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Updated: Jan 15, 2026
09:01
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Published on: July 3, 2025
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Statistical Inference for the Evolutionary History of Cancer Genomes
Khanh N Dinh1, Roman Jaksik2, Marek Kimmel3
1Department of Statistics, Columbia University, New York, New York 10027, USA.
Statistical Science : a Review Journal of the Institute of Mathematical Statistics
|October 16, 2025
Summary
This study compares cancer evolution models, finding birth-death and coalescent approaches yield comparable Site Frequency Spectra (SFS) for tumor cell populations. The research also introduces a selective sweep model to analyze tumor history and data preprocessing effects.
Area of Science:
- Computational Biology
- Cancer Genomics
- Evolutionary Genetics
Background:
- Modeling cancer evolution from mutation data is crucial.
- Classical population genetics and branching processes are common modeling frameworks.
- The Site Frequency Spectrum (SFS) is a key summary statistic for DNA sequence data.
Purpose of the Study:
- To compare the Site Frequency Spectrum (SFS) derived from birth-death processes versus coalescent models in cancer evolution.
- To introduce and evaluate a model of tumor evolution incorporating selective sweeps.
- To apply theoretical models to real cancer genome data.
Main Methods:
- Utilized birth-death processes and coalescent models for cancer evolution inference.
- Estimated SFS from bulk tumor sequencing data, grouping sites by mutant fractions.
- Developed a novel model for tumor evolution with selective sweeps.
Main Results:
- Birth-death and coalescent models produce quantitatively comparable SFS for typical tumor parameters, despite differing sampling mechanisms.
- The proposed selective sweep model aids in understanding tumor history.
- Demonstrated the influence of data pre-processing on evolutionary models.
Conclusions:
- Both birth-death and coalescent models offer valuable insights into cancer evolution.
- Selective sweep models enhance the analysis of tumor development.
- The findings are applicable to real-world cancer genomics datasets, such as those from The Cancer Genome Atlas.
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