Li Chen1, Zoya Voronovich1, Kenneth Clark1, Isaac Hands1, Jonathan Mannas1, Meggen Walsh1, Marina N Nikiforova1, Eric B Durbin1, Heidi Weiss1, Craig Horbinski1. 1. Biostatistics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, Kentucky (L.C., H.W.); Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, Kentucky (L.C., H.W.); Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania (Z.V., K.C., M.N.N.); Cancer Research Informatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, Kentucky (I.H., E.B.D.); Department of Neurosurgery, University of Kentucky, Lexington, Kentucky (J.M.); Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, Kentucky (M.W.); Division of Biomedical Informatics, Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, Kentucky (E.B.D.).
Abstract
BACKGROUND: Several variables are associated with the likelihood of isocitrate dehydrogenase 1 or 2 (IDH1/2) mutation in gliomas, though no guidelines yet exist for when testing is warranted, especially when an R132H IDH1 immunostain is negative. METHODS: A cohort of 89 patients was used to build IDH1/2 mutation prediction models in World Health Organization grades II-IV gliomas, and an external cohort of 100 patients was used for validation. Logistic regression and backward model selection with the Akaike information criterion were used to develop prediction models. RESULTS: A multivariable model, incorporating patient age, glioblastoma multiforme diagnosis, and prior history of grade II or III glioma, was developed to predict IDH1/2 mutation probability. This model generated an area under the curve (AUC) of 0.934 (95% CI: 0.878, 0.978) in the external validation cohort and 0.941 (95% CI: 0.918, 0.962) in the cohort of The Cancer Genome Atlas. When R132H IDH1 immunostain information was added, AUC increased to 0.986 (95% CI: 0.967, 0.998). This model had an AUC of 0.947 (95% CI: 0.891, 0.995) in predicting whether an R132H IDH1 immunonegative case harbored a less common IDH1 or IDH2 mutation. The models were also 94% accurate in predicting IDH1/2 mutation status in gliomas from The Cancer Genome Atlas. An interactive web-based application for calculating the probability of an IDH1/2 mutation is now available using these models. CONCLUSIONS: We have integrated multiple variables to generate a probability of an IDH1/2 mutation. The associated web-based application can help triage diffuse gliomas that would benefit from mutation testing in both clinical and research settings.
BACKGROUND: Several variables are associated with the likelihood of isocitrate dehydrogenase 1 or 2 (IDH1/2) mutation in gliomas, though no guidelines yet exist for when testing is warranted, especially when an R132H IDH1 immunostain is negative. METHODS: A cohort of 89 patients was used to build IDH1/2 mutation prediction models in World Health Organization grades II-IV gliomas, and an external cohort of 100 patients was used for validation. Logistic regression and backward model selection with the Akaike information criterion were used to develop prediction models. RESULTS: A multivariable model, incorporating patient age, glioblastoma multiforme diagnosis, and prior history of grade II or III glioma, was developed to predict IDH1/2 mutation probability. This model generated an area under the curve (AUC) of 0.934 (95% CI: 0.878, 0.978) in the external validation cohort and 0.941 (95% CI: 0.918, 0.962) in the cohort of The Cancer Genome Atlas. When R132H IDH1 immunostain information was added, AUC increased to 0.986 (95% CI: 0.967, 0.998). This model had an AUC of 0.947 (95% CI: 0.891, 0.995) in predicting whether an R132H IDH1 immunonegative case harbored a less common IDH1 or IDH2 mutation. The models were also 94% accurate in predicting IDH1/2 mutation status in gliomas from The Cancer Genome Atlas. An interactive web-based application for calculating the probability of an IDH1/2 mutation is now available using these models. CONCLUSIONS: We have integrated multiple variables to generate a probability of an IDH1/2 mutation. The associated web-based application can help triage diffuse gliomas that would benefit from mutation testing in both clinical and research settings.
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