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We developed a new clonal deconvolution method that uses temporal information from longitudinal tumor samples. This approach improves accuracy for analyzing cancer evolution, especially in liquid cancers and biopsies.

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Area of Science:

  • Computational Biology
  • Genomics
  • Cancer Research

Background:

  • Tumors consist of diverse cancer cell clones that adapt to their microenvironment.
  • Standard clonal deconvolution methods analyze tumor samples but often ignore the timing of sample collection.
  • Key questions involve incorporating temporal information and assessing its benefits in clonal deconvolution.

Purpose of the Study:

  • To develop a novel clonal deconvolution method that explicitly incorporates the temporal spacing of longitudinally sampled tumors.
  • To reconstruct the temporal profile of mutation cluster abundance as a continuous function of time.

Main Methods:

  • Developed a method merging a Dirichlet Process Mixture Model with Gaussian Process priors.
  • Utilized a sequence of sparsely collected samples as input.
  • Incorporated temporal spacing of longitudinally sampled tumors.

Main Results:

  • The developed method reconstructs mutation cluster abundance over time.
  • Benchmarking on various sequencing data (whole genome, whole exome, targeted, liquid biopsy) and synthetic data showed improved model performance.
  • Incorporating temporal information enhances model performance when sufficient data volume and complexity are available.

Conclusions:

  • Explicitly incorporating temporal spacing in clonal deconvolution improves model performance.
  • This approach is particularly beneficial for liquid cancers and liquid biopsies where sequential sampling is feasible.
  • The statistical methodology is publicly available.