Small Molecule Kinase Inhibitor Screen Identifies Polo-Like Kinase 1 as a Target for Neuroblastoma Tumor-Initiating Cells
Bibliographic record
Abstract
Neuroblastoma (NB) is an often fatal pediatric tumor of neural crest origin. We previously isolated NB tumor-initiating cells (NB TIC) from bone marrow metastases that resemble cancer stem cells and form metastatic NB in immunodeficient animals with as few as ten cells. To identify signaling pathways important for the survival and self-renewal of NB TICs and potential therapeutic targets, we screened a small molecule library of 143 protein kinase inhibitors, including 33 in clinical trials. Cytostatic or cytotoxic drugs were identified that targeted PI3K (phosphoinositide 3-kinase)/Akt, PKC (protein kinase C), Aurora, ErbB2, Trk, and Polo-like kinase 1 (PLK1). Treatment with PLK1 siRNA or low nanomolar concentrations of BI 2536 or BI 6727, PLK1 inhibitors in clinical trials for adult malignancies, were cytotoxic to TICs whereas only micromolar concentrations of the inhibitors were cytotoxic for normal pediatric neural stem cells. Furthermore, BI 2536 significantly inhibited TIC tumor growth in a therapeutic xenograft model, both as a single agent and in combination with irinotecan, an active agent for relapsed NB. Our findings identify candidate kinases that regulate TIC growth and survival and suggest that PLK1 inhibitors are an attractive candidate therapy for metastatic NB.
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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) | 0.001 | 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) | 0.002 | 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".