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

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

Research output: Contribution to journal › Article › peer-review

12 Scopus citations

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. Computational Bioinformatics; Cancer; Transcriptomics

Original language	English (US)
Pages (from-to)	451-460
Number of pages	10
Journal	iScience
Volume	9
DOIs	https://doi.org/10.1016/j.isci.2018.10.028
State	Published - Nov 30 2018
Externally published	Yes

Keywords

Cancer
Computational Bioinformatics
Transcriptomics

ASJC Scopus subject areas

General

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10.1016/j.isci.2018.10.028

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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. / Wang, Zeya; Cao, Shaolong; Morris, Jeffrey S.; Ahn, Jaeil; Liu, Rongjie; Tyekucheva, Svitlana; Gao, Fan; Li, Bo; Lu, Wei; Tang, Ximing; Wistuba, Ignacio I.; Bowden, Michaela; Mucci, Lorelei; Loda, Massimo; Parmigiani, Giovanni; Holmes, Chris C.; Wang, Wenyi.

In: iScience, Vol. 9, 30.11.2018, p. 451-460.

Research output: Contribution to journal › Article › peer-review

Wang, Zeya ; Cao, Shaolong ; Morris, Jeffrey S. ; Ahn, Jaeil ; Liu, Rongjie ; Tyekucheva, Svitlana ; Gao, Fan ; Li, Bo ; Lu, Wei ; Tang, Ximing ; Wistuba, Ignacio I. ; Bowden, Michaela ; Mucci, Lorelei ; Loda, Massimo ; Parmigiani, Giovanni ; Holmes, Chris C. ; Wang, Wenyi. / Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. In: iScience. 2018 ; Vol. 9. pp. 451-460.

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title = "Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration",

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. Computational Bioinformatics; Cancer; Transcriptomics",

keywords = "Cancer, Computational Bioinformatics, Transcriptomics",

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

note = "Funding Information: Z.W. and W.W. are supported by the U.S. National Cancer Institute through grant numbers R01CA174206 , R01 CA183793 , and P30 CA016672 . W.W. is supported by U.S. National Cancer Institute 2R01 CA158113 . Z.W. and J.S.M. are supported by NIH grants R01 CA178744 and P30 CA016672 and NSF grant 1550088 . J.S.M. is supported by U.S. NSF 1550088 and the MD Anderson Colorectal Cancer Moonshot. S.C., J.A., X.T., and I.I.W. are supported by NIH grant 1R01CA183793 . X.T. and I.I.W. are supported by The University of Texas Lung Specialized Programs of Research Excellence grant P50CA70907 . S.T. is supported by NIH grant R01 CA174206 and Prostate Cancer Foundation Challenge Award. G.P. is supported by NIH grants 5R01 CA174206-05 and 4P30CA006516-51 . M.L. is supported by NIH grants RO1CA131945 , R01CA187918 , DoD PC130716 , P50 CA90381 and the Prostate Cancer Foundation, United States . ",

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AU - Cao, Shaolong

AU - Morris, Jeffrey S.

AU - Ahn, Jaeil

AU - Liu, Rongjie

AU - Tyekucheva, Svitlana

AU - Gao, Fan

AU - Li, Bo

AU - Lu, Wei

AU - Tang, Ximing

AU - Wistuba, Ignacio I.

AU - Bowden, Michaela

AU - Mucci, Lorelei

AU - Loda, Massimo

AU - Parmigiani, Giovanni

AU - Holmes, Chris C.

AU - Wang, Wenyi

N1 - Funding Information: Z.W. and W.W. are supported by the U.S. National Cancer Institute through grant numbers R01CA174206 , R01 CA183793 , and P30 CA016672 . W.W. is supported by U.S. National Cancer Institute 2R01 CA158113 . Z.W. and J.S.M. are supported by NIH grants R01 CA178744 and P30 CA016672 and NSF grant 1550088 . J.S.M. is supported by U.S. NSF 1550088 and the MD Anderson Colorectal Cancer Moonshot. S.C., J.A., X.T., and I.I.W. are supported by NIH grant 1R01CA183793 . X.T. and I.I.W. are supported by The University of Texas Lung Specialized Programs of Research Excellence grant P50CA70907 . S.T. is supported by NIH grant R01 CA174206 and Prostate Cancer Foundation Challenge Award. G.P. is supported by NIH grants 5R01 CA174206-05 and 4P30CA006516-51 . M.L. is supported by NIH grants RO1CA131945 , R01CA187918 , DoD PC130716 , P50 CA90381 and the Prostate Cancer Foundation, United States .

PY - 2018/11/30

Y1 - 2018/11/30

N2 - 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. Computational Bioinformatics; Cancer; Transcriptomics

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