Telmisartan induces melanoma cell apoptosis and synergizes with vemurafenib in vitro by altering cell bioenergetics

Objective: Despite recent advancements in targeted therapy and immunotherapies, prognosis for metastatic melanoma patients remains extremely poor. Development of resistance to previously effective treatments presents a serious challenge and new approaches for melanoma treatment are urgently needed. The objective of this study was to examine the effects of telmisartan, an AGTR1 inhibitor and a partial agonist of PPAR gamma, on melanoma cells as a potential agent for repurposing in melanoma treatment. Methods: Expression of AGTR1 and PPAR gamma mRNA in melanoma patient tumor samples was examined in publicly available datasets and confirmed in melanoma cell lines by qRT-PCR. A panel of melanoma cell lines was tested in viability, apoptosis and metabolic assays in presence of telmisartan by flow cytometry and immunocytochemistry. A cytotoxic effect of combinations of telmisartan and targeted therapy vemurafenib was examined using the Chou-Talalay combination index method. Results: Both AGTR1 and PPAR gamma mRNA were expressed in melanoma patient tumor samples and decreased compared to the expression in the healthy skin. In vitro, we found that telmisartan decreased melanoma cell viability by inducing cell apoptosis. Increased glucose uptake, but not utilization, in the presence of telmisartan caused the fission of mitochondria and release of reactive oxygen species. Telmisartan altered the cell bioenergetics, thereby synergizing with vemurafenib in vitro, and even sensitized vemurafenib-resistant cells to the treatment. Conclusions: Given that the effective doses of telmisartan examined in our study can be administered to patients and that telmisartan is a widely used and safe antihypertensive drug, our findings provide the scientific rationale for testing its efficacy in treatment of melanoma progression

Comparison of Different Machine Learning Models in Prediction of Postirradiation Recurrence in Prostate Carcinoma Patients

After primary treatment of localized prostate carcinoma (PC), up to a third of patients have disease recurrence. Different predictive models have already been used either for initial stratification of PC patients or to predict disease recurrence. Recently, artificial intelligence has been introduced in the diagnosis and management of PC with a potential to revolutionize this field. The aim of this study was to analyze machine learning (ML) classifiers in order to predict disease progression in the moment of prostate-specific antigen (PSA) elevation during follow-up. The study cohort consisted of 109 PC patients treated with external beam radiotherapy alone or in combination with androgen deprivation therapy. We developed and evaluated the performance of two ML algorithms based on artificial neural networks (ANN) and naive Bayes (NB). Of all patients, 72.5% was randomly selected for a training set while the remaining patients were used for testing of the models. The presence/absence of disease progression was defined as the output variable. The input variables for models were conducted from the univariate analysis preformed among two groups of patients in the training set. They included two pretreatment variables (UICC stage and Gleason's score risk group) and five posttreatment variables (nadir PSA, time to nadir PSA, PSA doubling time, PSA velocity, and PSA in the moment of disease reevaluation). The area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and predictive accuracy was calculated to test the models' performance. The results showed that specificity was similar for both models, while NB achieved better sensitivity then ANN (100.0% versus 94.4%). The ANN showed an accuracy of 93.3%, and the matching for NB model was 96.7%. In this study, ML classifiers have shown potential for application in routine clinical practice during follow-up when disease progression was suspected

Iscador Qu inhibits doxorubicin-induced senescence of MCF7 cells (vol 7, 3763, 2017)

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper

Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells

Imidazoline I1 receptor signaling is associated with pathways that regulate cell viability leading to varied cell-type specific phenotypes. We demonstrated that the antihypertensive drug rilmenidine, a selective imidazoline I1 receptor agonist, modulates proliferation and stimulates the proapoptotic protein Bax thus inducing the perturbation of the mitochondrial pathway and apoptosis in human leukemic K562 cells. Rilmenidine acts through a mechanism which involves deactivation of Ras/MAP kinases ERK, p38 and JNK. Moreover, rilmenidine renders K562 cells, which are particularly resistant to chemotherapeutic agents, susceptible to the DNA damaging drug doxorubicin. The rilmenidine co-treatment with doxorubicin reverses G2/M arrest and triggers apoptotic response to DNA damage. Our data offer new insights into the pathways associated with imidazoline I1 receptor activation in K562 cells suggesting rilmenidine as a valuable tool to deepen our understanding of imidazoline I1 receptor signaling in hematologic malignancies and to search for medicinally active agents

Pharmacogenetics in cancer therapy-8 years of experience at the Institute for Oncology and Radiology of Serbia

Purpose: Pharmacogenetics is a study of possible mechanism by which an individual's response to drugs is genetically determined by variations in their DNA sequence. The aim of pharmacogenetics is to identify the optimal drug and dose for each individual based on their genetic constitution, i.e. to individualize drug treatment. This leads to achieving the maximal therapeutic response for each patient, while reducing adverse side effects of therapy and the cost of treatment. A centralized pharmacogenetics service was formed at the Institute for Oncology and Radiology of Serbia (IORS) with the aim to provide a personalized approach to cancer treatment of Serbian patients. Methods: Analyses of KRAS mutations in metastatic colorectal cancer, EGFR mutations in advanced non-small cell lung cancer, CYP2D6 polymorphism in breast cancer, DPD polymorphism in colorectal cancer and MTHFR polymorphism in osteosarcoma have been performed by real time polymerase chain reaction (PCR) and PCR-restriction fragment length polymorphism (PCR-RFLP). Results: Mutation testing analyses were successful for 1694 KRAS samples and 1821 EGFR samples, while polymorphism testing was successful for 9 CYP2D6 samples, 65 DPD samples and 35 MTHFR samples. Conclusions: Pharmacogenetic methods presented in this paper provide cancer patients in Serbia the best possible choice of treatment at the moment