Cross-species analysis of SHH medulloblastoma models reveals significant inhibitory effects of trametinib on tumor progression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it