Final Report Summary - APO-SYS (Apoptosis systems biology applied to cancer and AIDS. An integrated approach of experimental biology, data mining, mathematical modelling,( ) and molecular medicine)
Executive summary:
Cancer is the second leading cause of death in our society and together with AIDS represents two of the most serious health problems in Europe with an estimated cancer incidence of 3.2 million and cancer mortality of 1.7 million people in 2008. There are numerous factors affecting incidence rates in different countries (demographic aspects, external environmental factors, lifestyle, economical status) leading to tremendous societal and economic consequences for those affected as well as economic costs for the society. Although spectacular progress has been made in the treatment of different types of cancer as well as advancements in its early detection and identification, still the average mortality reduction for all forms of cancer is somewhat modest. In addition, at the end of 2009 it was estimated that around 840.000 people within the European community were living with HIV and there are evidence pointing to an increase in HIV transmission in a number of countries.
Project Context and Objectives:
Starting with the pioneering work by Kerr and colleagues in 1972, cell death studies have become an increasingly important area of biomedical research. In addition to apoptosis and necrosis, several other modes of cell death have been described and characterized based on various morphological and biochemical criteria. Moreover, cross-talk between different signaling pathways has been reported and might influence the sensitivity of tumor cells to treatment. The Nomenclature Committee on Cell Death proposed unified criteria for the definition of cell death and its different morphologies. According to this novel classification, twelve cell death modalities (four typical and eight atypical) can be recognized. Among the best characterized modes of cell death are apoptosis, autophagy, cornification and necrosis.
Project objectives
The multidisciplinary approach adopted in the APO-SYS consortium has aimed at obtaining major progress in the comprehension of cell death in human diseases, in particular cancer and AIDS by combining a series of systems biology approaches, in silico, in vitro (in organello and in cellula), in vivo, and by integrating experimental results with large data sets acquired on tissue samples from patients suffering from diseases that are caused by dysregulated apoptosis. The final results obtained during this project resulted in new refined cell death pathway models, in new knowledge for the optimization and calculation of prognoses and predictive parameters for the diseases as well as new guiding strategies for the amelioration of existing treatments and the identification of novel targets for therapeutic modulation of apoptosis.
The APO-SYS Consortium
The APO-SYS consortium started with 23 partner institutes from across Europe with a total of 26 principal investigators from various research areas including biology, biomedicine, bioinformatics, biomathematics and biostatistics. During the contract period one partner (Medicel Oy) left the consortium due to financial problems. Partner 6 (UUlm) has moved to another University and became Partner 24 (KGU) (a full list of partners can be found in Annex I). The Scientific Advisory Board in APO-SYS consortium, included Professor-Emeritus Sten Orrenius (Sweden), Prof. Pierre Golstein (France) and Prof. Olaf Wolkenhauer (Germany) and has supported the consortium during the 4-years project period (2008-2012). Coordination of the project was led by Prof. Boris Zhivotovsky from the Institute of Environmental Medicine, Karolinska Institutet.
The project was structured in 18 interdependent work packages (WPs) with the following partners responsible for the achievement of the tasks within each workpackage:
WP 1-3 (Management, integration and dissemination activities): Prof. Boris Zhivotovsky (Karolinska Institutet, Sweden), Prof. Emanuel Barillot (Institute Curie, France) and Project manager, Dr. Gabriela Imreh (Karolinska Institutet, Sweden).
WP 4-5 (Data management and dissemination for high quality manually curated data and Integration of the high-throughput experimental data) Dr. Henning Hermjacob, European Bioinformatic Institute, UK).
WP 6 (Construction and integration of the pathway maps and integration of models produced by the partners in the course of the project) Prof. Ron Shamir (Tel Aviv University, Israel).
WP 7 (Biostatistical analysis and data mining) Prof. Ralf Herwig (Max Plank Institute of Molecular Cell Biology and Genetics, Germany).
WP 8 (Experimental exploration of lethal apoptotic signalling: extrinsic pathway): Prof. Jurg Tschopp and Nicolas Fasel (University of Lausanne, Switzerland) and prof. Patrick Mehlen (Claude Bernard University of Lyon, France).
WP 9 (Experimental exploration of lethal apoptotic signalling: intrinsic pathway): Prof. Guido Kroemer (National Institute for Medical Research, France) and Prof. Marja Jaattela (Danish Cancer Society, Denmark).
WP 10 (Experimental exploration of lethal non-apoptotic signalling): Prof. Peter Vandenabeele (Flanders Institute for Biotechnology, Belgium) and Prof. Marino Zerial (Max Plank Institute of Molecular Cell Biology and Genetics, Germany).
WP 11 (Cell Death in non-mammalian model organisms): Prof. Frank Madeo (University of Graz, Austria) and Prof. Josef Penninger (Institute of Molecular Biotechnology, Austria).
WP 12 (Experimental exploration of 'switches'between lethal signalling pathways): Prof. Jens Andersen (University of Southern Denmark, Denmark).
WP 13 (Exploration of clinical samples: solid tumors): Prof. Olli Kallioniemi (Technical Research Centre of Finland, Finland) and Prof. Kari Hemminki (German Cancer Research Centre, Germany).
WP 14 (Exploration of clinical samples: haematological tumors): Prof. Rolf Lewensohn (Karolinska Institutet, Sweden) and Prof. Simone Fulda (Johann Wolfgang Goethe Clinical University, Germany).
WP 15 (Exploration of clinical samples: AIDS): Prof. Mauro Piacentini (National Institute for Infectious Diseases, Italy).
WP 16 (Computational systems biology modelling of cell death): Prof. Denis Thieffy (University d'Aix-Marseille, France).
WP 17 (Identification of intervention points in lethal pathways): Prof. Adi Kimchi and Prof. David Harel (Weizmann Institute of Science, Israel).
WP 18 (Identification of intervention points in diseases): Prof. Lucia Altucci (Second University of Naples, Italy) and Prof. Hinrich Gronemeyer (Institute of Genetics and Molecular and Cellular Biology, France).
Project Results:
Accordingly, we performed experimental exploration of cell-death pathways in human cell lines (in vitro) and model systems (in vitro) in human cells and in vivo in non-human model organism) using high-through put screening methods (genome and transcriptome microarrays, interactome data, mass spectrometry and RNAi assays) and hypothesis driven methods. Further, we developed tools for rational exploitation of large-scale RNAi screening results and we contributed to the improvement of statistical data analysis tools for extraction of correct and relevant biological data from the RNAi screenings. We continued the experimental exploration of lethal apoptotic signalling pathways (extrinsic, intrinsic and non-apoptotic) discovering new players and refining the existing pathways and lunched a systems biology approach to investigate cell death induction via representative death receptors as well as dependency receptors.
3.1 Creation of a data warehouse integrating knowledge on cell death-relevant regulators in diseases
3.1.1 Data management workshop and workshop report on consortium requirements for sharing of high quality annotations of molecular entities
Two main partners carried out the data management and dissemination activities related to this work-package: the Partner 13 (EBI) and Partner 23 Medicel Oy, the SMI company that was a part of our consortium for the first 24 months. Medicel Oy and EBI jointly organised one consortium-wide data management workshops at the beginning of the project (June 24-27, 2008, Cambridge) when particular emphasis was placed upon the available mechanisms for data submission to public data repositories to allow the sharing of high quality annotation and on the Medicel Integrator Software Platform.
3.1.2 Construction and integration of the pathway maps and models
A. Development of converters for exchanging models in BIOPAX format
One of the major goals of the APO-SYS consortium was to construct maps and mathematical models of pathways governing cell death, in particular apoptosis and cell fate switch between the possible ways to death. These maps and models are based on the accumulation of knowledge available both in the literature and in the mind of the experts from the consortium, and must be confronted to the experimental data produced in APO-SYS or coming from other sources. Thus, common language and tools for charting and analysis was desirable to be used by all partners of the consortium. Partner 3 (IC) has extensive experience in this question and adopted a well know software CellDesigner (Kitano et al., 2005) for map design and build an interface, BiNoM (see http://bioinfo-out.curie.fr/projects/binom/ online) (Zinovyev et al, 2007), which is a plug-in to the well-known graph software Cytoscape and allows analysis of networks created with CellDesigner.
