Analysis Of Gene Expression Signatures Research Articles

CDRgator: An Integrative Navigator of Cancer Drug Resistance Gene Signatures.

Understanding the mechanisms of cancer drug resistance is a critical challenge in cancer therapy. For many cancer drugs, various resistance mechanisms have been identified such as target alteration, alternative signaling pathways, epithelial–mesenchymal transition, and epigenetic modulation. Resistance may arise via multiple mechanisms even for a single drug, making it necessary to investigate multiple independent models for comprehensive understanding and therapeutic application. In particular, we hypothesize that different resistance processes result in distinct gene expression changes. Here, we present a web-based database, CDRgator (Cancer Drug Resistance navigator) for comparative analysis of gene expression signatures of cancer drug resistance. Resistance signatures were extracted from two different types of datasets. First, resistance signatures were extracted from transcriptomic profiles of cancer cells or patient samples and their resistance-induced counterparts for >30 cancer drugs. Second, drug resistance group signatures were also extracted from two large-scale drug sensitivity datasets representing ~1,000 cancer cell lines. All the datasets are available for download, and are conveniently accessible based on drug class and cancer type, along with analytic features such as clustering analysis, multidimensional scaling, and pathway analysis. CDRgator allows meta-analysis of independent resistance models for more comprehensive understanding of drug-resistance mechanisms that is difficult to accomplish with individual datasets alone (database URL: http://cdrgator.ewha.ac.kr).

Comprehensive molecular and immunological characterization of hepatocellular carcinoma.

BackgroundHepatocellular carcinoma (HCC) is a heterogeneous disease with various etiological factors, and ranks as the second leading cause of cancer-related mortality worldwide due to multi-focal recurrence. We herein identified three major subtypes of HCC by performing integrative analysis of two omics data sets, and clarified that this classification was closely correlated with clinicopathological factors, immune profiles and recurrence patterns.MethodsIn the test study, 183 tumor specimens surgically resected from HCC patients were collected for unsupervised clustering analysis of gene expression signatures and comparative analysis of gene mutations. These results were validated by using genome, methylome and transcriptome data of 373 HCC patients provided from the Cancer Genome Atlas Network. In addition, omics data were obtained from pairs of primary and recurrent HCC.FindingsComprehensive molecular evaluation of HCC by multi-platform analysis defined three major subtypes: (1) mitogenic and stem cell-like tumors with chromosomal instability; (2) CTNNB1-mutated tumors displaying immune suppression; and (3) metabolic disease-associated tumors, which included an immunogenic subgroup characterized by macrophage infiltration and favorable prognosis. Although genomic and epigenomic analysis explicitly distinguished between HCC with intrahepatic metastasis (IM) and multi-centric HCC (MC), the phenotypic similarity between the primary and recurrent tumors was not correlated to the IM/MC origin, but to the classification. Interpretation: Identification of these HCC subtypes provides further insights into patient stratification as well as presents opportunities for therapeutic development.FundMinistry of Education, Culture, Sports, Science and Technology of Japan (16H02670 and 18K19575), Japan Agency for Medical Research and Development (JP15cm0106064, JP17cm0106518, JP18cm0106540 and JP18fk0210040).

Weighted gene co-expression network analysis and connectivity map identifies lovastatin as a treatment option of gastric cancer by inhibiting HDAC2

ObjectiveThis study aimed to identifying and validating therapeutic compounds which might have positive effects on patients with gastric cancer (GC) based on weighted gene co-expression network analysis (WGCNA) and connectivity map (CMap). MethodsWe performed WGCNA to gain insights into the molecular aspects of GC. Raw microarray datasets (including 132 samples) were downloaded from the Gene Expression Omnibus (GEO) website. We utilized the WGCNA to identify the coexpressed genes (modules) and modular hub genes after non-specific filtering. Furthermore, these differentially expressed genes were submitted to CMap analysis to identify candidate therapeutic compounds for GC. In experimental part, cell growth inhibition was evaluated by Cell Counting Kit-8 (CCK-8) and colony formation assays. Tumor growth was assessed using nude mice with xenografts established in vivo. QRT-PCR and western blot were used for determination of HDAC2 expression level and immunohistochemistry was performed to quantify HDAC2 in gastric tumor samples. ResultsThrough WGCNA and CMap analysis, we found two potential therapeutic compounds, the valproic acid (VPA), which is the histone deacetylase (HDAC) inhibitor and lovastatin. HDAC2 was overexpressed in gastric cancer cell lines including AGS, BGC-823, NCI-N87 and MKN28. Dose-dependent inhibition of gastric cancer cells by VPA and lovastatin was verified in vitro. Apoptosis of GC cells was induced after treatment with VPA and lovastatin through suppressing HDAC2 expression. Furthermore, the inhibition of VPA with cisplatin and lovastatin with cisplatin were also dose-dependent and cisplatin exhibited synergistic effects. In the xenografts, similar results were found. ConclusionWGCNA was able to identify significant groups of genes associated with cancer prognosis. Moreover, analysis of gene expression signature using CMap is a powerful way to explore potential therapeutics for human diseases. For treating GC, lovastatin may be a potential drug.

