1. Computational and Systems Biology

Patient-specific Boolean models of signalling networks guide personalised treatments

  1. Arnau Montagud  Is a corresponding author
  2. Jonas Béal
  3. Luis Tobalina
  4. Pauline Traynard
  5. Vigneshwari Subramanian
  6. Bence Szalai
  7. Róbert Alföldi
  8. László Puskás
  9. Alfonso Valencia
  10. Emmanuel Barillot
  11. Julio Saez-Rodriguez
  12. Laurence Calzone  Is a corresponding author
  1. Barcelona Supercomputing Center (BSC), Spain
  2. Institut Curie, PSL Research University, France
  3. RWTH Aachen University, Germany
  4. Semmelweis University, Hungary
  5. Astridbio Technologies Ltd, Hungary
  6. Heidelberg University, Germany
https://doi.org/10.7554/eLife.72626
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Cite this article
  1. Arnau Montagud
  2. Jonas Béal
  3. Luis Tobalina
  4. Pauline Traynard
  5. Vigneshwari Subramanian
  6. Bence Szalai
  7. Róbert Alföldi
  8. László Puskás
  9. Alfonso Valencia
  10. Emmanuel Barillot
  11. Julio Saez-Rodriguez
  12. Laurence Calzone
(2022)
Patient-specific Boolean models of signalling networks guide personalised treatments
eLife 11:e72626.
https://doi.org/10.7554/eLife.72626
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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).

The following previously published data sets were used

Article and author information

Author details

  1. Arnau Montagud

    Barcelona Supercomputing Center (BSC), Barcelona, Spain
    For correspondence
    arnau.montagud@bsc.es
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7696-1241

  2. Jonas Béal

    Institut Curie, PSL Research University, Paris, France
    Competing interests
    No competing interests declared.

  3. Luis Tobalina

    Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany
    Competing interests
    Luis Tobalina, is a full-time employee and shareholder of AstraZeneca..

  4. Pauline Traynard

    Institut Curie, PSL Research University, Paris, France
    Competing interests
    No competing interests declared.

  5. Vigneshwari Subramanian

    Faculty of Medicine, Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Aachen, Germany
    Competing interests
    Vigneshwari Subramanian, is a full-time employee of AstraZeneca..

  6. Bence Szalai

    Department of Physiology, Semmelweis University, Budapest, Hungary
    Competing interests
    No competing interests declared.

  7. Róbert Alföldi

    Astridbio Technologies Ltd, Szeged, Hungary
    Competing interests
    Róbert Alföldi, is CEO of Astridbio Technologies Ltd..

  8. László Puskás

    Astridbio Technologies Ltd, Szeged, Hungary
    Competing interests
    László Puskás, is a scientific advisor of Astridbio Technologies Ltd..

  9. Alfonso Valencia

    Barcelona Supercomputing Center (BSC), Barcelona, Spain
    Competing interests
    Alfonso Valencia, Reviewing editor, eLife.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8937-6789

  10. Emmanuel Barillot

    Institut Curie, PSL Research University, Paris, France
    Competing interests
    No competing interests declared.

  11. Julio Saez-Rodriguez

    Institute of Computational Biomedicine, Heidelberg University, Heidelberg, Germany
    Competing interests
    Julio Saez-Rodriguez, receives funding from GSK and Sanofi and consultant fees from Travere Therapeutics. The other authors declare no conflicts of interest.-.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8552-8976

  12. Laurence Calzone

    Institut Curie, PSL Research University, Paris, France
    For correspondence
    Laurence.Calzone@curie.fr
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7835-1148

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.

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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  1. Arnau Montagud
  2. Jonas Béal
  3. Luis Tobalina
  4. Pauline Traynard
  5. Vigneshwari Subramanian
  6. Bence Szalai
  7. Róbert Alföldi
  8. László Puskás
  9. Alfonso Valencia
  10. Emmanuel Barillot
  11. Julio Saez-Rodriguez
  12. Laurence Calzone
(2022)
Patient-specific Boolean models of signalling networks guide personalised treatments
eLife 11:e72626.
https://doi.org/10.7554/eLife.72626

Share this article

https://doi.org/10.7554/eLife.72626

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