Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.
Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).
['Racle|Julien|J|http://orcid.org/0000-0002-0100-0323', 'de Jonge|Kaat|K|', 'Baumgaertner|Petra|P|', 'Speiser|Daniel E|DE|', 'Gfeller|David|D|http://orcid.org/0000-0002-3952-0930']
D002452:Cell Count D015179:Colorectal Neoplasms D005434:Flow Cytometry D020869:Gene Expression Profiling D006801:Humans D007150:Immunohistochemistry D008545:Melanoma D057089:Pathology, Molecular D017423:Sequence Analysis, RNA
gene expression human cell fraction predictions cancer biology computational biology systems biology tumor immune microenvironment