Increased signaling entropy in cancer requires the scale-free property of protein interaction networks

TL;DR

This paper reveals that the increased signaling entropy observed in cancer is primarily due to the scale-free topology of protein interaction networks and the correlation between gene expression changes and node connectivity.

Contribution

The study develops a computational framework to analyze how network topology influences signaling entropy in cancer, highlighting the role of scale-free networks.

Findings

Signaling entropy is higher in cancer cells compared to normal tissue.

The scale-free topology of protein interaction networks drives increased signaling entropy in cancer.

Random networks with Poisson degree distributions do not show increased signaling entropy in cancer.

Abstract

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.