Analytical performance of OncoPrism-HNSCC, an RNA-based assay to inform immune checkpoint inhibitor treatment decisions for recurrent/metastatic head and neck squamous cell carcinoma

While immune checkpoint inhibitor (ICI) therapies can significantly improve outcomes for patients with recurrent/metastatic head and neck squamous cell carcinoma (RM-HNSCC), only about 15–20% benefit from such treatments. Clinical tests that guide the use of ICIs are therefore critically needed. OncoPrism-HNSCC was developed to address this need. The assay combines next generation RNA sequencing-based immunomodulatory gene expression signatures with machine learning algorithms to generate an OncoPrism Score that classifies patients as having low, medium, or high likelihood of disease control in response to ICI treatment. Also, OncoPrism-HNSCC leverages the same FFPE patient tumor RNA used for ICI response prediction to identify rare cases where oncogenic rearrangements in NTRK1/2/3 or ALK genes, which may indicate the use of potentially highly effective targeted therapies. The clinical performance of OncoPrism-HNSCC has been validated. Here, we report its analytical performance in the presence of potentially confounding sources of variation.