Unsupervised hierarchical clustering of DNA methylation data indicates two clusters of DMGs that differ in the distribution of tumour localisation and patient age

To investigate potential epigenetic differences between DMGs from different, clearly defined localisations, we performed an unsupervised hierarchical cluster analysis of global DNA methylation data (149 DMGs in total; spinal cord n = 31, medulla n = 20, pons n = 64, thalamus n = 33, sella n = 1; table available as Online Resource 1). Unsupervised hierarchical clustering showed a separation of the DMGs into two main clusters. These two clusters corresponded to two subtypes of DMGs, DMG-A and DMG-B, with different clinical and molecular features that will be discussed below (Fig. 1a; DMG-A n = 45, DMG-B n = 104; average beta values in Online Resource 2). The majority of medullary cases was assigned to DMG-A (90.0%, n = 18/20, p < 0.0001). Contrarily, almost all pontine cases were assigned to DMG-B (95.3%, n = 61/64, p < 0.0001). Spinal and thalamic cases were rather evenly distributed amongst both subtypes (DMG-A: thalamus 30.3%, n = 10/33, p = 0.53; spinal cord 43.8%, n = 14/32, p = 0.17). One patient had both a spinal DMG and a second DMG in the sella. Both DMGs clustered together, belonging to DMG-B. For further statistical testing, only the spinal DMG was included.

Fig. 1

DMGs epigenetically split into two subtypes DMG-A and DMG-B that differ with respect to age, tumour localisation, TP53-mutations and MAPK-signalling pathway alterations. a Unsupervised hierarchical clustering of global DNA methylation data from 149 H3 K27M-mutant DMGs of four different localisations (spinal cord n = 31, medulla n = 20, pons n = 64, thalamus n = 33; one patient with an additional sellar tumour). DMGs subdivided into two clusters, corresponding to two DMG-subtypes DMG-A and DMG-B, that were enriched for different features. DMG-A (green, n = 45) was enriched for a medullary localisation (40.0%; n = 18 medullary cases), MAPK-associated mutations (55.6%; n = 15/27 cases sequenced) and cases with a methylated MGMT promoter (13.3%; n = 6/45). DMG-B (blue, n = 104) contained many pontine tumours (58.7%; n = 61) and TP53-mutant cases (78.9%; n = 30/38 cases sequenced). Most of the FGFR1-mutant cases formed a subcluster in the DMG-A cluster. b–f Uniform Manifold Approximation and Projection (UMAP) of the same cases shows similar results. b Again, DMG-A (green) and DMG-B (blue; subtype attribution from a) separated, as well as medullary versus pontine cases (c) and adult versus paediatric patients (d). e TP53-mutant cases were enriched in the part of the UMAP containing the DMG-B cases. f Most FGFR1- and NF1-mutant DMG were found in close proximity, mixed with few DMGs without known MAPK-associated alteration. g Violin plot of patient age. DMG-A (left) showed a bimodal age distribution with a median age at diagnosis of 31.0 ± 16.0 years. Patients with DMG-B were significantly younger (right; median age 7.6 ± 7.6 years; p < 0.001). The lines within the violin plots represent the quantiles (0.25, 0.50, 0.75), the red dots the median. h Distinct mutations were enriched in the two DMG-subtypes: TP53-mutations were enriched in DMG-B, while mutations associated with the MAPK-signalling pathway were enriched in DMG-A. Significance levels: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001

In addition to the differences in localisation between the two subtypes, we found a significant age difference (p < 0.001). The median age for DMG-A was 31.0 ± 16.0 years, with a bimodal age distribution centred around adolescents and adults (25% percentile 18.0 years, 75% percentile 47.8 years, Fig. 1g). 82.2% of patients with DMG-A were adults (n = 34/45; medullary/spinal n = 29/32, pontine/thalamic n = 5/13), and all patients were older than 11.0 years. In contrast, DMG-B predominantly consisted of DMGs from paediatric patients, with a median age of 7.6 ± 7.6 years (n = 84/100 < 18 years). The enrichment of medullary cases in DMG-A (median age 43.0 ± 15.6 years.) versus pontine cases in DMG-B (6.3 ± 7.4 years) was not the only reason for the difference in patient age between the two subtypes, as spinal cases split between DMG-A and DMG-B according to age (p < 0.001, Fisher’s exact test). Male and female cases were equally distributed between both subtypes (sex distribution m:f: DMG-A = 1:0.6; DMG-B = 1:1.1, Fig. 1a).

