Galectin-9/TIM-3 as a Key Regulator of Immune Response in Gliomas With Chromosome 1p/19q Codeletion.
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ABSTRACT:
SUBMITTER: Li G
PROVIDER: S-EPMC8692744 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAccurate classification of diffuse gliomas, the most common tumors of the central nervous system in adults, is important for appropriate treatment. However, detection of isocitrate dehydrogenase (IDH) mutation and chromosome1p/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance remains an issue. Adenosine to inosine (A-to-I) RNA editing has emerged as a novel cancer prognostic marker, but its value for glioma classification remains largely unexplored. We aim to (1) unravel the relationship between RNA editing and IDH mutation and 1p/19q codeletion and (2) predict IDH mutation and 1p/19q codeletion status using machine learning algorithms.ResultsBy characterizing genome-wide A-to-I RNA editing signatures of 638 gliomas, we found that tumors without IDH mutation exhibited higher total editing level compared with those carrying it (Kolmogorov-Smirnov test, p?<?0.0001). When tumor grade was considered, however, only grade IV tumors without IDH mutation exhibited higher total editing level. According to 10-fold cross-validation, support vector machines (SVM) outperformed random forest and AdaBoost (DeLong test, p?<?0.05). The area under the receiver operating characteristic curve (AUC) of SVM in predicting IDH mutation and 1p/19q codeletion were 0.989 and 0.990, respectively. After performing feature selection, AUCs of SVM and AdaBoost in predicting IDH mutation were higher than that of random forest (0.985 and 0.983 vs. 0.977; DeLong test, p?<?0.05), but AUCs of the three algorithms in predicting 1p/19q codeletion were similar (0.976-0.982). Furthermore, 67% of the six continuously misclassified samples by our 1p/19q codeletion prediction models were misclassifications in the original labelling after inspection of 1p/19q status and/or pathology report, highlighting the accuracy and clinical utility of our models.ConclusionsThe study represents the first genome-wide analysis of glioma editome and identifies RNA editing as a novel prognostic biomarker for glioma. Our prediction models provide standardized, accurate, reproducible and objective classification of gliomas. Our models are not only useful in clinical decision-making, but also able to identify editing events that have the potential to serve as biomarkers and therapeutic targets in glioma management and treatment.
| S-EPMC6929429 | biostudies-literature
Project description:BackgroundOligodendroglioma is genetically defined by concomitant IDH (IDH1/IDH2) mutation and whole-arm 1p/19q codeletion. Codeletion of 1p/19q traditionally evaluated by fluorescence in situ hybridization (FISH) cannot distinguish partial from whole-arm 1p/19q codeletion. Partial 1p/19q codeletion called positive by FISH is diagnostically a "false-positive" result. Chromosomal microarray (CMA) discriminates partial from whole-arm 1p/19q codeletion. Herein, we aimed to estimate the frequency of partial 1p/19q codeletion that would lead to a false-positive FISH result.MethodsFISH 1p/19q codeletion test probe coordinates were mapped onto Oncoscan CMA data to determine the rate of partial 1p/19q codeletion predicted to be positive by FISH. Diffuse astrocytic gliomas with available CMA data (2015-2018) were evaluated and classified based on IDH1-R132H/ATRX/p53 immunohistochemistry, IDH/TERT promoter targeted sequencing, and/or CMA according to classification updates. Predicted false-positive cases were verified by FISH whenever possible.ResultsThe overall estimated false-positive FISH 1p/19q codeletion rate was 3.6% (8/223). Predicted false positives were verified by FISH in 6 (of 8) cases. False-positive rates did not differ significantly (P = .49) between IDH-mutant (4.6%; 4/86) and IDH-wildtype (2.9%; 4/137) tumors. IDH-wildtype false positives were all WHO grade IV, whereas IDH-mutant false positives spanned WHO grades II-IV. Testing for 1p/19q codeletion would not have been indicated for most false positives based on current classification recommendations.ConclusionSelective 1p/19q codeletion testing and cautious interpretation for conflicting FISH and histopathological findings are recommended to avoid potential misdiagnosis.
