Modular organization of cancer signaling networks is associated with patient survivability

TL;DR

This study investigates how the modular organization of cancer signaling networks correlates with patient survival, finding that less modular networks are associated with higher curability, supporting the modularity-robustness hypothesis.

Contribution

It demonstrates that network modularity, rather than heterogeneous connectivity, better predicts cancer patient survivability across multiple cancer types.

Findings

Less modular networks are linked to higher patient survival.

Network modularity outperforms heterogeneous connectivity as a survival predictor.

Results support the modularity-robustness hypothesis in cancer networks.

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.