Chemical Genomics Reveals JAK STAT Activation As a Mechanism of Resistance to HDAC Inhibitors in B Cell Lymphomas
Notice bibliographique
Résumé
Abstract Background: HDAC inhibitors (HDACi) are being investigated as treatment for relapsed/refractory non Hodgkin lymphoma (NHL) and other cancers. However, the mechanisms underlying sensitivity and resistance to HDAC inhibition in lymphomas have not been fully characterized. We probed the cellular and molecular response to HDACi in vitro and in vivo in order to determine factors that dictate the response to HDACi and to enable design of approaches to incorporate HDACi into novel combination therapeutics. Methods: High-throughput cytotoxicity screening was performed using two different HDAC inhibitors, LBH589 (panobinostat) and SAHA (vorinostat) in 52 lymphoid cell lines characterized through RNA-seq and microarray gene expression profiling. This screen revealed a greater than 50-fold range in concentration needed to induce cytotoxicity for the 2 different HDAC inhibitors and there was moderate correlation between the 2 compounds in this panel (Pearson correlation r = 0.76, p < 0.01). By pairing this chemosensitivity data with gene expression profiles of the screened cell lines, we developed a gene expression classifier for LBH589 that identified resistant and sensitive cell line groups. This predictor was applied to B-cell NHL cell lines tested with LBH589 in the Cancer Cell Line Encyclopedia (CCLE) and we found that the sensitive and resistant cell line groups distinguished by this method differed more than 5-fold in IC50 (0.021 vs. 1.24 nM, P < 0.01 by Wilcoxon rank sum), thus validating the ability of this approach to distinguish HDACi resistant cell lines. We further initiated a clinical trial of LBH589 in relapsed/refractory diffuse large B cell lymphoma patients combined with RNAseq profiling of their tumors prior to embarking on treatment. We treated nine patients with LBH589, and application of our response predictor to scaled RNAseq gene expression data revealed 4 predicted responders and 5 predicted non-responders. Two of the predicted responders had a clinical response to LBH589, whereas none of the predicted non-responders had a clinical response, thus our classifier was able to identify all of the LBH589-responsive patients from this cohort (P = 0.08 by Fisher's exact test). Analysis of differentially expressed molecular pathways in HDACi sensitive and resistant samples by gene set enrichment revealed the JAK-STAT pathway as the most differentially expressed pathway associated with HDACi resistance (at P < 0.001 and FDR < 0.20). We further identified a number of distinct mutations including STAT3, SOCS1 and JAK1 that were associated with activation of the JAK-STAT pathway by gene expression signatures and the LBH589 response signature in DLBCL cell lines and patient samples by analysis of RNA-seq data. Phosphoprotein analysis by Western blot and Sis-inducible-element (SIE) luciferase reporter assays were used to confirm JAK-STAT activation in these samples and we found that overexpression of STAT3 Src-homology domain mutations activated JAK-STAT3 signaling in isogenic cell lines and fostered resistance to LBH589 in vitro. Conversely, using in vivo DLBCL xenograft models, we found that combining JAK-STAT and HDAC inhibition by treatment with LBH589 and ruxolitinib resulted in synergistic reduction of tumor cell viability and tumor growth with tolerable toxicity in mice. Conclusions: Sustained JAK-STAT activation appears to mediate resistance to HDAC inhibition in DLBCL and other NHLs and several recurrent genetic lesions drive JAK-STAT activation in these diseases. This process can be overcome by JAK 1/2 inhibition with ruxolitinib and these findings demonstrate a role for combination therapy with HDAC inhibitors and small molecules targeting the JAK-STAT pathway in lymphoid malignancies. Disclosures No relevant conflicts of interest to declare.
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) | 0,000 | 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».