Multiple factors influencing the release of hTERT mRNA from pancreatic cancer cell lines in in vitro culture

Potential clinical significance of perioperative levels of mRNA in plasma from patients with cancer of the larynx or hypopharynx

BACKGROUND

The use of plasma as a "liquid biopsy" has gained increasing attention. The purpose of the present study was to evaluate the diagnostic and prognostic utility of the perioperative detection and quantitation of mRNAs encoding human telomerase reverse transcriptase (hTERT) and glyceraldehyde 3-phosphate dehydrogenase (GAPDH) in plasma from patients with cancer of the larynx or hypopharynx.

METHODS

We recruited 47 patients with laryngeal cancer and 2 patients with hypopharyngeal cancer, plus 27 healthy subjects. A blood sample was taken from each patient before and after surgical resection of the tumor. We quantified hTERT mRNA and GAPDH mRNA in plasma by real-time polymerase chain reaction (PCR).

RESULTS

Detection of hTERT mRNA before surgery had diagnostic value (sensitivity, 22%; specificity, 100%). Detection was more frequent in patients with supraglottic tumors than glottic tumors (p = .02) and was related to subsequent recurrence (p = .02). Preoperative levels of hTERT mRNA in plasma were higher in patients with subsequent recurrence (p = .046) and/or metastases (p = .047). The disease-free survival (DFS) and overall survival (OS) of patients with plasma samples positive for hTERT mRNA was poorer than that of patients with negative samples. Mean levels of plasma GAPDH mRNA in untreated patients were higher than in healthy subjects (p < .001).

CONCLUSION

Detection and quantitation of hTERT and GAPDH mRNA in patients' plasma might be clinically significant in cases of laryngeal and hypopharyngeal cancer. © 2017 Wiley Periodicals, Inc. Head Neck 39: 647-655, 2017.

Improving pancreatic cancer diagnosis using circulating tumor cells: prospects for staging and single-cell analysis

Pancreatic cancer (PC) is the fourth most common cause of cancer-related death in the USA, primarily due to late presentation coupled with an aggressive biology. The lack of adequate biomarkers for diagnosis and staging confound clinical decision-making and delay potentially effective therapies. Circulating tumor cells (CTCs) are a promising new biomarker in PC. Preliminary studies have demonstrated their potential clinical utility, and newer CTC isolation platforms have the potential to provide clinicians access to tumor tissue in a reliable, real-time manner. Such a 'liquid biopsy' has been demonstrated in several cancers, and small studies have demonstrated its potential applications in PC. This article reviews the available literature on CTCs as a biomarker in PC and presents the latest innovations in CTC research as well as their potential applications in PC.

Mechanism-based mathematical modeling of combined gemcitabine and birinapant in pancreatic cancer cells

Combination chemotherapy is standard treatment for pancreatic cancer. However, current drugs lack efficacy for most patients, and selection and evaluation of new combination regimens is empirical and time-consuming. The efficacy of gemcitabine, a standard-of-care agent, combined with birinapant, a pro-apoptotic antagonist of Inhibitor of Apoptosis Proteins (IAPs), was investigated in pancreatic cancer cells. PANC-1 cells were treated with vehicle, gemcitabine (6, 10, 20 nM), birinapant (50, 200, 500 nM), and combinations of the two drugs. Temporal changes in cell numbers, cell cycle distribution, and apoptosis were measured. A basic pharmacodynamic (PD) model based on cell numbers, and a mechanism-based PD model integrating all measurements, were developed. The basic PD model indicated that synergistic effects occurred in both cell proliferation and death processes. The mechanism-based model captured key features of drug action: temporary cell cycle arrest in S phase induced by gemcitabine alone, apoptosis induced by birinapant alone, and prolonged cell cycle arrest and enhanced apoptosis induced by the combination. A drug interaction term Ψ was employed in the models to signify interactions of the combination when data were limited. When more experimental information was utilized, Ψ values approaching 1 indicated that specific mechanisms of interactions were captured better. PD modeling identified the potential benefit of combining gemcitabine and birinapant, and characterized the key interaction pathways. An optimal treatment schedule of pretreatment with gemcitabine for 24-48 h was suggested based on model predictions and was verified experimentally. This approach provides a generalizable modeling platform for exploring combinations of cytostatic and cytotoxic agents in cancer cell culture studies.

Nucleic Acids-Based Nanotechnology

Nanobiotechnology is emerging as a valuable field that integrates research from science and technology to create novel nanodevices and nanostructures with various applications in modern nanotechnology. Applications of nanobiotechnology are employed in biomedical and pharmaceutical research, biosensoring, nanofluidics, self-assembly of nanostructures, nanopharmaceutics, molecular computing, and others. It has been proven that nucleic acids are a very suitable medium for self-assembly of diverse nanostructures and catalytic nanodevices for various applications. In this chapter, the authors discuss various applications of nucleic-based nanotechnology. The areas discussed here include building nanostructures using DNA oligonucleodite, self-assembly of integrated RNA-based nanodevices for molecular computing and diagnostics, antibacterial drug discovery, exogenous control of gene expression, and gene silencing.

Computational selection and experimental validation of allosteric ribozymes that sense a specific sequence of human telomerase reverse transcriptase mRNAs as universal anticancer therapy agents

High expression levels of telomerase reverse transcriptase messenger RNAs in differentiated cells can be used as a common marker for cancer development. In this paper, we describe a novel computational method for selection of allosteric ribozymes that sense a specific sequence of human telomerase reverse transcriptase mRNAs. The in silico selection employed is based on computing secondary structures of RNA using the partition function in combination with a random search algorithm. We selected one of the ribozymes for experimental validation. The obtained results demonstrate that the tested ribozyme has a high-speed (∼1.8 per minute) of self-cleavage and is very selective. It can distinguish well between perfectly matching effector and the closest expressed RNA sequence in the human cell with 10 mismatches, with a ∼300-fold difference under physiologically relevant conditions. The presented algorithm is universal since the allosteric ribozymes can be designed to sense any specific RNA or DNA sequence of interest. Such designer ribozymes may be used for monitoring the expression of mRNAs in the cell and for developing novel anticancer gene therapies.