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Ayshwarya Subramanian , Carnegie Mellon University, Pittsburgh
Stanley Shackney , Oncotherapeutics, Pittsburgh
Russell Schwartz , Carnegie Mellon University, Pittsburgh
ABSTRACT
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.
INDEX TERMS
Segmentation, Computer Applications, Life and Medical Sciences, Biology and genetics, Health, Mathematics of Computing, Discrete Mathematics, Graph Theory, Trees, Computing Methodologies, Image Processing and Computer Vision
CITATION
Ayshwarya Subramanian, Stanley Shackney, Russell Schwartz, "Novel Multi-Sample Scheme for Inferring Phylogenetic Markers from Whole Genome Tumor Profiles", IEEE/ACM Transactions on Computational Biology and Bioinformatics, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TCBB.2013.33
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