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Tear Proteomic Predictive Biomarker Model for Ocular Graft Versus Host Disease Classification.
Olivia E O'Leary1, Andreas Schoetzau1, Ludovic Amruthalingam2
1Department of Ophthalmology, University Hospital Basel, University of Basel, Basel, Switzerland.
Translational Vision Science & Technology
|September 4, 2020
Summary
Tear protein biomarkers can predict ocular graft-versus-host disease (oGVHD) severity. This study identifies a 13-marker panel in mild oGVHD to forecast progression, aiding early diagnosis and management of this condition.
Area of Science:
- Ophthalmology
- Immunology
- Proteomics
Background:
- Diagnosis of ocular graft-versus-host disease (oGVHD) lacks validated biomarkers.
- Predicting oGVHD severity is crucial for patient management.
- Tear protein expression may offer insights into ocular surface inflammation in oGVHD.
Purpose of the Study:
- To predict oGVHD severity using tear protein expression in patients with mild oGVHD.
- To identify potential tear protein biomarkers for early oGVHD detection.
- To correlate tear proteomic profiles with oGVHD clinical phenotypes.
Main Methods:
- Cross-sectional observational study of 49 patients with and without oGVHD post-allogeneic hematopoietic stem cell transplantation (AHCT).
- Tear protein analysis using liquid chromatography-tandem mass spectrometry.
- Classification and prediction models (Random Forest, penalized logistic regression) developed to stratify oGVHD severity (NIH grades 0-3).
Main Results:
- Mass spectrometry identified 785 proteins in tear samples.
- Random forest models achieved high classification accuracy for different oGVHD grades (F1-scores 0.74-0.95).
- A 13-marker panel demonstrated significant diagnostic accuracy in identifying moderate-to-severe oGVHD.
Conclusions:
- Tear protein biomarkers are effective in classifying oGVHD severity.
- Ocular surface inflammation is a key factor in oGVHD clinical presentation.
- A 13-marker tear protein panel can predict the development of severe oGVHD phenotypes in AHCT patients.
