Targeting Sonic Hedgehog-Associated Medulloblastoma through Inhibition of Aurora and Polo-like Kinases
Bibliographic record
Abstract
Medulloblastoma is the most common malignant brain tumor in children. Although aggressive surgery, radiation, and chemotherapy have improved outcomes, survivors suffer severe long-term side effects, and many patients still succumb to their disease. For patients whose tumors are driven by mutations in the sonic hedgehog (SHH) pathway, SHH antagonists offer some hope. However, many SHH-associated medulloblastomas do not respond to these drugs, and those that do may develop resistance. Therefore, more effective treatment strategies are needed for both SHH and non-SHH-associated medulloblastoma. One such strategy involves targeting the cells that are critical for maintaining tumor growth, known as tumor-propagating cells (TPC). We previously identified a population of TPCs in tumors from patched mutant mice, a model for SHH-dependent medulloblastoma. These cells express the surface antigen CD15/SSEA-1 and have elevated levels of genes associated with the G2-M phases of the cell cycle. Here, we show that CD15(+) cells progress more rapidly through the cell cycle than CD15(-) cells and contain an increased proportion of cells in G2-M, suggesting that they might be vulnerable to inhibitors of this phase. Indeed, exposure of tumor cells to inhibitors of Aurora kinase (Aurk) and Polo-like kinases (Plk), key regulators of G2-M, induces cell-cycle arrest, apoptosis, and enhanced sensitivity to conventional chemotherapy. Moreover, treatment of tumor-bearing mice with these agents significantly inhibits tumor progression. Importantly, cells from human patient-derived medulloblastoma xenografts are also sensitive to Aurk and Plk inhibitors. Our findings suggest that targeting G2-M regulators may represent a novel approach for treatment of human medulloblastoma.
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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".