Montagud2022 - Prostate cancer Boolean model
Model Identifier
MODEL2106070001
Short description
Prostate cancer is the second most occurring cancer in men worldwide, and with the advances made with screening for prostate-specific antigen, it has been prone to early diagnosis and over-treatment. 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. The model includes pathways such as androgen receptor, MAPK, Wnt, NFkB, PI3K/AKT, MAPK, mTOR, SHH, the cell cycle, the epithelial-mesenchymal transition (EMT), apoptosis and DNA damage pathways. The final model accounts for 133 nodes and 449 edges. We applied a methodology to personalise this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients, using TCGA and GDSC datasets.
Format
SBML
(L3V1)
Related Publication
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Patient-specific Boolean models of signalling networks guide personalised treatments.
- Montagud A, Béal J, Tobalina L, Traynard P, Subramanian V, Szalai B, Alföldi R, Puskás L, Valencia A, Barillot E, Saez-Rodriguez J, Calzone L
- eLife , 2/ 2022 , Volume 11 , pages: e72626 , DOI: 10.7554/eLife.72626
- Institut Curie, PSL Research University, INSERM, U900, MINES ParisTech, 75005 Paris, France Barcelona Supercomputing Center (BSC), Barcelona, Spain Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC−COMBINE), RWTH Aachen University, 52074 Aachen, Germany Avidin Biotechnology Ltd., 6726 Szeged, Hungary Semmelweis University, Faculty of Medicine, Department of Physiology, Budapest, Hungary
- 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 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.
Contributors
Submitter of the first revision: Montagud A
Submitter of this revision: Montagud A
Modellers: Montagud A
Submitter of this revision: Montagud A
Modellers: Montagud A
Metadata information
hasProperty
Curation status
Non-curated
Modelling approach(es)
Tags
Connected external resources
SBGN view in Newt Editor
| Name | Description | Size | Actions |
|---|---|---|---|
Model files |
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| Montagud2021_Prostate_Cancer.sbml | SBML L3V1 representation of prostate cancer Boolean model | 426.48 KB | Preview | Download |
Additional files |
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| Montagud2021_Prostate_Cancer.bnd | MaBoSS network file of prostate cancer Boolean model | 16.92 KB | Preview | Download |
| Montagud2021_Prostate_Cancer.cfg | MaBoSS configuration file of prostate cancer Boolean model | 7.07 KB | Preview | Download |
| Montagud2021_Prostate_Cancer.png | PNG representation of prostate cancer Boolean model | 5.99 MB | Preview | Download |
| Montagud2021_Prostate_Cancer.svg | SVG representation of prostate cancer Boolean model | 860.17 KB | Preview | Download |
| Montagud2021_Prostate_Cancer.zginml | GINsim compressed file of prostate cancer Boolean model | 12.72 KB | Preview | Download |
- Model originally submitted by : Montagud A
- Submitted: Jun 22, 2022 10:43:32 AM
- Last Modified: Jun 22, 2022 10:43:32 AM
Revisions
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Version: 3
- Submitted on: Jun 22, 2022 10:43:32 AM
- Submitted by: Montagud A
- With comment: Model revised without commit message