Pyrvinium Targets CD133 in Human Glioblastoma Brain Tumor–Initiating Cells
Bibliographic record
Abstract
PURPOSE: Clonal evolution of cancer may be regulated by determinants of stemness, specifically self-renewal, and current therapies have not considered how genetic perturbations or properties of stemness affect such functional processes. Glioblastoma-initiating cells (GICs), identified by expression of the cell surface marker CD133, are shown to be chemoradioresistant. In the current study, we sought to elucidate the functional role of CD133 in self-renewal and identify compounds that can specifically target this CD133(+) treatment-refractory population. EXPERIMENTAL DESIGN: Using gain/loss-of-function studies for CD133 we assessed the in vitro self-renewal and in vivo tumor formation capabilities of patient-derived glioblastoma cells. We generated a CD133 signature combined with an in silico screen to find compounds that target GICs. Self-renewal and proliferation assays on CD133-sorted samples were performed to identify the preferential action of hit compounds. In vivo efficacy of the lead compound pyrvinium was assessed in intracranial GIC xenografts and survival studies. Lastly, microarray analysis was performed on pyrvinium-treated GICs to discover core signaling events involved. RESULTS: We discovered pyrvinium, a small-molecule inhibitor of GIC self-renewal in vitro and in vivo, in part through inhibition of Wnt/β-catenin signaling and other essential stem cell regulatory pathways. We provide a therapeutically tractable strategy to target self-renewing, chemoradioresistant, and functionally important CD133(+) stem cells that drive glioblastoma relapse and mortality. CONCLUSIONS: Our study provides an integrated approach for the eradication of clonal populations responsible for cancer progression, and may apply to other aggressive and heterogeneous cancers.
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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 | 0.000 |
| 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".