BMC Medical Genomics

Glioblastomas are the most common primary brain tumour in adults. While the prognosis for patients is poor, gene expression profiling has detected signatures that can sub-classify GBMs relative to histopathology and clinical variables. One category of GBM defined by a gene expression signature is termed ProNeural (PN), and has substantially longer patient survival relative to other gene expression-based subtypes of GBMs. Age of onset is a major predictor of the length of patient survival where younger patients survive longer than older patients. The reason for this survival advantage has not been clear.

We collected 267 GBM CEL files and normalized them relative to other microarrays of the same Affymetrix platform. 377 probesets on U133A and U133 Plus 2.0 arrays were used in a gene voting strategy with 177 probesets of matching genes on older U95Av2 arrays. Kaplan-Meier curves and Cox proportional hazard analyses were applied in distinguishing survival differences between expression subtypes and age.

This meta-analysis of published data in addition to new data confirms the existence of four distinct GBM expression-signatures. Further, patients with PN subtype GBMs had longer survival, as expected. However, the age of the patient at diagnosis is not predictive of survival time when controlled for the PN subtype.

The survival benefit of younger age is nullified when patients are stratified by gene expression group. Thus, the main cause of the age effect in GBMs is the more frequent occurrence of PN GBMs in younger patients relative to older patients.

Late-onset Alzheimer's disease (LOAD) is an age related neurodegenerative disease with a high prevalence that places major demands on healthcare resources in societies with increasingly aged populations. The only extensively replicable genetic risk factor for LOAD is the apolipoprotein E gene. In order to identify additional genetic risk loci we have conducted a genome-wide association (GWA) study in a large LOAD case – control sample, reducing costs through the use of DNA pooling.

DNA samples were collected from 1,082 individuals with LOAD and 1,239 control subjects. Age at onset ranged from 60 to 95 and Controls were matched for age (mean = 76.53 years, SD = 33), gender and ethnicity. Equimolar amounts of each DNA sample were added to either a case or control pool. The pools were genotyped using Illumina HumanHap300 and Illumina Sentrix HumanHap240S arrays testing 561,494 SNPs. 114 of our best hit SNPs from the pooling data were identified and then individually genotyped in the case – control sample used to construct the pools.

Highly significant association with LOAD was observed at the APOE locus confirming the validity of the pooled genotyping approach.

For 109 SNPs outside the APOE locus, we obtained uncorrected p-values ≤ 0.05 for 74 after individual genotyping. To further test these associations, we added control data from 1400 subjects from the 1958 Birth Cohort with the evidence for association increasing to 3.4 × 10^-6 for our strongest finding, rs727153.

rs727153 lies 13 kb from the start of transcription of lecithin retinol acyltransferase (phosphatidylcholine – retinol O-acyltransferase, LRAT). Five of seven tag SNPs chosen to cover LRAT showed significant association with LOAD with a SNP in intron 2 of LRAT, showing greatest evidence of association (rs201825, p-value = 6.1 × 10^-7).

We have validated the pooling method for GWA studies by both identifying the APOE locus and by observing a strong enrichment for significantly associated SNPs. We provide evidence for LRAT as a novel candidate gene for LOAD. LRAT plays a prominent role in the Vitamin A cascade, a system that has been previously implicated in LOAD.

Hyperactivation of the Ras signaling pathway is a driver of many cancers, and RAS pathway activation can predict response to targeted therapies. Therefore, optimal methods for measuring Ras pathway activation are critical. The main focus of our work was to develop a gene expression signature that is predictive of RAS pathway dependence.

We used the coherent expression of RAS pathway-related genes across multiple datasets to derive a RAS pathway gene expression signature and generate RAS pathway activation scores in pre-clinical cancer models and human tumors. We then related this signature to KRAS mutation status and drug response data in pre-clinical and clinical datasets.

The RAS signature score is predictive of KRAS mutation status in lung tumors and cell lines with high (> 90%) sensitivity but relatively low (50%) specificity due to samples that have apparent RAS pathway activation in the absence of a KRAS mutation. In lung and breast cancer cell line panels, the RAS pathway signature score correlates with pMEK and pERK expression, and predicts resistance to AKT inhibition and sensitivity to MEK inhibition within both KRAS mutant and KRAS wild-type groups. The RAS pathway signature is upregulated in breast cancer cell lines that have acquired resistance to AKT inhibition, and is downregulated by inhibition of MEK. In lung cancer cell lines knockdown of KRAS using siRNA demonstrates that the RAS pathway signature is a better measure of dependence on RAS compared to KRAS mutation status. In human tumors, the RAS pathway signature is elevated in ER negative breast tumors and lung adenocarcinomas, and predicts resistance to cetuximab in metastatic colorectal cancer.

These data demonstrate that the RAS pathway signature is superior to KRAS mutation status for the prediction of dependence on RAS signaling, can predict response to PI3K and RAS pathway inhibitors, and is likely to have the most clinical utility in lung and breast tumors.

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), infects ~8 million annually culminating in ~2 million deaths. Moreover, about one third of the population is latently infected, 10% of which develop disease during lifetime. Current approved prophylactic TB vaccines (BCG and derivatives thereof) are of variable efficiency in adult protection against pulmonary TB (0%–80%), and directed essentially against early phase infection.

