Gene Expression Meta-analysis Research Articles

NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis.

The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

Gene expression profiling meta-analysis reveals novel gene signatures and pathways shared between tuberculosis and rheumatoid arthritis.

Tuberculosis (TB) is among the leading causes of death by infectious diseases. An epidemiological association between Mycobacterium tuberculosis infection and autoimmune diseases like rheumatoid arthritis (RA) has been reported but it remains unclear if there is a causal relationship, and if so, which molecular pathways and regulatory mechanisms contribute to it. Here we used a computational biology approach by global gene expression meta-analysis to identify candidate genes and pathways that may link TB and RA. Data were collected from public expression databases such as NCBI GEO. Studies were selected that analyzed mRNA-expression in whole blood or blood cell populations in human case control studies at comparable conditions. Six TB and RA datasets (41 active TB patients, 33 RA patients, and 67 healthy controls) were included in the downstream analysis. This approach allowed the identification of deregulated genes that had not been identified in the single analysis of TB or RA patients and that were co-regulated in TB and RA patients compared to healthy subjects. The genes encoding TLR5, TNFSF10/TRAIL, PPP1R16B/TIMAP, SIAH1, PIK3IP1, and IL17RA were among the genes that were most significantly deregulated in TB and RA. Pathway enrichment analysis revealed ‘T cell receptor signaling pathway’, ‘Toll-like receptor signaling pathway,’ and ‘virus defense related pathways’ among the pathways most strongly associated with both diseases. The identification of a common gene signature and pathways substantiates the observation of an epidemiological association of TB and RA and provides clues on the mechanistic basis of this association. Newly identified genes may be a basis for future functional and epidemiological studies.

Gene expression meta-analysis of Parkinson\u2019s disease and its relationship with Alzheimer\u2019s disease

Parkinson’s disease (PD) and Alzheimer’s disease (AD) are the most common neurodegenerative diseases and have been suggested to share common pathological and physiological links. Understanding the cross-talk between them could reveal potentials for the development of new strategies for early diagnosis and therapeutic intervention thus improving the quality of life of those affected. Here we have conducted a novel meta-analysis to identify differentially expressed genes (DEGs) in PD microarray datasets comprising 69 PD and 57 control brain samples which is the biggest cohort for such studies to date. Using identified DEGs, we performed pathway, upstream and protein-protein interaction analysis. We identified 1046 DEGs, of which a majority (739/1046) were downregulated in PD. YWHAZ and other genes coding 14–3-3 proteins are identified as important DEGs in signaling pathways and in protein-protein interaction networks (PPIN). Perturbed pathways also include mitochondrial dysfunction and oxidative stress. There was a significant overlap in DEGs between PD and AD, and over 99% of these were differentially expressed in the same up or down direction across the diseases. REST was identified as an upstream regulator in both diseases. Our study demonstrates that PD and AD share significant common DEGs and pathways, and identifies novel genes, pathways and upstream regulators which may be important targets for therapy in both diseases.

Abstract P3-07-08: Multi-omic predictor of rapid and late relapse in primary triple negative breast cancer

