Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry
Standardization, data mining techniques, and comparison to normality are changing the landscape of multiparameter flow cytometry in clinical hematology. On the basis of these principles, a strategy was developed for measurable residual disease (MRD) assessment. Herein, suspicious cell clusters are f...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/10/5/317 |
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| author | Hugues Jacqmin Bernard Chatelain Quentin Louveaux Philippe Jacqmin Jean-Michel Dogné Carlos Graux François Mullier |
| author_facet | Hugues Jacqmin Bernard Chatelain Quentin Louveaux Philippe Jacqmin Jean-Michel Dogné Carlos Graux François Mullier |
| author_sort | Hugues Jacqmin |
| collection | DOAJ |
| description | Standardization, data mining techniques, and comparison to normality are changing the landscape of multiparameter flow cytometry in clinical hematology. On the basis of these principles, a strategy was developed for measurable residual disease (MRD) assessment. Herein, suspicious cell clusters are first identified at diagnosis using a clustering algorithm. Subsequently, automated multidimensional spaces, named “Clouds”, are created around these clusters on the basis of density calculations. This step identifies the immunophenotypic pattern of the suspicious cell clusters. Thereafter, using reference samples, the “Abnormality Ratio” (AR) of each Cloud is calculated, and major malignant Clouds are retained, known as “Leukemic Clouds” (L-Clouds). In follow-up samples, MRD is identified when more cells fall into a patient’s L-Cloud compared to reference samples (AR concept). This workflow was applied on simulated data and real-life leukemia flow cytometry data. On simulated data, strong patient-dependent positive correlation (<i>R</i><sup>2</sup> = 1) was observed between the AR and spiked-in leukemia cells. On real patient data, AR kinetics was in line with the clinical evolution for five out of six patients. In conclusion, we present a convenient flow cytometry data analysis approach for the follow-up of hematological malignancies. Further evaluation and validation on more patient samples and different flow cytometry panels is required before implementation in clinical practice. |
| first_indexed | 2024-03-10T19:45:29Z |
| format | Article |
| id | doaj.art-2ae255b74dab4b13bcb9b85aa99812dd |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T19:45:29Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-2ae255b74dab4b13bcb9b85aa99812dd2023-11-20T00:48:09ZengMDPI AGDiagnostics2075-44182020-05-0110531710.3390/diagnostics10050317Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow CytometryHugues Jacqmin0Bernard Chatelain1Quentin Louveaux2Philippe Jacqmin3Jean-Michel Dogné4Carlos Graux5François Mullier6Hematology Laboratory, NAmur Research Institute for LIfe Sciences (NARILIS), Namur Thrombosis and Hemostasis Center (NTHC), CHU UCL Namur, Université catholique de Louvain, 5530 Yvoir, BelgiumHematology Laboratory, NAmur Research Institute for LIfe Sciences (NARILIS), Namur Thrombosis and Hemostasis Center (NTHC), CHU UCL Namur, Université catholique de Louvain, 5530 Yvoir, BelgiumMontefiore Institute, University of Liege, 4000 Liège, BelgiumMnS–Modelling and Simulation, 5500 Dinant, BelgiumPharmacy Department, University of Namur, 5000 Namur, BelgiumDepartment of Hematology, Namur Research Institute for Life Sciences (NARILIS), Namur Thrombosis and Hemostasis Center (NTHC), CHU UCL Namur, Université catholique de Louvain, 5530 Yvoir, BelgiumHematology Laboratory, NAmur Research Institute for LIfe Sciences (NARILIS), Namur Thrombosis and Hemostasis Center (NTHC), CHU UCL Namur, Université catholique de Louvain, 5530 Yvoir, BelgiumStandardization, data mining techniques, and comparison to normality are changing the landscape of multiparameter flow cytometry in clinical hematology. On the basis of these principles, a strategy was developed for measurable residual disease (MRD) assessment. Herein, suspicious cell clusters are first identified at diagnosis using a clustering algorithm. Subsequently, automated multidimensional spaces, named “Clouds”, are created around these clusters on the basis of density calculations. This step identifies the immunophenotypic pattern of the suspicious cell clusters. Thereafter, using reference samples, the “Abnormality Ratio” (AR) of each Cloud is calculated, and major malignant Clouds are retained, known as “Leukemic Clouds” (L-Clouds). In follow-up samples, MRD is identified when more cells fall into a patient’s L-Cloud compared to reference samples (AR concept). This workflow was applied on simulated data and real-life leukemia flow cytometry data. On simulated data, strong patient-dependent positive correlation (<i>R</i><sup>2</sup> = 1) was observed between the AR and spiked-in leukemia cells. On real patient data, AR kinetics was in line with the clinical evolution for five out of six patients. In conclusion, we present a convenient flow cytometry data analysis approach for the follow-up of hematological malignancies. Further evaluation and validation on more patient samples and different flow cytometry panels is required before implementation in clinical practice.https://www.mdpi.com/2075-4418/10/5/317acute myeloid leukemia (AML)flow cytometrymultiparametric data analysisclusteringkernel density estimationpersonalized medicine |
| spellingShingle | Hugues Jacqmin Bernard Chatelain Quentin Louveaux Philippe Jacqmin Jean-Michel Dogné Carlos Graux François Mullier Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry Diagnostics acute myeloid leukemia (AML) flow cytometry multiparametric data analysis clustering kernel density estimation personalized medicine |
| title | Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry |
| title_full | Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry |
| title_fullStr | Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry |
| title_full_unstemmed | Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry |
| title_short | Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry |
| title_sort | clustering and kernel density estimation for assessment of measurable residual disease by flow cytometry |
| topic | acute myeloid leukemia (AML) flow cytometry multiparametric data analysis clustering kernel density estimation personalized medicine |
| url | https://www.mdpi.com/2075-4418/10/5/317 |
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