IMMU-18. FAVORABLE OUTCOME IN REPLICATION REPAIR DEFICIENT HYPERMUTANT BRAIN TUMORS TO IMMUNE CHECKPOINT INHIBITION: AN INTERNATIONAL RRD CONSORTIUM REGISTRY STUDY
Bibliographic record
Abstract
Abstract Pediatric brain tumors with replication repair deficiency (RRD) are hypermutant and may respond to immune checkpoint inhibition (ICI). We performed a consortium registry study of ICI in recurrent RRD cancers. Clinical and companion biomarkers were collected longitudinally on all patients. Biomarkers included tumor mutational burden (TMB), neoantigens and genetic signatures obtained from whole genome and exome sequencing. Immune inference was obtained by RNAseq and T cell rearrangement was collected in the tumor and in blood throughout treatment. Of the 46 tumors on the study, 32 were brain tumors with glioblastoma in 96%. Rapid, objective responses (>50%) were observed in 50% of glioblastomas. Three year overall survival for the whole cohort was 48+/-8% which compares favorably with historical controls. Brain tumors fared worse with OS of 39+/-10% and late recurrences observed even after 2 years of therapy (p=0.02). Tumor size and acute “flare” constitute poor outcome throughout all cancers. While all tumors are hypermutant, TMB and predicted neoantigens correlated with response to ICI (p=0.02). Specific signatures extracted from SNVs and total mutations predicted response to ICI and favorable outcome (p=0.005). RNA inference and TCR reveal that the FLARE phenotype is mostly acute nonspecific immune response and not true progression. Finally, glioblastomas (n=8) which failed single agent ICI had favorable responses to combinational immunotherapies with prolonged survival of 65%+/-8% at one year after failure vs 0 for other patients (p=0.01). RRD glioblastomas exhibit favorable outcome and responses to ICI. Combinational therapies based on tumor and immune signatures of these cancers are necessary.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".