NSC666715 and Its Analogs Inhibit Strand-Displacement Activity of DNA Polymerase β and Potentiate Temozolomide-Induced DNA Damage,Senescence and Apoptosis in Colorectal Cancer Cells

Recently approved chemotherapeutic agents to treat colorectal cancer (CRC) have made some impact; however, there is an urgent need for newer targeted agents and strategies to circumvent CRC growth and metastasis. CRC frequently exhibits natural resistance to chemotherapy and those who do respond initially later acquire drug resistance. A mechanism to potentially sensitize CRC cells is by blocking the DNA polymerase β (Pol-β) activity. Temozolomide (TMZ), an alkylating agent, and other DNA-interacting agents exert DNA damage primarily repaired by a Pol-β-directed base excision repair (BER) pathway. In previous studies, we used structure-based molecular docking of Pol-β and identified a potent small molecule inhibitor (NSC666715). In the present study, we have determined the mechanism by which NSC666715 and its analogs block Fen1-induced strand-displacement activity of Pol-β-directed LP-BER, cause apurinic/apyrimidinic (AP) site accumulation and induce S-phase cell cycle arrest. Induction of S-phase cell cycle arrest leads to senescence and apoptosis of CRC cells through the p53/p21 pathway. Our initial findings also show a 10-fold reduction of the IC₅₀ of TMZ when combined with NSC666715. These results provide a guide for the development of a target-defined strategy for CRC chemotherapy that will be based on the mechanisms of action of NSC666715 and TMZ. This combination strategy can be used as a framework to further reduce the TMZ dosages and resistance in CRC patients.     

SERPINs shelter the endowed migrants in a hostile land

Since metastatic lesions of solid tumors are the major cause of mortality in cancer patients, understanding the molecular mechanisms of metastasis is of paramount importance. Although extensive knowledge has been accumulated regarding the early steps in metastasis—starting with the departure of cancer cells from their primary sites, to their transit through the hematogenous and/or lymphatic systems, and ending with their entrance into the parenchyma of distant organs—it is difficult if not impossible to translate such knowledge into medicine due to the challenge of identifying patients with only primary tumors but otherwise pristine organs. In other words, autopsy studies indicate that a large proportion of patients already harbor dormant, undetectable micrometastases at the time of cancer diagnosis (Hensel et al, 2013 ). Accordingly, stopping tumor cell dissemination is too late for these patients. Therefore, understanding the survival and outgrowth of micrometastases may hold greater promise to combat metastatic disease.     

Bayesian nonparametric inference for panel count data with an informative observation process

In this paper, the panel count data analysis for recurrent events is considered. Such analysis is useful for studying tumor or infection recurrences in both clinical trial and observational studies. A bivariate Gaussian Cox process model is proposed to jointly model the observation process and the recurrent event process. Bayesian nonparametric inference is proposed for simultaneously estimating regression parameters, bivariate frailty effects, and baseline intensity functions. Inference is done through Markov chain Monte Carlo, with fully developed computational techniques. Predictive inference is also discussed under the Bayesian setting. The proposed method is shown to be efficient via simulation studies. A clinical trial dataset on skin cancer patients is analyzed to illustrate the proposed approach.     

Metastable behavior in Markov processes with internal states

A perturbation framework is developed to analyze metastable behavior in stochastic processes with random internal and external states. The process is assumed to be under weak noise conditions, and the case where the deterministic limit is bistable is considered. A general analytical approximation is derived for the stationary probability density and the mean switching time between metastable states, which includes the pre exponential factor. The results are illustrated with a model of gene expression that displays bistable switching. In this model, the external state represents the number of protein molecules produced by a hypothetical gene. Once produced, a protein is eventually degraded. The internal state represents the activated or unactivated state of the gene; in the activated state the gene produces protein more rapidly than the unactivated state. The gene is activated by a dimer of the protein it produces so that the activation rate depends on the current protein level. This is a well studied model, and several model reductions and diffusion approximation methods are available to analyze its behavior. However, it is unclear if these methods accurately approximate long-time metastable behavior (i.e., mean switching time between metastable states of the bistable system). Diffusion approximations are generally known to fail in this regard.