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ABSTRACT Brainstem gliomas are a heterogeneous group of tumors that encompass both benign tumors cured with surgical resection and highly lethal cancers with no efficacious therapies. We

perform a comprehensive study incorporating epigenetic and genomic analyses on a large cohort of brainstem gliomas, including Diffuse Intrinsic Pontine Gliomas. Here we report, from DNA

methylation data, distinct clusters termed H3-Pons, H3-Medulla, IDH, and PA-like, each associated with unique genomic and clinical profiles. The majority of tumors within H3-Pons

and-H3-Medulla harbors _H3F3A_ mutations but shows distinct methylation patterns that correlate with anatomical localization within the pons or medulla, respectively. Clinical data show

significantly different overall survival between these clusters, and pathway analysis demonstrates different oncogenic mechanisms in these samples. Our findings indicate that the integration

of genetic and epigenetic data can facilitate better understanding of brainstem gliomagenesis and classification, and guide future studies for the development of novel treatments for this

disease. SIMILAR CONTENT BEING VIEWED BY OTHERS MOLECULAR LANDSCAPE OF PEDIATRIC TYPE _IDH_ WILDTYPE, _H3_ WILDTYPE HEMISPHERIC GLIOBLASTOMAS Article 24 March 2022 THE DNA METHYLOME OF

PEDIATRIC BRAIN TUMORS APPEARS SHAPED BY STRUCTURAL VARIATION AND PREDICTS SURVIVAL Article Open access 08 August 2024 SPATIAL HETEROGENEITY IN DNA METHYLATION AND CHROMOSOMAL ALTERATIONS IN

DIFFUSE GLIOMAS AND MENINGIOMAS Article Open access 14 June 2022 INTRODUCTION Brainstem gliomas represent a heterogeneous group of tumors that arise from the midbrain, pons, or medulla.

Among these tumors, pediatric diffuse intrinsic pontine glioma (DIPG), with a median overall survival of 9–12 months1, has been the main research focus for the past five decades due to the

inoperability and resistance to chemotherapy and radiotherapy2,3,4,5,6. Approximately 80% of pediatric DIPGs harbor K27M mutations affecting _H3F3A_ or _HIST1H3B/C_1,7,8,9,10,11,12. These

K27M-mutant tumors are associated with a particularly poor prognosis13. In the 2016 World Health Organization (WHO) classification of CNS tumors, the term “diffuse midline gliomas,

H3K27M-mutant” was introduced to represent this DIPG tumor subset14. However, the molecular characteristics of the non-pediatric brainstem gliomas, such as midbrain and medulla oblongata

gliomas, remain poorly characterized. Research on these tumors has been challenging due to the relatively low incidence rate and the high risks related to surgical resection resulting in low

tissue availability. We collected, to our knowledge, the largest and most comprehensive cohort of brainstem gliomas, encompassing all age groups and anatomic locations, including medulla,

pons, and midbrain. We performed integrated whole genome sequencing, RNA sequencing, and array-based genome-wide methylation analysis to acquire a more comprehensive picture of the molecular

composition of these brain tumors. Here, we report methylation-based clusters that identify brainstem glioma subsets associated with tumor location and mutation landscape. We present two

distinct clusters of H3-mutant brainstem gliomas, H3-Pons and H3-Medulla, which despite their similar genetic mutations, differ not only in location, but in methylation pattern, gene

expression, and prognosis. RESULTS PATIENT COHORT CHARACTERISTICS We collected tumor samples and matched blood from 126 patients (detailed clinical information listed in Supplementary Table 

1). Tumor locations included midbrain tegmentum (11/126, 8.7%), tectum (5/126, 4.0%), pontomesencephalic junction (2/126, 1.6%), pons (38/126, 30.2%), middle cerebellar peduncle (7/126,

5.6%), pontomedullary (16/126, 12.6%), medulla (42/126, 33.3%), and midbrain-thalamus (5/126, 4.0%) (Supplementary Fig. 1; Supplementary Table 2). Patients were aged from 2 to 62 years, with

a median age of 23 years. Tumors were graded based on the WHO classification and included 8.7% (11/126) WHO grade I, 41.3% (52/126) WHO grade II, 31.0% (39/126) WHO grade III, and 19.0%