Abstract Background: Triple-negative breast cancer (TNBC) is a heterogeneous disease. Clinically, we observe three distinct TNBC outcomes: 1) rapid relapse (rrTNBC) characterized by aggressive drug resistant disease; 2) late relapse (lrTNBC) characterized by indolent or treatment responsive disease; and 3) no relapse (NoRTNBC). We hypothesized that distinct clinical and genomic features of primary tumors define rapid versus late relapse in TNBC. Approach: Using three publicly-available datasets (METABRIC, TCGA, and a prior gene expression meta-analysis), we identified 455 patients diagnosed with primary TNBC with adequate follow-up to be characterized as rrTNBC (relapse or death within 2 years of diagnosis), lrTNBC (relapse or death more than 2 years after diagnosis), or NoRTNBC (no relapse/death with at least 5 years follow-up). We compiled basic clinical (n=455 patients) and primary tumor multi-omic data, including whole transcriptome (n=455), whole genome copy number (n=317), and mutation data for 171 cancer-related genes (n=317). We evaluated intrinsic subtypes (PAM50, TNBCtype), 125 gene expression signatures, CIBERSORT immune subsets, copy number, and mutation frequency. Results: We first evaluated patients with relapse (rrTNBC+lrTNBC) vs. NoRTNBC. There was no significant difference in age, grade, stage at diagnosis, or PAM50 or TNBC subtype proportion between relapse and NoRTNBC. Among 125 expression signatures, five immune signatures were significantly higher in NoRTNBCs (FDR p<0.05) suggesting increased immune activity in patients who do not relapse. Using CIBERSORT inferred immune subsets, anti-tumor CD8 T-cell, M1 macrophage, and gamma-delta T-cell subsets were all highly correlated to these immune signatures (all Pearson's r >= 0.3, all p<1.2e-8). Among genomic features, patients who relapsed were significantly more likely to harbor a mutation in PIK3CA (Fisher exact FDR p=0.02) but there was no significant difference in tumor mutation burden or percent genome altered (Student's t-test p=0.83 and p=0.99, respectively). We then evaluated primary TNBC genomic data in patients who ultimately developed rapid vs. late relapse. Patients with rrTNBC were more likely to be higher stage (p<0.0001) while lrTNBC were more likely to be non-basal PAM50 subtype (p=0.03). Among 11 significantly altered gene expression signatures (FDR p<0.05), 6 estrogen/luminal signatures were significantly higher in lrTNBC. Mutations in DNAH11 and PIK3CA were more common in lrTNBC (Fisher exact FDR p=0.04 and p=0.05, respectively) but there were no significant differences in tumor mutation burden or copy number burden (Student's t-test p=0.13 and p=0.45, respectively). Using 317 cases with full genomic data divided into training and validation datasets, we will report a comparison of machine learning models for predicting relapse versus no relapse and rapid versus late relapse. Conclusions: Primary TNBC tumors destined for rapid, late, or no relapse reflect distinct genomic features. Anti-tumor immune signatures and subsets are enriched in patients who do not relapse yet no difference in mutational or copy number burden. Relative to rapid relapse TNBCs, late relapse TNBCs are enriched for non-basal tumors, estrogen/luminal expression signatures, and mutations in DNAH11 and PIK3CA. Citation Format: Zhang Y, Nock W, Asad S, Adams E, Singh J, Damicis A, Lustberg MB, Noonan A, Reinbolt R, Sardesai S, VanDeusen J, Wesolowski R, Williams N, Ramaswamy B, Stover DG. Multi-omic predictor of rapid and late relapse in primary triple negative breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P3-07-08.

Comprehensive gene expression meta-analysis identifies signature genes that distinguish microglia from peripheral monocytes/macrophages in health and glioma

Monocytes/macrophages have begun to emerge as key cellular modulators of brain homeostasis and central nervous system (CNS) disease. In the healthy brain, resident microglia are the predominant macrophage cell population; however, under conditions of blood-brain barrier leakage, peripheral monocytes/macrophages can infiltrate the brain and participate in CNS disease pathogenesis. Distinguishing these two populations is often challenging, owing to a paucity of universally accepted and reliable markers. To identify discriminatory marker sets for microglia and peripheral monocytes/macrophages, we employed a large meta-analytic approach using five published murine transcriptional datasets. Following hierarchical clustering, we filtered the top differentially expressed genes (DEGs) through a brain cell type-specific sequencing database, which led to the identification of eight microglia and eight peripheral monocyte/macrophage markers. We then validated their differential expression, leveraging a published single cell RNA sequencing dataset and quantitative RT-PCR using freshly isolated microglia and peripheral monocytes/macrophages from two different mouse strains. We further verified the translation of these DEGs at the protein level. As top microglia DEGs, we identified P2ry12, Tmem119, Slc2a5 and Fcrls, whereas Emilin2, Gda, Hp and Sell emerged as the best DEGs for identifying peripheral monocytes/macrophages. Lastly, we evaluated their utility in discriminating monocyte/macrophage populations in the setting of brain pathology (glioma), and found that these DEG sets distinguished glioma-associated microglia from macrophages in both RCAS and GL261 mouse models of glioblastoma. Taken together, this unbiased bioinformatic approach facilitated the discovery of a robust set of microglia and peripheral monocyte/macrophage expression markers to discriminate these monocyte populations in both health and disease.

Airway transcriptomic profiling after bronchial thermoplasty.

