PATH-09. THE IMPACT OF MISMATCH REPAIR DEFICIENCY ON GLIOMAS IN CHILDREN, ADOLESCENTS, AND YOUNG ADULTS; A MULTI-CENTRIC, COLLABORATIVE STUDY LED BY THE IRRDC AND GLIOMA TASKFORCE
Bibliographic record
Abstract
Abstract BACKGROUND Primary mismatch repair deficiency (pMMRD) is a pan-cancer mechanism caused by somatic mutations or inherited as part of Lynch Syndrome (LS) or constitutional mismatch repair deficiency (CMMRD). PMMRD results in universal hypermutation and microsatellite instability leading to chemoradiation resistance but sensitivity to immunotherapy. The prevalence of pMMRD in gliomas of children, adolescents, and young adults (CAYA), and the impact of germline inheritance is unknown. METHODS We harnessed functional genomic tools across population-based (Toronto), and multiple institutional (SJ, CBTN) cancer databases (n=1276) to determine the prevalence, subgroups, and impact of germline mutations in pMMRD gliomas. RESULTS Data from Toronto, SJ and CBTN reveals prevalence of 10%, 5% and 5% in pediatric high-grade gliomas (HGG). Molecularly, pMMRD is absent in gliomas harboring pediatric-type fusions, BRAF-V600E, and histone mutations, but enriched in HGG harboring IDH and TP53 mutations. In AYA (n=660), pMMRD was detected in 5% of IDH-WT and IDH-mutant high-grade astrocytomas, but notably absent in all oligodendrogliomas and other low-grade gliomas. Across this entire CAYA dataset, all except one pMMRD gliomas harbored germline MMR mutations (97%). Strikingly, LS predominated over CMMRD with significant difference in age of glioma onset (LS: 29-years versus CMMRD: 11-years; p< 0.001, IRRDC dataset). Survival analysis (Kaplan-Meier) confirmed the poor overall survival (OS) for pMMRD and other pediatric subtypes of HGG (3-year OS < 25%). In contrast, IDH-mutant pMMRD HGG have significantly worse OS than IDH-mutant non-pMMRD counterparts. Remarkably, immunotherapy significantly improves survival not only in children, but also for AYA patients with refractory pMMRD HGG (3-year OS > 50%). CONCLUSION This large study demonstrates that pMMRD drives a significant proportion of HGG across CAYA with an alarming impact of germline predisposition. Specifically, unrecognized AYA LS patients with MMRD HGG. These findings support universal screening for MMRD in HGG to identify patients for surveillance and immunotherapy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".