Unknown

Dataset Information

0

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


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.

SUBMITTER: Eduati F 

PROVIDER: S-EPMC7029724 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Eduati Federica F   Jaaks Patricia P   Wappler Jessica J   Cramer Thorsten T   Merten Christoph A CA   Garnett Mathew J MJ   Saez-Rodriguez Julio J  

Molecular systems biology 20200201 2


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 mi  ...[more]

Similar Datasets

| S-EPMC4818716 | biostudies-literature
| S-EPMC7980192 | biostudies-literature
| EGAS00001000655 | EGA
2024-03-01 | GSE213504 | GEO
| S-EPMC4768263 | biostudies-literature
| S-EPMC10068437 | biostudies-literature
| S-EPMC10907580 | biostudies-literature
| S-EPMC7054294 | biostudies-literature
| S-EPMC8604339 | biostudies-literature
| S-EPMC5120272 | biostudies-literature