Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients

Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.     

Genetics of sputum gene expression in chronic obstructive pulmonary disease

Previous expression quantitative trait loci (eQTL) studies have performed genetic association studies for gene expression, but most of these studies examined lymphoblastoid cell lines from non-diseased individuals. We examined the genetics of gene expression in a relevant disease tissue from chronic obstructive pulmonary disease (COPD) patients to identify functional effects of known susceptibility genes and to find novel disease genes. By combining gene expression profiling on induced sputum samples from 131 COPD cases from the ECLIPSE Study with genomewide single nucleotide polymorphism (SNP) data, we found 4315 significant cis-eQTL SNP-probe set associations (3309 unique SNPs). The 3309 SNPs were tested for association with COPD in a genomewide association study (GWAS) dataset, which included 2940 COPD cases and 1380 controls. Adjusting for 3309 tests (p<1.5e-5), the two SNPs which were significantly associated with COPD were located in two separate genes in a known COPD locus on chromosome 15: CHRNA5 and IREB2. Detailed analysis of chromosome 15 demonstrated additional eQTLs for IREB2 mapping to that gene. eQTL SNPs for CHRNA5 mapped to multiple linkage disequilibrium (LD) bins. The eQTLs for IREB2 and CHRNA5 were not in LD. Seventy-four additional eQTL SNPs were associated with COPD at p<0.01. These were genotyped in two COPD populations, finding replicated associations with a SNP in PSORS1C1, in the HLA-C region on chromosome 6. Integrative analysis of GWAS and gene expression data from relevant tissue from diseased subjects has located potential functional variants in two known COPD genes and has identified a novel COPD susceptibility locus.     

Network Signatures of Survival in Glioblastoma Multiforme

To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed a combinatorial network analysis framework to exhaustively search for molecular patterns in protein-protein interaction (PPI) networks. We identified a dysregulated molecular signature distinguishing short-term (survival<225 days) from long-term (survival>635 days) survivors of GBM using whole genome expression data from The Cancer Genome Atlas (TCGA). A 50-gene subnetwork signature achieved 80% prediction accuracy when tested against an independent gene expression dataset. Functional annotations for the subnetwork signature included “protein kinase cascade,” “IκB kinase/NFκB cascade,” and “regulation of programmed cell death” – all of which were not significant in signatures of existing subtypes. Finally, we used label-free proteomics to examine how our subnetwork signature predicted protein level expression differences in an independent GBM cohort of 16 patients. We found that the genes discovered using network biology had a higher probability of dysregulated protein expression than either genes exhibiting individual differential expression or genes derived from known GBM subtypes. In particular, the long-term survivor subtype was characterized by increased protein expression of DNM1 and MAPK1 and decreased expression of HSPA9, PSMD3, and CANX. Overall, we demonstrate that the combinatorial analysis of gene expression data constrained by PPIs outlines an approach for the discovery of robust and translatable molecular signatures in GBM.     

An Information-Theoretic Machine Learning Approach to Expression QTL Analysis

Expression Quantitative Trait Locus (eQTL) analysis is a powerful tool to study the biological mechanisms linking the genotype with gene expression. Such analyses can identify genomic locations where genotypic variants influence the expression of genes, both in close proximity to the variant (cis-eQTL), and on other chromosomes (trans-eQTL). Many traditional eQTL methods are based on a linear regression model. In this study, we propose a novel method by which to identify eQTL associations with information theory and machine learning approaches. Mutual Information (MI) is used to describe the association between genetic marker and gene expression. MI can detect both linear and non-linear associations. What’s more, it can capture the heterogeneity of the population. Advanced feature selection methods, Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS), were applied to optimize the selection of the affected genes by the genetic marker. When we applied our method to a study of apoE-deficient mice, it was found that the cis-acting eQTLs are stronger than trans-acting eQTLs but there are more trans-acting eQTLs than cis-acting eQTLs. We compared our results (mRMR.eQTL) with R/qtl, and MatrixEQTL (modelLINEAR and modelANOVA). In female mice, 67.9% of mRMR.eQTL results can be confirmed by at least two other methods while only 14.4% of R/qtl result can be confirmed by at least two other methods. In male mice, 74.1% of mRMR.eQTL results can be confirmed by at least two other methods while only 18.2% of R/qtl result can be confirmed by at least two other methods. Our methods provide a new way to identify the association between genetic markers and gene expression. Our software is available from supporting information.     

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.     

