Evaluation of the rapid Idylla IDH1-2 mutation assay in FFPE glioma samples.

IDH1 and IDH2 mutational status is a critical biomarker with diagnostic, prognostic, and treatment implications in glioma. Although IDH1 p.R132H-specific immunohistochemistry is available, it is unable to identify other mutations in IDH1/2. Next-generation sequencing can accurately determine IDH1/2 mutational status but suffers from long turnaround time when urgent treatment planning and initiation is medically necessary. The Idylla assay can detect IDH1/2 mutational status from unstained formalin-fixed paraffin-embedded (FFPE) slides in as little as a few hours. In a clinical validation, we demonstrate clinical accuracy of 97% compared to next-generation sequencing. Sensitivity studies demonstrated a limit of detection of 2.5-5% variant allele frequency, even at DNA inputs below the manufacturer's recommended threshold. Overall, the assay is an effective and accurate method for rapid determination of IDH1/2 mutational status.

Impact of Rare and Multiple Concurrent Gene Fusions on Diagnostic DNA Methylation Classifier in Brain Tumors.

DNA methylation is an essential molecular assay for central nervous system (CNS) tumor diagnostics. While some fusions define specific brain tumors, others occur across many different diagnoses. We performed a retrospective analysis of 219 primary CNS tumors with whole genome DNA methylation and RNA next-generation sequencing. DNA methylation profiling results were compared with RNAseq detected gene fusions. We detected 105 rare fusions involving 31 driver genes, including 23 fusions previously not implicated in brain tumors. In addition, we identified 6 multi-fusion tumors. Rare fusions and multi-fusion events can impact the diagnostic accuracy of DNA methylation by decreasing confidence in the result, such as BRAF, RAF, or FGFR1 fusions, or result in a complete mismatch, such as NTRK, EWSR1, FGFR, and ALK fusions. IMPLICATIONS: DNA methylation signatures need to be interpreted in the context of pathology and discordant results warrant testing for novel and rare gene fusions.

Polymorphous low-grade neuroepithelial tumor of the young with FGFR3-TACC3 fusion mimicking high-grade glioma: case report and series of high-grade correlates.

Background: Polymorphous low-grade neuroepithelial tumor of the young (PLNTY) is a recently described entity that can mimic high-grade glioma (HGG) in histologic and molecular features; however, factors predicting aggressive behavior in these tumors are unclear. Methods: We present an indolent neuroepithelial neoplasm in a 59-year-old female with imaging initially suggestive of HGG, and a series of adult patients with HGG harboring FGFR3-TACC3 fusions are also presented for comparison. Results: Pathology in the case patient revealed low-grade cytomorphology, microcalcifications, unusual neovascularization, and a low proliferation index. The lesion was diffusely CD34+ and harbored an FGFR3-TACC3 fusion and TERT promoter mutation. A diagnosis of PLNTY was therefore favored and the patient was observed with no progression at 15-month follow-up. In patients with HGG with FGFR3-TACC3 fusions, molecular findings included IDH-wildtype status, absence of 1p19q codeletion, CDKN2A loss, TERT promoter mutations and lack of MGMT promoter methylation. These patients demonstrated a median 15-month overall survival and a 6-month progression-free survival. Conclusion: PLNTY is a rare low-grade entity that can display characteristics of HGG, particularly in adults. Presence of FGFR3-TACC3 fusions and other high-grade features should raise concern for a more malignant precursor lesion when a diagnosis of PLNTY is considered.

Machine learning can aid in prediction of IDH mutation from H&E-stained histology slides in infiltrating gliomas.

