Critical transition and reversion of tumorigenesis

Abstract

Cancer is caused by the accumulation of genetic alterations and therefore has been historically considered to be irreversible. Intriguingly, several studies have reported that cancer cells can be reversed to be normal cells under certain circumstances. Despite these experimental observations, conceptual and theoretical frameworks that explain these phenomena and enable their exploration in a systematic way are lacking. In this review, we provide an overview of cancer reversion studies and describe recent advancements in systems biological approaches based on attractor landscape analysis. We suggest that the critical transition in tumorigenesis is an important clue for achieving cancer reversion. During tumorigenesis, a critical transition may occur at a tipping point, where cells undergo abrupt changes and reach a new equilibrium state that is determined by complex intracellular regulatory events. We introduce a conceptual framework based on attractor landscapes through which we can investigate the critical transition in tumorigenesis and induce its reversion by combining intracellular molecular perturbation and extracellular signaling controls. Finally, we present a cancer reversion therapy approach that may be a paradigm-changing alternative to current cancer cell-killing therapies.

Cancer: Mapping the tipping point to identify reversion therapies

Examining the critical point at which healthy cells become cancerous could provide clues about how to reverse tumor development. Under specific conditions, some cancer cells can revert back to normal, healthy cell types. Dongkwan Shin and Kwang-Hyun Cho at the Korea Advanced Institute of Science and Technology in Daejeon, South Korea, reviewed understanding of cancer reversion, and provide a modeling framework to identify potential reversion therapies. Reversion can occur in leukemia, but reverting cancers with solid tumors remains challenging. ‘Attractor landscape analysis’ maps cells, microenvironments, and signaling networks into a landscape to visualize how cell states change over time. It has been used to examine cancer reversion, but only with data from advanced cancer cells. Conducting this analysis at the tipping point, where a cell undergoes abrupt changes in state, could help identify reversion therapeutic targets.