Marija Krstic-Demonacos

Mutations in the p53 tumor suppressor are found in over 50% of cancers. p53 function is controlled through posttranslational modifications and cofactor interactions. In this study, we investigated the posttranslationally modified p53, including p53 acetylated at lysine 382 (K382), p53 phosphorylated at serine 46 (S46), and the p53 cofactor TTC5/STRAP (Tetratricopeptide repeat domain 5/ Stress-responsive activator of p300-TTC5) proteins in lung cancer. Immunohistochemical (IHC) analysis of lung cancer tissues from 250 patients was carried out and the results were correlated with clinicopathological features. Significant associations between total or modified p53 with a higher grade of the tumour and shorter overall survival (OS) probability were detected, suggesting that mutant and/or modified p53 acts as an oncoprotein in these patients. Acetylated at K382 p53 was predominantly nuclear in some samples and cytoplasmic in others. The localization of the K382 acetylated p53 was significantly associated with the gender and grade of the disease. The TTC5 protein levels were significantly associated with the grade, tumor size, and node involvement in a complex manner. SIRT1 expression was evaluated in 50 lung cancer patients and significant positive correlation was found with p53 S46 intensity, whereas negative TTC5 staining was associated with SIRT1 expression. Furthermore, p53 protein levels showed positive association with poor OS, whereas TTC5 protein levels showed positive association with better OS outcome. Overall, our results indicate that an analysis of p53 modified versions together with TTC5 expression, upon testing on a larger sample size of patients, could serve as useful prognostic factors or drug targets for lung cancer treatment.

Telomerase activity has been regarded as a critical step in cellular immortalization and carcinogenesis and because of this, regulation of telomerase represents an attractive target for anti-tumor specific therapeutics. Recently, one avenue of cancer research focuses on antisense strategy to target the oncogenes or cancer driver genes, in a sequence specific fashion to down-regulate the expression of the target gene. The protein catalytic subunit, human telomerase reverse transcriptase (hTERT) and the template RNA component (hTERC) are essential for telomerase function, thus theoretically, inhibition of telomerase activity can be achieved by interfering with either the gene expression of hTERT or the hTERC of the telomerase enzymatic complex. The present study showed that phosphorothioate antisense oligonucleotide (sASO)-nuclear localization signal (NLS) peptide conjugates targeting hTERC could inhibit telomerase activity very efficiently at 5 μM concentration but less efficiently at 1 μM concentration. On the other hand, siRNA targeting hTERT mRNA could strongly suppress hTERT expression at 200 nM concentration. It was also revealed that siRNA targeting hTERT could induce telomere attrition and then irreversible arrest of proliferation of cancer cells.

The class III NAD⁺ dependent deacetylases-sirtuins (SIRTs) link transcriptional regulation to DNA damage response and reactive oxygen species generation thereby modulating a wide range of cellular signaling pathways. Here, the contribution of SIRT1, SIRT3, and SIRT5 in the regulation of cellular fate through autophagy was investigated under diverse types of stress. The effects of sirtuins' silencing on cell survival and autophagy was followed in human osteosarcoma and mesothelioma cells exposed to DNA damage and oxidative stress. Our results suggest that the mitochondrial sirtuins SIRT3 and 5 are pro-proliferative under certain cellular stress conditions and this effect correlates with their role as positive regulators of autophagy. SIRT1 has more complex role which is cell type specific and can affect autophagy in both positive and negative ways. The mitochondrial sirtuins (SIRT3 and SIRT5) affect both early and late stages of autophagy, whereas SIRT1 acts mostly at later stages of the autophagic process. Investigation of potential crosstalk between SIRT1, SIRT3, and SIRT5 revealed several feedback loops and a significant role of SIRT5 in regulating SIRT3 and SIRT1. Results presented here support the notion that sirtuin family members play important as well as differential roles in the regulation of autophagy in osteosarcoma vs. mesothelioma cells exposed to DNA damage and oxidative stress, and this can be exploited in increasing the response of cancer cells to chemotherapy.

Malignant mesothelioma (MMe) is a cancer with poor prognosis and resistance to standard treatments. Recent reports have highlighted the role of the BRCA1 associated protein 1 gene (BAP1) in the development of MMe. In this study, the chemosensitivity of human mesothelioma cell lines carrying BAP1 wild-type (WT), mutant and silenced was analysed. The BAP1 mutant cells were significantly less sensitive than BAP1 WT cell lines to the clinically relevant drug gemcitabine. Silencing of BAP1 significantly increased resistance of MMe cells to gemcitabine. Cell cycle analysis suggested that gemcitabine induced Sub-G1 phase accumulation of the BAP1 WT cells and increased in the S-phase in both BAP1 WT and mutant cells. Analysis of the role of BAP1 in apoptosis suggested that gemcitabine induced early apoptosis in both BAP1 WT and BAP1 mutant cells but with a much higher degree in the WT cells. Effects on the population of cells in late apoptosis, which can mark necrosis and necroptosis, could not be seen in the mutant cells, highlighting the possibility that BAP1 plays a role in several types of cell death. Significantly decreased DNA damage in the form of double-strand breaks was observed in gemcitabine-treated BAP1 mutant cells, compared to BAP1 WT cells under the same conditions. After BAP1 silencing, a significant decrease in DNA damage in the form of double-strand breaks was observed compared to cells transfected with scramble siRNA. Taken together, the results presented in this manuscript shed light on the role of BAP1 in the response of MMe cells to gemcitabine treatment and in particular in the control of the DNA damage response, therefore providing a potential route for more efficient MMe therapy.

