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The Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) update 3 recommends that histologic grade II and III IDH-wildtype diffuse astrocytic gliomas that harbor EGFR amplification, the combination of whole chromosome 7 gain and whole chromosome 10 loss (7 + /10 −), or TERT promoter (pTERT) mutations should be considered as glioblastomas (GBM), World Health Organization grade IV. In this retrospective study, we examined the utility of molecular classification based on pTERT status and copy-number alterations (CNAs) in IDH-wildtype lower grade gliomas (LGGs, grade II, and III). The impact on survival was evaluated for the pTERT mutation and CNAs, including EGFR gain/amplification, PTEN loss, CDKN2A homozygous deletion, and PDGFRA gain/amplification. We analyzed 46 patients with IDH-wildtype/pTERT-mutant (mut) LGGs and 85 with IDH-wildtype/pTERT-wildtype LGGs. EGFR amplification and a combination of EGFR gain and PTEN loss (EGFR + /PTEN −) were significantly more frequent in pTERT-mut patients (p < 0.0001). Cox regression analysis showed that the pTERT mutation was a significant predictor of poor prognosis (hazard ratio [HR] 2.79, 95% confidence interval [CI] 1.55–4.89, p = 0.0008), but neither EGFR amplification nor EGFR + /PTEN − was an independent prognostic factor in IDH-wildtype LGGs. PDGFRA gain/amplification was a significant poor prognostic factor in IDH-wildtype/pTERT-wildtype LGGs (HR 2.44, 95% CI 1.09–5.27, p = 0.03, Cox regression analysis). The IDH-wildtype LGGs with either pTERT-mut or PDGFRA amplification were mostly clustered with GBM by DNA methylation analysis. Thus, our study suggests that analysis of pTERT mutation status is necessary and sufficient to diagnose IDH-wildtype diffuse astrocytic gliomas with molecular features of glioblastoma. The PDGFRA status may help further delineate IDH-wildtype/pTERT-wildtype LGGs. Methylation profiling showed that IDH-wildtype LGGs without molecular features of GBM were a heterogeneous group of tumors. Some of them did not fall into existing categories and had significantly better prognoses than those clustered with GBM.

Dysregulation of cell morphology and cell-cell interaction results in cancer cell growth, migration, invasion, and metastasis. Besides, a balance between the extracellular matrix (ECM) and matrix metalloprotease (MMP) is required for cancer cell morphology and angiogenesis. Here, we determined gene signatures associated with the morphology and microenvironment of primary central nervous system lymphoma (PCNSL) to enable prognosis prediction. Next-generation sequencing (NGS) on 31 PCNSL samples revealed gene signatures as follows: ACTA2 , ACTR10 , CAPG , CORO1C , KRT17 , and PALLD in cytoskeleton, CDH5 , CLSTN1 , ITGA10 , ITGAX , ITGB7 , ITGA8 , FAT4 , ITGAE , CDH10 , ITGAM , ITGB6 , and CDH18 in adhesion, COL8A2 , FBN1 , LAMB3 , and LAMA2 in ECM, ADAM22 , ADAM28 , MMP11 , and MMP24 in MMP. Prognosis prediction formulas with the gene expression values and the Cox regression model clearly divided survival curves of the subgroups in each status. Furthermore, collagen genes contributed to gene network formation in glasso, suggesting that the ECM balance controls the PCNSL microenvironment. Finally, the comprehensive balance of morphology and microenvironment enabled prognosis prediction by a combinatorial expression of 8 representative genes, including KRT17 , CDH10 , CDH18 , COL8A2 , ADAM22 , ADAM28 , MMP11 , and MMP24 . Besides, these genes could also diagnose PCNSL cell types with MTX resistances in vitro . These results would not only facilitate the understanding of biology of PCNSL but also consider targeting pathways for anti-cancer treatment in personalized precision medicine in PCNSL.

We attempted to establish a magnetic resonance imaging (MRI)-based radiomic model for stratifying prognostic subgroups of newly diagnosed glioblastoma (GBM) patients and predicting O (6)-methylguanine-DNA methyltransferase promotor methylation ( pMGMT -met) status of the tumor. Preoperative MRI scans from 201 newly diagnosed GBM patients were included in this study. A total of 489 texture features including the first-order feature, second-order features from 162 datasets, and location data from 182 datasets were collected. Supervised principal component analysis was used for prognostication and predictive modeling for pMGMT -met status was performed based on least absolute shrinkage and selection operator regression. 22 radiomic features that were correlated with prognosis were used to successfully stratify patients into high-risk and low-risk groups ( p  = 0.004, Log-rank test). The radiomic high- and low-risk stratification and pMGMT status were independent prognostic factors. As a matter of fact, predictive accuracy of the pMGMT methylation status was 67% when modeled by two significant radiomic features. A significant survival difference was observed among the combined high-risk group, combined intermediate-risk group (this group consists of radiomic low risk and pMGMT -unmet or radiomic high risk and pMGMT -met), and combined low-risk group ( p  = 0.0003, Log-rank test). Radiomics can be used to build a prognostic score for stratifying high- and low-risk GBM, which was an independent prognostic factor from pMGMT methylation status. On the other hand, predictive accuracy of the pMGMT methylation status by radiomic analysis was insufficient for practical use.

