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Identification of multivariable microRNA and clinical biomarker panels to predict imatinib response in chronic
Andrew Wu1,2, Ryan Yen1,2, Sarah Grasedieck3
1Terry Fox Laboratory, British Columbia Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada.
Abstract:
Imatinib Mesylate (imatinib) was once hailed as the magic bullet for chronic myeloid leukemia (CML) and remains a front-line therapy for CML to this day alongside other tyrosine kinase inhibitors (TKIs). However, TKI treatments are rarely curative and patients are often required to receive life-long treatment or otherwise risk relapse. Thus, there is a growing interest in identifying biomarkers in patients which can predict TKI response upon diagnosis. In this study, we analyze clinical data and differentially expressed miRNAs in CD34+ CML cells from 80 patients at diagnosis who were later classified as imatinib-responders or imatinib-nonresponders. A Cox Proportional Hazard (CoxPH) analysis identified 16 miRNAs that were associated with imatinib nonresponse and differentially expressed in these patients. We also trained a machine learning model with different combinations of the 16 miRNAs with and without clinical parameters and identified a panel with high predictive performance based on area-under-curve values of receiver-operating-characteristic and precision-recall curves. Interestingly, the multivariable panel consisting of both miRNAs and clinical features performed better than either miRNA or clinical panels alone. Thus, our findings may inform future studies on predictive biomarkers and serve as a tool to develop more optimized treatment plans for CML patients in the clinic.
Insights
Identifying specific microRNAs (miRNAs) in chronic myeloid leukemia (CML) patients can predict response to imatinib treatment. This discovery could lead to personalized treatment plans for CML patients.
Area of Science:
- Hematology
- Molecular Biology
- Oncology
Background:
- Imatinib Mesylate (imatinib) is a frontline therapy for chronic myeloid leukemia (CML), but treatments are rarely curative, necessitating lifelong therapy or risking relapse.
- Predictive biomarkers for tyrosine kinase inhibitor (TKI) response are crucial for optimizing CML treatment strategies.
Purpose of the Study:
- To identify differentially expressed microRNAs (miRNAs) in CD34+ CML cells that predict imatinib nonresponse.
- To develop a predictive model for imatinib response using a combination of miRNAs and clinical features.
Main Methods:
- Analysis of clinical data and miRNA expression in CD34+ CML cells from 80 patients at diagnosis.
- Cox Proportional Hazard (CoxPH) analysis to identify miRNAs associated with imatinib nonresponse.
- Machine learning models trained with combinations of miRNAs and clinical parameters to assess predictive performance.
Main Results:
- 16 differentially expressed miRNAs associated with imatinib nonresponse were identified.
- A multivariable predictive panel combining miRNAs and clinical features demonstrated high performance, outperforming panels using either miRNAs or clinical features alone.
- Receiver-operating-characteristic and precision-recall curves were used to evaluate model performance.
Conclusions:
- Specific miRNAs in CD34+ CML cells can serve as predictive biomarkers for imatinib response.
- A combined panel of miRNAs and clinical features offers a promising tool for optimizing CML treatment strategies.
- These findings support the development of personalized treatment plans for CML patients based on predictive biomarkers.
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