(24/126) WHO grade IV tumors. The majority of original histopathological diagnoses were astrocytoma (59, 46.8%), along with 27 oligoastrocytomas (21.4%), 24 glioblastomas (19.0%), 8

pilocytic astrocytomas (PA) (6.3%), 3 gangliogliomas (2.4%), 2 pilomyxoid astrocytomas (PMA) (1.6%), 1 pleomorphic xanthoastrocytoma (PXA) (0.8%), and 1 oligodendroglioma (0.8%). Among the

38 tumors located in the pons, 33 (86.8%) were diagnosed as diffuse high-grade midline gliomas, NOS (historically known as DIPG), and 5 were focal pontine tumors, most likely pilocytic

astrocytomas (13.2%). To analyze the tumor genomic and epigenomic characteristics of this tumor cohort, we performed methylation microarrays (_n_ = 123) and RNA sequencing (RNAseq) (_n_ = 

75) on tumors included in this study, and whole genome (_n_ = 97) and panel sequencing (_n_ = 21) on paired tumors and normal (germline) controls. METHYLATION CLASSIFICATION REVEALS DISTINCT

H3 CLUSTERS CORRELATED WITH TUMOR LOCATIONS IN BRAINSTEM GLIOMAS DNA methylation status has been utilized for classification of brain tumors, and could assist diagnosis and

prognostication2,13,15,16,17. We performed unsupervised hierarchical clustering (linkage method: WPGMA; distance: Euclidean) using the top 20,000 most variable probes (Supplementary Table 

3), excluding methylation probes on sex chromosomes and common single nucleotide polymorphisms sites. This approach revealed four distinct methylation clusters (Fig. 1). The hypermethylated

cluster (methylation cluster IDH) consisted primarily of tumors bearing _isocitrate dehydrogenase 1_ (_IDH1)_ mutations. Histone H3 mutant samples formed two different clusters associated

with tumor location (methylation clusters H3-Pons and H3-Medulla) (Fig. 1). The remaining cluster (methylation cluster PA-like) consisted largely of lower grade gliomas without detectable

_IDH1_ or _H3_ mutations. These clusters matched with the DKFZ methylation classifier by three main classes15, “diffuse midline gliomas H3 K27M mutant”, “pilocytic astrocytoma”, and “IDH

mutant”. However, our methylation-based clustering analysis revealed that H3-mutant samples were made up of two distinct sub-clusters, which correlated with their anatomic localization in

the brainstem of either the pons (methylation cluster H3-Pons) or medulla (methylation cluster H3-Medulla). Principal component analysis of the methylation array probe data using R packages

and functions (ggbiplot and prcomp)18,19 also revealed distinct groups based on the DKFZ methylation classifier (Fig. 2a; Supplementary Fig. 2a). When performing PCA specifically for whole

probes from tumors within methylation cluster H3-Pons and H3-Medulla (Fig. 2b), as well as locations in methylation cluster H3-Pons and H3-Medulla (Supplementary Fig. 2b), we observed

similar trends in these clusters and locations. The first principal component could explain most of the variance in the various principal component analyses performed (96.4% in Fig. 2a and

96.7% in Fig. 2b, Supplementary Fig. 2b), indicating methylation profiling could provide significant utility as a classifier of these samples. The four distinct methylation clusters,

corresponding to the H3-Pons, H3-Medulla, IDH, and PA-like subtypes, could be readily identified. Analysis using Tumor Map supported these findings by demonstrating a similar distinction of

clusters correlated to tumor location20 (Fig. 2c; Supplementary Fig. 3). The top 20,000 variable probes used above were based on methylation data from all samples, including samples from

methylation clusters IDH and PA-like. To prevent potential confounding factors for those differential probes mainly for methylation clusters IDH and PA-like, we selected the top 20,000

variable probes from only the methylation clusters H3-Pons and H3-Medulla, and we performed hierarchical clustering on these cases (Supplementary Fig. 4). Heatmap analysis showed this

hierarchical clustering of subclusters indeed maintained distinct clusters. Most samples from methylation cluster H3-Medulla are tumors from the medulla, or the nearby dorsal pontomedullary

junction, (23/27, 85.2%) (Supplementary Table 1, Supplementary Table 2; Supplementary Fig. 1). Tumors in the methylation cluster H3-Pons are largely pontine tumors (29 out of 47, 61.7%) and