B. Development of additional modelling tools to create compatibility with Reactome
Partner 14 (TAU) has advanced the EXPANDER (EXpression Analyzer and DisplayER) suite and published a protocol on the use of the package (Ulitsky et al, 2010). Access to SPIKE is being integrated into EXPANDER to facilitate effective analysis by allowing a user to easily combine EXPANDER's expression analysis tools with SPIKEs network analysis. In addition, CEZANNE, a version of the Matisse modelling tool that incorporates interaction confidence, was developed and published (Ulitsky and Shamir, 2009). Partner 20 (UnivMed) released a novel version of GINsim in 2009 (Naldi et al, 2009a). Furthermore, they developed an original and powerful method enabling the reduction of large logical models. The model reduction tool in GINsim was applied to the cell-fate decision model. Partner 15A (MPG) has developed GeNGe, a tool for modelling of gene regulatory interactions (Hache et al., 2009). Also in the context of APO-SYS, Partner 15A (MPG) uses the GeNGe tool to formally describe the effects of perturbations in apoptosis signalling on the gene regulatory layer. Partner 3 (IC) developed computational tools that are specific and necessary for modelling the cell fate decisions or systems of similar behaviour where the cell 'decides' on ending up in a particular phenotype. A model of cell fate decision process in response to death receptor engagement was developed by Partners 3 and 20 (Calzone et. al, 2010).
C. Construction of cell death pathways maps
A solid definition and comprehensive representation of cell death networks is essential for efficient and accurate dissemination of information. Using the experimental data obtained by several partners from the consortium (Partner 1,2,5,7,8,9,10,18 and 21) simple maps of two cell-death pathways the extrinsic TRAIL pathway and the intrinsic pathways were generated. The TRAIL pathway was constructed qualitatively using the Genomatix software. The scheme of the intrinsic pathway was created in an in-silico model based on ODE (Ordinary Differential Equations) with kinetic parameters from literature and validated in single cell experiments. The different maps are presented in Annex II. From the basic maps the partners of the APO-SYS consortium work on pathway integration and refinement. This includes: inclusion of the upstream processes by Partner 18 (RCSI) (Duessman et al., 2010); inclusion of spatial anisotropies of MOMP by Partner 18 (RCSI) (Rehm et al., 2009; Huber et al., 2010a, b); integration of DNA Damage, DNA DSB-inducing chemotherapy in combination with DNA-PK inhibition, alkylating agents in NSCLC, DNA-DSB-inducing chemotherapy in AML, Partner 1A (KI) (Stahl et al., 2009; Haag et al., 2010); Lysosomal Integration, Partner 8 (DCI) (Kirekegaard et al., 2009); Necrosis, Partner 2 (INSERM) and Partner 5 (VIB). Later on in the project, the maps were updated using additional data produced within the consortium.
D. Creation of APO-SYS model repository
Accordingly to the initial planning of the APO-SYS project, all mathematical models created in the course of the project had to be submitted to the integrative environment created by Medicel Oy (Partner 23). Following the departure of Medicel from the consortium, it was decided to host the collection of the mathematical models at the APO-SYS Intranet web-site. Some of the models placed in the model repository are already published and put in the public domain. The others are currently restricted to the members of APO-SYS consortium, but are due to be made available to the general public after publication.
E. Improved GO annotations for apoptosis
The use of structured controlled vocabularies (ontologies) is a fundamental part of modelling the biological processes. Analysis of such models requires standardized annotations that accurately depict the biology around the entities described. The Gene Ontology (GO) is a collaborative effort trying to standardize the representation of genes and gene products and it is arguably the most important resource of this type available nowadays. Discussions with the APO-SYS partners made clear that there was a requirement for a solid, updated ontology that could be used for the construction and integration of pathway maps and models depicting the apoptotic process. Both the lack of granular terms and the inadequate definitions hampered the use of data integration tools used to produce meaningful models of the apoptotic process. Thus, our aims was to reduce this gap and provide the community with a proper depiction of the apoptotic process in GO. The GO editorial team and the Proteomics Services team in the EBI have worked closely with the APO-SYS domain experts to create an extended version of the GO 'Apoptosis' branch which is currently being released.
3.1.3 Biostatistical analysis and data mining
A. Improvement and extension of computational functionalities for statistical analysis of large high-throughput data
Already from the beginning of the project we were well aware about the large amount of high-throughput data which will be produced during the course of the project, including genome microarrays, transcriptome microarrays, proteomics and RNAi data. The development of statistical tools for rigorous statistical analysis of large amount of high-throughput data was a high priority for the mathematicians and bionformaticians in our consortium.
B. Procedure and tools for obtaining validated RNAi experiments
Partner 11 (VTT) has developed the siRNA R/Bioconductor based data analysis workflow and is currently formulating an R package for publication. The workflow incorporates data import, QC, normalisation and a large number of graphical outputs. In order to facilitate the partner's ability to carry out RNAi screens in a homogenous and comparable fashion, the methods for high-throughput screening data analysis were presented at the APO-SYS Workshop titled 'Biostatistics', held on January 19-20, 2009 in Berlin at the Max Planck Institute for Molecular Genetics (Partner 15A). The presentations on the siRNA screening data analysis are available on the project web site (see http://www.apo-sys.eu online) and on the intranet. A standard operating procedures (SOP) document for high-throughput screening data analysis was formulated based on the experiences of Partner 11 (VTT) as well as other labs on screening data analysis.
C. Report on analysis of high-throughput data
The analysis of high-throughput data targeted a wide spectrum of research questions related to apoptosis signaling and used the tools and methods developed by the mathematicians and bioinformatitions from our consortium. The type of work carried out was either direct collaborative work between the computational groups and the experimental groups of the APO-SYS consortium solving specific research questions or integrative studies that incorporated different heterogeneous data sets within and outside the consortium.
3.1.4 Computational systems biology modeling of cell death
The aims for this part of the APO-SYS project was to building predictive models for programmed cell death in the context of alternative cell fate decisions, using different types of modelling formalisms (chemical kinetics, discrete or logical formalism). Particular emphasis was put on the definition of model parameters, the analysis of complexity and robustness, as well as on the delineation of validation experiments and the identification of intervention points into lethal pathways.
3.2 Experimental exploration of the extrinsic pathway
Signalling by death receptors (CD95/Fas, TNF-R, TRAIL) or absence of signalling by dependency receptors (p57NTTR, DCC, Patched, RET etc) can stimulate apoptosis through a pathway initiated at the plasma membrane. We applied a systems biology approach to understand the mechanisms through which such plasma membrane receptors trigger the apoptotic signal.
3.3 Experimental exploration of the intrinsic apoptotic pathway.
The intrinsic pathway also called 'stress pathway' or 'mitochondrial pathway' of apoptosis is initiated anywhere in the cell (except the plasma membrane), due to organelle-specific perturbation and damage or the absence of essential trophic factors. One limiting step of the intrinsic pathway is mitochondrial permeabilization. The consortium aimed at understanding the contribution of different cellular constituents and modules (e.g. the DNA damage response module, ER stress, etc).
3.4 Experimental exploration of lethal non-apoptotic signalling pathways
It is well acknowledged that non-apoptotic cell death signalling pathways (caspase-independent necrosis, autophagy, mitotic catastrophe) have important roles in pathological cell death. Our focus was on the functional characterization of siRNAs that inhibit or induce autophagy and in-depth characterization of the autophagic-regulatory machinery at the systems biology level. Three main non-apoptotic pathways were studied by the consortium, autophagy, necrosis and mitotic catastrophe.
3.5 Cell death in non-mammalian model organisms
The aim of the work we initiated in yeast (Saccharomyces cervisiae) and fruit fly (Drosophila melanogaster) was to consolidate data obtained in the human system and to discover new phylogenetically ancient (and hence important) cross-talks between distinct cell death modalities.
3.6. Experimental exploration of switches between lethal signalling pathways
There are some evidences that cells 'decide' between different cell death modalities (apoptosis versus autophagy versus necrosis versus mitotic catastrophe) and functional outcomes (immunogenic versus non-immunogenic cell death). Within the APO-SYS project we aimed at understanding the nature of these molecular switches and the candidate proteins participating in these 'switches'.
