EpiGRAPH: user-friendly software for statistical analysis and prediction of (epi)genomic data

EpiGRAPH is a genome-scale data-mining software tool that enables users to identify epigenetic and gene regulatory features in large datasets of genomic regions

Inhibition of HER Receptors Reveals Distinct Mechanisms of Compensatory Upregulation of Other HER Family Members: Basis for Acquired Resistance and for Combination Therapy

Overexpression of members of the HER/erbB transmembrane tyrosine kinase family like HER2/erbB2/neu is associated with various cancers. Some heterodimers, especially HER2/HER3 heterodimers, are particularly potent inducers of oncogenic signaling. Still, from a clinical viewpoint their inhibition has yielded only moderate success so far, despite promising data from cell cultures. This suggests acquired resistance upon inhibitor therapy as one putative issue, requiring further studies in cell culture also aiming at rational combination therapies. In this paper, we demonstrate in ovarian carcinoma cells that the RNAi-mediated single knockdown of HER2 or HER3 leads to the rapid counter-upregulation of the respective other HER family member, thus providing a rational basis for combinatorial inhibition. Concomitantly, combined knockdown of HER2/HER3 exerts stronger anti-tumor effects as compared to single inhibition. In a tumor cell line xenograft mouse model, therapeutic intervention with nanoscale complexes based on polyethylenimine (PEI) for siRNA delivery, again reveals HER3 upregulation upon HER2 single knockdown and a therapeutic benefit from combination therapy. On the mechanistic side, we demonstrate that HER2 knockdown or inhibition reduces miR-143 levels with subsequent de-repression of HER3 expression, and validates HER3 as a direct target of miR-143. HER3 knockdown or inhibition, in turn, increases HER2 expression through the upregulation of the transcriptional regulator SATB1. These counter-upregulation processes of HER family members are thus based on distinct molecular mechanisms and may provide the basis for the rational combination of inhibitors

Anticancer Therapy with HDAC Inhibitors: Mechanism-Based Combination Strategies and Future Perspectives

The increasing knowledge of molecular drivers of tumorigenesis has fueled targeted cancer therapies based on specific inhibitors. Beyond “classic” oncogene inhibitors, epigenetic therapy is an emerging field. Epigenetic alterations can occur at any time during cancer progression, altering the structure of the chromatin, the accessibility for transcription factors and thus the transcription of genes. They rely on post-translational histone modifications, particularly the acetylation of histone lysine residues, and are determined by the inverse action of histone acetyltransferases (HATs) and histone deacetylases (HDACs). Importantly, HDACs are often aberrantly overexpressed, predominantly leading to the transcriptional repression of tumor suppressor genes. Thus, histone deacetylase inhibitors (HDACis) are powerful drugs, with some already approved for certain hematological cancers. Albeit HDACis show activity in solid tumors as well, further refinement and the development of novel drugs are needed. This review describes the capability of HDACis to influence various pathways and, based on this knowledge, gives a comprehensive overview of various preclinical and clinical studies on solid tumors. A particular focus is placed on strategies for achieving higher efficacy by combination therapies, including phosphoinositide 3-kinase (PI3K)-EGFR inhibitors and hormone- or immunotherapy. This also includes new bifunctional inhibitors as well as novel approaches for HDAC degradation via PROteolysis-TArgeting Chimeras (PROTACs)

Nrf2/Keap1-Pathway Activation and Reduced Susceptibility to Chemotherapy Treatment by Acidification in Esophageal Adenocarcinoma Cells

