# A new subtype of diffuse midline glioma, H3 K27 and BRAF/FGFR1 co-altered: a clinico-radiological and histomolecular characterisation

Lucie Auffret, Yassine Ajlil, Arnault Tauziède-Espariat, Thomas Kergrohen, Chloé Puiseux, Laurent Riffaud, Pascale Blouin, Anne-Isabelle Bertozzi, Pierre Leblond, Klas Blomgren, et al.

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Lucie Auffret, Yassine Ajlil, Arnault Tauziède-Espariat, Thomas Kergrohen, Chloé Puiseux, et al. A new subtype of diffuse midline glioma, H3 K27 and BRAF/FGFR1 co-altered: a clinico-radiological and histomolecular characterisation. Acta Neuropathologica, 2023, 147 (1), pp.2. 10.1007/s00401-023-02651-4. hal-04490306

HAL Id: hal-04490306

https://hal.science/hal-04490306

Submitted on 23 May 2024

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<figure><img src="image_2.png" /></figure>
# A new subtype of diffuse midline glioma, H3 K27 and BRAF/FGFR1 co-altered: a clinico-radiological and histomolecular characterisation

Lucie Auffret, Yassine Ajlil, Arnault Tauziède-Espariat, Thomas Kergrohen, Chloé Puiseux, Laurent Riffaud, Pascale Blouin, Anne-Isabelle Bertozzi, Pierre Leblond, Klas Blomgren, et al.

<figure><img src="image_1.png" /></figure>

## ► To cite this version:

Lucie Auffret, Yassine Ajlil, Arnault Tauziède-Espariat, Thomas Kergrohen, Chloé Puiseux, et al. A new subtype of diffuse midline glioma, H3 K27 and BRAF/FGFR1 co-altered: a clinico-radiological and histomolecular characterisation. Acta Neuropathologica, 2023, 147 (1), pp.2. 10.1007/s00401-023-02651-4. hal-04490306

HAL Id: hal-04490306

https://hal.science/hal-04490306v1

Submitted on 23 May 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
# A new subtype of diffuse midline glioma, H3 K27 and BRAF/FGFR1 co-altered: a clinico-radiological and histomolecular characterisation

Lucie Auffret¹ · Yassine Ajlil¹ · Arnault Tauziède-Espariat²,³ · Thomas Kergrohen¹,⁴ · Chloé Puiseux⁵ ·

Laurent Riffaud⁶ · Pascale Blouin⁷ · Anne-Isabelle Bertozzi⁸ · Pierre Leblond⁹ · Klas Blomgren¹⁰,¹¹ ·

Sébastien Froelich¹² · Alberto Picca¹³ · Mehdi Touat¹³ · Marc Sanson¹³ · Kévin Beccaria¹,¹⁴ · Thomas Blauwblomme¹⁴ ·

Volodia Dangouloff-Ros¹⁵,¹⁶ · Nathalie Boddaert¹⁵,¹⁶ · Pascale Varlet²,³ · Marie-Anne Debily¹,¹⁷ · Jacques Grill¹,⁴ ·

David Castel¹,⁴

Received: 3 August 2023 / Revised: 22 October 2023 / Accepted: 24 October 2023
© The Author(s) 2023

## Abstract

Diffuse midline gliomas (DMG) H3 K27-altered are incurable grade 4 gliomas and represent a major challenge in neuro-oncology. This tumour type is now classified in four subtypes by the 2021 edition of the WHO Classification of the Central Nervous System (CNS) tumours. However, the H3.3-K27M subgroup still appears clinically and molecularly heterogeneous. Recent publications reported that rare patients presenting a co-occurrence of H3.3K27M with *BRAF* or *FGFR1* alterations tended to have a better prognosis. To better study the role of these co-driver alterations, we assembled a large paediatric and adult cohort of 29 tumours H3K27-altered with co-occurring activating mutation in *BRAF* or *FGFR1* as well as 31 previous cases from the literature. We performed a comprehensive histological, radiological, genomic, transcriptomic and DNA methylation analysis. Interestingly, unsupervised t-distributed Stochastic Neighbour Embedding (tSNE) analysis of DNA methylation profiles regrouped *BRAF*<sup>V600E</sup> and all but one *FGFR1*<sup>MUT</sup> DMG in a unique methylation cluster, distinct from the other DMG subgroups and also from ganglioglioma (GG) or high-grade astrocytoma with piloid features (HGAP). This new DMG subtype harbours atypical radiological and histopathological profiles with calcification and/or a solid tumour component both for *BRAF*<sup>V600E</sup> and *FGFR1*<sup>MUT</sup> cases. The analyses of a H3.3-K27M *BRAF*<sup>V600E</sup> tumour at diagnosis and corresponding in vitro cellular model showed that mutation in *H3-3A* was the first event in the oncogenesis. Contrary to other DMG, these tumours occur more frequently in the thalamus (70% for *BRAF*<sup>V600E</sup> and 58% for *FGFR1*<sup>MUT</sup>) and patients have a longer overall survival with a median above three years. In conclusion, *DMG, H3 K27 and BRAF/FGFR1 co-altered* represent a new subtype of DMG with distinct genotype/phenotype characteristics, which deserve further attention with respect to trial interpretation and patient management.

**Keywords** Paediatric-type high-grade glioma · Adult glioma · Midline glioma · BRAF-V600E mutation · FGFR1 mutation · DNA methylation profiling

## Introduction

Thanks to significant advances in genomics, the 2016 then-2021 World Health Organization (WHO) Classification of Tumours of the Central Nervous System (CNS) has defined tumour entities based on histological but also molecular features, like the driver genetic event [15, 16]. *Diffuse midline gliomas (DMG) H3 K27-altered* have now been identified as a new type of malignant gliomas which occur in the paediatric and adult populations, although

with disparities according to the preferential location, i.e. brainstem in children and thalamus in adults [18, 45].

DMG H3 K27-altered are either characterised by the substitution in histone H3 of the lysine at position 27 by a methionine (H3K27M), or the overexpression of *EZHIP* [3, 42]. Both mechanisms lead to Polycomb Repressor Complex 2 (PRC2) inhibition with a global loss of H3K27me3 [5, 13], and consequently a major epigenetic and transcriptional remodelling [1, 3]. According to different molecular and clinical parameters, including specific DNA methylation profiles, DMG H3 K27-altered appeared more heterogeneous than initially thought, and were further classified

<footnote>Extended author information available on the last page of the article</footnote>
# A new subtype of diffuse midline glioma, H3 K27 and BRAF/FGFR1 co-altered: a clinico-radiological and histomolecular characterisation

Lucie Auffret¹ · Yassine Ajlil¹ · Arnault Tauziède-Espariat²,³ · Thomas Kergrohen¹,⁴ · Chloé Puiseux⁵ ·

Laurent Riffaud⁶ · Pascale Blouin⁷ · Anne-Isabelle Bertozzi⁸ · Pierre Leblond⁹ · Klas Blomgren¹⁰,¹¹ ·

Sébastien Froelich¹² · Alberto Picca¹³ · Mehdi Touat¹³ · Marc Sanson¹³ · Kévin Beccaria¹,¹⁴ · Thomas Blauwblomme¹⁴ ·

Volodia Dangouloff-Ros¹⁵,¹⁶ · Nathalie Boddaert¹⁵,¹⁶ · Pascale Varlet²,³ · Marie-Anne Debily¹,¹⁷ · Jacques Grill¹,⁴ ·

David Castel¹,⁴

Received: 3 August 2023 / Revised: 22 October 2023 / Accepted: 24 October 2023
© The Author(s) 2023

## Abstract

Diffuse midline gliomas (DMG) H3 K27-altered are incurable grade 4 gliomas and represent a major challenge in neuro-oncology. This tumour type is now classified in four subtypes by the 2021 edition of the WHO Classification of the Central Nervous System (CNS) tumours. However, the H3.3-K27M subgroup still appears clinically and molecularly heterogeneous. Recent publications reported that rare patients presenting a co-occurrence of H3.3K27M with *BRAF* or *FGFR1* alterations tended to have a better prognosis. To better study the role of these co-driver alterations, we assembled a large paediatric and adult cohort of 29 tumours H3K27-altered with co-occurring activating mutation in *BRAF* or *FGFR1* as well as 31 previous cases from the literature. We performed a comprehensive histological, radiological, genomic, transcriptomic and DNA methylation analysis. Interestingly, unsupervised t-distributed Stochastic Neighbour Embedding (tSNE) analysis of DNA methylation profiles regrouped *BRAF*<sup>V600E</sup> and all but one *FGFR1*<sup>MUT</sup> DMG in a unique methylation cluster, distinct from the other DMG subgroups and also from ganglioglioma (GG) or high-grade astrocytoma with piloid features (HGAP). This new DMG subtype harbours atypical radiological and histopathological profiles with calcification and/or a solid tumour component both for *BRAF*<sup>V600E</sup> and *FGFR1*<sup>MUT</sup> cases. The analyses of a H3.3-K27M *BRAF*<sup>V600E</sup> tumour at diagnosis and corresponding in vitro cellular model showed that mutation in *H3-3A* was the first event in the oncogenesis. Contrary to other DMG, these tumours occur more frequently in the thalamus (70% for *BRAF*<sup>V600E</sup> and 58% for *FGFR1*<sup>MUT</sup>) and patients have a longer overall survival with a median above three years. In conclusion, *DMG, H3 K27 and BRAF/FGFR1 co-altered* represent a new subtype of DMG with distinct genotype/phenotype characteristics, which deserve further attention with respect to trial interpretation and patient management.

**Keywords** Paediatric-type high-grade glioma · Adult glioma · Midline glioma · BRAF-V600E mutation · FGFR1 mutation · DNA methylation profiling

## Introduction

Thanks to significant advances in genomics, the 2016 then-2021 World Health Organization (WHO) Classification of Tumours of the Central Nervous System (CNS) has defined tumour entities based on histological but also molecular features, like the driver genetic event [15, 16]. *Diffuse midline gliomas (DMG) H3 K27-altered* have now been identified as a new type of malignant gliomas which occur in the paediatric and adult populations, although

with disparities according to the preferential location, i.e. brainstem in children and thalamus in adults [18, 45].

DMG H3 K27-altered are either characterised by the substitution in histone H3 of the lysine at position 27 by a methionine (H3K27M), or the overexpression of *EZHIP* [3, 42]. Both mechanisms lead to Polycomb Repressor Complex 2 (PRC2) inhibition with a global loss of H3K27me3 [5, 13], and consequently a major epigenetic and transcriptional remodelling [1, 3]. According to different molecular and clinical parameters, including specific DNA methylation profiles, DMG H3 K27-altered appeared more heterogeneous than initially thought, and were further classified

<footnote>Extended author information available on the last page of the article</footnote>
in four subtypes: H3-3A K27-mutant (H3.3-K27M), H3C2 K27-mutant (H3.1-K27M), H3-wild-type (with EZHIP over-expression) or EGFR-altered [3, 4, 16, 19, 33]. Despite this subdivision, we still observed some clinical and molecular heterogeneity within the most common H3.3-K27M subtype. We previously demonstrated that TP53 co-driver mutations are associated with a worse tumour response to radiotherapy and a poorer outcome [41] suggesting that additional molec- ular alterations can deeply modify the phenotype induced by the driver histone H3 mutation. Recently, we and others have described single cases or small studies of tumours with concomitant alterations of H3-K27M and mitogen-activated protein kinase (MAPK) pathway showing a possible longer survival compared to patients with DMG H3 K27-altered, BRAF and FGFR1 wild-type. In these studies, diagnoses ranged from H3.3-K27M BRAF<sup>V600E</sup>/FGFR1<sup>MUT</sup> pilocytic astrocytoma or midline gangliogliomas grade 1–3 [11, 21–23, 26, 29, 44] to diffuse gliomas grade 4 [25, 31]. As BRAF and FGFR1 mutations are typical hallmarks of low-grade gliomas/glioneuronal tumours such as ganglioglioma or pilo- cytic astrocytoma, the co-occurrence of H3-K27 and these MAPK alterations makes diagnosis and grading difficult [30]. In order to understand how these alterations could mitigate the prognosis of these neoplasms, we analysed (radiologic, histologic, genomic, transcriptomic and DNA methylation analyses) a larger DMG H3 K27-altered cohort, comprising 29 tumours harbouring BRAF<sup>V600E</sup> or FGFR1<sup>MUT</sup> comple- mented by paediatric and adult cases from the literature.

# Patients, materials and methods

## Patients and tumour samples

The first part of the cohort is composed of 60 patients diag- nosed with H3-K27M or EZHIP-overexpressing tumours har- bouring a coding mutation in *BRAF* or *FGFR1* genes, from the Necker Enfants-Malades/GHU-Sainte Anne Hospital/ Gustave Roussy center and Biomede 1 Trial (NCT02233049) (n=29), or from other published cohorts (n=31) [3, 18, 23, 25, 30, 31, 37]. The control cohort includes patients with wild-type *FGFR1/BRAF*, in the above-mentioned cohorts of H3K27M or *EZHIP*-overexpressing DMG (flowchart in Supplementary Fig. 1, online resource). Tumour tissue and clinical data were collected under informed consent obtained from the parents or guardian according to the IRB approved protocol (CNIL 1176643).

## Radiology analysis

All available radiology outcomes at diagnosis (CT and MRI) of patients with H3.3-K27M tumours from our cohort were reviewed centrally by three experts (VDR, NB, and JG).

Parameters specifically recorded were: radiological presenta- tion (diffuse, circumscribed or nodular and diffuse), presence of contrast enhancement (yes/no) and calcifications (yes/no). For circumscribed tumours, the pattern at evolution was also analysed.

## Histopathological analyses and immunohistochemistry

Formalin-fixed paraffin-embedded (FFPE) tissue samples for each patient were retrieved and Haematoxylin–Phlox- ine–Saffron (HPS)-stained slides were analysed by two experienced neuropathologists (PV and ATE) to confirm morphological diagnoses. Micro-calcifications were noted as present or absent as well as granular bodies, ganglion neurons, necrosis, and microvascular proliferation. Mitotic activity (per 2 mm²) and tumour growth architecture were analysed within the inherent limits of a stereotaxic biopsy exploration. The latter was labelled as diffuse, compact tumoral areas or both. Morphological aspects were evalu- ated as ganglioglioma-like (GG-like), HGG with piloid astrocytic component or DMG-like. The infiltration pattern was also assessed by NF70 immunostaining (i.e. residual NF70 network or not). Immunostaining was performed from 3 µm-thick representative FFPE sections using a Dako OMNIS automate. The following primary antibodies were used: H3K27me3 (1:2500, polyclonal, Diagenode), H3-K27M (1:5000, clone EPR18340, Abcam), Neuro- filament Protein NF70 (1:100, clone 2F11), CD34 (1:40, clone QBEnd-10, Dako), BRAFV600E (1:100, clone VE1, Abcam), ATRX (1:200, polyclonal, Diagomics), Ki-67 (1:200, clone MIB-1, Dako). Antigen retrieval was per- formed at 95 °C, pH9 (GV80011-2, Dako) or pH6 (GV805, Dako). External positive and negative controls were used for antibody validation.

