Cluster analysis of flowcytometric immunophenotyping with extended T cell subsets in suspected immunodeficiency
Luca Seitz1,2, Daniel Gaitan1, Caroline M Berkemeier3
1Immunodeficiency Laboratory, Department of Biomedicine, University Hospital Basel and University of Basel, Basel, Switzerland.
Insights
Cluster analysis of immune variables, including T cell subsets, aids in diagnosing immunodeficiencies. This method improves diagnostic accuracy for patients with suspected immune disorders, supporting clinical decisions.
Area of Science:
- Immunology
- Computational Biology
- Clinical Diagnostics
Background:
- Diagnostic delays in immunodeficiencies lead to increased morbidity.
- Early identification of at-risk patients is crucial.
- Expanded T cell subset analysis is underutilized in initial evaluations.
Purpose of the Study:
- To assess the diagnostic utility of cluster analysis for immune variables in suspected immunodeficiency.
- To determine if clustering immune profiles can improve diagnostic accuracy.
Main Methods:
- Retrospective analysis of 38 immune variables in 107 adult patients.
- Included B cell and T cell subpopulations.
- Employed k-means cluster analysis and hierarchical heatmap visualization.
Main Results:
- Cluster analysis achieved 75% accuracy in classifying patients with immunodeficiency using key variables.
- Hierarchical heatmaps showed segregation of common variable immunodeficiency and combined immunodeficiency patients.
- Analysis of T cell subpopulations was particularly informative.
Conclusions:
- Cluster analysis of immune variables, including detailed T cell flow cytometry, can aid clinical decision-making.
- This approach offers potential for improved diagnosis of immunodeficiency in practice.
- Integrating comprehensive immune profiling supports earlier and more accurate patient management.
Background:
Patients with immunodeficiencies commonly experience diagnostic delays resulting in morbidity. There is an unmet need to identify patients earlier, especially those with high risk for complications. Compared to immunoglobulin quantification and flowcytometric B cell subset analysis, expanded T cell subset analysis is rarely performed in the initial evaluation of patients with suspected immunodeficiency. The simultaneous interpretation of multiple immune variables, including lymphocyte subsets, is challenging.
Objective:
To evaluate the diagnostic value of cluster analyses of immune variables in patients with suspected immunodeficiency.
Methods:
Retrospective analysis of 38 immune system variables, including seven B cell and sixteen T cell subpopulations, in 107 adult patients (73 with immunodeficiency, 34 without) evaluated at a tertiary outpatient immunology clinic. Correlation analyses of individual variables, k-means cluster analysis with evaluation of the classification into "no immunodeficiency" versus "immunodeficiency" and visual analyses of hierarchical heatmaps were performed.
Results:
Binary classification of patients into groups with and without immunodeficiency was correct in 54% of cases with the full data set and increased to 69% and 75% of cases, respectively, when only 16 variables with moderate (p < .05) or 7 variables with strong evidence (p < .01) for a difference between groups were included. In a cluster heatmap with all patients but only moderately differing variables and a heatmap with only immunodeficient patients restricted to T cell variables alone, segregation of most patients with common variable immunodeficiency and combined immunodeficiency was observed.
Conclusion:
Cluster analyses of immune variables, including detailed lymphocyte flowcytometry with T cell subpopulations, may support clinical decision making for suspected immunodeficiency in daily practice.
