Whole-Genome Profiling of Pediatric Diffuse Intrinsic Pontine Gliomas Highlights Platelet-Derived Growth Factor Receptor α and Poly (ADP-ribose) Polymerase As Potential Therapeutic Targets
Bibliographic record
Abstract
PURPOSE: Diffuse intrinsic pontine glioma (DIPG) is one of the most devastating of pediatric malignancies and one for which no effective therapy exists. A major contributor to the failure of therapeutic trials is the assumption that biologic properties of brainstem tumors in children are identical to cerebral high-grade gliomas of adults. A better understanding of the biology of DIPG itself is needed in order to develop agents targeted more specifically to these children's disease. Herein, we address this lack of knowledge by performing the first high-resolution single nucleotide polymorphism (SNP) -based DNA microarray analysis of a series of DIPGs. PATIENTS AND METHODS: Eleven samples (nine postmortem and two pretreatment surgical samples), the largest series thus far examined, were hybridized to SNP arrays (250 k or 6.0). The study was approved by the research ethics board at our institution. All array findings were validated using quantitative polymerase chain reaction, fluorescence in situ hybridization, immunohistochemistry, and/or microsatellite analysis. RESULTS: Analysis of DIPG copy number alterations showed recurrent changes distinct from those of pediatric supratentorial high-grade astrocytomas. Thirty-six percent of DIPGs had gains in platelet-derived growth factor receptor alpha (PDGFRA; 4 to 18 copies) and all showed PDGFR-alpha expression. Low-level gains in poly (ADP-ribose) polymerase (PARP) -1 were identified in three cases. Pathway analysis revealed genes with loss of heterozygosity were enriched for DNA repair pathways. CONCLUSION: To our knowledge, our data provides the first, comprehensive high-resolution genomic analysis of pediatric DIPG. Our findings of recurrent involvement of the PDGFR pathway as well as defects in DNA repair pathways coupled with gain of PARP-1 highlight two potential, biologically based, therapeutic targets directed specifically at this devastating disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".