Suchergebnisse

Cancer eradication and clinical outcome of immunotherapy depend on tumor cell immunogenicity, including HLA class I (HLA-I) and PD-L1 expression on malignant cells, and on the characteristics of the tumor microenvironment, such as tumor immune infiltration and stromal reaction. Loss of tumor HLA-I is a common mechanism of immune escape from cytotoxic T lymphocytes and is linked to cancer progression and resistance to immunotherapy with the inhibitors of PD-L1/PD-1 signaling. Here we observed that HLA-I loss in bladder tumors is associated with T cell exclusion and tumor encapsulation with stromal elements rich in FAP-positive cells. In addition, PD-L1 upregulation in HLA-I negative tumors demonstrated a correlation with high tumor grade and worse overall- and cancer-specific survival of the patients. These changes define common immuno-morphological signatures compatible with cancer immune escape and acquired resistance to therapeutic interventions across different types of malignancy. They also may contribute to the search of new targets for cancer treatment, such as FAP-expressing cancer-associated fibroblasts, in refractory bladder tumors. ; This work was supported by grants from the ISCIII Research Institute co-financed by the European Union (FED-ER-Fondo Europeo de Desarrollo Regional) (RETIC RD 06/020, RD09/0076/00165; PI14/01978, PI16/00752, Q2827015E, PI17/00197, PT17/0015/0041) and by the Junta de Andalucía in Spain (Groups CTS-143, CTS-695, CTS3952, CVI-4740). This study was partially financed by Abbott, and the Spanish Research Institute IDI-URO, Madrid. ; Yes

Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing. ; Nebraska NASA Space Grant Consortium grant: ("Technology for collaborative biomedical research"), National Institutes of Health grant support: (#5R01DA030962), US National Institute of General Medical Sciences support: (#GM070923), Swiss Federal Government through the Federal Office of Education Science and Innovation (SERI) and the European Commission FP6 project ENFIN (Experimental Network for Functional INtegration–LSHG-CT-2005-518254), FCT grant: (Pest-OE/EEI/LA002), , Federal Ministry of Education and Research (BMBF, Germany) as part of the Virtual Liver Network grants : (0315756, 0315744), EMBL-EBI.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developedSBMLLevel 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades ofSBMLand a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and howSBMLLevel 3 provides the foundation needed to support this evolution. ; National Institute of General Medical Sciences (NIGMS, US) [R01-GM070923]; Bundesministerium fur Bildung und Forschung (BMBF, DE)Federal Ministry of Education & Research (BMBF) [031L0104A]; NIH (US)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41-GM103313, R01-GM095485, 5R35-GM119770-03, GM57089]; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)German Research Foundation (DFG) [EXC 2124]; German Center for Infection Research (DZIF); JSPS KAKENHIMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of ScienceGrants-in-Aid for Scientific Research (KAKENHI) [21700328]; National Institutes of Health (NIH, US)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41-GM103712]; Biotechnology and Biological Sciences Research Council (BBSRC, UK) "MultiMod" projectBiotechnology and Biological Sciences Research Council (BBSRC) [BB/N019482/1]; NIGMS (US)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of General Medical Sciences (NIGMS) [P41-GM103313, R01-GM080219, R01-GM123032]; Federal Ministry of Education and Research (BMBF, DE), research network Systems Medicine of the Liver (LiSyM), Humboldt-University BerlinFederal Ministry of Education & Research (BMBF) [031L0054]; BBSRC (UK)Biotechnology and Biological Sciences Research Council (BBSRC); National Science Foundation (NSF, USA)National Science Foundation (NSF) [CCF-1748200, CCF-1856740]; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P41-GM103313, P41-EB023912]; Department of Biotechnology, Government of IndiaDepartment of Biotechnology (DBT) India [BT/PR4949/BRB/10/1048/2012]; Intramural Research Program of NIAID, NIH (US); BBSRC (UK) "MultiMod" project [BB/N019482/1]; Novo Nordisk Foundation Grant [NNF10CC1016517]; National Institute of Biomedical Imaging and Bioengineering (NIBIB, US) [P41-EB023912]; DDMoRe program (EU), Innovative Medicines Initiative Joint Undertaking [115156]; BBSRC (UK) grant "Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) " [BB/M017702/1] ; The principal authors thank many funding agencies for their support of this work. F.B., A.D., M.H., T.M.H., S.M.K., B.O., and L.S., as well as SBML.org and its online resources, were supported by the National Institute of General Medical Sciences (NIGMS, US), grant R01-GM070923 (PI: Hucka). In addition, F.B. has been supported by the Bundesministerium fur Bildung und Forschung (BMBF, DE), grant de.NBI ModSim1, 031L0104A (PI: Ursula Kummer). M.L.B. has been supported by NIH (US) grant P41-GM103313 and R01-GM095485. A.D. has been supported by infrastructural funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Cluster of Excellence EXC 2124 Controlling Microbes to Fight Infections, and by the German Center for Infection Research (DZIF). A.F. was supported by the Grant-in-Aid for Young Scientists (B), grant 21700328 from JSPS KAKENHI (JP) to Keio University. J.F. was supported by National Institutes of Health (NIH, US) grant P41-GM103712 to the National Center for Multiscale Modeling of Biological Systems (MMBioS). H.H. was supported by the Biotechnology and Biological Sciences Research Council (BBSRC, UK) "MultiMod" project (grant BB/N019482/1). T.H. was supported by NIH (US) grant 5R35-GM119770-03 to the University of Nebraska-Lincoln. S.H. was supported by NIGMS (US) grant R01-GM080219. M.K. was supported by the Federal Ministry of Education and Research (BMBF, DE), research network Systems Medicine of the Liver (LiSyM), grant 031L0054, Humboldt-University Berlin (PI: Konig). A.L. was supported by the BBSRC (UK) while working at the Centre for Integrated Systems Biology of Ageing and Nutrition (CISBAN), Newcastle University. C.M. was supported by the National Science Foundation (NSF, USA) under grant CCF-1748200 and CCF-1856740. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. I.M. was supported by NIH grant P41-EB023912 and P41-GM103313. K.R. was supported by the Department of Biotechnology, Government of India (grant BT/PR4949/BRB/10/1048/2012). M.M.-S. was supported by the Intramural Research Program of NIAID, NIH (US). R.M.-S. was supported by the BBSRC (UK) "MultiMod" project (grant BB/N019482/1). B.P.'s was supported by NIH (US) grant GM57089 to the University of California, San Diego, and by the Novo Nordisk Foundation Grant NNF10CC1016517. H.M.S. was supported by NIGMS (US) grant R01-GM123032 (PI: Sauro) and by the National Institute of Biomedical Imaging and Bioengineering (NIBIB, US) grant P41-EB023912 (PI: Sauro). J.C.S. was supported by NIGMS (US) grant P41-GM103313. M.S. was supported by the DDMoRe program (EU), Innovative Medicines Initiative Joint Undertaking under grant agreement 115156. N.S. was supported by BBSRC (UK) grant "Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM) ", grant BB/M017702/1 (PI: Nigel S. Scrutton). F.Z. was supported by the Intramural Research Program of NIAID, NIH (US).