Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Zeya Wang; Shaolong Cao; Jeffrey S Morris; Jaeil Ahn; Rongjie Liu; Svitlana Tyekucheva; Fan Gao; Bo Li; Wei Lu; Ximing Tang; Ignacio I Wistuba; Michaela Bowden; Lorelei Mucci; Massimo Loda; Giovanni Parmigiani; Chris C Holmes; Wenyi Wang

doi:10.1016/j.isci.2018.10.028

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

iScience. 2018 Nov 30:9:451-460. doi: 10.1016/j.isci.2018.10.028. Epub 2018 Nov 2.

Authors

Zeya Wang¹, Shaolong Cao², Jeffrey S Morris³, Jaeil Ahn⁴, Rongjie Liu³, Svitlana Tyekucheva⁵, Fan Gao¹, Bo Li⁶, Wei Lu⁷, Ximing Tang⁷, Ignacio I Wistuba⁷, Michaela Bowden⁸, Lorelei Mucci⁹, Massimo Loda¹⁰, Giovanni Parmigiani⁵, Chris C Holmes¹¹, Wenyi Wang¹²

Affiliations

¹ Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Statistics, Rice University, Houston, TX 77005, USA.
² Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
³ Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁴ Department of Biostatistics and Bioinformatics, Georgetown University, Washington, DC 20057, USA.
⁵ Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
⁶ Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Statistics, Harvard University, Cambridge, MA 02138, USA.
⁷ Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁸ Department of Oncologic Pathology, Dana Farber Cancer Institute, Boston, MA 02215, USA.
⁹ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
¹⁰ Department of Oncologic Pathology, Dana Farber Cancer Institute, Boston, MA 02215, USA; Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
¹¹ Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
¹² Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address: wwang7@mdanderson.org.

Abstract

Transcriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: https://github.com/wwylab/DeMixTallmaterials.

Keywords: Cancer; Computational Bioinformatics; Transcriptomics.

Published by Elsevier Inc.

Abstract

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