Abstract The H2020 project CanPathPro is building and validating a computational predictive modelling platform applied to cancer. To this end, we develop and refine bioinformatic and experimental tools, utilized in generation and evaluation of systems biology modeling predictions. The presented work employs the following technologies and methodologies: biologic systems representing 3 levels of biologic complexity (genetically engineered mouse models—GEMMs—of breast or lung cancer, organoids and cell lines derived thereof); next-generation sequencing and SWATH-based phospho/proteomics; and two large-scale computational mechanistic models. The highly defined biologic systems are used (i) to activate selected oncogenic stimuli that modulate pathway components in a systematic manner; (ii) to characterize the signaling changes occurring during cancer development—thus generating temporally resolved datasets for model training; and (iii) to validate, in vitro and in vivo, the modeling predictions. The mechanistic models, based on ordinary differential equations, enable prediction of phenotypes and drug response in mouse or human. Model parameters are defined using project-derived experimental data, either via parameter estimation strategies or via selection of parameter distributions by a Monte Carlo approach. For simulations, the models are initialized with transcriptome data, either from GEMM-derived cell lines grown under variable conditions or from GEMM-derived neoplastic and tumor tissue, representing lesion progression. The models also integrate the relevant mutations. Results, obtained by iterative rounds of in silico predictions and in vitro validation, include in silico identification (i) of the activation status of oncogenic pathways in GEMMs, organoids, and cell lines; (ii) of the signaling changes induced by the in vitro growth conditions (e.g., growth factor modulation, drug treatments) and by the mutational profile of cell lines/organoids, or by the mutational profile and lesion stage of each GEMM; and (iii) of the drug response of cell lines and GEMMs. In conclusion, this work encompasses a highly integrative systems biology approach generating and validating new hypotheses on cancer pathways signaling and crosstalk, identifying new signal flow, and suggests new ways to interfere with tumor growth. This abstract is also being presented as Poster B49. Citation Format: Julio Banga, Lucien Frappart, Jan Hasenauer, Yann Herault, Jos Jonkers, David Koubi, Bodo Lange, Glenn Terje Lines, Aspasia Plouidou, Oliver Rinner. Predictive modeling, applied to genetically engineered mouse models of breast or lung cancer, provides insights into major oncogenic pathways [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr PHB04.

Abstract Omics technologies are generating complex molecular datasets that are exponentially increasing the cancer knowledge base and opening up new therapeutic possibilities. However, current approaches to analysing such data are often confined to statistical and pattern recognition techniques, or at best modelling of a single cellular signalling pathway, rather than the complex cross-talks of pathways that determine cancer onset and progression and response to therapy. New solutions to optimally exploit this wealth of data for basic research, better treatment and stratification of patients, as well as more efficient targeted drug development are required. CanPathPro (www.canpathpro.eu), an EU Horizon 2020 project, is addressing the challenge of predictive modelling of biological data by developing and refining bioinformatic and experimental tools for the evaluation and control of systems biology modelling predictions. Components comprise highly defined mouse and organotypic experimental systems, next-generation sequencing, SWATH-based proteomics and a systems biology computational model for data integration, visualisation and predictive modelling. Within CanPathPro, genetically engineered mouse models are used to follow the temporal changes occurring during cancer development, including the histology of the tumour, the genome and transcriptome using next-generation sequencing and the (phospho-)proteome using SWATH technology. The systems biology computational model is optimised in an iterative fashion through perturbation experiments of tumor-tissue-derived cell lines and organoids, permitting the validation of pathway and parameter information. In this way, CanPathPro takes a unique approach combining classic cancer research with omics data and systems biology tools, to develop and validate a new biotechnological application: a combined systems and experimental biology platform for generating and testing cancer signalling hypotheses in biomedical research. The CanPathPro-generated platform will enable in silico identification of cancer signalling networks critical for tumour development and will allow users to predict activation status of individual pathways, following integration of user (or public) data sets in the pathway models. The innovative approach taken by CanPathPro is set to have broad and significant impact on diverse areas, from cancer research and personalised medicine to drug discovery and development, and ultimately improving outcomes for cancer patients. Citation Format: Christoph Wierling, Yann Herault, Jos Jonkers, Aspasia Ploubidou, Lucien Frappart, Jan Hasenauer, Julio Banga, Oliver Rinner, Valeriya Naumova, David Koubi, Bodo Lange. CanPathPro—development of a platform for predictive pathway modelling using genetically engineered mouse models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1296.

