Regulation of sonic hedgehog-GLI1 downstream target genes PTCH1, Cyclin D2, Plakoglobin, PAX6 and NKX2.2 and their epigenetic status in medulloblastoma and astrocytoma

Abstract Background The Sonic hedgehog (Shh) signaling pathway is critical for cell growth and differentiation. Impairment of this pathway can result in both birth defects and cancer. Despite its importance in cancer development, the Shh pathway has not been thoroughly investigated in tumorigenesis of brain tumors. In this study, we sought to understand the regulatory roles of GLI1, the immediate downstream activator of the Shh signaling pathway on its downstream target genes PTCH1, Cyclin D2, Plakoglobin, NKX2.2 and PAX6 in medulloblastoma and astrocytic tumors. Methods We silenced GLI1 expression in medulloblastoma and astrocytic cell lines by transfection of siRNA against GLI1. Subsequently, we performed RT-PCR and quantitative real time RT-PCR (qRT-PCR) to assay the expression of downstream target genes PTCH1, Cyclin D2, Plakoglobin, NKX2.2 and PAX6. We also attempted to correlate the pattern of expression of GLI1 and its regulated genes in 14 cell lines and 41 primary medulloblastoma and astrocytoma tumor samples. We also assessed the methylation status of the Cyclin D2 and PTCH1 promoters in these 14 cell lines and 58 primary tumor samples. Results Silencing expression of GLI1 resulted up-regulation of all target genes in the medulloblastoma cell line, while only PTCH1 was up-regulated in astrocytoma. We also observed methylation of the cyclin D2 promoter in a significant number of astrocytoma cell lines (63%) and primary astrocytoma tumor samples (32%), but not at all in any medulloblastoma samples. PTCH1 promoter methylation was less frequently observed than Cyclin D2 promoter methylation in astrocytomas, and not at all in medulloblastomas. Conclusions Our results demonstrate different regulatory mechanisms of Shh-GLI1 signaling. These differences vary according to the downstream target gene affected, the origin of the tissue, as well as epigenetic regulation of some of these genes.http://deepblue.lib.umich.edu/bitstream/2027.42/78313/1/1471-2407-10-614.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78313/2/1471-2407-10-614.pdfPeer Reviewe

Crossed Aphasia in a Patient with Anaplastic Astrocytoma of the Non-Dominant Hemisphere

Aphasia describes a spectrum of speech impairments due to damage in the language centers of the brain. Insult to the inferior frontal gyrus of the dominant cerebral hemisphere results in Broca\u27s aphasia - the inability to produce fluent speech. The left cerebral hemisphere has historically been considered the dominant side, a characteristic long presumed to be related to a person\u27s handedness . However, recent studies utilizing fMRI have shown that right hemispheric dominance occurs more frequently than previously proposed and despite a person\u27s handedness. Here we present a case of a right-handed patient with Broca\u27s aphasia caused by a right-sided brain tumor. This is significant not only because the occurrence of aphasia in right-handed-individuals with right hemispheric brain damage (so-called crossed aphasia ) is unusual but also because such findings support dissociation between hemispheric linguistic dominance and handedness. © 2017, EduRad. All rights reserved

Duplications of KIAA1549 and BRAF screening by Droplet Digital PCR from formalin-fixed paraffin-embedded DNA is an accurate alternative for KIAA1549-BRAF fusion detection in pilocytic astrocytomas

Pilocytic astrocytomas represent the most common glioma subtype in young patients and account for 5.4% of all gliomas. They are characterized by alterations in the RAS–MAP kinase pathway, the most frequent being a tandem duplication on chromosome 7q34 involving the BRAF gene, resulting in oncogenic BRAF fusion proteins. BRAF fusion involving the KIAA1549 gene is a hallmark of pilocytic astrocytoma, but it has also been recorded in rare cases of gangliogliomas, 1p/19q co-deleted oligodendroglial tumors, and it is also a common feature of disseminated oligodendroglial-like leptomeningeal neoplasm. In some difficult cases, evidence for KIAA1549-BRAF fusion is of utmost importance for the diagnosis. Moreover, because the KIAA1549-BRAF fusion constitutively activates the MAP kinase pathway, it represents a target for drugs such as MEK inhibitors, and therefore, the detection of this genetic abnormality is highly relevant in the context of clinical trials applying such new approaches. In the present study, we aimed to use the high sensitivity of Droplet Digital PCR (DDPCR™) to predict KIAA1549-BRAF fusion on very small amounts of formalin-fixed paraffin-embedded tissue in routine practice. Therefore, we analyzed a training cohort of 55 pilocytic astrocytomas in which the KIAA1549-BRAF fusion status was known by RNA sequencing used as our gold standard technique. Then, we analyzed a prospective cohort of 40 pilocytic astrocytomas, 27 neuroepithelial tumors remaining difficult to classify (pilocytic astrocytoma versus ganglioglioma or diffuse glioma), 15 dysembryoplastic neuroepithelial tumors, and 18 gangliogliomas. We could demonstrate the usefulness and high accuracy (100% sensitivity and specificity when compared to RNA sequencing) of DDPCR™ to assess the KIAA1549-BRAF fusion from very low amounts of DNA isolated from formalin-fixed paraffin-embedded specimens. BRAF duplication is both necessary and sufficient to predict this fusion in most cases and we propose that this single analysis could be used in routine practice to save time, money, and precious tissue

