Abstract B83: Combination therapy with the histone deacetylase inhibitor panobinostat and the CD20-targeting antibody rituximab in diffuse large B-cell lymphoma.
Bibliographic record
Abstract
Abstract Diffuse large B-cell lymphoma (DLBCL) is the most common sub-type of non-Hodgkin's lymphoma and although potentially curable with combination chemotherapy (CHOP-R), remains a therapeutic challenge in patients who do not respond. Recent work has demonstrated frequent mutations in histone modifying enzymes in DLBCL, particularly in histone acetyl transferase genes. We therefore, speculated that perturbations of epigenetic marks play a driving role in some of these cancers and that treatment with a histone deacetylase inhibitor (HDACi) might normalize acetylation levels and thus, alleviate the oncogenic potential of targeted cells. Several clinical trials have shown that HDACis as single agents are only effective in a minority of patients. We therefore, sought to find combination treatment partners that would lead to enhanced anti-cancer effects. Herein, we describe our results investigating combination therapy with the HDACi panobinostat (LBH-589; LBH) and the CD20 targeting antibody rituximab in DLBCL. Six DLBCL cell lines were treated with LBH and/or rituximab in vitro for 48 hours at concentrations tolerable in humans. Cell death and cell viability were measured by assay of DNA content by propidium iodide stain using flow cytometry, and manual cell counting using Trypan blue, respectively. No complement or immune effector cells were added to ensure that only direct signaling effects of rituximab binding to CD20 were measured. CD20 cell surface expression was measured by flow cytometry. A CD20-negative multiple myeloma cell line was included as a negative control. Synergy of combination treatment was calculated using CalcuSyn software. Protein expression was assessed by western blot. In four out of six DLBCL cell lines, we see a synergistic increase in cell death with the combination of LBH and rituximab. The effect of rituximab alone on cell death is negligible in all but one cell line, indicating that rituximab signaling on its own is not sufficient to induce cell death. In contrast, rituximab does sensitize four out of six cell lines to LBH-induced cell death. Although previously reported, we did not find that HDACi treatment increased cell surface expression of CD20, but rather CD20 expression remained unchanged or was reduced. Despite this, synergy between LBH and rituximab is still observed. No synergy was seen in a CD20-negative multiple myeloma cell line, suggesting that CD20 expression is necessary to transduce the signal from rituximab that sensitizes to HDACi treatment. Very preliminary data on potential down-stream targets of combination treatment shows a repression of BCL6 protein expression by LBH treatment alone and that this is potentiated by the addition of rituximab. Here, we present data showing synergistic effects on cell death in four out of six DLBCL cell lines with the combination of LBH and rituximab. The anti-cancer effect of combination therapy appears to depend on the presence of CD20 cell surface expression, but does not require an LBH-induced increase in CD20. We believe the combination of an HDACi with rituximab is a therapeutic strategy that merits further exploration in clinical trials. The work presented here suggests that there may be a synergistic effect despite decreased expression of CD20 which could be useful knowledge for investigators planning to use CD20 expression as a bio-marker. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr B83.
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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) | 0.001 | 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 | 0.001 |
| 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".