Microarray-based Gene Expression Studies Research Articles

Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data.

Ultrahigh-dimensional gene features are often collected in modern cancer studies in which the number of gene features is extremely larger than sample size . While gene expression patterns have been shown to be related to patients' survival in microarray-based gene expression studies, one has to deal with the challenges of ultrahigh-dimensional genetic predictors for survival predicting and genetic understanding of the disease in precision medicine. The problem becomes more complicated when two types of survival endpoints, distant metastasis-free survival and overall survival, are of interest in the study and outcome data can be subject to semi-competing risks due to the fact that distant metastasis-free survival is possibly censored by overall survival but not vice versa. Our focus in this paper is to extract important features, which have great impacts on both distant metastasis-free survival and overall survival jointly, from massive gene expression data in the semi-competing risks setting. We propose a model-free screening method based on the ranking of the correlation between gene features and the joint survival function of two endpoints. The method accounts for the relationship between two endpoints in a simply defined utility measure that is easy to understand and calculate. We show its favorable theoretical properties such as the sure screening and ranking consistency, and evaluate its finite sample performance through extensive simulation studies. Finally, an application to classifying breast cancer data clearly demonstrates the utility of the proposed method in practice.

Integrative Analysis of 1001 Diffuse Large B Cell Lymphoma Identifies Novel Oncogenic Roles for Rhoa

Abstract Diffuse large B cell lymphoma (DLBCL) is the most common hematologic malignancy and is characterized by a striking degree of clinical and molecular heterogeneity. Gene expression profiling has long been used to define subtypes and understand the heterogeneity within DLBCL. Microarray-based gene expression studies have identified cell of origin subgroups (Alizadeh et al., 2000), as well as gene expression signatures derived from host inflammatory response (Monti et al., 2005), stromal tissues (Lenz et al., 2008) as well as other biological processes. However, the connections between these gene expression signatures and genetic alterations is largely unknown. In this study, we sought to define connections between gene expression signatures and genetic alterations to better understand the biology of DLBCL. We enrolled a total of 1001 DLBCL patients and comprehensively defined the landscape of genetic mutations, copy number alterations and expression through whole-exome and transcriptome sequencing. We identified 150 genetic drivers of DLBCL including many novel, clinically relevant genes (e.g. SPEN, SETD1B and KLHL14). We began the gene expression analysis with a comprehensive universe of nearly 9500 annotated gene sets from widely used gene set databases (Kegg, Reactome, MSigDB), several lymphoma-specific gene signature sources (Lenz et al. 2008, Monti et al. 2005, and Shaffer et al.) and other published sources. From this large universe, we identified 1228 gene sets that comprised genes with high intra gene set correlation. The vast majority of these 1228 gene sets also exhibited high inter-gene set correlation indicating a high degree of redundancy between these gene sets. We developed an approach to singular value decomposition to define 31 exemplar gene sets that essentially replicated the patterns of the 1228 described gene sets. Our results recapitulated a number of described patterns underlying DLBCL gene expression including distinct gene sets reflecting cell of origin, host response, BCR and Oxidative Phosphorylation (Monti et al., 2005), as well as the “Stromal 1” and “Stromal 2” signatures (Lenz et al., 2008) signatures. There was no association between the expression of stromal signatures and overall mutational burden (p=0.4). Our data identified the genetic underpinnings of each of these distinct gene sets that provide a better understanding of the underlying biology affected by different genetic mutations. Interestingly, genetic alterations in RHOA were strongly associated with the proliferation gene signature and poor survival in DLBCL. We sought to better define the role of RHOA mutations in DLBCL which are mostly unknown. We knocked out the expression of RHOA in vitro through the CRISPR-Cas9 approach and found that RHOA deletion was lethal in both ABC DLBCL (LY3, TMD8, HBL1) and GCB DLBCLs (Pfeiffer and SUDHL4), results were validated with shRNAs. Conversely, cells overexpressing R5Q mutant proliferated 30% faster than vector control cells and more than 10% faster than wildtype overexpressing cells, directly implicating these mutations and RHOA overexpression in the growth of DLBCL cells. We further investigated the effects of RHOA knockout in vivo using two complementary mouse models. We bred Rhoa conditional knockout mice with Mb1-Cre transgenic mice, in which Cre is restricted to the B cell lineage as well as AID-Cre transgenic mice in which Cre is restricted to germinal center (GC) B cells. While B cells represented the vast majority of cells in the lymphoid organs of Rhoa wildtype mice, we found a dramatic reduction in B cell population and a resulting increase in T cell percentages in Rhoa deleted mice (p Collectively, our data define the genetic basis for observed changes in gene expression in DLBCL. Our experimental data define RHOA as an oncogene with roles in B cell development, germinal center maintenance, and DLCBL growth. Disclosures Leppa: Roche: Consultancy, Honoraria, Research Funding; Bayer: Research Funding; Merck: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Research Funding; Janssen Cilag: Consultancy, Research Funding. Flowers: Abbvie: Consultancy, Research Funding; Infinity: Research Funding; Clinical Care Options: Research Funding; National Cancer Institute: Research Funding; Spectrum: Consultancy; Onyx: Research Funding; OptumRx: Consultancy; Prime Oncology: Research Funding; Janssen Pharmaceutical: Research Funding; Acerta: Research Funding; Gilead: Consultancy; Seattle Genetics: Consultancy; Eastern Cooperative Oncology Group: Research Funding; Research to Practice: Research Funding; Burroughs Welcome Fund: Research Funding; Celgene: Consultancy, Research Funding; V Foundation: Research Funding; Bayer: Consultancy; Educational Concepts: Research Funding; Pharmacyclics LLC, an AbbVie Company: Research Funding; Genentech/Roche: Consultancy, Research Funding; Millennium/Takeda: Research Funding; National Institutes Of Health: Research Funding; TG Therapeutics: Research Funding. Gribben: TG Therapeutics: Honoraria; Kite: Honoraria; Janssen: Honoraria; Celgene: Honoraria; Pharmacyclics: Honoraria; Abbvie: Honoraria; Acerta: Honoraria; Karyopharm: Honoraria; Genentech/Roche: Honoraria. Hsi: Cellerant Therapeutics: Research Funding; Abbvie: Research Funding; Seattle Genetics: Consultancy, Honoraria, Speakers Bureau; Eli Lilly and Co.: Research Funding. Evens: Merck: Consultancy; Amgen: Consultancy; Seattle Genetics: Consultancy; Spectrum Pharmaceuticals: Consultancy; Novartis: Consultancy; Millennium: Consultancy; AbbVie: Consultancy; Affimed: Consultancy; Pharmacyclics: Consultancy; Celgene: Consultancy; Kite Pharma: Consultancy. Reddy: Abbvie: Consultancy; BMS: Consultancy; Celgene: Consultancy; Gilead: Speakers Bureau. Gordon: Janssen: Other: Data Monitoring Committee.

