Department of Medicine, University of California San Francisco, San Francisco, USA; Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA.
Department of Internal Medicine, Scripps Green Hospital, La Jolla, USA.
J Immunol Methods. 2021 Jul;494:113041. doi: 10.1016/j.jim.2021.113041. Epub 2021 Mar 19.
Neoadjuvant immunotherapy can induce immune responses within the tumor microenvironment. Gene expression can be used to assess responses with limited amounts of conventionally-fixed patient-derived samples. We aim to assess the cross-platform concordance of immune-related gene expression data. We performed comparisons across three panels in two platforms: Nanostring nCounter® PanCancer Immune Profiling Panel (nS), HTG EdgeSeq Oncology Biomarker Panel (HTG OBP) and Precision Immuno-Oncology Panel (HTG PIP). All tissue samples of 14 neoadjuvant GM-CSF treated, 14 neoadjuvant Provenge treated, and 12 untreated prostate cancer patients were radical prostatectomy (RP) tissues, while 6 prostatitis patients and 6 non-prostatitis subjects were biopsies. For all 52 patients, more than 90% of the common genes were significantly correlated (p < 0.05) and more than 76% of the common genes were highly correlated (r > 0.5) between any two panels. Co-inertia analysis also demonstrated high overall dataset structure similarity (correlation>0.84). Although both dimensionality reduction visualization analysis and unsupervised hierarchical cluster analysis for highly correlated common genes (r > 0.9) suggested a high-level of consistency across the panels, there were subsets of genes that were differentially expressed across the panels. In addition, while the effect size of the differential testing for neoadjuvant treated vs. untreated localized prostate cancer patients across the panels were significantly correlated, some genes were only differentially expressed in the HTG panels. Finally, the HTG PIP panel had the best classification performance among the 3 panels. These differences detected may be a result of the different panels or platforms due to their technical setting and focus. Thus, researchers should be aware of those potential differences when deciding which platform and panel to use.
新辅助免疫疗法可以在肿瘤微环境中诱导免疫反应。基因表达可用于评估使用有限数量的常规固定患者衍生样本的反应。我们旨在评估免疫相关基因表达数据的跨平台一致性。我们在两个平台上的三个面板中进行了比较：Nanostring nCounter® PanCancer Immune Profiling Panel (nS)、HTG EdgeSeq Oncology Biomarker Panel (HTG OBP) 和 Precision Immuno-Oncology Panel (HTG PIP)。所有 14 名新辅助 GM-CSF 治疗、14 名新辅助 Provenge 治疗和 12 名未经治疗的前列腺癌患者的组织样本均为根治性前列腺切除术 (RP) 组织，而 6 名前列腺炎患者和 6 名非前列腺炎患者为活检样本。对于所有 52 名患者，超过 90%的常见基因具有显著相关性（p < 0.05），超过 76%的常见基因具有高度相关性（r > 0.5）。协方差分析也证明了数据集结构高度相似（相关性>0.84）。虽然二维降维可视化分析和高度相关的常见基因的无监督层次聚类分析（r > 0.9）表明面板之间具有高度一致性，但存在一些基因在面板之间存在差异表达。此外，虽然面板之间新辅助治疗与未经治疗的局部前列腺癌患者的差异检测的效应大小显著相关，但某些基因仅在 HTG 面板中存在差异表达。最后，在 3 个面板中，HTG PIP 面板具有最佳的分类性能。这些检测到的差异可能是由于不同的面板或平台由于其技术设置和重点而存在差异。因此，研究人员在决定使用哪个平台和面板时应注意这些潜在差异。
