A predictive model of IDH mutation probability in gliomas

IDHWebApp

 

 

Predicting the likelihood of an isocitrate dehydrogenase 1 or 2 mutation in diagnoses of infiltrative glioma

Li Chen, Zoya Voronovich, Kenneth Clark, Isaac Hands, Jonathan Mannas, Meggen Walsh, Marina N. Nikiforova, Eric B. Durbin, Heidi Weiss and Craig Horbinski

Author Affiliations:
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.)

Corresponding author:
Craig Horbinski, M.D., Ph.D.
307 Combs Building
Department of Pathology and Laboratory Medicine
University of Kentucky
Lexington, KY 40536
Email: craig.horbinski@uky.edu

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.