IMMU-09. IMMUNOLOGICAL CHARACTERIZATION OF PEDIATRIC BRAIN TUMORS HAS CLINICAL IMPLICATIONS FOR PATIENT MANAGEMENT AND PROGNOSIS
Bibliographic record
Abstract
Abstract The tumor immune microenvironment (TIME) is a growing area of interest, however, the extent of immune activation in childhood CNS tumors is unknown. Although immunotherapy has not gained success in most CNS cancers, our group described gliomas with MMRD which exhibit hypermutation and favorable responses to immune checkpoint inhibition (ICI). Therefore, detailed characterization of the CNS TIME is key for the development of novel immunotherapeutic strategies and application of existing ones in childhood brain tumors. We developed a clinical NanoString immune-oncology panel that includes markers reflecting cell types, therapeutic targets, and cellular pathways, as well as the 18-gene tumor inflammation signature (TIS), a biomarker for ICI response. We tested over 500 brain tumors, including 266 low-grade gliomas (LGG), 170 high-grade gliomas (HGG), 91 MMRD tumors, 16 ependymomas, 46 medulloblastomas, and 36 non-tumor brain samples. Overall, ependymomas and medulloblastomas had low levels of inflammation, although SHH medulloblastomas had higher inflammation than other subtypes. IDH-mutant LGG were immunologically cold, while many gliomas with pediatric-LGG mutations had high levels of inflammation, including upregulation of immune checkpoints – indicating that ICI may be an effective strategy. Interestingly, in pediatric-LGG inflammation impacted outcome in tumors with the same genetic alterations. BRAF V600E-mutant LGG exhibiting high TIS had inferior prognosis (p = 0.02), while no such relationship was observed in BRAF-fused tumors. Diffuse midline gliomas had higher inflammation than hemispheric HGG, indicating that these tumors are not immunologically cold, as has been previously reported. In MMRD tumors treated with ICI, high TIS correlated with improved survival and was independent from hypermutation and mutational burden. Furthermore, MMRD gliomas had high expression of several other immune checkpoints including LAG3, suggesting its value as an additional therapeutic target. In summary, characterization of the TIME across pediatric brain tumors provides potential prognostic clues and suggest treatment strategies for further investigation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".