3.7 Exploration of clinical samples: solid tumors and hematological cancer
One of our primary goals was the retrieve of relevant information relevant to cell death pathways on clinical data. The important outcome of our approach was the possibility to be able to define better therapeutics for given tumors, to discover several new biomarkers and novel targets with potential anti-tumor activity.
3.7.1 Lung Cancer
Partner 1A (KI) has collected tumor, plasma and serum from non-small cell lung cancer (NSCLC) patients with the aim to find BM of prognosis and metastasis propensity. By performing MS-based proteomics Partner 1A (KI) identified several proteins which can be linked to lung cancer patient outcome including the S100 protein family (Pernemalm et al. 2009; de Petris et al. 2009). Partner 1A (KI) has constructed a tissue microarray (TMA) consisting of about 500 lung cancer cases with corresponding histological and clinical which has been used to validate S100 proteins for their prognostic capacity of NSCLC (De Petris et al., manuscript in preparation). The analyses show that S100A6-positive cases have longer survival compared with S100A6-negative cases, S100A6 and S100A4 showed a significant co-expression with a higher expression in well differentiated non-squamous tumors. All in all, Partner 1A (KI) have constructed a NSCLC TMA with corresponding clinical data and demonstrated its usefulness to validate apoptosis related proteins, identified by global proteomic methods. Partner 1A (KI) has also applied genomics and MS-based proteomics to identify signaling events critical for radiotherapy (RT)-induced apoptosis in NSCLC. By applying PSE pathway analysis on MS-data JNK was identified and validated to play an important role in RT-induced apoptosis in NSCLC cells (Stahl et al. 2009). Moreover, a novel role for Ephrin B3 and EphA2 in NSCLC survival and RT resistance was identified by genomic and phosphoproteomic MS analysis, respectively (Ståhl et al. 2011). As mutations and/or overexpression of Ephs recently have been revealed in NSCLC, the identification of Ephrin B3 and EphA2 in growth and RT response of NSCLC is very interesting our findings might suggest that targeting Ephrin/Ephs can be a way to improve RT response of NSCLC. Partner 1A (KI) has also analyzed apoptosis induction in response to the melphalan prodrug J1 (currently undergoing Phase II clinical trial), as well as in response to phenothiazines; compounds currently used antipsychotic agents. Analysis demonstrated that aminopeptidase N (ANPEP) is a potential BM of J1 response (Wikstrom et al., 2010) as it is required for intracellular accumulation of melphalan and subsequent apoptotic signaling upon J1 addition. With respect to phenothiazines, these agents were found to have CT sensitizing potential in NSCLC, illustrated by the increase of cisplatin-induced ROS and BAK/BAX, caspase-3 activation (Zong et al. 2011).
3.7.2 Colorectal cancer
Partner 18 (RCSI) in collaboration with RCSI Clinical Research Centre in Beaumont Hospital pioneered in validating systems model of apoptosis in clinical settings of colorectal cancer (CRC). These tools may aid to predict individual patient response to chemotherapy, thereby stratifying and individualising treatment. We developed a new model to understand how MOMP is regulated upon BCL-2 protein interaction subsequent to 5FU/Oxaliplatin. Our model mechanistically confirmed previous reports that cancer cells may undergo apoptosis at lower dose stimuli than cells of healthy tissue (cancer cell priming) and that the extent of this priming correlates with a better prognosis (Lindner et al. 2012, in preparation). A second model that analyses whether or not apoptosis is executed after MOMP, APOPTO-CELL was assessed in a retrospective study of 20 CRC patients (Hector et al. 2011).
3.7.3 Pediatric cancer
Core-binding factor (CBF) leukemias, characterized by translocations t(8;21) or inv(16)/t(16;16) targeting the CBF, constitute acute myeloid leukemia (AML) subgroups with favorable prognosis. However, about 40% of patients relapse and the current classification system do not fully reflect this clinical heterogeneity. Partner 6 (UUlm) and Partner 24 (KGU) revealed, using gene expression profiling (GEP), two distinct CBF leukemia subgroups displaying significant outcome differences and identified apoptotic signalling, MAPKinase signalling and chemotherapy-resistance mechanisms among the most significant differentially regulated pathways.
3.7.4 Genome wide association study on several collections of patient data
Genetic susceptibility of colorectal cancer (CRC) accounts for approximately 30% of its aetiology. Rare, high-penetrance germline mutations in a few genes (mainly APC and DNA mismatch repair genes) account for less than 5% of CRC cases. Much of the remaining variation in genetic risk is supposed to be attributable to common susceptibility loci, each exerting a small influence on risk. Partner 12 (DKFZ) applied an agnostic approach using a genome-wide association study on CRC by genotyping 371 German familial CRC cases and 1263 healthy controls using the Affymetrix Genome Wide Human SNP 6.0 Array. In the ensuing replication studies on four additional case-control sets (4915 cases and 5607 controls) known risk loci at 8q24.21 and 11q23 were confirmed, and a previously unreported association, rs12701937 (P = 10-3, OR 1.14 95% CI 1.05-1.23 dominant model) was identified (Lascorz J et al. 2010). This SNP is located between genes GLI3 and INHBA. Using software tools for pathway enrichment we were able to show over-representation of genes related to the mitogen-activated protein kinase (MAPK) signalling pathways among the most strongly associated markers from the GWAS. The risk of CRC increased significantly with an increasing number of risk alleles in these Thus the study replicated two of the known risk loci for CRC and identified a possible new risk polymorphism, with a stronger effect in familial cases.
3.7.5 Study on the level of expression of dependency receptors in solid cancer
Partner 21(UCBL) explored the level of expression of the pair ligand/receptor in a panel of solid tumor. Because chip analyses were found unreliable to measure expression of some ligands of dependence receptors (because of the amplification phase, Fitamant et al., 2008), Partner 21 employed Q-RT-PCR or immunohistochemistry 140 breast cancer, 101 neuroblastoma and a large panel of lung cancer. Partner 21 analyzed netrin-1 expression in a panel of 92 NSCLC tumors, including 45 adenocarcinomas and 47 squamous cell carcinomas, and found that Netrin-1 expression in normal bronchial and alveolar epithelial cells was absent or low but both tumor types expressed high levels of netrin-1. Netrin-1 expression was more frequent and more intense in adenocarcinomas (60% of samples were positive, with a median score of 100) than in squamous cell carcinomas (34 % positive samples with a median score of 50). In situ hybridization to detect netrin-1 mRNA identified a substantial level of netrin-1 mRNA in the epithelial tumor cells but netrin-1 mRNA levels were undetectable or very low in the stroma cells, indicating that netrin-1 may be produced by tumor cells. To quantify the expression of netrin-1 and its receptors, we determined the level of mRNAs encoding netrin-1 and its dependence receptors DCC, UNC5H1, UNC5H2, UNC5H3, and UNC5H4 by quantitative reverse-transcription polymerase chain reaction (Q-RT-PCR) in a panel of 25 lung carcinomas, including 14 adenocarcinomas and 11 squamous cell carcinomas, as well as in adjacent normal tissues that were at least 5 cm from the tumor. Netrin-1 expression was more than fourfold higher in lung tumors than in adjacent normal tissue. Netrin-1 overexpression relative to normal tissue was observed in 19 of the 25 (76%) tumors, with 13 of them (52%) showing more than a threefold increase in netrin-1 content relative to normal tissue.
3.7.6 Study of novel therapies in Myelodysplastic syndromes (MDS) and acute myelogenous leukemias (AML)
Partner 2 previously demonstrated that the epidermal growth factor receptor (EGFR) inhibitor erlotinib also mediates antiproliferative/cytotoxic effects in MDS and AML blasts and cell lines as an off-target effect (Boehrer et al. 2008). Currently two clinical trials (NCT00977548 and NCT01085838) are evaluating the therapeutic benefit of erlotinib in patients with MDS and AML. Thus, Partner 2 shows that erlotinib can reduce SFK over activation in MDS and AML blasts. Moreover, erlotinib was found to induce a G1 phase cell cycle arrest which either might reflect a reduction in proliferation rate or induction of reversible quiescence or irreversible senescence, potentially linked to the mTOR activation status (Boehrer et al. 2011) and that chemical inhibition of SFKs with 3-(4-chlorophenyl)1-(1,1-dimethylethyl)-1H-pyrazolo[3,4-a]pyrimidin-4-amine (PP2) similarly could induce G1 arrest in AML and recapitulate the effects induced by erlotinib.