4 from the nearby middle cerebellar peduncle (8.5%). Methylation cluster PA-like primarily consisted of medullary tumors (18 out of 34, 52.9%), along with 12 tumors from midbrain regions (8

from midbrain tegmentum and 4 from tectum, 35.3%). Unlike other clusters having focused locations, tumors from cluster _IDH_ were distributed across brainstem regions, 3 from the pons (20%),

3 from the middle cerebellar peduncle (20%), and 4 from the medulla (26.7%). Among all 31 DIPG tumors with methylation data, 27 of them were included in the H3-Pons cluster (87.1%), with

the remaining 4 DIPG cases clustering in the IDH cluster (12.9%). All patients in this study were of Asian ethnicity. To evaluate if these distinct H3 clusters can be found in a

predominantly non-Asian population, we combined our dataset with published studies of 28 DIPG samples (Buczkowicz et al.9). From tSNE results of those selected top 20,000 variable probes, we

found that those DIPG samples grouped closely with our H3-Pons samples as expected (Supplementary Fig. 5), indicating that classification according to the methylation cluster H3-Pons may be

robust across ethnicities. Notably, of the 6 DIPG cases from Buczkowicz et al. that clustered toward the PA-like group, 4 were H3WT and all were previously classified in either the “silent”

or “MYCN” methylation clusters of that study. TUMORS OF DISTINCT METHYLATION CLUSTERS DISPLAY DIFFERENT GENOMIC LANDSCAPES To identify somatic genetic alterations in this brainstem glioma

cohort, whole genome sequencing and panel targeted sequencing for 68 common mutated brain tumor genes were used on both the tumor samples and matched blood (Fig. 3). BWA and GATK

MuTect221,22 were used for variant calling, and IntOgen23,24 and FML25 were used to predict potential driver mutations and significant noncoding region mutations. The mutation landscape of

these tumors is grouped by the four distinct DNA methylation clusters defined above (Fig. 3; Supplementary Table 1). No significantly mutated noncoding regions were identified. Methylation

cluster H3-Pons and methylation cluster H3-Medulla are both enriched for H3 mutations, (H3-Pons: 40/43, 93.0%; H3-Medulla: 23/26, 88.5%), while the mutation frequencies of _PPM1D_, _FGFR1_,

and _NF1_ are higher in methylation cluster H3-Medulla (_PPM1D_ 46.15%, _FGFR1_ 19.23%, _NF1_ 46.15%) than in methylation cluster H3-Pons (_PPM1D_ 11.63%, _FGFR1_ 2.33%, _NF1_ 16.28%) (Fig. 

4a). _PPM1D_ truncating mutations have been reported in brainstem gliomas, but the frequency rate varies between different studies1,9,10,11,26. Three of the 30 DIPGs harbored _PPM1D_

mutations, as compared with 14 of 41 non-DIPG in H3 mutant brainstem gliomas. Methylation clusters H3-Pons and H3-Medulla shared several driver mutations, as predicated by FML/Intogen:

_PPM1D, NF1, ATRX, FGFR1, H3F3A, TP53_, and _SNRNP200_. However, methylation cluster H3-Pons specifically included other distinct driver alterations, such as _BCOR, TCF12, KRAS, PTEN, MGAT1,

and PIK3CA_ (Fig. 4b). As expected, methylation cluster IDH is enriched for _IDH1_ mutations (78.57%) and most of these cases harbored co-occurring _TP53_ (92.86%) and _ATRX_ (50%)

mutations. FML/Intogen analysis showed that _TP53_, _ATRX_, and _IDH1_ were significantly mutated and potential driver mutations in this methylation cluster. Of note, the patients in this

