Restricting datasets to classifiable samples augments discovery of immune disease biomarkers

Gunther Glehr, Paloma Riquelme, Katharina Kronenberg, Robert Lohmayer, Víctor J. López-Madrona, Michael Kapinsky, Hans J. Schlitt, Edward K. Geissler, Rainer Spang, Sebastian Haferkamp and James A. Hutchinson ()
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Gunther Glehr: University Hospital Regensburg
Paloma Riquelme: University Hospital Regensburg
Katharina Kronenberg: University Hospital Regensburg
Robert Lohmayer: Leibniz Institute for Immunotherapy
Víctor J. López-Madrona: Inst Neurosci Syst
Michael Kapinsky: Beckman Coulter Life Sciences GmbH
Hans J. Schlitt: University Hospital Regensburg
Edward K. Geissler: University Hospital Regensburg
Rainer Spang: University of Regensburg
Sebastian Haferkamp: University Hospital Regensburg
James A. Hutchinson: University Hospital Regensburg

Nature Communications, 2024, vol. 15, issue 1, 1-21

Abstract: Abstract Immunological diseases are typically heterogeneous in clinical presentation, severity and response to therapy. Biomarkers of immune diseases often reflect this variability, especially compared to their regulated behaviour in health. This leads to a common difficulty that frustrates biomarker discovery and interpretation – namely, unequal dispersion of immune disease biomarker expression between patient classes necessarily limits a biomarker’s informative range. To solve this problem, we introduce dataset restriction, a procedure that splits datasets into classifiable and unclassifiable samples. Applied to synthetic flow cytometry data, restriction identifies biomarkers that are otherwise disregarded. In advanced melanoma, restriction finds biomarkers of immune-related adverse event risk after immunotherapy and enables us to build multivariate models that accurately predict immunotherapy-related hepatitis. Hence, dataset restriction augments discovery of immune disease biomarkers, increases predictive certainty for classifiable samples and improves multivariate models incorporating biomarkers with a limited informative range. This principle can be directly extended to any classification task.

Date: 2024
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DOI: 10.1038/s41467-024-49094-3

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