3.8. Exploration of AIDS samples
AIDS is a chronic degenerative disease that involves the enhanced apoptotic turnover of lymphocytes and other cell types. Many studied dealing with the biology of the human immunodeficiency virus-1 (HIV-1) neglect the contribution of host cells to the pathogeny of AIDS.
A comprehensive comparative analysis of transcriptome vs proteome in the course of HIV-infection was performed by Partner 4 on in vitro and ex vivo cellular systems based on experimental results and database repository of high throughput gene expression data. The goal was to fully characterize new cell death signalling pathways activated by HIV infection, in addition to those already identified by the traditional approaches based on biochemistry and molecular biology. The comparison of the our proteomic data with the transcriptome evidences that the susceptibility to cell death is mediated by the interferon induction that leads to a sustained increase in p53 mRNA levels and therefore to a higher susceptibility of CD4+ T cells to pro-apoptotic signals. Besides the interferon responses, the up-regulation of genes associated with cell apoptosis has been detected both by proteomic and transcriptomic approaches in all HIV patients progressing towards AIDS.
3.9 Identification of intervention points in lethal pathways
The major goal of this work was to understand the structure/function organization of the molecular network of programmed cell death by studying the system through its intervention at different points. Our goal was to provide dynamic models of the system which quantify the network's output as a function of the input signal (the death inducing agent).
The first challenge was to construct a 'naive' protein-protein interaction map by critically mining the literature and the public data base sources. The resulting global signalling map consists of around 200 proteins (the network nodes) connected to each other by various enzymatic and functional interactions (the network edges). Most of the signalling pathways involve post-translational changes of the proteins including proteolysis, phosphorylation, ubiquitin and ubiquitin like conjugation reactions, assembly of protein complexes and changes in sub-cellular localization. The constructed maps are available in several visualization formats. One map was prepared manually (Bialik et al. 2010).
3.10 Identification of intervention points in diseases
The final goal of the comprehension of biological phenomena is the biomedical application of the knowledge. During the APO-SYS project we launched a variety of tentative approaches to validate current and future knowledge on disease-relevant deregulations of the apoptotic process, working on primary human specimen. One particularly important goal for the APO-SYS consortium was to selectively kill tumor cells subverting the chemotherapy resistance of cancer cells as well as potentiating the immunogenicity of the dying cell, based on the mathematical models developed in the course of the project.
Potential Impact:
Conclusions:
Through our comprehensive approach, uniquely combining biological, mathematical, biomedical and biostatistical knowledge, we have created mathematical model pathways governing cell death (intrinsic and extrinsic apoptosis pathways) as well as non-apoptotic pathways; we developed and improved already existing bioinformatic tools (EXPANDER, ITTACA, CAPweb) as well as community platforms such as R/Bioconductor for accurate biostatistical analysis of high-throughput information. Furthermore, we have reported several novel and important discoveries in peer-reviewed scientific articles dealing with the discovery of novel biomarkers and novel targets with anti-tumor potential as well as novel strategies for combinatorial treatment of tumors and HIV infection. We have also organized several workshops and participated in numerous conferences and meeting were we promoted the work conducted within the APO-SYS project. The success of the APO-SYS project can be appreciated by the publication of high number of peer review articles, of which a significant number are joint publications emphasizing the successful collaboration between the groups.
Dissemination activities
1 Project website
Project website (see http://www.apo-sys.eu online) was set-up at the start of the project and comprises several domains including a public access domain and a password protected domain (access provided to the project partners, project officer). During the course of the APO-SYS project, the webpage was updated continuously sharing with both the general public and research community the highlight of our research, and when appropriate availability of PDF files for download.
2 Scientific publications, press releases (radio, TV and newspapers) and conferences
It should also be mentioned that several members of the APO-SYS consortium were invited as authors in several books with a thematic within systems biology of cell death:
-Systems biology analysis of cell death pathways in cancer: how collaborative and interdisciplinary research helps. In Cesario A and Marcus F B (editors) (2011) Cancer Systems Biology: Bioinformatics and Medicine: Research and Clinical Applications. Springer Dordrecht Heidelberg London New York. pp. 267-296.
-Understanding different types of cell death using Systems Biology. In: Lavrik I (Editor). Calzone, L., Zinovyev, A., and Zhivotovsky, B. 'Systems Biology of apoptosis'. Springer Science, NY, USA, 2012 (In Press).
-Different modes of cell death induced by DNA damage. In: Greim H and Albertini R (Editors). Surova, O., and Zhivotovsky, B. 'The Cellular Response to the Genotoxic Insult: The Question of Threshold for Genotoxic Carcinogens'. RSC Publishing, 2012 (In Press).
-Chaouiya C, Naldi A, Thieffry D (2012). 'Logical modelling of gene regulatory networks with GINsim. ' In: van Helden J, Toussaint A, Thieffry D (eds.) Bacterial Molecular Networks, Methods in Molecular Biology series, vol 804, Humana Press, pp. 463-79.
-Thieffry D (accepted). 'Gene networks', 'logical modelling', and 'regulatory motifs' entries. In: Hancock JM, Zvelebil MJ (eds). Dictionary of Bioinformatics and Computational Biology. New York: John Wiley & Sons.
-Faure A, Thieffry D (in press). 'Cell cycle modelling using logical rules. ' In: Dubitzky W, Wolkenhauer O, Cho K-H, Yokota H (eds.) Encyclopedia of Systems Biology. Springer.
3. Workshops and Collaborative Meetings
-The first workshop on APO-SYS data analysis and pathway charting took place at Institute Curie, Paris in April 2008. The workshop was organized by Prof. Emmanuel Barillot in collaboration with Prof. Ron Shamir from Tel Aviv University with the thematic: 'APO-SYS data analysis and pathway charting'. The workshops focused on microarray data analysis, biological network analysis, pathway charting, and other omics data analysis.
-The second workshop took place at European Bioinformatics Institute, in Hinxton, Cambridge. The workshop was organized by Dr. Henning Hermjakob (EBI) and Dr. Christopher Roos (Medicel Oy) and focused on 'Data management and requirements for sharing of high quality annotations of molecular entities'. Several lectures took place during the 3 day workshop given by experts from EBI and Medicel Oy as well as training session for young scientists.
-The next important workshop was organized by Prof. Ralf Herwig and hosted at Max Plank Institute of Molecular Cell Biology and Genetics in Berlin in January 2009. The co-organizers were Christophe Roos, Medicel Oy and the Technical Research Centre of Finland, represented by Dr. Pekka Kohonen. The workshop focused on 'Data analysis with statistics' and 'Data storage, analysis and sharing on the Medicel platform'.
-In order to promote new collaboration opportunities between the members of the consortium and in special between young scientists working on the APO-SYS project, the management office organized three 'APO-SYS collaborative meetings' , two in Stockholm (June, 2009, 2010) and one in Hinxton, Cambridge (June, 2011). The meetings comprised presentations of the ongoing work within the APO-SYS project, discussion and poster presentations.
-The last workshop was organized by Dr. Henning Hermjakob and Dr. Samuel Kerrien form European Bioinformatics Institute, at Hinxton, Cambridge. The workshop focused on 'Data management' and 'GO content meeting'. The experts from EBI shared their knowledge with APO-SYS participants and several training sessions with different thematic took place. All presentations were made available to the participants and were applicable, made public towards other the research community.
4. Coordination activities
The APO-SYS project, coordinated at Karolinska Institute by Prof. Boris Zhivotovsky, was a challenging project at least of two reasons: first of all the project was a multidisciplinary project with partners specialized in biology, biomedicine/translational medicine, bioinformatics, biomathematicians and biostatisticians and spread all over Europe. Secondly the consortium was made up of 23 European partners (26 principal investigators) including, at least, for half through the project of a SMI company.
