Mouse models of colorectal cancer.

Colorectal cancer is one of the most common malignancies in the world. Many mouse models have been developed to evaluate features of colorectal cancer in humans. These can be grouped into genetically-engineered, chemically-induced, and inoculated models. However, none recapitulates all of the characteristics of human colorectal cancer. It is critical to use a specific mouse model to address a particular research question. Here, we review commonly used mouse models for human colorectal cancer

SAFS: A Deep Feature Selection Approach for Precision Medicine

In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prioritizing risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach is to use deep learning to identify significant risk factors affecting left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk. The results show that our feature learning and representation approach leads to better results in comparison with others

SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researchers to develop new approaches to subgroup detection and analysis, which is an effective strategy to personalize treatment. In this study, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variable

Short-hairpin RNA library: identification of therapeutic partners for gefitinib-resistant non-small cell lung cancer.

Somatic mutations of the epidermal growth factor receptor often cause resistance to therapy with tyrosine kinase inhibitor in non-small cell lung cancer (NSCLC). In this study, we aimed to identify partner drugs and pathways that can induce cell death in combination with gefitinib in NSCLC cells. We undertook a genome-wide RNAi screen to identify synthetic lethality with gefitinib in tyrosine kinase inhibitor resistant cells. The screening data were utilized in different approaches. Firstly, we identified PRKCSH as a candidate gene, silencing of which induces apoptosis of NSCLC cells treated with gefitinib. Next, in an in silico gene signature pathway analysis of shRNA library data, a strong correlation of genes involved in the CD27 signaling cascade was observed. We showed that the combination of dasatinib (NF-κB pathway inhibitor) with gefitinib synergistically inhibited the growth of NSCLC cells. Lastly, utilizing the Connectivity Map, thioridazine was identified as a top pharmaceutical perturbagen. In our experiments, it synergized with gefitinib to reduce p-Akt levels and to induce apoptosis in NSCLC cells. Taken together, a pooled short-hairpin library screen identified several potential pathways and drugs that can be therapeutic targets for gefitinib resistant NSCLC

Topological to magnetically ordered quantum phase transition in antiferromagnetic spin ladders with long-range interactions

We study a generalized quantum spin ladder with staggered long range interactions that decay as a power-law with exponent $\alpha$. Using large scale quantum Monte Carlo (QMC) and density matrix renormalization group (DMRG) simulations, we show that this model undergoes a transition from a rung-dimer phase characterized by a non-local string order parameter, to a symmetry broken N\'eel phase. We find evidence that the transition is second order.In the magnetically ordered phase, the spectrum exhibits gapless modes, while excitations in the gapped phase are well described in terms of triplons -- bound states of spinons across the legs. We obtain the momentum resolved spin dynamic structure factor numerically and find a well defined triplon band evolves into a gapless magnon dispersion across the transition. We further discuss the possibility of deconfined criticality in this model.Comment: 16 pages, 7 figure