recurring glioblastoma tumors, using data from a highly annotated clinical cohort that was selected through a national patient registry. We demonstrate the feasibility of DNA methylation mapping in a large set of routinely collected FFPE samples, and we validate bisulfite sequencing as a multipurpose assay that allowed us to infer a range of different genetic, epigenetic, and transcriptional characteristics of the profiled tumor samples. On the basis of these data, we identified subtle differences between primary and recurring tumors, links between DNA methylation and the tumor microenvironment, and an association of epigenetic tumor heterogeneity with patient survival. In summary, this study establishes an open resource for dissecting DNA methylation heterogeneity in a genetically diverse and heterogeneous cancer, and it demonstrates the feasibility of integrating epigenomics, radiology, and digital pathology for a national cohort, thereby leveraging existing samples and data collected as part of routine clinical practice.
Overview
Genome browser tracks
View the DNA methylation data as genome browser tracks. Note: The tracks are not displayed automatically because the large number of tracks can cause visualization problems. To view selected tracks, please click on the GBMatch_RRBS link below the browser window and change the display mode from "hide" to "dense" or "full" for the samples of interest.
| Name | Description and link |
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| Genome browser | |
| Tutorials |
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Histology
| Description | Preview | Download |
|---|---|---|
| Raw images |
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Progression cohort: GBMatch_histo_raw.zip (657 MB) Validation cohort: GBMatch_val_histo_raw.zip (195 MB) |
| Segmented images |
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Progression cohort: GBMatch_histo_seg.zip (616 MB) Validation cohort: GBMatch_val_histo_seg.zip (185 MB) Legend: Legend.txt |
| Image library (view with the NDP viewer) |
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Progression cohort: GBMatch_histo_lib.zip (419 GB) Validation cohort: GBMatch_val_histo_lib.zip (125 GB) Immunohistochemistry: IHC.zip (600 GB) |
Magnetic resonance imaging
| Description | Preview | Download |
|---|---|---|
| Raw images |
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Progression cohort: GBMatch_mri_raw.zip (234 MB) Validation cohort: GBMatch_val_mri_raw.zip (108 MB) |
| Segmented images |
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Progression cohort: GBMatch_mri_seg.zip (209 MB) Validation cohort: GBMatch_val_mri_seg.zip (97 MB) |
Data and software access
Note on how to best use the provided data for custom analysis: The master annotation table combines all different data types used in this study. Each row annotates one RRBS sample with information derived from all data types used in this study (e.g. histopathology, MRI, clinical, molecular, technical,...).
The master annotation table is linked to all raw and processed data through the first five colums ("patID", "surgery", "id", "EGA_id", "GEO_id"):
patID: Unique patient ID (together with "surgery" links to histopathological and MRI raw data)
surgery: Surgery number (together with "patID" links to histopathological and MRI raw data)
id: unique sample ID used in this study (links to RRBS data provided as R objects)
EGA_id: Unique sample ID assigned by EGA (links to raw sequencing data archived at EGA)
GEO_id: Unique sample ID assigned by GEO (links to processed sequencing data archived at GEO)
| Description | Preview | Download/access |
|---|---|---|
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Master annotation table |
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GBMatch_sampleAnnotation.tsv (1.4 MB) GBMatch_columnAnnotation.tsv (26.8 KB) |
| DNA Methylation data ready to use in R |
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5kb tiles (filtered): rrbsTiled5ksub.RData (778 MB) Promoters (1+0.5kb): rrbsProm1kb_geneNames.RData (187 MB) Single CpGs (filtered): rrbsCg.RData (1 GB) Single CpGs (all): rrbsCgNoMin.RData (1.6 GB) |
| Archived data access |
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Raw data at EGA: EGAS00001002538 Processed data at GEO: GSE100351 |
| Machine learning features |
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GBMatch_ML_features.zip (1.3 MB) |
| Software |
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Git repository at Github: https://github.com/epigen/GBMatch/ Git repository (GitHub snapshot): GBMatch-master.zip |
Data explorer
Interactive visualization of the data. Hover over (click on) a dot to highlight (select) all samples of a patient. Double-click to deselect. (r = Pearson correlation)
Stats:
X-axis:
Y-axis:
Color:
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Citation
If you use these resources in your research, please cite our publication in Nature Medicine (https://www.nature.com/articles/s41591-018-0156-x).
Klughammer J*, Kiesel B*, Roetzer T, Fortelny N, Nemc A, Nenning K, Furtner J, Sheffield NC, Datlinger P, Peter N, Nowosielski M, Augustin M, Mischkulnig M, Ströbel T, Alpar D, Ergüner B, Senekowitsch M, Moser P, Freyschlag CF, Kerschbaumer J, Thomé C, Grams AE, Stockhammer G, Kitzwoegerer M, Oberndorfer S, Marhold F, Weis S, Trenkler J, Buchroithner J, Pichler J, Haybaeck J, Krassnig S, Ali KM, von Campe G, Payer F, Sherif C, Preiser J, Hauser T, Winkler PA, Kleindienst W, Würtz F, Brandner-Kokalj T, Stultschnig M, Schweiger S, Dieckmann K, Preusser M, Langs G, Baumann B, Knosp E, Widhalm G, Marosi C, Hainfellner JA, Woehrer A#§, Bock C# (2018). The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space. Nature Medicine, doi: https://doi.org/10.1038/s41591-018-0156-x
The preliminary publication is freely available on bioRxiv: http://www.biorxiv.org/content/early/2017/08/09/173864