PDCT-09. HYPERMUTATION AND NEOANTIGEN FORMATION IS DIAGNOSTIC AND PREDICT RESPONSE TO IMMUNE CHECKPOINT INHIBITION IN CHILDHOOD BIALLELIC MISMATCH REPAIR DEFICIENT BRAIN TUMORS
Bibliographic record
Abstract
Recurrent malignant brain tumors (MBT) are universally lethal and common in the context of an aggressive cancer predisposition syndrome termed biallelic mismatch repair deficiency (bMMRD). bMMRD results in rapid onset of childhood cancers characterized by a high mutational burden. Evidence suggests that high mutation and neoantigen loads are associated with response to immune checkpoint inhibition (ICI). Exome sequencing and neoantigen prediction was performed on 50 bMMRD cancers and compared to childhood and adult neoplasms. Mutation and neoantigen load in bMMRD tumors were compared to adult melanomas, colorectal cancers and lung carcinomas that were responsive to ICIs. ICI were offered to BMMRD patients with recurrent tumors. bMMRD glioblastoma (n=25) and medulloblastoma/PNET had significantly higher mutational load than sporadic pediatric and adult gliomas (p<0.0001). bMMRD MBT with secondary polymerase mutations had the highest mutation load (mean 17,740+/-7703) in humans with mean neoantigen load 7-16 times higher than immunoresponsive adult tumors (p=0.00001). Spatial and temporal sampling of individual bMMRD tumors revealed large variations in mutation and neoantigen landscape which is related to prior therapy. Based on these preclinical data, bMMRD patients with recurrent MBT and other cancers are being treated with ICI with clinically significant and profound radiological responses. This report is the first to delineate the mutable nature of the neoantigen landscape in cancers where new mutations are constantly arising due to complete ablation of replication repair. The encouraging responses of recurrent malignant brain tumors to immune checkpoint inhibition may have implications to other hypermutant cancers arising from primary (genetic predisposition) or secondary somatic mismatch repair deficiency.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".