cluster are adults (age range 23–60 years). Methylation cluster PA-like, consisting primarily of grade I or II brainstem gliomas, showed distinct patterns in DNA methylation and genetic

mutations (Figs. 1 and  3). The number of mutations for each sample was lower in samples of cluster PA-like compared with samples in other clusters (mean mutation count: 6.9; methylation

cluster IDH, H3-Medulla, H3-Pons mean mutation count: 24.1). Interestingly, FML/Intogen driver analysis revealed potential driver mutations in _NF1_ and _SF3B1_ coding regions, and in the

noncoding 3′ UTR of _EXD3_, despite their low frequency of mutations. Overall, few of the commonly associated glioma driver genes were mutated in the methylation cluster PA-like, and _NF1_

and _SF3B1_ were the only recurrently mutated genes in this cluster. GENE EXPRESSION PROFILING REVEALS DISTINCT ENRICHED GENE SETS IN METHYLATION CLUSTERS H3-PONS AND H3-MEDULLA We performed

RNAseq on samples from the brainstem glioma cohort and evaluate patterns in gene expression (Supplementary Fig. 6). When selecting genes encoding transcription factors, similar to DNA

methylation profiling, methylation clusters H3-Pons and H3-Medulla could be differentiated by gene expression profiles27 (Supplementary Fig. 6). Next, we used HTseq28 and edgeR29,30 to

identify differentially expressed genes between methylation clusters H3-Pons and H3-Medulla, followed by enrichment analysis of these genes in pathways and gene ontology (GO) using DAVID31

(Supplementary Tables 4 and Supplementary Table 5). The top altered pathways and GOs identified in our analyses are related to cell cycle, cell division, or mitosis, showing the potential

different mechanisms involved in the tumors in methylation clusters H3-Pons or H3-Medulla. We also applied Gene Set Enrichment Analysis for the normalized FPKM values32,33 (Fig. 4c–h). Gene

sets enriched in methylation cluster H3-Medulla were primarily immune response-related gene sets such as interferon gamma signaling and cytokine receptor interaction, while gene sets

enriched in methylation cluster H3-Pons were cell cycle or mitosis-related such as DNA replication, mitotic phase, and checkpoints. FUSION GENES AND COPY NUMBER ALTERATIONS Using whole

genome sequencing data and RNA sequencing data, we evaluated our cohort for genomic rearrangements (Manta34) and fusion genes (STAR-fusion35). Several common fusion genes were detected,

including _KIAA1549_/_BRAF_ (_n_ = 3) and _NTRK_ fusions, which have been reported in gliomas11,36,37. The _KIAA1549/BRAF_ fusion was detected in 1 out of 8 pilocytic astrocytomas in our

cohort and in two grade II astrocytomas (all 3 in methylation cluster PA-like). We validated several recurrent fusion genes identified in our analyses, including _C15orf57-CBX3_ genes (_n_ =

 3), and _NTRK2_-other genes (_n_ = 6) (Fig. 5; Supplementary Table 6 and Supplementary Table 7). Sanger sequencing was performed to confirm the fusion genes and specific breakpoints in

these samples. We also used methylation array data to assess copy number alterations for each methylation cluster by conumee38 and GISTIC39 (Supplementary Tables 8 and  9; Supplementary Fig.

 7a,  b), which showed different patterns in copy number gains and deletions among the four methylation clusters. Although methylation clusters H3-Pons and H3-Medulla shared similar frequent

copy number alterations in 3p26.32, 8p23.1 (gains) and 5q31.3 (loss), H3-Medulla globally harbored more copy number gains (11 loci in H3-Medulla vs. 5 loci in H3-Pons) while H3-Pons

exhibited more frequent copy number losses (13 loci in H3-Pons vs. 5 loci in H3-Medulla). Interestingly, only H3-Pons showed 4q12 amplification which contains the frequently amplified gene

