IMMU-17. Comprehensive immunological gene expression profiling of pediatric brain tumors
Bibliographic record
Abstract
Abstract Immunotherapy, predominantly through immune checkpoint inhibition (ICI), has had incredible success in treating some metastatic cancers, however, outside of rare cases of mismatch repair deficient (MMRD) gliomas, brain tumors have not had consistent responses to ICI. This can be attributed to a variety of factors including a low tumor mutation burden, lack of T cell infiltrates, and the CNS immune privilege. There are numerous strategies to target the tumor immune microenvironment (TIME) beyond ICI, include CAR-T cells, tumor vaccines, and myeloid cell modulation. The investigation of these depends critically on detailed characterization of the cell populations and interactions in the CNS TIME. We developed a 103 gene NanoString immune-oncology gene expression panel that includes markers reflecting selected cell types, therapeutic targets, and cellular pathways, as well as the 18-gene Tumor Inflammation Signature, a well validate biomarker for ICI response. We have used this to characterize over 500 brain tumors, including a diverse set of 227 pediatric low-grade gliomas (LGG), 86 MMRD gliomas, 47 diffuse intrinsic pontine gliomas (DIPG), 26 ependymomas, 36 medulloblastomas, 70 adult gliomas, and 35 non-tumor brain samples. Our results demonstrate a broad range of immunologic states, including within groups of tumors with the same genetic driver alteration. In pediatric LGG with BRAF V600E, there was clear histologic correlation with immune status, as glioneuronal tumors had substantial upregulation of T cell markers and regulatory genes, while diffuse astrocytomas had a near normal immune profile. In DIPG there was strong upregulation of macrophage markers, contradicting prior reports that have characterized these tumors as immunologically neutral. In a set of MMRD gliomas treated with ICI we identified several differentially expressed genes correlating with therapeutic response, including CCL4, CXCL9, and HGPD. In sum, this provides a characterization of diverse immune activation states across pediatric gliomas and other brain tumors.
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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 | 0.000 |
| 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) | 0.001 | 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".