We then validated the analysis with consensus clustering. k-means clustering with a pre-defined number of two clusters, as indicated by elbow and silhouette plots, gave closely resembling results, proving the robustness of the cluster analysis (Online Resource 3). Compared to the initial cluster analysis, only 5.4% of cases switched the cluster (n = 8/149). Next, we performed a Uniform Manifold Approximation and Reduction (UMAP) and colour-coded the cases according to the DMG-subtype (Fig. 1b). The two clusters were recapitulated in this analysis, as well as the separation of medullary versus pontine cases and paediatric versus adult cases (Fig. 1c, d).

For an additional validation of the results, we repeated the cluster analysis and UMAP with a reference cohort of 227 cases from different glioma entities and normal CNS tissue (Online Resource 4). As expected, K27M-mutant DMGs formed a cluster separate from all other entities including the EGFR-altered DMGs. What is more, the two DMG clusters were again present, proving the reliability of the analysis. Only 4.8% of cases switched the cluster compared to Fig. 1a (n = 5/104).

This data indicates that medullary and pontine DMGs are epigenetically dissimilar, resulting in an assignment of the medullary cases to DMG-A and of the pontine cases to DMG-B. Spinal and thalamic DMGs are epigenetically more diverse and scatter across both DMG-subtypes, partially according to age. DMG-A showed a bimodal age distribution, arising mainly in adolescents and adults, whilst DMG-B was mainly detected in paediatric patients.

DMG-A has significantly more mutations associated with the MAPK-signalling pathway whereas DMG-B has more TP53-mutations

Next, we analysed the MGMT promoter methylation status and checked for mutations in a subset of DMGs.

13.3% (n = 6/45) of DMG-A had a methylated MGMT promoter as opposed to only 1.9% (n = 2/104) of DMG-B (p < 0.01; Fig. 1a). Half of the DMGs with a methylated MGMT promoter were from the spinal cord and about one third from the pons. DMGs from the spinal cord also showed the highest percentage of cases with a methylated MGMT promoter, whilst thalamic DMG never had a methylated MGMT promoter (spinal cord: n = 4/31, 12.9%, medulla: n = 1/20, 5.0%, pons: n = 3/64, 4.1%, thalamus: n = 0/33, 0.0%). 75% of DMGs with a methylated MGMT-promoter derived from adults (n = 6/8).

We subsequently analysed the CNP of DMGs for amplifications. The most frequent alteration, present in 16 DMGs, was an amplification of the platelet-derived growth factor alpha (PDGFRA), which also plays a role in tumour cell proliferation and migration [11, 16]. Amplifications of PDGFRA were exclusively found in H3.3-mutant DMGs, and significantly more often in DMG-B (p = 0.04; DMG-B 14.4%, n = 15/104; DMG-A 2.2%, n = 1/45; Fig. 1a, Online Resource 1). In addition, 2.2% (n = 1/45) of DMG-A and 2% (n = 3/104) of DMG-B showed a gain of PDGFRA (amplitude < 0.4). The second amplification that occurred in both subtypes was an amplification of CCND1 (DMG-A 2.2%, n = 1/45; DMG-B 2.9%, n = 3/104). Amplifications of EGFR, MDM2, CCND2, CDK4 and MET occurred in single cases only (Online Resource 1).

The different frequency of PDGFRA gains and amplifications was also visible in cumulative copy-number plots of DMG-A and DMG-B (Online Resource 5). In general, gains and losses were present in similar chromosomal regions in CNP from both DMG subtypes. However, DMG-B had significantly more copy-number alterations, especially losses, than DMG-A (p < 0.001; mean CNV load/Mb DMG-A 183.5 ± 168.5, DMG-B 258.1 ± 127.0).

