Patient-specific Boolean models of signalling networks guide personalised treatments.

ABSTRACT: Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionab

SUBMITTER: Montagud A 

PROVIDER: S-EPMC9018074 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature