Collection of gene signatures used for overrepresentation analysis.

<p>Collection of gene signatures used for overrepresentation analysis.</p

Association between published gene signatures and the CIG signature in human breast cancer.

<p>Heatmap showing the association between the expressions of several published gene signatures and the CIG signature in a set of approximately 2.500 breast tumour samples. The rows and columns represent the set of analysed gene expression signatures organized into groups related to prognosis, EMT, pathway activation, stem cell biology, breast tumour heterogeneity and stromal involvement. The cells at the intersection between the rows and the columns are colour-coded with red indicating a positive correlation between the respective gene signatures and white indicating a negative correlation. Colour saturation is associated the magnitude the correlation coefficient. The dendrogram is divided in 3 groups (red, blue and green) of strongly associated gene signatures. Underneath the heatmap the Spearman correlation coefficients between the CIG signature and the remaining signatures is represented as well as the ten signatures most strongly associated with the CIG signature.</p

Gene expression data sets used throughout this study.

<p>Gene expression data sets used throughout this study.</p

A Core Invasiveness Gene Signature Reflects Epithelial-to-Mesenchymal Transition but Not Metastatic Potential in Breast Cancer Cell Lines and Tissue Samples

<div><p>Introduction</p><p>Metastases remain the primary cause of cancer-related death. The acquisition of invasive tumour cell behaviour is thought to be a cornerstone of the metastatic cascade. Therefore, gene signatures related to invasiveness could aid in stratifying patients according to their prognostic profile. In the present study we aimed at identifying an invasiveness gene signature and investigated its biological relevance in breast cancer.</p><p>Methods & Results</p><p>We collected a set of published gene signatures related to cell motility and invasion. Using this collection, we identified 16 genes that were represented at a higher frequency than observed by coincidence, hereafter named the core invasiveness gene signature. Principal component analysis showed that these overrepresented genes were able to segregate invasive and non-invasive breast cancer cell lines, outperforming sets of 16 randomly selected genes (all P<0.001). When applied onto additional data sets, the expression of the core invasiveness gene signature was significantly elevated in cell lines forced to undergo epithelial-mesenchymal transition. The link between core invasiveness gene expression and epithelial-mesenchymal transition was also confirmed in a dataset consisting of 2420 human breast cancer samples. Univariate and multivariate Cox regression analysis demonstrated that CIG expression is not associated with a shorter distant metastasis free survival interval (HR = 0.956, 95%C.I. = 0.896–1.019, P = 0.186).</p><p>Discussion</p><p>These data demonstrate that we have identified a set of core invasiveness genes, the expression of which is associated with epithelial-mesenchymal transition in breast cancer cell lines and in human tissue samples. Despite the connection between epithelial-mesenchymal transition and invasive tumour cell behaviour, we were unable to demonstrate a link between the core invasiveness gene signature and enhanced metastatic potential.</p></div

Boxplots showing the relation between CIG expression and EMT.

<p>The top row (A–B) represents a time series of different cell lines treated with EMT-inducing factors. These data demonstrate that CIG expression increases by incubation time. The lower left boxplot (C) indicates that CIG expression is induced by all of the known EMT-inducing factors, but most strongly downstream of GSC. The lower right boxplot (D) indicates that CIG expression does not necessarily correlate with metastatic capability as the cell line with the highest metastatic capability has the lowest CIG expression.</p

Survival analysis.

<p>Survival analysis.</p

Forest plots of meta-analysis using a random effects model of the β-Catenin BRCA, E2F1, p63, PR, PI3K, RAS and VEGF pathway.

<p>The VEGF and BRCA signature was overall not significantly associated with clinical outcome. The other pathways showed significant association with survival after meta-analysis using 6 datasets (Québec, North Carolina, Melbourne, Niigata, Boston A, Boston B). Note the larger 95% Confidence intervals of the Québec dataset due to lower number of patients. Along the X-axis, hazard ratios are indicated by the centre of each square for each dataset. The meta-analysis used a weighted method (shown by the size of the squares/and the percentages indicated for each dataset) based upon confidence interval/number of patients. The 95% confidence interval for each hazard ratio is indicated by the width of the blue lines originating from the squares. The vertical red line shows the overall hazard ratio after meta-analysis, with the width of the diamond as the 95% confidence interval.</p

A Venn diagram is showing combined results of the meta-analysis: β-Catenin, E2F1, p63 and PI3K activation scores showed significant association with survival and were significantly correlated with all three prognostic signatures (WHR/IGS/GGI) after meta-analysis.

<p>PR and RAS activation scores were associated with clinical outcome, but did not consistently correlate with prognostic signatures. *Negative correlation coefficient **borderline significance with clinical outcome.</p

A VEGF-A signature was able to distinguish VEGF treated and naïve HUVEC's in the GSE18913 dataset.

<p>The 2D scatterplot of the principal component analysis showed that the centroïds had an observed Euclidean distance different from the expected Euclidean distance (p<0.0001). The first principal component of each sample is plotted along the X-axis, while the second principal component is plotted along the Y-axis. VEGF-A treated HUVEC samples are represented in blue and VEGF-A untreated samples are represented in red. Centroïds of both conditions are indicated by a black dot. (Panel A). After conversion to an activation score, the VEGF-A treated HUVEC's showed higher VEGF-A activation score in a time dependent relation (PANEL B AND C).</p

Histological heterogeneity of CD31-stained blood vessels in glioblastoma multiforme (a-d) and renal cell carcinoma (e-h).

<p>(a-b) Q_A = 15 vessels per mm², A_A = 1.56%, (c-d) Q_A = 77 vessels per mm², A_A = 3.70%, (e-f) Q_A = 183 vessels per mm², A_A = 13.10%, (g-h) Q_A = 81 vessels per mm², A_A = 6.17%. Low (a, b, e, f) heterogeneous samples showed a uniform distribution of vessel profiles as compared to high (c, d, g, h) heterogeneous samples. In glioblastoma multiforme, hotspots and garlands (arrows) were more easily recognized in heterogeneous than in homogeneous samples. Scale bar represents 500 μm (a, c, e, g) or 100 μm (b, d, f, h)</p