We then analysed the distribution of different H3-mutations, TP53-, ATRX- and MAPK-related mutations (NF1, FGFR1, FGFR2, KRAS and BRAF) in the two subtypes. Sequencing data was available for 94 cases (spinal cord n = 11, medulla n = 20, pons n = 50, thalamus n = 13, DMG-A: n = 30, DMG-B: n = 64). The vast majority of the samples harboured an H3-3A K27M mutation (88.3%, n = 83/94; Fig. 1a). The samples with an H3.1 K27M mutation formed clusters separate from the cases with H3.3 K27M, exclusively within the DMG-B cluster (Fig. 1a and Online Resources 3c, 4a). All cases with an H3.1 K27M mutation originated from the pons, which is in line with the literature [9].

Besides the H3 K27M mutation, DMGs frequently harboured mutations in the tumour suppressor genes TP53 (60.3%, n = 41/68), NF1 (22.4%, n = 13/58), ATRX (19.0%, n = 11/58) and PTEN (8.9%, n = 5/56), as well as in the proto-oncogenes FGFR1 (13.6%, n = 8/59), FGFR2 (5.0%, n = 1/20) and KRAS (8.6%, n = 5/58) (Online Resource 1).

Of note, mutations in genes associated with the MAPK signalling pathway (NF1, FGFR1, FGFR2 and KRAS) were significantly enriched in DMG-A: 55.6% of DMG-A had a MAPK-related mutation (n = 15/27), as opposed to only 25.0% of DMG-B (n = 8/32; p = 0.031; Fig. 1h, Online Resource 1). Mutations in NF1 and FGFR1 were predominant, whereas BRAF-mutations were not present. NF1-mutations were the most frequent MAPK-associated mutations, present in 38.5% of DMG-A (n = 10/26, p = 0.012; Fig. 1h). On the contrary, only 9.4% of DMG-B were NF1-mutant (n = 3/32). DMG-A also contained 25.9% FGFR1-mutant cases (n = 7/27), as opposed to only 3.1% FGFR1-mutant DMG-B (n = 1/32; p = 0.019; Fig. 1h). Of note, most FGFR1-mutant cases clustered together in one subcluster (Fig. 1a and Online Resources 3c, 4a). Still, we did not find a clear separation of FGFR1-mutant cases from all other cases in the UMAP, as described by Auffret and colleagues [1] (Fig. 1f). FGFR1-mutations were present in DMGs of all localisations, except the thalamus, and across all age groups. Conversely, more than two thirds of the NF1-mutant cases were detected in tumours from adult patients, predominantly in medullary localisation. This shows that some mutations occur in certain tumour localisations or related with a certain age at diagnosis, whilst other alterations are more universally found. Of note, 86.0% of all MAPK-mutations occurred in patients ≥ 10 years, i.e. in adolescents and adults.

Previous studies have described the coexistence of an NF1-mutation with further alterations in the MAPK-signalling pathway [29]. In our cohort, three medullary cases had mutations in both FGFR1 and NF1 and one spinal case had mutations in FGFR1 and KRAS (Fig. 1a, f; Online Resource 1). Hence, half of the FGFR1-mutant cases (n = 4/8) had an additional MAPK-related mutation.

Opposingly, TP53-mutations were significantly enriched in DMG-B (p < 0.001): 78.9% (n = 30/38) of DMG-B were TP53-mutant as opposed to only 36.6% (n = 11/30) of DMG-A (Fig. 1h). Prominent was the significantly higher percentage of TP53 mutations in pontine and thalamic DMGs, compared to spinal and medullary DMGs (spinal cord: n = 3/9, 33.3%, medulla: n = 5/20, 15.0%, pons: n = 20/25, 80.0%, thalamus: n = 11/13, 84.6%; p < 0.05), which is in line with the literature [12]. The percentage of ATRX-mutant cases was very similar in both subtypes (DMG-A: 18.5%, n = 5/27; DMG-B: 19.4%, n = 6/31; p > 0.99; Fig. 1h).