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List of Websites:
http://www.apo-sys.eu
Cancer is the second leading cause of death in our society and together with AIDS represents two of the most serious health problems in Europe with an estimated cancer incidence of 3.2 million and cancer mortality of 1.7 million people in 2008. There are numerous factors affecting incidence rates in different countries (demographic aspects, external environmental factors, lifestyle, economical status) leading to tremendous societal and economic consequences for those affected as well as economic costs for the society. Although spectacular progress has been made in the treatment of different types of cancer as well as advancements in its early detection and identification, still the average mortality reduction for all forms of cancer is somewhat modest. In addition, at the end of 2009 it was estimated that around 840.000 people within the European community were living with HIV and there are evidence pointing to an increase in HIV transmission in a number of countries.
Project Context and Objectives:
Starting with the pioneering work by Kerr and colleagues in 1972, cell death studies have become an increasingly important area of biomedical research. In addition to apoptosis and necrosis, several other modes of cell death have been described and characterized based on various morphological and biochemical criteria. Moreover, cross-talk between different signaling pathways has been reported and might influence the sensitivity of tumor cells to treatment. The Nomenclature Committee on Cell Death proposed unified criteria for the definition of cell death and its different morphologies. According to this novel classification, twelve cell death modalities (four typical and eight atypical) can be recognized. Among the best characterized modes of cell death are apoptosis, autophagy, cornification and necrosis.
Project objectives
The multidisciplinary approach adopted in the APO-SYS consortium has aimed at obtaining major progress in the comprehension of cell death in human diseases, in particular cancer and AIDS by combining a series of systems biology approaches, in silico, in vitro (in organello and in cellula), in vivo, and by integrating experimental results with large data sets acquired on tissue samples from patients suffering from diseases that are caused by dysregulated apoptosis. The final results obtained during this project resulted in new refined cell death pathway models, in new knowledge for the optimization and calculation of prognoses and predictive parameters for the diseases as well as new guiding strategies for the amelioration of existing treatments and the identification of novel targets for therapeutic modulation of apoptosis.
The APO-SYS Consortium
The APO-SYS consortium started with 23 partner institutes from across Europe with a total of 26 principal investigators from various research areas including biology, biomedicine, bioinformatics, biomathematics and biostatistics. During the contract period one partner (Medicel Oy) left the consortium due to financial problems. Partner 6 (UUlm) has moved to another University and became Partner 24 (KGU) (a full list of partners can be found in Annex I). The Scientific Advisory Board in APO-SYS consortium, included Professor-Emeritus Sten Orrenius (Sweden), Prof. Pierre Golstein (France) and Prof. Olaf Wolkenhauer (Germany) and has supported the consortium during the 4-years project period (2008-2012). Coordination of the project was led by Prof. Boris Zhivotovsky from the Institute of Environmental Medicine, Karolinska Institutet.
The project was structured in 18 interdependent work packages (WPs) with the following partners responsible for the achievement of the tasks within each workpackage:
WP 1-3 (Management, integration and dissemination activities): Prof. Boris Zhivotovsky (Karolinska Institutet, Sweden), Prof. Emanuel Barillot (Institute Curie, France) and Project manager, Dr. Gabriela Imreh (Karolinska Institutet, Sweden).
WP 4-5 (Data management and dissemination for high quality manually curated data and Integration of the high-throughput experimental data) Dr. Henning Hermjacob, European Bioinformatic Institute, UK).
WP 6 (Construction and integration of the pathway maps and integration of models produced by the partners in the course of the project) Prof. Ron Shamir (Tel Aviv University, Israel).
WP 7 (Biostatistical analysis and data mining) Prof. Ralf Herwig (Max Plank Institute of Molecular Cell Biology and Genetics, Germany).
WP 8 (Experimental exploration of lethal apoptotic signalling: extrinsic pathway): Prof. Jurg Tschopp and Nicolas Fasel (University of Lausanne, Switzerland) and prof. Patrick Mehlen (Claude Bernard University of Lyon, France).
WP 9 (Experimental exploration of lethal apoptotic signalling: intrinsic pathway): Prof. Guido Kroemer (National Institute for Medical Research, France) and Prof. Marja Jaattela (Danish Cancer Society, Denmark).
WP 10 (Experimental exploration of lethal non-apoptotic signalling): Prof. Peter Vandenabeele (Flanders Institute for Biotechnology, Belgium) and Prof. Marino Zerial (Max Plank Institute of Molecular Cell Biology and Genetics, Germany).
WP 11 (Cell Death in non-mammalian model organisms): Prof. Frank Madeo (University of Graz, Austria) and Prof. Josef Penninger (Institute of Molecular Biotechnology, Austria).
WP 12 (Experimental exploration of 'switches'between lethal signalling pathways): Prof. Jens Andersen (University of Southern Denmark, Denmark).
WP 13 (Exploration of clinical samples: solid tumors): Prof. Olli Kallioniemi (Technical Research Centre of Finland, Finland) and Prof. Kari Hemminki (German Cancer Research Centre, Germany).
WP 14 (Exploration of clinical samples: haematological tumors): Prof. Rolf Lewensohn (Karolinska Institutet, Sweden) and Prof. Simone Fulda (Johann Wolfgang Goethe Clinical University, Germany).
WP 15 (Exploration of clinical samples: AIDS): Prof. Mauro Piacentini (National Institute for Infectious Diseases, Italy).
WP 16 (Computational systems biology modelling of cell death): Prof. Denis Thieffy (University d'Aix-Marseille, France).
WP 17 (Identification of intervention points in lethal pathways): Prof. Adi Kimchi and Prof. David Harel (Weizmann Institute of Science, Israel).
WP 18 (Identification of intervention points in diseases): Prof. Lucia Altucci (Second University of Naples, Italy) and Prof. Hinrich Gronemeyer (Institute of Genetics and Molecular and Cellular Biology, France).
Project Results:
Accordingly, we performed experimental exploration of cell-death pathways in human cell lines (in vitro) and model systems (in vitro) in human cells and in vivo in non-human model organism) using high-through put screening methods (genome and transcriptome microarrays, interactome data, mass spectrometry and RNAi assays) and hypothesis driven methods. Further, we developed tools for rational exploitation of large-scale RNAi screening results and we contributed to the improvement of statistical data analysis tools for extraction of correct and relevant biological data from the RNAi screenings. We continued the experimental exploration of lethal apoptotic signalling pathways (extrinsic, intrinsic and non-apoptotic) discovering new players and refining the existing pathways and lunched a systems biology approach to investigate cell death induction via representative death receptors as well as dependency receptors.
3.1 Creation of a data warehouse integrating knowledge on cell death-relevant regulators in diseases
3.1.1 Data management workshop and workshop report on consortium requirements for sharing of high quality annotations of molecular entities
Two main partners carried out the data management and dissemination activities related to this work-package: the Partner 13 (EBI) and Partner 23 Medicel Oy, the SMI company that was a part of our consortium for the first 24 months. Medicel Oy and EBI jointly organised one consortium-wide data management workshops at the beginning of the project (June 24-27, 2008, Cambridge) when particular emphasis was placed upon the available mechanisms for data submission to public data repositories to allow the sharing of high quality annotation and on the Medicel Integrator Software Platform.
3.1.2 Construction and integration of the pathway maps and models
A. Development of converters for exchanging models in BIOPAX format
One of the major goals of the APO-SYS consortium was to construct maps and mathematical models of pathways governing cell death, in particular apoptosis and cell fate switch between the possible ways to death. These maps and models are based on the accumulation of knowledge available both in the literature and in the mind of the experts from the consortium, and must be confronted to the experimental data produced in APO-SYS or coming from other sources. Thus, common language and tools for charting and analysis was desirable to be used by all partners of the consortium. Partner 3 (IC) has extensive experience in this question and adopted a well know software CellDesigner (Kitano et al., 2005) for map design and build an interface, BiNoM (see http://bioinfo-out.curie.fr/projects/binom/ online) (Zinovyev et al, 2007), which is a plug-in to the well-known graph software Cytoscape and allows analysis of networks created with CellDesigner.