Power and sample size calculations for high-throughput sequencing-based experiments.

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type.

Thiol trapping and metabolic redistribution of sulfur metabolites enable cells to overcome cysteine overload.

Cysteine is an essential requirement in living organisms. However, due to its reactive thiol side chain, elevated levels of intracellular cysteine can be toxic and therefore need to be rapidly eliminated from the cellular milieu. In mammals and many other organisms, excess cysteine is believed to be primarily eliminated by the cysteine dioxygenase dependent oxidative degradation of cysteine, followed by the removal of the oxidative products. However, other mechanisms of tackling excess cysteine are also likely to exist, but have not thus far been explored. In this study, we use Saccharomyces cerevisiae, which naturally lacks a cysteine dioxygenase, to investigate mechanisms for tackling cysteine overload. Overexpressing the high affinity cysteine transporter, YCT1, enabled yeast cells to rapidly accumulate high levels of intracellular cysteine. Using targeted metabolite analysis, we observe that cysteine is initially rapidly interconverted to non-reactive cystine in vivo. A time course revealed that cells systematically convert excess cysteine to inert thiol forms; initially to cystine, and subsequently to cystathionine, S-Adenosyl-L-homocysteine (SAH) and S-Adenosyl L-methionine (SAM), in addition to eventually accumulating glutathione (GSH) and polyamines. Microarray based gene expression studies revealed the upregulation of arginine/ornithine biosynthesis a few hours after the cysteine overload, and suggest that the non-toxic, non-reactive thiol based metabolic products are eventually utilized for amino acid and polyamine biogenesis, thereby enabling cell growth. Thus, cells can handle potentially toxic amounts of cysteine by a combination of thiol trapping, metabolic redistribution to non-reactive thiols and subsequent consumption for anabolism.

Removing Batch Effects from Longitudinal Gene Expression - Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data.

Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data.

Bile acids promote diethylnitrosamine-induced hepatocellular carcinoma via increased inflammatory signaling.

Hepatocellular carcinoma (HCC) is the most common hepatic malignancy and the third leading cause of cancer related deaths. Previous studies have implicated bile acids in pathogenesis of HCC, but the mechanisms are not known. We investigated the mechanisms of HCC tumor promotion by bile acids the diethylnitrosamine (DEN)-initiation-cholic acid (CA)-induced tumor promotion protocol in mice. The data show that 0.2% CA treatment resulted in threefold increase in number and size of DEN-induced liver tumors. All tumors observed in DEN-treated mice were well-differentiated HCCs. The HCCs observed in DEN-treated CA-fed mice exhibited extensive CD3-, CD20-, and CD45-positive inflammatory cell aggregates. Microarray-based global gene expression studies combined with Ingenuity Pathway Analysis revealed significant activation of NF-κB and Nanog in the DEN-treated 0.2% CA-fed livers. Further studies showed significantly higher TNF-α and IL-1β mRNA, a marked increase in total and phosphorylated-p65 and phosphorylated IκBα (degradation form) in livers of DEN-treated 0.2% CA-fed mice. Treatment of primary mouse hepatocytes with various bile acids showed significant induction of stemness genes including Nanog, KLF4, Sox2, and Oct4. Quantification of total and 20 specific bile acids in liver, and serum revealed a tumor-associated bile acid signature. Finally, quantification of total serum bile acids in normal, cirrhotic, and HCC human samples revealed increased bile acids in serum of cirrhotic and HCC patients. Taken together, these data indicate that bile acids are mechanistically involved pathogenesis of HCC and may promote HCC formation via activation of inflammatory signaling.