Alterations of Glucose-Dependent Insulinotropic Polypeptide and Expression of Genes Involved in Mammary Gland and Adipose Tissue Lipid Metabolism during Pregnancy and Lactation

Gastric inhibitory polypeptide (GIP) is a gut derived peptide with multiple emerging physiological actions. Effects of pregnancy and lactation on GIP secretion and related gene expression were studied in Wistar rats. Pregnancy moderately increased feeding (p<0.05), whilst lactation substantially increased food intake (p<0.01 to p<0.001). Circulating GIP was unchanged during pregnancy, but non-fasting plasma glucose was significantly (p<0.01) decreased and insulin increased (p<0.05). Lactation was associated with elevated circulating GIP concentrations (p<0.001) without change of glucose or insulin. Oral glucose resulted in a significantly (p<0.001) decreased glycaemic excursion despite similar glucose-induced GIP and insulin concentrations in lactating rats. Pregnant rats had a similar glycaemic excursion but exhibited significantly lowered (p<0.05) GIP accompanied by elevated (p<0.001) insulin levels. Pregnant rats exhibited increased (p<0.001) islet numbers and individual islet areas were enlarged (p<0.05). There were no significant differences in islet alpha-cell areas, but all groups of rats displayed co-expression of glucagon and GIP in alpha-cells. Lactating rats exhibited significantly (p<0.01) increased intestinal weight, whereas intestinal GIP stores were significantly (p<0.01) elevated only in pregnant rats. Gene expression studies in lactating rats revealed prominent (p<0.01 to p<0.001) increases in mammary gland expression of genes involved in energy turnover, including GIP-R. GIP was present in intestines and plasma of 17 day old foetal rats, with substantially raised circulating concentrations in neonates throughout the period of lactation/suckling. These data indicate that changes in the secretion and action of GIP play an important role in metabolic adaptations during pregnancy and especially lactation.     

Functional Profiling of Precursor MicroRNAs Identifies MicroRNAs Essential for Glioma Proliferation

Cancer initiation and progression involve microRNAs that can function like tumor suppressors and oncogenes. The functional significance of most miRNAs is currently unknown. To determine systematically which microRNAs are essential for glioma growth, we screened a precursor microRNA library in three human glioblastoma and one astroglial cell line model systems. The most prominent and consistent cell proliferation–reducing hits were validated in secondary screening with an additional apoptosis endpoint. The functional screening data were integrated in the miRNA expression data to find underexpressed true functional tumor suppressor miRNAs. In addition, we used miRNA-target gene predictions and combined siRNA functional screening data to find the most probable miRNA-target gene pairs with a similar functional effect on proliferation. Nine novel functional miRNAs (hsa-miR-129, -136, -145, -155, -181b, -342-5p, -342-3p, -376a/b) in GBM cell lines were validated for their importance in glioma cell growth, and similar effects for six target genes (ROCK1, RHOA, MET, CSF1R, EIF2AK1, FGF7) of these miRNAs were shown functionally. The clinical significance of the functional hits was validated in miRNA expression data from the TCGA glioblastoma multiforme (GBM) tumor cohort. Five tumor suppressor miRNAs (hsa-miR-136, -145, -342, -129, -376a) showed significant underexpression in clinical GBM tumor samples from the TCGA GBM cohort further supporting the role of these miRNAs in vivo. Most importantly, higher hsa-miR-145 expression in GBM tumors yielded significantly better survival (p<0.005) in a subset of patients thus validating it as a genuine tumor suppressor miRNA. This systematic functional profiling provides important new knowledge about functionally relevant miRNAs in GBM biology and may offer new targets for treating glioma.     

Protein-Protein Interaction Analysis Highlights Additional Loci of Interest for Multiple Sclerosis

Genetic factors play an important role in determining the risk of multiple sclerosis (MS). The strongest genetic association in MS is located within the major histocompatibility complex class II region (MHC), but more than 50 MS loci of modest effect located outside the MHC have now been identified. However, the relative candidate genes that underlie these associations and their functions are largely unknown. We conducted a protein-protein interaction (PPI) analysis of gene products coded in loci recently reported to be MS associated at the genome-wide significance level and in loci suggestive of MS association. Our aim was to identify which suggestive regions are more likely to be truly associated, which genes are mostly implicated in the PPI network and their expression profile. From three recent independent association studies, SNPs were considered and divided into significant and suggestive depending on the strength of the statistical association. Using the Disease Association Protein-Protein Link Evaluator tool we found that direct interactions among genetic products were significantly higher than expected by chance when considering both significant regions alone (p<0.0002) and significant plus suggestive (p<0.007). The number of genes involved in the network was 43. Of these, 23 were located within suggestive regions and many of them directly interacted with proteins coded within significant regions. These included genes such as SYK, IL-6, CSF2RB, FCLR3, EIF4EBP2 and CHST12. Using the gene portal BioGPS, we tested the expression of these genes in 24 different tissues and found the highest values among immune-related cells as compared to non-immune tissues (p<0.001). A gene ontology analysis confirmed the immune-related functions of these genes. In conclusion, loci currently suggestive of MS association interact with and have similar expression profiles and function as those significantly associated, highlighting the fact that more common variants remain to be found to be associated to MS.     