B. Development of additional modelling tools to create compatibility with Reactome
Partner 14 (TAU) has advanced the EXPANDER (EXpression Analyzer and DisplayER) suite and published a protocol on the use of the package (Ulitsky et al, 2010). Access to SPIKE is being integrated into EXPANDER to facilitate effective analysis by allowing a user to easily combine EXPANDER's expression analysis tools with SPIKEs network analysis. In addition, CEZANNE, a version of the Matisse modelling tool that incorporates interaction confidence, was developed and published (Ulitsky and Shamir, 2009). Partner 20 (UnivMed) released a novel version of GINsim in 2009 (Naldi et al, 2009a). Furthermore, they developed an original and powerful method enabling the reduction of large logical models. The model reduction tool in GINsim was applied to the cell-fate decision model. Partner 15A (MPG) has developed GeNGe, a tool for modelling of gene regulatory interactions (Hache et al., 2009). Also in the context of APO-SYS, Partner 15A (MPG) uses the GeNGe tool to formally describe the effects of perturbations in apoptosis signalling on the gene regulatory layer. Partner 3 (IC) developed computational tools that are specific and necessary for modelling the cell fate decisions or systems of similar behaviour where the cell 'decides' on ending up in a particular phenotype. A model of cell fate decision process in response to death receptor engagement was developed by Partners 3 and 20 (Calzone et. al, 2010).
C. Construction of cell death pathways maps
A solid definition and comprehensive representation of cell death networks is essential for efficient and accurate dissemination of information. Using the experimental data obtained by several partners from the consortium (Partner 1,2,5,7,8,9,10,18 and 21) simple maps of two cell-death pathways the extrinsic TRAIL pathway and the intrinsic pathways were generated. The TRAIL pathway was constructed qualitatively using the Genomatix software. The scheme of the intrinsic pathway was created in an in-silico model based on ODE (Ordinary Differential Equations) with kinetic parameters from literature and validated in single cell experiments. The different maps are presented in Annex II. From the basic maps the partners of the APO-SYS consortium work on pathway integration and refinement. This includes: inclusion of the upstream processes by Partner 18 (RCSI) (Duessman et al., 2010); inclusion of spatial anisotropies of MOMP by Partner 18 (RCSI) (Rehm et al., 2009; Huber et al., 2010a, b); integration of DNA Damage, DNA DSB-inducing chemotherapy in combination with DNA-PK inhibition, alkylating agents in NSCLC, DNA-DSB-inducing chemotherapy in AML, Partner 1A (KI) (Stahl et al., 2009; Haag et al., 2010); Lysosomal Integration, Partner 8 (DCI) (Kirekegaard et al., 2009); Necrosis, Partner 2 (INSERM) and Partner 5 (VIB). Later on in the project, the maps were updated using additional data produced within the consortium.
D. Creation of APO-SYS model repository
Accordingly to the initial planning of the APO-SYS project, all mathematical models created in the course of the project had to be submitted to the integrative environment created by Medicel Oy (Partner 23). Following the departure of Medicel from the consortium, it was decided to host the collection of the mathematical models at the APO-SYS Intranet web-site. Some of the models placed in the model repository are already published and put in the public domain. The others are currently restricted to the members of APO-SYS consortium, but are due to be made available to the general public after publication.
E. Improved GO annotations for apoptosis
The use of structured controlled vocabularies (ontologies) is a fundamental part of modelling the biological processes. Analysis of such models requires standardized annotations that accurately depict the biology around the entities described. The Gene Ontology (GO) is a collaborative effort trying to standardize the representation of genes and gene products and it is arguably the most important resource of this type available nowadays. Discussions with the APO-SYS partners made clear that there was a requirement for a solid, updated ontology that could be used for the construction and integration of pathway maps and models depicting the apoptotic process. Both the lack of granular terms and the inadequate definitions hampered the use of data integration tools used to produce meaningful models of the apoptotic process. Thus, our aims was to reduce this gap and provide the community with a proper depiction of the apoptotic process in GO. The GO editorial team and the Proteomics Services team in the EBI have worked closely with the APO-SYS domain experts to create an extended version of the GO 'Apoptosis' branch which is currently being released.
3.1.3 Biostatistical analysis and data mining
A. Improvement and extension of computational functionalities for statistical analysis of large high-throughput data
Already from the beginning of the project we were well aware about the large amount of high-throughput data which will be produced during the course of the project, including genome microarrays, transcriptome microarrays, proteomics and RNAi data. The development of statistical tools for rigorous statistical analysis of large amount of high-throughput data was a high priority for the mathematicians and bionformaticians in our consortium.
B. Procedure and tools for obtaining validated RNAi experiments
Partner 11 (VTT) has developed the siRNA R/Bioconductor based data analysis workflow and is currently formulating an R package for publication. The workflow incorporates data import, QC, normalisation and a large number of graphical outputs. In order to facilitate the partner's ability to carry out RNAi screens in a homogenous and comparable fashion, the methods for high-throughput screening data analysis were presented at the APO-SYS Workshop titled 'Biostatistics', held on January 19-20, 2009 in Berlin at the Max Planck Institute for Molecular Genetics (Partner 15A). The presentations on the siRNA screening data analysis are available on the project web site (see http://www.apo-sys.eu online) and on the intranet. A standard operating procedures (SOP) document for high-throughput screening data analysis was formulated based on the experiences of Partner 11 (VTT) as well as other labs on screening data analysis.
C. Report on analysis of high-throughput data
The analysis of high-throughput data targeted a wide spectrum of research questions related to apoptosis signaling and used the tools and methods developed by the mathematicians and bioinformatitions from our consortium. The type of work carried out was either direct collaborative work between the computational groups and the experimental groups of the APO-SYS consortium solving specific research questions or integrative studies that incorporated different heterogeneous data sets within and outside the consortium.
3.1.4 Computational systems biology modeling of cell death
The aims for this part of the APO-SYS project was to building predictive models for programmed cell death in the context of alternative cell fate decisions, using different types of modelling formalisms (chemical kinetics, discrete or logical formalism). Particular emphasis was put on the definition of model parameters, the analysis of complexity and robustness, as well as on the delineation of validation experiments and the identification of intervention points into lethal pathways.
3.2 Experimental exploration of the extrinsic pathway
Signalling by death receptors (CD95/Fas, TNF-R, TRAIL) or absence of signalling by dependency receptors (p57NTTR, DCC, Patched, RET etc) can stimulate apoptosis through a pathway initiated at the plasma membrane. We applied a systems biology approach to understand the mechanisms through which such plasma membrane receptors trigger the apoptotic signal.
3.3 Experimental exploration of the intrinsic apoptotic pathway.
The intrinsic pathway also called 'stress pathway' or 'mitochondrial pathway' of apoptosis is initiated anywhere in the cell (except the plasma membrane), due to organelle-specific perturbation and damage or the absence of essential trophic factors. One limiting step of the intrinsic pathway is mitochondrial permeabilization. The consortium aimed at understanding the contribution of different cellular constituents and modules (e.g. the DNA damage response module, ER stress, etc).
3.4 Experimental exploration of lethal non-apoptotic signalling pathways
It is well acknowledged that non-apoptotic cell death signalling pathways (caspase-independent necrosis, autophagy, mitotic catastrophe) have important roles in pathological cell death. Our focus was on the functional characterization of siRNAs that inhibit or induce autophagy and in-depth characterization of the autophagic-regulatory machinery at the systems biology level. Three main non-apoptotic pathways were studied by the consortium, autophagy, necrosis and mitotic catastrophe.
3.5 Cell death in non-mammalian model organisms
The aim of the work we initiated in yeast (Saccharomyces cervisiae) and fruit fly (Drosophila melanogaster) was to consolidate data obtained in the human system and to discover new phylogenetically ancient (and hence important) cross-talks between distinct cell death modalities.
3.6. Experimental exploration of switches between lethal signalling pathways
There are some evidences that cells 'decide' between different cell death modalities (apoptosis versus autophagy versus necrosis versus mitotic catastrophe) and functional outcomes (immunogenic versus non-immunogenic cell death). Within the APO-SYS project we aimed at understanding the nature of these molecular switches and the candidate proteins participating in these 'switches'.
