Developmental phosphoproteomics identifies the kinase CK2 as a driver of Hedgehog signaling and a therapeutic target in medulloblastoma
Bibliographic record
Abstract
A major limitation of targeted cancer therapy is the rapid emergence of drug resistance, which often arises through mutations at or downstream of the drug target or through intrinsic resistance of subpopulations of tumor cells. Medulloblastoma (MB), the most common pediatric brain tumor, is no exception, and MBs that are driven by sonic hedgehog (SHH) signaling are particularly aggressive and drug-resistant. To find new drug targets and therapeutics for MB that may be less susceptible to common resistance mechanisms, we used a developmental phosphoproteomics approach in murine granule neuron precursors (GNPs), the developmental cell of origin of MB. The protein kinase CK2 emerged as a driver of hundreds of phosphorylation events during the proliferative, MB-like stage of GNP growth, including the phosphorylation of three of the eight proteins commonly amplified in MB. CK2 was critical to the stabilization and activity of the transcription factor GLI2, a late downstream effector in SHH signaling. CK2 inhibitors decreased the viability of primary SHH-type MB patient cells in culture and blocked the growth of murine MB tumors that were resistant to currently available Hh inhibitors, thereby extending the survival of tumor-bearing mice. Because of structural interactions, one CK2 inhibitor (CX-4945) inhibited both wild-type and mutant CK2, indicating that this drug may avoid at least one common mode of acquired resistance. These findings suggest that CK2 inhibitors may be effective for treating patients with MB and show how phosphoproteomics may be used to gain insight into developmental biology and pathology.
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".