HGG-17. TUMOR MUTATIONAL BURDEN ANALYSIS OF PEDIATRIC TUMORS PROVIDES A DIAGNOSTIC TOOL FOR GERMLINE REPLICATION REPAIR DEFICIENCY AND PREDICT RESPONSE TO IMMUNE CHECKPOINT INHIBITION
Bibliographic record
Abstract
Hypermutation constitute an important subgroup of cancers and confers sensitivity to immune checkpoint inhibition (ICI). Glioblastoma arising in children with Constitutional Mismatch Repair Deficiency Syndrome (CMMRD) are ultrahypermutant. Our objective was to quantify the frequency of hypermutant tumors in children, determine whether mutational signatures can predict germline mutations, and treat hypermutant tumors with ICIs. Deep panel sequencing of 2984 pediatric tumors (585 brain tumors). Tumor mutation burden was correlated to mutation burden from exome and genome sequencing (R2 = 0.94). Mutational signatures were analyzed to predict source of hypermutation. Data on 36 patients with hypermutant tumors identified by sequencing were enrolled on an ICI registry trial. Hypermutant tumors (>10 mut/MB) comprised 5% of all pediatric tumors (n=143). These were highly enriched for replication repair mutations (p<0.0001) and mutation loads correlated with hypermutant adult tumors that have shown demonstrable clinical response to ICI. Hypermutation was found in 5% of childhood and 6% of adult glioblastoma. All glioblastomas with greater than 100 Mut/MB harbored MMR/polymerase mutations suggesting germline bMMRD (p =10-7). Clinical data collected on 19 ultrahypermutant tumors revealed germline mutations in replication repair genes in all patients. Of the 36 patients with hypermutant cancers treated with ICI, 24 had brain tumors, and favorable sustained responses are observed. High mutation burden is a sensitive predictor of germline CMMRD. Hypermutant tumors are more common in the pediatric setting than previously appreciated, opening novel therapeutic avenues. ICI shows promise for hypermutant pediatric cancers including glioblastoma.
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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 | 0.000 |
| 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".