| S-EPMC7654379 | biostudies-literature
Project description:Accurate identification of molecular alterations in gliomas is crucial for their diagnosis and treatment. Although, fluorescence in situ hybridization (FISH) allows for the observation of diverse and heterogeneous alterations, it is inherently time-consuming and challenging due to the limitations of the molecular method. Here, we report the development of 1p/19qNET, an advanced deep-learning network designed to predict fold change values of 1p and 19q chromosomes and classify isocitrate dehydrogenase (IDH)-mutant gliomas from whole-slide images. We trained 1p/19qNET on next-generation sequencing data from a discovery set (DS) of 288 patients and utilized a weakly-supervised approach with slide-level labels to reduce bias and workload. We then performed validation on an independent validation set (IVS) comprising 385 samples from The Cancer Genome Atlas, a comprehensive cancer genomics resource. 1p/19qNET outperformed traditional FISH, achieving R2 values of 0.589 and 0.547 for the 1p and 19q arms, respectively. As an IDH-mutant glioma classifier, 1p/19qNET attained AUCs of 0.930 and 0.837 in the DS and IVS, respectively. The weakly-supervised nature of 1p/19qNET provides explainable heatmaps for the results. This study demonstrates the successful use of deep learning for precise determination of 1p/19q codeletion status and classification of IDH-mutant gliomas as astrocytoma or oligodendroglioma. 1p/19qNET offers comparable results to FISH and provides informative spatial information. This approach has broader applications in tumor classification.
| S-EPMC10505231 | biostudies-literature
Project description:High-grade gliomas are malignant neuroepithelial tumors. The current WHO classification for adult-type diffuse gliomas is based on IDH1/2 mutational and 1p/19q-codeletion status, which can be refined by emerging genomics, transcriptomics, and methylomics approaches. Nevertheless, therapy has been dominated by radiochemotherapy for about 15 years and progressed little in efficacy or precision through advanced molecular patient stratification. Glioma proteome alterations remain undercharacterized despite their promise for a better stratification and, in particular, the identification of therapeutic targets. Here, we used mass spectrometry (MS) to characterize 42 formalin-fixed, paraffin-embedded (FFPE) samples from IDH-wildtype (IDHwt), IDH-mutant (IDHmut) gliomas with and without 1p/19q-codeletion, and non-neoplastic brain tissue controls. Based on more than 5500 quantified proteins and 5000 phospho-sites, gliomas separated according to IDH1/2 mutational status and reflected loss of proteins encoded on 1p/19q in IDHmut, gains on chromosome 7 and losses on chromosome 10 in IDHwt. Unexpectedly, the 1p/19q-codeletion resulted in minor proteome changes. Instead, two proteomic subtypes of IDHmut gliomas showed major perturbations of mitochondrial DNA-encoded proteins, aerobic/anaerobic energy metabolism, RNA metabolism, chromatin dynamics, the extracellular matrix as well as tumor suppressor and oncoproteins. Reanalysis of three glioma proteomics studies independently validate the observed hallmarks and associate these proteomic subtypes with the well-established proneural and classic/mesenchymal IDHwt glioma subtypes. These results suggest an alternative stratification of IDHmut gliomas as part of common phenotypic subtypes independent of the IDH status, with broad therapeutic implications for patients with IDHmut gliomas in the future. Altogether, our study provides a rich protein and phospho-site resource restratifying glioma subtypes and supporting future mechanism of action and target discovery investigations.
2023-02-07 | GSE212838 | GEO
Project description:Background: Chromosome 1p/19q codeletion is one of the most important genetic alterations for low grade gliomas (LGGs), and patients with 1p/19q codeletion have significantly prolonged survival compared to those without the codeletion. And the tumor immune microenvironment also plays a vital role in the tumor progression and prognosis. However, the effect of 1p/19q codeletion on the tumor immune microenvironment in LGGs is unclear. Methods: Immune cell infiltration of 281 LGGs from The Cancer Genome Atlas (TCGA) and 543 LGGs from the Chinese Glioma Genome Atlas (CGGA) were analyzed for immune cell infiltration through three bioinformatics tools: ESTIMATE algorithm, TIMER, and xCell. The infiltrating level of immune cells and expression of immune checkpoint genes were compared between different groups classified by 1p/19q codeletion and IDH (isocitrate dehydrogenase) mutation status. The differential biological processes and signaling pathways were evaluated through Gene Set Enrichment Analysis (GSEA). Correlations were analyzed using Spearman correlation. Results: 1p/19q codeletion was associated with immune-related biological processes in LGGs. The infiltrating level of multiple kinds of immune cells and expression of immune checkpoint genes were significantly lower in 1p/19q codeletion LGGs compared to 1p/19q non-codeletion cohorts. There are 127 immune-related genes on chromosome 1p or 19q, such as TGFB1, JAK1, and CSF1. The mRNA expression of these genes was positively correlated with their DNA copy number. These genes are distributed in multiple immune categories, such as chemokines/cytokines, TGF-β family members, and TNF family members, regulating immune cell infiltration and expression of the immune checkpoint genes in tumors. Conclusion: Our results indicated that 1p/19q codeletion status is closely associated with the immunosuppressive microenvironment in LGGs. LGGs with 1p/19q codeletion display less immune cell infiltration and lower expression of immune checkpoint genes than 1p/19q non-codeletion cases. Mechanistically, this may be, at least in part, due to the deletion of copy number of immune-related genes in LGGs with 1p/19q codeletion. Our findings may be relevant to investigate immune evasion in LGGs and contribute to the design of immunotherapeutic strategies for patients with LGGs.
