Bioinformatics Research and Applications

Volume 7292 of the series Lecture Notes in Computer Science pp 250-262

Novel Multi-sample Scheme for Inferring Phylogenetic Markers from Whole Genome Tumor Profiles

  • Ayshwarya SubramanianAffiliated withCarnegie Mellon UniversityDepartment of Biological Sciences, Carnegie Mellon University
  • , Stanley ShackneyAffiliated withCarnegie Mellon UniversityOncotherapeutics
  • , Russell SchwartzAffiliated withCarnegie Mellon UniversityDepartment of Biological Sciences, Carnegie Mellon UniversityLane Center for Computational Biology, Carnegie Mellon University

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Computational cancer phylogenetics seeks to enumerate the temporal sequence of aberrations in tumor evolution, thereby delineating the evolution of possible tumor progression pathways, molecular subtypes and mechanisms of action. We previously developed a pipeline for constructing phylogenies describing evolution between major recurring cell types computationally inferred from whole-genome tumor profiles. The accuracy and detail of the phylogenies, however, depends on the identification of accurate, high-resolution molecular markers of progression, i.e., reproducible regions of aberration that robustly differentiate different subtypes and stages of progression. Here we present a novel hidden Markov model (HMM) scheme for the problem of inferring such phylogenetically significant markers through joint segmentation and calling of multi-sample tumor data. Our method classifies sets of genome-wide DNA copy number measurements into a partitioning of samples into normal (diploid) or amplified at each probe. It differs from other similar HMM methods in its design specifically for the needs of tumor phylogenetics, by seeking to identify robust markers of progression conserved across a set of copy number profiles. We show an analysis of our method in comparison to other methods on both synthetic and real tumor data, which confirms its effectiveness for tumor phylogeny inference and suggests avenues for future advances.


Bioinformatics cancer phylogenetics multi-sample array comparative genomic hybridization (aCGH)