Ultra high-risk PFA ependymoma is characterized by loss of chromosome 6q
Bibliographic record
Abstract
BACKGROUND: Within PF-EPN-A, 1q gain is a marker of poor prognosis, however, it is unclear if within PF-EPN-A additional cytogenetic events exist which can refine risk stratification. METHODS: Five independent non-overlapping cohorts of PF-EPN-A were analyzed applying genome-wide methylation arrays for chromosomal and clinical variables predictive of survival. RESULTS: Across all cohorts, 663 PF-EPN-A were identified. The most common broad copy number event was 1q gain (18.9%), followed by 6q loss (8.6%), 9p gain (6.5%), and 22q loss (6.8%). Within 1q gain tumors, there was significant enrichment for 6q loss (17.7%), 10q loss (16.9%), and 16q loss (15.3%). The 5-year progression-free survival (PFS) was strikingly worse in those patients with 6q loss, with a 5-year PFS of 50% (95% CI 45%-55%) for balanced tumors, compared with 32% (95% CI 24%-44%) for 1q gain only, 7.3% (95% CI 2.0%-27%) for 6q loss only and 0 for both 1q gain and 6q loss (P = 1.65 × 10-13). After accounting for treatment, 6q loss remained the most significant independent predictor of survival in PF-EPN-A but is not in PF-EPN-B. Distant relapses were more common in 1q gain irrespective of 6q loss. RNA sequencing comparing 6q loss to 6q balanced PF-EPN-A suggests that 6q loss forms a biologically distinct group. CONCLUSIONS: We have identified an ultra high-risk PF-EPN-A ependymoma subgroup, which can be reliably ascertained using cytogenetic markers in routine clinical use. A change in treatment paradigm is urgently needed for this particular subset of PF-EPN-A where novel therapies should be prioritized for upfront therapy.
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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 | 0.000 | 0.000 |
| 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".