| S-EPMC8322528 | biostudies-literature
Project description:BACKGROUND AND PURPOSE:Isocitrate dehydrogenase (IDH)-mutant lower grade gliomas are classified as oligodendrogliomas or diffuse astrocytomas based on 1p/19q-codeletion status. We aimed to test and validate neuroradiologists' performances in predicting the codeletion status of IDH-mutant lower grade gliomas based on simple neuroimaging metrics. MATERIALS AND METHODS:One hundred two IDH-mutant lower grade gliomas with preoperative MR imaging and known 1p/19q status from The Cancer Genome Atlas composed a training dataset. Two neuroradiologists in consensus analyzed the training dataset for various imaging features: tumor texture, margins, cortical infiltration, T2-FLAIR mismatch, tumor cyst, T2* susceptibility, hydrocephalus, midline shift, maximum dimension, primary lobe, necrosis, enhancement, edema, and gliomatosis. Statistical analysis of the training data produced a multivariate classification model for codeletion prediction based on a subset of MR imaging features and patient age. To validate the classification model, 2 different independent neuroradiologists analyzed a separate cohort of 106 institutional IDH-mutant lower grade gliomas. RESULTS:Training dataset analysis produced a 2-step classification algorithm with 86.3% codeletion prediction accuracy, based on the following: 1) the presence of the T2-FLAIR mismatch sign, which was 100% predictive of noncodeleted lower grade gliomas, (n = 21); and 2) a logistic regression model based on texture, patient age, T2* susceptibility, primary lobe, and hydrocephalus. Independent validation of the classification algorithm rendered codeletion prediction accuracies of 81.1% and 79.2% in 2 independent readers. The metrics used in the algorithm were associated with moderate-substantial interreader agreement (κ = 0.56-0.79). CONCLUSIONS:We have validated a classification algorithm based on simple, reproducible neuroimaging metrics and patient age that demonstrates a moderate prediction accuracy of 1p/19q-codeletion status among IDH-mutant lower grade gliomas.
| S-EPMC7028667 | biostudies-literature
Project description:BACKGROUND:Chromosome 1p/19q codeletion is increasingly being recognized as the crucial genetic marker for glioma patients and have been included in WHO classification of glioma in 2016. Fluorescent in situ hybridization, a widely used method in detecting 1p/19q status, has some methodological limitations which might influence the clinical management for doctors. Here, we attempted to explore an RNA sequencing computational method to detect 1p/19q status. METHODS:We included 692 samples with 1p/19q status information from TCGA cohort as training set and 222 samples with 1p/19q status information from REMBRANDT cohort as validation set. We reviewed and compared five tools: TSPairs, GSVA, PAM, Caret, smoother, with respect to their accuracy, sensitivity and specificity. RESULTS:In TCGA cohort, the GSVA method showed the highest accuracy (98.4%) in predicting 1p/19q status (sensitivity=95.5%, specificity=99.6%) and smoother method showed the second-highest accuracy (accuracy=97.8%, sensitivity=96.4%, specificity=98.3%). While in REMBRANDT cohort, smoother method exhibited the highest accuracy (98.6%) (sensitivity= 96.7%, specificity=98.9%) in 1p/19q status prediction. CONCLUSIONS:Our independent assessment of five tools revealed that smoother method was selected as the most stable and accurate method in predicting 1p/19q status. This method could be regarded as a potential alternative method for clinical practice in future.
