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Machine Learning Based Analysis of Relations between Antigen Expression and Genetic Aberrations in Childhood B-Cell
Jan Kulis1, Łukasz Wawrowski2, Łukasz Sędek3
1Department of Pediatric Hematology and Oncology, Medical University of Silesia in Katowice, ul. 3 Maja 13-15, 41-800 Zabrze, Poland.
Insights
Flow cytometry (FC) can identify genetic aberrations in B-cell precursor acute lymphoblastic leukemia (BCP-ALL) by analyzing blast cell immunophenotype. Machine learning models correlate specific antigen expression patterns with genetic changes, aiding prognosis.
Area of Science:
- Hematology
- Oncology
- Computational Biology
Background:
- Flow cytometry (FC) is crucial for diagnosing B-cell precursor acute lymphoblastic leukemia (BCP-ALL) by assessing blast cell immunophenotype.
- BCP-ALL prognosis is significantly influenced by underlying genetic aberrations.
- Early identification of these aberrations is vital for patient management.
Purpose of the Study:
- To identify specific genetic aberrations in BCP-ALL using FC immunophenotyping.
- To correlate multiple antigen expression patterns with known genetic alterations.
- To leverage machine learning for predicting genetic aberrations from immunophenotype data.
Main Methods:
- Analysis of FC immunophenotype data from BCP-ALL patients.
- Application of machine learning algorithms including gradient boosting, decision trees, and classification rules.
- Validation of results using repeated cross-validation.
Main Results:
- The t(12;21)/ETV6-RUNX1 aberration is associated with high CD10, CD38, low CD34, CD45, and low CD81 expression.
- The t(v;11q23)/KMT2A aberration correlates with positive NG2 and low CD10, CD34, TdT, CD24 expression.
- Hyperdiploidy is linked to CD123, CD66c, and CD34 expression; absence of these markers suggests no studied aberration.
Conclusions:
- Machine learning effectively correlates FC immunophenotype patterns with specific genetic aberrations in BCP-ALL.
- This approach offers a promising method for detecting genetic abnormalities and aiding prognosis in pediatric BCP-ALL.
- FC combined with ML provides a viable strategy for efficient aberration detection based on multi-marker expression.
Abstract:
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.
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