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Unearthing new genomic markers of drug response by improved measurement of discriminative power

机译：通过改进判别能力，发掘新的药物反应基因组标记

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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).
机译：肿瘤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反应性患者的能力仍然很差。因此，迫切需要发现市售和研究的肿瘤学药物的反应标记。针对一大批癌细胞系筛选这些药物导致发现了新的体外药物反应基因组标记。但是，尽管在数据中成千上万的候选药物基因关联中识别此类标记容易出错，但目前尚缺乏对此类检测任务有效性的评估。在这里，我们提出了一种新的非参数方法来测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的优点是不会对数据造成强烈假设，从而扭曲了基因组标记物的识别。此外，我们引入了新的基准，以使用未用于识别标记的最新数据进一步在体外验证这些标记。这种新方法的应用导致鉴定了分布在61％的被分析药物中的128种新基因组标记，包括5种没有先前已知标记的药物，而该方法最初被用于分析药物基因组数据的MANOVA测试所遗漏癌症联盟的敏感性。使用多个统计测试来发现标记并在独立数据上对其进行测试是不寻常的。我们发现这有助于丢弃实际上是虚假相关性的统计学上显着的药物基因关联。这种方法还揭示了新的，经过独立验证的体外药物反应标记物，如替莫罗莫司-CDKN2A（抗药性）和吉西他滨-EWS_FLI1（敏感性）。

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