Grebnev Pavel Alekseevich, Meshkov Ivan Olegovich, Ershov Pavel Viktorovich, Makhotenko Antonida Viktorovna, Azarian Valentina Bogdanovna, Erokhina Marina Vyacheslavovna, Galeta Anastasiya Aleksandrovna, Zakubanskiy Aleksandr Vladimirovich, Shingalieva Olga Sergeevna, Tregubova Anna Vasilevna, Asaturova Aleksandra Vyacheslavovna, Yudin Vladimir Sergeevich, Yudin Sergey Mihaylovich, Makarov Valentin Vladimirovich, Keskinov Anton Arturovich, Makarova Anna Sergeevna, Snigir Ekaterina Andreevna, Skvortsova Veronika Igorevna
Federal State Budgetary Institution "Centre for Strategic Planning and Management of Biomedical Health Risks" of the Federal Medical Biological Agency (Centre for Strategic Planning of FMBA of Russia), Bld. 1, Pogodinskaya Street, 10, 119121 Moscow, Russia.
Federal State Budgetary Institution "National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov", Ministry of Healthcare of The Russian Federation, Oparina Street, Bld. 4, 117997 Moscow, Russia.
Cancers (Basel). 2024 Sep 24;16(19):3252. doi: 10.3390/cancers16193252.
: The goal of this study was to compare the results of CNV detection by three different methods using 13 paired carcinoma samples, as well as to perform a statistical analysis of the agreement. : CNV was studied using NanoString nCounter v2 Cancer CN Assay (Nanostring), Illumina Infinium CoreExome microarrays (CoreExome microarrays) and digital droplet PCR (ddPCR). : There was a good level of agreement (PABAK score > 0.6) between the CoreExome microarrays and the ddPCR results for finding CNVs. There was a moderate level of agreement (PABAK values ≈ 0.3-0.6) between the NanoString Assay results and microarrays or ddPCR. For 83 out of 87 target genes studied (95%), the agreement between the CoreExome microarrays and NanoString nCounter was characterized by PABAK values < 0.75, except for MAGI3, PDGFRA, NKX2-1 and KDR genes (>0.75). The MET, HMGA2, KDR, C8orf4, PAX9, CDK6, and CCND2 genes had the highest agreement among all three approaches. : Therefore, to get a better idea of how to genotype an unknown CNV spectrum in tumor or normal tissue samples that are very different molecularly, it makes sense to use at least two CNV detection methods. One of them, like ddPCR, should be able to quantitatively confirm the results of the other.
本研究的目的是使用13对癌组织样本比较三种不同方法检测拷贝数变异（CNV）的结果，并对一致性进行统计分析。使用NanoString nCounter v2癌症CN分析（NanoString）、Illumina Infinium CoreExome微阵列（CoreExome微阵列）和数字液滴PCR（ddPCR）研究CNV。在检测CNV方面，CoreExome微阵列和ddPCR结果之间具有较高的一致性水平（PABAK评分>0.6）。NanoString分析结果与微阵列或ddPCR之间的一致性水平中等（PABAK值≈0.3 - 0.6）。在研究的87个靶基因中的83个（95%）中，CoreExome微阵列和NanoString nCounter之间的一致性以PABAK值<0.75为特征，但MAGI3、PDGFRA、NKX2 - 1和KDR基因除外（>0.75）。MET、HMGA2、KDR、C8orf4、PAX9、CDK6和CCND2基因在所有三种方法中具有最高的一致性。因此，为了更好地了解如何对分子差异很大的肿瘤或正常组织样本中的未知CNV谱进行基因分型，使用至少两种CNV检测方法是有意义的。其中一种方法，如ddPCR，应该能够定量地确认另一种方法的结果。
