Methylation silencing of ULK2, an autophagy gene, is essential for astrocyte transformation and tumor growth.

Glioblastoma (GBM) is the most aggressive type of brain tumor and shows very poor prognosis. Here, using genome-wide methylation analysis, we show that G-CIMP+ and G-CIMP-subtypes enrich distinct classes of biological processes. One of the hypermethylated genes in GBM, ULK2, an upstream autophagy inducer, was found to be down-regulated in GBM. Promoter hypermethylation of ULK2 was confirmed by bisulfite sequencing. GBM and glioma cell lines had low levels of ULK2 transcripts, which could be reversed upon methylation inhibitor treatment. ULK2 promoter methylation and transcript levels showed significant negative correlation. Ectopic overexpression of ULK2-induced autophagy, which further enhanced upon nutrient starvation or temozolomide chemotherapy. ULK2 also inhibited the growth of glioma cells, which required autophagy induction as kinase mutant of ULK2 failed to induce autophagy and inhibit growth. Furthermore, ULK2 induced autophagy and inhibited growth in Ras-transformed immortalized Baby Mouse Kidney (iBMK) ATG5(+/+) but not in autophagy-deficient ATG5(-/-) cells. Growth inhibition due to ULK2 induced high levels of autophagy under starvation or chemotherapy utilized apoptotic cell death but not at low levels of autophagy. Growth inhibition by ULK2 also appears to involve catalase degradation and reactive oxygen species generation. ULK2 overexpression inhibited anchorage independent growth, inhibited astrocyte transformation in vitro and tumor growth in vivo. Of all autophagy genes, we found ULK2 and its homologue ULK1 were only down-regulated in all grades of glioma. Thus these results altogether suggest that inhibition of autophagy by ULK1/2 down-regulation is essential for glioma development.

Higher topoisomerase 2 alpha gene transcript levels predict better prognosis in GBM patients receiving temozolomide chemotherapy: identification of temozolomide as a TOP2A inhibitor.

The search for molecular markers which predict response to chemotherapy is an important aspect of current neuro-oncology research. MGMT promoter methylation is the only proved marker of glioblastoma. The purpose of this study was to assess the effect of topoisomerase expression on glioblastoma survival and study the mechanisms involved. The transcript levels of all isoforms of the topoisomerase family in all grades of diffuse astrocytoma were assessed. A prospective study of patients with glioblastoma treated by a uniform treatment procedure was performed with the objective of correlating outcome with gene expression. The ability of TOP2A enzyme to relax the super coiled plasmid DNA in the presence of temozolomide was evaluated to assess its effect on TOP2A. The temozolomide cyctotoxicity of TOP2A-silenced U251 cells was assessed. The transcript levels of TOP2A, TOP2B, and TOP3A are upregulated significantly in GBM in comparison with lower grades of astrocytoma and normal brain samples. mRNA levels of TOP2A correlated significantly with survival of the patients. Higher TOP2A transcript levels in GBM patients predicted better prognosis (P = 0.043; HR = 0.889). Interestingly, we noted that temozolomide inhibited TOP2A activity in in-vitro enzyme assays. We also noted that siRNA knock down of TOP2A rendered a glioma cell line resistant to temozolomide chemotherapy. We demonstrated for the first time that temozolomide is also a TOP2A inhibitor and established that TOP2A transcript levels determine the chemosensitivity of glioblastoma to temozolomide therapy. Very high levels of TOP2A are a good prognostic indicator in GBM patients receiving temozolomide chemotherapy.

Genomic testing in 1019 individuals from 349 Pakistani families results in high diagnostic yield and clinical utility.

We implemented a collaborative diagnostic program in Lahore (Pakistan) aiming to establish the genetic diagnosis, and to asses diagnostic yield and clinical impact in patients with suspected genetic diseases. Local physicians ascertained pediatric patients who had no previous access to genetic testing. More than 1586 genetic tests were performed in 1019 individuals (349 index cases, 670 relatives). Most frequently performed tests were exome/genome sequencing (ES/GS, 284/78 index cases) and specific gene panels (55 index cases). In 61.3% of the patients (n = 214) a genetic diagnosis was established based on pathogenic and likely pathogenic variants. Diagnostic yield was higher in consanguineous families (60.1 vs. 39.5%). In 27 patients, genetic diagnosis relied on additional biochemical testing, allowing rapid assessment of the functional effect of the variants. Remarkably, the genetic diagnosis had a direct impact on clinical management. Most relevant consequences were therapy related such as initiation of the appropriated treatment in a timely manner in 51.9% of the patients (n = 111). Finally, we report 12 candidate genes among 66 cases with no genetic diagnosis. Importantly, three of these genes were validated as 'diagnostic' genes given the strong evidence supporting causality derived from our data repository (CAP2-dilated cardiomyopathy, ITFG2-intellectual disability and USP53-liver cholestasis). The high diagnostic yield, clinical impact, and research findings demonstrate the utility of genomic testing, especially when used as first-line genetic test. For patients with suspected genetic diseases from resource-limited regions, ES can be considered as the test of choice to achieve genetic diagnosis.