Adamantinomatous and papillary craniopharyngiomas are characterized by distinct epigenomic as well as mutational and transcriptomic profiles.

Introduction Craniopharyngiomas (CP) are rare epithelial tumors of the sellar region. Two subtypes, adamantinomatous (adaCP) and papillary CP (papCP), were previously identified based on histomorphological and epidemiological aspects. Recent data indicates that both variants are defined by specific genetic alterations, and influenced by distinct molecular pathways and particular origins. The fact that CP is an uncommon tumor entity renders studies on large cohorts difficult and exceptional. In order to achieve further insights distinguishing CP variants, we conducted whole genome methylation (450 k array) and microarray-based gene expression studies in addition to CTNNB1 and BRAF mutation analysis using a comprehensive cohort of 80 adaCP and 35 papCP.ResultsBRAFV600E mutations were solely found in the papCP subgroup and were not detectable in adaCP samples. In contrast, CTNNB1 mutations were exclusively detected in adaCP. The methylome fingerprints assigned DNA specimens to entity-specific groups (papCP (n = 18); adaCP (n = 25)) matching perfectly with histology-based diagnosis, suggesting that they represent truly distinct biological entities. However, we were not able to detect within the adaCP group (including 11 pediatric and 14 adult cases) a significant difference in methylation signature by age. Integrative comparison of the papCP with the adaCP group based on differential gene expression and methylation revealed a distinct upregulation of Wnt- and SHH signaling pathway genes in adaCP.ConclusionsAdaCP and papCP thus represent distinct tumor subtypes that harbor mutually exclusive gene mutations and methylation patterns, further reflected in differences in gene expression. This study demonstrates that DNA methylation analyses are an additional method to classify CP into subtypes, and implicates a role of epigenetic mechanisms in the genesis of the respective CP variants. Detection of tumor-specific signaling pathway activation enables the possibility of target-oriented intervention.Electronic supplementary materialThe online version of this article (doi:10.1186/s40478-016-0287-6) contains supplementary material, which is available to authorized users.

Carcinoma in situ to invasive breast cancer.

Invasive breast cancer (IBC) is a heterogeneous disease which can be divided in several molecular subtypes [1] with distinct biological behavior and clinical outcome. Although these subtypes are based on microarray-based gene expression studies, each molecular subtype has an immunohistochemical surrogate: luminal A (ER+ and PR+/Her2−, low Ki-67 index), luminal B (ER+, Her2−, PR− or low and/or high Ki-67 index or ER+, Her2+ with any PR expression and Ki-67 index), basal-like (ER−, PR− and Her2−) and Her2-overexpressed (ER−, PR− and Her2+) [2], which has been further refi ned with the addition of CK5/6 and EGFR [3]. The DNA mutation spectrum across these different breast cancer subtypes varies widely [4] and there is increased evidence that genetic alterations, and their prognostic and predictive signifi cance, differ among them. Ductal carcinoma in situ (DCIS) is regarded as a nonobligate precursor lesion of IBC, sharing a great degree of agreement regarding morphology and genetics [5]. In other words: ER+ and ER− DCIS correlate with respectively ER+ and ER− IBC. Reported incidences of DCIS subtypes suggest that progression of DCIS to IBC differs among subtypes [6]: Triple-negative DCIS seems to have the fastest progression. Luminal A type DCIS seems to progress slower to IBC than luminal B type DCIS. The high incidence of pure Her2+ DCIS compared with Her2+ IBC and the association of Her2+ IBC with an extensive DCIS component might indicate that Her2 amplifi cation does not play a key role in DCIS progression to IBC. The recent manuscript by Kim et al. [7] focuses on genomic differences between pure DCIS and synchronous DCIS with IBC in order to better understand the mechanisms of DCIS progression. In this interesting study, they compared 6 cases with pure DCIS and 5 pairs of synchronous DCIS and IBC by whole-exome sequencing and copy number profi ling. They concluded that pure DCIS harbored wellknown mutations (e.g. TP53, PIK3CA and AKT1), copy number alterations and chromothripsis, but had significantly fewer driver genes and co-occurrence of mutation/copy number alterations compared with synchronous DCIS with IBC. The authors proposed that although pure DCIS already has some driver mutations, additional changes are needed to progress to IBC. They stated that pure DCIS seems to have a less aggressive genome compared with synchronous DCIS and IBC. However, the study is based on a limited number of cases of different molecular subtypes. All six cases with pure DCIS were ER+, the majority (4 out of 6) was Her2− and all showed low or intermediate Ki-67 index. In conclusion: all pure DCIS cases were of the luminal type, mostly luminal A. On the contrary, the synchronous DCIS-IBC group included a total of fi ve cases, mainly (3 out of 5) ER−. The majority (4 out of 5) showed Her2 amplifi cation and all showed intermediate or high Ki-67 index, which means that this synchronous DCIS-IBC group includes only luminal B and Her2+ cases. Obviously, the pure DCIS group and the DCIS-IBC group consist of cases with a different molecular subtype; the pure DCIS group mostly luminal A, the DCIS− IBC group mostly luminal B and Her2+. Therefore, the question arises whether the reported genetic differences between these groups are a true refl ection of biological behavior (progression to IBC or not) or that these differences are the result of comparing different DCIS subtypes. In other words, in case one would compare luminal A type pure DCIS to luminal B type or Her2+ pure DCIS, would the results not be similar? In line with this, Kim et al. [7] reported that the ER− and PR− group harbored a higher number of somatic mutations and copy number alterations compared with the ER+ and PR+ group respectively, although this difference was only signifi cant for PR. In addition, the Her2+ cases seem to have more copy number alterations compared with the Her2− cases. Therefore, in our opinion, comparing different molecular DCIS subtypes with different biological behavior (progression or not) does not answer the question whether the reported molecular alterations of the DCIS component are truly responsible for progression since these differences could also be the result of comparing different DCIS subtypes. In order to study true progression-related alterations, we believe it might be preferable to do either paired analyses of synchronous DCIS-IBC or perform analyses of pure DCIS with molecular subtype matched cases with synchronous DCIS-IBC.

