EMT Network-based Lung Cancer Prognosis Prediction

Network-based feature selection methods on omics data have been developed in recent years. Their performance gain, however, is shown to be affected by the datasets, networks, and evaluation metrics. The reproducibility and robustness of biomarkers await to be improved. In this endeavor, one of the major challenges is the curse of dimensionality. To mitigate this issue, we proposed the Phenotype Relevant Network-based Feature Selection (PRNFS) framework. By employing a much smaller but phenotype relevant network, we could avoid irrelevant information and select robust molecular signatures. The advantages of PRNFS were demonstrated with the application of lung cancer prognosis prediction. Specifically, we constructed epithelial mesenchymal transition (EMT) networks and employed them for feature selection. We mapped multiple types of omics data on it alternatively to select single-omics signatures and further integrated them into multi-omics signatures. Then we introduced a multiplex network-based feature selection method to directly select multi-omics signatures. Both single-omics and multi-omics EMT signatures were evaluated on TCGA data as well as an independent multi-omics dataset. The results showed that EMT signatures achieved significant performance gain, although EMT networks covered less than 2.5% of the original data dimensions. Frequently selected EMT features achieved average AUC values of 0.83 on TCGA data. Employing EMT signatures on the independent dataset stratified the patients into significantly different prognostic groups. Multi-omics features showed superior performance over single-omics features on both TCGA data and the independent data. Additionally, we tested the performance of a few relational and non-relational databases for storing and retrieving omics data. Since biological data have large volume, high velocity, and wide varieties, it is necessary to have database systems that meet the need of integrative omics data analysis. Based on the results, we provided a few advices on building scalable omics data infrastructures

Coupled Reversible and Irreversible Bistable Switches Underlying TGF-\beta-induced Epithelial to Mesenchymal Transition

Epithelial to mesenchymal transition (EMT) plays important roles in embryonic development, tissue regeneration and cancer metastasis. While several feedback loops have been shown to regulate EMT, it remains elusive how they coordinately modulate EMT response to TGF-\beta treatment. We construct a mathematical model for the core regulatory network controlling TGF-\beta-induced EMT. Through deterministic analyses and stochastic simulations, we show that EMT is a sequential two-step program that an epithelial cell first transits to partial EMT then to the mesenchymal state, depending on the strength and duration of TGF-\beta stimulation. Mechanistically the system is governed by coupled reversible and irreversible bistable switches. The SNAIL1/miR-34 double negative feedback loop is responsible for the reversible switch and regulates the initiation of EMT, while the ZEB/miR-200 feedback loop is accountable for the irreversible switch and controls the establishment of the mesenchymal state. Furthermore, an autocrine TGF-\beta/miR-200 feedback loop makes the second switch irreversible, modulating the maintenance of EMT. Such coupled bistable switches are robust to parameter variation and molecular noise. We provide a mechanistic explanation on multiple experimental observations. The model makes several explicit predictions on hysteretic dynamic behaviors, system response to pulsed stimulation and various perturbations, which can be straightforwardly tested.Comment: 32 pages, 8 figures, accepted by Biophysical Journa

Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data

We discuss a cancer hallmark network framework for modelling genome-sequencing data to predict cancer clonal evolution and associated clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for a cancer patient, as well as cancer risks for a healthy individual are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized management and prevention of cancer.Comment: 5 figs, related papers, visit lab homepage: http://www.cancer-systemsbiology.org, Seminar in Cancer Biology, 201

Physiology-Aware Rural Ambulance Routing

In emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to low-speed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work. In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, 201