Analytical Performance of an Immunoprofiling Assay Based on RNA Models

Ian Schillebeeckx, Jon R. Armstrong, Jason T. Forys, Jeffrey Hiken, Jon Earls, Kevin C. Flanagan, Tiange Cui, Jarret I. Glasscock, David N. Messina, Eric J. Duncavage

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


As immuno-oncology drugs grow more popular in the treatment of cancer, better methods are needed to quantify the tumor immune cell component to determine which patients are most likely to benefit from treatment. Methods such as flow cytometry can accurately assess the composition of infiltrating immune cells; however, they show limited use in formalin-fixed, paraffin-embedded (FFPE) specimens. This article describes a novel hybrid-capture RNA sequencing assay, ImmunoPrism, that estimates the relative percentage abundance of eight immune cell types in FFPE solid tumors. Immune health expression models were generated using machine learning methods and used to uniquely identify each immune cell type using the most discriminatively expressed genes. The analytical performance of the assay was assessed using 101 libraries from 40 FFPE and 32 fresh-frozen samples. With defined samples, ImmunoPrism had a precision of ±2.72%, a total error of 2.75%, and a strong correlation (r2 = 0.81; P < 0.001) to flow cytometry. ImmunoPrism had similar performance in dissociated tumor cell samples (total error of 8.12%) and correlated strongly with immunohistochemistry (CD8: r2 = 0.83; P < 0.001) in FFPE samples. Other performance metrics were determined, including limit of detection, reportable range, and reproducibility. The approach used for analytical validation is shared here so that it may serve as a helpful framework for other laboratories when validating future complex RNA-based assays.

Original languageEnglish
Pages (from-to)555-570
Number of pages16
JournalJournal of Molecular Diagnostics
Issue number4
StatePublished - Apr 2020


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