Identification of Key microRNAs As Predictive Biomarkers of Nilotinib Response in Chronic Myeloid Leukemia: A Sub-Analysis of the Enestxtnd Clinical Trial
Notice bibliographique
Résumé
Despite the effectiveness of tyrosine kinase inhibitors (TKIs) against chronic myeloid leukemia (CML), acquired drug resistance often leads to relapse. CML has unique microRNA (miRNA) expression profiles at different disease stages and in response to TKI-treatment. However, the predictive utility of miRNAs is not yet conclusive and there is a need to identify key biomarkers for prognostic tools to be used in the clinic. We have recently generated global transcriptome profiles on treatment-naïve CD34+ CML cells with known subsequent imatinib (IM) responses and identified several differentially expressed miRNAs, including miR-185 and miR-145, in IM-nonresponders as compared to IM-responders. We also reported that IM response could be predicted in treatment-naïve CD34+ cells by an in vitro colony forming cell (CFC) assay. In this study, we evaluated miRNA expression changes in CD34+ CML cells pre- and post-nilotinib (NL) therapy from 58 CML patients enrolled in the Canadian sub-analysis of the ENESTxtnd phase IIIb clinical trial and assessed a potential correlation between miRNA expression and sensitivity of CD34+ cells to TKIs in CFC assays, to predict NL response in these patients. CFC assays were performed by plating pre-treated CD34+ cells with methylcellulose-medium ± Imatinib (IM), NL and Dasatinib (DA) and showed that only NL was able to differentiate responses between NL-responders and NL-nonresponders (p=0.011) and predict NL response (p=0.00016) based on calculated cutoffs. Microfluidic qRT-PCR and a univariate Cox proportional hazard (CoxPH) analysis on miRNA expression profiles were then performed in CD34+cells obtained at diagnosis (BL), 1-month (M1) and 3-month post-NL treatment (M3) from 58 CML patients; this showed that 17 out of 47 miRNAs examined were significantly associated with NL response (p<0.05). Further Welch t-test analysis revealed that nine of these miRNAs were differentially expressed between NL-responders and NL-nonresponders. These miRNA candidates were then subject to a multivariate CoxPH analysis ± CFC assay data from each patient, which demonstrated that miR-145 (p=0.013) and miR-708 (p=0.009) together could improve predictive power at the BL state. Additionally, receiver-operating-characteristic (ROC) and precision-recall (PR) area-under-curve (AUC) values (1.2-fold) of performance plots generated by random forest (RF) and Naïve-Bayes (NB) machine learning algorithms were increased in this multivariate panel compared to individual miRNA variables. At M1 and M3, four miRNAs were found to be stably associated with NL response individually and a combination of miR-150 (M1 p<0.001, M3 p=0.01) and miR-185 (M1 p=0.009, M3 p=0.01) was significantly associated with treatment response in multivariate CoxPH analysis. Again, the multivariate analysis improved ROC and PR AUC values, especially for miR-185 (1.2 to 2-fold). Most interestingly, incorporation of NL-CFC output consistently increased AUC values up to 2-fold at both BL and M1/M3 time points that enhanced predictive performance. Together, these findings offer two predictive models for NL response in treatment-naïve or post-treatment CML patients, which could be developed into prognostic biomarkers. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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 ».