Romidepsin and mogamulizumab sequential treatment for advanced cutaneous T-cell lymphoma
Bibliographic record
Abstract
Management of advanced stage cutaneous T-cell lymphoma (CTCL) can be challenging due to lack of durable responses to currently available therapies and their side effects and toxicities. Romidepsin, a histone deacetylase inhibitor, and mogamulizumab, an anti-CCR4 monoclonal antibody, have demonstrated some efficacy as monotherapies, however, survival outcomes remain poor. This retrospective study evaluates the effectiveness of sequential romidepsin-mogamulizumab (Romi-Moga) therapy in 18 patients with advanced CTCL. The overall response rate was 67% in our cohort and time to next treatment was 15 months, which compared favorably to clinical trials of monotherapies. Patients who transitioned to mogamulizumab within one month of romidepsin discontinuation exhibited superior responses in skin, blood, and lymph nodes and longer time to next treatment. These findings suggest a potential beneficial effect of Romi-Moga therapy when administered in close sequence. Prospective studies are needed to validate these results and optimize treatment strategies.
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".