A Meta-Analysis of Gene Expression Studies in Severe Sepsis and Septic Shock

Introduction: Sepsis represents a complex inflammatory response to infection. Gene expression studies based on microarrays have shown that this response can affect more than 80% of cellular functions and pathways, in what has been termed a “genomic storm”. For several years, sepsis was regarded as a pro-inflammatory condition, and this concept resulted in several experimental treatment strategies aimed to block inflammation. However, systematic failure of these therapies and recent evidence demonstrating that anti-inflammatory pathways are also activated during sepsis illustrate the complexity and our incomplete knowledge about the pathogenesis of this condition. In the last decade, microarray-based gene expression studies have been used in attempts to improve our understanding about sepsis. Raw data from most of these studies are now collected in public archives, thus offering a unique opportunity to combine the information from different studies by meta-analysis. It has been shown that by analyzing data from multiple experiments, biases and artifacts between datasets can be cancelled out, potentially allowing true relationships to stand out. Accordingly, an increasing number of bioinformatics protocols and guidelines about meta-analysis of gene expression studies have been published in the last years. In the context of sepsis, several high-quality microarray-based gene expression studies are available. However, no systematic meta-analysis of these studies has been performed. In order to identify genes and pathways robustly associated with the pathogenesis of sepsis, we performed a meta-analysis of gene expression studies in human severe sepsis and septic shock.Material and methods: Microarray data were identified by searching two public databases (Gene Expression Omnibus and Array-Express) using the following search criteria: (“sepsis or “septic shock”) AND (“peripheral blood” or “leukocytes”) AND (“homo sapiens”). Inclusion criteria were: studies in humans with severe sepsis or septic shock; RNA obtained from peripheral blood leukocytes; availability of raw data; and matched healthy controls from the same study. To improve consistency, only studies using similar platforms were compared. We used the R/BioConductor environment to preprocess the datasets using the Robust Multi-array Average algorithm (RMA) implemented in the ‘oligo’ package and to perform meta-analysis through the ‘RankProd’ package implementation. This is a non-parametric statistical method that utilizes ranks of the log-ratio statistics for all genes across different studies to generate a list of differentially expressed (DE) genes between two conditions, and considered superior to alternative methodologies. For this study, we selected genes with fold-change of expression above 2 and false discovery rate below 0.01, calculated based on 10,000 permutations. Gene set analysis was initially performed using WebGestalt and confirmed in similar tools (KEGG, Pathway Commons, WikiPathways). Only pathways identified by more than one tool were considered.Results: Forty-five studies were identified, of which five fulfilled inclusion criteria. Our meta-analysis included data from 259 patients and 132 controls. Out of 22,216 probesets, we observed 352 as candidates for DE, 215 of which were up-regulated and 137 down-regulated. Top 5 up-regulated genes were CD177, MMP8, HP, ARG1 and ANXA3. Top 5 down-regulated genes were FCER1A, YME1L1, TRDV3, LRRN3 and MYBL1. The gene ontology term associated with the set of DE genes in both analysis with higher statistical significance was “immune response” (adjP=2.85e-27), and the most significant pathways identified were “Hematopoietic cell lineage” (adjP=8.69e-13), “TCR signaling pathway” (adjP=3.04e-10) and “immune system” (adjP=1.08e-19).Discussion and conclusion: The combined analysis of data generated by high-throughput experiments is an attractive and validated strategy to improve the sensitivity and specificity of genome-wide expression data. This meta-analysis provides a comprehensive list of genes, pathways and expression signatures associated with severe sepsis and septic shock, confirming several results from individual studies. In addition, our meta-analysis potentially provides new biological insights about sepsis, by listing a comprehensive list of new candidate genes with robust associations with this condition. DisclosuresNo relevant conflicts of interest to declare.

