Dataset Information

Spinal Myxopapillary Ependymomas Demonstrate a Warburg Phenotype.

ABSTRACT: Myxopapillary ependymoma (MPE) is a distinct histologic variant of ependymoma arising commonly in the spinal cord. Despite an overall favorable prognosis, distant metastases, subarachnoid dissemination, and late recurrences have been reported. Currently, the only effective treatment for MPE is gross-total resection. We characterized the genomic and transcriptional landscape of spinal ependymomas in an effort to delineate the genetic basis of this disease and identify new leads for therapy.Gene expression profiling was performed on 35 spinal ependymomas, and copy number profiling was done on an overlapping cohort of 46 spinal ependymomas. Functional validation experiments were performed on tumor lysates consisting of assays measuring pyruvate kinase M activity (PKM), hexokinase activity (HK), and lactate production.At a gene expression level, we demonstrate that spinal grade II and MPE are molecularly and biologically distinct. These are supported by specific copy number alterations occurring in each histologic variant. Pathway analysis revealed that MPE are characterized by increased cellular metabolism, associated with upregulation of HIF1?. These findings were validated by Western blot analysis demonstrating increased protein expression of HIF1?, HK2, PDK1, and phosphorylation of PDHE1A. Functional assays were performed on MPE lysates, which demonstrated decreased PKM activity, increased HK activity, and elevated lactate production.Our findings suggest that MPE may be driven by a Warburg metabolic phenotype. The key enzymes promoting the Warburg phenotype: HK2, PKM2, and PDK are targetable by small-molecule inhibitors/activators, and should be considered for evaluation in future clinical trials for MPE.

SUBMITTER: Mack SC

PROVIDER: S-EPMC4537825 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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Spinal Myxopapillary Ependymomas Demonstrate a Warburg Phenotype.

Mack Stephen C SC Agnihotri Sameer S Bertrand Kelsey C KC Wang Xin X Wang Xin X Shih David J DJ Witt Hendrik H Hill Nadia N Zayne Kory K Barszczyk Mark M Ramaswamy Vijay V Remke Marc M Thompson Yuan Y Ryzhova Marina M Massimi Luca L Grajkowska Wieslawa W Lach Boleslaw B Gupta Nalin N Weiss William A WA Guha Abhijit A Hawkins Cynthia C Croul Sidney S Rutka James T JT Pfister Stefan M SM Korshunov Andrey A Pekmezci Melike M Tihan Tarik T Philips Joanna J JJ Jabado Nada N Zadeh Gelareh G Taylor Michael D MD

Clinical cancer research : an official journal of the American Association for Cancer Research 20150508 16

<h4>Purpose</h4>Myxopapillary ependymoma (MPE) is a distinct histologic variant of ependymoma arising commonly in the spinal cord. Despite an overall favorable prognosis, distant metastases, subarachnoid dissemination, and late recurrences have been reported. Currently, the only effective treatment for MPE is gross-total resection. We characterized the genomic and transcriptional landscape of spinal ependymomas in an effort to delineate the genetic basis of this disease and identify new leads fo ...[more]

PMID: 25957288

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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:BackgroundSpinal cord ependymomas (SCEs) represent the most common intramedullary spinal cord tumors among adults. Research shows that access to neurosurgical care and patient outcomes can be greatly influenced by patient location. This study investigates the association between the outcomes of patients with SCE in metropolitan and nonmetropolitan areas.MethodsCases of SCE between 2004 and 2019 were identified within the Central Brain Tumor Registry of the United States, a combined dataset including the Centers for Disease Control and Prevention's National Program of Cancer Registries and National Cancer Institute's Surveillance, Epidemiology, and End Results Program data. Multivariable logistic regression models were constructed to evaluate the association between urbanicity and SCE treatment, adjusted for age at diagnosis, sex, race and ethnicity. Survival data was available from 42 National Program of Cancer Registries (excluding Kansas and Minnesota, for which county data are unavailable), and Cox proportional hazard models were used to understand the effect of surgical treatment, county urbanicity, age at diagnosis, and the interaction effect between age at diagnosis and surgery, on the survival time of patients.ResultsOverall, 7577 patients were identified, with 6454 (85%) residing in metropolitan and 1223 (15%) in nonmetropolitan counties. Metropolitan and nonmetropolitan counties had different age, sex, and race/ethnicity compositions; however, demographics were not associated with differences in the type of surgery received when stratified by urbanicity. Irrespective of metropolitan status, individuals who were American Indian/Alaska Native non-Hispanic and Hispanic (all races) were associated with reduced odds of receiving surgery. Individuals who were Black non-Hispanic and Hispanic were associated with increased odds of receiving comprehensive treatment. Diagnosis of SCE at later ages was linked with elevated mortality (hazard ratio = 4.85, P < 0.001). Gross total resection was associated with reduced risk of death (hazard ratio = 0.37, P = 0.004), and age did not interact with gross total resection to influence risk of death.ConclusionsThe relationship between patients' residential location and access to neurosurgical care is critical to ensuring equitable distribution of care. This study represents an important step in delineating areas of existing disparities.

Dataset Information

Spinal Myxopapillary Ependymomas Demonstrate a Warburg Phenotype.

Publications

Spinal Myxopapillary Ependymomas Demonstrate a Warburg Phenotype.

Similar Datasets

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