High-throughput screening identifies the activity of histone deacetylase inhibitors in patient-derived models of soft tissue sarcoma
Bibliographic record
Abstract
Background Undifferentiated pleomorphic sarcoma (UPS) is a rare and aggressive soft tissue sarcoma with limited treatment options and a poor prognosis. As a complex karyotype tumor, UPS lacks recurrent targetable mutations, and response rates to standard first-line doxorubicin therapy are low. Phenotypic drug screening offers an alternative approach to identify new therapeutic targets without requiring prior knowledge of molecular mechanisms.Methods A library of FDA-approved compounds and a custom histone deacetylase (HDAC) inhibitor library were screened using well-annotated patient-derived cell lines. Hit compounds were further characterized using apoptosis assays and in vivo xenograft studies. Biomarkers of activity were evaluated using gene expression and western blot analyses. Synergy with doxorubicin was evaluated in combination assays.Results HDAC inhibitors emerged as a promising therapeutic class, demonstrating low IC50 values across cell lines (14.8−26.89 nM), with quisinostat taken forward for further evaluation. Gene expression changes in EPAS1, FOXO1, AMOT, and FOSL1 were observed as potential biomarkers of activity. Combination assays revealed synergy between quisinostat and doxorubicin (average ZIP score: 1.02−15.65; ZIPmax: 3.98−33.71), increasing apoptotic cell death in vitro. In vivo, quisinostat alone and in combination with doxorubicin significantly reduced the tumor volume (vehicle 160.0 ± 63.2 mm3, doxorubicin 78.0 ± 35.2 mm3, quisinostat 84.3 ± 13.1 mm3, and combination 49.2 ± 10.2 mm3). Quisinostat also showed potent activity in leiomyosarcoma (LMS) cell lines (5.82−31.32 nM), which represent an additional complex karyotype soft tissue sarcoma.Conclusions Quisinostat demonstrated strong preclinical activity and synergy with standard-of-care doxorubicin in models of UPS and LMS.
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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 | 0.001 |
| 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".