Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic Leukaemia
Flow cytometry technique (FC) is a standard diagnostic tool for diagnostics of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) assessing the immunophenotype of blast cells. BCP-ALL is often associated with underlying genetic aberrations, that have evidenced prognostic significance and can im...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/9/2281 |
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| author | Jan Kulis Łukasz Wawrowski Łukasz Sędek Łukasz Wróbel Łukasz Słota Vincent H. J. van der Velden Tomasz Szczepański Marek Sikora |
| author_facet | Jan Kulis Łukasz Wawrowski Łukasz Sędek Łukasz Wróbel Łukasz Słota Vincent H. J. van der Velden Tomasz Szczepański Marek Sikora |
| author_sort | Jan Kulis |
| collection | DOAJ |
| description | Flow cytometry technique (FC) is a standard diagnostic tool for diagnostics of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) assessing the immunophenotype of blast cells. BCP-ALL is often associated with underlying genetic aberrations, that have evidenced prognostic significance and can impact the disease outcome. Since the determination of patient prognosis is already important at the initial phase of BCP-ALL diagnostics, we aimed to reveal specific genetic aberrations by finding specific multiple antigen expression patterns with FC immunophenotyping. The FC immunophenotype data were analysed using machine learning methods (gradient boosting, decision trees, classification rules). The obtained results were verified with the use of repeated cross-validation. The t(12;21)/ETV6-RUNX1 aberration occurs more often when blasts present high expression of CD10, CD38, low CD34, CD45 and specific low expression of CD81. The t(v;11q23)/KMT2A is associated with positive NG2 expression and low CD10, CD34, TdT and CD24. Hyperdiploidy is associated with CD123, CD66c and CD34 expression on blast cells. In turn, high expression of CD81, low expression of CD45, CD22 and lack of CD123 and NG2 indicates that none of the studied aberrations is present. Detecting aberrations in pediatric BCP-ALL, based on the expression of multiple markers, can be done with decent efficiency. |
| first_indexed | 2024-03-10T04:02:48Z |
| format | Article |
| id | doaj.art-cf6eff7a3d2e446db652df2ec173f61f |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T04:02:48Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-cf6eff7a3d2e446db652df2ec173f61f2023-11-23T08:30:05ZengMDPI AGJournal of Clinical Medicine2077-03832022-04-01119228110.3390/jcm11092281Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic LeukaemiaJan Kulis0Łukasz Wawrowski1Łukasz Sędek2Łukasz Wróbel3Łukasz Słota4Vincent H. J. van der Velden5Tomasz Szczepański6Marek Sikora7Department of Pediatric Hematology and Oncology, Medical University of Silesia in Katowice, ul. 3 Maja 13-15, 41-800 Zabrze, PolandŁukasiewicz Research Network—Institute of Innovative Technologies EMAG, 40-189 Katowice, PolandDepartment of Microbiology and Immunology, Medical University of Silesia in Katowice, ul. Jordana 19, 41-808 Zabrze, PolandDepartment of Computer Networks and Systems, Silesian University of Technology, 44-100 Gliwice, PolandDepartment of Pediatric Hematology and Oncology, Medical University of Silesia in Katowice, ul. 3 Maja 13-15, 41-800 Zabrze, PolandDepartment of Immunology, Erasmus MC, University Medical Center Rotterdam, 3015 GD Rotterdam, The NetherlandsDepartment of Pediatric Hematology and Oncology, Medical University of Silesia in Katowice, ul. 3 Maja 13-15, 41-800 Zabrze, PolandDepartment of Computer Networks and Systems, Silesian University of Technology, 44-100 Gliwice, PolandFlow cytometry technique (FC) is a standard diagnostic tool for diagnostics of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) assessing the immunophenotype of blast cells. BCP-ALL is often associated with underlying genetic aberrations, that have evidenced prognostic significance and can impact the disease outcome. Since the determination of patient prognosis is already important at the initial phase of BCP-ALL diagnostics, we aimed to reveal specific genetic aberrations by finding specific multiple antigen expression patterns with FC immunophenotyping. The FC immunophenotype data were analysed using machine learning methods (gradient boosting, decision trees, classification rules). The obtained results were verified with the use of repeated cross-validation. The t(12;21)/ETV6-RUNX1 aberration occurs more often when blasts present high expression of CD10, CD38, low CD34, CD45 and specific low expression of CD81. The t(v;11q23)/KMT2A is associated with positive NG2 expression and low CD10, CD34, TdT and CD24. Hyperdiploidy is associated with CD123, CD66c and CD34 expression on blast cells. In turn, high expression of CD81, low expression of CD45, CD22 and lack of CD123 and NG2 indicates that none of the studied aberrations is present. Detecting aberrations in pediatric BCP-ALL, based on the expression of multiple markers, can be done with decent efficiency.https://www.mdpi.com/2077-0383/11/9/2281acute lymphoblastic leukaemiaflow cytometrycytogeneticsmachine learningknowledge discoveryclassification |
| spellingShingle | Jan Kulis Łukasz Wawrowski Łukasz Sędek Łukasz Wróbel Łukasz Słota Vincent H. J. van der Velden Tomasz Szczepański Marek Sikora Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic Leukaemia Journal of Clinical Medicine acute lymphoblastic leukaemia flow cytometry cytogenetics machine learning knowledge discovery classification |
| title | Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic Leukaemia |
| title_full | Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic Leukaemia |
| title_fullStr | Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic Leukaemia |
| title_full_unstemmed | Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic Leukaemia |
| title_short | Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell Precursor Acute Lymphoblastic Leukaemia |
| title_sort | machine learning based analysis of relations between antigen expression and genetic aberrations in childhood b cell precursor acute lymphoblastic leukaemia |
| topic | acute lymphoblastic leukaemia flow cytometry cytogenetics machine learning knowledge discovery classification |
| url | https://www.mdpi.com/2077-0383/11/9/2281 |
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