ObjectivePalladised cells of Desulfovibrio desulfuricans and Shewanella oneidensis have been reported as fuel cell electrocatalysts but growth at scale may be unattractive/costly; we have evaluated the potential of using E. coli, using H2/formate for Pd-nanoparticle manufacture.ResultsUsing ‘bio-Pd’ made under H2 (20 wt%) cyclic voltammograms suggested electrochemical activity of bio-NPs in a native state, attributed to proton adsorption/desorption. Bio-Pd prepared using formate as the electron donor gave smaller, well separated NPs; this material showed no electrochemical properties, and hence little potential for fuel cell use using a simple preparation technique. Bio-Pd on S. oneidensis gave similar results to those obtained using E. coli. ConclusionBio-Pd is sufficiently conductive to make an E. coli-derived electrochemically active material on intact, unprocessed bacterial cells if prepared at the expense of H2, showing potential for fuel cell applications using a simple one-step preparation method.

Research into rare diseases is typically fragmented by data type and disease. Individual efforts often have poor interoperability and do not systematically connect data across clinical phenotype, genomic data, biomaterial availability, and research/trial data sets. Such data must be linked at both an individual-patient and whole-cohort level to enable researchers to gain a complete view of their disease and patient population of interest. Data access and authorization procedures are required to allow researchers in multiple institutions to securely compare results and gain new insights. Funded by the European Union's Seventh Framework Programme under the International Rare Diseases Research Consortium (IRDiRC), RD-Connect is a global infrastructure project initiated in November 2012 that links genomic data with registries, biobanks, and clinical bioinformatics tools to produce a central research resource for rare diseases.

Purpose The SysCLAD study is an European Union-funded project under the FP-7 to predict CLAD signature by year-1 post lung transplantation (LT) before any decline in lung function. CLAD and its subtypes Bronchiolitis Obliterans Syndrome and Restrictive Allograft Syndrome represent the actual limitation resulting in a high morbi-mortality and costs. Specific aims of SysCLAD are identification of CLAD biomarkers, a better understanding of CLAD mechanisms and a predictive and personalized signature of CLAD. Methods and Materials Built upon a cohort of LT recipients already transplanted and recruited since September 2009 in 14 LT centres, SysCLAD signature will be based on a mathematical model developed through a systems medicine approach integrating clinical and biological data collected within the first year post LT in 2 steps, using calibration and validation samples within LT cohort. Clinical data include donors and recipients characteristics and post LT clinical events; biological data donors and recipients HLA, whole blood transcriptomics, BAL and blood proteomics at months 6 and 12, together with once miRNA, BAL microbiote and recipient genetic polymorphisms analysis at month 6. Results By November 2012, 820 LT candidates of whom 652 received a LT; indications were by digressive order CF, COPD/emphysema, IPF, PH, bronchiectasis and others; LT recipients were 42.7 ± 15.2 yrs old, 320 received a double LT; 2-years survival was 76%. In SysCLAD biobank, they are by year-1 post LT to predict besides “clinicome”: 1 152 TBB, 7 537 PBMC aliquots, 2362 BAL, 1311 blood samples for transcriptomics Paxgene®, 12 903 plasma and 11 008 sera aliquots. Conclusions CLAD tool is expected by early personalized interventions to improve morbi-mortality and increase the cost-effectiveness of LT. Funds from PHRC 2008 and Vaincre La Mucoviscidose within the 11 French lung Transplantation centres to launch Cohort in Lung Transplantation (COLT)-ClinicalTrials.gov Identifier: NCT00980967, Programme transplantation 2008, PRTP-13, and #HEALTH-F5-2012-305457, a FP-7 call.