A genome-scale dataset was constructed by analyzing published data of: (1) global gene expression studies under conditions which simulate intra-macrophage stress, dormancy, persistence and/or reactivation; (2) cellular and humoral immunity, and vaccine potential. This information was compiled along with revised annotation/bioinformatic characterization of selected gene products and in silico mapping of T-cell epitopes. Protocols for scoring, ranking and prioritization of the antigens were developed and applied.

Cross-matching of literature and in silico-derived data, in conjunction with the prioritization scheme and biological rationale, allowed for selection of 189 putative vaccine candidates from the entire genome. Within the 189 set, the relative distribution of antigens in 3 functional categories differs significantly from their distribution in the whole genome, with reduction in the Conserved hypothetical category (due to improved annotation) and enrichment in Lipid and in Virulence categories. Other prominent representatives in the 189 set are the PE/PPE proteins; iron sequestration, nitroreductases and proteases, all within the Intermediary metabolism and respiration category; ESX secretion systems, resuscitation promoting factors and lipoproteins, all within the Cell wall category. Application of a ranking scheme based on qualitative and quantitative scores, resulted in a list of 45 best-scoring antigens, of which: 74% belong to the dormancy/reactivation/resuscitation classes; 30% belong to the Cell wall category; 13% are classical vaccine candidates; 9% are categorized Conserved hypotheticals, all potentially very potent T-cell antigens.

The comprehensive literature and in silico-based analyses allowed for the selection of a repertoire of 189 vaccine candidates, out of the whole-genome 3989 ORF products. This repertoire, which was ranked to generate a list of 45 top-hits antigens, is a platform for selection of genes covering all stages of M. tuberculosis infection, to be incorporated in rBCG or subunit-based vaccines.

The role of microRNAs (miRNAs) in multiple myeloma (MM) has yet to be fully elucidated. To identify miRNAs that are potentially deregulated in MM, we investigated those mapping within transcription units, based on evidence that intronic miRNAs are frequently coexpressed with their host genes. To this end, we monitored host transcript expression values in a panel of 20 human MM cell lines (HMCLs) and focused on transcripts whose expression varied significantly across the dataset.

miRNA expression was quantified by Quantitative Real-Time PCR. Gene expression and genome profiling data were generated on Affymetrix oligonucleotide microarrays. Significant Analysis of Microarrays algorithm was used to investigate differentially expressed transcripts. Conventional statistics were used to test correlations for significance. Public libraries were queried to predict putative miRNA targets.

We identified transcripts specific to six miRNA host genes (CCPG1, GULP1, EVL, TACSTD1, MEST, and TNIK) whose average changes in expression varied at least 2-fold from the mean of the examined dataset. We evaluated the expression levels of the corresponding intronic miRNAs and identified a significant correlation between the expression levels of MEST, EVL, and GULP1 and those of the corresponding miRNAs miR-335, miR-342-3p, and miR-561, respectively. Genome-wide profiling of the 20 HMCLs indicated that the increased expression of the three host genes and their corresponding intronic miRNAs was not correlated with local copy number variations. Notably, miRNAs and their host genes were overexpressed in a fraction of primary tumors with respect to normal plasma cells; however, this finding was not correlated with known molecular myeloma groups. The predicted putative miRNA targets and the transcriptional profiles associated with the primary tumors suggest that MEST/miR-335 and EVL/miR-342-3p may play a role in plasma cell homing and/or interactions with the bone marrow microenvironment.

Our data support the idea that intronic miRNAs and their host genes are regulated dependently, and may contribute to the understanding of their biological roles in cancer. To our knowledge, this is the first evidence of deregulated miRNA expression in MM, providing insights that may lead to the identification of new biomarkers and altered molecular pathways of the disease.

Despite advances in the understanding of diabetic retinopathy, the nature and time course of molecular changes in the retina with diabetes are incompletely described. This study characterized the functional and molecular phenotype of the retina with increasing durations of diabetes.

Using the streptozotocin-induced rat model of diabetes, levels of retinal permeability, caspase activity, and gene expression were examined after 1 and 3 months of diabetes. Gene expression changes were identified by whole genome microarray and confirmed by qPCR in the same set of animals as used in the microarray analyses and subsequently validated in independent sets of animals. Increased levels of vascular permeability and caspase-3 activity were observed at 3 months of diabetes, but not 1 month. Significantly more and larger magnitude gene expression changes were observed after 3 months than after 1 month of diabetes. Quantitative PCR validation of selected genes related to inflammation, microvasculature and neuronal function confirmed gene expression changes in multiple independent sets of animals.

These changes in permeability, apoptosis, and gene expression provide further evidence of progressive retinal malfunction with increasing duration of diabetes. The specific gene expression changes confirmed in multiple sets of animals indicate that pro-inflammatory, anti-vascular barrier, and neurodegenerative changes occur in tandem with functional increases in apoptosis and vascular permeability. These responses are shared with the clinically documented inflammatory response in diabetic retinopathy suggesting that this model may be used to test anti-inflammatory therapeutics.