_PDGFRA_ in midline gliomas. Copy number alterations in methylation cluster IDH (7 loci in gains or losses) and PA-like (7 loci in gains or losses, including 7q34: KIAA1549 and BRAF

amplification) were fewer in comparison with methylation clusters H3. H3-MEDULLA IS CORRELATED WITH BETTER SURVIVAL THAN H3-PONS We performed survival analysis to investigate potential

differences in survival between the distinct methylation clusters we identified (Fig. 6a–d). Kaplan–Meier analyses showed distinct survival curves for patients stratified according to these

four methylation clusters (Fig. 6a). Methylation cluster IDH exhibited longer overall survival relative to methylation cluster H3 clusters (Median survival months, IDH: 141.2; H3-Pons: 9.47;

H3-Medulla: 26.33; Log-rank test: H3-Pons vs. IDH: _p_ < 0.0001; H3-Medulla vs. IDH: _p_ = 0.0269). Methylation clusters H3-Medulla and H3-Pons, despite sharing similar genetic

alterations of _H3_ and _TP53_ pathway mutations, had distinct overall survival trends (Log-rank test, _p_ < 0.0001) (Fig. 6b). Cases included in methylation cluster PA-like showed better

overall long-term survival compared with the other groups. Importantly, this improved survival trend for patients in the methylation cluster PA-like occurred in the context of the majority

of these cases being diagnosed histologically as astrocytoma or oligoastrocytoma, grades II-III (21 out of 34). Only 7 out of 34 tumors in this cluster were diagnosed as pilocytic

astrocytoma (Fig. 6a). Collectively, these results suggest that methylation classification into these subgroups may serve as a better correlate to identify patients with grade I–III tumors

that have a more benign clinical course. In terms of tumor grade, most of the tumors from methylation cluster H3-Pons were grade IV (20 out of 47, 42.6%), while 19 tumors were grade III

(40.4%), and 8 tumors were grade II (17.0%). Tumors classified in the methylation cluster H3-Medulla were composed of grade II and III cases (II: 11, 40.7%, III: 12, 44.4%;). When evaluating

only grade II and III tumors in both groups, H3-Medulla cases exhibited longer median overall survival (26.3 months) compared to tumors classified as H3-Pons (11.1 months) (Log-rank test,

_p_ = 0.00034) (Fig. 6c). DIPGs are known to have the worst prognosis among brainstem gliomas, and none of the DIPGs in our cohort were in methylation cluster H3-Medulla. When DIPGs in

methylation cluster H3-Pons were excluded, samples from methylation cluster H3-Medulla still showed a significantly longer overall survival relative to tumors classified as H3-Pons (26.3

months vs. 10.6 months) (_p_ value = 0.0064, log-rank test) (Fig. 6d). We also conducted Cox proportional hazards regression models for multivariate analysis (Supplementary Fig. 8). When

including methylation cluster and age as factors, H3-Pons still showed higher risk than H3-Medulla (hazard ratio: 1.04–6.6; _p_ value = 0.041), while age showed only limited effect (hazard

ratio: 0.95–1.0, _p_ value = 0.066) (Supplementary Fig. 8a). When including whether the sample is DIPG or non-DIPG, methylation cluster remains the most dominant factor (Supplementary Fig. 

8b). DISCUSSION Brainstem gliomas represent a heterogeneous group of tumors arising from the midbrain, pons, and the medulla, affecting both children and adults. These tumors have different

histologic features, but also differing levels of resectability and therefore variable clinical courses. Among these tumors, DIPG has been the most extensively studied, due to its relative

prevalence and lack of therapeutic options resulting in a poor prognosis. Integrated genomic, epigenomic and transcriptomic studies have provided insightful understanding of the

tumorigenesis of pediatric DIPG, which also might hold promise for future utilization of molecular marker-driven clinical trials and use of novel targeted therapies such as HDAC, JMJD3,