Transcriptional Profiling of Immune-Related Genes in Leishmania infantum-Infected Mice: Identification of Potential Biomarkers of Infection and Progression of Disease.

Leishmania spp. is a protozoan parasite that affects millions of people around the world. At present, there is no effective vaccine to prevent leishmaniases in humans. A major limitation in vaccine development is the lack of precise understanding of the particular immunological mechanisms that allow parasite survival in the host. The parasite-host cell interaction induces dramatic changes in transcriptome patterns in both organisms, therefore, a detailed analysis of gene expression in infected tissues will contribute to the evaluation of drug and vaccine candidates, the identification of potential biomarkers, and the understanding of the immunological pathways that lead to protection or progression of disease. In this large-scale analysis, differential expression of 112 immune-related genes has been analyzed using high-throughput qPCR in spleens of infected and naïve Balb/c mice at four different time points. This analysis revealed that early response against Leishmania infection is characterized by the upregulation of Th1 markers and M1-macrophage activation molecules such as Ifng, Stat1, Cxcl9, Cxcl10, Ccr5, Cxcr3, Xcl1, and Ccl3. This activation doesn't protect spleen from infection, since parasitic burden rises along time. This marked difference in gene expression between infected and control mice disappears during intermediate stages of infection, probably related to the strong anti-inflammatory and immunosuppresory signals that are activated early upon infection (Ctla4) or remain activated throughout the experiment (Il18bp). The overexpression of these Th1/M1 markers is restored later in the chronic phase (8 wpi), suggesting the generation of a classical “protective response” against leishmaniasis. Nonetheless, the parasitic burden rockets at this timepoint. This apparent contradiction can be explained by the generation of a regulatory immune response characterized by overexpression of Ifng, Tnfa, Il10, and downregulation Il4 that counteracts the Th1/M1 response. This large pool of data was also used to identify potential biomarkers of infection and parasitic burden in spleen, on the bases of two different regression models. Given the results, gene expression signature analysis appears as a useful tool to identify mechanisms involved in disease outcome and to establish a rational approach for the identification of potential biomarkers useful for monitoring disease progression, new therapies or vaccine development.

Comparative analysis of molecular signatures suggests the use of gabapentin for the management of endometriosis-associated pain.

BackgroundIt has been repetitively shown that the transcription factors DLX5 and DLX6 are drastically downregulated in endometriotic lesions when compared with eutopic endometrium. These findings suggest that regulatory cascades involving DLX5/6 might be at the origin of endometriosis symptoms such as chronic pelvic pain (CPP). We have shown that inactivation of Dlx5 and Dlx5/6 in the mouse uterus results in an endometrial phenotype reminiscent of endometriosis.MethodsWe focused on genes that present a similar deregulation in endometriosis and in Dlx5/6-null mice in search of new endometriosis targets.ResultsWe confirmed a strong reduction of DLX5 expression in endometriosis implants. We identified a signature of 30 genes similarly deregulated in human endometriosis implants and in Dlx5/6-null mouse uteri, reinforcing the notion that the downregulation of Dlx5/6 is an early event in the progress of endometriosis. CACNA2D3, a component of the α2δ family of voltage-dependent calcium channel complex, was strongly overexpressed both in mutant mouse uteri and in endometriosis implants, were also CACNA2D1 and CACNA2D2, other members of the α2δ family involved in nociception, are upregulated.ConclusionComparative analysis of gene expression signatures from endometriosis and mouse models showed that calcium channel subunits α2δ involved in nociception can be targets for the treatment of endometriosis-associated pain. CACNA2D3 has been associated with pain sensitization and heat nociception in animal models. In patients, CACNA2D3 variants were associated with reduced sensitivity to acute noxious stimuli. As α2δs were targets of gabapentinoid analgesics, the results suggested the use of these drugs for the treatment of endometriosis-associated pain. Indeed, recent small-scale clinical studies have shown that gabapentin could be effective in women with CPP. The findings of this study reinforce the need for a large definitive trial.