Chronic acid reflux causes cellular damage and inflammation in the lower esophagus. Due to these irritating insults, the squamous epithelium is replaced by metaplastic epithelium, which is a risk factor for the development of esophageal adenocarcinoma (EAC). In this study, we investigated the acid susceptibility in a Barrett’s cell culture in vitro model, using six cell lines, derived from squamous epithelium (EPC1 and EPC2), metaplasia (CP-A), dysplasia (CP-B), and EAC (OE33 and OE19) cells. Cells exposed to acidic pH showed a decreased viability dependent on time, pH, and progression status in the Barrett’s sequence, with the highest acid susceptibility in the squamous epithelium (EPC1 and EPC2), and the lowest in EAC cells. Acid pulsing was accompanied with an activation of the Nrf2/Keap1- and the NFκB-pathway, resulting in an increased expression of HO1—independent of the cellular context. OE33 showed a decreased responsiveness towards 5-FU, when the cells were grown in acidic conditions (pH 6 and pH 5.5). Our findings suggest a strong damage of squamous epithelium by gastroesophageal reflux, while Barrett’s dysplasia and EAC cells apparently exert acid-protective features, which lead to a cellular resistance against acid reflux

A genotypic method for determining HIV-2 coreceptor usage enables epidemiological studies and clinical decision support

Background: CCR5-coreceptor antagonists can be used for treating HIV-2 infected individuals. Before initiating treatment with coreceptor antagonists, viral coreceptor usage should be determined to ensure that the virus can use only the CCR5 coreceptor (R5) and cannot evade the drug by using the CXCR4 coreceptor (X4-capable). However, until now, no online tool for the genotypic identification of HIV-2 coreceptor usage had been available. Furthermore, there is a lack of knowledge on the determinants of HIV-2 coreceptor usage. Therefore, we developed a data-driven web service for the prediction of HIV-2 coreceptor usage from the V3 loop of the HIV-2 glycoprotein and used the tool to identify novel discriminatory features of X4-capable variants. Results: Using 10 runs of tenfold cross validation, we selected a linear support vector machine (SVM) as the model for geno2pheno[coreceptor-hiv2], because it outperformed the other SVMs with an area under the ROC curve (AUC) of 0.95. We found that SVMs were highly accurate in identifying HIV-2 coreceptor usage, attaining sensitivities of 73.5% and specificities of 96% during tenfold nested cross validation. The predictive performance of SVMs was not significantly different (p value 0.37) from an existing rules-based approach. Moreover, geno2pheno[coreceptor-hiv2] achieved a predictive accuracy of 100% and outperformed the existing approach on an independent data set containing nine new isolates with corresponding phenotypic measurements of coreceptor usage. geno2pheno[coreceptor-hiv2] could not only reproduce the established markers of CXCR4-usage, but also revealed novel markers: the substitutions 27K, 15G, and 8S were significantly predictive of CXCR4 usage. Furthermore, SVMs trained on the amino-acid sequences of the V1 and V2 loops were also quite accurate in predicting coreceptor usage (AUCs of 0.84 and 0.65, respectively). Conclusions: In this study, we developed geno2pheno[coreceptor-hiv2], the first online tool for the prediction of HIV-2 coreceptor usage from the V3 loop. Using our method, we identified novel amino-acid markers of X4-capable variants in the V3 loop and found that HIV-2 coreceptor usage is also influenced by the V1/V2 region. The tool can aid clinicians in deciding whether coreceptor antagonists such as maraviroc are a treatment option and enables epidemiological studies investigating HIV-2 coreceptor usage. geno2pheno[coreceptor-hiv2] is freely available at http://coreceptor-hiv2.geno2pheno.org

Role of Chemosensory TRP Channels in Lung Cancer

Transient receptor potential (TRP) channels represent a large family of cation channels and many members of the TRP family have been shown to act as polymodal receptor molecules for irritative or potentially harmful substances. These chemosensory TRP channels have been extensively characterized in primary sensory and neuronal cells. However, in recent years the functional expression of these proteins in non-neuronal cells, e.g., in the epithelial lining of the respiratory tract has been confirmed. Notably, these proteins have also been described in a number of cancer types. As sensor molecules for noxious compounds, chemosensory TRP channels are involved in cell defense mechanisms and influence cell survival following exposure to toxic substances via the modulation of apoptotic signaling. Of note, a number of cytostatic drugs or drug metabolites can activate these TRP channels, which could affect the therapeutic efficacy of these cytostatics. Moreover, toxic inhalational substances with potential involvement in lung carcinogenesis are well established TRP activators. In this review, we present a synopsis of data on the expression of chemosensory TRP channels in lung cancer cells and describe TRP agonists and TRP-dependent signaling pathways with potential relevance to tumor biology. Furthermore, we discuss a possible role of TRP channels in the non-genomic, tumor-promoting effects of inhalational carcinogens such as cigarette smoke

