How cancer emerges: Data-driven universal insights into tumorigenesis via hallmark networks

TL;DR

This study develops a data-driven network model to understand tumorigenesis, revealing early network topology changes and universal patterns across multiple cancer types, advancing systems biology insights into cancer progression.

Contribution

It introduces a novel dynamic network modeling approach based on hallmark interactions, integrating stochastic differential equations with gene regulatory data to analyze tumorigenesis.

Findings

Network topology reconfigures before hallmark expression shifts.

Universal patterns identified across 15 cancer types.

Tissue Invasion and Metastasis show the most significant differences.

Abstract

Cancer is a complex disease driven by dynamic regulatory shifts that cannot be fully captured by individual molecular profiling. We employ a data-driven approach to construct a coarse-grained dynamic network model based on hallmark interactions, integrating stochastic differential equations with gene regulatory network data to explore key macroscopic dynamic changes in tumorigenesis. Our analysis reveals that network topology undergoes significant reconfiguration before hallmark expression shifts, serving as an early indicator of malignancy. A pan-cancer examination across $15$ cancer types uncovers universal patterns, where Tissue Invasion and Metastasis exhibits the most significant difference between normal and cancer states, while the differences in Reprogramming Energy Metabolism are the least pronounced, consistent with the characteristic features of tumor biology. These findings reinforce the systemic nature of cancer evolution, highlighting the potential of network-based systems biology methods for understanding critical transitions in tumorigenesis.