System-wide coordinates of higher order functions in host-pathogen environment upon Mycobacterium tuberculosis infection

Molecular signatures and their interactions behind the successful establishment of infection of Mycobacterium tuberculosis (Mtb) inside macrophage are largely unknown. In this work, we present an inter-system scale atlas of the gene expression signatures, their interactions and higher order gene functions of macrophage-Mtb environment at the time of infection. We have carried out large-scale meta-analysis of previously published gene expression microarray studies andhave identified a ranked list of differentially expressed genes and their higher order functions in intracellular Mtb as well as the infected macrophage. Comparative analysis of gene expression signatures of intracellular Mtb with the in vitro dormant Mtb at different hypoxic and oxidative stress conditions led to the identification of the large number of Mtb functional groups, namely operons, regulons and pathways that were common and unique to the intracellular environment and dormancy state. Some of the functions that are specific to intracellular Mtb are cholesterol degradation and biosynthesis of immunomodulatory phenolic compounds. The molecular signatures we have identified to be involved in adaptation to different stress conditions in macrophage environment may be critical for designing therapeutic interventions against tuberculosis. And, our approach may be broadly applicable for investigating other host-pathogen interactions.

Integrated analysis of gene expression signatures associated with colon cancer from three datasets

PurposeThe present study aimed to elucidate the pathogenesis of colon cancer and identify genes associated with tumor development. MethodsThree datasets, two (GSE74602 and GSE44861) from the Gene Expression Omnibus database and RNA-Seq colon cancer data from The Cancer Genome Atlas data portal, were downloaded. These three datasets were grouped using a meta-analysis approach, and differentially expressed genes (DEGs) were identified between colon tumor samples and adjacent normal samples. Functional enrichment analysis and regulatory factor predication were performed for significant genes. Additionally, small-molecule drugs associated with colon cancer were predicted, and a prognostic risk model was constructed. ResultsThere were 251 overlapping DEGs (135 up- and 116 downregulated) between cancer samples and control samples in the three datasets. The DEGs were mainly involved in protein transport and apoptotic and neurotrophin signaling pathways. A total of 70 small-molecule drugs were predicated to be associated with colon cancer. Additionally, in the miRNA–target regulatory network, we found that SLC44A1 can be targeted by hsa-miR-183, hsa-miR-206, and hsa-miR-147, while KLF13 can be regulated by hsa-miR-182, hsa-miR-206, and hsa-miR-153. Moreover, the results of the prognostic risk model showed that four genes (VAMP1, P2RX5, CACNB1, and CRY2) could divide the samples into high and low risk groups. ConclusionSLC44A1 and KLF13 may be involved in tumorigenesis and the metastasis of colon cancer by miRNA regulation. In addition, a four-gene (VAMP1, P2RX5, CACNB1, and CRY2) expression signature may have prognostic and predictive value in colon cancer.

Analysis of Gene Expression Signatures in Cancer-Associated Stroma from Canine Mammary Tumours Reveals Molecular Homology to Human Breast Carcinomas

Cancer-associated stroma (CAS) plays a key role in cancer initiation and progression. Spontaneously occurring canine mammary carcinomas are viewed as excellent models of human breast carcinomas. Considering the importance of CAS for human cancer, it likely plays a central role in canine tumours as well. So far, however, canine CAS lacks characterisation, and it remains unclear whether the biology between CAS from canine and human tumours is comparable. In this proof-of-principle study, using laser-capture microdissection, we isolated CAS and normal stroma from 13 formalin-fixed paraffin embedded canine simple mammary carcinomas and analysed the expression of seven known human CAS markers by RT-qPCR (Reverse Transcription quantitative PCR) and validated some targets by immunohistochemistry. We found that Col1a1 (Collagen1α1), αSMA (alpha Smooth Muscle Actin), FAP (Fibroblast activation protein), PDGFRβ (Platelet-derived growth factor receptor beta), and Caveolin-1 were significantly upregulated in canine CAS, and the expression of CXCL12 (Stromal cell derived factor 1) significantly decreased, whereas MMP2 (Matrix Metalloproteinase 1) and IL6 (Interleukin 6) did not change. Our results suggest strong similarities in CAS biology in canine and human mammary carcinomas but also reveal some differences. To the best of our knowledge, this is the first report to provide a comprehensive expression analysis of the most important CAS markers in canine simple mammary carcinomas and further supports the validity of the dog as model for human cancer.

