Signaling pathway models as biomarkers: Patient-specific simulations of JNK activity predict the survival of neuroblastoma patients
Signaling pathways control cell fate decisions that ultimately determine the behavior of cancer cells. Therefore, the dynamics of pathway activity may contain prognostically relevant information different from that contained in the static nature of other types of biomarkers. To investigate this hypo...
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| Vydáno v: | Science signaling Ročník 8; číslo 408; s. ra130 |
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| Hlavní autoři: | , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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22.12.2015
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| ISSN: | 1937-9145, 1937-9145 |
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| Abstract | Signaling pathways control cell fate decisions that ultimately determine the behavior of cancer cells. Therefore, the dynamics of pathway activity may contain prognostically relevant information different from that contained in the static nature of other types of biomarkers. To investigate this hypothesis, we characterized the network that regulated stress signaling by the c-Jun N-terminal kinase (JNK) pathway in neuroblastoma cells. We generated an experimentally calibrated and validated computational model of this network and used the model to extract prognostic information from neuroblastoma patient-specific simulations of JNK activation. Switch-like JNK activation mediates cell death by apoptosis. An inability to initiate switch-like JNK activation in the simulations was significantly associated with poor overall survival for patients with neuroblastoma with or without MYCN amplification, indicating that patient-specific simulations of JNK activation could stratify patients. Furthermore, our analysis demonstrated that extracting information about a signaling pathway to develop a prognostically useful model requires understanding of not only components and disease-associated changes in the abundance or activity of the components but also how those changes affect pathway dynamics. |
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| AbstractList | Signaling pathways control cell fate decisions that ultimately determine the behavior of cancer cells. Therefore, the dynamics of pathway activity may contain prognostically relevant information different from that contained in the static nature of other types of biomarkers. To investigate this hypothesis, we characterized the network that regulated stress signaling by the c-Jun N-terminal kinase (JNK) pathway in neuroblastoma cells. We generated an experimentally calibrated and validated computational model of this network and used the model to extract prognostic information from neuroblastoma patient-specific simulations of JNK activation. Switch-like JNK activation mediates cell death by apoptosis. An inability to initiate switch-like JNK activation in the simulations was significantly associated with poor overall survival for patients with neuroblastoma with or without MYCN amplification, indicating that patient-specific simulations of JNK activation could stratify patients. Furthermore, our analysis demonstrated that extracting information about a signaling pathway to develop a prognostically useful model requires understanding of not only components and disease-associated changes in the abundance or activity of the components but also how those changes affect pathway dynamics.Signaling pathways control cell fate decisions that ultimately determine the behavior of cancer cells. Therefore, the dynamics of pathway activity may contain prognostically relevant information different from that contained in the static nature of other types of biomarkers. To investigate this hypothesis, we characterized the network that regulated stress signaling by the c-Jun N-terminal kinase (JNK) pathway in neuroblastoma cells. We generated an experimentally calibrated and validated computational model of this network and used the model to extract prognostic information from neuroblastoma patient-specific simulations of JNK activation. Switch-like JNK activation mediates cell death by apoptosis. An inability to initiate switch-like JNK activation in the simulations was significantly associated with poor overall survival for patients with neuroblastoma with or without MYCN amplification, indicating that patient-specific simulations of JNK activation could stratify patients. Furthermore, our analysis demonstrated that extracting information about a signaling pathway to develop a prognostically useful model requires understanding of not only components and disease-associated changes in the abundance or activity of the components but also how those changes affect pathway dynamics. Signaling pathways control cell fate decisions that ultimately determine the behavior of cancer cells. Therefore, the dynamics of pathway activity may contain prognostically relevant information different