DETAILED MOLECULAR CHARACTERISATION OF DIFFUSE INTRINSIC PONTINE GLIOMAS IDENTIFIES THREE MOLECULAR SUBGROUPS AND A NOVEL CANCER DRIVER, ACVR1
Bibliographic record
Abstract
BACKGROUND: Diffuse intrinsic pontine glioma (DIPG) is a devastating pediatric brain tumor with no effective therapy and near 100% fatality. The failure of most therapies can be attributed to the delicate location of these tumors and choosing therapies based on assumptions that DIPGs are molecularly similar to adult disease. Recent studies have unraveled the unique genetic make-up of this brain cancer with nearly 80% harboring a K27M-H3.3 or K27M-H3.1 mutation. However, DIPGs are still thought of as one disease with limited understanding of the genetic drivers of these tumors. This data is critical for the development of better therapies for these children. METHODS: Here we describe deep-sequencing analysis of 36 tumor–normal pairs (20 whole genome sequencing (WGS; Illumina Hiseq 2000) and 16 whole exome sequencing (WES; Applied Biosystems SOLiD 5500xl)), integrated with comprehensive methylation (28 DIPGs; Illumina Infinium450K methylation array), copy number (45 DIPGs, Affymetrix SNP6.0) and expression data (35 DIPGs; Illumina HT-12 v4). RESULTS: Unsupervised subgrouping of DIPGs based on CpG island methylation resulted in three distinct subgroups; MYCN, Silent, and H3-K27M. This subgrouping was supported by multiple analyses including principal components analysis, non-negative matrix factorization and consensus clustering. Subgroup-specific differences were supported by integration of mutation, structural, expression and clinical data. The MYCN subgroup DIPGs are not associated with histone mutations and are instead characterized by hypermethylation and catastrophic shattering of chromosome 2p with high-level copy number amplifications of MYCN and ID2. The Silent subgroup has genomes with minimal instability, fewer mutations and over-expression of WNT pathway genes. The H3-K27M subgroup is highly K27M-H3 mutated but is typically associated with additional genetic alterations including activating mutations in ACVR1, frequent RB1 deletions, TP53 deletions/mutations, PVT-1/MYC or PDGFRA gains/amplifications, genomic instability and alternative lengthening of telomeres. After H3F3A and TP53, the next most frequently mutated gene in DIPG is ACVR1 (activin A receptor, type I), a novel cancer gene. Mutations of ACVR1 in four DIPGs (c.617G > A) result in a R206H substitution. One DIPG had a mutation of a neighboring codon (Q207E). Two DIPGs had a c.983G > A (G328E) mutation and five DIPGs in our cohort had a c983G > T mutation which results in a G328V substitution. In total 20% of DIPGs have ACVR1 mutations. CONCLUSIONS: Our results highlight the many pathways to tumorigenesis in DIPG. This complexity needs to be considered when designing new therapeutic approaches in order to improve outcome for these children. SECONDARY CATEGORY: n/a.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".