bioRxivBioinformatics
Gene expression markers of Tumor Infiltrating Leukocytes
Patrick Danaher, Sarah Warren, Lucas Dennis, Leonard D'Amico, Andrew White, Mary L Disis, Melissa A Geller, Kunle Odunsi, Joseph Beechem, Stephen P Fling
Abstract
BackgroundAssays of the abundance of immune cell populations in the tumor microenvironment promise to inform immune oncology research and the choice of immunotherapy for individual patients. We propose to measure the intratumoral abundance of various immune cells populations with gene expression. In contrast to IHC and flow cytometry, gene expression assays yield high information content from a clinically practical workflow. Previous studies of gene expression in purified immune cells have reported hundreds of genes showing enrichment in a single cell type, but the utility of these genes in tumor samples is unknown. We describe a novel statistical method for using co-expression patterns in large tumor gene expression datasets to validate previously reported candidate cell type marker genes, and we use this method to winnow previously published gene lists down to a subset of high confidence marker genes.\n\nMethodsWe use co-expression patterns in 9986 samples from The Cancer Genome Atlas (TCGA) to validate previously reported cell type marker genes. We compare immune cell scores derived from these genes to measurements from flow cytometry and immunohistochemistry. We characterize the reproducibility of our cell scores in replicate runs of RNA extracted from FFPE tumor tissue.\n\nResultsWe identify a list of 60 marker genes whose expression levels quantify 14 immune cell populations. Cell type scores calculated from these genes are concordant with flow cytometry and IHC readings, show high reproducibility in replicate RNA samples from FFPE tissue, and reveal an intricate picture of the immune infiltrate in TCGA. Most genes previously reported to be enriched in a single cell type have co-expression patterns inconsistent with cell type specificity.\n\nConclusionsDue to their concise gene set, computational simplicity and utility in tumor samples, these cell type gene signatures may be useful in future discovery research and clinical trials to understand how tumors and therapeutic intervention shape the immune response.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Simultaneous Enumeration Of Cancer And Immune Cell Types From Bulk Tumor Gene Expression Data
J. Racle, K. de Jonge, … D. Gfeller·bioRxiv·Sep 1, 2017
- ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing For Aberrant Gene Expression Patterns
Douglas R. Wilson, Chong Jin, … Wei Sun·Journal of the American Statistical AssociationPeer-reviewed·Aug 14, 2019
- ILnc: Prioritizing Long Non-coding RNAs for Pan-cancer Analysis of Immune Cell Infiltration
Xinhui Li, Changbo Yang, … Xia Li·bioRxiv·Mar 10, 2022
- ImmuCellAI: a unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy
Ya‐Ru Miao, Qiong Zhang, … An‐Yuan Guo·Advanced SciencePeer-reviewed·Feb 11, 2020
- TIGER: A Web Portal of Tumor Immunotherapy Gene Expression Resource
Zhihang Chen, Ziwei Luo, … Zhixiang Zuo·Genomics Proteomics & BioinformaticsPeer-reviewed·Aug 29, 2022
- The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression
Yasin Senbabaoglu, Andrew G Winer, … Ari Hakimi·bioRxiv·Sep 1, 2015