BackgroundBronchial thermoplasty is a nonpharmacological, device-based treatment option for a specific population of severe asthmatic subjects, but the underlying mechanisms are largely unknown. The purpose of this study is to identify potential altered pathways by bronchial thermoplasty using a transcriptomic approach.MethodsPatients undergoing bronchial thermoplasty were recruited to the study, and a bronchial brushing sample was obtained before each bronchial thermoplasty session and sent for RNA sequencing. A variance component score test was performed to identify those genes whose expression varied after bronchial thermoplasty sessions. Differential gene expression meta-analysis of severe asthmatic subjects versus controls was performed using public repositories. Overlapping genes were included for downstream pathway and network analyses.Results12 patients were enrolled in our study. A total of 133 severe asthma cases and 107 healthy controls from the public repositories were included in the meta-analysis. Comparison of differentially expressed genes from our study patients with the public repositories identified eight overlapping genes: AMIGO2, CBX7, NR3C2, SETBP1, SHANK2, SNTB1, STXBP1 and ZNF853. Network analysis of these overlapping genes identified pathways associated with neurophysiological processes.ConclusionWe have shown that bronchial thermoplasty treatment alters several gene networks that are important in asthma pathogenesis. These results potentially elucidate the disease-modifying mechanisms of bronchial thermoplasty and provide several targets for further investigation.

GESgnExt: Gene Expression Signature Extraction and Meta-Analysis on Gene Expression Omnibus.

The gene expression omnibus (GEO) repository harbours an exponentially increasing number of gene expression studies. The expression data, as well as the related metadata, provides an abundant resource for knowledge discovery. Each study in GEO focuses on the gene expression perturbation of a specific subject (e.g., gene, drug, and disease). The identification of those subjects and the associations among them are beneficial for further in-depth studies. However, they cannot be directly inferred from the studies. A unified representation of those subjects (i.e., gene expression signatures) is desired. We developed GESgnExt for the automatic construction of gene expression signatures. The resultant 6542 signatures are built on 1934 series and 35919 samples from GEO. To evaluate its significance, we calculated the similarities among those signatures and compared the discovered associations against the existing interaction databases. The signatures connect the genes, drugs, and diseases, covering most of the experimentally validated interactions. Besides, we have discovered 3307 novel signatures and their related associations, complementing the existing signature knowledge. The biomedical relevance of GESgnExt is demonstrated further in multiple case studies, providing mechanistic insights into its knowledge discovery process.

SMAGEXP: a galaxy tool suite for transcriptomics data meta-analysis

BackgroundWith the proliferation of available microarray and high-throughput sequencing experiments in the public domain, the use of meta-analysis methods increases. In these experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably enhance the statistical power and give more accurate results. For those purposes, it combines either effect sizes or results of single studies in an appropriate manner. R packages metaMA and metaRNASeq perform meta-analysis on microarray and next generation sequencing (NGS) data, respectively. They are not interchangeable as they rely on statistical modeling specific to each technology.ResultsSMAGEXP (Statistical Meta-Analysis for Gene EXPression) integrates metaMA and metaRNAseq packages into Galaxy. We aim to propose a unified way to carry out meta-analysis of gene expression data, while taking care of their specificities. We have developed this tool suite to analyze microarray data from the Gene Expression Omnibus database or custom data from Affymetrix© microarrays. These data are then combined to carry out meta-analysis using metaMA package. SMAGEXP also offers to combine raw read counts from NGS experiments using DESeq2 and metaRNASeq package. In both cases, key values, independent from the technology type, are reported to judge the quality of the meta-analysis. These tools are available on the Galaxy main tool shed. A dockerized instance of galaxy containing SMAGEXP and its dependencies is available on Docker hub. Source code, help, and installation instructions are available on GitHub.ConclusionThe use of Galaxy offers an easy-to-use gene expression meta-analysis tool suite based on the metaMA and metaRNASeq packages.

HER2 regulates HIF-2\u03b1 and drives an increased hypoxic response in breast cancer

BackgroundTumour hypoxia is a driver of breast cancer progression associated with worse prognosis and more aggressive disease. The cellular response to hypoxia is mediated by the hypoxia-inducible transcription factors HIF-1 and HIF-2, whose transcriptional activity is canonically regulated through their oxygen-labile HIF-α subunits. These are constitutively degraded in the presence of oxygen; however, HIF-1α can be stabilised, even at high oxygen concentrations, through the activation of HER receptor signalling. Despite this, there is still limited understanding on how HER receptor signalling interacts with HIF activity to contribute to breast cancer progression in the context of tumour hypoxia.Methods2D and 3D cell line models were used alongside microarray gene expression analysis and meta-analysis of publicly available gene expression datasets to assess the impact of HER2 overexpression on HIF-1α/HIF-2α regulation and to compare the global transcriptomic response to acute and chronic hypoxia in an isogenic cell line model of HER2 overexpression.ResultsHER2 overexpression in MCF7 cells leads to an increase in HIF-2α but not HIF-1α expression in normoxia and an increased upregulation of HIF-2α in hypoxia. Global gene expression analysis showed that HER2 overexpression in these cells promotes an exaggerated transcriptional response to both short-term and long-term hypoxia, with increased expression of numerous hypoxia response genes. HIF-2α expression is frequently higher in HER2-overexpressing tumours and is associated with worse disease-specific survival in HER2-positive breast cancer patients. HER2-overexpressing cell lines demonstrate an increased sensitivity to targeted HIF-2α inhibition through either siRNA or the use of a small molecule inhibitor of HIF-2α translation.ConclusionsThis study suggests an important interplay between HER2 expression and HIF-2α in breast cancer and highlights the potential for HER2 to drive the expression of numerous hypoxia response genes in normoxia and hypoxia. Overall, these findings show the importance of understanding the regulation of HIF activity in a variety of breast cancer subtypes and points to the potential of targeting HIF-2α as a therapy for HER2-positive breast cancer.

Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies

BackgroundRandom-effects (RE) models are commonly applied to account for heterogeneity in effect sizes in gene expression meta-analysis. The degree of heterogeneity may differ due to inconsistencies in sample quality. High heterogeneity can arise in meta-analyses containing poor quality samples. We applied sample-quality weights to adjust the study heterogeneity in the DerSimonian and Laird (DSL) and two-step DSL (DSLR2) RE models and the Bayesian random-effects (BRE) models with unweighted and weighted data, Gibbs and Metropolis-Hasting (MH) sampling algorithms, weighted common effect, and weighted between-study variance. We evaluated the performance of the models through simulations and illustrated application of the methods using Alzheimer’s gene expression datasets.ResultsSample quality adjusting within study variance (wP6) models provided an appropriate reduction of differentially expressed (DE) genes compared to other weighted functions in classical RE models. The BRE model with a uniform(0,1) prior was appropriate for detecting DE genes as compared to the models with other prior distributions. The precision of DE gene detection in the heterogeneous data was increased with the DSLR2wP6 weighted model compared to the DSLwP6 weighted model. Among the BRE weighted models, the wP6weighted- and unweighted-data models and both Gibbs- and MH-based models performed similarly. The wP6 weighted common-effect model performed similarly to the unweighted model in the homogeneous data, but performed worse in the heterogeneous data. The wP6weighted data were appropriate for detecting DE genes with high precision, while the wP6weighted between-study variance models were appropriate for detecting DE genes with high overall accuracy. Without the weight, when the number of genes in microarray increased, the DSLR2 performed stably, while the overall accuracy of the BRE model was reduced. When applying the weighted models in the Alzheimer’s gene expression data, the number of DE genes decreased in all metadata sets with the DSLR2wP6weighted and the wP6weighted between study variance models. Four hundred and forty-six DE genes identified by the wP6weighted between study variance model could be potentially down-regulated genes that may contribute to good classification of Alzheimer’s samples.ConclusionsThe application of sample quality weights can increase precision and accuracy of the classical RE and BRE models; however, the performance of the models varied depending on data features, levels of sample quality, and adjustment of parameter estimates.

HOXA10 expression profiling in prostate cancer.

HOX genes encode transcription factors that play key roles in modulating normal tissue morphogenesis, differentiation and homeostasis. Disruption of normal HOX gene expression occurs frequently in human cancers and is associated with both tumor promoting and suppressing activities. Among these is, HOXA10, a pleiotropic gene that is critical for normal prostate development. In this study we characterized HOXA10 expression in human and mouse PCa to gain insights into its clinical significance. A meta-analysis of HOXA10 mRNA expression was carried out across several publicly available data sets. Expression of HOXA10 protein expression was assessed by immunohistochemistry (IHC) using human radical prostatectomy (RP) cases. We correlated HOXA10 expression to clinicopathological features and investigated its relationship to biochemical recurrence (BCR) after RP by the Kaplan-Meier method. HOXA10 mRNA and IHC protein expression was also examined in a mouse model of Pten-null PCa. A meta-analysis of HOXA10 gene expression indicated dysregulated expression of HOXA10 in human PCa. IHC profiling of HOXA10 revealed inverse correlations between HOXA10 expression and Gleason pattern, Gleason score, and pathological stage (P < 0.01). Patients with low expression profiles of HOXA10 were associated with a higher risk of BCR, (OR, 3.54; 95%CI, 1.21-16.14; P = 0.049) whereas patients with high HOXA10 expression experienced longer times to BCR (P = 0.045). However, HOXA10 was not an independent predictor of BCR (OR, 1.52; 95%CI, 0.42-5.54; P = 0.52). Evaluation of expression patterns of HOXA10 in mouse prostate tumors mimicked that of humans. Our findings show that HOXA10 expression is inversely associated with tumor differentiation and high HOXA10 expression is associated with improved BCR-free survival. This study provides human and mouse evidence to suggest tumor suppressive roles for HOXA10 in the context of prostate cancer.

