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Thursday, October 18
Thu, Oct 18, 5:00 PM - 7:00 PM
Hall of Mirrors
Opening Mixer and Speed Poster 1, Sponsored by Fifth Third Bank

Fast Deconvolution Tool for Separating Subtype-Specific Signals from Mixed Tumor Genomic Data (304930)

*Liuqing Yang, The University of North Carolina at Chapel Hill 

Keywords: Tumor Heterogeneity, Deconvolution, TCGA pan-cancer

With the advance of deep sequencing techniques, the heterogeneity within a tumor tissue, referred as intra-tumor heterogeneity, becomes a prevalent confounding factor to tumor genomic studies. Analysis on genomic profilings from heterogeneous tumor samples can potentially lead to false positive differential expression conclusions, and even influence patients’ clinical outcomes and therapeutic responses. To address the intra-tumor heterogeneity issue, we develop a Fast Tumor Deconvolution (FasTD) tool to separate the pure tumor signals from tumor-nontumor mixtures in an efficient way. Assuming a linear combination of the abundance of the mixing components and availability of some reference information for the non-tumor part, our semi-parametric regression-based model can quickly provide estimates for the tumor proportion in a mixture, as well as output the tumor specific genomic profile. We demonstrate FasTD is a competitive tumor deconvolution tool for both simulated data and The Cancer Genome Atlas RNA-seq datasets, with no requirement for pre-selected signature genes.