p53 modeling as a route to mesothelioma patients stratification and novel therapeutic identification

Our plain-English summary of p53 modeling as a route to mesothelioma patients stratification and novel therapeutic identification, by K. Tian, E. Bakker, M. Hussain, et al (Journal of Translational Medicine, 2018).

The study

• Authors: K. Tian, E. Bakker, M. Hussain, A. Guazzelli, H. Alhebshi, P. Meysami, C. Demonacos, J.M. Schwartz, L. Mutti, M. Krstic-Demonacos
• Journal: Journal of Translational Medicine
• Published: 2018;16(1):282
• Cited by: 12 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

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.

In plain English

This laboratory and computer-modeling study tested whether a computer model of the p53 gene network, called PKT206, could predict gene activity in malignant pleural mesothelioma. It did not examine asbestos exposure. The authors compared the model's predictions with gene-activity data from a mesothelioma cell line, Mero-14, treated with the drugs etoposide or gemcitabine. They also compared them with published tumor RNA data from 71 patients. In the cell line, between 71.36% and 85.34% of predictions were correct. In the patient data, between 52 and 84% were correct. Among patients with unmutated (wild-type) p53 who had chemotherapy, model scores for FEN1 had the strongest negative correlation with survival (−0.582). In untreated wild-type patients it was MMP2 (−0.521), and in untreated patients with mutated p53 it was SIAH1 (−0.702). Eight genes differed between stage 3 and stage 4 disease. In the cell line, epinephrine and myricetin (drugs predicted to target FEN1) reduced cell survival. Marimastat and batimastat (drugs that target MMP2) modestly but significantly slowed cell migration. The authors called the model a promising predictive tool. They noted that patient numbers were not high and described the drug tests as partial validation.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Computer modeling (p53 network model PKT206) checked against laboratory gene-expression data from a mesothelioma cell line and against published patient RNA-sequencing data, plus laboratory drug tests on the cell line
• Who was studied: The human malignant pleural mesothelioma cell line Mero-14, and published RNA-sequencing data from 71 mesothelioma patients originally reported by Bueno et al. Patients were grouped by p53 status (wild-type or mutant) and by whether they had received chemotherapy.
• Exposure: No asbestos exposure was studied. In the laboratory, Mero-14 cells were treated with the DNA-damaging drugs etoposide and gemcitabine. Later experiments tested epinephrine, myricetin, marimastat and batimastat.
• Main results:
  • In Mero-14 cells treated with gemcitabine, the LSSA method gave 149 correct predictions out of 199 genes (74.87%).
  • In Mero-14 cells treated with etoposide, the LSSA method gave 142 correct predictions out of 199 genes (71.36%).
  • With the STSFA method in gemcitabine-treated Mero-14 cells, 163 of 191 predictions were correct (85.34%).
  • With the STSFA method in etoposide-treated Mero-14 cells, 82.2% of predictions were correct.
  • In the patient RNA data, correct predictions ranged between 52 and 84%, depending on the comparison scenario.
  • In chemotherapy-treated patients with wild-type p53, FEN1 had the highest negative Pearson correlation with survival (−0.582).
  • In untreated patients with wild-type p53, MMP2 had the highest negative correlation with survival (−0.521).
  • In untreated patients with mutated p53, SIAH1 had the highest negative correlation with survival (−0.702).
  • In univariate Cox regression of the survival-correlated genes, FEN1 had a hazard ratio of 1.41 (95% CI 1.05 to 1.91), p equal to 0.024.
  • In the same Cox regression, SIAH1 had a hazard ratio of 1.15 (95% CI 0.974 to 1.37), p equal to 0.0986.
  • In the Cox regression, only CKB had a negative coefficient (associated with better survival). The other 29 genes had positive coefficients (associated with poorer survival).
  • Expression of 8 genes differed significantly between stage 3 and stage 4 disease.
  • Epinephrine, predicted to target FEN1, inhibited Mero-14 cell survival at high concentrations.
  • Myricetin, predicted to target FEN1, inhibited Mero-14 cell viability by up to 80% after 72 hours at a 100 µM dose.
  • Marimastat, which targets MMP2, caused cytotoxicity in Mero-14 cells of up to 60%.
  • Batimastat, which also targets MMP2, had a marginal effect on Mero-14 cell survival.
  • Both batimastat and marimastat caused modest but significant inhibition of wound closure (a measure of cell migration) in Mero-14 cells, with batimastat showing more potent effects.
• The authors' conclusion: The authors concluded that the PKT206 model is a promising predictive tool for developing stratified anti-cancer therapies.
• Limitations the authors state:
  • The number of patients analyzed was not high, and the groups contained different numbers of patients.
  • No p53 knockout scenario was modeled for the cell line, because no p53 null cell lines were used.
  • The laboratory drug results provided only partial validation of the computer predictions.

Topics

The National Library of Medicine indexes this study under subjects our site covers: Lung cancer, Mesothelioma.

Medical terms in this study, explained in our Lexicon: Mesothelioma, Pleural Mesothelioma, Chemotherapy.

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