Performance of the five methods based on the primary biliary cirrhosis of the liver data.

<p>Performance of the five methods based on the primary biliary cirrhosis of the liver data.</p

Survival Prediction Based on Compound Covariate under Cox Proportional Hazard Models

<div><p>Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models. We demonstrate via simulations and real data analysis that the compound covariate method generally competes well with ridge regression and Lasso methods, both already well-studied methods for predicting survival outcomes with a large number of covariates. Furthermore, we develop a refinement of the compound covariate method by incorporating likelihood information from multivariate Cox models. The new proposal is an adaptive method that borrows information contained in both the univariate and multivariate Cox regression estimators. We show that the new proposal has a theoretical justification from a statistical large sample theory and is naturally interpreted as a shrinkage-type estimator, a popular class of estimators in statistical literature. Two datasets, the primary biliary cirrhosis of the liver data and the non-small-cell lung cancer data, are used for illustration. The proposed method is implemented in R package “compound.Cox” available in CRAN at <a href="http://cran.r-project.org/">http://cran.r-project.org/</a>.</p> </div

The proposed shrinkage scheme applied for the compound covariate method.

<p>The proposed shrinkage scheme applied for the compound covariate method.</p

Simulation results under less sparse cases with <i>p</i> = 100 and <i>n</i> = 100 based on 50 replications.

<p>NOTE: For Scenario 1, each informative covariate is correlated with <i>s</i> non-informative covariates. For Scenario 2, the covariates for the right panel have two gene pathways and those for the left panel have one gene pathway. In each setting, <i>q</i> is the number of informative covariates (covariates with non-zero coefficients).</p

Kaplan-Meier curves for the 62 patients in the lung cancer data of Chen et al. [<b>6</b>].

<p>Good (blue), medium (black), and poor (red) groups are determined by the tertile of the PI’s in the test dataset.</p

Performance of the five methods based on the non-small-cell lung cancer data of Chen et al. [6].

<p>NOTE: Smaller values of the LR-test (log₁₀ P-value), Cox-test (log₁₀ P-value) and Deviance, and larger values of the <i>c</i>-index correspond to more accurate prediction performance.</p>*<p>If good and poor groups are separated by the median PI in the training set, the LR-test has P-value = 0.034 (log₁₀ P-value = −1.47) with <i>n</i> = 28 in the good and <i>n</i> = 34 in the poor groups (the same result as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047627#pone-0047627-g001" target="_blank">Figure 1C</a> of Chen et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047627#pone.0047627-Chen1" target="_blank">[6]</a>).</p><p>The methods: <b>CC</b>  =  compound covariate (using 97 or 16 genes), <b>CS</b>  =  compound shrinkage, <b>Ridge</b>  =  ridge regression, and <b>Lasso</b>  =  Lasso analyses are compared.</p

The <i>c</i>-index assessments of the four methods under varying number of top genes (<i>p</i> = 16 ∼ 124 ) in the lung cancer data of Chen et al. [6], where “top genes” refer to most strongly associated genes passing a univariate pre-filter for inclusion in the linear predictor (PI).

<p>The <i>c</i>-index assessments of the four methods under varying number of top genes (<i>p</i> = 16 ∼ 124 ) in the lung cancer data of Chen et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0047627#pone.0047627-Chen1" target="_blank">[6]</a>, where “top genes” refer to most strongly associated genes passing a univariate pre-filter for inclusion in the linear predictor (PI).</p

JAG1 enhances migration and invasion in different lung cancer cell lines and promotes metastasis <i>in vivo</i>.

<p>(A) JAG1 promotes cell migration and invasiveness <i>in vitro</i>. Left, JAG1 was transiently overexpressed in A549 and H226 cells. JAG1 mRNA was measured by real-time quantitative RT-PCR and normalized to TBP in triplicate. Middle and Right, migration ability and invasiveness of cells with transient JAG1 overexpression and control cells were evaluated by migration and matrigel invasion assays. *: <i>p</i> < 0.05, compared with mock. Data were represented as mean ± SD in triplicate. (B) JAG1 promotes metastasis <i>in vivo</i>. Left, number of nodules in SCID mice. The Mock CL1-0 cells and JAG1-overexpressing cell lines were inoculated into severe combined immunodeficiency (SCID) mice by tail vein injection. After 10 weeks, mice were sacrificed. Number of tumors derived from mock and JAG1 transfectants was measured under the dissection microscope. Data were represented as mean ± SD (n = 10 in mock control group and n = 13 in JAG1 transfectant group). *: <i>p</i> < 0.05, compared with mock control. Right, appearances of the lungs from mice injected with CL1-0 mock and JAG1 transfectants and the representative H&E staining sections of the lungs from mice. Red arrow heads indicate metastatic tumor nodules in lungs. Black arrows indicate the micrometastasis of lung cancer cells.</p

Proteolytic status of NOTCH family or downstream molecules changes in JAG1 manipulated lung cancer cell lines.

<p>Cell was transiently overexpressed JAG1 by expression plasmid or silenced JAG1 by siRNA strategy followed by immunoblotted with NICD1, NICD2, NICD3, and NICD4 antibodies or real-time quantitative PCR for NOTCH downstream molecules mRNA level analysis. (A) JAG1 overexpressed in CL1-0 cells followed by Western blot analysis for NICDs proteolytic status or real-time quantitative PCR for NOTCH downstream molecules mRNA level analysis. (B) JAG1 overexpressed in H1299 and H838 cell lines followed by Western blot analysis for NICDs proteolytic status. (C) JAG1 silenced in HOP62 and H322M cell lines followed by Western blot analysis for NICDs proteolytic status. DAPT is a gamma-secretase inhibitor and used for NOTCH signaling control. UD, undetermined due to undetectable expression level; *, <i>p</i> <0.05 compared with mock control. Data were represented as mean ± SD (n = 3 per group).</p

JAG1 acts as an invasion-promoting gene.

<p>(A) Identification of JAG1 in an invasion model of lung cancer cells. JAG1 is up-regulated in highly invasive lung cancer cells. Left, JAG1 mRNA level was assessed by oligonucleotide microarray analysis. Middle, JAG1 mRNA level was measured by real-time quantitative RT-PCR. The experiments were performed in triplicate and data were represented as mean ± SD. Right, JAG1 protein expression was evaluated by Western blot. β-actin was used as an internal control. (B) JAG1 promotes cell migration and invasiveness <i>in vitro</i>. Left, JAG1 was stably overexpressed in CL1-0 cells. JAG1 mRNA was measured by real-time quantitative RT-PCR in triplicate. Data were represented as mean ± SD. Middle and Right, migration and invasion ability of CL1-0 cells with constitutive JAG1 expression and mock control cells were evaluated by migration assay and matrigel invasion assay. Data presented as mean ± SD of three experiments. *: <i>p</i> < 0.05 as compared to the mock group. (C) Knockdown of JAG1 inhibits cell migration and invasiveness <i>in vitro</i>. Left, JAG1 mRNA was knocked down by siRNAs in CL1-5 cells assayed by real-time quantitative RT-PCR in triplicate. Two different siRNAs (siJAG1-1 and siJAG1-2) were used to silence JAG1. NC, negative control. Middle and Right, migration and invasion capability of JAG1-silencing transfectants were analyzed by migration and matrigel invasion assays. Data shown as mean ± SD; *: <i>p</i> < 0.05 compared with the negative control.</p