MB-05 * PHARMACOLOGICAL INHIBITION OF MRK/ZAK KINASE FOR THE TREATMENT OF MEDULLOBLASTOMA
Bibliographic record
Abstract
Medulloblastoma is a cerebellar tumor and the most common pediatric brain malignancy. Radiation therapy is part of the standard care for this tumor, but its effectiveness is accompanied by significant neurocognitive sequelae due to the deleterious effects of radiation on the developing brain. We have previously shown that the protein kinase MRK/ZAK protects tumor cells from radiation-induced cell death by regulating cell cycle arrest after ionizing radiation. Here, we show that siRNA-mediated MRK depletion sensitizes medulloblastoma primary cells to radiation. We have, therefore, designed and tested a small molecule inhibitor of MRK, M443, which binds to MRK in an irreversible fashion and inhibits its activity. We found that M443 strongly radio-sensitizes UW228 medulloblastoma cells as well as IMB226 patient-derived primary cells. M443 also inhibits radiation-induced activation of both p38 and Chk2, two proteins that act downstream of MRK and are involved in DNA damage-induced cell cycle arrest. We also tested the effect of M443 in an animal model of medulloblastoma that employs orthotopic implantation of IMB226 medulloblastoma cells in nude mice. Intra-tumoral delivery of M443 alone significantly extended animal survival by 5 days compared to vehicle treatment. Combination treatment of M443 with radiation at 2 x 3 Gy, that is not effective on its own (1 day of additional survival over control), achieved a synergistic increase in survival (15 days over the control survival time). Western blotting of tumor lysates demonstrated strong inhibition of MRK activity. In conclusion, we have developed a new small molecule inhibitor of MRK/ZAK that radio-sensitizes medulloblastoma cells. We hypothesize that combining radio-therapy with M443 will allow us to lower the radiation dose while maintaining therapeutic efficacy, thereby minimizing radiation-induced side effects.
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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) | 0.003 | 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".