Inhibition of the MiR-185-PAK6-Mediated Survival and Metabolic Pathways Selectively Targets Drug-Resistant CML Stem/Progenitor Cells
Notice bibliographique
Résumé
Overcoming drug resistance and targeting leukemic stem cells (LSCs) remain major challenges for curative treatment of human leukemia, including chronic myeloid leukemia (CML). Indeed, most patients with CML require life-long therapy with ABL1 tyrosine kinase inhibitors (TKIs), due to the persistence of residual LSCs that maintain the potential for relapse. Increasing evidence also indicates that LSCs are susceptible to cellular metabolic changes and seem to have a greater dependence on mitochondrial oxidative phosphorylation (OXPHOS) for survival. Previously, through global transcriptome profiling, we identified a key microRNA (miRNA), miR-185 as a predictive biomarker and it is also required for CML LSC survival. Its expression was significantly reduced in CD34+treatment-naïve CML cells and predictive of therapy response. Conversely, restored expression of miR-185 by lentiviral transduction in CD34+TKI-nonresponder cells significantly impaired survival of these cells, sensitizing them to TKIs in vitroand in pre-clinical xenotransplantation models, indicating that miR-185 acts as a tumor suppressor and is critical in regulating TKI response/resistance of CML stem/progenitor cells. PAK6, a serine/threonine-protein kinase, was identified as a target gene of miR-185; it is upregulated in CD34+TKI-nonresponder cells vs. TKI-responders, correlating with reduced miR-185 expression. To further investigate the molecular and biological roles of the miR-185-PAK6 axisin the regulation of survival of drug-resistant cells, including LSCs, we performed RNA-seq and gene set enrichment analysis (GSEA) in the same CD34+patient cells where miR-185 and PAK6 were identified as being differentially expressed. Interestingly, this analysis has now identified a significant gene set enrichment of OXPHOS, reactive oxygen species (ROS), and adipogenesis pathways in CD34+CML cells compared to healthy CD34+cells (Normalized Enrichment Scores (NES): 2.44, 1.65 and 1.8). Moreover, these changes were significantly higher in TKI-nonresponder cells than in TKI-responders (NES: 1.73, 0.49 and 0.34). We have thus hypothesized that the miR-185-PAK6 axis may contribute to the perturbation of specific metabolic pathways in TKI-nonresponder LSC/progenitor cells and confer therapy-resistance to these cells. Indeed, a pre-clinically validated pan-PAK inhibitor (PF-3758309) alone, or in combination with a TKI, greatly reduced mitochondrial activity in TKI-nonresponder cells, in MitoTracker analysis, an effect that was not seen in the same cells treated with a TKI. Notably, ROS production was also significantly reduced in these cells treated with PF-3758309 and further reduction was observed with a combination of PF-3758309 and TKIs. Notably, PF-3758309 significantly reduced the growth of IM-resistant cell lines (IC50 25-70 nM) and CD34+TKI-nonresponder cells, as assessed by viability and colony-forming cell assays, and increased their apoptosis; these effects were greatly enhanced by TKIs (2-fold, P<0.05). These results were further confirmed in TKI-resistant cells using a lentiviral CRISPR/Cas9 knockout system that specifically targets PAK6. In addition, specific molecular changes associated with PF-3758309 treatment were also investigated using the PharmacoDB database and PharmacoGx R-package. Several candidates were identified, including growth factor independent 1B transcriptional repressor (GFI1B), a myeloid-enriched transcription factor. Its expression was reduced by PF-3758309 treatment and significantly increased in CML compared to healthy controls (>2-fold). Interestingly, expression of GFI1B is further upregulated in CD34+TKI-nonresponders compared to responders. Taken together, these findings indicate that dual targeting of miR-185-PAK6-mediated survival and metabolic pathways, along with BCR-ABL, selectively eradicates therapy-resistant LSC/progenitors, providing a valuable therapeutic strategy for improved treatment and care. 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 | 0,001 |
| 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 ».