Patient-specific Boolean models of signalling networks guide personalised treatments
- Barcelona Supercomputing Center (BSC), Spain
- Institut Curie, PSL Research University, France
- RWTH Aachen University, Germany
- Semmelweis University, Hungary
- Astridbio Technologies Ltd, Hungary
- Heidelberg University, Germany
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.
Data availability
Code (and processed data) to reproduce the analyses can be found in a dedicated GitHub (https://github.com/ArnauMontagud/PROFILE_v2), some of the code used in the work can be found in other GitHub repositories (https://github.com/sysbio-curie/PROFILE; https://github.com/sysbio-curie/Logical_modelling_pipeline).The model built can be accessed on the SuppFile1 and on BioModels and GINsim model repositories (https://www.ebi.ac.uk/biomodels/MODEL2106070001; http://ginsim.org/model/signalling-prostate-cancer).
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GDSC 1 and 2Genomics of Drug Sensitivity in Cancer.
Article and author information
Author details
Julio Saez-Rodriguez
Funding
European Commission (H2020-PHC-668858)
- Arnau Montagud
- Jonas Béal
- Luis Tobalina
- Pauline Traynard
- Vigneshwari Subramanian
- Bence Szalai
- Róbert Alföldi
- László Puskás
- Emmanuel Barillot
- Julio Saez-Rodriguez
- Laurence Calzone
European Commission (H2020-ICT-825070)
- Arnau Montagud
- Alfonso Valencia
European Commission (H2020-ICT-951773)
- Arnau Montagud
- Alfonso Valencia
- Emmanuel Barillot
- Julio Saez-Rodriguez
- Laurence Calzone
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Jennifer Flegg, The University of Melbourne, Australia
Version history
- Preprint posted: July 29, 2021 (view preprint)
- Received: July 30, 2021
- Accepted: January 27, 2022
- Accepted Manuscript published: February 15, 2022 (version 1)
- Version of Record published: April 19, 2022 (version 2)
Copyright
© 2022, Montagud et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Patient-specific Boolean models of signalling networks guide personalised treatments
eLife 11:e72626.
https://doi.org/10.7554/eLife.72626
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