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Expression alterations define unique molecular characteristics of spinal ependymomas.

ABSTRACT: Ependymomas are glial tumors that originate in either intracranial or spinal regions. Although tumors from different regions are histologically similar, they are biologically distinct. We therefore sought to identify molecular characteristics of spinal ependymomas (SEPN) in order to better understand the disease biology of these tumors. Using gene expression profiles of 256 tumor samples, we identified increased expression of 1,866 genes in SEPN when compared to intracranial ependymomas. These genes are mainly related to anterior/posterior pattern specification, response to oxidative stress, glial cell differentiation, DNA repair, and PPAR signalling, and also significantly enriched with cellular senescence genes (P = 5.5 × 10-03). In addition, a high number of significantly down-regulated genes in SEPN are localized to chromosome 22 (81 genes from chr22: 43,325,255 - 135,720,974; FDR = 1.77 × 10-23 and 22 genes from chr22: 324,739 - 32,822,302; FDR = 2.07 × 10-09) including BRD1, EP300, HDAC10, HIRA, HIC2, MKL1, and NF2. Evaluation of NF2 co-expressed genes further confirms the enrichment of chromosome 22 regions. Finally, systematic integration of chromosome 22 genes with interactome and NF2 co-expression data identifies key candidate genes. Our results reveal unique molecular characteristics of SEPN such as altered expression of cellular senescence and chromosome 22 genes.

SUBMITTER: Lourdusamy A

PROVIDER: S-EPMC4637320 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Expression alterations define unique molecular characteristics of spinal ependymomas.

Lourdusamy Anbarasu A Rahman Ruman R Grundy Richard G RG

Oncotarget 20150801 23

Ependymomas are glial tumors that originate in either intracranial or spinal regions. Although tumors from different regions are histologically similar, they are biologically distinct. We therefore sought to identify molecular characteristics of spinal ependymomas (SEPN) in order to better understand the disease biology of these tumors. Using gene expression profiles of 256 tumor samples, we identified increased expression of 1,866 genes in SEPN when compared to intracranial ependymomas. These g ...[more]

PMID: 25909290

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Project description:Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the existing discrepancy between histomorphogical diagnoses and classification using methylation data, we asked whether deep neural networks can predict the DNA methylation class of spinal cord ependymomas from hematoxylin and eosin stained whole-slide images. Using explainable AI, we further aimed to prospectively improve the consistency of histology-based diagnoses with DNA methylation profiling by identifying and quantifying distinct morphological patterns of these molecular ependymoma types. We assembled a case series of 139 molecularly characterized spinal cord ependymomas (nMPE = 84, nSP-EPN = 55). Self-supervised and weakly-supervised neural networks were used for classification. We employed attention analysis and supervised machine-learning methods for the discovery and quantification of morphological features and their correlation to the diagnoses of experienced neuropathologists. Our best performing model predicted the DNA methylation class with 98% test accuracy and used self-supervised learning to outperform pretrained encoder-networks (86% test accuracy). In contrast, the diagnoses of neuropathologists matched the DNA methylation class in only 83% of cases. Domain-adaptation techniques improved model generalization to an external validation cohort by up to 22%. Statistically significant morphological features were identified per molecular type and quantitatively correlated to human diagnoses. The approach was extended to recently defined subtypes of myxopapillary ependymomas (MPE-(A/B), 80% test accuracy). In summary, we demonstrated the accurate prediction of the DNA methylation class of spinal cord ependymomas (SP-EPN, MPE(-A/B)) using hematoxylin and eosin stained whole-slide images. Our approach may prospectively serve as a supplementary resource for integrated diagnostics and may even help to establish a standardized, high-quality level of histology-based diagnostics across institutions-in particular in low-income countries, where expensive DNA-methylation analyses may not be readily available.

