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Identifying Robust Subclonal Structures through Tumor Progression Tree Alignment.

Jacob Gilbert1, Chih Hao Wu2, Marina Knittel3

  • 1Department of Computer Science, University of Maryland, College Park, MD, USA.

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Summary
This summary is machine-generated.

Comparing tumor evolution is key in cancer genomics. We developed an algorithm for optimal multi-label tree alignment (omlta) to compare clonal trees by minimizing removed mutations, aiding cancer research.

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Area of Science:

  • Computational Biology
  • Cancer Genomics
  • Evolutionary Biology

Background:

  • Understanding tumor evolutionary histories is crucial for cancer genomics.
  • Clonal trees model tumor progression using rooted, unordered trees where nodes represent subclones labeled by mutations.

Purpose of the Study:

  • To introduce and computationally solve the optimal multi-label tree alignment (omlta) problem for comparing clonal trees.
  • To develop the first computational tool for optimal clonal tree alignment.

Main Methods:

  • Introduced omlta, an algorithm to find the minimum number of mutation labels to remove for clonal tree isomorphism.
  • Developed an algorithm with a running time of O(L^k), where L is the total number of mutation labels and k is the minimum number of labels to remove.
  • Implemented the algorithm as a computational tool available at https://github.com/algo-cancer/omlta.

Main Results:

  • Presented an algorithm to compute the optimal multi-label tree alignment (omlta).
  • The implemented tool is the first to compute optimal alignments between clonal trees.
  • Applied omlta to analyze 126 non-small cell lung cancer cases from the TRACERx study and melanoma single-cell data.

Conclusions:

  • The developed omlta algorithm and tool enable optimal comparison of tumor evolutionary histories.
  • This method advances the field of cancer genomics by providing a robust way to compare clonal trees.
  • The application to real-world cancer data demonstrates the utility of omlta in understanding tumor evolution.