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

Abstract

Abstract Background 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 may occur, and 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. Methods The assay’s analytical sensitivity was assessed by varying RNA input quantity and quality, observing the effect on ICI response prediction scores. Analytical specificity was tested by spiking increasing percentages of genomic DNA into input RNA. Intra-assay and inter-assay precision were evaluated, and the analytical sensitivity, specificity, and precision of gene fusion detection were assessed. Concordance with orthogonal methods of gene fusion detection was tested on 67 FFPE clinical samples. Results Varying RNA inputs as low as four-fold below the nominal input amount had little effect on ICI response prediction scores. RNA quality levels below the test threshold had no significant effect. Genomic DNA spike-ins up to 30% had only a small effect on scores. The pooled standard deviation for multiple operators, reagent lots, batches, and sequencers yielded an overall variance represented by just 0.87% of the score range of the test (0–100). NTRK and ALK gene fusion detection was 100% concordant with orthogonal methods. Conclusions Robust and reliable analytical performance of the OncoPrism-HNSCC assay supports its clinical use, even in the presence of variation typically encountered in the laboratory setting.

Keywords

• OncoPrism
• Head and neck cancer
• HNSCC
• Analytical validation
• Biomarker
• Classifier