| S-EPMC6382420 | biostudies-literature
Project description:The prognosis of small-cell lung cancer (SCLC) is poor despite reports suggesting modest improvement in survival. To date, chemotherapy remains the cornerstone treatment for SCLC patients, and many studies have focused on identifying the molecular characteristics of SCLC, which serve as the basis for precision treatments that improve the prognosis of SCLC. For instance, the therapeutic effect of temozolomide, recommended for patients with relapsed SCLC, is linked to 1p/19q codeletion in anaplastic oligodendroglial tumors. A subpopulation of SCLC patients may derive benefit from tyrosine kinase inhibitors targeting RET. In order to identify 1p/19q codeletion and RET rearrangement in SCLC patients, 32 SCLC resected specimens were retrospectively collected between 2008 and 2014 from the Zhejiang Cancer Hospital in People's Republic of China. Fluorescence in situ hybridization was used to detect 1p/19q codeletion and RET rearrangement in the specimens. A 1p single deletion was detected in eight specimens, 19q single deletion was detected in three specimens, and only three specimens had a 1p/19q codeletion. None of the specimens had a RET rearrangement. The three patients whose specimens had a 1p/19q codeletion were alive after 58, 50, and 30 months of follow-up care. There was a trend toward prolonged overall survival for the patients with codeletion compared to no codeletion, 1p single deletion, 19q single deletion, and without 1p and 19q deletion (P=0.113, 0.168, 0.116, and 0.122, respectively). Our data showed that RET rearrangement may be not an ideal molecular target for SCLC therapies in People's Republic of China. Instead, 1p/19q codeletion is a promising marker for a good prognosis and treatment with temozolomide in SCLC.
| S-EPMC4913998 | biostudies-literature
Project description:Glial cell-derived brain tumors (gliomas) are devastating diseases without effective curative therapies. Gliomas are classified according to various schemes including the cancer-initiating cell type, World Health Organization tumor grades, and genetic markers such as Isocitrate dehydrogenase (IDH) mutations and chromosomal 1p/19q codeletion status. Genomics, transcriptomics and methylomics approaches are emerging as powerful means to refine classification. However, various aspects of cancer biology are solely reflected on the proteomelevel. Here, we employ mass spectrometry (MS) to characterize the proteomes and phospho-proteomes of IDHmut glioma entities with and without 1p/19q codeletion, IDHwt gliomas and control tissue from a total of 42 patients. Our data-dependent acquisition MS workflow on average quantifies more than 5000 proteins and 3000 phospho-sites per sample of formalin-fixed paraffin-embedded (FFPE) brain sections. The tumor proteomes reflected genetic alterations such as the loss of chromosome 1p/19q proteins, the loss of ATRX in non-codeletion IDHmut glioma, and the frequent loss and amplification of chromosomes 10 and 7, respectively,in IDH-wild type glioma. Nevertheless, 1p/19q codeletion status was not the major determinant of IDHmut glioma proteomes. Instead, the proteome profiles suggest an alternative classification of IDHmut gliomas that correlates with the loss of mitochondrial DNA-encoded proteins, the abundance of respiratory chain proteins, a distinct tumor suppressor and oncoprotein profile, an extracellular matrix signature, and alterations in the phospho-proteome. Moreover, the glioma association of numerous proteins implicated in other cancers by this dataset provides a resource for further mechanistic investigation of glioma genesis and progression.
2022-11-09 | PXD024427 | Pride
Project description:BackgroundIDH mutation and 1p/19q codeletion status are important prognostic markers for glioma that are currently determined using invasive procedures. Our goal was to develop artificial intelligence-based methods to noninvasively determine molecular alterations from MRI.MethodsPre-operative MRI scans of 2648 glioma patients were collected from Washington University School of Medicine (WUSM; n = 835) and publicly available Brain Tumor Segmentation (BraTS; n = 378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41), The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD; n = 774) datasets. A 2.5D hybrid convolutional neural network was proposed to simultaneously localize glioma and classify its molecular status by leveraging MRI imaging features and prior knowledge features from clinical records and tumor location. The models were trained on 223 and 348 cases for IDH and 1p/19q tasks, respectively, and tested on one internal (TCGA) and two external (WUSM and EGD) test sets.ResultsFor IDH, the best-performing model achieved areas under the receiver operating characteristic (AUROC) of 0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of 0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For 1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of 0.588, 0.713, 0.782, on those three data-splits, respectively.ConclusionsThe high accuracy of the model on unseen data showcases its generalization capabilities and suggests its potential to perform "virtual biopsy" for tailoring treatment planning and overall clinical management of gliomas.
| S-EPMC10162113 | biostudies-literature