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Updated: May 8, 2026

Anti-Nuclear Antibody Screening Using HEp-2 Cells
Published on: June 23, 2014
Speckled antinuclear antibody pattern as a potential predictor for immune-related adverse events in cancer patients
Hiroki Kohno1, Masaaki Yanai2,3, Miyu Nishigami2
1Division of Respiratory Medicine and Rheumatology, Department of Multidisciplinary Internal Medicine, Faculty of Medicine, Tottori University, Yonago, Japan. hkohno@tottori-u.ac.jp.
Background:
Immune checkpoint inhibitors (ICIs) improve cancer survival but can induce immune-related adverse events (irAEs). Pre-existing autoantibodies, particularly antinuclear antibodies (ANAs), have been suggested as predictive biomarkers for irAE development. However, prior studies have yielded mixed results. Furthermore, the predictive value of specific ANA staining patterns has remained unexplored. We aimed to investigate the relationship between baseline autoantibody levels and irAE development.
Methods:
This retrospective observational study included 626 patients with cancer receiving ICI therapy between November 2020 and August 2024 at the Tottori University Hospital. irAEs were graded using Common Terminology Criteria for Adverse Events v5.0, with grade ≥ 2 considered clinically significant. Logistic regression analysis assessed the association between baseline factors and irAE development. Receiver operating characteristic (ROC) curve analysis evaluated model performance.
Results:
Elevated ANA titers (≥ ×40), speckled ANA staining pattern, and combination ICI therapy were significantly associated with an increased risk of grade ≥ 2 irAEs in both univariable and multivariable analyses. The predictive performance of the area under the ROC curve for the multivariable models using either elevated ANA titers or the speckled pattern was similar (approximately 0.617).
Conclusions:
Baseline ANA titer and speckled ANA pattern are potential predictive biomarkers for grade ≥ 2 irAEs, particularly in patients receiving combination ICI therapy. Comprehensive ANA testing may improve risk stratification and inform personalized irAE management.
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