from that contained in the static nature of other types of biomarkers. To investigate this hypothesis, we characterized the network that regulated stress signaling by the c-Jun N-terminal kinase (JNK) pathway in neuroblastoma cells. We generated an experimentally calibrated and validated computational model of this network and used the model to extract prognostic information from neuroblastoma patient-specific simulations of JNK activation. Switch-like JNK activation mediates cell death by apoptosis. An inability to initiate switch-like JNK activation in the simulations was significantly associated with poor overall survival for patients with neuroblastoma with or without MYCN amplification, indicating that patient-specific simulations of JNK activation could stratify patients. Furthermore, our analysis demonstrated that extracting information about a signaling pathway to develop a prognostically useful model requires understanding of not only components and disease-associated changes in the abundance or activity of the components but also how those changes affect pathway dynamics. |
| Author | Munoz, Amaya Garcia Hastings, Jordan F Kennedy, Sean P Kholodenko, Boris N Westermann, Frank Fischer, Matthias Kolch, Walter Halasz, Melinda Rauch, Nora Fey, Dirk Dreidax, Daniel Pilkington, Ruth Croucher, David R |
| Author_xml | – sequence: 1 givenname: Dirk surname: Fey fullname: Fey, Dirk organization: Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland – sequence: 2 givenname: Melinda surname: Halasz fullname: Halasz, Melinda organization: Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland – sequence: 3 givenname: Daniel surname: Dreidax fullname: Dreidax, Daniel organization: Department of Neuroblastoma Genomics (B087), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany – sequence: 4 givenname: Sean P surname: Kennedy fullname: Kennedy, Sean P organization: Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland – sequence: 5 givenname: Jordan F surname: Hastings fullname: Hastings, Jordan F organization: The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia – sequence: 6 givenname: Nora surname: Rauch fullname: Rauch, Nora organization: Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland – sequence: 7 givenname: Amaya Garcia surname: Munoz fullname: Munoz, Amaya Garcia organization: Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland – sequence: 8 givenname: Ruth surname: Pilkington fullname: Pilkington, Ruth organization: Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland – sequence: 9 givenname: Matthias surname: Fischer fullname: Fischer, Matthias organization: Department of Pediatric Hematology and Oncology, University Hospital Cologne, 50937 Cologne, Germany. Center for Molecular Medicine Cologne, University of Cologne, 50931 Cologne, Germany. Max Planck Institute for Metabolism Research, 50931 Cologne, Germany – sequence: 10 givenname: Frank surname: Westermann fullname: Westermann, Frank organization: Department of Neuroblastoma Genomics (B087), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany – sequence: 11 givenname: Walter surname: Kolch fullname: Kolch, Walter organization: Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland. School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland. Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland – sequence: 12 givenname: Boris N surname: Kholodenko fullname: Kholodenko, Boris N email: d.croucher@garvan.org.au, boris.kholodenko@ucd.ie organization: Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland. School of Medicine and Medical Science, University College Dublin, Belfield, Dublin 4, Ireland. Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin 4, Ireland. d.croucher@garvan.org.au boris.kholodenko@ucd.ie – sequence: 13 givenname: David R surname: Croucher fullname: Croucher, David R email: d.croucher@garvan.org.au, boris.kholodenko@ucd.ie organization: Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland. The Kinghorn Cancer Centre, Garvan Institute of Medical Research, Sydney, New South Wales 2010, Australia. St Vincent's Clinical School, University of New South Wales, Sydney, New South Wales 2052, Australia. d.croucher@garvan.org.au boris.kholodenko@ucd.ie |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26696630$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Adolescent Animals Biomarkers, Tumor - metabolism Cell Line, Tumor Child Child, Preschool Disease-Free Survival Female Follow-Up Studies Humans Infant Male MAP Kinase Kinase 4 - metabolism Models, Biological N-Myc Proto-Oncogene Protein Neoplasms, Experimental - metabolism Neuroblastoma - metabolism Neuroblastoma - mortality Nuclear Proteins - metabolism Oncogene Proteins - metabolism Predictive Value of Tests Signal Transduction Survival Rate Zebrafish - metabolism Zebrafish Proteins - metabolism |
| Title | Signaling pathway models as biomarkers: Patient-specific simulations of JNK activity predict the survival of neuroblastoma patients |
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