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a diagnosis based on conventional radiomic features and patient age. Multisite preoperative MR images of 164 patients with grade II/III glioma were grouped by IDH and TERT promoter (pTERT) mutations as follows: (1) IDH wild type, (2) IDH and pTERT co-mutations, (3) IDH mutant and pTERT wild type. We applied a CNN (AlexNet) to four types of MR sequence and obtained the CNN texture features to classify the groups with a linear support vector machine. The classification was also performed using conventional radiomic features and/or patient age. Using all features, we succeeded in classifying patients with an accuracy of 63.1%, which was significantly higher than the accuracy obtained from using either the radiomic features or patient age alone. In particular, prediction of the pTERT mutation was significantly improved by the CNN texture features. In conclusion, the pretrained CNN texture features capture the information of IDH and TERT genotypes in grade II/III gliomas better than the conventional radiomic features.

Molecular biological characterization of tumors has become a pivotal procedure for glioma patient care. The aim of this study is to build conventional MRI-based radiomics model to predict genetic alterations within grade II/III gliomas attempting to implement lesion location information in the model to improve diagnostic accuracy. One-hundred and ninety-nine grade II/III gliomas patients were enrolled. Three molecular subtypes were identified: IDH1/2 -mutant, IDH1/2 -mutant with TERT promoter mutation, and IDH- wild type. A total of 109 radiomics features from 169 MRI datasets and location information from 199 datasets were extracted. Prediction modeling for genetic alteration was trained via LASSO regression for 111 datasets and validated by the remaining 58 datasets. IDH mutation was detected with an accuracy of 0.82 for the training set and 0.83 for the validation set without lesion location information. Diagnostic accuracy improved to 0.85 for the training set and 0.87 for the validation set when lesion location information was implemented. Diagnostic accuracy for predicting 3 molecular subtypes of grade II/III gliomas was 0.74 for the training set and 0.56 for the validation set with lesion location information implemented. Conventional MRI-based radiomics is one of the most promising strategies that may lead to a non-invasive diagnostic technique for molecular characterization of grade II/III gliomas.

MicroRNAs (miRNAs) inhibit protein function by silencing the translation of target mRNAs. However, in primary central nervous system lymphoma (PCNSL), the expression and functions of miRNAs are inadequately known. Here, we examined the expression of 847 miRNAs in 40 PCNSL patients with a microarray and investigated for the miRNA predictors associated with cancer immunity-related genes such as T helper cell type 1/2 (Th-1/Th-2) and regulatory T cell (T-reg) status, and stimulatory and inhibitory checkpoint genes, for prognosis prediction in PCNSL. The aim of this study is to find promising prognosis markers based on the miRNA expression in PCNSL. We detected 334 miRNAs related to 66 cancer immunity-related genes in the microarray profiling. Variable importance measured by the random survival forest analysis and Cox proportional hazards regression model elucidated that 11 miRNAs successfully constitute the survival formulae dividing the Kaplan-Meier curve of the respective PCNSL subgroups. On the other hand, univariate analysis shortlisted 23 miRNAs for overall survival times, with four miRNAs clearly dividing the survival curves-miR-101/548b/554/1202. These miRNAs regulated Th-1/Th-2 status, T-reg cell status, and immune checkpoints. The miRNAs were also associated with gene ontology terms as Ras/MAP-kinase, ubiquitin ligase, PRC2 and acetylation, CDK, and phosphorylation, and several diseases including acquired immunodeficiency syndrome, glioma, and those related to blood and hippocampus with statistical significance. In conclusion, the results demonstrated that the four miRNAs comprising miR-101/548b/554/1202 associated with cancer immunity can be a useful prognostic marker in PCNSL and would help us understand target pathways for PCNSL treatments.

Primary central nervous system lymphoma (PCNSL) is a brain malignant non-Hodgkin’s B-cell lymphoma. The standard treatments are high-dose methotrexate (MTX)-based chemotherapies and deferred whole brain radiotherapy. However, MTX resistance-dependent global expression and signaling pathway changes and their relationship with prognoses have not yet been elucidated. Here, we conducted a global expression analysis with next-generation sequencing and gene set enrichment analysis (GSEA) in MTX-resistant PCNSL cell lines (HKBML-MTX and TK-MTX) and PCNSL tissues. In rank scores, genes listed in HKBML-MTX and TK-MTX were enriched in PCNSL with poor prognoses. In fold changes, a part of differentially-expressed genes in PCNSL tissues were also detected in HKBML-MTX and TK-MTX cells; FOXD2-AS1 and MMP19 were commonly expressed in both HKBML-MTX and TK-MTX, FABP5 and CD70 were HKBML-MTX-specifically expressed, and CLCN2 , HOXB9 , INE1 , and LRP5L were TK-MTX-specifically expressed, which may provide a combination of prognostic markers on MTX-sensitivities in PCNSL. Additionally, PCNSL subgroups, divided with hierarchical clustering and Kaplan-Meier methods, included twenty commonly expressed genes in both HKBML-MTX and TK-MTX, ten HKBML-MTX-specifically expressed genes, and two TK-MTX-specifically expressed genes. These results suggest that the GSEA-assisted gene signatures can provide a combination for prognostic markers in recurrent PCNSL with MTX resistances.

