Analytical Performance of an Immunoprofiling Assay Based on RNA Models

Abstract 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 show limited use in FFPE specimens. Here we describe a novel hybrid-capture RNA sequencing assay, ImmunoPrism, that estimates the relative percent abundance of 8 immune cell types in FFPE solid tumors. Immune health expression models (iHEMs) were created 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 (r 2 =0.81, p