Predicting the Response to Combination Antiretroviral Therapy: Retrospective Validation of geno2pheno-THEO on a Large Clinical Database

BackgroundExpert-based genotypic interpretation systems are standard methods for guiding treatment selection for patients infected with human immunodeficiency virus type 1. We previously introduced the software pipeline geno2pheno-THEO (g2p-THEO), which on the basis of viral sequence predicts the response to treatment with a combination of antiretroviral compounds by applying methods from statistical learning and the estimated potential of the virus to escape from drug pressure MethodsWe retrospectively validated the statistical model used by g2p-THEO in ∼7600 independent treatment-sequence pairs extracted from the EuResist integrated database, ranging from 1990 to 2007. Results were compared with the 3 most widely used expert-based interpretation systems: Stanford HIVdb, ANRS, and Rega ResultsThe difference in receiver operating characteristic curves between g2p-THEO and expert-based approaches was significant (P<.001; paired Wilcoxon test). Indeed, at 80% specificity, g2p-THEO found 16.2%-19.8% more successful regimens than did the expert-based approaches. The increased performance of g2p-THEO was confirmed in a 2001-2007 data set from which most obsolete therapies had been removed ConclusionFinding drug combinations that increase the chances of therapeutic success is the main reason for using decision support systems. The present analysis of a large data set derived from clinical practice demonstrates that g2p-THEO solves this task significantly better than state-of-the-art expert-based systems. The tool is available at http://www.geno2pheno.or

Comparison of Classifier Fusion Methods for Predicting Response to Anti HIV-1 Therapy

BACKGROUND: Analysis of the viral genome for drug resistance mutations is state-of-the-art for guiding treatment selection for human immunodeficiency virus type 1 (HIV-1)-infected patients. These mutations alter the structure of viral target proteins and reduce or in the worst case completely inhibit the effect of antiretroviral compounds while maintaining the ability for effective replication. Modern anti-HIV-1 regimens comprise multiple drugs in order to prevent or at least delay the development of resistance mutations. However, commonly used HIV-1 genotype interpretation systems provide only classifications for single drugs. The EuResist initiative has collected data from about 18,500 patients to train three classifiers for predicting response to combination antiretroviral therapy, given the viral genotype and further information. In this work we compare different classifier fusion methods for combining the individual classifiers. PRINCIPAL FINDINGS: The individual classifiers yielded similar performance, and all the combination approaches considered performed equally well. The gain in performance due to combining methods did not reach statistical significance compared to the single best individual classifier on the complete training set. However, on smaller training set sizes (200 to 1,600 instances compared to 2,700) the combination significantly outperformed the individual classifiers (p<0.01; paired one-sided Wilcoxon test). Together with a consistent reduction of the standard deviation compared to the individual prediction engines this shows a more robust behavior of the combined system. Moreover, using the combined system we were able to identify a class of therapy courses that led to a consistent underestimation (about 0.05 AUC) of the system performance. Discovery of these therapy courses is a further hint for the robustness of the combined system. CONCLUSION: The combined EuResist prediction engine is freely available at http://engine.euresist.org

DASMI: exchanging, annotating and assessing molecular interaction data

Motivation: Ever increasing amounts of biological interaction data are being accumulated worldwide, but they are currently not readily accessible to the biologist at a single site. New techniques are required for retrieving, sharing and presenting data spread over the Internet