Overcoming resistance to sonic hedgehog inhibition by targeting p90 ribosomal S6 kinase in pediatric medulloblastoma
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Bibliographic record
Abstract
BACKGROUND: Molecular subtyping has allowed for the beginning of personalized treatment in children suffering from medulloblastoma (MB). However, resistance inevitably emerges against these therapies, particularly in the Sonic Hedgehog (SHH) subtype. We found that children with SHH subtype have the worst outcome underscoring the need to identify new therapeutic targets. PROCEDURE: High content screening of a 129 compound library identified agents that inhibited SHH MB growth. Lead molecular target levels, p90 ribosomal S6 kinase (RSK) were characterized by immunoblotting and qRT-PCR. Comparisons were made to human neural stem cells (hNSC). Impact of inhibiting RSK with the small molecule BI-D1870 or siRNA was assessed in growth assays (monolayer, neurosphere, and soft agar). NanoString was used to detect RSK in a cohort of 66 patients with MB. To determine BI-D1870 pharmacokinetics/pharmacodynamics, 100 mg/kg was I.P. injected into mice and tissues were collected at various time points. RESULTS: Daoy, ONS76, UW228, and UW426 MB cells were exquisitely sensitive to BI-D1870 but unresponsive to SHH inhibitors. Anti-tumor growth corresponded with inactivation of RSK in MB cells. BI-D1870 had no effect on hNSCs. Inhibiting RSK with siRNA or BI-D1870 suppressed growth, induced apoptosis, and sensitized cells to SHH agents. Notably, RSK expression is correlated with SHH patients. In mice, BI-D1870 was well-tolerated and crossed the blood-brain barrier (BBB). CONCLUSIONS: RSK inhibitors are promising because they target RSK which is correlated with SHH patients as well as cause high levels of apoptosis to only MB cells. Importantly, BI-D1870 crosses the BBB, acting as a scaffold for development of more long-lived RSK inhibitors.
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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) | 0.000 | 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