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Phylogenetic Copy-Number Factorization of Multiple Tumor Samples.

Simone Zaccaria1,2, Mohammed El-Kebir1, Gunnar W Klau3

  • 11 Department of Computer Science, Princeton University , Princeton, New Jersey.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|April 17, 2018
PubMed
Summary

This study reconstructs tumor evolutionary trees using copy-number aberrations (CNAs) from bulk sequencing data. Our novel algorithm accurately identifies cancer clones and their relationships, offering a high-resolution view of tumor evolution.

Keywords:
copy-number aberrationsfactorizationinteger linear programmingintratumor heterogeneitymultiple tumor samplestumor phylogeny

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Area of Science:

  • Oncology
  • Computational Biology
  • Genetics

Background:

  • Cancer evolution is driven by somatic mutations, often visualized as phylogenetic trees.
  • Analyzing bulk tumor sequencing data is challenging due to mixed cell populations and diverse mutations.
  • Copy-number aberrations (CNAs) are key genomic alterations in cancer evolution.

Purpose of the Study:

  • To develop a method for reconstructing tumor phylogenies from CNA data in bulk sequencing.
  • To introduce and solve the Copy-Number Tree Mixture Deconvolution (CNTMD) problem.
  • To provide a higher resolution view of cancer's copy-number evolution.

Main Methods:

  • Formulated the Copy-Number Tree Mixture Deconvolution (CNTMD) problem.
  • Designed an algorithm to solve the CNTMD problem.
  • Applied the algorithm to simulated and real prostate cancer patient data.

Main Results:

  • The CNTMD algorithm outperformed existing methods on simulated data.
  • The algorithm successfully identified distinct cancer clones and their proportions in real patient samples.
  • A novel phylogenetic tree revealed detailed copy-number evolution in prostate cancer.

Conclusions:

  • The developed algorithm accurately reconstructs tumor phylogenies from bulk CNA data.
  • This approach enhances understanding of cancer evolution and clonal heterogeneity.
  • The findings offer a more precise view of copy-number changes during tumor progression.