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Extreme value theory as a framework for understanding mutation frequency distribution in cancer genomes
Natsuki Tokutomi1, Kenta Nakai1,2, Sumio Sugano3,4
1Department of Computational Biology and Medical Science, Graduate School of Frontier Science, University of Tokyo, Kashiwa, Chiba, Japan.
Plos One
|August 23, 2021
Summary
This study models preclonal cancer cell dynamics using extreme value theory (EVT). We found mutation frequencies follow a Fréchet distribution, offering a new framework for understanding cancer driver mutations.
Area of Science:
- Evolutionary biology
- Genomics
- Statistical modeling
Background:
- Population dynamics of preclonal cancer cells remain understudied.
- Understanding early cancer evolution is crucial for effective treatment strategies.
Purpose of the Study:
- To apply extreme value theory (EVT) to model preclonal cancer cell population dynamics.
- To analyze the mutation frequency among tumors (MFaT) as a proxy for driver mutation fitness.
- To investigate the applicability of EVT in cancer genome sequencing data.
Main Methods:
- Formulated preclonal cancer cell population dynamics as a Darwinian evolutionary system.
- Utilized extreme value theory (EVT) to analyze mutation frequency among tumors (MFaT).
- Analyzed three large-scale cancer genome datasets (>10,000 tumors each) and >177,000 mutation sites.
Main Results:
- Clarified premises for EVT application in the strong selection and weak mutation (SSWM) regime for cancer genomes.
- Confirmed the stochastic distribution of MFaT aligns with the Fréchet type, not the Gumbel hypothesis.
- Demonstrated EVT's potential as a population genetics framework for driver mutation frequency.
Conclusions:
- Extreme value theory (EVT) provides a robust framework for analyzing preclonal cancer cell population dynamics.
- The Fréchet distribution of MFaT offers new insights into the evolutionary behavior of cancer driver mutations.
- EVT is applicable to real-world cancer genome sequence data for understanding mutation stochasticity.
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