Hendrik Hache

Hedgehog‐EGFR cooperation response genes determine the oncogenic phenotype of basal cell carcinoma and tumour‐initiating pancreatic cancer cells

Inhibition of Hedgehog (HH)/GLI signalling in cancer is a promising therapeutic approach. Interactions between HH/GLI and other oncogenic pathways affect the strength and tumourigenicity of HH/GLI. Cooperation of HH/GLI with epidermal growth factor receptor (EGFR) signalling promotes transformation and cancer cell proliferation in vitro. However, the in vivo relevance of HH‐EGFR signal integration and the critical downstream mediators are largely undefined. In this report we show that genetic and pharmacologic inhibition of EGFR signalling reduces tumour growth in mouse models of HH/GLI driven basal cell carcinoma (BCC). We describe HH‐EGFR cooperation response genes including SOX2, SOX9, JUN, CXCR4 and FGF19 that are synergistically activated by HH‐EGFR signal integration and required for in vivo growth of BCC cells and tumour‐initiating pancreatic cancer cells. The data validate EGFR signalling as drug target in HH/GLI driven cancers and shed light on the molecular processes controlled by HH‐EGFR signal cooperation, providing new therapeutic strategies based on combined targeting of HH‐EGFR signalling and selected downstream target genes.

Reverse engineering of gene regulatory networks: a comparative study

Reverse engineering of gene regulatory networks has been an intensively studied topic in bioinformatics since it constitutes an intermediate step from explorative to causative gene expression analysis. Many methods have been proposed through recent years leading to a wide range of mathematical approaches. In practice, different mathematical approaches will generate different resulting network structures, thus, it is very important for users to assess the performance of these algorithms. We have conducted a comparative study with six different reverse engineering methods, including relevance networks, neural networks, and Bayesian networks. Our approach consists of the generation of defined benchmark data, the analysis of these data with the different methods, and the assessment of algorithmic performances by statistical analyses. Performance was judged by network size and noise levels. The results of the comparative study highlight the neural network approach as best performing method among those under study.

Synergism between Hedgehog-GLI and EGFR Signaling in Hedgehog-Responsive Human Medulloblastoma Cells Induces Downregulation of Canonical Hedgehog-Target Genes and Stabilized Expression of GLI1

Aberrant activation of Hedgehog (HH) signaling has been identified as a key etiologic factor in many human malignancies. Signal strength, target gene specificity, and oncogenic activity of HH signaling depend profoundly on interactions with other pathways, such as epidermal growth factor receptor-mediated signaling, which has been shown to cooperate with HH/GLI in basal cell carcinoma and pancreatic cancer. Our experimental data demonstrated that the Daoy human medulloblastoma cell line possesses a fully inducible endogenous HH pathway. Treatment of Daoy cells with Sonic HH or Smoothened agonist induced expression of GLI1 protein and simultaneously prevented the processing of GLI3 to its repressor form. To study interactions between HH- and EGF-induced signaling in greater detail, time-resolved measurements were carried out and analyzed at the transcriptomic and proteomic levels. The Daoy cells responded to the HH/EGF co-treatment by downregulating GLI1, PTCH, and HHIP at the transcript level; this was also observed when Amphiregulin (AREG) was used instead of EGF. We identified a novel crosstalk mechanism whereby EGFR signaling silences proteins acting as negative regulators of HH signaling, as AKT- and ERK-signaling independent process. EGFR/HH signaling maintained high GLI1 protein levels which contrasted the GLI1 downregulation on the transcript level. Conversely, a high-level synergism was also observed, due to a strong and significant upregulation of numerous canonical EGF-targets with putative tumor-promoting properties such as MMP7, VEGFA, and IL-8. In conclusion, synergistic effects between EGFR and HH signaling can selectively induce a switch from a canonical HH/GLI profile to a modulated specific target gene profile. This suggests that there are more wide-spread, yet context-dependent interactions, between HH/GLI and growth factor receptor signaling in human malignancies.

GeNGe: Systematic Generation of Gene Regulatory Networks

Summary: The analysis of gene regulatory networks (GRNs) is a central goal of bioinformatics highly accelerated by the advent of new experimental techniques, such as RNA interference. A battery of reverse engineering methods has been developed in recent years to reconstruct the underlying GRNs from these and other experimental data. However, the performance of the individual methods is poorly understood and validation of algorithmic performances is still missing to a large extent. To enable such systematic validation, we have developed the web application GeNGe (GEne Network GEnerator), a controlled framework for the automatic generation of GRNs. The theoretical model for a GRN is a non-linear differential equation system. Networks can be user-defined or constructed in a modular way with the option to introduce global and local network perturbations. Resulting data can be used, e.g. as benchmark data for evaluating GRN reconstruction methods or for predicting effects of perturbations as theoretical counterparts of biological experiments. Availability: Available online at http://genge.molgen.mpg.de Contact: hache@molgen.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Prediction in the face of uncertainty: A Monte Carlo-based approach for systems biology of cancer treatment

Cancer is known to be a complex disease and its therapy is difficult. Much information is available on molecules and pathways involved in cancer onset and progression and this data provides a valuable resource for the development of predictive computer models that can help to identify new potential drug targets or to improve therapies. Modeling cancer treatment has to take into account many cellular pathways usually leading to the construction of large mathematical models. The development of such models is complicated by the fact that relevant parameters are either completely unknown, or can at best be measured under highly artificial conditions. Here we propose an approach for constructing predictive models of such complex biological networks in the absence of accurate knowledge on parameter values, and apply this strategy to predict the effects of perturbations induced by anti-cancer drug target inhibitions on an epidermal growth factor (EGF) signaling network. The strategy is based on a Monte Carlo approach, in which the kinetic parameters are repeatedly sampled from specific probability distributions and used for multiple parallel simulations. Simulation results from different forms of the model (e.g., a model that expresses a certain mutation or mutation pattern or the treatment by a certain drug or drug combination) can be compared with the unperturbed control model and used for the prediction of the perturbation effects. This framework opens the way to experiment with complex biological networks in the computer, likely to save costs in drug development and to improve patient therapy.