Differentially Expressed Genes and Pathways in Endothelial Cells Exposed to Hemin or Plasma from Patients with Sickle Cell Disease: A Meta-Analysis of Gene Expression Studies

Introduction: Vaso-occlusion and chronic hemolysis are recognized as the most important pathogenic mechanisms of sickle cell disease (SCD), feeding a vicious circle that leads to acute and chronic complications. Although phenotypic aspects of this pro-inflammatory state have been described in detail, much less is known about the upstream pathways that activate and perpetuate inflammation in SCD. It has been known for more than 50 years that patients with SCD present higher plasma concentrations of heme. More recently, the role of heme as a mediator of inflammation in SCD has been confirmed in relevant models, suggesting that free heme can be a trigger for both microvascular occlusion and acute chest syndrome (ACS). In the past, microarray-based gene expression experiments have been used to study the effects of heme on endothelial cells (EC), as well as gene expression signatures of SCD. These studies can be analyzed in combination, since original raw data are now collected in public archives. In fact, it has been shown that by analyzing data from multiple experiments by meta-analysis, biases and artifacts between datasets can be cancelled out, potentially allowing true relationships to stand out. In order to gain insights into the cellular and molecular pathways activated by heme in endothelial cells (EC), as well as about their potential relevance in SCD, we performed a meta-analysis of microarray-based gene expression studies involving heme, EC and SCD.Material and methods: Microarray data were identified by searching two public databases (GEO and Array-Express) using the following search criteria: (“sickle cell disease” and “homo sapiens”). Eleven studies were identified, of which two were selected for our meta-analysis (GSE1849; GSE25014). One study evaluated the effect of heme 5µM in human primary pulmonary artery (PAECs) and microvascular EC (PMVECs) (12 samples), while the other study evaluated the effect of plasma from SCD patients (9 patients in steady state and 12 patients during ACS) in PAECs. To perform the meta-analysis we used INMEX, an integrative web-based tool for meta-analysis of expression data. For the meta-analysis, we applied a combining rank orders method based in the RankProd package. Genes with expression fold-changes (FC) in the same direction (either up or down) of 1.4 in at least one study were selected as candidates for differentially expressed (cDE) genes. Selected genes were ranked based on p value, and a p value ≤0.05 was considered statistically significant. To further understand functions of the subset of genes that were cDE in both studies, we performed gene ontology enrichment analysis. The functional analysis was undertaken using INMEX, and confirmed in other three gene set analysis tools (Pathway Commons, WikiPathways and KEGG). Only pathways that were identified in more than one tool were considered in the analysis.Results: Two different meta-analysis were performed. Gene expression data from heme-stimulated EC was compared to: (i) data from EC stimulated by plasma from SCD patients at steady-state; or (ii) data from EC stimulated by plasma obtained during ACS. In the first (heme x steady-state) and second (heme x ACS) analysis, 799 and 786 genes were consistently up- or down-regulated in both studies. The up- and down-regulated genes with the lowest p values were C2CD4A (C2 calcium-dependent domain containing 4A), and KLHL23 (kelch-like family member 23), respectively. In addition, genes associated with depletion of reactive oxygen species and coagulation activation were also identified. The most significant pathways identified in the gene set analysis were “IL5-mediated signaling events” (heme x steady-state; p= 0.0012) and “MAPK signaling pathway” (heme x ACS; p= 0.0073512) respectively.Results and conclusion: Genes and pathways that are DE both in EC stimulated by heme or by plasma from SCD patients could be relevant elements in the pathogenesis of inflammation in SCD. Heme has been shown to increase the generation of ROS and to induce the expression of inflammatory and pro-coagulant proteins by EC. The results of our meta-analysis are consistent with these effects. Therefore, the comprehensive list of genes and pathways identified in our study could help the generation new hypothesis about the mechanisms involved in heme-induced activation and perpetuation of inflammation in SCD. DisclosuresNo relevant conflicts of interest to declare.

HIF-1 is involved in the negative regulation of AURKA expression in breast cancer cell lines under hypoxic conditions

