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. 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 actionable points of interventions in one cell-line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.

PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

“…Then, with PhysiBoSS 2.0, we can perform multiscale simulations and find synergistic drug combinations for a cell line of interest. We hereby provide an example study on the LNCaP prostate cell line (Montagud et al , 2022) with six different drugs (Supplementary Information).…”

“…The second model is a prostate cancer model (Montagud et al , 2022). This model was used to identify druggable targets in prostate cancer that are personalised to patients and cell lines.…”

“…The model has 133 total nodes, 9 inputs and 6 outputs nodes. This model is available as a Supplementary material of the corresponding paper (Montagud et al , 2022).…”

In silicoInvestigation of Molecular Networks Linking Gastrointestinal Diseases, Malnutrition, and Sarcopenia

“…Moreover, in Boolean models, the computational time increases only proportionally to the complexity of the network, allowing efficient high-throughput analyses. Recently, Montagud et al created personalized Boolean models from clinical data of cancer patients to predict drug targets and validated their results with cell line-specific models 30 . The development of a Boolean model simulating the influence of nutrition and metabolism on sarcopenia may therefore prove useful in assessing the effects of various physiological and pathological conditions.…”

Discrete modeling for integration and analysis of large-scale signaling networks

“…However, this prevents any dynamical analysis on the models. A second strategy is to extract specific, non-exhaustive information from the datasources, as done in the Omnipath framework [15], which uses conversion rules to extract only binary interactions from a subset of the BioPAX databases, which can then be manually enriched with generic Boolean rules [16]. All of these initiatives show that combining BioPAX data sources to analyze their combined dynamics is currently out of reach.…”