3.7 Exploration of clinical samples: solid tumors and hematological cancer
One of our primary goals was the retrieve of relevant information relevant to cell death pathways on clinical data. The important outcome of our approach was the possibility to be able to define better therapeutics for given tumors, to discover several new biomarkers and novel targets with potential anti-tumor activity.
3.7.1 Lung Cancer
Partner 1A (KI) has collected tumor, plasma and serum from non-small cell lung cancer (NSCLC) patients with the aim to find BM of prognosis and metastasis propensity. By performing MS-based proteomics Partner 1A (KI) identified several proteins which can be linked to lung cancer patient outcome including the S100 protein family (Pernemalm et al. 2009; de Petris et al. 2009). Partner 1A (KI) has constructed a tissue microarray (TMA) consisting of about 500 lung cancer cases with corresponding histological and clinical which has been used to validate S100 proteins for their prognostic capacity of NSCLC (De Petris et al., manuscript in preparation). The analyses show that S100A6-positive cases have longer survival compared with S100A6-negative cases, S100A6 and S100A4 showed a significant co-expression with a higher expression in well differentiated non-squamous tumors. All in all, Partner 1A (KI) have constructed a NSCLC TMA with corresponding clinical data and demonstrated its usefulness to validate apoptosis related proteins, identified by global proteomic methods. Partner 1A (KI) has also applied genomics and MS-based proteomics to identify signaling events critical for radiotherapy (RT)-induced apoptosis in NSCLC. By applying PSE pathway analysis on MS-data JNK was identified and validated to play an important role in RT-induced apoptosis in NSCLC cells (Stahl et al. 2009). Moreover, a novel role for Ephrin B3 and EphA2 in NSCLC survival and RT resistance was identified by genomic and phosphoproteomic MS analysis, respectively (Ståhl et al. 2011). As mutations and/or overexpression of Ephs recently have been revealed in NSCLC, the identification of Ephrin B3 and EphA2 in growth and RT response of NSCLC is very interesting our findings might suggest that targeting Ephrin/Ephs can be a way to improve RT response of NSCLC. Partner 1A (KI) has also analyzed apoptosis induction in response to the melphalan prodrug J1 (currently undergoing Phase II clinical trial), as well as in response to phenothiazines; compounds currently used antipsychotic agents. Analysis demonstrated that aminopeptidase N (ANPEP) is a potential BM of J1 response (Wikstrom et al., 2010) as it is required for intracellular accumulation of melphalan and subsequent apoptotic signaling upon J1 addition. With respect to phenothiazines, these agents were found to have CT sensitizing potential in NSCLC, illustrated by the increase of cisplatin-induced ROS and BAK/BAX, caspase-3 activation (Zong et al. 2011).
3.7.2 Colorectal cancer
Partner 18 (RCSI) in collaboration with RCSI Clinical Research Centre in Beaumont Hospital pioneered in validating systems model of apoptosis in clinical settings of colorectal cancer (CRC). These tools may aid to predict individual patient response to chemotherapy, thereby stratifying and individualising treatment. We developed a new model to understand how MOMP is regulated upon BCL-2 protein interaction subsequent to 5FU/Oxaliplatin. Our model mechanistically confirmed previous reports that cancer cells may undergo apoptosis at lower dose stimuli than cells of healthy tissue (cancer cell priming) and that the extent of this priming correlates with a better prognosis (Lindner et al. 2012, in preparation). A second model that analyses whether or not apoptosis is executed after MOMP, APOPTO-CELL was assessed in a retrospective study of 20 CRC patients (Hector et al. 2011).
3.7.3 Pediatric cancer
Core-binding factor (CBF) leukemias, characterized by translocations t(8;21) or inv(16)/t(16;16) targeting the CBF, constitute acute myeloid leukemia (AML) subgroups with favorable prognosis. However, about 40% of patients relapse and the current classification system do not fully reflect this clinical heterogeneity. Partner 6 (UUlm) and Partner 24 (KGU) revealed, using gene expression profiling (GEP), two distinct CBF leukemia subgroups displaying significant outcome differences and identified apoptotic signalling, MAPKinase signalling and chemotherapy-resistance mechanisms among the most significant differentially regulated pathways.
3.7.4 Genome wide association study on several collections of patient data
Genetic susceptibility of colorectal cancer (CRC) accounts for approximately 30% of its aetiology. Rare, high-penetrance germline mutations in a few genes (mainly APC and DNA mismatch repair genes) account for less than 5% of CRC cases. Much of the remaining variation in genetic risk is supposed to be attributable to common susceptibility loci, each exerting a small influence on risk. Partner 12 (DKFZ) applied an agnostic approach using a genome-wide association study on CRC by genotyping 371 German familial CRC cases and 1263 healthy controls using the Affymetrix Genome Wide Human SNP 6.0 Array. In the ensuing replication studies on four additional case-control sets (4915 cases and 5607 controls) known risk loci at 8q24.21 and 11q23 were confirmed, and a previously unreported association, rs12701937 (P = 10-3, OR 1.14 95% CI 1.05-1.23 dominant model) was identified (Lascorz J et al. 2010). This SNP is located between genes GLI3 and INHBA. Using software tools for pathway enrichment we were able to show over-representation of genes related to the mitogen-activated protein kinase (MAPK) signalling pathways among the most strongly associated markers from the GWAS. The risk of CRC increased significantly with an increasing number of risk alleles in these Thus the study replicated two of the known risk loci for CRC and identified a possible new risk polymorphism, with a stronger effect in familial cases.
3.7.5 Study on the level of expression of dependency receptors in solid cancer
Partner 21(UCBL) explored the level of expression of the pair ligand/receptor in a panel of solid tumor. Because chip analyses were found unreliable to measure expression of some ligands of dependence receptors (because of the amplification phase, Fitamant et al., 2008), Partner 21 employed Q-RT-PCR or immunohistochemistry 140 breast cancer, 101 neuroblastoma and a large panel of lung cancer. Partner 21 analyzed netrin-1 expression in a panel of 92 NSCLC tumors, including 45 adenocarcinomas and 47 squamous cell carcinomas, and found that Netrin-1 expression in normal bronchial and alveolar epithelial cells was absent or low but both tumor types expressed high levels of netrin-1. Netrin-1 expression was more frequent and more intense in adenocarcinomas (60% of samples were positive, with a median score of 100) than in squamous cell carcinomas (34 % positive samples with a median score of 50). In situ hybridization to detect netrin-1 mRNA identified a substantial level of netrin-1 mRNA in the epithelial tumor cells but netrin-1 mRNA levels were undetectable or very low in the stroma cells, indicating that netrin-1 may be produced by tumor cells. To quantify the expression of netrin-1 and its receptors, we determined the level of mRNAs encoding netrin-1 and its dependence receptors DCC, UNC5H1, UNC5H2, UNC5H3, and UNC5H4 by quantitative reverse-transcription polymerase chain reaction (Q-RT-PCR) in a panel of 25 lung carcinomas, including 14 adenocarcinomas and 11 squamous cell carcinomas, as well as in adjacent normal tissues that were at least 5 cm from the tumor. Netrin-1 expression was more than fourfold higher in lung tumors than in adjacent normal tissue. Netrin-1 overexpression relative to normal tissue was observed in 19 of the 25 (76%) tumors, with 13 of them (52%) showing more than a threefold increase in netrin-1 content relative to normal tissue.
3.7.6 Study of novel therapies in Myelodysplastic syndromes (MDS) and acute myelogenous leukemias (AML)
Partner 2 previously demonstrated that the epidermal growth factor receptor (EGFR) inhibitor erlotinib also mediates antiproliferative/cytotoxic effects in MDS and AML blasts and cell lines as an off-target effect (Boehrer et al. 2008). Currently two clinical trials (NCT00977548 and NCT01085838) are evaluating the therapeutic benefit of erlotinib in patients with MDS and AML. Thus, Partner 2 shows that erlotinib can reduce SFK over activation in MDS and AML blasts. Moreover, erlotinib was found to induce a G1 phase cell cycle arrest which either might reflect a reduction in proliferation rate or induction of reversible quiescence or irreversible senescence, potentially linked to the mTOR activation status (Boehrer et al. 2011) and that chemical inhibition of SFKs with 3-(4-chlorophenyl)1-(1,1-dimethylethyl)-1H-pyrazolo[3,4-a]pyrimidin-4-amine (PP2) similarly could induce G1 arrest in AML and recapitulate the effects induced by erlotinib.
