LGG-26. Predicting MAPK inhibitor sensitivity in pediatric low-grade gliomas with novel gene expression-derived signatures
Bibliographic record
Abstract
Abstract Pediatric low-grade glioma (pLGG), the most common brain tumors in children, are driven by alterations in the MAPK pathway. Several clinical trials have shown the potential for MAPK inhibitor (MAPKi) treatment in pLGG. However, the range of response to MAPKi is heterogeneous, even between tumors sharing the same driving MAPK alteration. A predictive stratification tool is needed to identify tumors that will be sensitive to MAPK inhibition. We generated sensitivity gene signatures for each MAPKi class (BRAFi, MEKi, ERKi), based on MAPK-related genes differentially regulated between MAPKi sensitive and non-sensitive cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) dataset. Single sample Gene Set Enrichment Analysis was used to measure and validate the MAPKi predictive sensitivity scores in the GDSC dataset and an independent patient-derived xenograft (PDX) dataset (XevaDB). The validated signatures were tested in a pLGG-specific background, using gene expression data from pLGG cell lines and primary pLGG samples. Our MAPKi sensitivity signatures discriminated MAPKi sensitive and non-sensitive cells in the GDSC dataset, and significantly correlated with MAPKi response in the PDX dataset. The sensitivity scores discerned gliomas with varying MAPK alterations from those without MAPK alterations, and showed higher scores in pLGG compared to high-grade gliomas and normal brain tissue. MAPKi-predicted sensitivity was heterogeneous within pLGG groups with a common MAPK alteration, as observed in MAPKi clinical trials. Intriguingly, we observed a strong positive correlation between our MAPKi sensitivity signature scores and the predicted immune cell infiltration rate as determined by the ESTIMATE score. These data demonstrate the potential relevance of gene-expression signatures to predict response to MAPKi treatment in pLGG patients, worth of further investigation in a prospective manner in upcoming clinical trials. In addition, our data could support a role of immune cell infiltration in the response to MAPKi in pLGG, warranting further validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".