DIPG-29. GENOMIC LANDSCAPE OF DIFFUSE INTRINSIC PONTINE GLIOMA: AN ANALYSIS OF THE DIPG-BATS COHORT
Bibliographic record
Abstract
INTRODUCTION: Diffuse intrinsic pontine glioma (DIPG) remains a devastating and incurable disease. The DIPG-BATs clinical trial incorporates diagnostic biopsy with molecularly determined treatment stratification. Here we present the initial genomic analysis of the DIPG-BATs cohort. METHODS: Children enrolled on the DIPG-BATs clinical trial underwent upfront diagnostic biopsies prior to commencement of therapy. RNA and DNA were extracted from single core biopsies and subjected to whole-genome sequencing (WGS) and RNA-sequencing. Tumor samples were also collected at autopsy if there was parental consent. RESULTS: Fifty-three patients were enrolled on study, of whom 50 underwent biopsy. There were no biopsy-related deaths. A mean of 5ug of RNA and 10ug of DNA were extracted from single frozen cores. WGS on 41 DIPG samples (including eight autopsy samples) revealed a mean mutation rate of 0.753 mutations per Mb. We confirmed TP53, PIK3CA, H3F3A, ACVR1, PPM1D, and HIST1H3B to be recurrently mutated in DIPG. Additional mutations were found in epigenetic modifiers including ASXL1. Copy-number analysis revealed PDGFRA to meet statistical significance as a recurrent amplification peak in DIPG (q<0.25). Gene-set analysis revealed mutations in the TERT pathway, ARF pathway, AKT/PTEN pathway, TP53 pathway, cell cycle and apoptosis pathways to be statistically enriched across the cohort (q<0.10). Analysis of paired diagnosis and autopsy samples revealed evolution of tumors following treatment. Samples obtained at autopsy exhibited a significantly increased mean mutation rate compared to untreated biopsies (p<0.0001). CONCLUSIONS: Whole-genome sequencing of DIPG-BATs samples confirms driver mutations in multiple pathways implicated in cancer. Initial investigation of paired biopsy and autopsy samples allows the analysis of the genomic evolution of DIPG. These findings shed insight into both oncogenic and resistance drivers in DIPG. *equal contribution
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".