Selective targeting of neuroblastoma tumour‐initiating cells by compounds identified in stem cell‐based small molecule screens
Bibliographic record
Abstract
Neuroblastoma (NB) is the most deadly extra-cranial solid tumour in children necessitating an urgent need for effective and less toxic treatments. One reason for the lack of efficacious treatments may be the inability of existing drugs to target the tumour-initiating or cancer stem cell population responsible for sustaining tumour growth, metastases and relapse. Here, we describe a strategy to identify compounds that selectively target patient-derived cancer stem cell-like tumour-initiating cells (TICs) while sparing normal paediatric stem cells (skin-derived precursors, SKPs) and characterize two therapeutic candidates. DECA-14 and rapamycin were identified as NB TIC-selective agents. Both compounds induced TIC death at nanomolar concentrations in vitro, significantly reduced NB xenograft tumour weight in vivo, and dramatically decreased self-renewal or tumour-initiation capacity in treated tumours. These results demonstrate that differential drug sensitivities between TICs and normal paediatric stem cells can be exploited to identify novel, patient-specific and potentially less toxic therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".