## DNA/RNA extraction and sequencing

DNA and RNA were extracted from frozen tumours using Allprep DNA/RNA kit (Qiagen) and were quantified using, respectively, the Qubit Broad Range double-stranded DNA assay (Life Technologies) or the Qubit RNA high sensibility (Life Technologies). When no frozen material was available for DNA methylation profiling, tumour DNA was extracted from formol-fixed paraffin-embedded (FFPE) block sections using dedicated protocols at Diagenode or Integragen. Tar- geted DNA sequencing (≥ 6000× coverage) or whole exome sequencing (≥ 130× coverage) was performed as previously described [10, 41]. WES was aligned with BWA according to GATK best practice guidelines, then Mutect2 was used for the DNA calling. RNAseq on DMG primary tumours was

<figure><img src="image_3.png" /></figure>
in four subtypes: H3-3A K27-mutant (H3.3-K27M), H3C2 K27-mutant (H3.1-K27M), H3-wild-type (with EZHIP over-expression) or EGFR-altered [3, 4, 16, 19, 33]. Despite this subdivision, we still observed some clinical and molecular heterogeneity within the most common H3.3-K27M subtype. We previously demonstrated that TP53 co-driver mutations are associated with a worse tumour response to radiotherapy and a poorer outcome [41] suggesting that additional molec- ular alterations can deeply modify the phenotype induced by the driver histone H3 mutation. Recently, we and others have described single cases or small studies of tumours with concomitant alterations of H3-K27M and mitogen-activated protein kinase (MAPK) pathway showing a possible longer survival compared to patients with DMG H3 K27-altered, BRAF and FGFR1 wild-type. In these studies, diagnoses ranged from H3.3-K27M BRAF<sup>V600E</sup>/FGFR1<sup>MUT</sup> pilocytic astrocytoma or midline gangliogliomas grade 1–3 [11, 21–23, 26, 29, 44] to diffuse gliomas grade 4 [25, 31]. As BRAF and FGFR1 mutations are typical hallmarks of low-grade gliomas/glioneuronal tumours such as ganglioglioma or pilo- cytic astrocytoma, the co-occurrence of H3-K27 and these MAPK alterations makes diagnosis and grading difficult [30]. In order to understand how these alterations could mitigate the prognosis of these neoplasms, we analysed (radiologic, histologic, genomic, transcriptomic and DNA methylation analyses) a larger DMG H3 K27-altered cohort, comprising 29 tumours harbouring BRAF<sup>V600E</sup> or FGFR1<sup>MUT</sup> comple- mented by paediatric and adult cases from the literature.

# Patients, materials and methods

## Patients and tumour samples

The first part of the cohort is composed of 60 patients diag- nosed with H3-K27M or EZHIP-overexpressing tumours har- bouring a coding mutation in *BRAF* or *FGFR1* genes, from the Necker Enfants-Malades/GHU-Sainte Anne Hospital/ Gustave Roussy center and Biomede 1 Trial (NCT02233049) (*n* = 29), or from other published cohorts (*n* = 31) [3, 18, 23, 25, 30, 31, 37]. The control cohort includes patients with wild-type *FGFR1/BRAF*, in the above-mentioned cohorts of H3K27M or *EZHIP*-overexpressing DMG (flowchart in Supplementary Fig. 1, online resource). Tumour tissue and clinical data were collected under informed consent obtained from the parents or guardian according to the IRB approved protocol (CNIL 1176643).

## Radiology analysis

All available radiology outcomes at diagnosis (CT and MRI) of patients with H3.3-K27M tumours from our cohort were reviewed centrally by three experts (VDR, NB, and JG).

Parameters specifically recorded were: radiological presenta- tion (diffuse, circumscribed or nodular and diffuse), presence of contrast enhancement (yes/no) and calcifications (yes/no). For circumscribed tumours, the pattern at evolution was also analysed.

## Histopathological analyses and immunohistochemistry

Formalin-fixed paraffin-embedded (FFPE) tissue samples for each patient were retrieved and Haematoxylin–Phlox- ine–Saffron (HPS)-stained slides were analysed by two experienced neuropathologists (PV and ATE) to confirm morphological diagnoses. Micro-calcifications were noted as present or absent as well as granular bodies, ganglion neurons, necrosis, and microvascular proliferation. Mitotic activity (per 2 mm²) and tumour growth architecture were analysed within the inherent limits of a stereotaxic biopsy exploration. The latter was labelled as diffuse, compact tumoral areas or both. Morphological aspects were evalu- ated as ganglioglioma-like (GG-like), HGG with piloid astrocytic component or DMG-like. The infiltration pattern was also assessed by NF70 immunostaining (i.e. residual NF70 network or not). Immunostaining was performed from 3 µm-thick representative FFPE sections using a Dako OMNIS automate. The following primary antibodies were used: H3K27me3 (1:2500, polyclonal, Diagenode), H3-K27M (1:5000, clone EPR18340, Abcam), Neuro- filament Protein NF70 (1:100, clone 2F11), CD34 (1:40, clone QBEnd-10, Dako), BRAFV600E (1:100, clone VE1, Abcam), ATRX (1:200, polyclonal, Diagomics), Ki-67 (1:200, clone MIB-1, Dako). Antigen retrieval was per- formed at 95 °C, pH9 (GV80011-2, Dako) or pH6 (GV805, Dako). External positive and negative controls were used for antibody validation.

## DNA/RNA extraction and sequencing

DNA and RNA were extracted from frozen tumours using Allprep DNA/RNA kit (Qiagen) and were quantified using, respectively, the Qubit Broad Range double-stranded DNA assay (Life Technologies) or the Qubit RNA high sensibility (Life Technologies). When no frozen material was available for DNA methylation profiling, tumour DNA was extracted from formol-fixed paraffin-embedded (FFPE) block sections using dedicated protocols at Diagenode or Integragen. Tar- geted DNA sequencing (≥ 6000× coverage) or whole exome sequencing (≥ 130× coverage) was performed as previously described [10, 41]. WES was aligned with BWA according to GATK best practice guidelines, then Mutect2 was used for the DNA calling. RNAseq on DMG primary tumours was

<figure><img src="image_3.png" /></figure>
performed at Integragen (Evry, France). PolyA mRNA molecules were initially purified from at least 100 ng total RNA (NEBNext® Poly(A) mRNA Magnetic Isolation Module, NEB) and libraries were then prepared using the NEBNext Ultra II Directional RNA Library Prep Kit (NEB). Paired-end reads of 100 bp were generated on an Illumina NovaSeq reaching an average sequencing depth of 60 million reads.

## DNA Methylation array processing

Genome-wide DNA methylation analysis was performed using either the Illumina HumanMethylation450 BeadChip (450 k) or EPIC arrays as previously published [2, 3]. Data were obtained from different platforms (DKFZ Heidelberg; Integragen; Diagenode; published data) and were analysed with R (v4.0.4). For t-Distributed Stochastic Neighbour Embedding (t-SNE) analysis, the *minfi* package was used to load idat files and preprocessed with the function *preproccess.illumina* for dye bias and background correction. Probes located on sex chromosomes or not uniquely mapped to the human reference genome were removed. Probes containing single-nucleotide polymorphisms or that were not present in both EPIC and 450 k methylation array were also eliminated. A batch effect correction was done with *removebatchEffect* function from *limma* package, to remove difference between formalin-fixed paraffin-embedded and frozen samples. The probes were sorted by standard deviation. The 10,000 most variable probes were used for subsequent clustering analysis and to compute the 1-variance weighted Pearson correlation between samples. The distance matrix was used as input in t-SNE from *Rtsne* package. For the second analysis, DNA methylation-based classification of CNS tumours from DKFZ-Heidelberg was used in order to predict the CNS tumour class based on the V12.7 of the classifier (www.molecularneuropathology.org).

## Gene expression analysis

Reads were pre-processed using the nf-core RNAseq pipeline (v3.0), mapped to the reference genome GRC38/hg38 with the *STAR* tool (v2.6.1d), annotated with GENCODE v36 and counting was performed with the Salmon quantification tool (v1.4.0). Differential gene expression analysis was performed with the *DESeq2* package (v1.30.0, *minReplicatesForReplace* = 7, *betaPrior* = TRUE) with a threshold of 0.01 for Benjamini-Hochberg adjusted p value (adj-p). For gene set enrichment analysis (GSEA), hypergeometric tests were used to identify overrepresented gene sets from the *MSigDB* v7.4 database, amongst genes ranked by significance and fold-change in differential expression analysis, with Benjamini-Hochberg multiple testing correction using the package *Clusterprofiler*. Catalogues considered included Hallmark and C2. Differences were considered as

significant when false discovery rate adj-q value was < 0.02. Gene expression comparison was evaluated with Wald test using *DESeq2*.

## Univariate and multivariate survival analyses

Overall survival (OS) was estimated with the Kaplan-Meier method and median overall survival was computed using a log-rank test. OS was obtained from the post-diagnosis until death patient or last known information. The analysis was realised in Prism9 software. Multivariable Cox proportional hazards regression model on OS was performed including histone H3, *BRAF*, *FGFR1*, *TP53* status, age at diagnosis and tumour location with R software using the function *coxph()* of the *survival* package (Version 3.2-13).

## Statistical analyses

Distribution of age at diagnosis according to different parameters was accessed by Mann-Whitney test. Presence of macro-calcification, contrast enhancement, radiologic profile and sex ratio were evaluated by Fisher's exact test. Chi-square test for trend was used to evaluate tumour type and location. All statistic tests were performed using Prism 9 software (GraphPad).

## Results

### Genomic landscape of DMG H3K27-altered with *BRAF/FGFR1* alterations

We analysed a study cohort of H3K27-altered gliomas harbouring mutations in *BRAF* (*n* = 22), *FGFR* (*n* = 37), or both (*n* = 1; Supplementary Fig. 1 and Table 1, online resource). Of these, 43 were children (< 18 years) and 16 adults. In parallel, we analysed control cases of patients with *BRAF*<sup>WT</sup>/*FGFR*<sup>WT</sup> DMG H3-K27 from the corresponding cohorts. All DMG H3-K27M with *BRAF* alterations harboured a somatic V600E substitution, but no fusion (Fig. 1). Concerning *FGFR*, all cases displayed *FGFR1* hotspot substitutions N546K/D (73%, 27/37) and/or K656E/M (23%, 11/37) preferentially occurring in CNS tumours, but neither *FGFR1* fusion/duplication nor *FGFR2/3* alterations (Fig. 1) [20]. All these mutations in *BRAF* and *FGFR1* are widely known to induce an aberrant MAPK activation [12, 20, 27, 38, 43]. We estimated the overall prevalence of DMG *BRAF*<sup>V600E</sup> and DMG *FGFR1*<sup>MUT</sup> within the H3.3-K27M tumours in our composite paediatric-adult cohort at 7.6% and 12.3%, respectively. Thus, DMG *BRAF*<sup>MUT</sup>/*FGFR1*<sup>MUT</sup> together could represent close to 20% of all DMG H3.3-K27M.

We further analysed the genomic landscape of tumour for which material or data were available and observed that
performed at Integragen (Evry, France). PolyA mRNA molecules were initially purified from at least 100 ng total RNA (NEBNext® Poly(A) mRNA Magnetic Isolation Module, NEB) and libraries were then prepared using the NEBNext Ultra II Directional RNA Library Prep Kit (NEB). Paired-end reads of 100 bp were generated on an Illumina NovaSeq reaching an average sequencing depth of 60 million reads.

## DNA Methylation array processing

Genome-wide DNA methylation analysis was performed using either the Illumina HumanMethylation450 BeadChip (450 k) or EPIC arrays as previously published [2, 3]. Data were obtained from different platforms (DKFZ Heidelberg; Integragen; Diagenode; published data) and were analysed with R (v4.0.4). For t-Distributed Stochastic Neighbour Embedding (t-SNE) analysis, the *minfi* package was used to load idat files and preprocessed with the function *preproccess.illumina* for dye bias and background correction. Probes located on sex chromosomes or not uniquely mapped to the human reference genome were removed. Probes containing single-nucleotide polymorphisms or that were not present in both EPIC and 450 k methylation array were also eliminated. A batch effect correction was done with *removebatchEffect* function from *limma* package, to remove difference between formalin-fixed paraffin-embedded and frozen samples. The probes were sorted by standard deviation. The 10,000 most variable probes were used for subsequent clustering analysis and to compute the 1-variance weighted Pearson correlation between samples. The distance matrix was used as input in t-SNE from *Rtsne* package. For the second analysis, DNA methylation-based classification of CNS tumours from DKFZ-Heidelberg was used in order to predict the CNS tumour class based on the V12.7 of the classifier (www.molecularneuropathology.org).

## Gene expression analysis

Reads were pre-processed using the nf-core RNAseq pipeline (v3.0), mapped to the reference genome GRC38/hg38 with the *STAR* tool (v2.6.1d), annotated with GENCODE v36 and counting was performed with the Salmon quantification tool (v1.4.0). Differential gene expression analysis was performed with the *DESeq2* package (v1.30.0, *minReplicatesForReplace* = 7, *betaPrior* = TRUE) with a threshold of 0.01 for Benjamini-Hochberg adjusted p value (adj-p). For gene set enrichment analysis (GSEA), hypergeometric tests were used to identify overrepresented gene sets from the *MSigDB* v7.4 database, amongst genes ranked by significance and fold-change in differential expression analysis, with Benjamini-Hochberg multiple testing correction using the package *Clusterprofiler*. Catalogues considered included Hallmark and C2. Differences were considered as

significant when false discovery rate adj-q value was < 0.02. Gene expression comparison was evaluated with Wald test using *DESeq2*.

## Univariate and multivariate survival analyses

Overall survival (OS) was estimated with the Kaplan-Meier method and median overall survival was computed using a log-rank test. OS was obtained from the post-diagnosis until death patient or last known information. The analysis was realised in Prism9 software. Multivariable Cox proportional hazards regression model on OS was performed including histone H3, *BRAF*, *FGFR1*, *TP53* status, age at diagnosis and tumour location with R software using the function *coxph*() of the *survival* package (Version 3.2-13).

## Statistical analyses

Distribution of age at diagnosis according to different parameters was accessed by Mann-Whitney test. Presence of macro-calcification, contrast enhancement, radiologic profile and sex ratio were evaluated by Fisher's exact test. Chi-square test for trend was used to evaluate tumour type and location. All statistic tests were performed using Prism 9 software (GraphPad).

