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PurposeTo identify stage I lung adenocarcinoma patients with a poor prognosis who will benefit from adjuvant therapy.Patients and MethodsWhole gene expression profiles were obtained at 19 time points over a 48-hour time course from human primary lung epithelial cells that were stimulated with epidermal growth factor (EGF) in the presence or absence of a clinically used EGF receptor tyrosine kinase (RTK)-specific inhibitor, gefitinib. The data were subjected to a mathematical simulation using the State Space Model (SSM). “Gefitinib-sensitive” genes, the expressional dynamics of which were altered by addition of gefitinib, were identified. A risk scoring model was constructed to classify high- or low-risk patients based on expression signatures of 139 gefitinib-sensitive genes in lung cancer using a training data set of 253 lung adenocarcinomas of North American cohort. The predictive ability of the risk scoring model was examined in independent cohorts of surgical specimens of lung cancer.ResultsThe risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (P = 0.029) and 3.26 (P = 0.0072), respectively. It also enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (P = 0.001) and 3.72 (P = 0.0049), respectively.ConclusionThe set of 139 gefitinib-sensitive genes includes many genes known to be involved in biological aspects of cancer phenotypes, but not known to be involved in EGF signaling. The present result strongly re-emphasizes that EGF signaling status in cancer cells underlies an aggressive phenotype of cancer cells, which is useful for the selection of early-stage lung adenocarcinoma patients with a poor prognosis.Trial RegistrationThe Gene Expression Omnibus (GEO) GSE31210

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We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.

[1] The source regions of region-0 (R0), region-1 (R1), and region-2 (R2) field-aligned currents (FACs) were statistically determined using DMSP particle precipitation and magnetometer data. Each FAC sheet originates from more than one region in the magnetosphere, depending on the latitude and the magnetic local time (MLT). R2 originates mostly from the central plasma sheet (CPS) and boundary plasma sheet (BPS) in the morning and from the CPS, BPS, and inner magnetosphere in the afternoon, all of which are on closed field lines. Near noon, some R2 may originate from the low-latitude boundary layer (LLBL), which is located near the magnetopause and can be open or closed. R1 mostly maps to the BPS, hence on closed field lines, in morning and afternoon, but near noon, it maps mostly to the LLBL. The LLBL source region can be found more frequently in the dawn-noon sector than in the noon-dusk sector. On the other hand, R0 is located mostly on open field lines and is associated mostly with mantle precipitation. However, the mantle precipitation has a dependency on the polarity of R0. Within up-flowing R0, sometimes an upward field-aligned electric field, which accelerates electron downward and retards ion precipitation, modifies mantle distribution to look more like those of polar rain or BPS. This electric field has the opposite polarity to the background electric field that maintains charge-quasi-neutrality and that limits some solar wind electrons from entering the magnetosphere in the mantle and polar rain regions. Implications to current generation mechanisms are discussed.