Extreme value statistics of mutation accumulation in renewing cell populations

TL;DR

This paper develops an analytical framework to estimate the risk of extreme mutation accumulation in renewing cell populations, which is crucial for understanding tumor initiation and other phenotypic changes.

Contribution

It introduces a novel application of branching random walk theory to model the maximum mutation numbers in cell populations, providing analytical estimates for mutation risk.

Findings

Probability of exceeding mutation thresholds scales with age and population size

Analytical estimates for maximum mutation distribution derived

Model validated against biological data

Abstract

The emergence of a predominant phenotype within a cell population is often triggered by a rare accumulation of DNA mutations in a single cell. For example, tumors may be initiated by a single cell in which multiple mutations cooperate to bypass a cell's defense mechanisms. The risk of such an event is thus determined by the extremal accumulation of mutations across tissue cells. To address this risk, we study the statistics of the maximum mutation numbers in a generic, but tested, model of a renewing cell population. By drawing an analogy between the genealogy of a cell population and the theory of branching random walks, we obtain analytical estimates for the probability of exceeding a threshold number of mutations and determine how the statistical distribution of maximum mutation numbers scales with age and cell population size.