Androgen Receptor Activity and Radiotherapeutic Sensitivity in African-American Men with Prostate Cancer: A Large Scale Gene Expression Analysis and Meta-Analysis of RTOG Trials

Population data suggests that African-American (AfA) men have an increased mortality from prostate cancer (PCa) compared to White (C) men. Socioeconomic variables contribute to this disparity, yet intrinsic biological differences remain plausible. Herein, we investigate the interplay of androgen receptor activity (AR-A) and radiotherapeutic sensitivity to provide a molecular rationale to help explain the disparity in outcomes for AfA men with PCa. Transcriptome-wide expression profiles of FFPE tumor samples from 5,831 localized PCa patients were used. Tissue was obtained from a prospective population cohort (n=5,239) and two retrospective cohorts with long-term outcomes (n=592). Predicted radiation sensitivity was measured using the 24-gene post-operative radiation therapy (RT) outcome score (PORTOS). AR-A was defined from the pooled expression of nine canonical AR-target genes. Clinical radiosensitivity was validated using individual patient data from four RTOG trials (92-02, 94-08, 94-13, and 99-10), comprised of 6,011 patients (18.5% AfA). Competing risk adjustments were used for all survival analyses for biochemical recurrence (BCR) and distant metastases (DM). In men treated by surgery from the prospective cohort, low AR-A tumors were significantly more likely to develop DM (10-year rate: 37% vs 17%, p=0.008). On multivariable analysis after adjusting for Gleason grade, T-stage, PSA, margin status, and lymph node invasion, low AR-A remained independently prognostic for DM (p=0.03). After generating a matched cohort of AfA and C patients, AfA tumors were more likely to have low AR-A (p<0.001). However, AfA tumors had decreased double strand break repair pathway expression (p<0.001) and increased predicted RT sensitivity (p<0.001). This suggests that AfA men may have improved outcomes with RT. To clinically test whether AfA tumors are more radiosensitive, we leveraged four large RTOG trials of men treated with RT. On both unadjusted and propensity weighted cohorts (adjusting for age, performance status, PSA, Gleason grade, T-stage, N-stage, and use/duration of hormone therapy), AfA men had significantly improved outcomes compared to C men (BCR: HR 0.82; 95% CI 0.74-0.92; p = 0.0005 and DM: HR 0.70; (95% CI 0.57-0.86; p = 0.0008). Our data suggest that there are population-level differences in AR signaling and DNA repair in AfA and C men’s PCa, which transcriptionally suggest that AfA men may harbor more radiosensitive tumors. To our knowledge, this is the first report demonstrating that AfA men may have improved outcomes compared to C men treated with RT, which is consistent with our hypothesis regarding diversity in AR-A, and warrants further investigation.

Identification of target gene and prognostic evaluation for lung adenocarcinoma using gene expression meta-analysis, network analysis and neural network algorithms.

Lung adenocarcinoma (LUAD) is a heterogeneous disease with poor survival in the advanced stage and a high incidence rate in the world. Novel drug targets are urgently required to improve patient treatment. Therefore, we aimed to identify therapeutic targets for LUAD based on protein-protein and protein-drug interaction network analysis with neural network algorithms using mRNA expression profiles. A comprehensive meta-analysis of selective non-small cell lung cancer (NSCLC) mRNA expression profile datasets from Gene Expression Omnibus were used to identify potential biomarkers and the molecular mechanisms related to the prognosis of NSCLC patients. Using the Network Analyst tool, based on combined effect size (ES) methods, we recognized 6566 differentially expressed genes (DEGs), which included 3036 downregulated and 3530 upregulated genes linked to NSCLC patient survival. ClueGO, a Cytoscape plugin, was exploited to complete the function and pathway enrichment analysis, which disclosed "regulated exocytosis", "purine nucleotide binding", "pathways in cancer", and "cell cycle" between exceptionally supplemented terms. Enrichr, a web tool examination, demonstrated "early growth response protein 1 (EGR-1)", "hepatocyte nuclear factor 4α (HNF4A)", "mitogen-activated protein kinase 14 (MAP3K14)", and "cyclin-dependent kinase 1 (CDK1)" to be among the most prevalent TFs and kinases associated with NSCLC. Our meta-analysis identified that MAPK1 and aurora kinase (AURKA) are the most obvious class of hub nodes. Furthermore, protein-drug interaction network and neural network algorithms identified candidate drugs such as phosphothreonine and 4-(4-methylpiperazin-1-yl)-n-[5-(2-thienylacetyl)-1,5-dihydropyrrolo[3,4-c]pyrazol-3-yl] benzamide and for the targets MAPK1 and AURKA, respectively. Our study has identified novel candidate biomarkers, pathways, transcription factors (TFs), and kinases associated with NSCLC prognosis, as well as drug candidates, which may assist treatment strategy for NSCLC patients.