Genetic studies in a patient with X-linked retinoschisis coexisting with developmental delay and sensorineural hearing loss.

BACKGROUND: In this study, we present a juvenile retinoschisis patient with developmental delay, sensorineural hearing loss, and reduced axial tone. X-linked juvenile retinoschisis (XLRS) is a retinal dystrophy, most often not associated with systemic anomalies and also not showing any locus heterogeneity. Therefore it was of interest to understand the genetic basis of the condition in this patient. MATERIALS AND METHODS: RS1 gene screening for XLRS was performed by Sanger sequencing. Whole genome SNP 6.0 array analysis was carried out to investigate gross chromosomal aberrations that could result in systemic phenotype. In addition, targeted next generation sequencing (NGS) was employed to determine any possible involvement of X-linked syndromic and non-syndromic mental retardation genes. This NGS panel consisted of 550 genes implicated in several other rare inherited diseases. RESULTS: RS1 gene screening revealed a pathogenic hemizygous splice site mutation (c.78+1G>T), inherited from the mother. SNP 6.0 array analysis did not indicate any significant chromosomal aberrations that could be disease-associated. Targeted resequencing did not identify any mutations in the X-linked mental retardation genes. However, variations in three other genes (NSD1, LARGE, and POLG) were detected, which were all inherited from the patient's unaffected father. CONCLUSIONS: Taken together, RS1 mutation was found to segregate with retinoschisis phenotype while none of the other identified variations were co-segregating with the systemic defects. Hereby, we infer that the multisystemic defects harbored by the patient are a rare coexistence of XLRS, developmental delay, sensorineural hearing loss, and reduced axial tone reported for the first time in the literature.

A 16-gene signature distinguishes anaplastic astrocytoma from glioblastoma.

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.

A fourteen gene GBM prognostic signature identifies association of immune response pathway and mesenchymal subtype with high risk group.

BACKGROUND: Recent research on glioblastoma (GBM) has focused on deducing gene signatures predicting prognosis. The present study evaluated the mRNA expression of selected genes and correlated with outcome to arrive at a prognostic gene signature. METHODS: Patients with GBM (n = 123) were prospectively recruited, treated with a uniform protocol and followed up. Expression of 175 genes in GBM tissue was determined using qRT-PCR. A supervised principal component analysis followed by derivation of gene signature was performed. Independent validation of the signature was done using TCGA data. Gene Ontology and KEGG pathway analysis was carried out among patients from TCGA cohort. RESULTS: A 14 gene signature was identified that predicted outcome in GBM. A weighted gene (WG) score was found to be an independent predictor of survival in multivariate analysis in the present cohort (HR = 2.507; B = 0.919; p<0.001) and in TCGA cohort. Risk stratification by standardized WG score classified patients into low and high risk predicting survival both in our cohort (p = <0.001) and TCGA cohort (p = 0.001). Pathway analysis using the most differentially regulated genes (n = 76) between the low and high risk groups revealed association of activated inflammatory/immune response pathways and mesenchymal subtype in the high risk group. CONCLUSION: We have identified a 14 gene expression signature that can predict survival in GBM patients. A network analysis revealed activation of inflammatory response pathway specifically in high risk group. These findings may have implications in understanding of gliomagenesis, development of targeted therapies and selection of high risk cancer patients for alternate adjuvant therapies.

A ten-microRNA expression signature predicts survival in glioblastoma.

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n=222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR]=2.4; 95% CI=1.4-3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR=1.7; 95% CI=1.1-2.8; p=0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR=2.0; 95% CI=1.4-2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR=1.120; 95% CI=1.04-1.20; p=0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.