Abstract 44: Towards novel marker discovery from phylogenetic analysis of heterogeneous tumor samples

Abstract

Abstract Computational methods based on phylogenetics, or the inference of evolutionary trees, have been useful for inferring likely pathways of tumor progression, but their use has been limited by our inability to precisely characterize the mutational states of individual cells in heterogeneous tumor samples. We describe a novel approach to tumor phylogenetics using computationally inferred sub-populations of cells derived from whole-genome data on heterogeneous tumor samples. We previously proposed a strategy for computationally separating cell types from heterogeneous tumor samples and inferring genome-wide profiles of RNA expression or DNA copy number for these discrete cell populations from profiles of the full tumor samples. In the present work, we extend this method by showing how one can use these computationally inferred cell states to identify markers informative of progression state and use them in phylogenetic analysis of tumor samples. We demonstrate the approach on a publicly available data set of array comparative genome hybridization (aCGH) data profiles applied to sectioned ductal breast carcinomas (Navin et al., 2010; NCBI GEO GSE16672). Our approach begins by applying our previously described method for cell type separation to generate inferred profiles of six cell types, and computationally inferring virtual aCGH profiles for these types. We then scan over sliding windows of 20 consecutive probes per window, identifying windows for which probe values are significantly aberrant relative to diploid by a modified chi-squared test (p-value 0.001 with Bonferroni correction), and merging overlapping windows to identify phylogenetically informative amplicons. We then identify, for each amplicon, those components that have significantly aberrant copy number by Gaussian test with diploid mean (p-value 0.001). Finally, the identified amplicon states for the inferred cell types, as well as an additional all-diploid normal type, are used as input for a maximum parsimony phylogenetic tree reconstruction using the PAUP package with 300 bootstrap replicates. The method applied to the Navin et al. data identified 15 amplicons phylogenetically informative for breast tumor progression. Bootstrap replicates from the phylogenetic tree reconstruction reveal a robust grouping of cell states into three distinct subgroups and suggest several possible progression pathways by which the identified amplicons may successively accumulate in particular breast cancer sub-types. Continuing work will examine robustness of the inferred markers and pathways to independent data sources. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 44. doi:10.1158/1538-7445.AM2011-44