Cross-species analysis of SHH medulloblastoma models reveals significant inhibitory effects of trametinib on tumor progression
Bibliographic record
Abstract
Sonic Hedgehog (SHH) medulloblastomas (MBs) exhibit an intermediate prognosis and extensive intertumoral heterogeneity. While SHH pathway antagonists are effective in post-pubertal patients, younger patients exhibit significant side effects, and tumors that harbor mutations in downstream SHH pathway genes will be drug resistant. Thus, novel targeted therapies are needed. Here, we performed preclinical testing of the potent MEK inhibitor (MEKi) trametinib on tumor properties across 2 human and 3 mouse SHH MB models in vitro and in 3 orthotopic MB xenograft models in vivo. Trametinib significantly reduces tumorsphere size, stem/progenitor cell proliferation, viability, and migration. RNA-sequencing on human and mouse trametinib treated cells corroborated these findings with decreased expression of cell cycle, stem cell pathways and SHH-pathway related genes concomitant with increases in genes associated with cell death and ciliopathies. Importantly, trametinib also decreases tumor growth and increases survival in vivo. Cell cycle related E2F target gene sets are significantly enriched for genes that are commonly downregulated in both trametinib treated tumorspheres and primary xenografts. However, IL6/JAK STAT3 and TNFα/NFκB signaling gene sets are specifically upregulated following trametinib treatment in vivo indicative of compensatory molecular changes following long-term MEK inhibition. Our study reveals a novel role for trametinib in effectively attenuating SHH MB tumor progression and warrants further investigation of this potent MEK1/2 inhibitor either alone or in combination with other targeted therapies for the treatment of SHH MB exhibiting elevated MAPK pathway activity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".