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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Zeya Wang, Jeffrey S. Morris, Shaolong Cao, Jaeil Ahn, Rongjie Liu, Svitlana Tyekucheva, Bo Li, Wei Lu, Ximing Tang, Ignacio I. Wistuba, Michaela Bowden, Lorelei Mucci, Massimo Loda, Giovanni Parmigiani, Chris C. Holmes, Wenyi Wang
doi: https://doi.org/10.1101/146795
Zeya Wang
1 Department of Statistics, Rice University, Houston, TX;
2 Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX;
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Jeffrey S. Morris
3 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX;
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Shaolong Cao
2 Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX;
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Jaeil Ahn
4 Department of Biostatistics and Bioinformatics, Georgetown University, Washington, DC;
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Rongjie Liu
3 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX;
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Svitlana Tyekucheva
5 Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA;
11 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA;
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Bo Li
5 Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA;
6 Department of Statistics, Harvard University, Cambridge, MA;
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Wei Lu
7 Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX;
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Ximing Tang
7 Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX;
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Ignacio I. Wistuba
7 Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX;
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Michaela Bowden
8 Center for Molecular Oncologic Pathology, Dana Farber Cancer Institute, Boston, MA;
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Lorelei Mucci
9 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA;
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Massimo Loda
8 Center for Molecular Oncologic Pathology, Dana Farber Cancer Institute, Boston, MA;
10 Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA;
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Giovanni Parmigiani
5 Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA;
11 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA;
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Chris C. Holmes
12 Department of Statistics, University of Oxford, Oxford, United Kingdom. Correspondence should be addressed to W.W. ().
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  • For correspondence: wwang7@mdanderson.org
Wenyi Wang
2 Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX;
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ABSTRACT

We develop a novel method DeMixT for the gene expression deconvolution of three compartments in cancer patient samples: tumor, immune and surrounding stromal cells. In validation studies using mixed cell line and laser-capture microdissection data, DeMixT yielded accurate estimates for both cell proportions and compartment-specific expression profiles. Application to the head and neck cancer data shows DeMixT-based deconvolution provides an important step to link tumor transcriptome data with clinical outcomes.

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Posted June 08, 2017.
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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration
Zeya Wang, Jeffrey S. Morris, Shaolong Cao, Jaeil Ahn, Rongjie Liu, Svitlana Tyekucheva, Bo Li, Wei Lu, Ximing Tang, Ignacio I. Wistuba, Michaela Bowden, Lorelei Mucci, Massimo Loda, Giovanni Parmigiani, Chris C. Holmes, Wenyi Wang
bioRxiv 146795; doi: https://doi.org/10.1101/146795
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Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration
Zeya Wang, Jeffrey S. Morris, Shaolong Cao, Jaeil Ahn, Rongjie Liu, Svitlana Tyekucheva, Bo Li, Wei Lu, Ximing Tang, Ignacio I. Wistuba, Michaela Bowden, Lorelei Mucci, Massimo Loda, Giovanni Parmigiani, Chris C. Holmes, Wenyi Wang
bioRxiv 146795; doi: https://doi.org/10.1101/146795

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