GeneNominator™

Do you want to know which pathway your lead molecule is targeting? Or which molecularly defined subtype of cancer cells are most sensitive to it? GeneNominator™ combines the Oncolines™ responses of your compound with the known expression levels of more than 18,000 genes in the Oncolines™ cell lines. GeneNominator™ is ideally suited to identify the pathways targeted by your molecule, to search for efficacy and resistance mechanisms and to predict responder populations.

GeneNominator™ characteristics

For more information and request for a quotation, please send an e-mail to services@ntrc.nl

Reporting of significant genes

  1. proven cancer-driving genes
  2. clinically actionable genes
  3. genes known to be involved in drug resistance

sensitivity to afatinib – cancer genes

Correlation of the expression of cancer-driving genes and sensitivity to afatinib. The correlation coefficients for genes in grey are considered significant after testing for false positives

Sigma score to filter out false-positive genes

afatinib vs compound library

Sigma scores of the correlations between afatinib response and expression of ten cancer genes. Afatinib is depicted as a red circle, while the library compounds are depicted as blue dots.

Interaction analysis

protein network plot for sensitivity to afatinib

Interaction analysis of genes correlated with afatinib response. This shows that EGFR is central. Genes of which the protein products have interactions are connected by lines.

Gene Set Analysis

Gene Set Analysis network plot for sensitivity to afatinib

Gene Set Analysis of afatinib response. Each colored circle represents a set of genes that are active in a particular pathway, connections represent overlapping genes. In the open circle four gene sets that are related to EGFR pathway activation.

[1] M.G. Rees et al. (2015) Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nature Chem. Biol. 12: 109-116
[2] J. Barretina et al. (2012), The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483: 603-607
[3] J. Uitdehaag et al. (2016), Cell panel profiling reveals conserved therapeutic clusters and differentiates the mechanism of action of different PI3K/mTOR, Aurora kinase and EZH2 inhibitors. J. Mol. Cancer Ther. 15: 3097-3109
[4] D. Sklarczyk et al. (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43: D447-D452
[5] A. Subramanian et al. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci USA 102: 15545-15550