A logistic regression showed that the odds for cases with a MAPK-associated mutation to be included in the DMG-A cluster were significantly higher than to be included in the DMG-B cluster (p = 0.019, Odds ratio (OR) 3.75), whilst for TP53-mutant cases the opposite was true (p < 0.001, (OR) 0.113).

In summary, we find that DMG-A contains significantly more cases with a methylated MGMT promoter and mutations associated with the MAPK-signalling pathway, and DMG-B significantly more PDGFRA-amplifications and TP53-mutations.

The prognosis of DMG-A is significantly better than of DMG-B

Next, we analysed the OS for all cases with available data (n = 65; spinal cord: n = 8, medulla: n = 20, pons: n = 25, thalamus: n = 12). OS for patients with DMG-A was significantly better compared to DMG-B (Fig. 2a; p < 0.001; DMG-A: median OS = 32.0 ± 20.0 months; DMG-B: median OS = 11.0 ± 10.3 months).

Fig. 2

Patients with DMG-A have a significantly better survival than patients with DMG-B. Overall survival (OS) for all cases with survival data available (n = 65; spinal cord: n = 8, medulla: n = 20, pons: n = 25, thalamus: n = 12). a Patients with DMG-A have a significantly better survival than patients with DMG-B (DMG-A: median OS = 32.0 ± 20.0 months; DMG-B: 11.0 ± 10.3 months, p < 0.001). In univariate analyses, OS also differed for spinal versus pontine DMGs (b; spinal cord: median OS = 24.1 ± 26.9 months, medulla: median OS = 18.1 ± 9.9 months, pons 11.0 ± 11.0 months, thalamus: median OS = 19.0 ± 17.0 months), for children versus adults (c; < 18 years: median OS = 11.0 ± 8.9 months, ≥ 18 years: 23.4 ± 19.0 months), for patients with TP53-mutant versus TP53-wild type (wt) DMGs (d; TP53-mutant: median OS = 11.0 ± 7.3 months, TP53-wild type: 23.4 ± 13.9 months), for DMGs that were MAPK-mutant, TP53-wild type versus MAPK-mutant, TP53-mutant (e, MAPK-mut/TP53-wt: OS = 17.8 ± 16.2 months and MAPK-mut/TP53-mut: OS = 9.0 ± 4.6 months), for DMG-A, MAPK wild type versus DMG-B, MAPK wild type (f, DMG-A, MAPK-wt: OS = 23.4 ± 8.2 months and DMG-B, MAPK-wt: OS = 8.4 ± 6.8 months) and DMG-B without and with PDGFRA-amplification (g, DMG-B, PDGFRA-balanced: OS = 12.0 ± 8.3 months; DMG-B, PDGFRA-amplified: OS = 7.5 ± 3.4 months). However, tumour localisation, TP53-status, PDGFRA-status and age were not independent of the cluster attribution. Significance levels: *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001

We also detected significant differences in OS associated with further parameters, which, as discussed below, were not independent of the cluster attribution: Compared to patients with pontine DMGs, both the survival of patients with spinal DMGs and all non-pontine DMGs combined were significantly better (Fig. 2b; spinal cord: median OS = 24.1 ± 26.9 months, medulla: median OS = 18.1 ± 9.9 months, pons: median OS = 11.0 ± 11.0 months, thalamus: median OS = 19.0 ± 17.0 months; p = 0.03 resp. 0.006). However, amongst DMG-B, the survival of patients with DMG-B of non-pontine localisations was as poor as the survival of patients with pontine DMG-B (DMG-B, non-pontine: median OS = 10.5 ± 8.1 months, DMG-B, pons: median OS = 11.0 ± 7.8 months, p = 0.7, Online Resource 6d). This confirms that patients with DMG-B have a worse prognosis compared to DMG-A, independent of the localisation.