MicroRNAs (miRNAs) are small RNA molecules that inhibit gene function by suppressing translation of target genes. However, in primary central nervous system lymphoma (PCNSL), the biological significance of miRNAs is largely unknown, although some miRNAs are known to be prognosis markers. Here, we analyzed 847 miRNAs expressed in 27 PCNSL specimens using microarray profiling and surveyed miRNA signature for prognostic prediction. Of these, 16 miRNAs were expressed in 27 PCNSL specimens at a frequency of 48%. Their variable importance measured by Random forest model revealed miR-192, miR-486, miR-28, miR-52, miR-181b, miR-194, miR-197, miR-93, miR-708, and let-7g as having positive effects; miR-29b-2*, miR-126, and miR-182 as having negative effects; and miR-18a*, miR-425, and miR-30d as neutral. After principal component analysis, the prediction formula for prognosis, consisting of the expression values of the above-mentioned miRNAs, clearly divided Kaplan-Meier survival curves by the calculated Z-score (HR = 6.4566, P = 0.0067). The 16 miRNAs were enriched by gene ontology terms including angiogenesis, cell migration and proliferation, and apoptosis, in addition to signaling pathways including TGF-β/SMAD, Notch, TNF, and MAPKinase. Their target genes included BCL2-related genes, HMGA2 oncogene, and LIN28B cancer stem cell marker. Furthermore, three miRNAs including miR-181b, miR-30d, and miR-93, selected from the 16 miRNAs, also showed comparable results for survival (HR = 8.9342, P = 0.0007), suggestive of a miRNA signature for prognostic prediction in PCNSL. These results indicate that this miRNA signature is useful for prognostic prediction in PCNSL and would help us understand target pathways for therapies in PCNSL.

Intracranial germ cell tumors (IGCTs) are rare brain neoplasms that mainly occur in children and adolescents with a particularly high incidence in East Asian populations. Here, we conduct a genome-wide association study (GWAS) of 133 patients with IGCTs and 762 controls of Japanese ancestry. A common 4-bp deletion polymorphism in an enhancer adjacent to BAK1 is significantly associated with the disease risk (rs3831846; P  = 2.4 × 10 −9 , odds ratio = 2.46 [95% CI: 1.83–3.31], minor allele frequency = 0.43). Rs3831846 is in strong linkage disequilibrium with a testicular GCTs susceptibility variant rs210138. In-vitro reporter assays reveal rs3831846 to be a functional variant attenuating the enhancer activity, suggesting its contribution to IGCTs predisposition through altering BAK1 expression. Risk alleles of testicular GCTs derived from the European GWAS show significant positive correlations in the effect sizes with the Japanese IGCTs GWAS ( P  = 1.3 × 10 −4 , Spearman’s ρ  = 0.48). These results suggest the shared genetic susceptibility of GCTs beyond ethnicity and primary sites. Intracranial germ cell tumors (IGCTs) are rare brain tumors mainly diagnosed in children and young adults. Here, the authors conduct a genome-wide association study for IGCTs, identify a risk locus at BAK1 , and characterize its functional consequences.

PurposeHere, we investigated expression modules reflecting the reciprocal expression of the cancer microenvironment and immune response-related genes associated with poor prognosis in primary central nervous system lymphoma (PCNSL).MethodsWeighted gene coexpression network analysis revealed representative modules, including neurogenesis, immune response, anti-virus, microenvironment, gene expression and translation, extracellular matrix, morphogenesis, and cell adhesion in the transcriptome data of 31 PCNSL samples.Results Gene expression networks were also reflected by protein–protein interaction networks. In particular, some of the hub genes were highly expressed in patients with PCNSL with prognoses as follows: AQP4, SLC1A3, GFAP, CXCL9, CXCL10, GBP2, IFI6, OAS2, IFIT3, DCN, LRP1, and LUM with good prognosis; and STAT1, IFITM3, GZMB, ISG15, LY6E, TGFB1, PLAUR, MMP4, FTH1, PLAU, CSF3R, FGR, POSTN, CCR7, TAS1R3, small ribosomal subunit genes, and collagen type 1/3/4/6 genes with poor prognosis. Furthermore, prognosis prediction formulae were constructed using the Cox proportional-hazards regression model, which demonstrated that the IP-10 receptor gene CXCR3 and type I interferon-induced protein gene IFI44L could predict patient survival in PCNSL.ConclusionThese results indicate that the differential expression and balance of immune response and microenvironment genes may be required for PCNSL tumor growth or prognosis prediction, which would help understanding the mechanism of tumorigenesis and potential therapeutic targets in PCNSL.