cloneRate: fast estimation of single-cell clonal dynamics using coalescent theory

Area of Science:

• Evolutionary medicine
• Computational biology
• Cancer genomics

Background:

• Measuring in vivo evolution, particularly cancer evolution, is challenging due to experimental limitations and the dynamic nature of biological systems.
• Continuous observation of clonal architecture is often impossible, and longitudinal multi-timepoint samples are scarce.
• Single-cell resolution DNA sequencing data offers a way to reconstruct past evolutionary dynamics using mutational history.

Purpose of the Study:

• To address the need for an accurate, fast, and user-friendly method for quantifying clone growth dynamics from DNA sequencing datasets.
• To develop and validate analytical methods for estimating clone net growth rates.
• To provide a publicly available tool for researchers in cancer evolution and related fields.

Main Methods:

• Derived methods based on coalescent theory to estimate clone net growth rates.
• Utilized reconstructed phylogenies or shared mutations for growth rate estimation.
• Validated analytical methods, replacing the need for complex simulations.

Main Results:

• Clones with multiple driver mutations exhibit significantly increased growth rates (median 0.94/year) compared to those with single or unknown drivers (median 0.25/year).
• Higher fittest clone growth rates in myeloproliferative neoplasms correlate with shorter time to diagnosis (13.9 vs 26.4 months).
• The developed methods show broad applications for improving mechanistic understanding and prognostic ability in hematopoietic data.

Conclusions:

• The `cloneRate` R package provides an accessible tool for estimating clone growth dynamics.
• Accurate quantification of clone growth dynamics can enhance understanding of cancer evolution.
• This approach has the potential to improve prognostic capabilities for hematologic malignancies.