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Statistical Inference for the Evolutionary History of Cancer Genomes.

Khanh N Dinh1, Roman Jaksik2, Marek Kimmel3

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Summary
This summary is machine-generated.

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.

Keywords:
Cancer evolutionbirth-death processesbulk sequencingclonal selectioncoalescentsploidysite frequency spectrumtumor heterogeneity

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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.