Project description:The brain tumor immune microenvironment (TIME) continuously evolves during glioma progression, but only a limited view of a highly complex glioma associated immune contexture across isocitrate dehydrogenase mutation (IDH) classified gliomas is known. Herein, we present an unprecedentedly comprehensive view of myeloid and lymphoid cell type diversity with our single cell RNA sequencing interrogation.
Project description:The brain tumor immune microenvironment (TIME) continuously evolves during glioma progression, but only a limited view of a highly complex glioma associated immune contexture across isocitrate dehydrogenase mutation (IDH) classified gliomas is known. Herein, we present an unprecedentedly comprehensive view of myeloid and lymphoid cell type diversity with our m-RNA sequencing interrogation.
Project description:The brain tumor immune microenvironment (TIME) continuously evolves during glioma progression, but only a limited view of a highly complex glioma associated immune contexture across isocitrate dehydrogenase mutation (IDH) classified gliomas is known. Herein, we present an unprecedentedly comprehensive view of T cells from brain TIME at single cell resolution, which served as part of our pan-cancer T cell atlas analysis.
Project description:Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient's treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients' age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas' IDH status prediction.
Project description:Over the last decade, extraordinary progress has been made in elucidating the underlying genetic causes of gliomas. In 2008, our understanding of glioma genetics was revolutionized when mutations in isocitrate dehydrogenase 1 and 2 (IDH1/2) were identified in the vast majority of progressive gliomas and secondary glioblastomas (GBMs). IDH enzymes normally catalyze the decarboxylation of isocitrate to generate ?-ketoglutarate (?KG), but recurrent mutations at Arg(132) of IDH1 and Arg(172) of IDH2 confer a neomorphic enzyme activity that catalyzes reduction of ?KG into the putative oncometabolite D-2-hydroxyglutate (D2HG). D2HG inhibits ?KG-dependent dioxygenases and is thought to create a cellular state permissive to malignant transformation by altering cellular epigenetics and blocking normal differentiation processes. Herein, we discuss the relevant literature on mechanistic studies of IDH1/2 mutations in gliomas, and we review the potential impact of IDH1/2 mutations on molecular classification and glioma therapy.
Project description:BackgroundInhibition of the isocitrate dehydrogenase (IDH)-mutant enzyme is a novel therapeutic target in IDH-mutant gliomas. Imaging biomarkers of IDH inhibitor treatment efficacy in human IDH-mutant gliomas are largely unknown. This study investigated early volumetric, perfusion, and diffusion MRI changes in IDH1-mutant gliomas during IDH inhibitor treatment.MethodsTwenty-nine IDH1-mutant glioma patients who received IDH inhibitor and obtained anatomical, perfusion, and diffusion MRI pretreatment at 3-6 weeks (n = 23) and/or 2-4 months (n = 14) of treatment were retrospectively studied. Normalized relative cerebral blood volume (nrCBV), apparent diffusion coefficient (ADC), and fluid-attenuated inversion recovery (FLAIR) hyperintensity volume were analyzed.ResultsAfter 3-6 weeks of treatment, nrCBV was significantly increased (P = .004; mean %change = 24.15%) but not FLAIR volume (P = .23; mean %change = 11.05%) or ADC (P = .52; mean %change = -1.77%). Associations between shorter progression-free survival (PFS) with posttreatment nrCBV > 1.55 (P = .05; median PFS, 240 vs 55 days) and increased FLAIR volume > 4 cm3 (P = .06; 227 vs 29 days) trended toward significance. After 2-4 months, nrCBV, FLAIR volume, and ADC were not significantly different from baseline, but an nrCBV increase > 0% (P = .002; 1121 vs 257 days), posttreatment nrCBV > 1.8 (P = .01; 1121 vs. 270 days), posttreatment ADC < 1.15 μm2/ms (P = .02; 421 vs 215 days), median nrCBV/ADC ratio increase > 0% (P = .02; 1121 vs 270 days), and FLAIR volume change > 4 cm3 (P = .03; 421 vs 226.5 days) were associated with shorter PFS.ConclusionsIncreased nrCBV at 3-6 weeks of treatment may reflect transient therapeutic and/or tumor growth changes, whereas nrCBV, ADC, and FLAIR volume changes occurring at 2-4 months of treatment may more accurately reflect antitumor response to IDH inhibition.
