Hiken Jeffrey, Earls Jon, Flanagan Kevin C, Wellinghoff Rachel L, Ponder Michelle, Messina David N, Glasscock Jarret I, Duncavage Eric J
Cofactor Genomics, Inc, 4044 Clayton Ave, St. Louis, MO, 63110, USA.
Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA.
BMC Cancer. 2025 Jan 7;25(1):21. doi: 10.1186/s12885-024-13362-8.
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.
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.
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.
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.
虽然免疫检查点抑制剂（ICI）疗法可显著改善复发/转移性头颈部鳞状细胞癌（RM-HNSCC）患者的预后，但只有约15%-20%的患者能从此类治疗中获益。因此，迫切需要指导ICI使用的临床试验。OncoPrism-HNSCC检测方法就是为满足这一需求而开发的。该检测方法将基于下一代RNA测序的免疫调节基因表达特征与机器学习算法相结合，生成一个OncoPrism评分，将患者分类为对ICI治疗疾病控制可能性低、中或高。此外，OncoPrism-HNSCC利用用于预测ICI反应的相同FFPE患者肿瘤RNA来识别可能发生NTRK1/2/3或ALK基因致癌重排的罕见病例，这可能表明可使用潜在高效的靶向疗法。OncoPrism-HNSCC的临床性能已得到验证。在此，我们报告其在存在潜在混杂变异来源情况下的分析性能。
通过改变RNA输入量和质量来评估该检测方法的分析灵敏度，观察其对ICI反应预测评分的影响。通过向输入RNA中加入不同百分比的基因组DNA来测试分析特异性。评估批内和批间精密度，并评估基因融合检测的分析灵敏度、特异性和精密度。在67份FFPE临床样本上测试与基因融合检测正交方法的一致性。
RNA输入量低至标称输入量的四倍以下对ICI反应预测评分影响不大。低于测试阈值的RNA质量水平无显著影响。高达30%的基因组DNA掺入对评分只有微小影响。多个操作人员、试剂批次、批次和测序仪的合并标准偏差产生的总体方差仅占测试评分范围（0-100）的0.87%。NTRK和ALK基因融合检测与正交方法的一致性为100%。
OncoPrism-HNSCC检测方法强大且可靠的分析性能支持其临床应用，即使在实验室环境中存在典型变异的情况下也是如此。