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. 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. 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. 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.

Glucocorticoid hormones (GCs) are used to treat a variety of diseases because of their potent anti-inflammatory effect and their ability to induce apoptosis in lymphoid malignancies through the glucocorticoid receptor (GR). Despite ongoing research, high glucocorticoid efficacy and widespread usage in medicine, resistance, disease relapse and toxicity remain factors that need addressing. Understanding the mechanisms of glucocorticoid signalling and how resistance may arise is highly important towards improving therapy. To gain insight into this we undertook a systems biology approach, aiming to generate a Boolean model of the glucocorticoid receptor protein interaction network that encapsulates functional relationships between the GR, its target genes or genes that target GR, and the interactions between the genes that interact with the GR. This model named GEB052 consists of 52 nodes representing genes or proteins, the model input (GC) and model outputs (cell death and inflammation), connected by 241 logical interactions of activation or inhibition. 323 changes in the relationships between model constituents following in silico knockouts were uncovered, and steady-state analysis followed by cell-based microarray genome-wide model validation led to an average of 57% correct predictions, which was taken further by assessment of model predictions against patient microarray data. Lastly, semi-quantitative model analysis via microarray data superimposed onto the model with a score flow algorithm has also been performed, which demonstrated significantly higher correct prediction ratios (average of 80%), and the model has been assessed as a predictive clinical tool using published patient microarray data. In summary we present an in silico simulation of the glucocorticoid receptor interaction network, linked to downstream biological processes that can be analysed to uncover relationships between GR and its interactants. Ultimately the model provides a platform for future development both by directing laboratory research and allowing for incorporation of further components, encapsulating more interactions/genes involved in glucocorticoid receptor signalling. Here we present modelling of the glucocorticoid receptor (GR) signalling network. The GR is the effector for a class of drugs known as corticosteroids, which are widely used in medicine for their anti-inflammatory effects and ability to induce apoptosis in leukaemic cells. However, side effects, treatment-related toxicity and glucocorticoid resistance remain and therefore increased understanding of the glucocorticoid receptor mechanism of action may improve therapeutic outcomes. The GEB052 model presented herein has been used to generate predictions for how the network is altered between glucocorticoid-sensitive and glucocorticoid-resistant scenarios, and these predictions have been verified using published gene expression data from established cell lines (for both qualitative and semi-quantitative analysis). The model has also been preliminarily assessed as a predictive clinical tool by correlating model predictions with clinical outcomes of thirteen leukaemia patients. Thus, the GEB052 model demonstrates successful modelling to understand GR function. GEB052 provides accurate predictions and has indicated potential routes through which glucocorticoid resistance may arise. The work presented herein thus demonstrates a proof-of-principle of this modelling approach to furthering GR research, and provides insight into potential mechanisms of corticosteroids resistance.

Glucocorticoids (GCs) and topoisomerase II inhibitors are used to treat acute lymphoblastic leukaemia (ALL) as they induce death in lymphoid cells through the glucocorticoid receptor (GR) and p53 respectively. Mechanisms underlying ALL cell death and the contribution of the bone marrow microenvironment to drug response/resistance remain unclear. The role of the microenvironment and the identification of chemoresistance determinants were studied by transcriptomic analysis in ALL cells treated with Dexamethasone (Dex), and Etoposide (Etop) grown in the presence or absence of bone marrow conditioned media (CM). The necroptotic (RIPK1) and the apoptotic (caspase-8/3) markers were downregulated by CM, whereas the inhibitory effects of chemotherapy on the autophagy marker Beclin-1 (BECN1) were reduced suggesting CM exerts cytoprotective effects. GCs upregulated the RIPK1 ubiquitinating factor BIRC3 (cIAP2), in GC-sensitive (CEM-C7-14) but not in resistant (CEM-C1-15) cells. In addition, CM selectively affected GR phosphorylation in a site and cell-specific manner. GR is recruited to RIPK1, BECN1 and BIRC3 promoters in the sensitive but not in the resistant cells with phosphorylated GR forms being generally less recruited in the presence of hormone. FACS analysis and caspase-8 assays demonstrated that CM promoted a pro-survival trend. High molecular weight proteins reacting with the RIPK1 antibody were modified upon incubation with the BIRC3 inhibitor AT406 in CEM-C7-14 cells suggesting that they represent ubiquitinated forms of RIPK1. Our data suggest that there is a correlation between microenvironment-induced ALL proliferation and altered response to chemotherapy.