ACVR1, PPM1D, and BET bromodomain inhibitors, and CDK7 blockade40,41,42,43,44. However, the molecular profiling of the many other brainstem tumors that are non-pediatric DIPG has remained

elusive due in large part to the lack of sample availability. This has limited our understanding of these diseases and ability to objectively stratify these patients and implement use of

novel targeted therapies. Here, we provide a comprehensive integrated genomic analysis of gliomas of the brainstem, with tumors spanning from the most rostral midbrain to the pons and

medulla. The newly updated WHO guidelines use a new diagnostic term for brainstem gliomas with the H3 K27M mutation, however, this classification is unable to distinguish the variable

prognoses of these gliomas, categorizing all of them as WHO Grade IV. From our study, we show using the epigenetic and genetic signatures that tumors from various locations in the brainstem

can be classified into four major epigenetic subtypes, each with distinct clinical courses and potential therapeutic targets. Using methylation data we showed that brainstem gliomas could be

classified into four major methylation clusters: H3-Pons, H3-Medulla, IDH, and PA-like. We summarized the integrated genetic and clinical features of these subtypes in Fig. 7. Our study

revealed the presence of two distinct epigenetic subgroups of H3-mutant tumors, H3-Pons and H3-Medulla. There were significant differences in the survival trends between these two clusters,

with the H3-Pons group having a more aggressive course as compared to the H3-Medulla tumors. Based on RNA-seq based differential expression analysis, we found these tumors to have different

enrichment of gene expression pathways, with the H3-Medulla tumors enriched for immune-response related pathways, as compared with the more aggressive H3-Pons tumors having more cell

cycle-related pathways. Despite these tumors having similar mutation patterns, with common alterations in core mutations such as _H3F3A_, these striking differences in epigenetic and

expression patterns may inform distinct origins or influences of the tumor microenvironment, and warrant further investigation. These discoveries indicate that methylation status might

improve the classification for brainstem gliomas and guide clinical decision making for patients. Tumors in the PA-like cluster consisted primarily of tumors originally diagnosed as grades

II-III infiltrative gliomas, pilocytic astrocytomas, PMA, and PXA. The PA-like-group tumors had a benign clinical course, despite variability in grade. Based on histologic criteria, such

variability may lead to classification of a subset of these cases as higher grade gliomas and lead to overtreatment in clinical practice. Here again we demonstrate that epigenetic and

genomic patterns can more precisely stratify patient tumors diagnosed with small biopsies. Using methylation-based classification of tumors could better inform clinical decision making and

identify patients that are candidates for therapeutic intervention. We also utilized whole genome sequencing data to establish the mutation landscape of brainstem gliomas and discovered

methylation patterns closely matched with mutation landscapes. Several frequently mutated genes were identified in these clusters, including _H3F3A, HIST1H3B, IDH1, TP53, PPM1D, ATM, ATRX,

FGFR1, PIK3CA, NF1, PTEN, PDGFRA_, and _TCF12_. We used additional algorithms to predict driver mutations in noncoding regions of genes, such as _UBE3C_, _CXorf28_, and _EXD3_, as well as

structural variants and copy number changes. Although several genes could be identified in multiple clusters, certain genes, such as _NF1_ and _PPM1D_ were more frequent in cluster

H3-Medulla, while the percentage of _TP53_ mutations was higher in H3-Pons. Also, we identified the cluster IDH in brainstem gliomas, with tumors in this cluster harboring co-occurring

_IDH1, TP53_, and _ATRX_ mutations. The majority of these IDH cluster tumors were restricted to adult patients, consistent with previous studies focusing on pediatric brainstem tumors and

showing very rare _IDH_ mutations in these pediatric tumors. This indicates age is a key factor in developing brainstem glioma with _IDH1_ mutation. This comprehensive study of brainstem

gliomas provides an overview of this heterogeneous tumor entity. Using an integrated genomic analysis of more than one hundred brainstem gliomas from various anatomic locations, we show the

promise of molecular profiling of brainstem tumors for improved tumor classification and understanding of their molecular underpinnings, and identify new potential therapeutic targets, all

to improve outcomes for these patients. METHODS SAMPLE COLLECTION AND COHORT CHARACTERISTICS Brain tumor and peripheral blood samples were collected from patients at Beijing Tiantan

Hospital, Capital Medical University, China between 2013 and 2017 with informed consent reviewed by Institutional Review Board of Beijing Tiantan Hospital, with accreditation of the

