Abstract B83: Combination therapy with the histone deacetylase inhibitor panobinostat and the CD20-targeting antibody rituximab in diffuse large B-cell lymphoma.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».