Restricting datasets to classifiable samples allows prediction of immune checkpoint blockade-related hepatitis

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 problem that complicates biomarker discovery and interpretation – namely, unequal dispersion of immune disease biomarker expression between clinical subgroups 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. In advanced melanoma, restriction finds biomarkers of immune-related adverse event (irAE) risk after immunotherapy and enables us to build multivariate models that accurately predict immunotherapy-related hepatitis. The correct classification rate was significantly enhanced (73.3%) after restriction in contrast to the baseline models (56.7%). 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.

Publication History

Article published online:
20 January 2025