Bioinformatical analysis of gene expression signatures of different glioma subtypes

The aim of the present study was to identify the common molecular mechanisms of multiple glioma subtypes, including astrocytoma, glioblastoma and oligodendroglioma, in addition to the specific mechanisms of different types. The gene expression profile set GSE4290 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) from three types of glioma, relative to non-tumor tissue, were calculated by the t-test method with a linear regression model. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs was performed. GeneVenn online analysis software was used for the comparison of the DEGs between subtypes. A total of 795 DEGs, including 619 up and 176 downregulated DEGs were screened from the astrocytoma expression profiles; these were enriched in the KEGG pathways of ‘neuroactive ligand-receptor interaction’ (upregulated) and ‘Wnt signaling pathway’ (downregulated). Protein-protein interaction networks for astrocytoma, glioblastoma and oligodendroglioma were constructed with 1,617, 7,027 and 1,172 pairs, respectively. A total of 595 common DEGs were obtained between the three subtypes, which were enriched in pathways associated with neural signaling. Glioblastoma is a subtype of astrocytoma; there were 195 DEGs common between these subtypes that were not also associated with oligodendroglioma. DEGs unique to astrocytoma, glioblastoma and oligodendroglioma were associated with the development of the nervous system, the cell cycle and cell matrix components, respectively. The screened DEG p53 gene is likely to be critical for glioma development, including via the Wnt and p53 signaling pathways. Brain-derived neurotrophic factor and cyclin-dependent kinase 1 genes were also likely to be important in the mechanism of glioma development, and were associated with the cell cycle and p53 signaling pathways. Immune system-associated and cell matrix component pathways may be unique signaling pathways associated with astrocytoma and oligodendroglioma, respectively.

Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC

Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non–small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.

Abstract PD1-03: Multivariate analysis of subtype and gene expression signatures predictive of pathologic complete response (pCR) in triple-negative breast cancer (TNBC): CALGB 40603 (Alliance)

Abstract Support: U10CA180821, U10CA180882 Background: The addition of either carboplatin (Cb) or bevacizumab (Bev) to standard neoadjuvant chemotherapy (NACT) increases pCR rates in TNBC overall and in the dominant subset of basal-like cancers (Sikov et al, JCO 2015; Sikov et al, SABCS 2014). Multigene expression signatures more accurately reflect tumor biology for response prediction and prognosis than individual gene expression. We evaluated the ability of multivariate analysis of gene expression signatures to create predictive models for achievement of pCR in TNBC. Methods: RNA sequencing was successful on 389 pretreatment samples from patients with available pCR data, and used to assign PAM50 subtype and calculate gene signatures scores for 489 published expression signatures. Elastic net, a penalized regression model for high dimensional variable selection, was used to select features associated with pCR in all TNBC and in the basal-like subset. Models were derived in a training set (2/3 of samples) and validated in a separate test set (1/3). A separate model was derived using 196 TNBC samples from patients treated only on the standard NACT +/- Cb arms for application to external TNBC neoadjuvant data sets not treated with Bev. Results: Consistent with our prior partial data set, 343 (88%) of the cancers were classified basal-like, in whom the in breast pCR rate was 54%; the remainder were classified normal-like (n=32) or HER2-enriched (n=14) with a non-basal pCR rate of 56%. Elastic Net analysis in all TNBC generated a model of 23 signatures and treatment assignment with 68% sensitivity and 64% specificity. The area under the curve was 0.64 (p-value=0.0019). Nineteen modules, including immune cell signatures (Th1, NK, IgG), immunoglobulin variable region expression, addition of Cb and Bev and expression of genes at regions 15q25, 17p11.2-13.3, and 8p22 were positively associated with response. The latter two regions are associated with aggressive breast cancer, and while not part of the 17p13 signature, this region contains TP53, a gene important in TNBC. Six modules were associated with resistance, including luminal progenitor, TGFB, NOTCH, FOS/JUN, 8p amplicon, and eosinophil signatures. When limited to basal-like samples, a model including 32 modules and addition of Cb and Bev was generated, with 62.3% sensitivity and 59.1% specificity. Seventeen features were selected in both models. Omitting Bev-treated patients, a model using just the gene expression signatures was developed. The predictive value of this model will be assessed using an external cohort of TNBC patients treated with neoadjuvant docetaxel and Cb (NCT01560663) and results presented. Conclusions: Multivariate analysis of gene expression signatures derived from pretreatment samples enabled the construction of models to predict achievement of pCR in TNBC. These models performed well on our test set, and will be assessed for their predictive ability in other TNBC data sets. If validated by future analyses, this could help us identify patients likely to achieve pCR with standard NACT and may benefit from the addition of agents such as Cb or Bev. ClinicalTrials.gov Identifier: NCT00861705. Citation Format: Hoadley KA, Hyslop T, Fan C, Berry DA, Hahn O, Tolaney SM, Sikov WM, Perou CM, Carey LA. Multivariate analysis of subtype and gene expression signatures predictive of pathologic complete response (pCR) in triple-negative breast cancer (TNBC): CALGB 40603 (Alliance) [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr PD1-03.