## Results

### Genomic landscape of DMG H3K27-altered with *BRAF/FGFR1* alterations

We analysed a study cohort of H3K27-altered gliomas harbouring mutations in *BRAF* (*n* = 22), *FGFR* (*n* = 37), or both (*n* = 1; Supplementary Fig. 1 and Table 1, online resource). Of these, 43 were children (< 18 years) and 16 adults. In parallel, we analysed control cases of patients with *BRAF*<sup>WT</sup>/*FGFR*<sup>WT</sup> DMG H3-K27 from the corresponding cohorts. All DMG H3-K27M with *BRAF* alterations harboured a somatic V600E substitution, but no fusion (Fig. 1). Concerning *FGFR*, all cases displayed *FGFR1* hotspot substitutions N546K/D (73%, 27/37) and/or K656E/M (23%, 11/37) preferentially occurring in CNS tumours, but neither *FGFR1* fusion/duplication nor *FGFR2/3* alterations (Fig. 1) [20]. All these mutations in *BRAF* and *FGFR1* are widely known to induce an aberrant MAPK activation [12, 20, 27, 38, 43]. We estimated the overall prevalence of DMG *BRAF*<sup>V600E</sup> and DMG *FGFR1*<sup>MUT</sup> within the H3.3-K27M tumours in our composite paediatric-adult cohort at 7.6% and 12.3%, respectively. Thus, DMG *BRAF*<sup>MUT</sup>/*FGFR1*<sup>MUT</sup> together could represent close to 20% of all DMG H3.3-K27M.

We further analysed the genomic landscape of tumour for which material or data were available and observed that
<table><caption>**Table 1** Multivariable Cox proportional hazards regression model for the OS of patients with H3-K27M DMG</caption><thead><tr><th>Variable</th><th>Hazard ratio (95%CI)</th><th>p value</th></tr></thead><tbody><tr><td><strong>H3 status</strong></td><td></td><td></td></tr><tr><td>H3.1-K27M</td><td>1</td><td></td></tr><tr><td>H3.3-K27M</td><td>1.5820 (1.0529-2.3769)</td><td>0.0274</td></tr><tr><td>H3-WT</td><td>1.0210 (0.5452-1.9119)</td><td>0.9482</td></tr><tr><td><strong>BRAF status</strong></td><td></td><td></td></tr><tr><td>BRAF wild-type</td><td>1</td><td></td></tr><tr><td>BRAF mutated</td><td>0.2132 (0.1098-0.4140)</td><td>5.03e-06</td></tr><tr><td><strong>FGFR1 status</strong></td><td></td><td></td></tr><tr><td>FGFR1 wild-type</td><td>1</td><td></td></tr><tr><td>FGFR1 mutated</td><td>0.3414 (0.1963-0.5939)</td><td>0.0001</td></tr><tr><td><strong>TP53 status</strong></td><td></td><td></td></tr><tr><td>TP53 wild-type</td><td>1</td><td></td></tr><tr><td>TP53 mutated</td><td>1.4622 (1.0601-2.0169)</td><td>0.0205</td></tr><tr><td>Age at diagnosis</td><td>0.9582 (0.9361-0.9809)</td><td>0.0003</td></tr><tr><td><strong>Tumour location</strong></td><td></td><td></td></tr><tr><td>Pons</td><td>1</td><td></td></tr><tr><td>Thalamus</td><td>0.7084 (0.4802-1.0452)</td><td>0.0823</td></tr><tr><td>Other midline</td><td>1.0039 (0.6263-1.6089)</td><td>0.9872</td></tr></tbody></table>

CI confidence interval, WT wild-type

BRAF<sup>MUT</sup> or FGFR1<sup>MUT</sup> was mostly associated with H3.3-K27M mutation but not H3.1-K27M mutation. One tumour harboured a FGFR1<sup>MUT</sup> in the context of a DMG H3-K27 wild-type with EZHIP overexpression presenting an ACVR1 mutation (case #60). Second, BRAF and FGFR1 mutations were mutually exclusive, as only one tumour presented both hits (case #23; Fig. 1). However, no clonality information was available for this case to confirm subclonality of the two mutations. Finally, we observed that in 90% (9/10) of FGFR1<sup>MUT</sup> cases for which we had the information, FGFR1 mutation was clonal to H3-3A mutation with a similar variant allele frequency suggesting a role in the early steps of oncogenesis of these tumours. It was less frequently the case for patients with DMG H3-K27M BRAF<sup>V600E</sup> with only 33% (2/6) tumours where BRAF mutation appeared clonal to H3-K27M mutation. In addition, H3-K27 FGFR1<sup>MUT</sup> tumours presented often other hits in the MAPK pathway with NF1 (13/31; 42%) or PTPN11 in (3/22; 13.6%) as the topmost mutated genes (Fig. 1). Additional MAPK-activating mutations seemed to be less frequent for BRAF<sup>MUT</sup> tumours with 1/12 (8.3%) case harbouring an NF1 mutation. TP53 mutations were found in 5% (1/20) and 9.3% (3/32) of the BRAF<sup>MUT</sup> and FGFR1<sup>MUT</sup> tumours, respectively but not PPM1D (Fig. 1). For one H3.3-K27M FGFR1<sup>MUT</sup> patient, TP53 mutation was clonal to H3.3-K27M and it was sub-clonal for the other case for which information was available.

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Clinical and molecular characteristics of the patient cohort of DMG H3-K27M with BRAF and FGFR1 mutations. Overview of the clinical and molecular annotations of 60 paediatric and adult DMG H3-K27 patients presenting BRAF or FGFR1 mutations. Cases are presented in columns and genes status in rows. Age is reported</figcaption></figure>

in years. All molecular information is derived from DNA or RNA sequencing analyses, except immuno-histological data (including histological profile, EZHIP and ATRX expression). For survival, patients still alive at last follow-up are indicated by a half-filled square

<figure><img src="image_5.png" /></figure>
<table><caption>**Table 1** Multivariable Cox proportional hazards regression model for the OS of patients with H3-K27M DMG</caption><thead><tr><th>Variable</th><th>Hazard ratio (95%CI)</th><th>p value</th></tr></thead><tbody><tr><td><strong>H3 status</strong></td><td></td><td></td></tr><tr><td>H3.1-K27M</td><td>1</td><td></td></tr><tr><td>H3.3-K27M</td><td>1.5820 (1.0529-2.3769)</td><td>0.0274</td></tr><tr><td>H3-WT</td><td>1.0210 (0.5452-1.9119)</td><td>0.9482</td></tr><tr><td><strong>BRAF status</strong></td><td></td><td></td></tr><tr><td>BRAF wild-type</td><td>1</td><td></td></tr><tr><td>BRAF mutated</td><td>0.2132 (0.1098-0.4140)</td><td>5.03e-06</td></tr><tr><td><strong>FGFR1 status</strong></td><td></td><td></td></tr><tr><td>FGFR1 wild-type</td><td>1</td><td></td></tr><tr><td>FGFR1 mutated</td><td>0.3414 (0.1963-0.5939)</td><td>0.0001</td></tr><tr><td><strong>TP53 status</strong></td><td></td><td></td></tr><tr><td>TP53 wild-type</td><td>1</td><td></td></tr><tr><td>TP53 mutated</td><td>1.4622 (1.0601-2.0169)</td><td>0.0205</td></tr><tr><td>Age at diagnosis</td><td>0.9582 (0.9361-0.9809)</td><td>0.0003</td></tr><tr><td><strong>Tumour location</strong></td><td></td><td></td></tr><tr><td>Pons</td><td>1</td><td></td></tr><tr><td>Thalamus</td><td>0.7084 (0.4802-1.0452)</td><td>0.0823</td></tr><tr><td>Other midline</td><td>1.0039 (0.6263-1.6089)</td><td>0.9872</td></tr></tbody></table>

CI confidence interval, WT wild-type

BRAF<sup>MUT</sup> or FGFR1<sup>MUT</sup> was mostly associated with H3.3-K27M mutation but not H3.1-K27M mutation. One tumour harboured a FGFR1<sup>MUT</sup> in the context of a DMG H3-K27 wild-type with EZHIP overexpression presenting an ACVR1 mutation (case #60). Second, BRAF and FGFR1 mutations were mutually exclusive, as only one tumour presented both hits (case #23; Fig. 1). However, no clonality information was available for this case to confirm subclonality of the two mutations. Finally, we observed that in 90% (9/10) of FGFR1<sup>MUT</sup> cases for which we had the information, FGFR1 mutation was clonal to H3-3A mutation with a similar variant allele frequency suggesting a role in the early steps of oncogenesis of these tumours. It was less frequently the case for patients with DMG H3-K27M BRAF<sup>V600E</sup> with only 33% (2/6) tumours where BRAF mutation appeared clonal to H3-K27M mutation. In addition, H3-K27 FGFR1<sup>MUT</sup> tumours presented often other hits in the MAPK pathway with NF1 (13/31; 42%) or PTPN11 in (3/22; 13.6%) as the topmost mutated genes (Fig. 1). Additional MAPK-activating mutations seemed to be less frequent for BRAF<sup>MUT</sup> tumours with 1/12 (8.3%) case harbouring an NF1 mutation. TP53 mutations were found in 5% (1/20) and 9.3% (3/32) of the BRAF<sup>MUT</sup> and FGFR1<sup>MUT</sup> tumours, respectively but not PPM1D (Fig. 1). For one H3.3-K27M FGFR1<sup>MUT</sup> patient, TP53 mutation was clonal to H3.3-K27M and it was sub-clonal for the other case for which information was available.

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Clinical and molecular characteristics of the patient cohort of DMG H3-K27M with BRAF and FGFR1 mutations. Overview of the clinical and molecular annotations of 60 paediatric and adult DMG H3-K27 patients presenting BRAF or FGFR1 mutations. Cases are presented in columns and genes status in rows. Age is reported</figcaption></figure>

in years. All molecular information is derived from DNA or RNA sequencing analyses, except immuno-histological data (including histological profile, EZHIP and ATRX expression). For survival, patients still alive at last follow-up are indicated by a half-filled square

<figure><img src="image_5.png" /></figure>
In addition, ATRX was mutated, or its expression was lost on IHC in 59.3% (16/27) of *FGFR1*MUT DMG, albeit never in *BRAF*MUT tumours, and this was not associated to *TP53*MUT in all but one case (#24). A mutation in a member of the PI3K/AKT/mTOR signalling pathway was present in 19% (4/21) of H3-K27M-*FGFR1*MUT tumours (Fig. 1).

**BRAF**<sup>MUT</sup>/<em>FGFR1</em><sup>MUT</sup> **H3 K27-altered tumours are**
**histologically heterogeneous with frequent**
**mixed diffuse-circumscribed presentation**
**and calcifications**

We next analysed the histo-pathological profile of cases of
our own cohort (Supplementary Table 1). These 29 tumours
were initially diagnosed as DMG H3K27 (n = 13), gangli-
oglioma grade 1 (n = 5), anaplastic ganglioglioma grade 3
(n = 4) or other gliomas (GBM n = 3; pilocytic astrocytoma
n = 1; LGG n = 1 or oligo-astrocytoma grade 3 n = 2). All
BRAF MUT/FGFRI MUT DMG presented a H3K27 trimethyla-
tion loss as expected and were positive for H3K27M stain-
ing with the exception of the case #60, harbouring EZHIP
overexpression. Tumour growth pattern evaluated morpho-
logically and based on NF70 immunostaining revealed that
12 tumours were only diffuse (n = 3 in FGFRI MUT group
and n = 9 in BRAF MUT), 4 were only composed of compact
tumoral tissue and 13 were mixed diffuse with nodular com-
pact areas. We observed three major histological patterns:
GG-like in 41.3% (12/29), DMG-like in 34.4% (10/29), and
HGG with a piloid astrocytic component (piloid-HGG) in
24.3% (7/29). BRAF MUT and FGFRI MUT tumours presented
a classic DMG histological profile only in 25% (4/16) and
46% (6/13) of cases, respectively (Figs. 1, 2, Supplementary
Fig. 2a, online resource). A GG-like profile, characterised by
mixed neuronal and glial tumour cells, was present in 50%
(8/16) and 30% (4/13) of BRAF MUT or FGFRI MUT cases,
respectively (Fig. 2b-i). Finally, 25% (4/16) of BRAF MUT
and 23% (3/13) of FGFRI MUT DMG presented a piloid-
HGG profile with piloid cell morphology (Fig. 2j, k). The
remaining FGFRI MUT DMG case (3/13) harboured a mixed
DMG-like and GG-like or piloid-HGG, and the last GG-
like and piloid-HGG profile (Fig. 2l). Micro-calcifications,
albeit rare in classic DMG, were observed in 56% (9/16) of
BRAF MUT and 38% (5/13) FGFRI MUT DMG (Fig. 2j). Gan-
glion cells and eosinophilic granular bodies were seen in
37% (11/29) and 31% (9/29) mainly in the BRAF MUT group
(7/16) (Fig. 2b). GFAP whorls defined as a small gliofila-
ment tangle, represented a rare but particular aspect (n = 4)
(Fig. 2m-o). Perivascular lymphoid infiltrates were fre-
quent (48%, 14/29) and especially enriched in the BRAF MUT
group (62%, 10/16) (Fig. 2c). CD34 extravascular staining
was observed in the majority of tested cases: 50% (4/8) in
FGFRI MUT and 60% (9/15) in BRAF MUT. ATRX loss of
expression was frequently seen in FGFRI MUT cases tested

(8/11) but absent in *BRAF*<sup>MUT</sup> (0/16) (Figs. 1, 2). The his-
topathological profile of *BRAF*<sup>MUT</sup> or *FGFRI*<sup>MUT</sup> DMG thus
appeared very heterogeneous and distinct compared to clas-
sical DMG H3-K27M.

Radiologically, we observed that DMG H3-K27 *BRAF*-MUT or *FGFRI*^MUT were mostly diffuse but presented a significant enrichment of a mixed nodular-diffuse aspect compared to control DMG H3-K27 (Fig. 3a–b; (9/11) 82% and (5/9) 56% versus (4/47) 8%; *p* value = <0.0001, chi-square test for trend). Few tumours were completely circumscribed (Fig. 3a, b—(2/11) 18% and (2/9) 22% versus (0/47) 0%; *p* value = <0.0001, Chi-square test for trend), and radiological analysis at progression identified a diffuse evolution pattern in half (2/4). Second, these tumours were more often contrast enhancing (Supplementary Fig. 2b, online resource; (11/11) 100% and (9/9) 100% versus 47% (22/47); *p* value = 0.0013 and 0.0029, respectively; Fisher’s exact test). Third, they developed more calcifications than DMG H3-K27 (Fig. 3a, c; (6/11) 55% and (5/9) 56% versus (1/38) 3%; *p* value = <0.0001, Fisher’s exact test). Presence of micro-calcifications detected in histological analyses correlated with macro-calcifications seen by CT scan in 4/8 (50%) DMG *BRAF*^MUT and 2/5 (33.3%) *FGFRI*^MUT (Supplementary Fig. 3c, d, online resource). In total in histologic and radiologic analyses, DMG H3K27M *BRAF*^MUT or *FGFRI*^MUT was calcified in 8/11 (72.7%) and 6/9 (66.7%), respectively. To evaluate how radiological features could detect DMG with MAPK alterations, we classified tumours according to a ‘classic’ (diffuse) or ‘atypical’ (nodular-diffuse or circumscribed aspect and/or presence of macro-calcifications) radiological profile independently of their genotype. This showed that 91% (10/11) of DMG with *BRAF*^V600E and 78% (7/9) DMG with *FGFRI*^MUT H3K27M DMG are classified as atypical versus only 8.5% (4/47) classical DMG H3K27 (Supplementary Fig. 3e, online resource; *p* value = <0.0001, Fisher’s exact test).