Numerous microarray-based gene expression studies performed on several types of solid tumors revealed significant changes in key genes involved in progression and regulation of the cell cycle, including AURKA that is known to be overexpressed in many types of human malignancies. Tumor hypoxia is associated with poor prognosis in several cancer types, including breast cancer (BC). Since hypoxia is a condition that influences the expression of many genes involved in tumorigenesis, proliferation, and cell cycle regulation, we performed a microarray-based gene expression analysis in order to identify differentially expressed genes in BC cell lines exposed to hypoxia. This analysis showed that hypoxia induces a down-regulation of AURKA expression. Although hypoxia is a tumor feature, the molecular mechanisms that regulate AURKA expression in response to hypoxia in BC are still unknown. For the first time, we demonstrated that HIF-1 activation downstream of hypoxia could drive AURKA down-regulation in BC cells. In fact, we found that siRNA-mediated knockdown of HIF-1α significantly reduces the AURKA down-regulation in BC cells under hypoxia. The aim of our study was to obtain new insights into AURKA transcriptional regulation in hypoxic conditions. Luciferase reporter assays showed a reduction of AURKA promoter activity in hypoxia. Unlike the previous findings, we hypothesize a new possible mechanism where HIF-1, rather than inducing transcriptional activation, could promote the AURKA down-regulation via its binding to hypoxia-responsive elements into the proximal region of the AURKA promoter. The present study shows that hypoxia directly links HIF-1 with AURKA expression, suggesting a possible pathophysiological role of this new pathway in BC and confirming HIF-1 as an important player linking an environmental signal to the AURKA promoter. Since AURKA down-regulation overrides the estrogen-mediated growth and chemoresistance in BC cells, these findings could be important for the development of new possible therapies against BC.

An effect size filter improves the reproducibility in spectral counting-based comparative proteomics

The microarray community has shown that the low reproducibility observed in gene expression-based biomarker discovery studies is partially due to relying solely on p-values to get the lists of differentially expressed genes. Their conclusions recommended complementing the p-value cutoff with the use of effect-size criteria. The aim of this work was to evaluate the influence of such an effect-size filter on spectral counting-based comparative proteomic analysis. The results proved that the filter increased the number of true positives and decreased the number of false positives and the false discovery rate of the dataset. These results were confirmed by simulation experiments where the effect size filter was used to evaluate systematically variable fractions of differentially expressed proteins. Our results suggest that relaxing the p-value cut-off followed by a post-test filter based on effect size and signal level thresholds can increase the reproducibility of statistical results obtained in comparative proteomic analysis. Based on our work, we recommend using a filter consisting of a minimum absolute log2 fold change of 0.8 and a minimum signal of 2-4 SpC on the most abundant condition for the general practice of comparative proteomics. The implementation of feature filtering approaches could improve proteomic biomarker discovery initiatives by increasing the reproducibility of the results obtained among independent laboratories and MS platforms. Quality control analysis of microarray-based gene expression studies pointed out that the low reproducibility observed in the lists of differentially expressed genes could be partially attributed to the fact that these lists are generated relying solely on p-values. Our study has established that the implementation of an effect size post-test filter improves the statistical results of spectral count-based quantitative proteomics. The results proved that the filter increased the number of true positives whereas decreased the false positives and the false discovery rate of the datasets. The results presented here prove that a post-test filter applying a reasonable effect size and signal level thresholds helps to increase the reproducibility of statistical results in comparative proteomic analysis. Furthermore, the implementation of feature filtering approaches could improve proteomic biomarker discovery initiatives by increasing the reproducibility of results obtained among independent laboratories and MS platforms. This article is part of a Special Issue entitled: Standardization and Quality Control in Proteomics.

Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis

Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

Computational interrogation of cis-regulatory elements of genes that are common targets of luteotropin and luteolysin in the primate corpus luteum

The rapid recent increase in microarray-based gene expression studies in the corpus luteum (CL) utilizing macaque models gathered increasing volume of data in publically accessible microarray expression databases. Examining gene pathways in different functional states of CL may help to understand the factors that control luteal function and hence human fertility. Co-regulation of genes in microarray experiments may imply common transcriptional regulation by sequence-specific DNA-binding transcriptional factors. We have computationally analyzed the transcription factor binding sites (TFBS) in a previously reported macaque luteal microarray gene set (n=15) that are common targets of luteotropin (luteinizing hormone (LH) and human chorionic gonadotropin (hCG)) and luteolysin (prostaglandin (PG) F2α). This in silico approach can reveal transcriptional networks that control these important genes which are representative of the interplay between luteotropic and luteolytic factors in the control of luteal function. Our computational analyses revealed 6 matrix families whose binding sites are significantly over-represented in promoters of these genes. The roles of these factors are discussed, which might help to understand the transcriptional regulatory network in the control of luteal function. These factors might be promising experimental targets for investigation of human luteal insufficiency.

Implications of TGFβ on Transcriptome and Cellular Biofunctions of Palatal Mesenchyme