Abstract P1-07-04: Unique overlapping subtypes of triple-negative breast and ovarian cancers and sensitivity of “mesenchymal-like” cancers to HSP90 inhibition is revealed by integrated gene expression and drug sensitivity profiling

Abstract Next-generation sequencing and gene expression signature analysis of primary tumors from The Cancer Genome Atlas (TCGA) revealed striking similarities between triple-negative breast cancer (TNBC) and ovarian cancer (OVCA). We hypothesized that these similarities may reveal transcriptionally-identifiable subgroups within a mix of TNBC and OVCA that are uniquely sensitive to certain drugs. To test this hypothesis, gene expression profiles for TNBC and OVCA cell lines in the Cancer Cell Line Encyclopedia were analyzed by unsupervised hierarchical clustering, which revealed two major unique subgroups containing both TNBC and OVCA cell lines that clustered according to the previously defined Mesenchymal and Basal subclasses of TNBC. Differential gene expression between “Mesenchymal-like” and “Basal-like” TNBC/OVCA cell lines was used to generate a gene signature that was subsequently validated using gene expression data in the Genomics of Drug Sensitivity in Cancer (GDSC) database. Drug sensitivity data from GDSC was then utilized to profile differential sensitivity of “Mesenchymal-like” and “Basal-like” cell lines to the 99 anti-cancer drugs with coverage of >50 breast and ovarian cell lines. Mesenchymal-like TNBC/OVCA cells were uniquely sensitive to HSP90 inhibition compared to Basal-like TNBC/OVCA cells for both HSP90 inhibitors in GDSC: CCT018159 (p<0.001) and 17-AAG (p=0.012). Strikingly, Mesenchymal-like TNBC/OVCA cells were most sensitive to HSP90 inhibition among all 33 solid tumor cancer lineages in GDSC, as well as other subgroups of breast and ovarian cancers. Differential sensitivity of Mesenchymal-like and Basal-like TNBC/OVCA cells to HSP90 inhibition with 4 agents (CCT018159, 17-AAG, AT13387, PU-H71) was validated using growth assays for 12 cell lines (6 of each subgroup) previously characterized in GDSC. To further validate the predictive value of our gene signature, gene expression data for 6 TNBC/OVCA cell lines with uncharacterized drug sensitivity was used to classify cell lines as “Mesenchymal-like” (n=4) or “Basal-like” (n=2). Our gene signature successfully predicted differential sensitivity to HSP90 inhibition, with the 4 Mesenchymal-like cell lines displaying greater sensitivity to HSP90 inhibition. The gene signature has been used to select PDX models with predicted differential sensitivity to HSP90 inhibition, and treatment studies are ongoing. In summary, we developed a novel approach to generate a gene expression signature that robustly predicts sensitivity to HSP90 inhibitors in preclinical models of TNBC and OVCA. HSP90 thus represents a therapeutic opportunity in an overlapping subpopulation of TNBC and OVCA. Our approach to identify novel combinations of drugs and histology/lineage-independent cancer subgroups may be used to discover new therapeutic opportunities in other cancer types. Citation Format: Shee K, Ung MH, Cheng C, Miller TW. Unique overlapping subtypes of triple-negative breast and ovarian cancers and sensitivity of “mesenchymal-like” cancers to HSP90 inhibition is revealed by integrated gene expression and drug sensitivity profiling [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-07-04.

GEN3VA: aggregation and analysis of gene expression signatures from related studies

Genome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expression Omnibus (GEO) for potential reuse. However, these repositories currently do not provide simple interfaces to systematically analyze collections of related studies. Here we present GENE Expression and Enrichment Vector Analyzer (GEN3VA), a web-based system that enables the integrative analysis of aggregated collections of tagged gene expression signatures identified and extracted from GEO. Each tagged collection of signatures is presented in a report that consists of heatmaps of the differentially expressed genes; principal component analysis of all signatures; enrichment analysis with several gene set libraries across all signatures, which we term enrichment vector analysis; and global mapping of small molecules that are predicted to reverse or mimic each signature in the aggregate. We demonstrate how GEN3VA can be used to identify common molecular mechanisms of aging by analyzing tagged signatures from 244 studies that compared young vs. old tissues in mammalian systems. In a second case study, we collected 86 signatures from treatment of human cells with dexamethasone, a glucocorticoid receptor (GR) agonist. Our analysis confirms consensus GR target genes and predicts potential drug mimickers. GEN3VA can be used to identify, aggregate, and analyze themed collections of gene expression signatures from diverse but related studies. Such integrative analyses can be used to address concerns about data reproducibility, confirm results across labs, and discover new collective knowledge by data reuse. GEN3VA is an open-source web-based system that is freely available at: http://amp.pharm.mssm.edu/gen3va .

GEN3VA: aggregation and analysis of gene expression signatures from related studies.

