Veracyte, Inc., South San Francisco, CA, 94080, USA.
BMC Cancer. 2021 Apr 13;21(1):400. doi: 10.1186/s12885-021-08130-x.
Bronchoscopy is a common procedure used for evaluation of suspicious lung nodules, but the low diagnostic sensitivity of bronchoscopy often results in inconclusive results and delays in treatment. Percepta Genomic Sequencing Classifier (GSC) was developed to assist with patient management in cases where bronchoscopy is inconclusive. Studies have shown that exposure to tobacco smoke alters gene expression in airway epithelial cells in a way that indicates an increased risk of developing lung cancer. Percepta GSC leverages this idea of a molecular "field of injury" from smoking and was developed using RNA sequencing data generated from lung bronchial brushings of the upper airway. A Percepta GSC score is calculated from an ensemble of machine learning algorithms utilizing clinical and genomic features and is used to refine a patient's risk stratification.
The objective of the analysis described and reported here is to validate the analytical performance of Percepta GSC. Analytical performance studies characterized the sensitivity of Percepta GSC test results to input RNA quantity, the potentially interfering agents of blood and genomic DNA, and the reproducibility of test results within and between processing runs and between laboratories.
Varying the amount of input RNA into the assay across a nominal range had no significant impact on Percepta GSC classifier results. Bronchial brushing RNA contaminated with up to 10% genomic DNA by nucleic acid mass also showed no significant difference on classifier results. The addition of blood RNA, a potential contaminant in the bronchial brushing sample, caused no change to classifier results at up to 11% contamination by RNA proportion. Percepta GSC scores were reproducible between runs, within runs, and between laboratories, varying within less than 4% of the total score range (standard deviation of 0.169 for scores on 4.57 scale).
The analytical sensitivity, analytical specificity, and reproducibility of Percepta GSC laboratory results were successfully demonstrated under conditions of expected day to day variation in testing. Percepta GSC test results are analytically robust and suitable for routine clinical use.
支气管镜检查是一种常用于评估可疑肺结节的常见程序，但支气管镜检查的低诊断灵敏度通常导致结果不确定，并延迟治疗。Percepta 基因组测序分类器 (GSC) 的开发是为了协助处理支气管镜检查结果不确定的患者。研究表明，吸烟会改变气道上皮细胞的基因表达，从而增加患肺癌的风险。Percepta GSC 利用了吸烟导致的分子“损伤场”的这一理念，并利用来自上呼吸道支气管刷取的 RNA 测序数据开发而成。Percepta GSC 评分是从利用临床和基因组特征的机器学习算法的集合中计算得出的，用于细化患者的风险分层。
本文所述和报告的分析的目的是验证 Percepta GSC 的分析性能。分析性能研究表征了 Percepta GSC 测试结果对输入 RNA 量、血液和基因组 DNA 潜在干扰剂以及测试结果在处理运行内、运行之间以及实验室之间的重现性的敏感性。
在标称范围内改变检测中的输入 RNA 量对 Percepta GSC 分类器结果没有显著影响。用核酸质量污染多达 10%基因组 DNA 的支气管刷 RNA 也未对分类器结果产生显著差异。多达 11% RNA 比例的血液 RNA （支气管刷样本中的潜在污染物）的添加对分类器结果没有影响。Percepta GSC 评分在运行之间、运行内和实验室之间具有重现性，在总评分范围内的变化小于 4%（在 4.57 刻度上评分的标准偏差为 0.169）。
在日常测试中预期的变化条件下，成功证明了 Percepta GSC 实验室结果的分析灵敏度、分析特异性和重现性。Percepta GSC 测试结果具有分析稳健性，适合常规临床使用。
