Cancer systems biology in the genome sequencing era: Part 2, evolutionary dynamics of tumor clonal networks and drug resistance

TL;DR

This paper discusses how tumor clones evolve and interact, using genome sequencing data, to improve understanding and treatment of cancer by viewing tumors as evolving systems.

Contribution

It introduces a systems biology framework for analyzing tumor evolution, heterogeneity, and drug resistance through computational and genomic methods.

Findings

Clonal networks exhibit dynamic evolutionary patterns.

Early-warning signals can predict fast-growing clone formation.

Modeling clone interactions aids in understanding drug resistance.

Abstract

A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor reoccurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows to profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a whole-system understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.