Abstract
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).
Highlights
Tumors form complex microenvironments composed of various cell types such as cancer, immune, stromal and endothelial cells (Hanahan and Weinberg, 2011; Joyce and Fearon, 2015)
By combining RNA-Seq profiles of all major immune and other non-malignant cell types established from both circulating and tumor-infiltrating cells together with information about cell morphology and algorithmic developments to consider uncharacterized and possibly highly variable cell types, EPIC overcomes several limitations of previous approaches to predict the fraction of both cancer and immune or other non-malignant cell types from bulk tumor gene expression data
The method can be broadly applied to most solid tumors, as confirmed by our validation in melanoma and colorectal samples, but it will not be suitable for hematological malignancies like leukemia or lymphoma
Summary
Tumors form complex microenvironments composed of various cell types such as cancer, immune, stromal and endothelial cells (Hanahan and Weinberg, 2011; Joyce and Fearon, 2015). Immune cells infiltrating the tumor microenvironment play a major role in shaping tumor progression, response to (immuno-)therapy and patient survival (Fridman et al, 2012). Flow cytometry or immunohistochemistry (IHC) measurements to quantify the number of both malignant and tumor-infiltrating immune cells are rarely performed for samples analyzed at the gene expression level. To correctly interpret these data in particular from an immuno-oncology point of view (Angelova et al, 2015; Gentles et al, 2015; Hackl et al, 2016; Li et al, 2016; Linsley et al, 2015; Rooney et al, 2015; Senbabaoglu et al, 2016; Zheng et al, 2017), reliable and carefully validated bioinformatics tools are required to infer the fraction of cancer and immune cell types from bulk tumor gene expression data
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