ImaGEO: integrative gene expression meta-analysis from GEO database.

The Gene Expression Omnibus (GEO) database provides an invaluable resource of publicly available gene expression data that can be integrated and analyzed to derive new hypothesis and knowledge. In this context, gene expression meta-analysis (geMAs) is increasingly used in several fields to improve study reproducibility and discovering robust biomarkers. Nevertheless, integrating data is not straightforward without bioinformatics expertise. Here, we present ImaGEO, a web tool for geMAs that implements a complete and comprehensive meta-analysis workflow starting from GEO dataset identifiers. The application integrates GEO datasets, applies different meta-analysis techniques and provides functional analysis results in an easy-to-use environment. ImaGEO is a powerful and useful resource that allows researchers to integrate and perform meta-analysis of GEO datasets to lead robust findings for biomarker discovery studies. ImaGEO is accessible at http://bioinfo.genyo.es/imageo/. Supplementary data are available at Bioinformatics online.

Abstract A050: Integrated system-level analyses define an androgen receptor variant oncogenic network for advanced prostate cancer

Abstract Unbiased high-throughput approaches provide a global view of the massive genetic and transcriptomic changes that occur in and potentially drive cancer. Such approaches permit the discovery of novel networks that can serve as prognostic markers and therapeutic targets. We performed a systems-biology approach to identify genes linked to the androgen receptor (AR) variant AR-V7 in castration-resistant prostate cancer (CRPC). AR variants are C-terminal truncated, constitutively active, and play key transcriptional regulatory roles resulting in treatment resistance and disease progression. These AR variants lack the C-terminal ligand binding domain, which is the target of all approved drugs against AR, and consequently there is considerable interest in novel therapeutic strategies that are effective against AR variant-expressing prostate tumors. Directly targeting AR variants is challenging, and thus an attractive alternative approach is to identify downstream AR-V7 hub genes that not only drive cancer progression but also feedback to enhance AR-V7 activity. Thus, our goals were to: 1) define clinically relevant gene sets or modules in human PC progression using unbiased gene expression meta-analysis of PC patient samples by Weighted Gene-Co-expression Network Analysis (WGCNA); 2) map the AR-V7 functional interactome using a unique high-throughput synthetic genetic array (SGA) screening in the yeast S. pombe; and 3) identify AR-V7 regulated genes in PC cells. Integration of these approaches resulted in the identification of seven genes that are upregulated in human PC (as determined by WGCNA), functionally interact with AR-V7 (as determined by SGA), and are regulated by AR-V7. This gene set encoded select proteins previously linked to PC, such as members of the cyclin and topoisomerase families, but also proteins with no prior association with AR-V7 or PC. The seven genes comprised a signature that correlated with patient Gleason score and predicted disease free-survival and overall survival in large independent PC patient cohorts. Individual depletion of the expression of these genes decreased ligand-independent AR transcriptional activity concomitant with reduced CRPC cell proliferation. Because CRPC cells were highly dependent on these inter-related genes, combination therapy targeting two of the seven genes led to potent inhibition of CRPC cell proliferation. In contrast, the combination of these agents had little to no effect on nontumorigenic prostate epithelial cells or AR-null PC cells. In summary, we utilized an unbiased and novel gene discovery strategy that combined sophisticated bioinformatics and experimental approaches. This multifaceted approach resulted in the identification of disease-relevant genes that were regulated by AR-V7 and that reciprocally enhanced AR-V7 oncogenic activity. This novel gene set has a prognostic value and provides a rationale for combinatorial CRPC therapy. Citation Format: Fiorella Magani, Eric Bray, Maria Julia Martinez, Valeria Copello, Ning Zhao, Stephanie Peacock, David Wiley, Gennaro D’urso, Kerry L. Burnstein. Integrated system-level analyses define an androgen receptor variant oncogenic network for advanced prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Prostate Cancer: Advances in Basic, Translational, and Clinical Research; 2017 Dec 2-5; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(16 Suppl):Abstract nr A050.

