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CHRONIC MYELOGENOUS LEUKEMIA

Identification of multivariable microRNA and clinical biomarker panels to predict imatinib response in chronic myeloid leukemia at diagnosis

Leukemia volume 37, pages 2426–2435 (2023)Cite this article

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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.

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Fig. 1: Study design and characterization of patient clinical features between imatinib-responders and imatinib-nonresponders.
Fig. 2: Multivariate analysis and predictive performance of combined clinical features in patients at diagnosis.
Fig. 3: Transcript levels of 16 differentially expressed miRNAs univariately associated with imatinib response between imatinib-responders and imatinib-nonresponders.
Fig. 4: Multivariate analysis and predictive performance of combined miRNAs in patients at diagnosis.
Fig. 5: Multivariate analysis and predictive performance of combined clinical and miRNA features in patients at diagnosis.

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Data availability

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

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Acknowledgements

The authors thank the Stem Cell Assay Laboratory staff for processing patient samples, Josephine Leung and Kyi Min Saw for excellent technical assistance, members of the Leukemia/Bone Marrow Transplant Program of British Columbia and the Hematology Cell Bank of British Columbia for patient samples, the Terry Fox Laboratory FACS Facility for cell sorting and STEMCELL Technologies for culture reagents.

Funding

This work was supported by the Canadian Cancer Society, the Leukemia & Lymphoma Society of Canada, the Canadian Institutes of Health Research (CIHR) and the Collings Stevens Chronic Leukemia Research Fund (XJ). RY and HL both received Four-Year Fellowships from UBC and CIHR Frederick Banting and Charles Best Canada Graduate Scholarships; SG is supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation – project 446251518); AW received MITACS Accelerate Fellowship.

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Authors and Affiliations

  1. Terry Fox Laboratory, British Columbia Cancer Research Institute, University of British Columbia, Vancouver, BC, Canada

    Andrew Wu, Ryan Yen, Hanyang Lin, Helen Nakamoto, Connie J. Eaves & Xiaoyan Jiang

  2. Department of Medicine, University of British Columbia, Vancouver, BC, Canada

    Andrew Wu, Ryan Yen, Donna L. Forrest, Connie J. Eaves & Xiaoyan Jiang

  3. Michael Smith Laboratories, Dept of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada

    Sarah Grasedieck

  4. Leukemia/Bone Marrow Transplant Program of British Columbia, University of British Columbia, Vancouver, BC, Canada

    Donna L. Forrest

  5. Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada

    Connie J. Eaves & Xiaoyan Jiang

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Contributions

DLF, CE and XJ developed the concept and designed the experiments; AW and RY performed data analyses and statistical and bioinformatics analyses; SG provided expertise in complex statistical and data analyses and insightful discussions; HL, HN, AW performed qRT-PCR, CFC experiments and data analyses; DLF provided the clinical data and insightful discussions; AW, RY, SG, and XJ wrote the manuscript and all authors commented on it.

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Correspondence to Xiaoyan Jiang.

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Research funding: Bristol-Myers Squibb Canada (DLF, CE and XJ). Other authors declare no conflicts of interests.

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Wu, A., Yen, R., Grasedieck, S. et al. Identification of multivariable microRNA and clinical biomarker panels to predict imatinib response in chronic myeloid leukemia at diagnosis. Leukemia 37, 2426–2435 (2023). https://doi.org/10.1038/s41375-023-02062-0

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