Association for the Accreditation of Human Research Protection Program. Biopsy or resected tumors were for clinical diagnosis and therapy. All the FFPE and snap-frozen tumor tissues used for

sequencing were reviewed by an experienced team of neuropathologists at Beijing Tiantan Hospital, Captial Medical University. Tissues whose tumor content were less than 70% were excluded

from subsequent sequencing. 126 leftover samples were used in this analysis. Among these samples, 97 samples were used for whole genome sequencing, 123 for methylation microarray, 75 for

RNAseq, and 21 samples for panel sequencing. Clinical information and survival data are available for these patients. Kaplan–Meier analysis, log-rank test, and Cox proportional hazards

regression model (R package survminer) were used to test for survival analysis. WHOLE GENOME SEQUENCING AND RNA SEQUENCING Whole exome sequencing, RNAseq, and panel targeted sequencing (for

68 common mutated brain tumor genes) were performed by GenetronHealth, Beijing, China. For whole genome sequencing and panel sequencing, BWA was used for alignment and GATK mutect2 was

utilized for variant calling. For RNAseq, STAR, or hisat2 was use for alignment, cufflinks was used for gene expression profiling, Htseq and edgeR were used for differential counts analysis,

and GSEA was used for gene sets analysis. METHYLATION MICROARRAYS The Illumina HumanMethylation450 BeadChip and Infinium MethylationEPIC BeadChip were used for assessing genome-wide

methylation profiling of 123 samples. GenomeStudio Methylation Module was used for data processing and quality check. Hierarchical clustering, t-distributed stochastic neighbor embedding

(tSNE), and principal component analysis by R package Rtsne, and pheatmap with linkage method WPGMA and Euclidean distance were performed for evaluation of subgroups18,45,46,47. COPY NUMBER

ALTERATIONS Segmentation was calculated by Conumee from methylation arrays. GISTIC 2.0 was used for four different methylation clusters. Parameters setting: -genegistic 1 -smallmem 1 -broad

1 -conf 0.95 -armpeel 1 -savegene 1 -gcm mean -maxseg 2500 -ta 0.1 -td 0.1. STRUCTURAL VARIANTS Manta was applied for structural variant calling from whole genome sequencing data. We also

used RNAseq data for checking structural variants, and STAR-fusion was applied for RNAseq data. Selected fusion genes detected from both whole genome sequencing and RNAseq were validated by

Sanger sequencing, including: CAPZA2-MET, KMT2E-MET, MET-CTTNBP2, ST7-CAPZA2, CAPZA2-CLCN1, CAPZA2-THAP5, CAPZA2-PNPLA8, RB11-2B6.3-MET, SYS1-DBNDD2, C15orf57-CBX3. Primers for PCR and

Sanger sequencing were listed in Supplementary Table 10. REPORTING SUMMARY Further information on research design is available in the Nature Research Reporting Summary linked to this

article. DATA AVAILABILITY Whole Genome Sequencing data of this study has been deposited to Genome Sequence Archive in BIG Data Center, Beijing Institute of Genomics (BIG), Chinese Academy

of Sciences48,49, https://bigd.big.ac.cn/gsa-human, accession number HRA000092, and RNAseq, panel targeted sequencing, and methylation microarray data to the European Genome-phenome Archive

(EGA, http://ega-archive.org) under accession number EGAS00001004341. The deposited and publicly available data are compliant with the regulations of the Ministry of Science and Technology

of the People’s Republic of China. The data will be available for sharing and data use agreements are available in Supplementary materials. REFERENCES * Fontebasso, A. M. et al. Recurrent

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Res._ 46, D14–D20 (2018). Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS The project was supported by National Key Technology Research and Development Program of the

Ministry of Science and Technology of China (Grant nos. 2014BAI04B01 and 2015BAI12B04), Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support

(Grant no. ZYLX201608), the National Natural Science Foundation of China (Grant no. 81872048), Beijing Municipal Natural Science Foundation (Grant no.7161004), 2016 Development Project of

Science and Technology Innovation Base (Grant no.206028), and Beijing Municipal Special Funds for Medical Research No. JingYiYan 2018-7 (PI: Liwei Zhang), and by a Duke Brain Tumor Center