DNA methylation profiling distinguishes a subgroup
of DMG H3 K27-altered with MAPK-activating
mutations

Given the disparities between DMG_K27-BRAF/FGFR1 and classical DMG H3K27-altered, we hypothesised that DMG H3 K27-altered with MAPK-activating mutations might correspond to either (i) a new subtype of DMG or (ii) atypical aggressive MAPK-driven low-grade gliomas/ glioneuronal tumours. To test these hypotheses, we analysed the DNA methylation profile of the whole cohort. Based on the Heidelberg DNA methylation brain classifier V12.8, 54% (7/13) of *BRAF*<sup>MUT</sup> and 67% (10/15) of *FGFR*<sup>MUT</sup> tumours classified as DMG_K27 and the remaining corresponded to other classes or were undefined (score <0.9) (Supplementary Fig. 3a, online resource), which was consistent with
In addition, ATRX was mutated, or its expression was lost on IHC in 59.3% (16/27) of *FGFR1*MUT DMG, albeit never in *BRAF*MUT tumours, and this was not associated to *TP53*MUT in all but one case (#24). A mutation in a member of the PI3K/AKT/mTOR signalling pathway was present in 19% (4/21) of H3-K27M-*FGFR1*MUT tumours (Fig. 1).

**BRAF**<sup>MUT</sup>/<em>FGFR1</em><sup>MUT</sup> **H3 K27-altered tumours are**
**histologically heterogeneous with frequent**
**mixed diffuse-circumscribed presentation**
**and calcifications**

We next analysed the histo-pathological profile of cases of
our own cohort (Supplementary Table 1). These 29 tumours
were initially diagnosed as DMG H3K27 (n = 13), gangli-
oglioma grade 1 (n = 5), anaplastic ganglioglioma grade 3
(n = 4) or other gliomas (GBM n = 3; pilocytic astrocytoma
n = 1; LGG n = 1 or oligo-astrocytoma grade 3 n = 2). All
BRAF MUT/FGFRI MUT DMG presented a H3K27 trimethyla-
tion loss as expected and were positive for H3K27M stain-
ing with the exception of the case #60, harbouring EZHIP
overexpression. Tumour growth pattern evaluated morpho-
logically and based on NF70 immunostaining revealed that
12 tumours were only diffuse (n = 3 in FGFRI MUT group
and n = 9 in BRAF MUT), 4 were only composed of compact
tumoral tissue and 13 were mixed diffuse with nodular com-
pact areas. We observed three major histological patterns:
GG-like in 41.3% (12/29), DMG-like in 34.4% (10/29), and
HGG with a piloid astrocytic component (piloid-HGG) in
24.3% (7/29). BRAF MUT and FGFRI MUT tumours presented
a classic DMG histological profile only in 25% (4/16) and
46% (6/13) of cases, respectively (Figs. 1, 2, Supplementary
Fig. 2a, online resource). A GG-like profile, characterised by
mixed neuronal and glial tumour cells, was present in 50%
(8/16) and 30% (4/13) of BRAF MUT or FGFRI MUT cases,
respectively (Fig. 2b-i). Finally, 25% (4/16) of BRAF MUT
and 23% (3/13) of FGFRI MUT DMG presented a piloid-
HGG profile with piloid cell morphology (Fig. 2j, k). The
remaining FGFRI MUT DMG case (3/13) harboured a mixed
DMG-like and GG-like or piloid-HGG, and the last GG-
like and piloid-HGG profile (Fig. 2l). Micro-calcifications,
albeit rare in classic DMG, were observed in 56% (9/16) of
BRAF MUT and 38% (5/13) FGFRI MUT DMG (Fig. 2j). Gan-
glion cells and eosinophilic granular bodies were seen in
37% (11/29) and 31% (9/29) mainly in the BRAF MUT group
(7/16) (Fig. 2b). GFAP whorls defined as a small gliofila-
ment tangle, represented a rare but particular aspect (n = 4)
(Fig. 2m-o). Perivascular lymphoid infiltrates were fre-
quent (48%, 14/29) and especially enriched in the BRAF MUT
group (62%, 10/16) (Fig. 2c). CD34 extravascular staining
was observed in the majority of tested cases: 50% (4/8) in
FGFRI MUT and 60% (9/15) in BRAF MUT. ATRX loss of
expression was frequently seen in FGFRI MUT cases tested

(8/11) but absent in *BRAF*<sup>MUT</sup> (0/16) (Figs. 1, 2). The his-
topathological profile of *BRAF*<sup>MUT</sup> or *FGFR1*<sup>MUT</sup> DMG thus
appeared very heterogeneous and distinct compared to clas-
sical DMG H3-K27M.

Radiologically, we observed that DMG H3-K27 *BRAF*-MUT or *FGFR1*-MUT were mostly diffuse but presented a significant enrichment of a mixed nodular-diffuse aspect compared to control DMG H3-K27 (Fig. 3a–b; (9/11) 82% and (5/9) 56% *versus* (4/47) 8%; *p* value = <0.0001, chi-square test for trend). Few tumours were completely circumscribed (Fig. 3a, b—(2/11) 18% and (2/9) 22% versus (0/47) 0%; *p* value = <0.0001, Chi-square test for trend), and radiological analysis at progression identified a diffuse evolution pattern in half (2/4). Second, these tumours were more often contrast enhancing (Supplementary Fig. 2b, online resource; (11/11) 100% and (9/9) 100% *versus* 47% (22/47); *p* value = 0.0013 and 0.0029, respectively; Fisher’s exact test). Third, they developed more calcifications than DMG H3-K27 (Fig. 3a, c; (6/11) 55% and (5/9) 56% *versus* (1/38) 3%; *p* value = <0.0001, Fisher’s exact test). Presence of micro-calcifications detected in histological analyses correlated with macro-calcifications seen by CT scan in 4/8 (50%) DMG *BRAF*<sup>MUT</sup> and 2/5 (33.3%) *FGFR1*<sup>MUT</sup> (Supplementary Fig. 3c, d, online resource). In total in histologic and radiologic analyses, DMG H3K27M *BRAF*<sup>MUT</sup> or *FGFR1*<sup>MUT</sup> was calcified in 8/11 (72.7%) and 6/9 (66.7%), respectively. To evaluate how radiological features could detect DMG with MAPK alterations, we classified tumours according to a ‘classic’ (diffuse) or ‘atypical’ (nodular-diffuse or circumscribed aspect and/or presence of macro-calcifications) radiological profile independently of their genotype. This showed that 91% (10/11) of DMG with *BRAF*<sup>V600E</sup> and 78% (7/9) DMG with *FGFR1*<sup>MUT</sup> H3K27M DMG are classified as atypical *versus* only 8.5% (4/47) classical DMG H3K27 (Supplementary Fig. 3e, online resource; *p* value = <0.0001, Fisher’s exact test).

DNA methylation profiling distinguishes a subgroup
of DMG H3 K27-altered with MAPK-activating
mutations

Given the disparities between DMG_K27-BRAF/FGFR1 and classical DMG H3K27-altered, we hypothesised that DMG H3 K27-altered with MAPK-activating mutations might correspond to either (i) a new subtype of DMG or (ii) atypical aggressive MAPK-driven low-grade gliomas/ glioneuronal tumours. To test these hypotheses, we analysed the DNA methylation profile of the whole cohort. Based on the Heidelberg DNA methylation brain classifier V12.8, 54% (7/13) of *BRAF*<sup>MUT</sup> and 67% (10/15) of *FGFR1*<sup>MUT</sup> tumours classified as DMG_K27 and the remaining corresponded to other classes or were undefined (score <0.9) (Supplementary Fig. 3a, online resource), which was consistent with
<figure><img src="image_6.png" /><figcaption>**Fig. 2** Multiple histopathological profiles of DMG H3-K27 with *BRAF* or *FGFR1* mutations. Case 14 **a** A glioneuronal proliferation with ganglion cells, eosinophilic granular bodies and some microcalcifications (HPS, magnification×400). Case 11 **b** A glioneuronal proliferation with numerous ganglion cells (HPS, magnification×400). Case 13 **c** A glioneuronal proliferation with numerous ganglion cells and lymphocytic infiltrates (HPS, magnification×400). Case 31 **d** A mainly circumscribed proliferation (neurofilament, magnification×30). Case 32 **e** A mainly circumscribed proliferation (neurofilament, magnification×400) with a diffuse component at the periphery of the tumour **f** (neurofilament, magnification×400). **g** Diffuse chromogranin A immunoreactivity staining neuron cells (magnification×400). **h** *BRAF*<sup>V600E</sup> expression in all tumour cells including ganglion cells (magnification×400). **i** H3K27M expression in all tumour cells including ganglion cells (magnification×400). Case 7 **j** A glial proliferation with oligo-like features and microcalcifications (magnification×400). Case 11 **k** Global loss of H3K27me3 (magnification×400). **l** Loss of ATRX in tumour cells (magnification×400). **m** Whorls of gliofibrillary processes (HPS, magnification×20). **n** Whorls of gliofibrillary processes (HPS, magnification×400), stained using GFAP antibody **o**, magnification×400). Black scale bars represent 50 µm (**a**–**c**, **e**–**l** and **n**–**o**), 100 µm (**m**), and 500 µm (**d**)</figcaption></figure>

the hypothesis that DMG H3 K27-altered with MAPK-activating mutation constitute a unique subtype of DMG. In contrast, all DMG H3.3-K27M *BRAF*<sup>WT</sup>/*FGFR*<sup>WT</sup> clustered

as DMG_K27. We then performed an unsupervised clustering based on DNA methylation profiles of these samples together with reference gliomas from the literature [2]. All

<figure><img src="image_7.png" /></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 2** Multiple histopathological profiles of DMG H3-K27 with *BRAF* or *FGFR1* mutations. Case 14 **a** A glioneuronal proliferation with ganglion cells, eosinophilic granular bodies and some microcalcifications (HPS, magnification×400). Case 11 **b** A glioneuronal proliferation with numerous ganglion cells (HPS, magnification×400). Case 13 **c** A glioneuronal proliferation with numerous ganglion cells and lymphocytic infiltrates (HPS, magnification×400). Case 31 **d** A mainly circumscribed proliferation (neurofilament, magnification×30). Case 32 **e** A mainly circumscribed proliferation (neurofilament, magnification×400) with a diffuse component at the periphery of the tumour **f** (neurofilament, magnification×400). **g** Diffuse chromogranin A immunoreactivity staining neuron cells (magnification×400). **h** BRAF<sup>V600E</sup> expression in all tumour cells including ganglion cells (magnification×400). **i** H3K27M expression in all tumour cells including ganglion cells (magnification×400). Case 7 **j** A glial proliferation with oligo-like features and microcalcifications (magnification×400). Case 11 **k** Global loss of H3K27me3 (magnification×400). **l** Loss of ATRX in tumour cells (magnification×400). **m** Whorls of gliofibrillary processes (HPS, magnification×20). **n** Whorls of gliofibrillary processes (HPS, magnification×400), stained using GFAP antibody **o**, magnification×400). Black scale bars represent 50 µm (**a**–**c**, **e**–**l** and **n**–**o**), 100 µm (**m**), and 500 µm (**d**)</figcaption></figure>

the hypothesis that DMG H3 K27-altered with MAPK-activating mutation constitute a unique subtype of DMG. In contrast, all DMG H3.3-K27M *BRAF*<sup>WT</sup>/*FGFR*<sup>WT</sup> clustered

as DMG_K27. We then performed an unsupervised clustering based on DNA methylation profiles of these samples together with reference gliomas from the literature [2]. All

<figure><img src="image_7.png" /></figure>
**Fig. 3** Radiological specificities of DMG H3-K27M *BRAF<sup>V600E</sup>* or ► *FGFR1<sup>MUT</sup>*. **a** T2-FLAIR (Fluid-attenuated inversion recovery) MR images sequences or CT scans (computed tomography) of DMG H3-K27M according to *BRAF* or *FGFR1* mutation status. **b** Comparison of the tumour radiological presentation (diffuse, circumscribed, or mixed) of DMG according to their genotype (chi-square test for trend: ns, ****p value < 0.0001). **c** Comparison of presence of macro-calcifications in DMG CT-scans according to the presence of MAPK alteration (Fisher's exact test: ****p value < 0.0001, *p* = 0.0210, *p* = 0.0348)

DMG H3-K27 *BRAF*<sup>MUT</sup> or *FGFR1*<sup>MUT</sup> but one (case #42) separated from DMG H3-K27 on the tSNE, independently of their H3 mutational status or brain classifier score (Fig. 4, and supplementary Fig. 3b, online resource). They also separated from midline *BRAF*<sup>V600E</sup> LGG and GG grade 1 (Supplementary Fig. 3c, online resource). Furthermore, they were grouped in subclasses according to the nature of the secondary MAPK mutation in *BRAF* or *FGFR1* (Fig. 4, and supplementary Fig. 3b, online resource).

**BRAF and FGFR1 mutational status are prognostic in paediatric and adult DMG H3 K27-altered**

TP53 mutations and *BRAF*<sup>V600E</sup>*/FGFR1* mutations are mostly mutually exclusive in DMG. In order to avoid the confounding effect of *TP53* mutations on the outcome of H3.3-K27M DMG without MAPK-activating alterations, we stratified patients on *Histone* H3 and *TP53* genotypes of both subgroups in Kaplan–Meier overall survival (OS) analyses. This analysis showed a significant better OS for paediatric and adult DMG H3-K27 patients with activating *BRAF* (median OS 37 mo.) or *FGFR1* mutations (median OS 36 mo.) compared to DMG H3.3-K27M *TP53*<sup>WT</sup> (median OS 12 mo.) and other DMG subtypes (Fig. 5a; *p* value < 0.0001, global log-rank test). Further, we showed that there was no impact of histopathological features such as microvascular proliferation, necrosis or mitotic index, on the OS of patients with *BRAF* or *FGFR1*-mutated DMG (supplemental Fig. 2f-h, online resource). To pursue, we performed a multivariable analysis to evaluate the association of Histone H3, *BRAF*, *FGFR1* and *TP53* mutational status, age at diagnosis and tumoral location with survival. As expected from previous publications, histone H3 and *TP53* status were significantly associated with OS (Table 1) [4, 41]. Age was significantly associated with prognosis, but its overall impact was only marginal compared to other variables. We also showed that *BRAF*<sup>V600E</sup> (HR: 0.2132, 95% CI 0.1098–0.4140, *p* value = 5.03e-06) and *FGFR1*<sup>MUT</sup> (0.3414, 95%CI 0.1963–0.5939, *p* value = 0.0001) are strong and independent prognostic markers in H3K27-altered DMG (Table 1).

