Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

• DOI: 10.15252/msb.20188664
• Publication Date: 2020-02-19T13:57:58Z
• Authors: Federica Eduati; Patricia Jaaks; Jessica Wappler; Thorsten Cramer; Christoph A Merten; Mathew J Garnett; Julio Saez‐Rodriguez
• Keywords: Patient-Specific Modeling; drug combinations; Medicine (General); QH301-705.5; Cell Survival; Biopsy; INHIBITION; Antineoplastic Agents; SDG 3 – Goede gezondheid en welzijn; MECHANISMS; patient‐specific models; Genetic Heterogeneity; Mice; R5-920; SDG 3 - Good Health and Well-being; Cell Line, Tumor; patient-specific models; Animals; Humans; Biology (General); Precision Medicine; MUTANT P53; Articles; Microfluidic Analytical Techniques; logic modeling; Xenograft Model Antitumor Assays; signaling pathways; NETWORKS; APOPTOSIS; 3. Good health; Pancreatic Neoplasms; Logistic Models; precision oncology; SURVIVAL; GROWTH; Female; Drug Screening Assays, Antitumor; Phosphatidylinositol 3-Kinase; Proto-Oncogene Proteins c-akt; RESISTANCE; Signal Transduction

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.