  Differentiation is a key cellular process in normal tissue development that
is significantly altered in cancer. Although molecular signatures
characterising pluripotency and multipotency exist, there is, as yet, no single
quantitative mark of a cellular sample's position in the global differentiation
hierarchy. Here we adopt a systems view and consider the sample's network
entropy, a measure of signaling pathway promiscuity, computable from a sample's
genome-wide expression profile. We demonstrate that network entropy provides a
quantitative, in-silico, readout of the average undifferentiated state of the
profiled cells, recapitulating the known hierarchy of pluripotent, multipotent
and differentiated cell types. Network entropy further exhibits dynamic changes
in time course differentiation data, and in line with a sample's
differentiation stage. In disease, network entropy predicts a higher level of
cellular plasticity in cancer stem cell populations compared to ordinary cancer
cells. Importantly, network entropy also allows identification of key
differentiation pathways. Our results are consistent with the view that
pluripotency is a statistical property defined at the cellular population
level, correlating with intra-sample heterogeneity, and driven by the degree of
signaling promiscuity in cells. In summary, network entropy provides a
quantitative measure of a cell's undifferentiated state, defining its elevation
in Waddington's landscape.
