Andrey Alexeyenko M edical E pidemiology and B iostatistics Network biology and cancer data integration.

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Andrey Alexeyenko M edical E pidemiology and B iostatistics 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.

Single molecular markers are often far from perfect. Combinations (signatures) should perform better. How to select optimal combinations? × Severity, Optimal treatment, Prognosis etc. Biomarker signatures in the network

Candidate signature in the network Biomarker candidates

Ready network-based signature RELAPSE = γ 1 EIF3S9 + γ 2 CRHR1 + γ 3 LYN + … + γ N KCNA5

Mutations: distinguishing drivers from passengers Functional coupling transcription  transcription transcription  methylation methylation  methylation mutation  methylation mutation  transcription mutation  mutation + mutated gene

Pathway cross-talk

From genes to pathways: growing confidence Inositol phosphate metabolism (KEGG) Glioblastoma (TCGARN, 2008)

Analysis of cancer-specific wiring Pathway network of normal vs. tumor tissues Edges connect pathways given a higher ( N>9; p ) 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 s i z e : number of pathway members in the network. Edge opacity : p 0. Edge t hi ck ne ss : 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