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.

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.

Molecular Epidemiology of HIV-1 Subtypes in India: Origin and Evolutionary History of the Predominant Subtype C

Background: India has the third largest HIV-1 epidemic with 2.4 million infected individuals. Molecular epidemiological analysis has identified the predominance of HIV-1 subtype C (HIV-1C). However, the previous reports have been limited by sample size, and uneven geographical distribution. The introduction of HIV-1C in India remains uncertain due to this lack of structured studies. To fill the gap, we characterised the distribution pattern of HIV-1 subtypes in India based on data collection from nationwide clinical cohorts between 2007 and 2011. We also reconstructed the time to the most recent common ancestor (tMRCA) of the predominant HIV-1C strains. Methodology/Principal Findings: Blood samples were collected from 168 HIV-1 seropositive subjects from 7 different states. HIV-1 subtypes were determined using two or three genes, gag, pol, and env using several methods. Bayesian coalescent-based approach was used to reconstruct the time of introduction and population growth patterns of the Indian HIV-1C. For the first time, a high prevalence (10%) of unique recombinant forms (BC and A1C) was observed when two or three genes were used instead of one gene (p<0.01; p = 0.02, respectively). The tMRCA of Indian HIV-1C was estimated using the three viral genes, ranged from 1967 (gag) to 1974 (env). Pol-gene analysis was considered to provide the most reliable estimate 1971, (95% CI: 1965-1976)]. The population growth pattern revealed an initial slow growth phase in the mid-1970s, an exponential phase through the 1980s, and a stationary phase since the early 1990s. Conclusions/Significance: The Indian HIV-1C epidemic originated around 40 years ago from a single or few genetically related African lineages, and since then largely evolved independently. The effective population size in the country has been broadly stable since the 1990s. The evolving viral epidemic, as indicated by the increase of recombinant strains, warrants a need for continued molecular surveillance to guide efficient disease intervention strategies.

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 DNA methylation prognostic signature of glioblastoma: identification of NPTX2-PTEN-NF-kappa B nexus

Glioblastoma (GBM) is the most common, malignant adult primary tumor with dismal patient survival, yet the molecular determinants of patient survival are poorly characterized. Global methylation profile of GBM samples (our cohort; n = 44) using high-resolution methylation microarrays was carried out. Cox regression analysis identified a 9-gene methylation signature that predicted survival in GBM patients. A risk-score derived from methylation signature predicted survival in univariate analysis in our and The Cancer Genome Atlas (TCGA) cohort. Multivariate analysis identified methylation risk score as an independent survival predictor in TCGA cohort. Methylation risk score stratified the patients into low-risk and high-risk groups with significant survival difference. Network analysis revealed an activated NF-kappa B pathway association with high-risk group. NF-kappa B inhibition reversed glioma chemoresistance, and RNA interference studies identified interleukin-6 and intercellular adhesion molecule-1 as key NF-kappa B targets in imparting chemoresistance. Promoter hypermethylation of neuronal pentraxin II (NPTX2), a risky methylated gene, was confirmed by bisulfite sequencing in GBMs. GBMs and glioma cell lines had low levels of NPTX2 transcripts, which could be reversed upon methylation inhibitor treatment. NPTX2 overexpression induced apoptosis, inhibited proliferation and anchorage-independent growth, and rendered glioma cells chemosensitive. Furthermore, NPTX2 repressed NF-kappa B activity by inhibiting AKT through a p53-PTEN-dependent pathway, thus explaining the hypermethylation and downregulation of NPTX2 in NF-kappa B-activated high-risk GBMs. Taken together, a 9-gene methylation signature was identified as an independent GBM prognosticator and could be used for GBM risk stratification. Prosurvival NF-kappa B pathway activation characterized high-risk patients with poor prognosis, indicating it to be a therapeutic target. (C) 2013 AACR.

Variation at Diabetes- and Obesity-Associated Loci May Mirror Neutral Patterns of Human Population Diversity and Diabetes Prevalence in India

South Asian populations harbor a high degree of genetic diversity, due in part to demographic history. Two studies on genome-wide variation in Indian populations have shown that most Indian populations show varying degrees of admixture between ancestral north Indian and ancestral south Indian components. As a result of this structure, genetic variation in India appears to follow a geographic cline. Similarly, Indian populations seem to show detectable differences in diabetes and obesity prevalence between different geographic regions of the country. We tested the hypothesis that genetic variation at diabetes-and obesity-associated loci may be potentially related to different genetic ancestries. We genotyped 2977 individuals from 61 populations across India for 18 SNPs in genes implicated in T2D and obesity. We examined patterns of variation in allele frequency across different geographical gradients and considered state of origin and language affiliation. Our results show that most of the 18 SNPs show no significant correlation with latitude, the geographic cline reported in previous studies, or by language family. Exceptions include KCNQ1 with latitude and THADA and JAK1 with language, which suggests that genetic variation at previously ascertained diabetes-associated loci may only partly mirror geographic patterns of genome-wide diversity in Indian populations.

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.

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.

Genome-wide methylation profiling identifies an essential role of reactive oxygen species in pediatric glioblastoma multiforme and validates a methylome specific for H3 histone family 3A with absence of G-CIMP/isocitrate dehydrogenase 1 mutation

Background. Pediatric glioblastoma multiforme (GBM) is rare, and there is a single study, a seminal discovery showing association of histone H3.3 and isocitrate dehydrogenase (IDH) 1 mutation with a DNA methylation signature. The present study aims to validate these findings in an independent cohort of pediatric GBM, compare it with adult GBM, and evaluate the involvement of important functionally altered pathways. Methods. Genome-wide methylation profiling of 21 pediatric GBM cases was done and compared with adult GBM data (GSE22867). We performed gene mutation analysis of IDH1 and H3 histone family 3A (H3F3A), status evaluation of glioma cytosine-phosphate-guanine island methylator phenotype (G-CIMP), and Gene Ontology analysis. Experimental evaluation of reactive oxygen species (ROS) association was also done. Results. Distinct differences were noted between methylomes of pediatric and adult GBM. Pediatric GBM was characterized by 94 hypermethylated and 1206 hypomethylated cytosine-phosphate-guanine (CpG) islands, with 3 distinct clusters, having a trend to prognostic correlation. Interestingly, none of the pediatric GBM cases showed G-CIMP/IDH1 mutation. Gene Ontology analysis identified ROS association in pediatric GBM, which was experimentally validated. H3F3A mutants (36.4%; all K27M) harbored distinct methylomes and showed enrichment of processes related to neuronal development, differentiation, and cell-fate commitment. Conclusions. Our study confirms that pediatric GBM has a distinct methylome compared with that of adults. Presence of distinct clusters and an H3F3A mutation-specific methylome indicate existence of epigenetic subgroups within pediatric GBM. Absence of IDH1/G-CIMP status further indicates that findings in adult GBM cannot be simply extrapolated to pediatric GBM and that there is a strong need for identification of separate prognostic markers. A possible role of ROS in pediatric GBM pathogenesis is demonstrated for the first time and needs further evaluation.