Agapito Giuseppe, Botta Cirino, Guzzi Pietro Hiram, Arbitrio Mariamena, Di Martino Maria Teresa, Tassone Pierfrancesco, Tagliaferri Pierosandro, Cannataro Mario
Department of Medical and Surgical Science, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
Department of Experimental Medicine and Clinic, University Magna Graecia of Catanzaro, Catanzaro 88100, Italy.
Microarrays (Basel). 2016 Sep 23;5(4):24. doi: 10.3390/microarrays5040024.
The identification of biomarkers for the estimation of cancer patients' survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants.
In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants.
The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data.
Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients.
识别用于评估癌症患者生存情况的生物标志物是现代肿瘤学中的一个关键问题。最近,Affymetrix DMET(药物代谢酶和转运体)微阵列平台提供了确定患者的吸收、分布、代谢和排泄(ADME)基因变异并将其与药物依赖性不良事件相关联的可能性。因此,从使用DMET数据获得的患者概况出发分析患者的生存分布,可能会向临床医生揭示有关药物反应、生存率和基因变异之间可能存在的相关性的重要信息。
为了支持这一分析,我们开发了OSAnalyzer,这是一个软件工具,能够计算癌症患者的总生存期(OS)和无进展生存期(PFS),并评估它们与ADME基因变异的关联。
该工具能够对富含生存事件的DMET数据进行自动分析。此外,根据通过对数秩检验计算的曲线下面积比较获得的统计显著性对结果进行排序,从而实现对高通量数据的快速简便分析和可视化。
最后,我们展示了一个案例研究,以突出OSAnalyzer在分析大量患者时的实用性。