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Journal of Biomedicine and Biotechnology
Volume 2012, Article ID 797812, 16 pages
Research Article

Inference of Tumor Phylogenies from Genomic Assays on Heterogeneous Samples

1Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2Intelligent Oncotherapeutics, Pittsburgh, PA 15243, USA
3Department of Biological Sciences and Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Received 2 November 2011; Accepted 21 February 2012

Academic Editor: Ali Khraibi

Copyright © 2012 Ayshwarya Subramanian et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Tumorigenesis can in principle result from many combinations of mutations, but only a few roughly equivalent sequences of mutations, or “progression pathways,” seem to account for most human tumors. Phylogenetics provides a promising way to identify common progression pathways and markers of those pathways. This approach, however, can be confounded by the high heterogeneity within and between tumors, which makes it difficult to identify conserved progression stages or organize them into robust progression pathways. To tackle this problem, we previously developed methods for inferring progression stages from heterogeneous tumor profiles through computational unmixing. In this paper, we develop a novel pipeline for building trees of tumor evolution from the unmixed tumor data. The pipeline implements a statistical approach for identifying robust progression markers from unmixed tumor data and calling those markers in inferred cell states. The result is a set of phylogenetic characters and their assignments in progression states to which we apply maximum parsimony phylogenetic inference to infer tumor progression pathways. We demonstrate the full pipeline on simulated and real comparative genomic hybridization (CGH) data, validating its effectiveness and making novel predictions of major progression pathways and ancestral cell states in breast cancers.