Rhabdoid Tumors Are Sensitive to the Protein-Translation Inhibitor Homoharringtonine
Bibliographic record
Abstract
Abstract Purpose: Rhabdoid tumors are devastating pediatric cancers in need of improved therapies. We sought to identify small molecules that exhibit in vitro and in vivo efficacy against preclinical models of rhabdoid tumor. Experimental Design: We screened eight rhabdoid tumor cell lines with 481 small molecules and compared their sensitivity with that of 879 other cancer cell lines. Genome-scale CRISPR–Cas9 inactivation screens in rhabdoid tumors were analyzed to confirm target vulnerabilities. Gene expression and CRISPR–Cas9 data were queried across cell lines and primary rhabdoid tumors to discover biomarkers of small-molecule sensitivity. Molecular correlates were validated by manipulating gene expression. Subcutaneous rhabdoid tumor xenografts were treated with the most effective drug to confirm in vitro results. Results: Small-molecule screening identified the protein-translation inhibitor homoharringtonine (HHT), an FDA-approved treatment for chronic myelogenous leukemia (CML), as the sole drug to which all rhabdoid tumor cell lines were selectively sensitive. Validation studies confirmed the sensitivity of rhabdoid tumor to HHT was comparable with that of CML cell lines. Low expression of the antiapoptotic gene BCL2L1, which encodes Bcl-XL, was the strongest predictor of HHT sensitivity, and HHT treatment consistently depleted Mcl-1, the synthetic-lethal antiapoptotic partner of Bcl-XL. Rhabdoid tumor cell lines and primary-tumor samples expressed low BCL2L1, and overexpression of BCL2L1 induced resistance to HHT in rhabdoid tumor cells. Furthermore, HHT treatment inhibited rhabdoid tumor cell line and patient-derived xenograft growth in vivo. Conclusions: Rhabdoid tumor cell lines and xenografts are highly sensitive to HHT, at least partially due to their low expression of BCL2L1. HHT may have therapeutic potential against rhabdoid tumors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".