Identification of Key microRNAs As Predictive Biomarkers of Nilotinib Response in Chronic Myeloid Leukemia: A Sub-Analysis of the Enestxtnd Clinical Trial
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".