The Neurokinin-1 Receptor Is a Target in Pediatric Rhabdoid Tumors
Bibliographic record
Abstract
Rhabdoid tumors (RT) are among the most aggressive tumors in early childhood. Overall survival remains poor, and treatment only effectively occurs at the cost of high toxicity and late adverse effects. It has been reported that the neurokinin-1 receptor/ substance P complex plays an important role in cancer and proved to be a promising target. However, its role in RT has not yet been described. This study aims to determine whether the neurokinin-1 receptor is expressed in RT and whether neurokinin-1 receptor (NK1R) antagonists can serve as a novel therapeutic approach in treating RTs. By in silico analysis using the cBio Cancer Genomics Portal we found that RTs highly express neurokinin-1 receptor. We confirmed these results by RT-PCR in both tumor cell lines and in human tissue samples of various affected organs. We demonstrated a growth inhibitory and apoptotic effect of aprepitant in viability assays and flow cytometry. Furthermore, this effect proved to remain when used in combination with the cytostatic cisplatin. Western blot analysis showed an upregulation of apoptotic signaling pathways in rhabdoid tumors when treated with aprepitant. Overall, our findings suggest that NK1R may be a promising target for the treatment of RT in combination with other anti-cancer therapies and can be targeted with the NK1R antagonist aprepitant.
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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".