A common molecular signature of patients with sickle cell disease revealed by microarray meta-analysis and a genome-wide association study.

A chronic inflammatory state to a large extent explains sickle cell disease (SCD) pathophysiology. Nonetheless, the principal dysregulated factors affecting this major pathway and their mechanisms of action still have to be fully identified and elucidated. Integrating gene expression and genome-wide association study (GWAS) data analysis represents a novel approach to refining the identification of key mediators and functions in complex diseases. Here, we performed gene expression meta-analysis of five independent publicly available microarray datasets related to homozygous SS patients with SCD to identify a consensus SCD transcriptomic profile. The meta-analysis conducted using the MetaDE R package based on combining p values (maxP approach) identified 335 differentially expressed genes (DEGs; 224 upregulated and 111 downregulated). Functional gene set enrichment revealed the importance of several metabolic pathways, of innate immune responses, erythrocyte development, and hemostasis pathways. Advanced analyses of GWAS data generated within the framework of this study by means of the atSNP R package and SIFT tool identified 60 regulatory single-nucleotide polymorphisms (rSNPs) occurring in the promoter of 20 DEGs and a deleterious SNP, affecting CAMKK2 protein function. This novel database of candidate genes, transcription factors, and rSNPs associated with SCD provides new markers that may help to identify new therapeutic targets.

Abstract 246: An integrative genetic epidemiologic approach to identify susceptibility genes for post-menopausal breast cancer

Abstract Background: Up to 30% of common (peri- and post-menopausal) breast cancer cases in the general population are believed to be due to segregation of mutations and variations with low to medium risk [i.e., 1.5&amp;lt; relative risk &amp;lt;20] for breast cancer. The majority of these genes remain to be identified. We used a hypothesis-driven integrative genetic epidemiologic approach to test the association of post-menopausal breast cancer with Nucleotide Excision Repair (NER) genes using several gene expression microarray and genome-wide association studies (GWAS) datasets. Methods: As per inclusion criteria, all datasets contained Caucasian cases with invasive ductal breast carcinoma diagnosed post-menopause (≥50 years of age) and healthy controls (with no personal history of breast cancer). Raw data from four gene expression microarray datasets (GSE5764, GSE10797, GSE10780, E-TABM-276) were used for pathway and meta-analysis. Raw data from three GWAS datasets, the Cancer Genetic Markers of Susceptibility (CGEMS) (1089 cases, 1093 controls), a nested case-control (351 cases, 884 controls) within National Surgical Adjuvant Breast and Bowel Project (NSABP), and a nested case-control (465 cases and 1394 controls) within the Women's Health Initiative (WHI) Hormone Therapy Trials, were analyzed using logistic regression models adjusting for covariates. Linkage disequilibrium patterns and deviations from Hardy-Weinberg Equilibrium (HWE) were assessed for single nucleotide polymorphisms (SNPs) within all candidate genes. Individual haplotypes and diplotypes were determined for all subjects. Odd ratios (OR) and 95% confidence intervals (CI) were calculated. Results: Individual analysis of gene expression microarray datasets and meta-analysis of GSE10780 and E-TABM-276 identified significant up-regulation of several NER genes including ERCC6 (P=7.95*10-6) and ERCC8 (P=4.67*10-6). Increased risk of breast cancer was found in association with variations in ERCC6 in all GWAS datasets. ERCC6 SNPs were in strong LD in all datasets with no deviations from HWE. In CGEMS, increased risk of breast cancer was found with ERCC6 rs4253082 (OR=1.32, 95%CI: 1.09-1.61, P=0.005), rs3750749 (OR=1.40, 95%CI: 1.07-1.8, P=0.01), and rs3750751 (OR=2.91, 95%CI: 1.05-8.06, P=0.04). In NSABP, increased risk of breast cancer was found with ERCC6 rs2229760 (OR=1.37, 95%CI: 1.02-1.83, P=0.03). In WHI, increased risk of breast cancer was found with ERCC6 rs1012553 (OR=1.35, 95%CI: 1.07-1.71, P=0.01) and rs2228528 (OR=1.29, 95%CI: 1.01-1.66, P=0.04). Gene-gene interaction analysis was significant for interactions between ERCC6 and several NER genes, including between specific diplotypes in ERCC6 and ERCC8 (Pinteraction=0.0009 in CGEMS). Conclusion: Genes involved in the NER pathway, including ERCC6 and ERCC8, may be associated with low to medium risk for susceptibility to post-menopausal breast cancer. Citation Format: Roxana Moslehi, Nur Zeinomar, Cristy Stagnar, Hui-Shien Tsao, Amiran Dzutsev. An integrative genetic epidemiologic approach to identify susceptibility genes for post-menopausal breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 246.

