Phase 2 study of panobinostat with or without rituximab in relapsed diffuse large B-cell lymphoma
Bibliographic record
Abstract
The majority of diffuse large B-cell lymphoma (DLBCL) tumors contain mutations in histone-modifying enzymes (HMEs), indicating a potential therapeutic benefit of histone deacetylase inhibitors (HDIs), and preclinical data suggest that HDIs augment the effect of rituximab. In this randomized phase 2 study, we evaluated the response rate and toxicity of panobinostat, a pan-HDI administered 30 mg orally 3 times weekly, with or without rituximab, in 40 patients with relapsed or refractory de novo (n = 27) or transformed (n = 13) DLBCL. Candidate genes and whole exomes were sequenced in relapse tumor biopsies to search for molecular correlates, and these data were used to quantify circulating tumor DNA (ctDNA) in serial plasma samples. Eleven of 40 patients (28%) responded to panobinostat (95% confidence interval [CI] 14.6-43.9) and rituximab did not increase responses. The median duration of response was 14.5 months (95% CI 9.4 to "not reached"). At time of data censoring, 6 of 11 patients had not progressed. Of the genes tested for mutations, only those in MEF2B were significantly associated with response. We detected ctDNA in at least 1 plasma sample from 96% of tested patients. A significant increase in ctDNA at day 15 relative to baseline was strongly associated with lack of response (sensitivity 71.4%, specificity 100%). We conclude that panobinostat induces very durable responses in some patients with relapsed DLBCL, and early responses can be predicted by mutations in MEF2B or a significant change in ctDNA level at 15 days after treatment initiation. This clinical trial was registered at www.ClinicalTrials.gov (#NCT01238692).
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".