Separating Subtype Specific Signals from Mixed Tumor Genomic Data

• Activity Number: 527 - Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
• Type: Contributed
• Date/Time: Wednesday, July 31, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Liuqing Yang* and Hongtu Zhu and Steve Marron
• Companies: AbbVie and DiDi Chuxing and UNC-Chapel Hill and University of North Carolina at Chapel Hill
• Keywords: Tumor Heterogeneity; Deconvolution; Cancer Genomics

Abstract

The heterogeneity within a bulk tumor tissue, referred as intra­tumor 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 intra­tumor heterogeneity issue, we develop a Fast Tumor Deconvolution (FasTD) tool to separate the pure tumor signals from mixture samples in an efficient way. Assuming a linear combination of the abundance of the mixing components and availability of reference information for the non­tumor component(s), our semi­parametric 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 RNAseq datasets, with no requirement for pre­selected signature genes.