Meta-analysis of airway epithelium gene expression in asthma.

Differential gene expression in the airway epithelium of patients with asthma versus controls has been reported in several studies. However, there is no consensus on which genes are reproducibly affected in asthma. We sought to identify a consensus list of differentially expressed genes (DEGs) using a meta-analysis approach.We identified eight studies with data that met defined inclusion criteria. These studies comprised 355 cases and 193 controls and involved sampling either bronchial or nasal epithelium. We conducted study-level analyses, followed by a meta-analysis. Likewise, we applied a meta-analysis framework to the results of study-level pathway enrichment.We identified 1273 DEGs, 431 of which had not been identified in previous studies. 450 DEGs exhibited large effect sizes and were robust to study population differences in age, sex, race/ethnicity, medication use, smoking status and exacerbations. The magnitude of differential expression of these 450 genes was highly similar in bronchial and nasal airway epithelia. Meta-analysis of pathway enrichment revealed a number of consistently dysregulated biological pathways, including putative transcriptional and post-transcriptional regulators.In total, we identified a set of genes that is consistently dysregulated in asthma, that links to known and novel biological pathways, and that will inform asthma subtype identification.

Gene expression profiling of low-grade endometrial stromal sarcoma indicates fusion protein-mediated activation of the Wnt signaling pathway

ObjectiveLow-grade endometrial stromal sarcomas (LGESS) harbor chromosomal translocations that affect proteins associated with chromatin remodeling Polycomb Repressive Complex 2 (PRC2), including SUZ12, PHF1 and EPC1. Roughly half of LGESS also demonstrate nuclear accumulation of β-catenin, which is a hallmark of Wnt signaling activation. However, the targets affected by the fusion proteins and the role of Wnt signaling in the pathogenesis of these tumors remain largely unknown. MethodsHere we report the results of a meta-analysis of three independent gene expression profiling studies on LGESS and immunohistochemical evaluation of nuclear expression of β-catenin and Lef1 in 112 uterine sarcoma specimens obtained from 20 LGESS and 89 LMS patients. ResultsOur results demonstrate that 143 out of 310 genes overexpressed in LGESS are known to be directly regulated by SUZ12. In addition, our gene expression meta-analysis shows activation of multiple genes implicated in Wnt signaling. We further emphasize the role of the Wnt signaling pathway by demonstrating concordant nuclear expression of β-catenin and Lef1 in 7/16 LGESS. ConclusionsBased on our findings, we suggest that LGESS-specific fusion proteins disrupt the repressive function of the PRC2 complex similar to the mechanism seen in synovial sarcoma, where the SS18-SSX fusion proteins disrupt the mSWI/SNF (BAF) chromatin remodeling complex. We propose that these fusion proteins in LGESS contribute to overexpression of Wnt ligands with subsequent activation of Wnt signaling pathway and formation of an active β-catenin/Lef1 transcriptional complex. These observations could lead to novel therapeutic approaches that focus on the Wnt pathway in LGESS.

4q21.2q21.3 Duplication: Molecular and Neuropsychological Aspects.

During the last decades, a large amount of newly described microduplications and microdeletions associated with intellectual disability (ID) and related neuropsychiatric diseases have been discovered. However, due to natural limitations, a significant part of them has not been the focus of multidisciplinary approaches.Here, we address previously undescribed chromosome 4q21.2q21.3 microduplication for gene prioritization, evaluation of cognitive abilities and estimation of genomic mechanisms for brain dysfunction by molecular cytogenetic (cytogenomic) and gene expression (meta-) analyses as well as for neuropsychological assessment. We showed that duplication at 4q21.2q21.3 is associated with moderate ID, cognitive deficits, developmental delay, language impairment, memory and attention problems, facial dysmorphisms, congenital heart defect and dentinogenesis imperfecta. Gene-expression meta-analysis prioritized the following genes: ENOPH1, AFF1, DSPP, SPARCL1, and SPP1. Furthermore, genotype/phenotype correlations allowed the attribution of each gene gain to each phenotypic feature.Neuropsychological testing showed visual-perceptual and fine motor skill deficits, reduced attention span, deficits of the nominative function and problems in processing both visual and aural information. Finally, emerging approaches including molecular cytogenetic, bioinformatic (genome/epigenome meta-analysis) and neuropsychological methods are concluded to be required for comprehensive neurological, genetic and neuropsychological descriptions of new genomic rearrangements/diseases associated with ID.