Dang Cuong C, Peón Antonio, Ballester Pedro J
Cancer Research Center of Marseille, INSERM U1068, F-13009, Marseille, France.
Institut Paoli-Calmettes, F-13009, Marseille, France.
BMC Med Genomics. 2018 Feb 6;11(1):10. doi: 10.1186/s12920-018-0336-z.
Oncology drugs are only effective in a small proportion of cancer patients. Our current ability to identify these responsive patients before treatment is still poor in most cases. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs. Screening these drugs against a large panel of cancer cell lines has led to the discovery of new genomic markers of in vitro drug response. However, while the identification of such markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking.
Here we present a new non-parametric method to measuring the discriminative power of a drug-gene association. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers.
The application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium.
Discovering markers using more than one statistical test and testing them on independent data is unusual. We found this helpful to discard statistically significant drug-gene associations that were actually spurious correlations. This approach also revealed new, independently validated, in vitro markers of drug response such as Temsirolimus-CDKN2A (resistance) and Gemcitabine-EWS_FLI1 (sensitivity).
肿瘤学药物仅对一小部分癌症患者有效。在大多数情况下，我们目前在治疗前识别这些有反应患者的能力仍然很差。因此，迫切需要发现已上市和正在研究的肿瘤学药物的反应标志物。针对大量癌细胞系筛选这些药物已导致发现了体外药物反应的新基因组标志物。然而，虽然在数据中的数千个候选药物 - 基因关联中识别此类标志物容易出错，但目前缺乏对此类检测任务有效性的评估。
在此，我们提出一种新的非参数方法来衡量药物 - 基因关联的判别力。与参数统计检验不同，所采用的非参数检验具有不对数据做出强假设从而扭曲基因组标志物识别的优点。此外，我们引入了一个新的基准，以使用未用于识别标志物的更新数据在体外进一步验证这些标志物。
这种新方法的应用已导致识别出128个新的基因组标志物，分布在61%的分析药物中，包括5种以前没有已知标志物的药物，这些药物被最初用于分析癌症药物敏感性基因组学联盟数据的多变量方差分析测试遗漏了。
使用不止一种统计检验发现标志物并在独立数据上对其进行测试并不常见。我们发现这有助于舍弃实际上是虚假相关性的具有统计学意义的药物 - 基因关联。这种方法还揭示了新的、经过独立验证的体外药物反应标志物（如替西罗莫司 - CDK2N1（耐药）和吉西他滨 - EWS_FLI1（敏感））。
