Andrey Alexeyenko

Medical

Epidemiology and

Biostatistics

Network biology and cancer data integration

FunCoup: on-line interactome resource

Andrey Alexeyenko and Erik L.L. Sonnhammer (2009) Global networks of functional coupling in eukaryotes from comprehensive data integration. Genome Research.

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Single molecular markers are often far from perfect. Combinations (signatures) should perform better.

How to select optimal combinations?

×

Severity,Optimal treatment,

Prognosisetc.

Biomarker signatures in the network

Candidate signature in the network

Biomarker candidates

Ready network-based signature

RELAPSE = γ1EIF3S9+ γ2CRHR1 + γ3LYN + … + γNKCNA5

Mutations: distinguishing drivers from passengers

Functional couplingtranscription transcription transcription methylation methylation methylation mutation methylation mutation transcriptionmutation mutation

+ mutated gene

Pathway cross-talk

From genes to pathways:growing confidence

Inositol phosphate metabolism (KEGG)

Glioblastoma (TCGARN, 2008)

Analysis of cancer-specific wiringPathway network of

normal vs. tumor tissues

Edges connect pathways given a higher (N>9; p0<0.01; pFDR<0.20) number of gene-gene links (pfc>0.5) between them (seen as edge labels). Known pathways (circles) are classified as:

•signaling,•metabolic,•cancer,•other disease.

Blue lines: evidence from mRNA co-expression under normal conditions + ALL human & mouse data.

Red lines: evidence from mRNA co-expression in expO tumor samples + ALL human data + mouse PPI.

Node size: number of pathway

members in the network.

Edge opacity: p0.

Edge thickness: number of gene-gene links.

Arrow of time: network prospective

Alexeyenko et al. Zebrafish transcriptome under dioxin treatment. PLoS One. In press

Acknowledgements:

Erik Sonnhammer’s bioinformatics group

KICancer

http://FunCoup.sbc.su.se

http://FunCoup.sbc.su.se