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>
**Fig. 3** Radiological specificities of DMG H3-K27M *BRAF*<sup>V600E</sup> or ► *FGFR1*<sup>MUT</sup>. **a** T2-FLAIR (Fluid-attenuated inversion recovery) MR images sequences or CT scans (computed tomography) of DMG H3-K27M according to *BRAF* or *FGFR1* mutation status. **b** Comparison of the tumour radiological presentation (diffuse, circumscribed, or mixed) of DMG according to their genotype (chi-square test for trend: ns, ****p value < 0.0001). **c** Comparison of presence of macro-calcifications in DMG CT-scans according to the presence of MAPK alteration (Fisher's exact test: ****p value < 0.0001, *p* = 0.0210, *p* = 0.0348)

DMG H3-K27 *BRAF*<sup>MUT</sup> or *FGFR1*<sup>MUT</sup> but one (case #42) separated from DMG H3-K27 on the tSNE, independently of their H3 mutational status or brain classifier score (Fig. 4, and supplementary Fig. 3b, online resource). They also separated from midline *BRAF*<sup>V600E</sup> LGG and GG grade 1 (Supplementary Fig. 3c, online resource). Furthermore, they were grouped in subclasses according to the nature of the secondary MAPK mutation in *BRAF* or *FGFR1* (Fig. 4, and supplementary Fig. 3b, online resource).

**BRAF and FGFR1 mutational status are prognostic in paediatric and adult DMG H3 K27-altered**

TP53 mutations and *BRAF*<sup>V600E</sup>*/FGFR1* mutations are mostly mutually exclusive in DMG. In order to avoid the confounding effect of *TP53* mutations on the outcome of H3.3-K27M DMG without MAPK-activating alterations, we stratified patients on *Histone* H3 and *TP53* genotypes of both subgroups in Kaplan–Meier overall survival (OS) analyses. This analysis showed a significant better OS for paediatric and adult DMG H3-K27 patients with activating *BRAF* (median OS 37 mo.) or *FGFR1* mutations (median OS 36 mo.) compared to DMG H3.3-K27M *TP53*<sup>WT</sup> (median OS 12 mo.) and other DMG subtypes (Fig. 5a; *p* value < 0.0001, global log-rank test). Further, we showed that there was no impact of histopathological features such as microvascular proliferation, necrosis or mitotic index, on the OS of patients with *BRAF* or *FGFR1*-mutated DMG (supplemental Fig. 2f-h, online resource). To pursue, we performed a multivariable analysis to evaluate the association of Histone H3, *BRAF*, *FGFR1* and *TP53* mutational status, age at diagnosis and tumoral location with survival. As expected from previous publications, histone H3 and *TP53* status were significantly associated with OS (Table 1) [4, 41]. Age was significantly associated with prognosis, but its overall impact was only marginal compared to other variables. We also showed that *BRAF*<sup>V600E</sup> (HR: 0.2132, 95% CI 0.1098–0.4140, *p* value = 5.03e-06) and *FGFR1*<sup>MUT</sup> (0.3414, 95%CI 0.1963–0.5939, *p* value = 0.0001) are strong and independent prognostic markers in H3K27-altered DMG (Table 1).

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 4** Analysis of DNA methylation profiles of DMG H3.3-K27M *BRAF*<sup>MUT</sup>/*FGFR1*<sup>MUT</sup>. CNS tumour classification based on DNA methylation profiles. Unsupervised clustering by t-SNE analysis of tumours based on their DNA methylation profiles using 10,000 top-</figcaption></figure>

most differentially methylated probes across the reference sample set composed of samples from Capper et al. (n=936) and Castel et al. (n=41)

<figure><img src="image_12.png" /></figure>
<figure><img src="image_11.png" /><figcaption>**Fig. 4** Analysis of DNA methylation profiles of DMG H3.3-K27M *BRAF*<sup>MUT</sup>/*FGFR1*<sup>MUT</sup>. CNS tumour classification based on DNA methylation profiles. Unsupervised clustering by t-SNE analysis of tumours based on their DNA methylation profiles using 10,000 top-</figcaption></figure>

most differentially methylated probes across the reference sample set composed of samples from Capper et al. (n=936) and Castel et al. (n=41)

<figure><img src="image_12.png" /></figure>
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 5** Clinical specificities of patients with DMG H3-K27M *BRAF*<sup>MUT</sup>*/FGFR1*<sup>MUT</sup> and transcriptomic tumour profiling. **a** Comparison of OS estimated using Kaplan–Meier method according to the mutation status of Histone H3, *BRAF*, *FGFR1* and *TP53* (log-rank test, *p* value = <0.0001). **b** Distribution of age at diagnosis according to *BRAF* and *FGFR1* mutation status (Mann–Whitney test; \* *p* value = 0.0168, \*\* *p* value = 0.0001, \*\*\* *p* value = <0.0001). **c**</figcaption></figure>

<figure><img src="image_16.png" /></figure>

Comparison of tumour location according to the mutation status of *BRAF* and *FGFR1* (chi-square test for trend: ns, \*\* *p* value = 0.0004 \*\*\* *p* value = <0.0001). **d** GSEA plot showing common transcriptomic signature from DMG H3.3-K27M vs. DMG H3.3-K27M *BRAF*<sup>V600E</sup> or *FGFR1*<sup>MUT</sup>. The normalised enrichment score (NES) and the false discovery rate (FDR q) are indicated in each plot

## Clinical disparities in paediatric and adult patients with DMG H3-K27M *BRAF/FGFR1*-mutated

We next compared other clinical parameters. The sex ratio was balanced in DMG with MAPK-activating mutations (Supplementary Fig. 4a, online resource). However, we identified a significant difference in age at diagnosis between H3.3-K27M DMG, *BRAF*<sup>MUT</sup> and *FGFR1*<sup>MUT</sup> DMG (Fig. 5b). More precisely, H3.3-K27M *BRAF*<sup>V600E</sup> DMG developed only in children (<18 years) with a median age at onset of 7.2 years lower than DMG H3.3K27M with

9.85 years (Fig. 5b; *p* value = 0.0123, Mann–Whitney test) but similar to DMG H3K27-altered paediatric cases with 7.6 years in a restricted paediatric cohort (supplementary Fig. 4b, online resource). In contrast, patients with *FGFR1*<sup>MUT</sup> have a significant higher age at diagnosis compared to those *FGFR1*<sup>WT</sup> with median of 14.8 and 9.8 years respectively (Fig. 5b; *p* value = 0.0001, Mann–Whitney test). The age at diagnosis of H3-K27M DMG can vary according to initial tumour locations, with a higher age of onset for thalamic *versus* pontine tumours [18, 34]. We found this difference of age according to tumour location
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Fig. 5** Clinical specificities of patients with DMG H3-K27M *BRAF*<sup>MUT</sup>*/FGFR1*<sup>MUT</sup> and transcriptomic tumour profiling. **a** Comparison of OS estimated using Kaplan–Meier method according to the mutation status of Histone H3, *BRAF*, *FGFR1* and *TP53* (log-rank test, *p* value = <0.0001). **b** Distribution of age at diagnosis according to *BRAF* and *FGFR1* mutation status (Mann–Whitney test; \* *p* value = 0.0168, \*\* *p* value = 0.0001, \*\*\* *p* value = <0.0001). **c**</figcaption></figure>

<figure><img src="image_16.png" /></figure>

Comparison of tumour location according to the mutation status of *BRAF* and *FGFR1* (chi-square test for trend: ns, \*\* *p* value = 0.0004 \*\*\* *p* value = <0.0001). **d** GSEA plot showing common transcriptomic signature from DMG H3.3-K27M vs. DMG H3.3-K27M *BRAF*<sup>V600E</sup> or *FGFR1*<sup>MUT</sup>. The normalised enrichment score (NES) and the false discovery rate (FDR q) are indicated in each plot

## Clinical disparities in paediatric and adult patients with DMG H3-K27M *BRAF/FGFR1*-mutated

We next compared other clinical parameters. The sex ratio was balanced in DMG with MAPK-activating mutations (Supplementary Fig. 4a, online resource). However, we identified a significant difference in age at diagnosis between H3.3-K27M DMG, *BRAF*<sup>MUT</sup> and *FGFR1*<sup>MUT</sup> DMG (Fig. 5b). More precisely, H3.3-K27M *BRAF*<sup>V600E</sup> DMG developed only in children (<18 years) with a median age at onset of 7.2 years lower than DMG H3.3K27M with

9.85 years (Fig. 5b; *p* value = 0.0123, Mann–Whitney test) but similar to DMG H3K27-altered paediatric cases with 7.6 years in a restricted paediatric cohort (supplementary Fig. 4b, online resource). In contrast, patients with *FGFR1*<sup>MUT</sup> have a significant higher age at diagnosis compared to those *FGFR1*<sup>WT</sup> with median of 14.8 and 9.8 years respectively (Fig. 5b; *p* value = 0.0001, Mann–Whitney test). The age at diagnosis of H3-K27M DMG can vary according to initial tumour locations, with a higher age of onset for thalamic *versus* pontine tumours [18, 34]. We found this difference of age according to tumour location
in DMG H3.3-K27M and in DMG *BRAF*<sup>V600E</sup> but not in DMG *FGFR1*<sup>MUT</sup> (Supplementary Fig. 4c, online resource). Finally, DMG with *BRAF* and *FGFR1* mutations were significantly more frequent in the thalamus compared to DMG H3.3-K27M (Fig. 5c; 70% (16/23) and 58% (21/36), respectively *versus* 28% (55/199); *p* value = 0.0004, and *p* value < 0.0001, Chi-square test for trend). No variation in term of OS, age at diagnosis and tumour location was noted according to *FGFR1*-mutated variant: *FGFR1*<sup>N546K/D</sup> *versus* *FGFR1*<sup>N656E</sup> (Supplementary Fig. 5, online resource).

## H3.3-K27M mutation occurs prior to *BRAF*<sup>V600E</sup> during oncogenesis

We next wondered if the sequence of appearance of mutations was identical in these tumours, and more largely if they correspond to (i) DMG (H3-K27M as a first hit) or (ii) atypical aggressive low-grade gliomas (*BRAF*<sup>V600E</sup> as first hit). To address this question, we analysed *BRAF* copy number variation (CNV) by digital droplet Polymerase Chain Reaction (ddPCR) on genomic DNA from one *BRAF*<sup>V600E</sup> H3.3-K27M tumour (case #2) and from a H3.3-K27M *BRAF*<sup>WT</sup> clone derived from this primary tumour in vitro (Supplementary Fig. 6, online resource). The *BRAF*<sup>WT</sup> clone had two *BRAF* alleles and thus resulted from the re-amplification of an ancestral H3.3-K27M-only clone, but not from a genetic loss of the *BRAF*<sup>V600E</sup> allele, demonstrating that, in one DMG H3.3-K27M *BRAF*<sup>V600E</sup>, H3.3-K27M was the first hit. Interestingly, DNA methylation profiles from this in vitro amplified *BRAF*<sup>WT</sup> H3.3-K27M ancestral clone from case #2 and also from a tumour relapse enriched in H3.3-K27M *BRAF*<sup>WT</sup> clone from patient #7 (initially diagnosed with DMG H3.3-K27M *BRAF*<sup>V600E</sup>), clustered with DMG H3-K27 with MAPK alterations instead of classical DMG H3-K27 (Supplementary Fig. 3b, online resource).

## DMG H3.3K27M with MAPK alterations show a transcriptomic signature of senescence with up-regulation of CDKN1A (P21)

In order to investigate the possible specificities of the DMG H3.3-K27M with *BRAF*<sup>MUT</sup> or *FGFR1*<sup>MUT</sup>, we compared their transcriptome to regular DMG H3.3-K27M *TP53*<sup>WT</sup> separately. We found 676 significantly up-regulated and 633 down-regulated genes in the contrast H3.3K27M *BRAF*<sup>WT</sup> *versus* H3.3-K27M *BRAF*<sup>V600E</sup> DMG and 228 up-regulated and 274 down-regulated genes in the contrast H3.3-K27M *FGFR1*<sup>WT</sup> *versus* H3.3-K27M *FGFR1*<sup>MUT</sup> DMG (adj. *p* value ≤ 0.001) (Supplementary Fig. 7a, b, online resource). Ninety-four up-regulated and 111 down-regulated genes were common to the two comparisons. Using gene set enrichment analyses (GSEA), we observed an enrichment for MAPK signalling and PI3K/AKT/MTOR signalling

signatures in both *BRAF*<sup>MUT</sup> and *FGFR1*<sup>MUT</sup> DMG (Fig. 5d; Supplementary Fig. 7e, f, online resource) as well as angiogenesis and hypoxia signatures (Supplementary Fig. 7 g-j, online resource). In addition, transcriptomic signatures highlighted activation of senescence and P53 signalling pathway in both comparisons (Fig. 5d). P53 protein is a tumour suppressor implicated in the permanent cell cycle arrest by inducing senescence or apoptosis in response to stress like oncogene activation [6, 14]. TP53 pathway activation was validated at the protein level by immunohistochemistry (IHC) in 71% (10/14) of DMG H3.3-K27M *TP53*<sup>WT</sup> with *BRAF*/*FGFR1* alterations, showing heterogeneous, weak to strong TP53 staining. Moreover *CDKN1A*, encoding the senescence marker P21, was overexpressed at the RNA level in both *BRAF*<sup>MUT</sup> and *FGFR1*<sup>MUT</sup> gliomas compared to those only H3.3-K27M mutated (adj. *p* value < 0.0001) (Supplementary Fig. 7 k online resource). *CDKN2A*, which encodes the tumour suppressor P16, was overexpressed only in *BRAF*-mutant gliomas (Supplementary Fig. 7 l online resource; adj. *p* value = 0.0048). As a whole, based on transcriptome and IHC, DMG H3-K27 with *BRAF*<sup>MUT</sup>/<i>FGFR1</i><sup>MUT</sup> were characterised by a senescence programme, likely induced trough a P16/P21-P53 axis.

## Discussion

The recent description of ‘DMG, H3 K27-altered’ and its sub-classification into four molecular subgroups does not capture completely the diversity of this disease [16]. Our data support the individualization of an additional new subtype of DMG with distinct histological, radiological, clinical, genomic, transcriptomic and epigenetic features that we provisionally termed *DMG, H3 K27 and BRAF/FGFR1 co-altered* (DMG_K27-BRAF/FGFR1) which may represent 20% of *DMG H3 K27-altered*. Using unsupervised analysis of DNA methylation tumour profiles, *DMG_K27-BRAF/FGFR1* formed a specific cluster, separated from other *DMG_K27* gliomas, others adult/paediatric diffuse gliomas, low-grade glial/glioneuronal tumours and more specifically *BRAF*<sup>V600E</sup>-mutated ganglioglioma even midline located.

