Project description:Recent genomic approaches have suggested the existence of multiple distinct subtypes of medulloblastoma. We studied a large cohort of medulloblastomas to determine how many subgroups of the disease exist, how they differ, and the extent of overlap between subgroups. We determined gene expression profiles and DNA copy number aberrations for 103 primary medulloblastomas. Bioinformatic tools were used for class discovery of medulloblastoma subgroups based on the most informative genes in the dataset. Immunohistochemistry for subgroup-specific ‘signature’ genes was used to determine subgroup affiliation for 294 non-overlapping medulloblastomas on two independent tissue microarrays (TMAs). Multiple unsupervised analyses of transcriptional profiles identified four distinct, non-overlapping molecular variants: WNT, SHH, Group C, and Group D. Supervised analysis of these four subgroups revealed significant subgroup-specific demographics, histology, metastatic status, and DNA copy number aberrations. Immunohistochemistry for DKK1 (WNT), SFRP1 (SHH), NPR3 (Group C), and KCNA1 (Group D) could reliably and uniquely classify formalin fixed medulloblastomas in ~98% of cases. Group C patients (NPR3 +ve tumors) exhibited a significantly diminished progression free and overall survival irrespective of their metastatic status. Our integrative genomics approach to a large cohort of medulloblastomas has identified four disparate subgroups with distinct demographics, clinical presentation, transcriptional profiles, genetic abnormalities, and clinical outcome. Medulloblastomas can be reliably assigned to subgroups through immunohistochemistry, thereby making medulloblastoma sub-classification widely available. Future research on medulloblastoma and the development of clinical trials should take into consideration these four distinct types of medulloblastoma. A total of 103 primary medulloblastoma specimens were profiled by Affymetrix exon array and gene-level analysis was performed.

Project description:BACKGROUND AND PURPOSE:Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma. MATERIALS AND METHODS:In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance. RESULTS:Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma. CONCLUSIONS:This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.

Project description:Human angiostrongyliasis results from accidental infection with Angiostrongylus, an intra-arterial nematode. Angiostrongylus cantonensis infections result in eosinophilic meningitis, and A. costaricensis infections cause eosinophilic enteritis. Immunological methodologies are critical to the diagnosis of both infections, since these parasites cannot be isolated from fecal matter and are rarely found in cerebrospinal fluid samples. A. costaricensis and A. cantonensis share common antigenic epitopes which elicit antibodies that recognize proteins present in either species. Detection of antibodies to a 31-kDa A. cantonensis protein present in crude adult worm extracts is a sensitive and specific method for immunodiagnosis of cerebral angiostrongyliasis. The objective of the present work was to isolate and characterize the 31-kDa proteins using soluble protein extracts derived from adult female worms using both one- (1DE) and two-dimensional (2DE) gel electrophoresis. Separated proteins were blotted onto nitrocellulose and probed using sera from infected and non-infected controls. The 31-kDa band present in 1DE gels and the 4 spots identified in 2DE gels were excised and analyzed by electrospray ionization mass spectrometry. Using the highest scores obtained following Mascot analysis, amino acid sequences were obtained that matched four unique proteins: tropomyosin, the 14-3-3 phosphoserine-binding protein, a protein containing a nascent polypeptide-associated complex domain, and the putative epsilon subunit of coatomer protein complex isoform 2. Oxidative cleavage of diols using sodium m-periodate demonstrated that carbohydrate moieties are essential for the antigenicity of all four spots of the 31-kDa antigen. In this article we describe the identification of the 31-kDa antigen, and provide DNA sequencing of the targets. In conclusion, these data suggest that reactivity to the 31-kDa proteins may represent antibody recognition of more than one protein, and recombinant protein-based assays for cerebral angiostrongyliasis diagnosis may require eukaryotic expression systems to maintain antigenicity.

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Medulloblastoma comprises four distinct molecular variants.

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