LGG-13. UNRAVELING THE SPATIAL TUMOR MICROENVIRONMENT OF PEDIATRIC LOW-GRADE GLIOMAS USING IMAGING MASS CYTOMETRY
Bibliographic record
Abstract
Abstract Pediatric low-grade gliomas (pLGG) are the most common brain tumors in children and are associated with significant morbidity. Therefore, there is an urgent need for novel therapeutic strategies. While some studies have described pLGG’s tumor microenvironment (TME) from bulk RNAseq and scRNAseq, little is known about the spatial architecture of pLGG and its association with clinico-molecular features. Our study utilized imaging mass cytometry and a panel of 35 metal-labelled antibodies to unravel the spatial organization of key TME cell populations in 120 primary pLGG samples from the LOGGIC Core BioClinical DataBank. Cellular neighborhood analysis was performed to map the spatial organization of pLGG TME. Several clinic-molecular features (entity, tumor location, genetic driver alteration, disease progression status, age and sex) were used to measure their putative association with the enrichment of key cell populations and cellular structures to identify diagnosis and prognosis markers. Here, we identified a predominant presence of myeloid cells in the TME, particularly notable in optic pathway tumors, which exhibited unique immune profiles. Optic pathway tumors demonstrated elevated immune subsets, delineating distinct architectural features in the TME of this brain region. Spatial analysis defined cellular neighborhoods and specific interactions thereof, including myeloid interaction and macrophage-abundant regions. Clinically, these myeloid cell populations, associated with an increased expression of the immune checkpoint protein TIM3, suggesting the presence of an immuno-suppressive environment, were associated with inferior survival outcomes. Importantly, we identified an immunophenotype signature based on the presence of 4 myeloid cell populations significantly associated with progression free survival in our cohort. Our study underscored the need for accurate identification of immune cell populations influencing tumor progression, offering valuable insights for the identification of prognostic markers, and for the development of effective therapeutic strategies, such as immune checkpoint inhibitors, for the treatment of pLGG.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".