Machine Learning Decision Tree Models for Differentiation of Posterior Fossa Tumors Using Diffusion Histogram Analysis and Structural MRI Findings.

We applied machine learning algorithms for differentiation of posterior fossa tumors using apparent diffusion coefficient (ADC) histogram analysis and structural MRI findings. A total of 256 patients with intra-axial posterior fossa tumors were identified, of whom 248 were included in machine learning analysis, with at least 6 representative subjects per each tumor pathology. The ADC histograms of solid components of tumors, structural MRI findings, and patients' age were applied to construct decision models using Classification and Regression Tree analysis. We also compared different machine learning classification algorithms (i.e., naïve Bayes, random forest, neural networks, support vector machine with linear and polynomial kernel) for dichotomized differentiation of the 5 most common tumors in our cohort: metastasis (n = 65), hemangioblastoma (n = 44), pilocytic astrocytoma (n = 43), ependymoma (n = 27), and medulloblastoma (n = 26). The decision tree model could differentiate seven tumor histopathologies with terminal nodes yielding up to 90% accurate classification rates. In receiver operating characteristics (ROC) analysis, the decision tree model achieved greater area under the curve (AUC) for differentiation of pilocytic astrocytoma (p = 0.020); and atypical teratoid/rhabdoid tumor ATRT (p = 0.001) from other types of neoplasms compared to the official clinical report. However, neuroradiologists' interpretations had greater accuracy in differentiating metastases (p = 0.001). Among different machine learning algorithms, random forest models yielded the highest accuracy in dichotomized classification of the 5 most common tumor types; and in multiclass differentiation of all tumor types random forest yielded an averaged AUC of 0.961 in training datasets, and 0.873 in validation samples. Our study demonstrates the potential application of machine learning algorithms and decision trees for accurate differentiation of brain tumors based on pretreatment MRI. Using easy to apply and understandable imaging metrics, the proposed decision tree model can help radiologists with differentiation of posterior fossa tumors, especially in tumors with similar qualitative imaging characteristics. In particular, our decision tree model provided more accurate differentiation of pilocytic astrocytomas from ATRT than by neuroradiologists in clinical reads

BRAF V600E, TERT promoter mutations and CDKN2A/B homozygous deletions are frequent in epithelioid glioblastomas: a histological and molecular analysis focusing on intratumoral heterogeneity

学位記番号:医博甲173

Child-related characteristics predicting subsequent health-related quality of life in 8- to 14-year-old children with and without cerebellar tumors: a prospective longitudinal study

BackgroundWe identified child-related determinants of health-related quality of life (HRQoL) in children aged 8–14 years who were treated for 2 common types of pediatric brain tumors. MethodsQuestionnaire measures of HRQoL and psychometric assessments were completed by 110 children on 3 occasions over 24 months. Of these 110, 72 were within 3 years of diagnosis of a cerebellar tumor (37 standard-risk medulloblastoma, 35 low-grade cerebellar astrocytoma), and 38 were in a nontumor group. HRQoL, executive function, health status, and behavioral difficulties were also assessed by parents and teachers as appropriate. Regression modeling was used to relate HRQoL z scores to age, sex, socioeconomic status, and 5 domains of functioning: Cognition, Emotion, Social, Motor and Sensory, and Behavior. ResultsHRQoL z scores were significantly lower after astrocytoma than those in the nontumor group and significantly lower again in the medulloblastoma group, both by self-report and by parent-report. In regression modeling, significant child-related predictors of poorer HRQoL z scores by self-report were poorer cognitive and emotional function (both z scores) and greater age (years) at enrollment (B = 0.038, 0.098, 0.136, respectively). By parent-report, poorer cognitive, emotional and motor or sensory function (z score) were predictive of lower subsequent HRQoL of the child (B = 0.043, 0.112, 0.019, respectively), while age at enrollment was not. ConclusionsEarly screening of cognitive and emotional function in this age group, which are potentially amenable to change, could identify those at risk of poor HRQoL and provide a rational basis for interventions to improve HRQoL