Project description:Of nine ependymoma molecular groups detected by DNA methylation profiling, the posterior fossa type A (PFA) is most prevalent. We used DNA methylation profiling to look for further molecular heterogeneity among 675 PFA ependymomas. Two major subgroups, PFA-1 and PFA-2, and nine minor subtypes were discovered. Transcriptome profiling suggested a distinct histogenesis for PFA-1 and PFA-2, but their clinical parameters were similar. In contrast, PFA subtypes differed with respect to age at diagnosis, gender ratio, outcome, and frequencies of genetic alterations. One subtype, PFA-1c, was enriched for 1q gain and had a relatively poor outcome, while patients with PFA-2c ependymomas showed an overall survival at 5 years of >?90%. Unlike other ependymomas, PFA-2c tumors express high levels of OTX2, a potential biomarker for this ependymoma subtype with a good prognosis. We also discovered recurrent mutations among PFA ependymomas. H3 K27M mutations were present in 4.2%, occurring only in PFA-1 tumors, and missense mutations in an uncharacterized gene, CXorf67, were found in 9.4% of PFA ependymomas, but not in other groups. We detected high levels of wildtype or mutant CXorf67 expression in all PFA subtypes except PFA-1f, which is enriched for H3 K27M mutations. PFA ependymomas are characterized by lack of H3 K27 trimethylation (H3 K27-me3), and we tested the hypothesis that CXorf67 binds to PRC2 and can modulate levels of H3 K27-me3. Immunoprecipitation/mass spectrometry detected EZH2, SUZ12, and EED, core components of the PRC2 complex, bound to CXorf67 in the Daoy cell line, which shows high levels of CXorf67 and no expression of H3 K27-me3. Enforced reduction of CXorf67 in Daoy cells restored H3 K27-me3 levels, while enforced expression of CXorf67 in HEK293T and neural stem cells reduced H3 K27-me3 levels. Our data suggest that heterogeneity among PFA ependymomas could have clinicopathologic utility and that CXorf67 may have a functional role in these tumors.

Project description:The diagnosis of ependymoma has moved from a purely histopathological review with limited prognostic value to an integrated diagnosis, relying heavily on molecular information. However, as the integrated approach is still novel and some molecular ependymoma subtypes are quite rare, few studies have correlated integrated pathology and clinical outcome, often focusing on small series of single molecular types. We collected data from 2023 ependymomas as classified by DNA methylation profiling, consisting of 1736 previously published and 287 unpublished methylation profiles. Methylation data and clinical information were correlated, and an integrated model was developed to predict progression-free survival. Patients with EPN-PFA, EPN-ZFTA, and EPN-MYCN tumors showed the worst outcome with 10-year overall survival rates of 56%, 62%, and 32%, respectively. EPN-PFA harbored chromosome 1q gains and/or 6q losses as markers for worse survival. In supratentorial EPN-ZFTA, a combined loss of CDKN2A and B indicated worse survival, whereas a single loss did not. Twelve out of 200 EPN-ZFTA (6%) were located in the posterior fossa, and these tumors relapsed or progressed even earlier than supratentorial tumors with a combined loss of CDKN2A/B. Patients with MPE and PF-SE, generally regarded as non-aggressive tumors, only had a 10-year progression-free survival of 59% and 65%, respectively. For the prediction of the 5-year progression-free survival, Kaplan-Meier estimators based on the molecular subtype, a Support Vector Machine based on methylation, and an integrated model based on clinical factors, CNV data, and predicted methylation scores achieved balanced accuracies of 66%, 68%, and 73%, respectively. Excluding samples with low prediction scores resulted in balanced accuracies of over 80%. In sum, our large-scale analysis of ependymomas provides robust information about molecular features and their clinical meaning. Our data are particularly relevant for rare and hardly explored tumor subtypes and seemingly benign variants that display higher recurrence rates than previously believed.

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Expression alterations define unique molecular characteristics of spinal ependymomas.

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Expression alterations define unique molecular characteristics of spinal ependymomas.

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