BackgroundGenome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expression Omnibus (GEO) for potential reuse. However, these repositories currently do not provide simple interfaces to systematically analyze collections of related studies.ResultsHere we present GENE Expression and Enrichment Vector Analyzer (GEN3VA), a web-based system that enables the integrative analysis of aggregated collections of tagged gene expression signatures identified and extracted from GEO. Each tagged collection of signatures is presented in a report that consists of heatmaps of the differentially expressed genes; principal component analysis of all signatures; enrichment analysis with several gene set libraries across all signatures, which we term enrichment vector analysis; and global mapping of small molecules that are predicted to reverse or mimic each signature in the aggregate. We demonstrate how GEN3VA can be used to identify common molecular mechanisms of aging by analyzing tagged signatures from 244 studies that compared young vs. old tissues in mammalian systems. In a second case study, we collected 86 signatures from treatment of human cells with dexamethasone, a glucocorticoid receptor (GR) agonist. Our analysis confirms consensus GR target genes and predicts potential drug mimickers.ConclusionsGEN3VA can be used to identify, aggregate, and analyze themed collections of gene expression signatures from diverse but related studies. Such integrative analyses can be used to address concerns about data reproducibility, confirm results across labs, and discover new collective knowledge by data reuse. GEN3VA is an open-source web-based system that is freely available at: http://amp.pharm.mssm.edu/gen3va.

Cogena, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery.

BackgroundDrug repositioning, finding new indications for existing drugs, has gained much recent attention as a potentially efficient and economical strategy for accelerating new therapies into the clinic. Although improvement in the sensitivity of computational drug repositioning methods has identified numerous credible repositioning opportunities, few have been progressed. Arguably the “black box” nature of drug action in a new indication is one of the main blocks to progression, highlighting the need for methods that inform on the broader target mechanism in the disease context.ResultsWe demonstrate that the analysis of co-expressed genes may be a critical first step towards illumination of both disease pathology and mode of drug action. We achieve this using a novel framework, co-expressed gene-set enrichment analysis (cogena) for co-expression analysis of gene expression signatures and gene set enrichment analysis of co-expressed genes. The cogena framework enables simultaneous, pathway driven, disease and drug repositioning analysis. Cogena can be used to illuminate coordinated changes within disease transcriptomes and identify drugs acting mechanistically within this framework. We illustrate this using a psoriatic skin transcriptome, as an exemplar, and recover two widely used Psoriasis drugs (Methotrexate and Ciclosporin) with distinct modes of action. Cogena out-performs the results of Connectivity Map and NFFinder webservers in similar disease transcriptome analyses. Furthermore, we investigated the literature support for the other top-ranked compounds to treat psoriasis and showed how the outputs of cogena analysis can contribute new insight to support the progression of drugs into the clinic. We have made cogena freely available within Bioconductor or https://github.com/zhilongjia/cogena.ConclusionsIn conclusion, by targeting co-expressed genes within disease transcriptomes, cogena offers novel biological insight, which can be effectively harnessed for drug discovery and repositioning, allowing the grouping and prioritisation of drug repositioning candidates on the basis of putative mode of action.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2737-8) contains supplementary material, which is available to authorized users.

Broad Activity of Apto-253 in AML and Other Hematologic Malignancies Correlates with KLF4 Expression Level

