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are associated with an autosomal dominant disease of the connective tissue, fibrodysplasia ossificans progressiva (FOP). G328V-ACVR1 mutations have never.
Uncovering Molecular Subgroups and a Novel Cancer Driver, ACVR1, in Diffuse Intrinsic Pontine Gliomas Paweł Buczkowicz1,3,5, Christine Hoeman4, Patricia Rakopoulos3,5, Sanja Pajovic3, Andrew Morrison3, Eric Bouffet2 , Ute Bartels2, Oren Becher 4 and Cynthia Hawkins1,3,5 (1) Division of Pathology - (2) Division of Haematology and Oncology , Hospital for Sick Children, Toronto, ON, Canada

Background Diffuse intrinsic pontine glioma (DIPG) is a devastating paediatric brain tumour with no effective therapy and near 100% fatality1. The failure of most therapies can be attributed to the delicate location of these tumours and choosing therapies based on assumptions that DIPGs are molecularly similar to adult disease2. Recent studies have unravelled the unique genetic make-up of this paediatric brain cancer with nearly 80% harbouring a K27M-H3.3 or K27M-H3.1 mutation3. However, DIPGs are still thought of as one disease with limited understanding of the genetic drivers of these tumours. To understand what drives DIPGs we integrated whole-genomesequencing with methylation, expression and copy-number profiling, discovering that DIPGs are three molecularly distinct subgroups (H3-K27M, Silent, MYCN) and uncovering a novel cancer driver, the activin receptor ACVR1, in 20% of DIPGs.

(3) The Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for Sick Children, Toronto, ON, Canada (4) Division of Pediatric Hematology/Oncology, Duke University Medical Center, Durham, USA (5) Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Canada

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Materials & Methods Patients and Samples. Biological material and clinical data was gathered for 74 DIPG samples, including normal brain and/or peripheral blood if available. DIPGs were diagnosed by a neuroradiologist based on MRI imaging. A contrasting lesion with diffuse involvement of at least 50% of the pons was required for DIPG diagnosis. All patient material was collected after receiving informed consent and was approved by the institutional review board of contributing centers. Twenty of these patients were pre-treatment samples (2 non-treated patients from autopsy), and 54 were post-treatment autopsy samples. The median age of diagnosis was 6.37 years with a median survival of 10.4 months. Methylation Profiling. Comprehensive methylation profiling of 28 DIPG samples was conducted using the Illumina Infunium450K array. Methylation profiling iNHA transfected with K27M-H3.3 and WT-H3.3 vectors was also assessed on this array. Subgrouping and clustering based on differential CpG probe methylation was performed and validated using multiple programs and algorithms including non-negative matrix factorization (NMF) (GenePattern), consensus hierarchical clustering (GenePattern, Bioconductor/R and MultiExperiment Viewer), and significance analysis of microarrays (SAM). Next generation sequencing alignment and structural variants. Sequencing was performed on 35 DIPG samples (20 whole genome & 15 whole exome) with next generation Illumina and SOLiD (Applied Biosystems) technologies, respectively. One hundred and fifty one sequence variants in 128 genes were validated using PCR amplification by Fluidigm arrays and ion torrent chips (Life Technologies). Structural variants (SV) were identified using PRISM 1.1.6 and PRISM CTX 1.0.14. Recurrent SVs were found after subtracting common SVs and all SVs found in normal tissue and were visually validated using DNAC algorithm and Savant (v2.0.3)5. Prediction of structural variants in chromothriptic regions was performed by discordant read-pair clustering. Discordant readpairs (fragment size > 3 · σ + µ) were initially selected for by greedy clustering using a sliding window of 15 · σ and refined by clipped read mappings. Localization of chromothriptic event was predicted using Hidden-Markov-Model (HMM) informed by depth of coverage and discordant read-pairs. Copy number analysis. Copy number analysis was conducted for 48 DIPG samples using SNP6.0 (Affymetrix, Santa Clara, CA). Analysis was conducted in Partek Genomics Suite (v6.6) (Partek Incorporated, St. Louis, MO) and Genotyping Console 4.1 (GTC4.1; Affymetix).

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Figure 1. Methylation profiling reveals 3 molecular subgroups of DIPG – (a) Heat map of methylation levels in three DIPG subgroups identified by unsupervised hierarchical clustering and supported by (b) principal component anlaysis, (c) non-negative matrix factorization (cophenetic coefficient = 0.9934, k=3) and (d) consensus clustering represented by cumulitive distribution function and change in Gini.

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Conclusions 1. DIPG comprise three molecular subgroups (MYCN, Silent and H3-K27M) with unique DNA methylation patterns. 2. DIPG molecular subgroups have common clinical and genetic events. MYCN: no recurrent mutations, high-grade histology, high level amplification of MYCN and ID2. Silent: silent genomes based on WGS structural and SNP6.0 copy number analysis. All low-grade tumours are from this group yet this group does not have different overall survival from the other two groups. H3-K27M: highly mutated in either H3.3 (H3F3A) or H3.1 (HIST1H3B and HIST1H3C), alternative lengthening of telomeres (ALT) is exclusive to this group, recurrent CAN including PDGFRA and PVT-1/MYC gains and TP53 and RB1 losses. 3. High-level MYCN and ID2 amplifications occur in the context of chromothripsis and exist as double minutes. MYCN subgroup DIPGs exhibit chromosome 2p SVs consistent with chromothripsis. This leads to the formation of double minutes containing on average 20 to 50 copies of MYCN and ID2 per tumour cell. 4. K27M-H3.3 affects cell morphology and global DNA methylation and expression in vitro.

