Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies
| Autor: | Christoph A. Merten, Patricia Jaaks, Mathew J. Garnett, Federica Eduati, Julio Saez-Rodriguez, Jessica Wappler, Thorsten Cramer |
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| Přispěvatelé: | Computational Biology, ICMS Core, EAISI Health |
| Předmět: |
Patient-Specific Modeling
Medicine (General) Response to therapy Biopsy Model parameters SDG 3 – Goede gezondheid en welzijn patient‐specific models Mice 0302 clinical medicine Tumor stage patient-specific models Precision Medicine Biology (General) ComputingMilieux_MISCELLANEOUS Cancer 0303 health sciences Applied Mathematics Articles Microfluidic Analytical Techniques Patient specific 3. Good health Computational Theory and Mathematics Dynamic models 030220 oncology & carcinogenesis precision oncology ComputingMethodologies_DOCUMENTANDTEXTPROCESSING Female Signal transduction Corrigendum General Agricultural and Biological Sciences Signal Transduction Information Systems drug combinations Cell Survival QH301-705.5 ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Antineoplastic Agents Computational biology Biology Article General Biochemistry Genetics and Molecular Biology Genetic Heterogeneity 03 medical and health sciences R5-920 SDG 3 - Good Health and Well-being Cell Line Tumor Pancreatic cancer medicine Animals Humans 030304 developmental biology General Immunology and Microbiology Computational Biology Precision medicine medicine.disease logic modeling Xenograft Model Antitumor Assays signaling pathways Pancreatic Neoplasms Logistic Models Drug Screening Assays Antitumor Phosphatidylinositol 3-Kinase Proto-Oncogene Proteins c-akt 030217 neurology & neurosurgery |
| Zdroj: | Molecular Systems Biology, Vol 16, Iss 6, Pp n/a-n/a (2020) Molecular Systems Biology Molecular Systems Biology, 16(2):e8664. EMBO Press Mol Syst Biol Molecular Systems Biology, Vol 16, Iss 2, Pp n/a-n/a (2020) |
| ISSN: | 1744-4292 |
| Popis: | Mechanistic modeling of signaling pathways mediating patient‐specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available that precludes the generation of large‐scale perturbation data. Here, we present an approach that couples ex vivo high‐throughput screenings of cancer biopsies using microfluidics with logic‐based modeling to generate patient‐specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K‐Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest that our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine. Patient‐specific signaling models are built from microfludic‐based perturbation screenings on cells from tumour biopsies and pathway knowledge. Combination therapies predicted by the models are validated experimentally. |
| Databáze: | OpenAIRE |
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