Integrative analysis of cancer-related signaling pathways.

Identification and classification of cancer types and subtypes is a major issue in current cancer research. Whole genome expression profiling of cancer tissues is often the basis for such subtype classifications of tumors and different signatures for individual cancer types have been described. However, the search for best performing discriminatory gene-expression signatures covering more than one cancer type remains a relevant topic in cancer research as such a signature would help understanding the common changes in signaling networks in these disease types. In this work, we explore the idea of a top down approach for sample stratification based on a module-based network of cancer relevant signaling pathways. For assembly of this network, we consider several of the most established cancer pathways. We evaluate our sample stratification approach using expression data of human breast and ovarian cancer signatures. We show that our approach performs equally well to previously reported methods besides providing the advantage to classify different cancer types. Furthermore, it allows to identify common changes in network module activity of those cancer samples.

Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

The response of cancer cells to drugs is determined by various factors, including the cells’ mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited by the availability of mathematical models and scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs.

Comparative analysis and modeling of the severity of steatohepatitis in DDC-treated mouse strains

Background Non-alcoholic fatty liver disease (NAFLD) has a broad spectrum of disease states ranging from mild steatosis characterized by an abnormal retention of lipids within liver cells to steatohepatitis (NASH) showing fat accumulation, inflammation, ballooning and degradation of hepatocytes, and fibrosis. Ultimately, steatohepatitis can result in liver cirrhosis and hepatocellular carcinoma. Methodology and Results In this study we have analyzed three different mouse strains, A/J, C57BL/6J, and PWD/PhJ, that show different degrees of steatohepatitis when administered a 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) containing diet. RNA-Seq gene expression analysis, protein analysis and metabolic profiling were applied to identify differentially expressed genes/proteins and perturbed metabolite levels of mouse liver samples upon DDC-treatment. Pathway analysis revealed alteration of arachidonic acid (AA) and S-adenosylmethionine (SAMe) metabolism upon other pathways. To understand metabolic changes of arachidonic acid metabolism in the light of disease expression profiles a kinetic model of this pathway was developed and optimized according to metabolite levels. Subsequently, the model was used to study in silico effects of potential drug targets for steatohepatitis. Conclusions We identified AA/eicosanoid metabolism as highly perturbed in DDC-induced mice using a combination of an experimental and in silico approach. Our analysis of the AA/eicosanoid metabolic pathway suggests that 5-hydroxyeicosatetraenoic acid (5-HETE), 15-hydroxyeicosatetraenoic acid (15-HETE) and prostaglandin D2 (PGD2) are perturbed in DDC mice. We further demonstrate that a dynamic model can be used for qualitative prediction of metabolic changes based on transcriptomics data in a disease-related context. Furthermore, SAMe metabolism was identified as being perturbed due to DDC treatment. Several genes as well as some metabolites of this module show differences between A/J and C57BL/6J on the one hand and PWD/PhJ on the other.

Synergistic cross-talk of hedgehog and interleukin-6 signaling drives growth of basal cell carcinoma: Synergistic cross-talk of hedgehog and interleukin-6 signaling drives growth of basal cell carcinoma

Persistent activation of hedgehog (HH)/GLI signaling accounts for the development of basal cell carcinoma (BCC), a very frequent nonmelanoma skin cancer with rising incidence. Targeting HH/GLI signaling by approved pathway inhibitors can provide significant therapeutic benefit to BCC patients. However, limited response rates, development of drug resistance, and severe side effects of HH pathway inhibitors call for improved treatment strategies such as rational combination therapies simultaneously inhibiting HH/GLI and cooperative signals promoting the oncogenic activity of HH/GLI. In this study, we identified the interleukin‐6 (IL6) pathway as a novel synergistic signal promoting oncogenic HH/GLI via STAT3 activation. Mechanistically, we provide evidence that signal integration of IL6 and HH/GLI occurs at the level of cis‐regulatory sequences by co‐binding of GLI and STAT3 to common HH‐IL6 target gene promoters. Genetic inactivation of Il6 signaling in a mouse model of BCC significantly reduced in vivo tumor growth by interfering with HH/GLI‐driven BCC proliferation. Our genetic and pharmacologic data suggest that combinatorial HH‐IL6 pathway blockade is a promising approach to efficiently arrest cancer growth in BCC patients.

Computational Tools and Resources for Integrative Modeling in Systems Biology

Mathematical modeling is key for systems level understanding of cellular processes. The development of mathematical models demands advanced computational tools that keep track of heterogeneous data of molecules and their interactions. Especially the integration of experimental data and pre-existing knowledge into computational models of biological systems is of considerable importance. In silico simulations of model behavior under similar conditions as in the experiment give the possibility for model validation regarding specific experimental data. Such an integrative approach leads eventually to a more accurate and consistent description of the observed biological system. We review several resources and computational tools which support the investigation of biological networks and describe several resources and methods for integrative modeling.