Increased signaling entropy in cancer requires the scale-free property of protein interaction networks.
One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is unclear how these perturbations give rise to the observed increased plasticity. Elucidating such systems-level principles is key for an improved understanding of cancer. Recently, it has been shown that signaling entropy, an overall measure of signaling pathway promiscuity, and computable from integrating a sample's gene expression profile with a protein interaction network, correlates with phenotypic plasticity and is increased in cancer compared to normal tissue. Here we develop a computational framework for studying the effects of network perturbations on signaling entropy. We demonstrate that the increased signaling entropy of cancer is driven by two factors: (i) the scale-free (or near scale-free) topology of the interaction network, and (ii) a subtle positive correlation between differential gene expression and node connectivity. Indeed, we show that if protein interaction networks were random graphs, described by Poisson degree distributions, that cancer would generally not exhibit an increased signaling entropy. In summary, this work exposes a deep connection between cancer, signaling entropy and interaction network topology.
1] Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, United Kingdom [2] CAS Key Lab for Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China.
Andrew E
AE
Teschendorff
1] Statistical Cancer Genomics, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, United Kingdom [2] Centre for Mathematics and Physics in the Life Sciences and Experimental Biology, University College London, London WC1E6BT United Kingdom [3] Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United Kingdom.
Christopher R S
CR
Banerji
Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United Kingdom.
Simone
S
Severini
Department of Mathematics, King's College London, London WC2R 2LS, UK.
Reimer
R
Kuehn
Department of Mathematics, King's College London, London WC2R 2LS, UK.
Peter
P
Sollich
D000465:Algorithms; D030541:Databases, Genetic; D019277:Entropy; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D016012:Poisson Distribution; D060066:Protein Interaction Maps / Q000235:genetics*; D015398:Signal Transduction / Q000235:genetics*; D059467:Transcriptome / Q000235:genetics