Grant and Zachem Family Fund (PI: Hai Yan). The authors would like to thank Guilin Li, Lin Luo, Jiang Du, and Junmei Wang (Department of Pathology, Beijing Tiantan Hospital, Capital Medical

University) for their assistance in evaluating the histopathological material, Lin Qiao (Beijing Tiantan Hospital, Capital Medical University) for her help in collecting samples, and the

research computing facility Duke Compute Cluster (Mark DeLong and Tom Milledge) at Duke University. AUTHOR INFORMATION Author notes * These authors contributed equally: Lee H. Chen, Changcun

Pan. AUTHORS AND AFFILIATIONS * Department of Pathology, Duke University Medical Center, Durham, 27710, NC, USA Lee H. Chen, Bill H. Diplas, Cheng Xu, Landon J. Hansen, Matthew S. Waitkus, 

Yiping He, Roger E. McLendon & Hai Yan * Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, Durham, 27710, NC, USA Lee H. Chen, Bill H. Diplas, Cheng Xu, Landon J.

Hansen, Matthew S. Waitkus, Yiping He, David M. Ashley & Hai Yan * Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Nan Si Huan Xi Lu 119, Fengtai

District, 100070, Beijing, China Changcun Pan, Cheng Xu, Yuliang Wu, Xin Chen, Yibo Geng, Tao Sun, Yu Sun, Peng Zhang, Zhen Wu, Junting Zhang, Deling Li, Yang Zhang, Wenhao Wu, Yu Wang &

 Liwei Zhang * China National Clinical Research Center for Neurological Diseases, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China Changcun Pan & Liwei Zhang * Beijing Key

Laboratory of Brain Tumor, Nan Si Huan Xi Lu 119, Fengtai District, 100070, Beijing, China Changcun Pan & Liwei Zhang * Genetron Health (Beijing) Co. Ltd, 102208, Beijing, China Guangyu

Li, Jie Yang, Ce Xu & Sizhen Wang * State Key Laboratory of Medical Molecular Biology, Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical

Sciences, Peking Union Medical College, 100005, Beijing, China Xiaoyue Wang Authors * Lee H. Chen View author publications You can also search for this author inPubMed Google Scholar *

Changcun Pan View author publications You can also search for this author inPubMed Google Scholar * Bill H. Diplas View author publications You can also search for this author inPubMed 

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publications You can also search for this author inPubMed Google Scholar * Sizhen Wang View author publications You can also search for this author inPubMed Google Scholar * Matthew S.

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author inPubMed Google Scholar CONTRIBUTIONS L.H.C., B.H.D., L.Z., and H.Y. conceived and designed this study; L.H.C. and B.H.D developed the methodology; G.L., J.Y., X.W., Ce.X. and S.W.

analyzed and interpreted the data; C.P., Y.Wu., X.C., Y.G., T.S., Y.S., P.Z., Z.W., J.Z., D.L., Y.Z., W.W., Y.Wa., and L.Z. contributed to the acquisition of data; L.H.C. drafted the

manuscript; B.H.D., L.J.H., M.S.W., C.P., Ch.X., R.E.M., D.M.A., Y.H., and H.Y. reviewed, and/or revised the manuscript; H.Y. and L.Z. supervised this study. CORRESPONDING AUTHORS

Correspondence to Hai Yan or Liwei Zhang. ETHICS DECLARATIONS COMPETING INTERESTS H.Y. is the chief scientific officer and has owner interest in Genetron Holdings, and receives royalties

from Genetron, Agios, and Personal Genome Diagnostics (PGDX). ADDITIONAL INFORMATION PEER REVIEW INFORMATION _Nature Communications_ thanks Roel Verhaak and the other, anonymous, reviewer(s)

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ARTICLE Chen, L.H., Pan, C., Diplas, B.H. _et al._ The integrated genomic and epigenomic landscape of brainstem glioma. _Nat Commun_ 11, 3077 (2020).

https://doi.org/10.1038/s41467-020-16682-y Download citation * Received: 18 April 2019 * Accepted: 14 May 2020 * Published: 17 June 2020 * DOI: https://doi.org/10.1038/s41467-020-16682-y

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