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Figure 2. Molecular subgroups of DIPG share common clinical features and recurrent genomic events. (a) Clinical and genomic features such as gender, histology, frequency of recurrent mutations, alternative lengthening of telomeres and copy number alterations are represented in a DIPG subgroup specific manner. (b) Probability of two mutational or structural features of DIPG co-occurring based on odds ratio suggests statistically significant association between K27M-H3.3 and PDGFRA amplifications (OR = 8.0, p = 0.0127) and between K27MH3.1 and ACVR1 mutations (OR = 15.8, p < 0.001). (C) Probability of mutations or structural event of DIPG occurring with a clinical feature such as gender or tumor histology based on odds ratio shows statistically significant correlation between P53 mutations and GBM histology (OR = 10.8, p < 0.005), among others.

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Figure 4. K27M-H3.3 affects cell morphology and global H3K27me3 while ACVR1 mutations constitutively activate BMP signaling in vitro. (a) Immortalized NHAs transfected with K27M-H3.3 show phenotypic changes compared to empty vector and WT-H3.3 control, forming cell clusters at high density when seeded in DMEM and growing semi-adherently in neural stem cell media. (b) K27M-H3.3 NHAs have different methylation and expression profiles as compared to controls. (c) Four mutations (R206H, Q207E, G328E and G328V) were detected in 12/61 DIPG patients. The R206H and Q207E mutations occur in the GS domain and the G328-mutations occur in the protein kinase domain. (d) Western blot showing increased pSMAD1/5 in ACVR1 mutant NHA and DIPG58 cells transfected with G328V-ACVR1 as compared to control cells. (e) Real-time PCR in NHA transfected with empty vector, K27M-H3.3, G328VACVR1 or a combination of K27M-H3.3 and G328V-ACVR1 shows increase in ID1 and ID2 gene expression as compared to empty vector control. Error bars represent standard deviation.

6. ACVR1 mutations activate BMP signaling through phospho-SMAD1/5 and increased ID1 and ID2 expression. Our results show that this seemingly homogeneous entity in fact comprises three distinct subgroups with different clinical and molecular features. This complexity needs to be considered when designing new therapeutic approaches in order to improve outcome for these children.

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References

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Figure 3. MYCN subgroup show recurrent structural variants involving high level amplification and rearrangement of MYCN, ID2 and KIDINS220 on chromosome 2p. SNP6.0 copy number profiles of chromosome 2p focal, high-level amplifications in (a) DIPG38, (b) DIPG49, (c) DIPG01 and (d) DIPG29. These amplifications always involve the genes MYCN, ID2 and KIDINS220. CIRCOS plots of structural variants in (e) DIPG01 and (f) DIPG29 as determine from WGS data. (g) The bi-directed event graph for a chromothripsis event in DIPG29. Red edges represent the genomic interval of their respective nodes. Blue edges represent groups of discordant read pairs supporting the same breakpoint. Arc width is proportional to the maximal likely copy count.

5. ACVR1 mutations are a novel driver in 20% of DIPG patients. G328E, R206H and Q207E ACVR1 mutations are associated with an autosomal dominant disease of the connective tissue, fibrodysplasia ossificans progressiva (FOP). G328V-ACVR1 mutations have never previously been reported and ACVR1 mutations have never been associated with cancer.

Figure 5. Molecular subgroups of diffuse intrinsic pontine gliomas. Schematic representation of clinical, genetic and epigenetic features seen in three DIPG subgroups; MYCN, Silent and H3-K27M.

1. Hargrave D, Bartels U, Bouffet E. (2006) “Diffuse brainstem glioma in children: critical review of clinical trials” The Lancet Oncology 7: 241-248. 2. Donaldson SS, Laningham F, Fisher PG (2006) “Advances toward an understanding of brainstem gliomas” Journal of Clinical Oncology 24: 1266-1272. 3. Khuong-Quang DA, Buczkowicz P, Rakopoulos P et al. (2012) “K27M mutation in histone H3.3 defines clinically and biologically distinct subgroups of pediatric diffuse intrinsic pontine gliomas” Acta Neuropathologica 123(3): 439-447. 4. Jiang Y, Wang Y, Brudno M. (2012) “PRISM: pair-read informed split-read mapping for base-pair level detection of insertion, deletion and structural variants. Bioinformatics 28: 2576-2583. 5. Fiume M, Smith EJ, Brook A et al. (2012) “Savant Genome Browser 2: visualization and analysis for population-scale genomics. 6. Buczkowicz P, Hoeman C, Rakopoulos P et al. (2013) “Comprehensive genomic analysis of diffuse intrinsic pontine gliomas unravels three molecular subgroups and a novel cancer driver, ACVR1” Nature Genetics (submitted).