Multiple PET tracers for use in the classification of gliomas according to the 2016 WHO criteria

Background The molecular diagnosis of gliomas such as isocitrate dehydrogenase (IDH) status (wild-type [wt] or mutation [mut]) is especially important in the 2016 WHO classification. Positron emission tomography (PET) has afforded molecular and metabolic diagnostic imaging. The present study aimed to define the interrelationship between the 2016 WHO classification of gliomas and the integrated data from PET images using multiple tracers, including 18F-fluorodeoxyglucose ( 18F-FDG), 11C-methionine ( 11C-MET), 18F-fluorothymidine ( 18F-FLT), and 18F-fluoromisonidazole ( 18F-FMISO). Methods This retrospective, single-center study comprised 113 patients with newly diagnosed glioma based on the 2016 WHO criteria. Patients were divided into four glioma subtypes (Mut, Codel, Wt, and glioblastoma multiforme [GBM]). Tumor standardized uptake value (SUV) divided by mean normal cortical SUV (tumor-normal tissue ratio [TNR]) was calculated for 18F-FDG, 11C-MET, and 18F-FLT. Tumor-blood SUV ratio (TBR) was calculated for 18F-FMISO. To assess the diagnostic accuracy of PET tracers in distinguishing glioma subtypes, a comparative analysis of TNRs and TBR as well as the metabolic tumor volume (MTV) were calculated by Scheffe’s multiple comparison procedure for each PET tracer following the Kruskal–Wallis test. Results The differences in mean 18F-FLT TNR and 18F-FMISO TBR were significant between GBM and other glioma subtypes (p < 0.001). Regarding the comparison between Gd-T1WI volumes and 18F-FLT MTVs or 18F-FMISO MTVs, we identified significant differences between Wt and Mut or Codel (p < 0.01). Conclusion Combined administration of four PET tracers might aid in the preoperative differential diagnosis of gliomas according to the 2016 WHO criteria.

Immunological classification of gliomas based on immunogenomic profiling

Background Gliomas are heterogeneous in the tumor immune microenvironment (TIM). However, a classification of gliomas based on immunogenomic profiling remains lacking. Methods We hierarchically clustered gliomas based on the enrichment levels of 28 immune cells in the TIM in five datasets and obtained three clusters: immunity-high, immunity-medium, and immunity-low. Results Glioblastomas were mainly distributed in immunity-high and immunity-medium, while lower-grade gliomas were distributed in all the three subtypes and predominated in immunity-low. Immunity-low displayed a better survival than other subtypes, indicating a negative correlation between immune infiltration and survival prognosis in gliomas. IDH mutations had a negative correlation with glioma immunity. Immunity-high had higher tumor stemness and epithelial-mesenchymal transition scores and included more high-grade tumors than immunity-low, suggesting that elevated immunity is associated with tumor progression in gliomas. Immunity-high had higher tumor mutation burden and more frequent somatic copy number alterations, suggesting a positive association between tumor immunity and genomic instability in gliomas. Conclusions The identification of immune-specific glioma subtypes has potential clinical implications for the immunotherapy of gliomas.

Comparative analysis of transcriptomic profile, histology, and IDH mutation for classification of gliomas

Gliomas are currently classified through integration of histology and mutation information, with new developments in DNA methylation classification. However, discrepancies exist amongst the major classification methods. This study sought to compare transcriptome-based classification to the established methods. RNAseq and microarray data were obtained for 1032 gliomas from the TCGA and 395 gliomas from REMBRANDT. Data were analyzed using unsupervised and supervised learning and other statistical methods. Global transcriptomic profiles defined four transcriptomic glioma subgroups with 91.4% concordance with the WHO-defined mutation subtypes. Using these subgroups, 168 genes were selected for the development of 1000 linear support vector classifiers (LSVC). Based on plurality voting of 1000 LSVC, the final ensemble classifier confidently classified all but 17 TCGA gliomas to one of the four transcriptomic profile (TP) groups. The classifier was validated using a gene expression microarray dataset. TP1 cases include IDHwt, glioblastoma high immune infiltration and cellular proliferation and poor survival prognosis. TP2a is characterized as IDHmut-codel, oligodendrogliomas with high tumor purity. TP2b tissue is mostly composed of neurons and few infiltrating malignant cells. TP3 exhibit increased NOTCH signaling, are astrocytoma and IDHmut-non-codel. TP groups are highly concordant with both WHO integrated histology and mutation classification as well as methylation-based classification of gliomas. Transcriptomic profiling provides a robust and objective method to classify gliomas with high agreement to the current WHO guidelines and may provide additional survival prediction to the current methods.

Cellular Plasticity and Tumor Microenvironment in Gliomas: The Struggle to Hit a Moving Target

Brain tumors encompass a diverse group of neoplasias arising from different cell lineages. Tumors of glial origin have been the subject of intense research because of their rapid and fatal progression. From a clinical point of view, complete surgical resection of gliomas is highly difficult. Moreover, the remaining tumor cells are resistant to traditional therapies such as radio- or chemotherapy and tumors always recur. Here we have revised the new genetic and epigenetic classification of gliomas and the description of the different transcriptional subtypes. In order to understand the progression of the different gliomas we have focused on the interaction of the plastic tumor cells with their vasculature-rich microenvironment and with their distinct immune system. We believe that a comprehensive characterization of the glioma microenvironment will shed some light into why these tumors behave differently from other cancers. Furthermore, a novel classification of gliomas that could integrate the genetic background and the cellular ecosystems could have profound implications in the efficiency of current therapies as well as in the development of new treatments.