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On measuring selection in cancer from subclonal mutation frequencies
Ivana Bozic1, Chay Paterson1, Bartlomiej Waclaw2
1Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.
Plos Computational Biology
|September 27, 2019
Summary
Cancer evolution models show driver mutations are rarely found at intermediate frequencies. This finding challenges the common assumption of neutral evolution in tumors, suggesting many cancers may not be evolving neutrally.
Area of Science:
- Oncology
- Evolutionary Biology
- Computational Biology
Background:
- Cancer genomes exhibit numerous mutations, comprising drivers and neutral passengers.
- Distinguishing drivers from passengers is crucial for developing targeted cancer therapies.
- Current models suggest a significant fraction of cancers evolve neutrally, following a 1/f power law.
Purpose of the Study:
- To investigate the frequency distribution of subclonal driver mutations in cancer evolution.
- To assess the validity of the 1/f statistic for detecting neutral evolution in tumors.
- To provide a quantitative framework for analyzing cancer genome data.
Main Methods:
- Development of a stochastic model for cancer evolution.
- Derivation of the probability distribution for subclonal driver mutation frequencies.
- Analysis of model predictions under various growth scenarios (exponential, spatial 3D, sigmoidal).
Main Results:
- Driver mutation frequencies are statistically biased towards fixation (frequency 1) or loss (frequency 0).
- Intermediate frequencies for driver mutations are improbable, making them difficult to detect.
- The 1/f statistic may significantly overestimate neutral evolution due to the rarity of intermediate-frequency drivers.
Conclusions:
- The assumption of neutral evolution in a third of cancers may be an overestimation.
- The derived model offers a more accurate assessment of selection in cancer genomes.
- Findings are robust across different cancer growth models, applicable to early and late stages.
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