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Comparative Lesions Analysis Through a Targeted Sequencing Approach

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Quantifying Clonal and Subclonal Passenger Mutations in Cancer Evolution.

Ivana Bozic^1,2, Jeffrey M Gerold¹, Martin A Nowak^1,2,3

¹Program for Evolutionary Dynamics, Harvard University, Cambridge, Massachusetts, United States of America.

Plos Computational Biology

|February 2, 2016

Summary

This summary is machine-generated.

Passenger mutations in cancer cells act as a molecular clock, revealing evolutionary history and potential treatment targets. This study models their stochastic expansion to reconstruct cancer evolution and estimate cell birth/death rates.

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Area of Science:

Genetics
Evolutionary Biology
Computational Biology

Background:

Most cancer exome mutations are passengers, not affecting cell proliferation.
Passenger mutations offer insights into cancer evolution, acting as molecular clocks.
These mutations can inform immunotherapy targets and predict treatment resistance.

Purpose of the Study:

To model the stochastic expansion of cancer cell populations.
To analyze passenger mutations for phylogenetic reconstruction of cancer evolution.
To estimate cancer cell birth and death rates from mutation data.

Main Methods:

Forward-time analysis of passenger mutation fixation probabilities and frequencies.
Calculation of likelihoods for cancer evolutionary trees.
Backward-time analysis to estimate cell population size at mutation origination.

Main Results:

Derived formulas for fixation probabilities and frequencies of successive passenger mutations.
Computed likelihoods enabling phylogenetic reconstruction of individual cancer evolution.
Developed methods to estimate subclonal mutation numbers and infer early-stage cancer cell dynamics.

Conclusions:

Passenger mutations are valuable for reconstructing cancer evolutionary history.
Stochastic modeling provides tools for analyzing cancer progression and inferring population dynamics.
This framework aids in estimating cancer cell proliferation rates from sequencing data.