Comparing cancer models using gene expression of genetic pathways - PowerPoint PPT Presentation

Comparing cancer models using gene expression of genetic pathways and other gene lists Tauno Metsalu Data mining research seminar 17.12.2012

Introduction • Before testing a drug on human, it has to be tested and effective on a simpler model • Most potential drugs lack the expected efficacy on human, even when working properly on a model • It is important to understand the exact aspects how models could be improved

Drug development pipeline

Model systems

Cell line as a model system • Cheaper • Stays alive for longer • Less variable • Differs from real cancer! • How much and in which sense does it differ?

Collected expression data

Microarrays (1)

Microarrays (2)

Gene lists • We use two different ways to define gene lists • Pathways – groups of genes with a specific biological function (KEGG database) • Interaction partners – we take all interaction partners of a specified gene (FunCoup database)

Similarity • Absolute similarity – Spearman’s correlation converted into 0-1 scale • Relative similarity – relative rank of absolute similarity among all gene lists (used just for better intuition)

Data integration InParanoid ortholog mapping mouse samples human samples mouse human pathway pathway genes genes green: pairwise data comparisons used

Analysis • We used two different genes as basis: EGFR and MTOR • They have enough data for both gene list definitions (pathways and interaction partners)

EGFR

MTOR

Summary • We proposed a pipeline to compare cancer models • It is possible to integrate data from different platforms and species (human and mouse) • MTOR is better retained in cancer models than EGFR

Acknowledgements • Prof Jaak Vilo • Priit Adler • Raivo Kolde