3.8. Exploration of AIDS samples
AIDS is a chronic degenerative disease that involves the enhanced apoptotic turnover of lymphocytes and other cell types. Many studied dealing with the biology of the human immunodeficiency virus-1 (HIV-1) neglect the contribution of host cells to the pathogeny of AIDS.
A comprehensive comparative analysis of transcriptome vs proteome in the course of HIV-infection was performed by Partner 4 on in vitro and ex vivo cellular systems based on experimental results and database repository of high throughput gene expression data. The goal was to fully characterize new cell death signalling pathways activated by HIV infection, in addition to those already identified by the traditional approaches based on biochemistry and molecular biology. The comparison of the our proteomic data with the transcriptome evidences that the susceptibility to cell death is mediated by the interferon induction that leads to a sustained increase in p53 mRNA levels and therefore to a higher susceptibility of CD4+ T cells to pro-apoptotic signals. Besides the interferon responses, the up-regulation of genes associated with cell apoptosis has been detected both by proteomic and transcriptomic approaches in all HIV patients progressing towards AIDS.
3.9 Identification of intervention points in lethal pathways
The major goal of this work was to understand the structure/function organization of the molecular network of programmed cell death by studying the system through its intervention at different points. Our goal was to provide dynamic models of the system which quantify the network's output as a function of the input signal (the death inducing agent).
The first challenge was to construct a 'naive' protein-protein interaction map by critically mining the literature and the public data base sources. The resulting global signalling map consists of around 200 proteins (the network nodes) connected to each other by various enzymatic and functional interactions (the network edges). Most of the signalling pathways involve post-translational changes of the proteins including proteolysis, phosphorylation, ubiquitin and ubiquitin like conjugation reactions, assembly of protein complexes and changes in sub-cellular localization. The constructed maps are available in several visualization formats. One map was prepared manually (Bialik et al. 2010).
3.10 Identification of intervention points in diseases
The final goal of the comprehension of biological phenomena is the biomedical application of the knowledge. During the APO-SYS project we launched a variety of tentative approaches to validate current and future knowledge on disease-relevant deregulations of the apoptotic process, working on primary human specimen. One particularly important goal for the APO-SYS consortium was to selectively kill tumor cells subverting the chemotherapy resistance of cancer cells as well as potentiating the immunogenicity of the dying cell, based on the mathematical models developed in the course of the project.
Potential Impact:
Conclusions:
Through our comprehensive approach, uniquely combining biological, mathematical, biomedical and biostatistical knowledge, we have created mathematical model pathways governing cell death (intrinsic and extrinsic apoptosis pathways) as well as non-apoptotic pathways; we developed and improved already existing bioinformatic tools (EXPANDER, ITTACA, CAPweb) as well as community platforms such as R/Bioconductor for accurate biostatistical analysis of high-throughput information. Furthermore, we have reported several novel and important discoveries in peer-reviewed scientific articles dealing with the discovery of novel biomarkers and novel targets with anti-tumor potential as well as novel strategies for combinatorial treatment of tumors and HIV infection. We have also organized several workshops and participated in numerous conferences and meeting were we promoted the work conducted within the APO-SYS project. The success of the APO-SYS project can be appreciated by the publication of high number of peer review articles, of which a significant number are joint publications emphasizing the successful collaboration between the groups.
Dissemination activities
1 Project website
Project website (see http://www.apo-sys.eu online) was set-up at the start of the project and comprises several domains including a public access domain and a password protected domain (access provided to the project partners, project officer). During the course of the APO-SYS project, the webpage was updated continuously sharing with both the general public and research community the highlight of our research, and when appropriate availability of PDF files for download.
2 Scientific publications, press releases (radio, TV and newspapers) and conferences
It should also be mentioned that several members of the APO-SYS consortium were invited as authors in several books with a thematic within systems biology of cell death:
-Systems biology analysis of cell death pathways in cancer: how collaborative and interdisciplinary research helps. In Cesario A and Marcus F B (editors) (2011) Cancer Systems Biology: Bioinformatics and Medicine: Research and Clinical Applications. Springer Dordrecht Heidelberg London New York. pp. 267-296.
-Understanding different types of cell death using Systems Biology. In: Lavrik I (Editor). Calzone, L., Zinovyev, A., and Zhivotovsky, B. 'Systems Biology of apoptosis'. Springer Science, NY, USA, 2012 (In Press).
-Different modes of cell death induced by DNA damage. In: Greim H and Albertini R (Editors). Surova, O., and Zhivotovsky, B. 'The Cellular Response to the Genotoxic Insult: The Question of Threshold for Genotoxic Carcinogens'. RSC Publishing, 2012 (In Press).
-Chaouiya C, Naldi A, Thieffry D (2012). 'Logical modelling of gene regulatory networks with GINsim. ' In: van Helden J, Toussaint A, Thieffry D (eds.) Bacterial Molecular Networks, Methods in Molecular Biology series, vol 804, Humana Press, pp. 463-79.
-Thieffry D (accepted). 'Gene networks', 'logical modelling', and 'regulatory motifs' entries. In: Hancock JM, Zvelebil MJ (eds). Dictionary of Bioinformatics and Computational Biology. New York: John Wiley & Sons.
-Faure A, Thieffry D (in press). 'Cell cycle modelling using logical rules. ' In: Dubitzky W, Wolkenhauer O, Cho K-H, Yokota H (eds.) Encyclopedia of Systems Biology. Springer.
3. Workshops and Collaborative Meetings
-The first workshop on APO-SYS data analysis and pathway charting took place at Institute Curie, Paris in April 2008. The workshop was organized by Prof. Emmanuel Barillot in collaboration with Prof. Ron Shamir from Tel Aviv University with the thematic: 'APO-SYS data analysis and pathway charting'. The workshops focused on microarray data analysis, biological network analysis, pathway charting, and other omics data analysis.
-The second workshop took place at European Bioinformatics Institute, in Hinxton, Cambridge. The workshop was organized by Dr. Henning Hermjakob (EBI) and Dr. Christopher Roos (Medicel Oy) and focused on 'Data management and requirements for sharing of high quality annotations of molecular entities'. Several lectures took place during the 3 day workshop given by experts from EBI and Medicel Oy as well as training session for young scientists.
-The next important workshop was organized by Prof. Ralf Herwig and hosted at Max Plank Institute of Molecular Cell Biology and Genetics in Berlin in January 2009. The co-organizers were Christophe Roos, Medicel Oy and the Technical Research Centre of Finland, represented by Dr. Pekka Kohonen. The workshop focused on 'Data analysis with statistics' and 'Data storage, analysis and sharing on the Medicel platform'.
-In order to promote new collaboration opportunities between the members of the consortium and in special between young scientists working on the APO-SYS project, the management office organized three 'APO-SYS collaborative meetings' , two in Stockholm (June, 2009, 2010) and one in Hinxton, Cambridge (June, 2011). The meetings comprised presentations of the ongoing work within the APO-SYS project, discussion and poster presentations.
-The last workshop was organized by Dr. Henning Hermjakob and Dr. Samuel Kerrien form European Bioinformatics Institute, at Hinxton, Cambridge. The workshop focused on 'Data management' and 'GO content meeting'. The experts from EBI shared their knowledge with APO-SYS participants and several training sessions with different thematic took place. All presentations were made available to the participants and were applicable, made public towards other the research community.
4. Coordination activities
The APO-SYS project, coordinated at Karolinska Institute by Prof. Boris Zhivotovsky, was a challenging project at least of two reasons: first of all the project was a multidisciplinary project with partners specialized in biology, biomedicine/translational medicine, bioinformatics, biomathematicians and biostatisticians and spread all over Europe. Secondly the consortium was made up of 23 European partners (26 principal investigators) including, at least, for half through the project of a SMI company.
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