Cancer Progression as a Learning Process

TL;DR

This paper proposes that cancer progression, including drug resistance and metastasis, can be understood as a cellular learning process driven by stress-induced trial-and-error adaptation, offering a new framework beyond traditional Darwinian models.

Contribution

It introduces a novel learning theory framework to explain cancer cell adaptation, emphasizing stress-driven exploratory search at the single-cell level and tissue-level network interactions.

Findings

Feasibility of learning-based adaptation in high-dimensional cellular systems

Cancer progression linked to breakdown of cellular-tissue homeostasis

Learning model supports stress-driven exploratory behavior in cancer cells

Abstract

Drug resistance and metastasis - the major complications in cancer - both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory adaptation is feasible in a high dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate and restrain cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.