Bone Marrow Stromal Cells Induce Bortezomib Resistance Partly through a Transcriptome Modulation in Multiple Myeloma Cells
Notice bibliographique
Résumé
Abstract INTRODUCTION It has been suggested that environment-mediated drug resistance (EMDR) might account for the intrinsic (de novo) resistance to therapy in some myeloma patients developing in early stages of the disease and contribute to acquired drug resistance in the course of treatment. Understanding the mechanisms exploited by bone marroe stromal cells (BMSCs) to generate such resistance has long been sought. Furthermore, knowledge of the role that changes in expression of various genes could play is scanty. In this study we explored how BMSCs could induce resistance to bortezomib and whether it would change the expression of an array of genes in MM cells in the presence of bortezomib. METHODS Using patient-derived BMSCs and a normal immortalized human BMSC line (HS-5), we set up a model in which several human myeloma cell lines (HMCLs), RPMI-8226, U266, MM1.S, OPM2 and H929, were co-cultured with stromal cells in the presence or absence of bortezomib. Drug cytotoxicity in HS-5 context was performed using XTT assay. Transwell system, Paraformaldehyde (PFA) fixation of BMSCs and treatment of BMSCs with Brefeldin-A (BFA, an inhibitor of intracellular protein transport) were used as controls to delineate the direct or indirect roles of BMSCs. Percentage of apoptotic cells was determined with anti-CD138/APC, annexin-V/FITC and PI staining in FACS. For gene expression array, co-cultures were applied to magnetic cell separation (EasySep, Stem Cell Technologies), total RNA was isolated from MM cell pellets and synthesized cDNAs were applied to real time PCR gene expression arrays. After normalization to 4 different housekeeping genes, fold changes in gene expression were analyzed in co-cultures compared to MM cell monocultures in the presence of bortezomib using the 2-ΔΔCtalgorithm. RESULTS In the XTT cytotoxicity assay, all tested HMCLs displayed a higher level of viability in the presence of stroma than in stroma-free conditions (average bortezomib IC50s: stroma 22nM vs no stroma 8.53nM, p<0.02). Moreover, FACS analysis for all HMCLs showed that the percentage of bortezomib-induced apoptosis in stroma context was significantly lower than that in stroma-free conditions (p<0.05). Notably, in BFA and PFA treatment settings the level of bortezomib-induced apoptosis was lower (but not significant) than that in Transwell system suggesting that both soluble factors secreted by BMSCs and direct adhesion contribute to stroma-induced resistance to bortezomib. Gene expression analyses in real time PCR showed that the induced resistance was associated with downregulation of some genes involved in cell cycle and proliferation control (CDKN2A, CDC6, CDK2, EGR1, PTEN), downregulation of some genes involved in p53 signaling pathway (PUMA, P73, P53, MDM2, NOXA, BAX) and downregulation of some genes involved in apoptosis (CASP3, PARP). CONCLUSION Our findings suggest that BMSCs induce resistance against cytotoxic and apoptotic effects of bortezomib in MM cells. To this end, in our tested HMCLs, the gene expression patterns may imply involvement of multiple pathways in controlling stroma-induced resistance to bortezomib. Although gene expression results were verified real time PCR readouts, changes in genes may not always correlate with protein changes. Hence, our study has given us the rationale to proceed with more in depth exploration as to whether BMSCs may render MM cells resistant to bortezomib by negative modulation of p53 signaling pathway controlling apoptosis and cell cycle. Disclosures No relevant conflicts of interest to declare.
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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) | 0,002 | 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 ».