Development of the palate comprises sequential stages of growth, elevation, and fusion of the palatal shelves. The mesenchymal component of palates plays a major role in early phases of palatogenesis, such as growth and elevation. Failure in these steps may result in cleft palate, the second most common birth defect in the world. These early stages of palatogenesis require precise and chronological orchestration of key physiological processes, such as growth, proliferation, differentiation, migration, and apoptosis. There is compelling evidence for the vital role of TGFβ-mediated regulation of palate development. We hypothesized that the isoforms of TGFβ regulate different cellular biofunctions of the palatal mesenchyme to various extents. Human embryonic palatal mesenchyme (HEPM) cells were treated with TGFβ1, β2, and β3 for microarray-based gene expression studies in order to identify the roles of TGFβ in the transcriptome of the palatal mesenchyme. Following normalization and modeling of 28,869 human genes, 566 transcripts were detected as differentially expressed in TGFβ-treated HEPM cells. Out of these altered transcripts, 234 of them were clustered in cellular biofunctions, including growth and proliferation, development, morphology, movement, cell cycle, and apoptosis. Biological interpretation and network analysis of the genes active in cellular biofunctions were performed using IPA. Among the differentially expressed genes, 11 of them are known to be crucial for palatogenesis (EDN1, INHBA, LHX8, PDGFC, PIGA, RUNX1, SNAI1, SMAD3, TGFβ1, TGFβ2, and TGFβR1). These genes were used for a merged interaction network with cellular behaviors. Overall, we have determined that more than 2% of human transcripts were differentially expressed in response to TGFβ treatment in HEPM cells. Our results suggest that both TGFβ1 and TGFβ2 orchestrate major cellular biofunctions within the palatal mesenchyme in vitro by regulating expression of 234 genes.

Is gene array testing to be considered routine now?

There are five multi-gene expression based prognostic tests for breast cancer offered as reference lab tests - Mammaprint, MapQuant Dx, OncotypeDx, PAM50 Breast Cancer Intrinsic Subtype Classifier, and Theros Breast Cancer Index. Each claims to provide additional prognostic information beyond conventional prognostic markers and to aid in determining who should receive systemic therapy. Evidence for their clinical utility was reviewed to determine whether any of them should be considered as routine clinical test. Peer reviewed publications, meeting abstracts, and information provided by company web sites have been reviewed to compile information regarding their clinical utility according to the following criteria; (1) Analytical validity and regulatory approval of the reference lab test. (2) Level of evidence for clinical utility. (3) Whether published evidences support prognostic and/or predictive claim. While published evidences for clinical claims for OncotypeDx and Mammaprint used reference lab tests, and the supporting evidences for other tests come from academic assays before being converted to reference lab tests, results from two large randomized clinical trials testing the clinical utility of OncotypeDx and Mammaprint are still several years away and until that time none of the markers would reach level I evidence by Marker Utility Grading System. However Oncotype Dx has reached a level IB evidence according to Simon modification to Marker Utility Grading System. Therefore OncotypeDx may be considered for routine clinical use as an adjunct to clinical and pathological information and has been incorporated into clinical guidelines in USA. While Mammaprint, MapQuantDx, and PAM50 have been repeatedly demonstrated to provide robust prognostic information, evidence for its worth as a predictive marker for chemotherapy benefit is yet to come from randomize clinical trials and therefore its utility is limited to prognostication. Meta-analysis of publicly available microarray based gene expression studies demonstrated that gene expression assays provide similar information and the most important information they provide is the proliferation activity. In untreated population, the prognostic impact of proliferation genes is limited to ER+HER2- subset since HER2+ or ER-HER2- subsets are associated with high proliferation activity. Therefore the clinical utility of these gene expression based tests is mainly for ER+HER2- subset. Since they are usually treated with adjuvant anti-estrogen therapies, for their clinical utility, demonstration of the interaction between the gene expression markers and chemotherapy in anti-estrogen treated cohort in a randomized clinical trial would be required. While OncoytpeDx is the only test supported by studies in a randomized clinical trial for adjuvant chemotherapy, other gene expression based tests are expected to provide similar information. Gene expression profiling assays as more reproducible and precise surrogates for tumor grade (MapQauntDx and Theros Breast Cancer Index) are very promising assays. However, absence of definitive predefined cut-off for defining the subset that benefit from chemotherapy validated in cohorts from randomized trials limit their clinical application.

NF-κB-mediated anti-inflammatory activity of the sesquiterpene lactone 7-hydroxyfrullanolide

Microarray technology can be used to study the molecular mechanisms of new chemical entities with the aim to develop effective therapeutics. 7-Hydroxyfrullanolide (7HF) is a sesquiterpene lactone that was found to be efficacious in multiple animal models of inflammation by suppression of pro-inflammatory cytokines; however, its molecular mechanism of action remains unclear. We investigated the effects of 7HF on lipopolysaccharide (LPS)-stimulated human peripheral blood mononuclear cells using microarray-based gene expression studies and explored the molecular targets affected. Gene expression profiles and pathway analysis revealed that 7HF potently suppressed multiple inflammatory pathways induced by LPS. More importantly, 7HF was found to inhibit NF-κB related transcripts. These transcripts were further validated using freshly isolated synovial cells from rheumatoid arthritis patients, thus clinically validating our findings. Cell-based imaging and subsequent Western blot analysis demonstrated that 7HF inhibited the translocation of NF-κB into the nucleus by directly inhibiting the phosphorylation of IKK-β. Since the transcription of adhesion molecules is regulated by NF-κB, further investigation showed that 7HF dose-dependently suppressed ICAM-1, VCAM-1 and E-selectin expression on LPS-stimulated endothelial cells as well as inhibited the adhesion of monocytes to LPS-stimulated endothelial cells. Taken together, our results reveal that 7HF possesses NF-κB inhibitory potential and suggest a likely molecular mechanism of its anti-inflammatory activity.

