Copy-number alterations reshape the classification of diffuse intrinsic pontine gliomas. First exome sequencing results of the BIOMEDE trial
Bibliographic record
Abstract
Abstract Diffuse intrinsic pontine gliomas (DIPG) is an incurable neoplasm occurring mainly in children for which no progress was made in the last decades. The randomized phase II BIOMEDE trial compared three drugs (everolimus, dasatinib, erlotinib) combined with irradiation. The present report describes whole exome sequencing (WES) results for the first 100 patients randomized. Copy-number-Alteration (CNA) unsupervised clustering identified four groups with different outcomes and biology. This classification improved prognostication compared to models based on known biomarkers (Histone H3 and TP53 mutations). The cluster presenting complex genomic rearrangements was associated with significantly worse outcome and TP53 dysfunction. Mutation and CNA signatures confirmed the frequent alteration in DNA repair machinery. With respect to potential targetable pathways, PI3K/AKT/mTOR activation occurred in all the samples through multiples mechanisms. In conclusion, WES at diagnosis was feasible in most patients and provides a better patient stratification and theranostic information for precision medicine.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".