Patient‐specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies. Issue 2 (19th February 2020)

• Record Type: Journal Article
• Authors: Eduati, Federica; Jaaks, Patricia; Wappler, Jessica; Cramer, Thorsten; Merten, Christoph A; Garnett, Mathew J; Saez‐Rodriguez, Julio
• Is Part Of: Molecular systems biology. Volume 16:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-19
• Subjects: drug combinations -- logic modeling -- patient‐specific models -- precision oncology -- signaling pathways; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.20188664
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: 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. Synopsis: 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. Microfluidic‐based high‐throughput perturbation screenings performed ex vivo on pancreatic cancer patients' resections and on cell lines provide functional insights into signaling processes. Training a generic network built from prior knowledge of the underlying signaling pathways to this data provides patient specific logic models of the pathways. The models can be used to uncover mechanistic differences among patients, and to simulate the effect of novel perturbations. Simulation of drug combinations generated personalized candidate therapies that were validated experimentally. Abstract : 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.