ArticleeLife2022
Patient-specific Boolean models of signalling networks guide personalised treatments.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Succession-diagram-based Markov chains reveal the attractor landscape of asynchronous Boolean networks.NPJ systems biology and applications · 2026Article
- Targeting HSP90 in cancer: advances in the development of inhibitors, mechanisms of action, and therapeutic applications.Molecular cancer · 2026Review
- Data driven network inference and longitudinal transcriptomics unveil dynamic regulation in Chronic Lymphocytic Leukaemia models.NPJ systems biology and applications · 2026Article
- Integrating dynamic modeling of signaling pathways with subject-specific transcriptomic data to assess breast cancer risk.PloS one · 2026Article
- PatientProfiler: building patient-specific signaling models from proteogenomic data.Molecular systems biology · 2025Article
- Review
- Constraint based modeling of drug induced metabolic changes in a cancer cell line.NPJ systems biology and applications · 2025Article
- Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming.NPJ systems biology and applications · 2025Article
- Systems biology successes and areas for opportunity in prostate cancer.Endocrine-related cancer · 2025Review
- Mapping the attractor landscape of Boolean networks with biobalm.Bioinformatics (Oxford, England) · 2025Article
- Systems Biology of the Cancer Cell.Annual review of biomedical engineering · 2025Review
- CanSeer: a translational methodology for developing personalized cancer models and therapeutics.Scientific reports · 2025Article
- Article
- Challenges and opportunities for digital twins in precision medicine from a complex systems perspective.NPJ digital medicine · 2025Review
- Improving neuroendocrine tumor treatments with mathematical modeling: lessons from other endocrine cancers.Endocrine oncology (Bristol, England) · 2025Review
- pastboon: an R package to simulate parameterized stochastic Boolean networks.Bioinformatics advances · 2025Article
- Immune digital twins for complex human pathologies: applications, limitations, and challenges.NPJ systems biology and applications · 2024Review
- Proteome-wide copy-number estimation from transcriptomics.Molecular systems biology · 2024Article
- Cohort-specific boolean models highlight different regulatory modules during Parkinson's disease progression.iScience · 2024Article
- Maboss for HPC environments: implementations of the continuous time Boolean model simulator for large CPU clusters and GPU accelerators.BMC bioinformatics · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.