Inflammatory Signalling in Fetal Membranes: Increased Expression Levels of TLR 1 in the Presence of Preterm Histological Chorioamnionitis

Histological chorioamnionitis (HCA) is an established marker of ascending infection, a major cause of preterm birth. No studies have characterised the global change in expression of genes involved in the toll-like receptor (TLR) signalling pathways in the presence of HCA in the setting of preterm birth (pHCA). Fetal membranes were collected immediately after delivery and underwent histological staging for inflammation to derive 3 groups; term spontaneous labour without HCA (n = 9), preterm birth <34 weeks gestation without HCA (n = 8) and pHCA <34 weeks (n = 12). Profiling arrays ran in triplicate for each group were used to determine the expression of 84 genes associated with TLR signalling and screen for genes of interest (fold change >2; p<0.1). Expression of identified genes was validated individually for all samples, relative to GAPDH, using RT-PCR. Expression of TLR 1, TLR 2, lymphocyte antigen 96, interleukin 8 and Interleukin-1 receptor-associated kinase-like 2 was increased in pHCA (p<0.05). Degree of expression was positively associated with histological staging of both maternal and fetal inflammation (p<0.05). The inflammatory expression profile at the maternal/fetal interface associated with pHCA, a reflection of ascending infection, is extremely heterogeneous suggesting polymicrobial involvement with activation of a common pathway. Antagonism of TLR 1 and TLR 2 signalling in this setting warrants further assessment.     

Involvement of the Clock Gene Rev-erb alpha in the Regulation of Glucagon Secretion in Pancreatic Alpha-Cells

Disruption of pancreatic clock genes impairs pancreatic beta-cell function, leading to the onset of diabetes. Despite the importance of pancreatic alpha-cells in the regulation of glucose homeostasis and in diabetes pathophysiology, nothing is known about the role of clock genes in these cells. Here, we identify the clock gene Rev-erb alpha as a new intracellular regulator of glucagon secretion. Rev-erb alpha down-regulation by siRNA (60–70% inhibition) in alphaTC1-9 cells inhibited low-glucose induced glucagon secretion (p<0.05) and led to a decrease in key genes of the exocytotic machinery. The Rev-erb alpha agonist GSK4112 increased glucagon secretion (1.6 fold) and intracellular calcium signals in alphaTC1-9 cells and mouse primary alpha-cells, whereas the Rev-erb alpha antagonist SR8278 produced the opposite effect. At 0.5 mM glucose, alphaTC1-9 cells exhibited intrinsic circadian Rev-erb alpha expression oscillations that were inhibited by 11 mM glucose. In mouse primary alpha-cells, glucose induced similar effects (p<0.001). High glucose inhibited key genes controlled by AMPK such as Nampt, Sirt1 and PGC-1 alpha in alphaTC1-9 cells (p<0.05). AMPK activation by metformin completely reversed the inhibitory effect of glucose on Nampt-Sirt1-PGC-1 alpha and Rev-erb alpha. Nampt inhibition decreased Sirt1, PGC-1 alpha and Rev-erb alpha mRNA expression (p<0.01) and glucagon release (p<0.05). These findings identify Rev-erb alpha as a new intracellular regulator of glucagon secretion via AMPK/Nampt/Sirt1 pathway.     

The Uremic Toxin Adsorbent AST-120 Abrogates Cardiorenal Injury Following Myocardial Infarction

An accelerated progressive decline in renal function is a frequent accompaniment of myocardial infarction (MI). Indoxyl sulfate (IS), a uremic toxin that accumulates from the early stages of chronic kidney disease (CKD), is contributory to both renal and cardiac fibrosis. IS levels can be reduced by administration of the oral adsorbent AST-120, which has been shown to ameliorate pathological renal and cardiac fibrosis in moderate to severe CKD. However, the cardiorenal effect of AST-120 on less severe renal dysfunction in the post-MI setting has not previously been well studied. MI-induced Sprague-Dawley rats were randomized to receive either AST-120 (MI+AST-120) or were untreated (MI+Vehicle) for 16 weeks. Serum IS levels were measured at baseline, 8 and 16 weeks. Echocardiography and glomerular filtration rate (GFR) were assessed prior to sacrifice. Renal and cardiac tissues were assessed for pathological changes using histological and immunohistochemical methods, Western blot analysis and real-time PCR. Compared with sham, MI+Vehicle animals had a significant reduction in left ventricular ejection fraction (by 42%, p<0.001) and fractional shortening (by 52%, p<0.001) as well as lower GFR (p<0.05) and increased serum IS levels (p<0.05). A significant increase in interstitial fibrosis in the renal cortex was demonstrated in MI+Vehicle animals (p<0.001). Compared with MI+Vehicle, MI+AST-120 animals had increased GFR (by 13.35%, p<0.05) and reduced serum IS (p<0.001), renal interstitial fibrosis (p<0.05), and renal KIM-1, collagen-IV and TIMP-1 expression (p<0.05). Cardiac function did not change with AST-120 treatment, however gene expression of TGF-β1 and TNF-α as well as collagen-I and TIMP-1 protein expression was decreased in the non-infarcted myocardium (p<0.05). In conclusion, reduction of IS attenuates cardio-renal fibrotic processes in the post-MI kidney. KIM-1 appears to be a sensitive renal injury biomarker in this setting and is correlated with serum IS levels.