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SUMMARY:Increased entropy of signal transduction in the cancer metastasis 
 phenotype - Xin Wang (Cacner Research UK Cambridge Research Institute)
DTSTART:20101011T143000Z
DTEND:20101011T153000Z
UID:TALK25394@talks.cam.ac.uk
CONTACT:Stefan Gräf
DESCRIPTION:Andrew E Teschendorff and Simone Severini\, published in BMC S
 ystems Biology\n\nBackground\n\nThe statistical study of biological networ
 ks has led to important novel biological insights\, such as the presence o
 f hubs and hierarchical modularity. There is also a growing interest in st
 udying the statistical properties of networks in the context of cancer gen
 omics. However\, relatively little is known as to what network features di
 ffer between the cancer and normal cell physiologies\, or between differen
 t cancer cell phenotypes.\n\nResults\n\nBased on the observation that freq
 uent genomic alterations underlie a more aggressive cancer phenotype\, we 
 asked if such an effect could be detectable as an increase in the randomne
 ss of local gene expression patterns. Using a breast cancer gene expressio
 n data set and a model network of protein interactions we derive constrain
 ed weighted networks defined by a stochastic information flux matrix refle
 cting expression correlations between interacting proteins. Based on this 
 stochastic matrix we propose and compute an entropy measure that quantifie
 s the degree of randomness in the local pattern of information flux around
  single genes. By comparing the local entropies in the non-metastatic vers
 us metastatic breast cancer networks\, we here show that breast cancers th
 at metastasize are characterised by a small yet significant increase in th
 e degree of randomness of local expression patterns. We validate this resu
 lt in three additional breast cancer expression data sets and demonstrate 
 that local entropy better characterises the metastatic phenotype than othe
 r non-entropy based measures. We show that increases in entropy can be use
 d to identify genes and signalling pathways implicated in breast cancer me
 tastasis and provide examples of de-novo discoveries of gene modules with 
 known roles in apoptosis\, immune-mediated tumour suppression\, cell-cycle
  and tumour invasion. Importantly\, we also identify a novel gene module w
 ithin the insulin growth factor signalling pathway\, alteration of which m
 ay predispose the tumour to metastasize.\n\nConclusions\n\nThese results d
 emonstrate that a metastatic cancer phenotype is characterised by an incre
 ase in the randomness of the local information flux patterns. Measures of 
 local randomness in integrated protein interaction mRNA expression network
 s may therefore be useful for identifying genes and signalling pathways di
 srupted in one phenotype relative to another. Further exploration of the s
 tatistical properties of such integrated cancer expression and protein int
 eraction networks will be a fruitful endeavour.\n\n[ "link":http://www.bio
 medcentral.com/1752-0509/4/104 ]
LOCATION:Room 132\, CRI
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