PURPOSE: To develop a tear molecule level-based predictive model based on a panel of tear cytokines and their correlation with clinical features in ocular chronic graft versus host disease (cGVHD). METHODS: Twenty-two ocular cGVHD patients and 21 healthy subjects were evaluated in a controlled environmental research laboratory (CERLab). Clinical parameters were recorded, and tears were collected. Levels of 15 molecules (epidermal growth factor [EGF], IL receptor antagonist [IL-1Ra], IL-1β, IL-2, IL-6, IL-8/CXCL8, IL-10, IL-12p70, IL-17A, interferon inducible protein [IP]-10/CXCL10, IFN-γ, VEGF, TNF-α, eotaxin 1, and regulated on activation normal T cell expressed and secreted [RANTES]) were measured by multiplex-bead assay and correlated with clinical parameters. Logistic regression was used to develop a predictive model. Leave-one-out cross-validation was applied. Classification capacity was evaluated in a cohort of individuals with dry eye (DE) of other etiologies different from GVHD. RESULTS: Epidermal growth factor and IP-10/CXCL10 levels were significantly decreased in ocular cGVHD, positively correlating with tear production and stability and negatively correlating with symptoms, hyperemia, and vital staining. Interleukin-1Ra, IL-8/CXCL8, and IL-10 were significantly increased in ocular cGVHD, and the first two correlated positively with symptoms, hyperemia, and ocular surface integrity while negatively correlating with tear production and stability. Predictive models were generated, and the best panel was based on IL-8/CXCL8 and IP-10/CXCL10 tear levels along with age and sex, with an area under the receiving operating curve of 0.9004, sensitivity of 86.36%, and specificity of 95.24%. CONCLUSIONS: A predictive model based on tear levels of IL-8/CXCL8 and IP-10/CXCL10 resulted in optimal sensitivity and specificity. These results add further knowledge to the search for potential biomarkers in this devastating ocular inflammatory disease.
PURPOSE: To develop a tear molecule level-based predictive model based on a panel of tear cytokines and their correlation with clinical features in ocular chronic graft versus host disease (cGVHD). METHODS: Twenty-two ocular cGVHD patients and 21 healthy subjects were evaluated in a controlled environmental research laboratory (CERLab). Clinical parameters were recorded, and tears were collected. Levels of 15 molecules (epidermal growth factor [EGF], IL receptor antagonist [IL-1Ra], IL-1β, IL-2, IL-6, IL-8/CXCL8, IL-10, IL-12p70, IL-17A, interferon inducible protein [IP]-10/CXCL10, IFN-γ, VEGF, TNF-α, eotaxin 1, and regulated on activation normal T cell expressed and secreted [RANTES]) were measured by multiplex-bead assay and correlated with clinical parameters. Logistic regression was used to develop a predictive model. Leave-one-out cross-validation was applied. Classification capacity was evaluated in a cohort of individuals with dry eye (DE) of other etiologies different from GVHD. RESULTS: Epidermal growth factor and IP-10/CXCL10 levels were significantly decreased in ocular cGVHD, positively correlating with tear production and stability and negatively correlating with symptoms, hyperemia, and vital staining. Interleukin-1Ra, IL-8/CXCL8, and IL-10 were significantly increased in ocular cGVHD, and the first two correlated positively with symptoms, hyperemia, and ocular surface integrity while negatively correlating with tear production and stability. Predictive models were generated, and the best panel was based on IL-8/CXCL8 and IP-10/CXCL10 tear levels along with age and sex, with an area under the receiving operating curve of 0.9004, sensitivity of 86.36%, and specificity of 95.24%. CONCLUSIONS: A predictive model based on tear levels of IL-8/CXCL8 and IP-10/CXCL10 resulted in optimal sensitivity and specificity. These results add further knowledge to the search for potential biomarkers in this devastating ocular inflammatory disease.
Authors: D Wolff; H Greinix; S J Lee; T Gooley; S Paczesny; S Pavletic; F Hakim; F Malard; M Jagasia; A Lawitschka; J A Hansen; D Pulanic; E Holler; A Dickinson; E Weissinger; M Edinger; S Sarantopoulos; K R Schultz Journal: Bone Marrow Transplant Date: 2018-01-24 Impact factor: 5.483
Authors: David Miklos; Corey S Cutler; Mukta Arora; Edmund K Waller; Madan Jagasia; Iskra Pusic; Mary E Flowers; Aaron C Logan; Ryotaro Nakamura; Bruce R Blazar; Yunfeng Li; Stephen Chang; Indu Lal; Jason Dubovsky; Danelle F James; Lori Styles; Samantha Jaglowski Journal: Blood Date: 2017-09-18 Impact factor: 22.113