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

Abstract

BackgroundMalignant 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.MethodsWe 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.ResultsIn 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.ConclusionsClinical 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.Electronic supplementary materialThe online version of this article (10.1186/s12967-018-1650-0) contains supplementary material, which is available to authorized users.

Sirtuin Family Members Selectively Regulate Autophagy in Osteosarcoma and Mesothelioma Cells in Response to Cellular Stress

“…The effects of silencing of sirtuin family members on cell viability were also tested in the REN cell line which differs from Mero-14 in terms of expression of crucial genes involved in cancer cell viability and response to cytotoxic drugs including p53 (44, 45). Substantial and statistically significant downregulation of cell viability was observed in REN cells treated with the studied drugs, however silencing of SIRT1, SIRT3, and SIRT5 did not exert significant effects (Figure 1C) although downward trend was observed in SIRT3 and SIRT5 silenced cells.…”

Systematic Analysis of Aberrant Biochemical Networks and Potential Drug Vulnerabilities Induced by Tumor Suppressor Loss in Malignant Pleural Mesothelioma

“…Notably, synthetic lethal targets with p53 inactivation have been investigated [ 49 , 50 , 51 ]. In particular, MDM2, a nuclear E3 ubiquitin ligase that binds and targets p53 for proteasomal degradation, is detected in 21.3% of clinical MPM samples, and its expression is significantly associated with poor survival [ 52 ].…”

Drug Repositioning and Subgroup Discovery for Precision Medicine Implementation in Triple Negative Breast Cancer

“…Different molecular features have been used to stratify patients and reposition drugs. These molecular features range from using a single gene to using heterogeneous molecular data types; for example, using P53 to stratify patients based on their P53 status and using the RACK1 status to determine personalized treatment [ 7 , 8 ]. Moreover, heterogeneous molecular data was used for cancer subtyping and the repositioning of drugs after ranking genes based on the Gene Ontology (GO) pathways analysis [ 9 ].…”