This highlights a possible distinct cell of origin for *DMG_K27- BRAF/FGFR1*, able to exhibit a mixed glial and neuronal differentiation, mostly noticeable in the *BRAF* subclass [2, 39]. Schüller et al. did not mention any of these phenotypes in DMG H3-K27M *FGFR1*<sup>MUT</sup>, due to the limited number of tumours [31]. The analysis of the H3.3-K27M ancestral clone derived from a DMG *BRAF*<sup>V600E</sup> H3.3K27M harboured this same DNA methylation profile, confirming that the specific DNA methylation profile of *DMG_K27-BRAF/FGFR1* is not a strict consequence of MAPK alterations. The fair discrimination of tumours on the tSNE based on the type of the secondary MAPK mutation
in DMG H3.3-K27M and in DMG *BRAF*<sup>V600E</sup> but not in DMG *FGFR1*<sup>MUT</sup> (Supplementary Fig. 4c, online resource). Finally, DMG with *BRAF* and *FGFR1* mutations were significantly more frequent in the thalamus compared to DMG H3.3-K27M (Fig. 5c; 70% (16/23) and 58% (21/36), respectively *versus* 28% (55/199); *p* value = 0.0004, and *p* value < 0.0001, Chi-square test for trend). No variation in term of OS, age at diagnosis and tumour location was noted according to *FGFR1*-mutated variant: *FGFR1*<sup>N546K/D</sup> *versus* *FGFR1*<sup>N656E</sup> (Supplementary Fig. 5, online resource).

## H3.3-K27M mutation occurs prior to *BRAF*<sup>V600E</sup> during oncogenesis

We next wondered if the sequence of appearance of mutations was identical in these tumours, and more largely if they correspond to (i) DMG (H3-K27M as a first hit) or (ii) atypical aggressive low-grade gliomas (*BRAF*<sup>V600E</sup> as first hit). To address this question, we analysed *BRAF* copy number variation (CNV) by digital droplet Polymerase Chain Reaction (ddPCR) on genomic DNA from one *BRAF*<sup>V600E</sup> H3.3-K27M tumour (case #2) and from a H3.3-K27M *BRAF*<sup>WT</sup> clone derived from this primary tumour in vitro (Supplementary Fig. 6, online resource). The *BRAF*<sup>WT</sup> clone had two *BRAF* alleles and thus resulted from the re-amplification of an ancestral H3.3-K27M-only clone, but not from a genetic loss of the *BRAF*<sup>V600E</sup> allele, demonstrating that, in one DMG H3.3-K27M *BRAF*<sup>V600E</sup>, H3.3-K27M was the first hit. Interestingly, DNA methylation profiles from this in vitro amplified *BRAF*<sup>WT</sup> H3.3-K27M ancestral clone from case #2 and also from a tumour relapse enriched in H3.3-K27M *BRAF*<sup>WT</sup> clone from patient #7 (initially diagnosed with DMG H3.3-K27M *BRAF*<sup>V600E</sup>), clustered with DMG H3-K27 with MAPK alterations instead of classical DMG H3-K27 (Supplementary Fig. 3b, online resource).

## DMG H3.3K27M with MAPK alterations show a transcriptomic signature of senescence with up-regulation of CDKN1A (P21)

In order to investigate the possible specificities of the DMG H3.3-K27M with *BRAF*<sup>MUT</sup> or *FGFR1*<sup>MUT</sup>, we compared their transcriptome to regular DMG H3.3-K27M *TP53*<sup>WT</sup> separately. We found 676 significantly up-regulated and 633 down-regulated genes in the contrast H3.3K27M *BRAF*<sup>WT</sup> *versus* H3.3-K27M *BRAF*<sup>V600E</sup> DMG and 228 up-regulated and 274 down-regulated genes in the contrast H3.3-K27M *FGFR1*<sup>WT</sup> *versus* H3.3-K27M *FGFR1*<sup>MUT</sup> DMG (adj. *p* value ≤ 0.001) (Supplementary Fig. 7a, b, online resource). Ninety-four up-regulated and 111 down-regulated genes were common to the two comparisons. Using gene set enrichment analyses (GSEA), we observed an enrichment for MAPK signalling and PI3K/AKT/MTOR signalling

signatures in both *BRAF*<sup>MUT</sup> and *FGFR1*<sup>MUT</sup> DMG (Fig. 5d; Supplementary Fig. 7e, f, online resource) as well as angiogenesis and hypoxia signatures (Supplementary Fig. 7 g-j, online resource). In addition, transcriptomic signatures highlighted activation of senescence and P53 signalling pathway in both comparisons (Fig. 5d). P53 protein is a tumour suppressor implicated in the permanent cell cycle arrest by inducing senescence or apoptosis in response to stress like oncogene activation [6, 14]. TP53 pathway activation was validated at the protein level by immunohistochemistry (IHC) in 71% (10/14) of DMG H3.3-K27M *TP53*<sup>WT</sup> with *BRAF*/*FGFR1* alterations, showing heterogeneous, weak to strong TP53 staining. Moreover *CDKN1A*, encoding the senescence marker P21, was overexpressed at the RNA level in both *BRAF*<sup>MUT</sup> and *FGFR1*<sup>MUT</sup> gliomas compared to those only H3.3-K27M mutated (adj. *p* value < 0.0001) (Supplementary Fig. 7 k online resource). *CDKN2A*, which encodes the tumour suppressor P16, was overexpressed only in *BRAF*-mutant gliomas (Supplementary Fig. 7 l online resource; adj. *p* value = 0.0048). As a whole, based on transcriptome and IHC, DMG H3-K27 with *BRAF*<sup>MUT</sup>/ *FGFR1*<sup>MUT</sup> were characterised by a senescence programme, likely induced trough a P16/P21-P53 axis.

## Discussion

The recent description of 'DMG, H3 K27-altered' and its sub-classification into four molecular subgroups does not capture completely the diversity of this disease [16]. Our data support the individualization of an additional new subtype of DMG with distinct histological, radiological, clinical, genomic, transcriptomic and epigenetic features that we provisionally termed DMG, H3 K27 and *BRAF*/*FGFR1* co-altered (DMG_K27-BRAF/FGFR1) which may represent 20% of DMG H3 K27-altered. Using unsupervised analysis of DNA methylation tumour profiles, DMG_K27-BRAF/FGFR1 formed a specific cluster, separated from other DMG_K27 gliomas, others adult/paediatric diffuse gliomas, low-grade glial/glioneuronal tumours and more specifically *BRAF*<sup>V600E</sup>-mutated ganglioglioma even midline located.

This highlights a possible distinct cell of origin for DMG_K27- BRAF/FGFR1, able to exhibit a mixed glial and neuronal differentiation, mostly noticeable in the BRAF subclass [2, 39]. Schüller et al. did not mention any of these phenotypes in DMG H3-K27M *FGFR1*<sup>MUT</sup>, due to the limited number of tumours [31]. The analysis of the H3.3-K27M ancestral clone derived from a DMG *BRAF*<sup>V600E</sup> H3.3K27M harboured this same DNA methylation profile, confirming that the specific DNA methylation profile of DMG_K27-BRAF/FGFR1 is not a strict consequence of MAPK alterations. The fair discrimination of tumours on the tSNE based on the type of the secondary MAPK mutation

<figure><img src="image_17.png" /></figure>
(i.e. *BRAF* vs. *FGFR1*) even suggests that this entity could
be further subdivided.

Genotype–morphotype correlations support the distinc-
tion from classical DMG, H3 K27-altered and from glial/
glioneuronal tumours MAPK-altered: (i) whilst ependymal
differentiation has been described in rare DMG H3-K27
[36], a mixed glioneuronal differentiation associated with
CD34 positivity and eosinophilic granular bodies or a piloid
differentiation are not yet described; (ii) only an exceptional
subset of ganglioglioma grade 1 present *FGFR1* alteration,
more characteristic of other glioneuronal tumours [24] and
(iii) the existence of true malignant transformation in gan-
glioglioma is a matter of debate. The majority of reported
cases were published before the advent of molecular biology,
reclassified in a wide spectrum of CNS WHO tumour types
without a distinct methylation class [28] or more interest-
ingly were midline-located with a co-occurring *BRAF* and
H3-K27M mutations [11, 21–23, 26, 29, 30, 44]. In the uni-
fied methylation class that we describe, the radiological and
histopathological presentations are thus highly heterogene-
ous including tumours with mixed glioneuronal or pilocytic
differentiation, and do not always fulfil a strict diagnostic
criterion of DMG. Indeed, these tumours are less diffuse,
with a frequent nodular to circumscribed radiological aspect
(91% for *BRAF*<sup>MUT</sup> and 78% for *FGFR1*<sup>MUT</sup> DMG) and
calcifications.

Several other clinical and biological characteristics sup-
port the individualization of this new entity from classical
DMG, *H3 K27-altered*. First, OS is significantly different
from classical DMG\_K27, with a median around three years
for both *FGFRI*<sup>MUT</sup> and *BRAF*<sup>MUT</sup> H3.3K27M DMG.

Moreover, our multivariate analysis demonstrates for the
first time that the presence of these mutations is an inde-
pendent prognostic factor for improved OS in DMG_K27.
Previously, Picca et al. and Schüller et al. showed in a small
populations (n=6 or n=7), by univariate analysis and with-
out taking into account TP53 status, that DMG patients with
*FGFRI*<sup>MUT</sup> have a better survival [25, 31]. The identification
of a new subtype of *DMG H3 K27-altered* with longer sur-
vival is also a step forward for clinical research, highlight-
ing the need for patient’s stratification in trials or at least
molecular documentation of the cases.

Patients from Necker/Gustave Roussy cohort with *BRAF/ FGFR1-mutated* DMG_K27 received, over a large period of time, quite heterogeneous treatment which did not allow specific statistical conclusion. It thus remains to be defined whether these patients could respond to a targeted therapy against *BRAF*<sup>V600E</sup> or *FGFR1*.

Another meaningful difference is that DMG_K27-BRAF/
FGFR1 are more frequent in the thalamus than the brain-
stem compared to DMG from the H3.3-K27M subtype.
The age at diagnosis also differs according to the presence
of the MAPK alteration. The age at onset for *FGFR1*<sup>MUT</sup>

DMG is significantly higher (median 14.8 years) and to our
knowledge no adults were affected by a H3.3-K27M *BRAF*-
mutated gliomas. In this new subtype, some heterogeneity
remains present at various level between DMG H3K27M
*BRAF* or *FGFR1*-mutated. This finding cannot be presently
explained, but it may also point towards different oncogen-
esis, which could be individualised in the future studies.

We also investigated gene expression in these tumours
and observed a senescence signature including an up-reg-
ulation of CDKN1A (P21) specific to DMG_K27-MAPK
which is usually more present in paediatric LGGs. LGGs
are characterised by an over-activated MAPK signalling in
consequence to oncogenic alteration of *BRAF* or *FGFR1*
[15] and the main hypothesis for a slow tumour evolution
in LGG is based on the induction of oncogene-induced
senescence which gives a growth advantage in a restricted
window during brain development [7, 8, 27, 35, 40]. Senes-
cence triggered via the P53/P21 axis could in part explain
the slower tumour evolution in DMG_K27-MAPK. Of note,
the DMG_K27-BRAF/FGFR1 share other characteristics
with LGGs like calcifications and preferential association
of mutations: *FGFR1* with *NF1*, *PI3KCA*, *PTPN11* [9, 17,
32, 38]. We also demonstrated in one case from this new
DMG subtype harbouring *BRAF*<sup>V600E</sup>, that H3.3K27M was
the first mutational event in its oncogenesis. Thus, *BRAF*
and *FGFR1* mutations would be secondary driver events in
the oncogenesis of these tumours and could give a prolifera-
tive advantage to the H3.3-K27M ancestral clone in a spe-
cific developmental window similarly to paediatric LGGs.
Extending the analysis of sequential acquisition of muta-
tions in DMG_K27-MAPK oncogenesis will be essential
for designing future therapeutic interventions.

In conclusion, we have identified a fifth subtype of DMG,
*H3 K27-altered* that we named ‘*DMG H3 K27 and BRAF/
*FGFR1 co-altered*’ (DMG_K27- BRAF/FGFR1), which
harbours specific clinical and biological characteristics. We
hypothesise that the better OS of DMG_K27-BRAF/FGFR1
compared to other DMG_K27 could be the result of both a
specific cell origin and the oncogene-induced senescence.
Individualization of this subtype is of importance for the
interpretation of trials and affected patients may deserve
specific treatment strategies.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s00401-023-02651-4.

**Acknowledgements** LA, MAD, JG and DC acknowledge the Etoile de Martin charity (annual grant support to LA and RARE program with the Fondation Carrefour "Les Boucles du Coeur"), Imagine for Margo, La Marche de l'Ecureuil charity and programme GLCE fondation Gustave Roussy for the financial support of this project, Necker Imagine Tumor and DNA biobank (BB-033-00065) for tumor and blood samples and centers participating in the BIOMEDE International Study and national PIs (Karsten Nysom, Klas Blomgren, v Hargrave, Dannis Van Vuurden, Francisco Bautista-Sirvent, Geoffrey McCowage).
(i.e. *BRAF* vs. *FGFR1*) even suggests that this entity could
be further subdivided.

Genotype–morphotype correlations support the distinc-
tion from classical DMG, H3 K27-altered and from glial/
glioneuronal tumours MAPK-altered: (i) whilst ependymal
differentiation has been described in rare DMG H3-K27
[36], a mixed glioneuronal differentiation associated with
CD34 positivity and eosinophilic granular bodies or a piloid
differentiation are not yet described; (ii) only an exceptional
subset of ganglioglioma grade 1 present *FGFR1* alteration,
more characteristic of other glioneuronal tumours [24] and
(iii) the existence of true malignant transformation in gan-
glioglioma is a matter of debate. The majority of reported
cases were published before the advent of molecular biology,
reclassified in a wide spectrum of CNS WHO tumour types
without a distinct methylation class [28] or more interest-
ingly were midline-located with a co-occurring *BRAF* and
H3-K27M mutations [11, 21–23, 26, 29, 30, 44]. In the uni-
fied methylation class that we describe, the radiological and
histopathological presentations are thus highly heterogene-
ous including tumours with mixed glioneuronal or pilocytic
differentiation, and do not always fulfil a strict diagnostic
criterion of DMG. Indeed, these tumours are less diffuse,
with a frequent nodular to circumscribed radiological aspect
(91% for *BRAF*<sup>MUT</sup> and 78% for *FGFR1*<sup>MUT</sup> DMG) and
calcifications.

Several other clinical and biological characteristics sup-
port the individualization of this new entity from classical
DMG, *H3 K27-altered*. First, OS is significantly different
from classical DMG_K27, with a median around three years
for both *FGFRI*<sup>MUT</sup> and *BRAF*<sup>MUT</sup> H3.3K27M DMG.

Moreover, our multivariate analysis demonstrates for the
first time that the presence of these mutations is an inde-
pendent prognostic factor for improved OS in DMG_K27.
Previously, Picca et al. and Schüller et al. showed in a small
populations (n=6 or n=7), by univariate analysis and with-
out taking into account TP53 status, that DMG patients with
*FGFRI*<sup>MUT</sup> have a better survival [25, 31]. The identification
of a new subtype of *DMG H3 K27-altered* with longer sur-
vival is also a step forward for clinical research, highlight-
ing the need for patient’s stratification in trials or at least
molecular documentation of the cases.

