PATH-14. GENETIC SUSCEPTIBILITY AND OUTCOMES OF PEDIATRIC, ADOLESCENT AND YOUNG ADULT IDH-MUTANT ASTROCYTOMAS
Bibliographic record
Abstract
Abstract INTRODUCTION Previously thought to be rare, recent case series have shown that IDH mutations in young patients are more common than previously described. In this study, we analyzed IDH-mutant tumors to determine clinical significance of these mutations in children, adolescents and young adults. METHODS Through this multi-institution study (10 institutions), we collected 64 IDH1/2-mutant infiltrating astrocytoma specimens from 58 patients aged 4–26 (M:F, 0.4:0.6). Specimens included 46 low-grade (LGG) and 18 high-grade (HGG) astrocytomas. Tumor sequencing data (n=45), germline sequencing data (n=37) and outcome data (n=40) was analyzed. RESULTS Similar to adults, most sequenced tumors had a co-mutation in the TP53 gene, while ATRX mutations were less common and primarily seen in HGGs. Approximately 60% (n=21) of patients with germline data available had a mutation in a cancer predisposition gene. Mismatch repair (MMR) mutations were most common (n=12; MSH6 n=9), followed by TP53mutations (n=7). All patients with MMR gene mutations had HGGs and poor progression free (PFS=10% at 2 years, mean TTP=9 months) and overall (OS <30% at 2 years) survival. Despite an OS of 90% at 5 years, many LGG patients had tumor progression/recurrence requiring additional treatment (PFS= 80% at 2 yrs, 40% at 5 yrs, mean TTP=3.5 years). Four LGG tumors (2 with TP53+ATRXloss, 2 with TP53 loss+1p19q co-deletion) underwent malignant transformation. CONCLUSION IDH-mutant tumors in pediatric patients are strongly associated with cancer predisposition and increased risk for progression/recurrence or malignant transformation. Routine screening for IDH1/2 mutations in children with grade 2–4 astrocytomas could greatly impact patient management.
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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 | 0.000 |
| 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) | 0.002 | 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".