Prognostic power of the stem cell distance-based risk predictor.

<p><b>(A-B)</b> Classifier performance of novel and published classifiers in lung adenocarcinoma <b>(A)</b> and breast cancer <b>(B)</b> validation cohorts. All risk scores were quantile normalized, so that the risk scores of all predictors had an IQR of 1 and a mean of 0. This approach allowed for a comparison of predictors by risk score hazard ratios. Hazard ratios and 95% confidence intervals of normalized risk scores for the stem cell distance-based predictor (SC) as well as for competing predictors are shown. A hazard ratio significantly larger than 1 indicates that patients with a high predicted risk had a poor outcome. Numbers on the right of the each row of plots represent the CPEs. Shown on the top are the results of a model using only gene expression information; at the bottom, we show the results of a multivariate model in which clinical covariates were incorporated. <b>(A)</b> Classifiers A-N are the published results of the mostly gene signature-based Director's Challenge predictors [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173589#pone.0173589.ref014" target="_blank">14</a>]. <b>(B)</b> The AURKA prediction is obtained by a univariate model using only the expression of the <i>AURKA</i> gene as covariate. The model GENE70 represents the prediction of the van't Veer gene signature [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173589#pone.0173589.ref002" target="_blank">2</a>] comprising 70 genes. The GGI prediction represents the Gene expression Grade Index [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173589#pone.0173589.ref003" target="_blank">3</a>]. Tumor size was not available in the VDX cohort. <b>(C)</b> Comparison in high grade, serous ovarian cancer with the survival signature published by the TCGA project. See the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173589#sec017" target="_blank">Supporting Information</a> on details of the datasets and methodology.</p

Predicted activation/inhibition states of transcription factors based on our hESC gene signature.

<p>The table shows the Ingenuity analysis for prediction of top 5 activated or inhibited transcription factors (TFs). The p-value of the overlap is calculated using Fisher’s exact test and indicates the overlap between the signature genes and genes regulated by that TF.</p

Clustering of the lung adenocarcinoma validation dataset (MSK cohort).

<p>Patient samples are clustered based on their distances of gene expression profiles from stem cells (see hierarchical cluster analysis section of the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173589#sec002" target="_blank">Methods</a>). Samples marked with a bold, black label indicate deceased patients. Plotted below the dendrogram are lymph node involvement (node-negative versus node-positive) and the presence of KRAS, EGFR and/or TP53 mutations. Stars indicate the patients with positive lymph nodes test results or corresponding gene mutations. Furthermore, we show the patient risk scores, obtained by a Cox proportional hazards model using the distance to hESC as covariate. This model was fitted in the UM/HLM training set. Dotted grey lines indicate the risk score tertiles in the training cohort. A subset of patients had FDG-PET imaging prior to treatment. The SUV_max describes the maximal measured glucose uptake of the tumors, and is plotted below the risk score. Risk score and SUV_max were highly correlated (ρ = -0.613, P < 0.001). Finally, the size of the tumor was plotted for the 63 patients for whom this information was available (ρ = -0.209, P = 0.1). Curves of risk score and tumor size were smoothed with a 3-point simple moving average (SMA).</p

Survival analysis of the stem cell distance-based risk predictor.

<p><b>(A)</b> Dependence of the prediction concordance of the choice of the stem cell dataset and the variance probe set filter, the two main parameters of the model, in all tuning datasets. The concordance is given on the y-axis as the concordance probability estimates (CPEs), with a value of 0.5 indicating a random model, and a value of 1.0 a perfect model. (<b>B</b>) Kaplan-Meier plots for all analyzed cancer types, visualizing survival differences among three risk groups. Samples of all validation cohorts were trisected into three equally sized groups based on their expression distances to stem cells. The high-risk group represents the samples close to the stem cells, and the low-risk group represents the samples farthest from stem cells. Validation cohorts were then combined for each cancer type (see Figs B-G in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173589#pone.0173589.s001" target="_blank">S1 File</a> for Kaplan-Meier plots for all cohorts, including tuning datasets, separately). Note that the distance in gene expression of a sample from that of stem cells is a continuous measure; the subdivision of samples was chosen only to visualize the differences in survival between these groups. For all cancer types, data from validation cohorts (Table A in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173589#pone.0173589.s001" target="_blank">S1 File</a>) is used for the analysis. The stem cell signatures used are PB_CD34 for AML and DLBCL, hESC for breast cancer and lung cancer, hMSC for colorectal cancer, liposarcoma and ovarian cancer (Table M in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173589#pone.0173589.s001" target="_blank">S1 File</a>). Hazard ratios and 95% confidence intervals of normalized risk scores are shown. P values were calculated with the log-rank test.</p

Additional file 1: of Comprehensive detection of germline variants by MSK-IMPACT, a clinical diagnostic platform for solid tumor molecular oncology and concurrent cancer predisposition testing

Supplementary Methods and Tables S1-S6. (DOCX 93 kb

Assessment of sustainability of forest management practices on the operational level in northwestern Russia – a case study from the Republic of Karelia

The main aim of the article is to assess the consequences of newly proposed legislative initiative on introducing intensive forest management practices in Russia. Implementation of norms and its effect on sustainable forest management practices have been analysed in this study on one enterprise operating in the Republic of Karelia. This meant modelling of forest growth, clear cuts and regeneration within 100 km radius from the mill for two alternative management scenarios with fixed demand of wood based on current norms and decreasing harvesting age to half from the current. Wood demand of the enterprise, structure and accessibility of forest resources, i.e. forest road infrastructure were taken into account in the analysis. Both forest management scenarios decreased the total growing stock significantly, and therefore considered as non-sustainable practices. In addition, forest age structure was more uneven for both scenarios at the end of the simulation period. Comparison of two alternative management practices showed that the implementation of intensive forest management in Russia requires new norms that would be based on principles of sustainable forest management.201