Introduction: Aberrant expression of the homeodomain transcription factor CDX2 has recently been reported in a large proportion of AML cases. One consequence of CDX2 deregulation appears to be repressed expression of the transcription factor KLF4. Repression of KLF4 was shown to be critical for CDX2-mediated tumorigenesis, and forced genetic de-repression of KLF4 led to apoptosis of AML cells. APTO-253 is a novel small molecule that induces the expression of KLF4 and is cytotoxic to AML cell lines at low-nanomolar concentrations. We evaluated the activity of APTO-253 against a broad panel of primary specimens from patients with acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and myelodysplastic syndromes/myeloproliferative neoplasms (MDS/MPN). APTO-253 was tested both as a single agent and in combinations with 2 other emerging targeted therapies, the BET bromodomain inhibitor JQ1 and the FLT3 inhibitor quizartinib.Methods: We used an ex vivo drug sensitivity assay to determine the activity of APTO-253, JQ1, and quizartinib across increasing concentrations of each agent up to 10 μM. Combinations were tested at fixed, equimolar ratios over the same concentration range. After a 3-day ex vivo culture, cell viability was assessed using a colorimetric tetrazolium-based MTS assay, and IC50 values were calculated. RNA-Seq was performed on AML specimens to permit investigation of correlations of drug sensitivity with gene expression levels.Results: We evaluated specimens from 177 patients with a variety of hematologic malignancy diagnoses (80 AML, 72 CLL, 25 MDS/MPN). The highest frequency of APTO-253 sensitivity occurred in AML, with 43/80 (54%) samples exhibiting an IC50 <1 μM. At this cutoff, 25/72 (35%) CLL samples and 3/25 (12%) MDS/MPN samples were sensitive to APTO-253. The average expression of KLF4 mRNA was 2-fold lower among AML samples with an IC50 <1 µM compared to those with IC50 >1 µM (p=0.07). Approximately 65% (56/87) of cases tested with a combination of APTO-253 and JQ1 showed the combination IC50 to be at least 2-fold lower than the IC50 of either agent alone. This enhanced efficacy of APTO-253 with JQ1 was observed across all 3 hematologic malignancies tested, whereas quizartinib enhancement of APTO-253 sensitivity was confined to AML (14/38, or 37% showed reduced IC50).Conclusions: These results support the potential of KLF4 as an important and frequently dysregulated master transcription factor in AML and suggest that the KLF4 inducer APTO-253 is effective at killing tumor cells in a majority of AML samples. The data also indicate activity of APTO-253 in other hematologic malignancies, namely CLL. Expression level of KLF4 may be one component of a biomarker for prediction of APTO-253 efficacy; a more extensive global gene expression signature analysis is under way. Finally, these data have identified prominent interaction of APTO-253 with the BET bromodomain inhibitor JQ1, as well as AML-restricted interaction of APTO-253 with the FLT3 inhibitor quizartinib, suggesting these classes of drugs as potential combination partners for APTO-253. DisclosuresRice:Aptose Biosciences: Employment, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Howell:Aptose Biosciences: Consultancy, Equity Ownership; Angstrom: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Abeoda: Equity Ownership, Membership on an entity's Board of Directors or advisory committees; InhibRx: Equity Ownership. Vellanki:Aptose Biosciences: Employment, Equity Ownership. Druker:Oncotide Pharmaceuticals: Research Funding; Sage Bionetworks: Research Funding; Fred Hutchinson Cancer Research Center: Research Funding; Bristol-Myers Squibb: Research Funding; Novartis Pharmaceuticals: Research Funding; Henry Stewart Talks: Patents & Royalties; McGraw Hill: Patents & Royalties; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; ARIAD: Research Funding; AstraZeneca: Consultancy; Aptose Therapeutics, Inc (formerly Lorus): Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; CTI Biosciences: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Millipore: Patents & Royalties; Roche TCRC, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees; Cylene Pharmaceuticals: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees. Tyner:Incyte: Research Funding; Janssen Pharmaceuticals: Research Funding; Constellation Pharmaceuticals: Research Funding; Array Biopharma: Research Funding; Aptose Biosciences: Research Funding.

Abstract 5169: Omics approach to identify driver genes for peritoneal dissemination of gastric cancer cells

Abstract Background Little is known about the molecular mechanisms of peritoneal dissemination in gastric cancer cells. In this study, to clear the molecular mechanisms of accelerating the peritoneal dissemination, we performed to identify significant genes which give rise to peritoneal dissemination and poor prognosis of gastric cancer. We conducted expression array analysis in mice model of peritoneal dissemination and in 200 cases of gastric cancer cases, and compared with each other to identify driver genes. Methods As described previously, three parental scirrhous gastric carcinoma-derived cell lines (HSC-39, HSC-44PE and HSC-58) were established from human scirrhous gastric carcinoma. Moreover, inoculating each parental cell lines into the gastric wall of nude mice, four matched highly peritoneal-metastatic cell lines (HSC-39As8, HSC-44As3, HSC-58As1 and HSC-58As9) were established after 12 cycles of stepwise selection. To identify gene expression signatures for peritoneal dissemination, we performed expression analysis for matched parental and each resultant cell line. Next, to examine the relationship between gene expression signatures and clinical behaviors, we performed synthetic analysis of gene expression signatures from in vivo peritoneal dissemination mouse model data and expression data set containing 200 gastric tumors from the Singapore cohort. Results (1) Our synthetic analysis, EEM analysis, which tested the functionality of the input genes sets on the basis of their coherence, showed that one gene set from the 58As9 cell line harbored a coherently expressed subset of significant size. (2) We found 57 genes in the coherent subset which had clinical correlation. (3) We found that peritoneal metastases patients tended to show higher expression of such genes, and patients prognosis with higher expression of such genes tended to be poor. (4) Among such genes, we focused on chemokine(C-X-C motif) receptor 7(CXCR7). CXCR7 expression was significantly higher in 58As9 than in HSC58(P&amp;lt;0.005). We also found that overall survival of patients with CXCR7 high expression gastric cancer tended to be poorer than that with CXCR7 low expression gastric cancer in both the Singapore and Japanese cohorts. Conclusions In conclusion, synthetic analysis from two data sets indicated several candidate driver genes for strongly accelerating gastric cancer peritoneal dissemination, the current approach to compare gene signatures in vivo and clinical samples with accurate clinical annotations enabled us to identify brand-new driver candidate genes. Among them, CXCR7 might play an important role in peritoneal dissemination and CXCR7 might be a therapeutic target for suppression of gastric cancer peritoneal dissemination in the near future. Note: This abstract was not presented at the meeting. Citation Format: Sho Nambara, Junji Kurashige, Tomoko Saito, Hisateru Komatsu, Masami Ueda, Shotaro Sakimura, Hidenari Hirata, Ryutaro Uchi, Yuki Takano, Yoshiaki Shinden, Tomohiro Iguchi, Hidetoshi Eguchi, Keishi Sugimachi, Yoshihiko Maehara, Koshi Mimori. Omics approach to identify driver genes for peritoneal dissemination of gastric cancer cells. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5169. doi:10.1158/1538-7445.AM2015-5169

