Small interfering RNA library screen of human kinases and phosphatases identifies polo-like kinase 1 as a promising new target for the treatment of pediatric rhabdomyosarcomas
Bibliographic record
Abstract
Rhabdomyosarcoma, consisting of alveolar (aRMS) and embryonal (eRMS) subtypes, is the most common type of sarcoma in children. Currently, there are no targeted drug therapies available for rhabdomyosarcoma. In searching for new molecular therapeutic targets, we carried out genome-wide small interfering RNA (siRNA) library screens targeting human phosphatases (n = 206) and kinases (n = 691) initially against an aRMS cell line, RH30. Sixteen phosphatases and 50 kinases were identified based on growth inhibition after 72 hours. Inhibiting polo-like kinase 1 (PLK1) had the most remarkable impact on growth inhibition (approximately 80%) and apoptosis on all three rhabdomyosarcoma cell lines tested, namely, RH30, CW9019 (aRMS), and RD (eRMS), whereas there was no effect on normal muscle cells. The loss of PLK1 expression and subsequent growth inhibition correlated with decreased p-CDC25C and Cyclin B1. Increased expression of WEE 1 was also noted. The induction of apoptosis after PLK1 silencing was confirmed by increased p-H2AX, propidium iodide uptake, and chromatin condensation, as well as caspase-3 and poly(ADP-ribose) polymerase cleavage. Pediatric Ewing's sarcoma (TC-32), neuroblastoma (IMR32 and KCNR), and glioblastoma (SF188) models were also highly sensitive to PLK1 inhibition. Finally, based on cDNA microarray analyses, PLK1 mRNA was overexpressed (>1.5 fold) in 10 of 10 rhabdomyosarcoma cell lines and in 47% and 51% of primary aRMS (17 of 36 samples) and eRMS (21 of 41 samples) tumors, respectively, compared with normal muscles. Similarly, pediatric Ewing's sarcoma, neuroblastoma, and osteosarcoma tumors expressed high PLK1. We conclude that PLK1 could be a promising therapeutic target for the treatment of a wide range of pediatric solid tumors including rhabdomyosarcoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".