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Updated: Feb 4, 2026
Implantation and Monitoring by PET/CT of an Orthotopic Model of Human Pleural Mesothelioma in Athymic Mice
Published on: December 21, 2019
p53 modeling as a route to mesothelioma patients stratification and novel therapeutic identification
Kun Tian1, Emyr Bakker2, Michelle Hussain3
1School of Environment and Life Sciences, University of Salford, Salford, UK.
Background:
Malignant pleural mesothelioma (MPM) is an orphan disease that is difficult to treat using traditional chemotherapy, an approach which has been effective in other types of cancer. Most chemotherapeutics cause DNA damage leading to cell death. Recent discoveries have highlighted a potential role for the p53 tumor suppressor in this disease. Given the pivotal role of p53 in the DNA damage response, here we investigated the predictive power of the p53 interactome model for MPM patients' stratification.
Methods:
We used bioinformatics approaches including omics type analysis of data from MPM cells and from MPM patients in order to predict which pathways are crucial for patients' survival. Analysis of the PKT206 model of the p53 network was validated by microarrays from the Mero-14 MPM cell line and RNA-seq data from 71 MPM patients, whilst statistical analysis was used to identify the deregulated pathways and predict therapeutic schemes by linking the affected pathway with the patients' clinical state.
Results:
In silico simulations demonstrated successful predictions ranging from 52 to 85% depending on the drug, algorithm or sample used for validation. Clinical outcomes of individual patients stratified in three groups and simulation comparisons identified 30 genes that correlated with survival. In patients carrying wild-type p53 either treated or not treated with chemotherapy, FEN1 and MMP2 exhibited the highest inverse correlation, whereas in untreated patients bearing mutated p53, SIAH1 negatively correlated with survival. Numerous repositioned and experimental drugs targeting FEN1 and MMP2 were identified and selected drugs tested. Epinephrine and myricetin, which target FEN1, have shown cytotoxic effect on Mero-14 cells whereas marimastat and batimastat, which target MMP2 demonstrated a modest but significant inhibitory effect on MPM cell migration. Finally, 8 genes displayed correlation with disease stage, which may have diagnostic implications.
Conclusions:
Clinical decisions related to MPM personalized therapy based on individual patients' genetic profile and previous chemotherapeutic treatment could be reached using computational tools and the predictions reported in this study upon further testing in animal models.
Insights
This study uses a p53 interactome model to predict outcomes for malignant pleural mesothelioma (MPM) patients. Computational tools identified survival-associated genes and potential drug targets for personalized cancer therapy.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Malignant pleural mesothelioma (MPM) is a rare cancer with limited treatment options.
- Traditional chemotherapy effectiveness is variable in MPM.
- The p53 tumor suppressor's role in MPM is increasingly recognized.
Purpose of the Study:
- To investigate the predictive power of the p53 interactome model for MPM patient stratification.
- To identify crucial pathways for MPM patient survival using bioinformatics.
- To link deregulated pathways with clinical states for predicting therapeutic schemes.
Main Methods:
- Utilized omics data analysis from MPM cells and patients.
- Validated the p53 network model (PKT206) using microarrays and RNA-seq data.
- Employed statistical analysis to identify deregulated pathways and correlate them with clinical outcomes.
Main Results:
- In silico simulations achieved 52-85% prediction accuracy for patient stratification.
- Identified 30 genes correlating with survival, including FEN1, MMP2, and SIAH1 based on p53 status.
- Discovered repositioned drugs targeting FEN1 and MMP2, with some showing cytotoxic or inhibitory effects in MPM cells.
Conclusions:
- Computational tools and the p53 interactome model can aid in personalized MPM therapy decisions.
- Further testing in animal models is recommended to validate findings.
- 8 genes correlated with disease stage, suggesting potential diagnostic applications.
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