Spatial measures of genetic heterogeneity during carcinogenesis

TL;DR

This paper models the spatial and temporal dynamics of genetic heterogeneity during tumor development, providing new measures and estimates that enhance understanding of cancer initiation and progression.

Contribution

It introduces a spatial stochastic process model for tumorigenesis, deriving new measures of spatial heterogeneity and estimating clone sizes from clinical biopsies.

Findings

Derived estimates of Simpson's Index in premalignant tissue

Analyzed the typical length scale of genetic heterogeneity

Estimated the extent of premalignant clones from biopsies

Abstract

In this work we explore the temporal dynamics of spatial heterogeneity during the process of tumorigenesis from healthy tissue. We utilize a spatial stochastic process model of mutation accumulation and clonal expansion in a structured tissue to describe this process. Under a two-step tumorigenesis model, we first derive estimates of a non-spatial measure of diversity: Simpson's Index, which is the probability that two individuals sampled at random from the population are identical, in the premalignant population. We next analyze two new measures of spatial population heterogeneity. In particular we study the typical length scale of genetic heterogeneity during the carcinogenesis process and estimate the extent of a surrounding premalignant clone given a clinical observation of a premalignant point biopsy. This evolutionary framework contributes to a growing literature focused on developing a better understanding of the spatial population dynamics of cancer initiation and progression. Although initially motivated by understanding questions in cancer, these results can be applied more generally to help understand the dynamics of heterogeneity and diversity in a variety of spatially structured, evolving populations.