Frequent false-negative immunohistochemical staining with IDH1 (R132H)-specific H09 antibody on frozen section control slides: a potential pitfall in glioma diagnosis

Characterization of an IDH1 R132H Rabbit Monoclonal Antibody, MRQ-67, and Its Applications in the Identification of Diffuse Gliomas

The current diagnosis of diffuse glioma involves isocitrate dehydrogenase (IDH) mutation testing. Most IDH mutant gliomas carry a G-to-A mutation at IDH1 position 395, resulting in the R132H mutant. R132H immunohistochemistry (IHC), therefore, is used to screen for the IDH1 mutation. In this study, the performance of MRQ-67, a recently generated IDH1 R132H antibody, was characterized in comparison with H09, a frequently used clone. Selective binding was demonstrated by an enzyme-linked immunosorbent assay for MRQ-67 to the R132H mutant, with an affinity higher than that for H09. By Western and dot immunoassays, MRQ-67 was found to bind specifically to the IDH1 R1322H, with a higher capacity than H09. IHC testing with MRQ-67 demonstrated a positive signal in most diffuse astrocytomas (16/22), oligodendrogliomas (9/15), and secondary glioblastomas tested (3/3), but not in primary glioblastomas (0/24). While both clones demonstrated a positive signal with similar patterns and equivalent intensities, H09 exhibited a background stain more frequently. DNA sequencing on 18 samples showed the R132H mutation in all IHC positive cases (5/5), but not in negative cases (0/13). These results demonstrate that MRQ-67 is a high-affinity antibody suitable for specific detection of the IDH1 R132H mutant by IHC and with less background as compared with H09.

Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection-an application in metabolomics studies

Background

Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies. We developed a novel primal-dual based classification method (PD-CR) that can perform classification with rejection and feature selection on high dimensional datasets. PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost. It simultaneously optimizes the selected features and the prediction accuracy with a new tailored, constrained primal-dual method. The primal-dual framework is general enough to encompass various robust losses and to allow for convergence analysis. Here, we compare PD-CR to three commonly used methods: partial least squares discriminant analysis (PLS-DA), random forests and support vector machines (SVM). We analyzed two metabolomics datasets: one urinary metabolomics dataset concerning lung cancer patients and healthy controls; and a metabolomics dataset obtained from frozen glial tumor samples with mutated isocitrate dehydrogenase (IDH) or wild-type IDH.

Results

PD-CR was more accurate than PLS-DA, Random Forests and SVM for classification using the 2 metabolomics datasets. It also selected biologically relevant metabolites. PD-CR has the advantage of providing a confidence score for each prediction, which can be used to perform classification with rejection. This substantially reduces the False Discovery Rate.

Conclusion

PD-CR is an accurate method for classification of metabolomics datasets which can outperform PLS-DA, Random Forests and SVM while selecting biologically relevant features. Furthermore the confidence score provided with PD-CR can be used to perform classification with rejection and reduce the false discovery rate.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04478-w.

Simplified approach for pathological diagnosis of diffuse gliomas in adult patients

The most recent WHO classification (2016) for gliomas introduced integrated diagnoses requiring both phenotypic and genotypic data. This approach presents difficulties for countries with limited resources for laboratory testing. The present study describes a series of 118 adult Thai patients with diffuse gliomas, classified by the WHO 2016 classification. The purpose was to demonstrate how a diagnosis can still be achieved using a simplified approach that combines clinical, morphological, immunohistochemical, and fewer molecular assays than typically performed. This algorithm starts with tumor location (midline vs. non-midline) with diffuse midline glioma identified by H3 K27M immunostaining. All other tumors are placed into one of 6 categories, based on morphologic features rather than specific diagnoses. Molecular testing is limited to IDH1/IDH2 mutations, plus co-deletion of 1p/19q for cases with oligodendroglial features and TERT promoter mutation for cases without such features. Additional testing for co-deletion of 1p/19q, TERT promoter mutation and BRAF mutations are only used in selected cases to refine diagnosis and prognosis. With this approach, we were able to reach the integrated diagnosis in 117/118 cases, saving 50 % of the costs of a more inclusive testing panel. The demographic data and tumor subtypes were found to be similar to series from other regions of the world. To the best of our knowledge, this is to the first reported series of diffuse gliomas in South-East Asia categorized by the WHO 2016 classification system.