While Machine Learning (ML) models have been increasingly applied to a range of histopathology tasks, there has been little emphasis on characterizing these models and contrasting them with human experts. We present a detailed empirical analysis comparing expert neuropathologists and ML models at predicting IDH mutation status in H&E-stained histology slides of infiltrating gliomas, both independently and synergistically. We find that errors made by neuropathologists and ML models trained using the TCGA dataset are distinct, representing modest agreement between predictions (human-vs.-human κ = 0.656; human-vs.-ML model κ = 0.598). While no ML model surpassed human performance on an independent institutional test dataset (human AUC = 0.901, max ML AUC = 0.881), a hybrid model aggregating human and ML predictions demonstrates predictive performance comparable to the consensus of two expert neuropathologists (hybrid classifier AUC = 0.921 vs. two-neuropathologist consensus AUC = 0.920). We also show that models trained at different levels of magnification exhibit different types of errors, supporting the value of aggregation across spatial scales in the ML approach. Finally, we present a detailed interpretation of our multi-scale ML ensemble model which reveals that predictions are driven by human-identifiable features at the patch-level.

Towards a single-assay approach: a combined DNA/RNA sequencing panel eliminates diagnostic redundancy and detects clinically-relevant fusions in neuropathology.

Since the introduction of integrated histological and molecular diagnoses by the 2016 World Health Organization (WHO) Classification of Tumors of the Nervous System, an increasing number of molecular markers have been found to have prognostic significance in infiltrating gliomas, many of which have now become incorporated as diagnostic criteria in the 2021 WHO Classification. This has increased the applicability of targeted-next generation sequencing in the diagnostic work-up of neuropathology specimens and in addition, raises the question of whether targeted sequencing can, in practice, reliably replace older, more traditional diagnostic methods such as immunohistochemistry and fluorescence in-situ hybridization. Here, we demonstrate that the Oncomine Cancer Gene Mutation Panel v2 assay targeted-next generation sequencing panel for solid tumors is not only superior to IHC in detecting mutation in IDH1/2 and TP53 but can also predict 1p/19q co-deletion with high sensitivity and specificity relative to fluorescence in-situ hybridization by looking at average copy number of genes sequenced on 1p, 1q, 19p, and 19q. Along with detecting the same molecular data obtained from older methods, targeted-next generation sequencing with an RNA sequencing component provides additional information regarding the presence of RNA based alterations that have diagnostic significance and possible therapeutic implications. From this work, we advocate for expanded use of targeted-next generation sequencing over more traditional methods for the detection of important molecular alterations as a part of the standard diagnostic work up for CNS neoplasms.

The effect of succinic acid on the metabolic profile in high-fat diet-induced obesity and insulin resistance.

Obesity, insulin resistance, and poor metabolic profile are hallmarks of a high-fat diet (HFD), highlighting the need to understand underlying mechanisms. Therefore, we sought to determine the effect of succinic acid (SA) on metabolism in high-fat diet (HFD)-induced obesity. Animals were randomly assigned to either low-fat diet (LFD) or a high-fat diet (HFD). Mice consumed their respective diets for 4.5 months and then assigned to the following groups: (LFD)+vehicle, LFD + SA (0.75 mg/ml), HFD + vehicle, or HFD + SA. Body weight (BW), food, and water intake, were tracked weekly. After 6 weeks, insulin, glucose, and pyruvate tolerance tests were completed, and spontaneous physical activity was assessed. Epididymal white adipose tissue (EWAT) mass and in vitro measurements of oxidative skeletal muscle (soleus) respiration were obtained. Expectedly, the HFD increased BW and EWAT mass, and reduced glucose and insulin tolerance. SA significantly reduced EWAT mass, more so in HFD (p < .05), but had no effect on any in vivo measurements (BW, insulin, glucose, or pyruvate tolerance, nor physical activity, all p > .05). A significant (p < .05) interaction was observed between mitochondrial respiration and treatment, where SA increased respiration, likely owed to greater mitochondrial content, as assessed by complex IV activity in both LFD and HFD. In HFD-induced obesity, coupled with insulin desensitization, we found no favorable effect of succinic acid on glucose regulation, though adiposity was attenuated. In oxidative skeletal muscle, there was a tendency for increased respiratory capacity, likely owed to greater mitochondrial content, suggestive of a succinic acid-induced mitochondrial biogenesis.