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Evolution under Spatially Heterogeneous Selection in Solid Tumors.

Guanghao Li1,2,3,4,5, Zuyu Yang2,3,6, Dafei Wu2,3,7

  • 1State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.

Molecular Biology and Evolution
|December 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a neutral spatial (NS) model for tumor evolution, revealing that mutations increase towards the tumor periphery. This spatial pattern is key to understanding natural selection in solid tumors, particularly smaller ones.

Keywords:
cancer evolutionintra-tumoral heterogeneitynatural selectionphenotypic diversitytumor spatial growth model

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Area of Science:

  • Evolutionary biology
  • Cancer genomics
  • Tumorigenesis

Background:

  • Spatial genetic and phenotypic diversity in solid tumors is known.
  • The impact of this heterogeneity on tumorigenesis dynamics remains under-examined due to spatial constraints.
  • Standard population genetic models do not fully capture solid tumor evolution.

Purpose of the Study:

  • To propose and test a neutral spatial (NS) model for tumor evolution.
  • To investigate how spatial distribution of mutations influences tumorigenesis.
  • To understand the role of spatial constraints in blunting selection efficacy.

Main Methods:

  • Developed a neutral spatial (NS) model incorporating spatial mutation accumulation and kin competition.
  • Performed three-dimensional multiple microsampling of two hepatocellular carcinomas.
  • Utilized whole-genome sequencing (WGS) to analyze mutation patterns and clonal relationships.

Main Results:

  • Observed a 2-fold increase in mutations from the tumor center to the periphery in hepatocellular carcinomas.
  • Demonstrated that spatial determination of genealogical relationships and kin competition influence selection.
  • Found that limited migration restricts the detection of selective advantages to highly advantageous clones.

Conclusions:

  • The neutral spatial (NS) model provides a framework for understanding tumor evolution under spatial constraints.
  • Spatial patterns of mutation accumulation are critical for assessing natural selection during tumorigenesis.
  • Multiregional sampling may only reveal a subset of fitness differences in solid tumors, especially smaller ones.