Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models
Abstract Background The proportion of residual leukemic blasts after chemotherapy assessed by multiparameter flow cytometry, is an important prognostic factor for the risk of relapse and overall survival in acute myeloid leukemia (AML). This measurable residual disease (MRD) is used in clinical tria...
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| Format: | Article |
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Nature Portfolio
2024-12-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-024-00700-x |
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| author | Tim R. Mocking Angèle Kelder Tom Reuvekamp Lok Lam Ngai Philip Rutten Patrycja Gradowska Arjan A. van de Loosdrecht Jacqueline Cloos Costa Bachas |
| author_facet | Tim R. Mocking Angèle Kelder Tom Reuvekamp Lok Lam Ngai Philip Rutten Patrycja Gradowska Arjan A. van de Loosdrecht Jacqueline Cloos Costa Bachas |
| author_sort | Tim R. Mocking |
| collection | DOAJ |
| description | Abstract Background The proportion of residual leukemic blasts after chemotherapy assessed by multiparameter flow cytometry, is an important prognostic factor for the risk of relapse and overall survival in acute myeloid leukemia (AML). This measurable residual disease (MRD) is used in clinical trials to stratify patients for more or less intensive consolidation therapy. However, an objective and reproducible analysis method to assess MRD status from flow cytometry data is lacking, yet is highly anticipated for broader implementation of MRD testing. Methods We propose a computational pipeline based on Gaussian mixture modeling that allows a fully automated assessment of MRD status while remaining completely interpretable for clinical diagnostic experts. Our pipeline requires limited training data, which makes it easily transferable to other medical centers and cytometry platforms. Results We identify all healthy and leukemic immature myeloid cells in with high concordance (Spearman’s Rho = 0.974) and classification performance (median F-score = 0.861) compared to manual analysis. Using control samples (n = 18), we calculate a computational MRD percentage with high concordance to expert gating (Spearman’s rho = 0.823) and predict MRD status in a cohort of 35 AML follow-up measurements with high accuracy (97%). Conclusions We demonstrate that our pipeline provides a powerful tool for fast (~3 s) and objective automated MRD assessment in AML. |
| format | Article |
| id | doaj-art-7f4b7758c1cb4b5f80f3860d2a55c52e |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-7f4b7758c1cb4b5f80f3860d2a55c52e2025-01-05T12:44:10ZengNature PortfolioCommunications Medicine2730-664X2024-12-01411910.1038/s43856-024-00700-xComputational assessment of measurable residual disease in acute myeloid leukemia using mixture modelsTim R. Mocking0Angèle Kelder1Tom Reuvekamp2Lok Lam Ngai3Philip Rutten4Patrycja Gradowska5Arjan A. van de Loosdrecht6Jacqueline Cloos7Costa Bachas8Department of Hematology, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Hematology, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Hematology, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Hematology, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Hematology, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Hematology, Erasmus MC Cancer InstituteDepartment of Hematology, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Hematology, Amsterdam UMC, Vrije Universiteit AmsterdamDepartment of Hematology, Amsterdam UMC, Vrije Universiteit AmsterdamAbstract Background The proportion of residual leukemic blasts after chemotherapy assessed by multiparameter flow cytometry, is an important prognostic factor for the risk of relapse and overall survival in acute myeloid leukemia (AML). This measurable residual disease (MRD) is used in clinical trials to stratify patients for more or less intensive consolidation therapy. However, an objective and reproducible analysis method to assess MRD status from flow cytometry data is lacking, yet is highly anticipated for broader implementation of MRD testing. Methods We propose a computational pipeline based on Gaussian mixture modeling that allows a fully automated assessment of MRD status while remaining completely interpretable for clinical diagnostic experts. Our pipeline requires limited training data, which makes it easily transferable to other medical centers and cytometry platforms. Results We identify all healthy and leukemic immature myeloid cells in with high concordance (Spearman’s Rho = 0.974) and classification performance (median F-score = 0.861) compared to manual analysis. Using control samples (n = 18), we calculate a computational MRD percentage with high concordance to expert gating (Spearman’s rho = 0.823) and predict MRD status in a cohort of 35 AML follow-up measurements with high accuracy (97%). Conclusions We demonstrate that our pipeline provides a powerful tool for fast (~3 s) and objective automated MRD assessment in AML.https://doi.org/10.1038/s43856-024-00700-x |
| spellingShingle | Tim R. Mocking Angèle Kelder Tom Reuvekamp Lok Lam Ngai Philip Rutten Patrycja Gradowska Arjan A. van de Loosdrecht Jacqueline Cloos Costa Bachas Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models Communications Medicine |
| title | Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models |
| title_full | Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models |
| title_fullStr | Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models |
| title_full_unstemmed | Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models |
| title_short | Computational assessment of measurable residual disease in acute myeloid leukemia using mixture models |
| title_sort | computational assessment of measurable residual disease in acute myeloid leukemia using mixture models |
| url | https://doi.org/10.1038/s43856-024-00700-x |
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