Adults had a better OS than children (Fig. 2c; < 18 years: median OS = 11.0 ± 8.9 months, ≥ 18 years: 23.4 ± 19.0 months, p = 0.0006). As published previously [32], the OS of patients with TP53-wild type (wt) DMGs was significantly better than that of patients with TP53-mutant DMGs (Fig. 2d; TP53-mutant: median OS = 11.0 ± 7.3 months, TP53-wild type: median OS = 23.4 ± 13.9 months, p = 0.006). We then combined these two factors for further analyses. Only two DMG-A, TP53-mutant were derived from paediatric patients, as opposed to 20 TP53-mutant DMG-B. Amongst paediatric patients with TP53-mutant DMG-B, the survival of patients with non-pontine DMG-B was as poor as the survival of those with pontine DMG-B (DMG-B, TP53-mutant, non-pontine, < 18 years: median OS = 12.0 ± 8.9 months, DMG-B, TP53-mutant, pons, < 18 years: median OS = 8.3 ± 4.5 months, p = 0.2, Online Resource 6e). This shows that DMG-B integrates different parameters associated with a poor prognosis, irrespective of the tumour localisation.

MAPK-mutant, TP53-wild type cases had a significantly better survival than MAPK-mutant, TP53-mutant cases (Fig. 2e; MAPK-mut/TP53-wt: median OS = 17.8 ± 16.2 months and MAPK-mut/TP53-mut: median OS = 9.0 ± 4.6 months; p = 0.03). The MAPK-status alone did not significantly influence survival (MAPK-mutant: median OS = 12.0 ± 15.3 months, MAPK-wt: median OS = 11.8 ± 7.4 months, p = 0.2, Online Resource 6a). This was also not the case when looking at individual MAPK-alterations (FGFR1-mut: median OS = 17.9 ± 28.3 months, NF1-mut: median OS = 33.8 ± 25.0 months, KRAS-mut: median OS = 9.0 ± 13.9 months; p = 0.3, Online Resource 6b). However, DMG-A, MAPK-wt cases had a significantly better survival than DMG-B, MAPK-wt cases (Fig. 2f; DMG-A, MAPK-wt: OS = 23.4 ± 8.2 months and DMG-B, MAPK-wt: OS = 8.4 ± 6.8 months; p = 0.007). The prognosis of patients with DMGs harbouring a PDGFRA-amplification was in general worse than that of patients with DMGs having a balanced PDGFRA-locus (p < 0.001). As 88% of PDGFRA-amplified cases with available sequencing data were also TP53-mutant (n = 7/8), we also tested the survival of patients with DMG-B harbouring a PDGFRA-amplification versus DMG-B with a balanced PDGFRA-locus, and again detected a significant difference (p = 0.01; Fig. 2g; DMG-B, PDGFRA-balanced: OS = 12.0 ± 8.3 months; DMG-B, PDGFRA-amplified: OS = 7.5 ± 3.4 months). A difference in the OS between male and female patients was not detected (Online Resource 6c; female: median OS = 13.0 ± 17.5 months, male: median OS = 14.8 ± 12.1 months, p = 0.2).

Summarising these findings, individual features primarily detected in DMG-B (pontine localisation, TP53-mutant, PDGFRA-amplified, paediatric patients) were associated with a poorer prognosis, compared to features enriched in DMG-A. In line with this, a univariate cox regression also identified subtype, tumour localisation, PDGFRA copy-number status, TP53-status and age as significant variables (Online Resource 6f). Fisher’s Exact tests proved that tumour localisation, PDGFRA-status, TP53-status and age at diagnosis were dependent on the DMG-subtype (p < 0.05 for all tests). In agreement, a multivariate Cox-regression did not indicate any significant parameter.

In summary, an unbiased stratification via unsupervised hierarchical clustering is well suited to predict OS as it integrates different parameters that influence survival. Hence, a stratification according to the two DMG-subtypes A and B showed the most significant difference in survival. The features of DMG-A and DMG-B are summarised in Fig. 3.

Fig. 3

Summary of the clinical and molecular features of DMG-A and DMG-B. DMG-A is enriched for adult patients, medullary localisation and MAPK-associated mutations, and contains more cases with a methylated MGMT promoter. Contrarily, DMG-B is enriched for paediatric patients, pontine localisation and cases with TP53-mutations. The overall survival of patients with DMG-A is superior to that of patients with DMG-B