Death by HDAC Inhibition in Synovial Sarcoma Cells
Bibliographic record
Abstract
Abstract Conventional cytotoxic therapies for synovial sarcoma provide limited benefit, and no drugs specifically targeting the causative SS18-SSX fusion oncoprotein are currently available. Histone deacetylase (HDAC) inhibition has been shown in previous studies to disrupt the synovial sarcoma oncoprotein complex, resulting in apoptosis. To understand the molecular effects of HDAC inhibition, RNA-seq transcriptome analysis was undertaken in six human synovial sarcoma cell lines. HDAC inhibition induced pathways of cell-cycle arrest, neuronal differentiation, and response to oxygen-containing species, effects also observed in other cancers treated with this class of drugs. More specific to synovial sarcoma, polycomb group targets were reactivated, including tumor suppressor CDKN2A, and proapoptotic transcriptional patterns were induced. Functional analyses revealed that ROS-mediated FOXO activation and proapoptotic factors BIK, BIM, and BMF were important to apoptosis induction following HDAC inhibition in synovial sarcoma. HDAC inhibitor pathway activation results in apoptosis and decreased tumor burden following a 7-day quisinostat treatment in the Ptenfl/fl;hSS2 mouse model of synovial sarcoma. This study provides mechanistic support for a particular susceptibility of synovial sarcoma to HDAC inhibition as a means of clinical treatment. Mol Cancer Ther; 16(12); 2656–67. ©2017 AACR.
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) | 0.002 | 0.001 |
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".