The emerging role of microRNAs in resistance to lung cancer treatments

One of the major challenges in the treatment of lung cancer is the development of drug resistance. This represents a major obstacle in the treatment of patients, limiting the efficacy of both conventional chemotherapy and biological therapies. Deciphering the mechanisms of resistance is critical to further understanding the multifactorial pathways involved, and in developing more specific targeted treatments. To date, numerous studies have reported the potential role of microRNAs (miRNAs) in resistance to various cancer treatments. MicroRNAs are a family of small non-coding RNAs that regulate gene expression by sequence-specific targeting of mRNAs causing translational repression or mRNA degradation. More than 1200 validated human miRNAs have been identified to date. While as little as one miRNA can regulate hundreds of targets, a single target can also be affected by multiple miRNAs. Evidence suggests that dysregulation of specific miRNAs may be involved in the acquisition of resistance to a number of cancer treatments, thereby modulating the sensitivity of cancer cells to such therapies. Therefore, targeting miRNAs may be an attractive strategy for developing novel and more effective individualized therapies, improving drug efficiency, and for predicting patient response to different treatments. In this review, we provide an overview on the role of miRNAs in resistance to current lung cancer therapies and novel biological agents.

Smoothing a Discrete Hazard Function for the Number of Patients Colonized with Methicillin-Resistance Staphylococcus Aureus in an Intensive Care Unit

A new method for estimating the time to colonization of Methicillin-resistant Staphylococcus Aureus (MRSA) patients is developed in this paper. The time to colonization of MRSA is modelled using a Bayesian smoothing approach for the hazard function. There are two prior models discussed in this paper: the first difference prior and the second difference prior. The second difference prior model gives smoother estimates of the hazard functions and, when applied to data from an intensive care unit (ICU), clearly shows increasing hazard up to day 13, then a decreasing hazard. The results clearly demonstrate that the hazard is not constant and provide a useful quantification of the effect of length of stay on the risk of MRSA colonization which provides useful insight.