Germline analysis of an international cohort of pediatric diffuse midline glioma patients
Bibliographic record
Abstract
BACKGROUND: Factors that drive the development of diffuse midline gliomas (DMG) are unknown. Our study aimed to determine the prevalence of pathogenic/likely pathogenic (P/LP) germline variants in pediatric patients with DMG. METHODS: We assembled an international cohort of 252 pediatric patients with DMG, including diffuse intrinsic pontine glioma (n = 153), with germline whole genome or whole exome sequencing. RESULTS: We identified P/LP germline variants in cancer predisposition genes in 7.5% (19/252) of patients. Tumor profiles differed, with the absence of somatic drivers in the PI3K/mTOR pathway in patients with germline P/LP variants versus those without (P = .023). P/LP germline variants were recurrent in homologous recombination (n = 9; BRCA1, BRCA2, PALB2) and Fanconi anemia genes (n = 4). Somatic findings established that the germline variants definitively contributed to tumorigenesis in at least 1% of cases. One patient with recurrent DMG and pathogenic germline variants (BRCA2, FANCE) showed a near-complete radiological response to PARP and immune checkpoint inhibition. CONCLUSIONS: Our study determined the prevalence of pathogenic germline variants in pediatric DMG and suggests a role in tumorigenesis for a subset of patients.
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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 | 0.001 |
| 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".