Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data

By Julien Racle, Kaat de Jonge, Petra Baumgaertner, D. Speiser, David Gfeller. bioRxiv, 2017

1,103 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/f5403f6b55797286baac54a2d02abc75f9b01053

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Citations in the corpus

RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, background): “While bulk RNA-seq can be computationally deconvoluted to estimate the composition of cells present [44], it is not possible to discover new cell types or perform cell-type-specific analyses with bulk RNA-seq and thus single cell RNA-seq opens the door to new applications.”
Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals Disease-Specific Alterations of Immune Cells (2020, background): “, 2016) in a bulk tissue transcriptome deconvolution approach (Racle et al., 2017).”
TIMER2.0 for analysis of tumor-infiltrating immune cells (2020, methodology): “0 utilizes the immunedeconv (16), an R package which integrates six state-of-the-art algorithms, including TIMER (9), xCell (7), MCP-counter (8), CIBERSORT (10), EPIC (11) and quanTIseq (12).”
IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures (2020, methodology): “EPIC decodes the proportion of immune and cancer cells from the expression of genes and compares it with the gene expression pro ﬁ les from speci ﬁ c cells to predict the cell subpopulation landscape (19).”
Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology (2019, methodology): “EPIC EPC D Cell fractions, relative to all cells in sample Intra, inter constrained least square regression 6 immune cell types, fibroblasts, endothelial cells Racle et al. (2017)”