Gene Expression Profiling and Pathway Network Analysis Predicts a Novel Antitumor Function for a Botanical-Derived Drug, PG2.

PG2 is a botanical drug that is mostly composed of Astragalus polysaccharides (APS). Its role in hematopoiesis and relieving cancer-related fatigue has recently been clinically investigated in cancer patients. However, systematic analyses of its functions are still limited. The aim of this study was to use microarray-based expression profiling to evaluate the quality and consistency of PG2 from three different product batches and to study biological mechanisms of PG2. An integrative molecular analysis approach has been designed to examine significant PG2-induced signatures in HL-60 leukemia cells. A quantitative analysis of gene expression signatures was conducted for PG2 by hierarchical clustering of correlation coefficients. The results showed that PG2 product batches were consistent and of high quality. These batches were also functionally equivalent to each other with regard to how they modulated the immune and hematopoietic systems. Within the PG2 signature, there were five genes associated with doxorubicin: IL-8, MDM4, BCL2, PRODH2, and BIRC5. Moreover, the combination of PG2 and doxorubicin had a synergistic effect on induced cell death in HL-60 cells. Together with the bioinformatics-based approach, gene expression profiling provided a quantitative measurement for the quality and consistency of herbal medicines and revealed new roles (e.g., immune modulation) for PG2 in cancer treatment.

Chloroquine eliminates cancer stem cells through deregulation of Jak2 and DNMT1.

Triple negative breast cancer (TNBC) is known to contain a high percentage of CD44(+) /CD24(-/low) cancer stem cells (CSCs), corresponding with a poor prognosis despite systemic chemotherapy. Chloroquine (CQ), an antimalarial drug, is a lysotropic reagent which inhibits autophagy. CQ was identified as a potential CSC inhibitor through in silico gene expression signature analysis of the CD44(+) /CD24(-/low) CSC population. Autophagy plays a critical role in adaptation to stress conditions in cancer cells, and is related with drug resistance and CSC maintenance. Thus, the objectives of this study were to examine the potential enhanced efficacy arising from addition of CQ to standard chemotherapy (paclitaxel) in TNBC and to identify the mechanism by which CQ eliminates CSCs in TNBCs. Herein, we report that CQ sensitizes TNBC cells to paclitaxel through inhibition of autophagy and reduces the CD44(+) /CD24(-/low) CSC population in both preclinical and clinical settings. Also, we are the first to report a mechanism by which CQ regulates the CSCs in TNBC through inhibition of the Janus-activated kinase 2 (Jak2)-signal transducer and activator of transcription 3 signaling pathway by reducing the expression of Jak2 and DNA methyltransferase 1.

Personalized therapy for breast cancer

Breast cancer is a complex disease characterized by many morphological, clinical and molecular features. For many years, this disease has been classified according to histopathologic criteria, known as the tumor, node and metastasis (TNM) staging system. Clinical criteria that include immunohistochemical markers, such as the estrogen receptor (ER), the progesterone receptor (PR), and the human epidermal growth factor receptor 2 (HER2), provide a classification of breast cancer and dictates the optimal therapeutic approach for treatment. With genomic techniques, such as real-time reverse transcriptase PCR (RT-PCR), microarrays, next-generation sequencing, and whole-exome sequencing, breast cancer diagnostics is going through a significant evolution. Genomic and transcriptomic technologies make the analysis of gene expression signatures and mutation status possible so that tumors may now be classified more accurately with respect to diagnosis and prognosis. The -omic era has also made the possible identification of new biomarkers involved in breast cancer development, survival and invasion that can be gradually incorporated either into clinical testing or clinical trials. Together, clinical and molecular criteria can contribute to a more personalized management of the breast cancer patient. This article will present the progress made in the diagnosis and management of breast cancer using molecular information provided by genomic and transcriptomic technologies.