Mir-34a Sensitizes Multiple Myeloma (MM) Cells to the Proteasome Inhibitor Bortezomib
Notice bibliographique
Résumé
Abstract Abstract 138 Background: miRNA are small non-coding RNAs that regulate mRNA at the post-transcriptional level and their deregulation contributes to the initiation and progression of several cancers. In MM, an association between high-risk disease and global elevation of miRNA expression has been reported. In addition, miRNAs involvement in the clonal evolution of plasma cells from MGUS to MM stages and correlation with specific MM molecular subgroups were also described. Therapeutically, overexpression of selected miRNAs are shown to reduce MM cells growth and viability. In the current study, we have investigated the role miRNAs play in MM cells sensitivity to the proteasome inhibitor Bortezomib. Methods and results: Microarray profiling, followed by short stem loop-PCR confirmation, of miRNA signatures of MM cell lines with differential sensitivity to bortezomib (3 fold δ in IC50) identified 24 differentially expressed miRNAs and clustered MM cell lines into 2 distinct groups. In particular, mir-34a had high expression in sensitive (S: MM1S, RPMI8226, NCI-H929 and U266 with an IC50 < 1.25nM), in comparison to relatively resistant cells (R: KMS11, INA6 and OPM2 with an IC50 ∼ 5nM). In all cell lines with low miR-34a expression, epigenetic silencing of miR-34a through its promoter methylation was identified with bisulfite conversion based PCR and treatment with the demethylating agent 5'-Azacytidine did upregulate miR34a expression. Furthermore, and since p53 is reported to activate miR-34a expression, we have correlated miR-34a levels with the TP53 mutational status and del17p.13 (by FISH) in MM cells. Mutations in exons 5 and 8 of TP53 were identified in all cell lines with low miR-34a. In particular del17p.13 by FISH was noted in KMS11 cells, consistent with their very low or undetectable miR-34a. We next sought to determine if restoration of mir-34a levels would increase sensitivity to bortezomib. While lentivirus-mediated stable expression of miR-34a in KMS11 and OPM2 cells had a minimal effect on their viability (∼ 5% increase in Annexin V staining compared to infection with mock lentiviral control), it significantly increased (∼ 20% increase in Annexin V staining) their sensitivity to bortezomib. Using western blot analysis, we confirmed that several predicted targets of miR-34a are altered with a dramatic reduction in Bcl-2, CDK4, CDK6, CEBPα and YY1 in cells infected with lentivirus expressing miR-34a. Lastly, we examined in vivo in SCID mice xenografted with KMS11 cells expressing miR-34a (KMS11-34a) or empty vector (KMS11-EV) the effects of miR-34a on tumor growth and response to bortezomib treatment. The growth kinetics of KMS11-34a xenografts was significantly slower compared to that of KMS11-EV (tumor volumes 1000 mm3 vs 2000 mm3 respectively on day 10 post KMS11 cells implantation) and this effects was even more evident in bortezomib treated mice (tumor volume 3000 mm3 on day 14 in KMS11-EV versus 1200 mm3 in KMS11-34a on day 14). Conclusion: TP53 loss/mutation or epigenetic silencing through promoter methylation reduces miR-34a expression in MM cells. Conversely, restored miR-34a expression downregulates Bcl2, CDK4/6 and YY1 and sensitizes MM cells to bortezomib. These results support a potential therapeutic role for miR-34a in MM. Disclosures: Neri: Celgene: Honoraria, Research Funding. Bahlis:Celgene: Honoraria, Speakers Bureau.
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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 ».