miRNA Expression in Multiple Myeloma as Predictive Model of Response to Bortezomib.
Notice bibliographique
Résumé
Abstract Abstract 4918 Background Bortezomib therapeutic efficacy is well established in multiple myeloma (MM) however response to this therapy remains difficult to predict with resistant disease observed in nearly 20% of MM patients. Through DNA microarrays, predictive models of response to stem cell transplant and Bortezomib were reported correlating mRNA expression data with disease outcomes and response to therapy. MicroRNAs (miRNAs) are a key class of small, non-coding RNA molecules that modulate post-transcriptional regulation of gene expression and were recently described to be involved in deregulation of gene expression in many cancers including MM. Little evidence however is available concerning the role of miRNA expression in the prediction of response to Bortezomib in MM. We aimed to assess the expression of miRNAs in a panel of Bortezomib highly sensitive and relatively resistant MM cell lines as well as primary MM cells and identify miRNA expression patterns that are associated with response to Bortezomib. Methods We have used miRNA microarrays (Affymetrix miRNA GeneChip) as well as liquid phase Luminex microbead miRNA profiling (Flexmir, Luminex) to profile miRNA expression in MM cell lines (MM1S, KMS11, INA6, U266) and sorted CD138+ bone marrow PCs from MM patients prior to treatment with Bortezomib (n=5; 3 sensitive and 2 resistant) and PCs from a healthy normal donor (n=1). The MM cell lines included in this analysis were classified as sensitive (S) or resistant (R) based on their Bortezomib IC50 at 48 hours (IC50 for MM1S and KMS11 ∼ 5 nM versus INA6 and U266 ∼ 20nM). For the microarray studies the hybridization signal values for the multiple probes for each miRNA were obtained and normalized with the use of miRNA QC tool (Affymetrix) and analyzed using Partek Genomics Suite software. Thereafter, filters were applied to identify the miRNA probes whose normalized signal were at least 2 folds differentially expressed between sensitive (MM1S) and resistant (INA6) cell lines with a P value < 0.05 (ANOVA) and a FDR of 0.05. Bortezomib sensitive (n=3) and resistant (n=2) primary MM samples were subjected to the same miRNA array analysis and filtering. Liquid phase Luminex microbead miRNA profiling (FlexmiR) was used for the confirmation (MM1S and INA6) and validation of the array results in other MM cell lines KMS11 (IC50 5nM) and U266 (IC50 20nM). Results Using Affymetrix miRNA GeneChip we identified 22 differentially expressed miRNA with overexpression of miR-155, miR-342-3p, miR-181a and b, miR-128, miR-20b and downregulation miR-let-7b, miR-let-7i, miR-let-7d, miR-let-7c, miR-222, miR-221, miR-23a, miR-27a and miR-29a in bortezomib relatively resistant (INA6) versus sensitive (MM1S) cell line. These results were confirmed in INA6 and MM1S cells with the use of Luminex microbead miRNA profiling and validated to be similarly differentially expressed between KMS11 (sensitive) and relatively U266 (resistant) cell lines. Furthermore, TargetScan algorithms and Ingenuity Pathway Analysis software were used to identify predicted miRNAs-targeted mRNAs or potentially regulated networks and included genes involved in cell cycle regulation, cell growth, apoptosis and ubiquitin-conjugation pathways. Lastly to further investigate the clinical relevance of miRNAs in MM in terms of prediction of response and outcome to Bortezomib, we correlated miRNA expression profile of sorted CD138+ bone marrow PCs from Bortezomib sensitive (n=3) and resistant (n=2) MM patients with their response to therapy. Unsupervised analysis of the data revealed that the Bortezomib sensitive MM patients clustered with MM1S cell line while resistant patients segregated into the INA6 cluster. Conclusion In summary, we have described a MM miRNA signature, which includes miRNAs that modulate the expression of proteins critical to myeloma pathogenesis and is predictive of response to Bortezomib. Further validation of this miRNA signature in a larger cohort of Bortezomib-treated MM patients is ongoing. Disclosures Stewart: Glaxo-Smith-Kline: Research Funding.
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 ».