Commo F, Ferté C, Soria J C, Friend S H, André F, Guinney J
Sage Bionetworks, Seattle, USA INSERM U981, Gustave Roussy, University Paris XI, Villejuif.
Sage Bionetworks, Seattle, USA INSERM U981, Gustave Roussy, University Paris XI, Villejuif Department of Medical Oncology, Gustave Roussy, Villejuif, France.
Ann Oncol. 2015 Mar;26(3):582-8. doi: 10.1093/annonc/mdu582. Epub 2014 Dec 23.
Comparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may profoundly affect CGH array results and therefore may adversely affect outcomes in the precision medicine context.
The effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia (CCLE) panel, and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n = 283, MOSCATO-01 n = 309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively.
Using the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (P < 0.05). Using the CCLE dataset, selected genes (ERBB2, EGFR) were better or equally correlated to the IC50 of their companion drug (lapatinib, erlotinib), when applying the alternative centralization. Finally, focusing on 24 actionable genes, we observed as many as 7.1% (SAFIR-01) and 6.8% (MOSCATO-01) of patients originally not oriented to a specific treatment, but who could have been proposed a treatment based on the alternative peak centralization method.
The centralization method substantially affects the call detection of CGH profiles and may thus impact precision medicine approaches. Among the three methods described, the alternative peak method addresses limitations associated with existing approaches.
比较基因组杂交（CGH）阵列在个性化医疗项目中越来越多地用于识别基因拷贝数畸变（CNA），这些畸变可用于指导分子肿瘤委员会做出的临床决策。然而，诸如集中化步骤等分析过程可能会深刻影响CGH阵列结果，因此可能会在精准医疗背景下对结果产生不利影响。
评估了三种不同的集中化方法（中位数、最大峰值、替代峰值）对三个数据集的影响：（i）NCI60细胞系面板，（ii）癌症细胞系百科全书（CCLE）面板，以及（iii）参与前瞻性分子筛查试验的患者（SAFIR-01，n = 283；MOSCATO-01，n = 309），并分别与核型分析、药物敏感性和患者-药物匹配进行比较。
使用NCI60细胞系面板时，替代峰值法生成的图谱比其他集中化策略生成的图谱更接近细胞核型（P < 0.05）。使用CCLE数据集时，应用替代集中化时，所选基因（ERBB2、EGFR）与其配套药物（拉帕替尼、厄洛替尼）的IC50具有更好或同等的相关性。最后，聚焦于24个可操作基因，我们观察到，原本未被指定特定治疗方案，但基于替代峰值集中化方法本可被建议接受治疗的患者，在SAFIR-01中多达7.1%，在MOSCATO-01中多达6.8%。
集中化方法会显著影响CGH图谱的调用检测，从而可能影响精准医疗方法。在所描述的三种方法中，替代峰值法解决了现有方法的局限性。
