Embryo-Engineered Nonhuman Primate Models: Progress and Gap to Translational Medicine

Animal models of human diseases are vital in better understanding the mechanism of pathogenesis and essential for evaluating and validating potential therapeutic interventions. As close relatives of humans, nonhuman primates (NHPs) play an increasingly indispensable role in advancing translational medicine research. In this review, we summarized the progress of NHP models generated by embryo engineering, analyzed their unique advantages in mimicking clinical patients, and discussed the remaining gap between basic research of NHP models to translational medicine.

PowerTools: A web based user-friendly tool for future translational study design

BackgroundBiomarker identification is one of the major goals of functional genomics and translational medicine research. The advent of NGS lead to a constant and exponential increase of large datasets that have the potential of providing the means for novel biomarker identification for the early diagnosis of complex diseases and/or for patient/disease stratification. Once a biomarker has been identified, a validation study is necessary to assess its value. A study design that considers its appropriateness and cost-effectiveness is paramount. The calculation of a sample size is a challenge that needs to be addressed.MethodsThe workflow of our tool, termed PowerTools, is based on based on the method described by Blaise et al., (2016) [1]. For a given number of input data sets, a simulation step with the random multivariate normal distribution including correlation is considered. As a next step, datasets of variable sizes are generated by random selection of samples. Based on the outcome variable, either classification or regression modes are available. For binary classification ANOVA and linear regression test can be performed and then performance matrices can be evaluated.ResultsWe developed an online framework to streamline power calculations to aid future omics study designs within a translational medicine research context. We make our code freely available on GitHub [2] and we have provided a web interface that can be accessed at online [3].ConclusionsPowerTools offers the potential for designing appropriate and cost-effective subsequent omics study designs.

Network Analysis of Cancer-focused Association Network Reveals Distinct Network Association Patterns

Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other biological entities (eg, genes and drugs) in cancer network, however, are usually scattered in biomedical publications. Systematic analyses of these cancer-specific associations can help highlight the hidden associations between different cancer types and related genes/drugs. In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug–disease–gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts. Eight significant NMs were identified and considered as the backbone of the cancer association network. Each NM corresponds to specific biological meanings. We demonstrated that such approaches will facilitate the formulization of novel cancer research hypotheses, which is critical for translational medicine research and personalized medicine in cancer.