Modular organization of cancer signaling networks is associated with patient survivability

Abstract

Molecular signaling networks are believed to determine cancer robustness. Although cancer patient survivability was reported to correlate with the heterogeneous connectivity of the signaling networks inspired by theoretical studies on the increase of network robustness due to the heterogeneous connectivity, other theoretical and data analytic studies suggest an alternative explanation: the impact of modular organization of networks on biological robustness or adaptation to changing environments. In this study, thus, we evaluate whether the modularity-robustness hypothesis is applicable to cancer using network analysis. We focus on 14 specific cancer types whose molecular signaling networks are available in databases, and show that modular organization of cancer signaling networks is associated with the patient survival rate. In particular, the cancers with less modular signaling networks are more curable. This result is consistent with a prediction from the modularity-robustness hypothesis. Furthermore, we show that the network modularity is a better descriptor of the patient survival rate than the heterogeneous connectivity. However, these results do not contradict the importance of the heterogeneous connectivity. Rather, they provide new and different insights into the relationship between cellular networks and cancer behaviors. Despite several limitations of data analysis, these findings enhance our understanding of adaptive and evolutionary mechanisms of cancer cells.

Thermodynamic measures of cancer: Gibbs free energy and entropy of protein–protein interactions

“…Recent papers [5,6] describe topological metrics of PPI cancer networks that correlate with 5-year cancer patient survival. Of particular interest are Breitkreutz et al [7] and Takemoto and Kaori [8] who introduce a thermodynamic measure based on degree distribution. A degree distribution is essentially a Boltzmann probability distribution [9,10], which allows us to consider realworld statistical thermodynamics as a conceptual framework within which to view cancer initiation and progression.…”

Gibbs Free Energy of Protein-Protein Interactions reflects tumor stage

“…Recent papers [3,4] describe topological metrics of PPI cancer networks that correlate with 5-yr cancer patient survival. Breitkreutz et al (2012) [5] and Takemoto and Kaori (2013) [6] describe a thermodynamic measure based on degree distribution. A degree distribution is essentially a Boltzmann [7]distribution, which allows us to consider real-world thermodynamics.…”

“…PPI networks are being used with increasing frequency for mining information about cancer dynamics, cancer progression and therapy, but there are no meaningful tools to analyze them. Breitkreutz et al (2012) found a correlation of degree-entropy of PPI with 5-yr survival [5] , introducing the concept, and the work was further elaborated on by Takemoto and Kaori in 2013 [6]. Thus, the concept of mathematically analyzing complexity of networks is not new.…”

Climatic seasonality may affect ecological network structure: Food webs and mutualistic networks

“…Several data analytical studies have found a positive correlation between environmental variability and network modularity in several types of biological system (e.g. metabolic networks (Parter et al, 2007) and cancer signalling networks (Takemoto and Kihara, 2013)). Nevertheless, scepticism still exists regarding the impact of environmental variability on modularity in intracellular networks (Clune et al, 2013;Hansen, 2003;Holme, 2011;Takemoto, 2013Takemoto, , 2012.…”