Patients from Necker/Gustave Roussy cohort with *BRAF/ FGFR1-mutated* DMG_K27 received, over a large period of time, quite heterogeneous treatment which did not allow specific statistical conclusion. It thus remains to be defined whether these patients could respond to a targeted therapy against *BRAF*<sup>V600E</sup> or *FGFR1*.

Another meaningful difference is that DMG_K27-BRAF/
FGFR1 are more frequent in the thalamus than the brain-
stem compared to DMG from the H3.3-K27M subtype.
The age at diagnosis also differs according to the presence
of the MAPK alteration. The age at onset for *FGFR1*<sup>MUT</sup>

DMG is significantly higher (median 14.8 years) and to our
knowledge no adults were affected by a H3.3-K27M *BRAF*-
mutated gliomas. In this new subtype, some heterogeneity
remains present at various level between DMG H3K27M
*BRAF* or *FGFR1*-mutated. This finding cannot be presently
explained, but it may also point towards different oncogen-
esis, which could be individualised in the future studies.

We also investigated gene expression in these tumours
and observed a senescence signature including an up-reg-
ulation of CDKN1A (P21) specific to DMG_K27-MAPK
which is usually more present in paediatric LGGs. LGGs
are characterised by an over-activated MAPK signalling in
consequence to oncogenic alteration of *BRAF* or *FGFR1*
[15] and the main hypothesis for a slow tumour evolution
in LGG is based on the induction of oncogene-induced
senescence which gives a growth advantage in a restricted
window during brain development [7, 8, 27, 35, 40]. Senes-
cence triggered via the P53/P21 axis could in part explain
the slower tumour evolution in DMG_K27-MAPK. Of note,
the DMG_K27-BRAF/FGFR1 share other characteristics
with LGGs like calcifications and preferential association
of mutations: *FGFR1* with *NF1*, *PI3KCA*, *PTPN11* [9, 17,
32, 38]. We also demonstrated in one case from this new
DMG subtype harbouring *BRAF*<sup>V600E</sup>, that H3.3K27M was
the first mutational event in its oncogenesis. Thus, *BRAF*
and *FGFR1* mutations would be secondary driver events in
the oncogenesis of these tumours and could give a prolifera-
tive advantage to the H3.3-K27M ancestral clone in a spe-
cific developmental window similarly to paediatric LGGs.
Extending the analysis of sequential acquisition of muta-
tions in DMG_K27-MAPK oncogenesis will be essential
for designing future therapeutic interventions.

In conclusion, we have identified a fifth subtype of DMG,
*H3 K27-altered* that we named ‘*DMG H3 K27 and BRAF/
*FGFR1 co-altered*’ (DMG_K27- BRAF/FGFR1), which
harbours specific clinical and biological characteristics. We
hypothesise that the better OS of DMG_K27-BRAF/FGFR1
compared to other DMG_K27 could be the result of both a
specific cell origin and the oncogene-induced senescence.
Individualization of this subtype is of importance for the
interpretation of trials and affected patients may deserve
specific treatment strategies.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s00401-023-02651-4.

**Acknowledgements** LA, MAD, JG and DC acknowledge the Etoile de Martin charity (annual grant support to LA and RARE program with the Fondation Carrefour "Les Boucles du Coeur"), Imagine for Margo, La Marche de l'Ecureuil charity and programme GLCE fondation Gustave Roussy for the financial support of this project, Necker Imagine Tumor and DNA biobank (BB-033-00065) for tumor and blood samples and centers participating in the BIOMEDE International Study and national PIs (Karsten Nysom, Klas Blomgren, v Hargrave, Dannis Van Vuurden, Francisco Bautista-Sirvent, Geoffrey McCowage).
**Data availability** DNA methylation data analysed in the current study are accessible in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-13471 [https://www.ebi.ac.uk/array express/]. RNAseq data were deposited in the European Genome-Phenome Archive (EGA) [https://ega-archive.org/].

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

1. Bender S, Tang Y, Lindroth AM, Hovestadt V, Jones DTW, Kool M et al (2013) Reduced H3K27me3 and DNA Hypomethylation Are Major Drivers of Gene Expression in K27M Mutant Pediatric High-Grade Gliomas. Cancer Cell 24:660-672. https://doi.org/10.1016/j.ccr.2013.10.006

2. Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D et al (2018) DNA methylation-based classification of central nervous system tumours. Nature 555:469-474. https://doi.org/10.1038/nature26000

3. Castel D, Kergrohen T, Tauziède-Espariat A, Mackay A, Ghermaoui S, Lechapt E et al (2020) Histone H3 wild-type DIPG/DMG overexpressing EZHIP extend the spectrum diffuse mid-line gliomas with PRC2 inhibition beyond H3-K27M mutation. Acta Neuropathol (Berl) 139:1109-1113. https://doi.org/10.1007/s00401-020-02142-w

4. Castel D, Philippe C, Calmon R, Le Dret L, Truffaux N, Boddaert N et al (2015) Histone H3F3A and HIST1H3B K27M mutations define two subgroups of diffuse intrinsic pontine gliomas with different prognosis and phenotypes. Acta Neuropathol (Berl) 130:815-827. https://doi.org/10.1007/s00401-015-1478-0

5. Chan K-M, Fang D, Gan H, Hashizume R, Yu C, Schroeder M et al (2013) The histone H3.3K27M mutation in pediatric glioma reprograms H3K27 methylation and gene expression. Genes Dev 27:985-990. https://doi.org/10.1101/gad.217778.113

6. Childs BG, Baker DJ, Kirkland JL, Campisi J, Deursen JM (2014) Senescence and apoptosis: dueling or complementary cell fates? EMBO Rep 15:1139-1153. https://doi.org/10.15252/embr.201439245

7. Gnekow AK, Walker DA, Kandels D, Picton S, Perilongo G, Grill J et al (2017) A European randomised controlled trial of the addition of etoposide to standard vincristine and carboplatin induction as part of an 18-month treatment programme for childhood (≤16 years) low grade glioma - A final report. Eur J Cancer 81:206-225. https://doi.org/10.1016/j.ejca.2017.04.019

8. Jacob K, Quang-Khuong D-A, Jones DTW, Witt H, Lambert S, Albrecht S et al (2011) Genetic Aberrations Leading to MAPK Pathway Activation Mediate Oncogene-Induced Senescence in Sporadic Pilocytic Astrocytomas. Clin Cancer Res 17:4650-4660. https://doi.org/10.1158/1078-0432.CCR-11-0127

9. Johnson DR, Giannini C, Jenkins RB, Kim DK, Kaufmann TJ (2019) Plenty of calcification: imaging characterization of

polymorphous low-grade neuroepithelial tumor of the young. Neuroradiology 61:1327-1332. https://doi.org/10.1007/s00234-019-02269-y

10. Kergrohen T, Castel D, Le Teuff G, Tauziède-Espariat A, Lechapt-Zalcman E, Nysom K, Blomgren K, Leblond P, Ber-tozzi A-I, De Carli E, Faure-Conter C, Chappe C, Entz-Werlé N, Moussa A, Ghermaoui S, Barret E, Picot S, Sabourin-Cousin M, Beccaria K, Vassal G, Varlet P, Puget S, Grill J, Debily M-A (2021) Copy-number alterations reshape the classification of diffuse intrinsic pontine gliomas. First exome sequencing results of the BIOMEDE trial. medRxiv. https://doi.org/10.1101/2021.04.29.21256183

11. Kleinschmidt-DeMasters BK, Donson A, Foreman NK, Dorris K (2017) H3 K27M Mutation in Gangliogliomas can be Associated with Poor Prognosis. Brain Pathol Zurich Switz 27:846-850. https://doi.org/10.1111/bpa.12455

12. Lew ED, Furdui CM, Anderson KS, Schlessinger J (2009) The Precise Sequence of FGF Receptor Autophosphorylation Is Kinetically Driven and Is Disrupted by Oncogenic Mutations. Sci Signal. https://doi.org/10.1126/scisignal.2000021

13. Lewis PW, Muller MM, Koletsky MS, Cordero F, Lin S, Banaszynski LA et al (2013) Inhibition of PRC2 Activity by a Gain-of-Function H3 Mutation Found in Pediatric Glioblastoma. Science 340:857-861. https://doi.org/10.1126/science.1232245

14. Liu X, Ding J, Meng L (2018) Oncogene-induced senescence: a double edged sword in cancer. Acta Pharmacol Sin 39:1553-1558. https://doi.org/10.1038/aps.2017.198

15. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK et al (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol (Berl) 131:803-820. https://doi.org/10.1007/s00401-016-1545-1

16. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D et al (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncol. https://doi.org/10.1093/neuonc/noab106

17. Lucas C-HG, Gupta R, Doo P, Lee JC, Cadwell CR, Ramani B et al (2020) Comprehensive analysis of diverse low-grade neuroepithelial tumors with FGFR1 alterations reveals a distinct molecular signature of rosette-forming glioneuronal tumor. Acta Neuropathol Commun 8:151. https://doi.org/10.1186/s40478-020-01027-z

18. Mackay A, Burford A, Carvalho D, Izquierdo E, Fazal-Salom J, Taylor KR et al (2017) Integrated Molecular Meta-Analysis of 1,000 Pediatric High-Grade and Diffuse Intrinsic Pontine Glioma. Cancer Cell 32:520-537.e5. https://doi.org/10.1016/j.ccell.2017.08.017

19. Mondal G, Lee JC, Ravindranathan A, Villanueva-Meyer JE, Tran QT, Allen SJ et al (2020) Pediatric bithalamic gliomas have a distinct epigenetic signature and frequent EGFR exon 20 insertions resulting in potential sensitivity to targeted kinase inhibition. Acta Neuropathol (Berl) 139:1071-1088. https://doi.org/10.1007/s00401-020-02155-5

20. Nakamura IT, Kohsaka S, Ikegami M, Ikeuchi H, Ueno T, Li K et al (2021) Comprehensive functional evaluation of variants of fibroblast growth factor receptor genes in cancer. Npj Precis Oncol 5:66. https://doi.org/10.1038/s41698-021-00204-0

21. Nguyen AT, Colin C, Nanni-Metellus I, Padovani L, Maurage C-A, Varlet P et al (2015) Evidence for *BRAF* V600E and *H3F3A* K27M double mutations in paediatric glial and glioneuronal tumours. Neuropathol Appl Neurobiol 41:403-408. https://doi.org/10.1111/nan.12196

<figure><img src="image_17.png" /></figure>
**Data availability** DNA methylation data analysed in the current study are accessible in the ArrayExpress database at EMBL-EBI under accession number E-MTAB-13471 [https://www.ebi.ac.uk/array express/]. RNAseq data were deposited in the European Genome-Phenome Archive (EGA) [https://ega-archive.org/].

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

## References

1. Bender S, Tang Y, Lindroth AM, Hovestadt V, Jones DTW, Kool M et al (2013) Reduced H3K27me3 and DNA Hypomethylation Are Major Drivers of Gene Expression in K27M Mutant Pediatric High-Grade Gliomas. Cancer Cell 24:660-672. https://doi.org/10.1016/j.ccr.2013.10.006

2. Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D et al (2018) DNA methylation-based classification of central nervous system tumours. Nature 555:469-474. https://doi.org/10.1038/nature26000

3. Castel D, Kergrohen T, Tauziède-Espariat A, Mackay A, Ghermaoui S, Lechapt E et al (2020) Histone H3 wild-type DIPG/DMG overexpressing EZHIP extend the spectrum diffuse mid-line gliomas with PRC2 inhibition beyond H3-K27M mutation. Acta Neuropathol (Berl) 139:1109-1113. https://doi.org/10.1007/s00401-020-02142-w

4. Castel D, Philippe C, Calmon R, Le Dret L, Truffaux N, Boddaert N et al (2015) Histone H3F3A and HIST1H3B K27M mutations define two subgroups of diffuse intrinsic pontine gliomas with different prognosis and phenotypes. Acta Neuropathol (Berl) 130:815-827. https://doi.org/10.1007/s00401-015-1478-0

5. Chan K-M, Fang D, Gan H, Hashizume R, Yu C, Schroeder M et al (2013) The histone H3.3K27M mutation in pediatric glioma reprograms H3K27 methylation and gene expression. Genes Dev 27:985-990. https://doi.org/10.1101/gad.217778.113

6. Childs BG, Baker DJ, Kirkland JL, Campisi J, Deursen JM (2014) Senescence and apoptosis: dueling or complementary cell fates? EMBO Rep 15:1139-1153. https://doi.org/10.15252/embr.201439245

7. Gnekow AK, Walker DA, Kandels D, Picton S, Perilongo G, Grill J et al (2017) A European randomised controlled trial of the addition of etoposide to standard vincristine and carboplatin induction as part of an 18-month treatment programme for childhood (≤16 years) low grade glioma - A final report. Eur J Cancer 81:206-225. https://doi.org/10.1016/j.ejca.2017.04.019

8. Jacob K, Quang-Khuong D-A, Jones DTW, Witt H, Lambert S, Albrecht S et al (2011) Genetic Aberrations Leading to MAPK Pathway Activation Mediate Oncogene-Induced Senescence in Sporadic Pilocytic Astrocytomas. Clin Cancer Res 17:4650-4660. https://doi.org/10.1158/1078-0432.CCR-11-0127

9. Johnson DR, Giannini C, Jenkins RB, Kim DK, Kaufmann TJ (2019) Plenty of calcification: imaging characterization of

polymorphous low-grade neuroepithelial tumor of the young. Neuroradiology 61:1327-1332. https://doi.org/10.1007/s00234-019-02269-y

10. Kergrohen T, Castel D, Le Teuff G, Tauziède-Espariat A, Lechapt-Zalcman E, Nysom K, Blomgren K, Leblond P, Ber-tozzi A-I, De Carli E, Faure-Conter C, Chappe C, Entz-Werlé N, Moussa A, Ghermaoui S, Barret E, Picot S, Sabourin-Cousin M, Beccaria K, Vassal G, Varlet P, Puget S, Grill J, Debily M-A (2021) Copy-number alterations reshape the classification of diffuse intrinsic pontine gliomas. First exome sequencing results of the BIOMEDE trial. medRxiv. https://doi.org/10.1101/2021.04.29.21256183

11. Kleinschmidt-DeMasters BK, Donson A, Foreman NK, Doris K (2017) H3 K27M Mutation in Gangliogliomas can be Associated with Poor Prognosis. Brain Pathol Zurich Switz 27:846-850. https://doi.org/10.1111/bpa.12455

12. Lew ED, Furdui CM, Anderson KS, Schlessinger J (2009) The Precise Sequence of FGF Receptor Autophosphorylation Is Kinetically Driven and Is Disrupted by Oncogenic Mutations. Sci Signal. https://doi.org/10.1126/scisignal.2000021

13. Lewis PW, Muller MM, Koletsky MS, Cordero F, Lin S, Banaszynski LA et al (2013) Inhibition of PRC2 Activity by a Gain-of-Function H3 Mutation Found in Pediatric Glioblastoma. Science 340:857-861. https://doi.org/10.1126/science.1232245