CDX2 Regulates Multidrug Resistance 1 Gene Expression in Malignant Intestinal Epithelium

The caudal-related homeobox transcription factor CDX2 has a key role in intestinal development and differentiation. CDX2 heterozygous mutant mice develop colonic polyps, and loss of CDX2 expression is seen in a subset of colon carcinomas in humans. Ectopic CDX2 expression in the stomach of transgenic mice promotes intestinal metaplasia, and CDX2 expression is frequently detected in intestinal metaplasia in the stomach and esophagus. We sought to define CDX2-regulated genes to enhance knowledge of CDX2 function. HT-29 colorectal cancer cells have minimal endogenous CDX2 expression, and HT-29 cells with ectopic CDX2 expression were generated. Microarray-based gene expression studies revealed that the Multidrug Resistance 1 (MDR1/P-glycoprotein/ABCB1) gene was activated by CDX2. Evidence that the MDR1 gene was a direct transcriptional target of CDX2 was obtained, including analyses with MDR1 reporter gene constructs and chromatin immunoprecipitation assays. RNA interference-mediated inhibition of CDX2 decreased endogenous MDR1 expression. In various colorectal cancer cell lines and human tissues, endogenous MDR1 expression was well correlated to CDX2 expression. Overexpression of CDX2 in HT-29 cells revealed increased resistance to the known substrate of MDR1, vincristine and paclitaxel, which was reversed by an MDR1 inhibitor, verapamil. These data indicate that CDX2 directly regulates MDR1 gene expression through binding to elements in the promoter region. Thus, CDX2 is probably important for basal expression of MDR1, regulating drug excretion and absorption in the lower gastrointestinal tract, as well as for multidrug resistance to chemotherapy reagent in CDX2-positive gastrointestinal cancers.

Examining smoking-induced differential gene expression changes in buccal mucosa

BackgroundGene expression changes resulting from conditions such as disease, environmental stimuli, and drug use, can be monitored in the blood. However, a less invasive method of sample collection is of interest because of the discomfort and specialized personnel necessary for blood sampling especially if multiple samples are being collected. Buccal mucosa cells are easily collected and may be an alternative sample material for biomarker testing. A limited number of studies, primarily in the smoker/oral cancer literature, address this tissue's efficacy as an RNA source for expression analysis. The current study was undertaken to determine if total RNA isolated from buccal mucosa could be used as an alternative tissue source to assay relative gene expression.MethodsTotal RNA was isolated from swabs, reverse transcribed and amplified. The amplified cDNA was used in RT-qPCR and microarray analyses to evaluate gene expression in buccal cells. Initially, RT-qPCR was used to assess relative transcript levels of four genes from whole blood and buccal cells collected from the same seven individuals, concurrently. Second, buccal cell RNA was used for microarray-based differential gene expression studies by comparing gene expression between a group of female smokers and nonsmokers.ResultsAn amplification protocol allowed use of less buccal cell total RNA (50 ng) than had been reported previously with human microarrays. Total RNA isolated from buccal cells was degraded but was of sufficient quality to be used with RT-qPCR to detect expression of specific genes. We report here the finding of a small number of statistically significant differentially expressed genes between smokers and nonsmokers, using buccal cells as starting material. Gene Set Enrichment Analysis confirmed that these genes had a similar expression pattern to results from another study.ConclusionsOur results suggest that despite a high degree of degradation, RNA from buccal cells from cheek mucosa could be used to detect differential gene expression between smokers and nonsmokers. However the RNA degradation, increase in sample variability and microarray failure rate show that buccal samples should be used with caution as source material in expression studies.

Transforming growth factor β as a therapeutic target in systemic sclerosis

Transforming growth factor beta (TGF-beta) is a pleiotropic cytokine with vital homeostatic functions. Aberrant TGF-beta expression is implicated in the pathogenesis of fibrosis in systemic sclerosis (SSc); thus, TGF-beta represents a molecular therapeutic target in this disease. Anti-TGF-beta monoclonal antibody has been evaluated in a small trial of early SSc, with disappointing results. Antibodies against the alphavbeta6 integrin that prevent latent TGF-beta activation, however, have shown promise in preclinical studies. Small-molecule inhibitors of TGF-beta-receptor activity are effective in animal models of fibrosis. Imatinib mesylate and related tyrosine kinase inhibitors also block TGF-beta pathways and abrogate fibrotic responses. The blocking of TGF-beta activity might lead to spontaneous immune activation, epithelial hyperplasia and impaired wound healing. Loss of immune tolerance is a potential concern in an autoimmune disease such as SSc. Novel insights from microarray-based gene expression analyses and studies of genetic polymorphisms in TGF-beta signaling could aid in identifying patients who are most likely to respond to anti-TGF-beta treatment. This intervention promises to have a major impact on the treatment of SSc. Concerns regarding efficacy and safety and whether biomarkers can indicate these features, questions regarding appropriate dosing and timing of therapy, and identification of potential responders are critical challenges ahead.