The class‐I HDAC inhibitor MGCD0103 induces apoptosis in Hodgkin lymphoma cell lines and synergizes with proteasome inhibitors by an HDAC6‐independent mechanism
Bibliographic record
Abstract
Inhibition of histone deacetylase 6 (HDAC6)-dependent aggresome function by pan HDAC inhibitors was recently reported to be a key mechanism underlying the synergistic activity between proteasome inhibitors and HDAC inhibitors in a variety of tumour types. Because these combinations induce significant thrombocytopenia in vivo, we examined whether less toxic, isotype-selective HDAC inhibitors may still synergize with proteasome inhibitors, and if so, by what mechanisms. Here, we showed that the class I HDAC inhibitor, MGCD0103, has a potent antiproliferative activity in Hodgkin lymphoma (HL) cell lines. Furthermore, MGCD0103 induced tumour necrosis factor α (TNF-α) expression and secretion, which was associated with nuclear factor (NF)-κB activation. Selective inhibition of TNF-α expression by short interfering mRNA, or inhibition of MGCD0103-induced NF-kB activation by proteasome inhibitors enhanced MGCD0103-induced cell death. Thus, our results demonstrate that MGCD0103 may synergize with proteasome inhibitors by HDAC6-independent mechanisms, providing mechanistic rationale for exploring this potentially less toxic combination for the treatment of lymphoma.
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 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 | 0.001 | 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 | 0.001 |
| 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".