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

Neuro-Oncology Neuro-Oncology 16(11), 1478– 1483, 2014 doi:10.1093/neuonc/nou097 Advance Access date 23 May 2014 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 Corresponding Author: Craig Horbinski, MD, PhD, 307 Combs Building, Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY 40536 (). 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. Keywords: glioma, IDH1, IDH2. Mutations in isocitrate dehydrogenase types 1 and 2 (IDH1/2) are present in the majority of grades II and III astrocytomas, oligodendrogliomas, and mixed oligoastrocytomas, as well as about 10% of grade IV glioblastoma multiforme (GBM).1 With rare exceptions, these heterozygous point mutations focus on codon 132 of IDH1 and 172 of IDH2, producing mutant enzymes that reduce a-ketoglutarate to D-2-hydroxyglutarate (D-2-HG).2 Because mutant tumors tend to be far less aggressive than their grade-matched wild-type counterparts, and because the presence of a mutation can help differentiate infiltrative gliomas from nonneoplastic mimickers and noninfiltrative tumors,3 – 5 IDH1/2 testing has become part of the routine workup of any lesion even suspected of being a diffusely infiltrative glioma. Overall, nearly 90% of gliomas that have an IDH1/2 mutation will contain the R132H IDH1 variant, which prompted the development of an R132H IDH1–specific monoclonal antibody that works very well as a rapid, specific, inexpensive screen.6 – 8 But while a positive immunohistochemical result does not need confirmatory sequencing, sometimes testing for less common IDH1 and IDH2 mutations is warranted. This is also relevant for any retrospective brain tumor studies that utilize tissues predating routine clinical IDH1/2 testing, because immunostaining and reflex sequencing of all archived tumors would not be cost-effective. Received 7 November 2013; accepted 23 April 2014 # The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: . 1478 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.) Chen et al.: Predicting IDH1/2 mutations in gliomas Methods Cohorts The original cohort for model development included 123 untreated WHO grades II –IV gliomas, patient age 16+ years, from the University of Pittsburgh Medical Center who had been tested for IDH1 and IDH2 mutations by R132H IDH1 immunohistochemistry (Dianova) and follow-up sequencing. The original validation cohort included 100 untreated WHO grades II – IV gliomas, age 16+ years, from the University of Kentucky Markey Cancer Center who had also been tested for IDH1/2 mutations using the same approach. The variables initially considered for predicting IDH1/2 mutations included patient age, tumor location, GBM diagnosis, prior history of grade II or III glioma, and R132H IDH1 immunohistochemistry results. Patients with complete data on these variables were used for the development and validation of the prediction models. Institutional review boards from the University of Pittsburgh and the University of Kentucky approved this study prior to intramural case collections. Data from The Cancer Genome Atlas (TCGA) on grades II – IV gliomas were downloaded through the University of California, Santa Cruz Cancer Browser at https://genome-cancer.ucsc.edu.9 Statistical Methods We first considered using patient age and clinical characteristics, including GBM status, tumor location, and prior history of grade II or III glioma to predict the likelihood of IDH1/2 mutation. A multivariable logistic regression model was built using backward model selection with the Akaike information criterion that ended up eliminating location in the final model, which is referred to as model A. We also considered using R132H IDH1 immunostain status in addition to age and the above clinical characteristics to predict the likelihood of IDH1/2 mutation. Let R denote R132H IDH1 immunostain status (1 ¼ positive, 0 ¼ negative) and X denote age and clinical characteristics. Based on the law of total probability and the fact that the predicted probability of IDH1/2 mutation is given a positive R132H IDH1 immunostain result, some simple algebra yields that given R132H IDH1 immunostain status and X, the predicted probability of IDH1/2 mutation is equal to R + (1 – R)*(p1 – p2)/(1 – p2), where p1 ¼ the predicted probab (...truncated)