Gene expression profiles of gliomas in formalin-fixed paraffin-embedded material

Background: We have recently demonstrated that expression profiling is a more accurate and objective method to classify gliomas than histology. Similar to most expression profiling studies, our experiments were performed using fresh frozen (FF) glioma samples whereas most archival samples are fixed in formalin and embedded in paraffin (FFPE). Identification of the same, expression-based intrinsic subtypes in FFPE-stored samples would enable validation of the prognostic value of these subtypes on these archival samples. In this study, we have therefore determined whether the intrinsic subtypes identified using FF material can be reproduced in FFPE-stored samples.Methods: We have performed expression profiling on 55 paired FF-FFPE glioma samples using HU133 plus 2.0 arrays (FF) and Exon 1.0 ST arrays (FFPE). The median time in paraffin of the FFPE samples was 14.1 years (range 6.6-26.4 years). Results: In general, the correlation between FF and FFPE expression in a single sample was poor. We then selected the most variable probe sets per gene (n17 583), and of these, the 5000 most variable probe sets on FFPE expression profiles. This unsupervised selection resulted in a better concordance (R 2 0.54) between expression of FF and FFPE samples. Importantly, this probe set selection resulted in a correct assignment of 87% of FFPE samples into one of seven intrinsic subtypes identified using FF samples. Assignment to the same molecular cluster as the paired FF tissue was not correlated to time in paraffin. Conclusion: We are the first to examine a large cohort of paired FF and FFPE samples. We show that expression data from FFPE material can be used to assign samples to intrinsic molecular subtypes identified using FF material. This assignment allows the use of archival material, including material derived from large-randomised clinical trials, to determine the predictive and/or prognostic value of intrinsic glioma subtypes on Exon arrays. This would enable clinicians to provide patients with an objective and accurate diagnosis and prognosis, and a personalised treatment strategy.

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