PDTM-21. LARGE SCALE TUMOR MUTATIONAL BURDEN ANALYSIS OF PEDIATRIC TUMORS PROVIDES A DIAGNOSTIC TOOL FOR GERMLINE PREDISPOSITION AND REVEALS NOVEL CANDIDATES FOR IMMUNE CHECKPOINT INHIBITION
Bibliographic record
Abstract
A significant amount of childhood brain tumors emerge from cancer predisposition syndromes. Although tumor sequencing is common practice, it is currently impossible to infer germline mutations from tumor data. Glioblastoma Multiforme arising in children with Biallelic Mismatch Repair Deficiency Syndrome (bMMRD) are ultrahypermutant which is highly specific to this syndrome and confers eligibility for immune checkpoint inhibition therapy (ICI). 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 (1000x) of 315 genes in 2984 pediatric tumors (585 brain tumors) and 79 842 adult tumors (3910 brain tumors) was performed. An algorithm was devised to determine mutation burden from 2 MB of panel sequencing data and results correlated strongly with mutation burden from exome sequencing (R2 = 0.94). Mutational signatures were analyzed to predict source of hypermutation. Thirteen patients with hypermutant brain tumors identified by panel sequencing were enrolled on an ICI 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 cases of GBM greater than 100 Mut/MB harbored MMR/polymerase mutations suggesting germline bMMRD (p =10-7). Clinical data collected on 15 ultrahypermutant tumors revealed germline mutations in replication repair genes in all patients. Of the 22 patients with hypermutant cancers treated with ICI, 16 had brain tumors, and favorable sustained responses are observed. High mutation burden is a sensitive predictor of germline bMMRD. Hypermutant tumors are more common in the pediatric setting than previously appreciated, opening novel therapeutic avenues. ICI shows promise for hypermutant pediatric cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".