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Comparative Lesions Analysis Through a Targeted Sequencing Approach
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GraPhyC: Using Consensus to Infer Tumor Evolution.

Kiya Govek, Camden Sikes, Yangqiaoyu Zhou

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |October 8, 2020
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    Summary
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    GraPhyC identifies a consensus tumor evolution tree from conflicting data. This algorithm uses a weighted graph to find the best tree, outperforming other methods on simulated and real cancer data.

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    Area of Science:

    • Computational biology
    • Cancer genomics
    • Bioinformatics

    Background:

    • Reconstructing tumor evolution from sequencing data is challenging due to conflicting phylogenetic trees.
    • Existing methods struggle with tumor trees that lack consistent leaf labels.

    Purpose of the Study:

    • To develop a novel algorithm, GraPhyC, for finding a consensus tumor evolution tree from heterogeneous input trees.
    • To introduce new distance measures for comparing tumor evolution trees.

    Main Methods:

    • GraPhyC constructs a weighted directed graph where nodes represent mutation sets and edges reflect observed parental relationships.
    • The algorithm finds a minimum weight spanning arborescence, minimizing distance to input trees.
    • Extensions and clustering applications of the GraPhyC approach are explored.

    Main Results:

    • GraPhyC outperforms a baseline method on simulated tumor evolution data.
    • The algorithm effectively computes centroids in k-medians clustering.
    • Analysis of real sequencing data identified a novel consensus tree consistent with other findings.

    Conclusions:

    • GraPhyC provides an effective method for tumor evolution tree consensus.
    • The algorithm demonstrates utility in both tree reconstruction and clustering applications.
    • GraPhyC successfully identifies biologically plausible evolutionary paths in cancer.