14. Liu X, Ding J, Meng L (2018) Oncogene-induced senescence: a double edged sword in cancer. Acta Pharmacol Sin 39:1553-1558. https://doi.org/10.1038/aps.2017.198

15. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK et al (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol (Berl) 131:803-820. https://doi.org/10.1007/s00401-016-1545-1

16. Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D et al (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro-Oncol. https://doi.org/10.1093/neuonc/noab106

17. Lucas C-HG, Gupta R, Doo P, Lee JC, Cadwell CR, Ramani B et al (2020) Comprehensive analysis of diverse low-grade neuroepithelial tumors with FGFR1 alterations reveals a distinct molecular signature of rosette-forming glioneuronal tumor. Acta Neuropathol Commun 8:151. https://doi.org/10.1186/s40478-020-01027-z

18. Mackay A, Burford A, Carvalho D, Izquierdo E, Fazal-Salom J, Taylor KR et al (2017) Integrated Molecular Meta-Analysis of 1,000 Pediatric High-Grade and Diffuse Intrinsic Pontine Glioma. Cancer Cell 32:520-537.e5. https://doi.org/10.1016/j.ccell.2017.08.017

19. Mondal G, Lee JC, Ravindranathan A, Villanueva-Meyer JE, Tran QT, Allen SJ et al (2020) Pediatric bithalamic gliomas have a distinct epigenetic signature and frequent EGFR exon 20 insertions resulting in potential sensitivity to targeted kinase inhibition. Acta Neuropathol (Berl) 139:1071-1088. https://doi.org/10.1007/s00401-020-02155-5

20. Nakamura IT, Kohsaka S, Ikegami M, Ikeuchi H, Ueno T, Li K et al (2021) Comprehensive functional evaluation of variants of fibroblast growth factor receptor genes in cancer. Npj Precis Oncol 5:66. https://doi.org/10.1038/s41698-021-00204-0

21. Nguyen AT, Colin C, Nanni-Metellus I, Padovani L, Maurage C-A, Varlet P et al (2015) Evidence for *BRAF* V600E and *H3F3A* K27M double mutations in paediatric glial and glioneuronal tumours. Neuropathol Appl Neurobiol 41:403-408. https://doi.org/10.1111/nan.12196

<figure><img src="image_18.png" /></figure>
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23. Pagès M, Beccaria K, Boddaert N, Saffroy R, Besnard A, Castel D et al (2018) Co-occurrence of histone H3 K27M and BRAF V600E mutations in paediatric midline grade I ganglioglioma: Histone H3 K27M and BRAF V600E mutations in GG. Brain Pathol 28:103–111. https://doi.org/10.1111/bpa.12473

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33. Sievers P, Sill M, Schrimpf D, Stichel D, Reuss DE, Sturm D et al (2021) A subset of pediatric-type thalamic gliomas share a distinct DNA methylation profile, H3K27me3 loss and frequent alteration of EGFR. Neuro-Oncol 23:34–43. https://doi.org/10.1093/neuonc/noaa251

34. Solomon DA, Wood MD, Tihan T, Bollen AW, Gupta N, Phillips JJJ et al (2016) Diffuse Midline Gliomas with Histone H3–K27M Mutation: A Series of 47 Cases Assessing the Spectrum of Morphologic Variation and Associated Genetic Alterations: Diffuse midline gliomas with histone H3–K27M mutation. Brain Pathol 26:569–580. https://doi.org/10.1111/bpa.12336

35. Tabori U, Vukovic B, Zielenska M, Hawkins C, Braude I, Rutka J et al (2006) The Role of Telomere Maintenance in the Spontaneous Growth Arrest of Pediatric Low-Grade Gliomas. Neoplasia 8:136–142. https://doi.org/10.1593/neo.05715

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43. Yoon K (2004) Fibroblast Growth Factor Receptor Signaling Promotes Radial Glial Identity and Interacts with Notch1 Signaling in Telencephalic Progenitors. J Neurosci 24:9497–9506. https://doi.org/10.1523/JNEUROSCI.0993-04.2004

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45. Zheng L, Gong J, Yu T, Zou Y, Zhang M, Nie L et al (2022) Diffuse Midline Gliomas With Histone H3 K27M Mutation in Adults and Children: A Retrospective Series of 164 Cases. Am J Surg Pathol 46:863–871. https://doi.org/10.1097/PAS.0000000000001897

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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32. Sievers P, Appay R, Schrimpf D, Stichel D, Reuss DE, Wefers AK et al (2019) Rosette-forming glioneuronal tumors share a distinct DNA methylation profile and mutations in FGFR1, with recurrent co-mutation of PIK3CA and NF1. Acta Neuropathol (Berl) 138:497–504. https://doi.org/10.1007/s00401-019-02038-4

33. Sievers P, Sill M, Schrimpf D, Stichel D, Reuss DE, Sturm D et al (2021) A subset of pediatric-type thalamic gliomas share a distinct DNA methylation profile, H3K27me3 loss and frequent alteration of EGFR. Neuro-Oncol 23:34–43. https://doi.org/10.1093/neuonc/noaa251

34. Solomon DA, Wood MD, Tihan T, Bollen AW, Gupta N, Phillips JJJ et al (2016) Diffuse Midline Gliomas with Histone H3–K27M Mutation: A Series of 47 Cases Assessing the Spectrum of Morphologic Variation and Associated Genetic Alterations: Diffuse midline gliomas with histone H3–K27M mutation. Brain Pathol 26:569–580. https://doi.org/10.1111/bpa.12336

35. Tabori U, Vukovic B, Zielenska M, Hawkins C, Braude I, Rutka J et al (2006) The Role of Telomere Maintenance in the Spontaneous Growth Arrest of Pediatric Low-Grade Gliomas. Neoplasia 8:136–142. https://doi.org/10.1593/neo.05715

36. Tauziède-Espariat A, Métais A, Mariet C, Castel D, Grill J, Saffroy R et al (2023) The pontine diffuse midline glioma, EGFR-subtype with ependymal features: Yet another face of diffuse midline glioma, H3K27-altered. Brain Pathol. https://doi.org/10.1111/bpa.13181

37. Taylor KR, Mackay A, Truffaux N, Butterfield YS, Morozova O, Philippe C, Castel D, Grasso CS, Vinci M, Carvalho D, Carcaboso AM, de Torres C, Cruz O, Mora J, Entz-Werle N, Ingram WJ, Monje M, Hargrave D, Bullock AN, Puget S, Yip S, Jones C, Grill J (2014) Recurrent activating ACVR1 mutations in diffuse intrinsic pontine glioma. Nat Genet 46:457–461. https://doi.org/10.1038/ng.2925

38. The International Cancer Genome Consortium PedBrain Tumor Project, Jones DTW, Hutter B, Jäger N, Korshunov A, Kool M et al (2013) Recurrent somatic alterations of FGFR1 and NTRK2 in pilocytic astrocytoma. Nat Genet 45:927–932. https://doi.org/10.1038/ng.2682

39. Visvader JE (2011) Cells of origin in cancer. Nature 469:314–322. https://doi.org/10.1038/nature09781

40. Warrington NM, Woerner BM, Daginakatte GC, Dasgupta B, Perry A, Gutmann DH et al (2007) Spatiotemporal Differences in CXCL12 Expression and Cyclic AMP Underlie the Unique Pattern of Optic Glioma Growth in Neurofibromatosis Type 1. Cancer Res 67:8588–8595. https://doi.org/10.1158/0008-5472.CAN-06-2220

41. Werbrouck C, Evangelista CCS, Lobón-Iglesias M-J, Barret E, Le Teuff G, Merlevede J et al (2019) TP53 Pathway Alterations Drive Radioresistance in Diffuse Intrinsic Pontine Gliomas (DIPG). Clin Cancer Res 25:6788–6800. https://doi.org/10.1158/1078-0432.CCR-19-0126

42. Wu G, Broniscer A, McEachron TA, Lu C, Paugh BS, Beckfort J et al (2012) Somatic Histone H3 Alterations in Paediatric Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Glioblastomas. Nat Genet 44:251–253. https://doi.org/10.1038/ng.1102

43. Yoon K (2004) Fibroblast Growth Factor Receptor Signaling Promotes Radial Glial Identity and Interacts with Notch1 Signaling in Telencephalic Progenitors. J Neurosci 24:9497–9506. https://doi.org/10.1523/JNEUROSCI.0993-04.2004

44. Zanello M, Pages M, Tauziède-Espariat A, Saffroy R, Puget S, Lacroix L et al (2016) Clinical, Imaging, Histopathological and Molecular Characterization of Anaplastic Ganglioglioma. J Neuropathol Exp Neurol 75:971–980. https://doi.org/10.1093/jnen/nlw074

45. Zheng L, Gong J, Yu T, Zou Y, Zhang M, Nie L et al (2022) Diffuse Midline Gliomas With Histone H3 K27M Mutation in Adults and Children: A Retrospective Series of 164 Cases. Am J Surg Pathol 46:863–871. https://doi.org/10.1097/PAS.0000000000001897

**Publisher's Note** Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## Authors and Affiliations

Lucie Auffret¹ · Yassine Ajlil¹ · Arnault Tauziède-Espariat²,³ · Thomas Kergrohen¹,⁴ · Chloé Puiseux⁵ ·

Laurent Riffaud⁶ · Pascale Blouin⁷ · Anne-Isabelle Bertozzi⁸ · Pierre Leblond⁹ · Klas Blomgren¹⁰,¹¹ ·

Sébastien Froelich¹² · Alberto Picca¹³ · Mehdi Touat¹³ · Marc Sanson¹³ · Kévin Beccaria¹,¹⁴ · Thomas Blauwblomme¹⁴ ·

Volodia Dangouloff-Ros¹⁵,¹⁶ · Nathalie Boddaert¹⁵,¹⁶ · Pascale Varlet²,³ · Marie-Anne Debily¹,¹⁷ · Jacques Grill¹,⁴ ·

David Castel¹,⁴

<figure><img src="image_18.png" /></figure>

Jacques Grill
jacques.grill@gustaveroussy.fr

<figure><img src="image_19.png" /></figure>

David Castel
david.castel@gustaveroussy.fr

¹ Molecular Predictors and New Targets in Oncology, Inserm, Gustave Roussy, Université Paris-Saclay, Villejuif, France

² Department of Neuropathology, GHU Paris-Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France

³ UMR 1266, IMA-Brain, Institut de Psychiatrie et Neurosciences de Paris, Inserm, Paris, France

⁴ Département de Cancérologie de L'Enfant et de L'Adolescent, Gustave Roussy, Université Paris-Saclay, Villejuif, France

⁵ Department of Pediatric Oncology, Rennes University Hospital, Rennes, France

⁶ Department of Pediatric Neurosurgery, Rennes University Hospital, Rennes, France

⁷ Department of Pediatric Hematology, CHRU de Tours, Tours, France

⁸ Department of Pediatric Hematology-Oncology, Hôpital des Enfants, Toulouse, France

⁹ Institute of Pediatric Hematology and Oncology, Centre Léon Bérard, Lyon, France

¹⁰ Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden

¹¹ Pediatric Hematology and Oncology, Karolinska University Hospital, Stockholm, Sweden

¹² Service de Neurochirurgie-Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France

¹³ Inserm U1127, CNRS UMR 7225, Institut du Cerveau, ICM, Charles Foix, Service de Neurologie 2-Mazarin, Sorbonne Université, AP-HP, Hôpitaux Universitaires la Pitié Salpêtrière, Paris, France

¹⁴ Department of Pediatric Neurosurgery, Hôpital Necker-Enfants Malades, AP-HP, Université Paris Cité, Paris, France

¹⁵ Department of Pediatric Radiology, Hôpital Necker-Enfants Malades, AP-HP, Université Paris-Cité, Paris, France

¹⁶ INSERM U1163, Institut Imagine, Université Paris Cité, Paris, France

¹⁷ Département de Biologie, Université Évry Paris-Saclay, Évry, France

<figure><img src="image_20.png" /></figure>
## Authors and Affiliations

Lucie Auffret¹ · Yassine Ajlil¹ · Arnault Tauziède-Espariat²,³ · Thomas Kergrohen¹,⁴ · Chloé Puiseux⁵ ·

Laurent Riffaud⁶ · Pascale Blouin⁷ · Anne-Isabelle Bertozzi⁸ · Pierre Leblond⁹ · Klas Blomgren¹⁰,¹¹ ·

Sébastien Froelich¹² · Alberto Picca¹³ · Mehdi Touat¹³ · Marc Sanson¹³ · Kévin Beccaria¹,¹⁴ · Thomas Blauwblomme¹⁴ ·

Volodia Dangouloff-Ros¹⁵,¹⁶ · Nathalie Boddaert¹⁵,¹⁶ · Pascale Varlet²,³ · Marie-Anne Debily¹,¹⁷ · Jacques Grill¹,⁴ ·

David Castel¹,⁴

<figure><img src="image_19.png" /></figure>

Jacques Grill
jacques.grill@gustaveroussy.fr

<figure><img src="image_20.png" /></figure>

David Castel
david.castel@gustaveroussy.fr

¹ Molecular Predictors and New Targets in Oncology, Inserm, Gustave Roussy, Université Paris-Saclay, Villejuif, France

² Department of Neuropathology, GHU Paris-Psychiatrie et Neurosciences, Sainte-Anne Hospital, Paris, France

³ UMR 1266, IMA-Brain, Institut de Psychiatrie et Neurosciences de Paris, Inserm, Paris, France

⁴ Département de Cancérologie de L'Enfant et de L'Adolescent, Gustave Roussy, Université Paris-Saclay, Villejuif, France

⁵ Department of Pediatric Oncology, Rennes University Hospital, Rennes, France

⁶ Department of Pediatric Neurosurgery, Rennes University Hospital, Rennes, France

⁷ Department of Pediatric Hematology, CHRU de Tours, Tours, France

⁸ Department of Pediatric Hematology-Oncology, Hôpital des Enfants, Toulouse, France

⁹ Institute of Pediatric Hematology and Oncology, Centre Léon Bérard, Lyon, France

¹⁰ Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden

¹¹ Pediatric Hematology and Oncology, Karolinska University Hospital, Stockholm, Sweden

¹² Service de Neurochirurgie-Hôpital Lariboisière, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France

¹³ Inserm U1127, CNRS UMR 7225, Institut du Cerveau, ICM, Charles Foix, Service de Neurologie 2-Mazarin, Sorbonne Université, AP-HP, Hôpitaux Universitaires la Pitié Salpêtrière, Paris, France

¹⁴ Department of Pediatric Neurosurgery, Hôpital Necker-Enfants Malades, AP-HP, Université Paris Cité, Paris, France

¹⁵ Department of Pediatric Radiology, Hôpital Necker-Enfants Malades, AP-HP, Université Paris-Cité, Paris, France

¹⁶ INSERM U1163, Institut Imagine, Université Paris Cité, Paris, France

¹⁷ Département de Biologie, Université Évry Paris-Saclay, Évry, France

<figure><img src="image_21.png" /></figure>