Relator Raissa T, Terada Aika, Sese Jun
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, 135-0064, Japan.
PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan.
BMC Med Genomics. 2018 Apr 20;11(Suppl 2):31. doi: 10.1186/s12920-018-0346-x.
Survival analysis methods have been widely applied in different areas of health and medicine, spanning over varying events of interest and target diseases. They can be utilized to provide relationships between the survival time of individuals and factors of interest, rendering them useful in searching for biomarkers in diseases such as cancer. However, some disease progression can be very unpredictable because the conventional approaches have failed to consider multiple-marker interactions. An exponential increase in the number of candidate markers requires large correction factor in the multiple-testing correction and hide the significance.
We address the issue of testing marker combinations that affect survival by adapting the recently developed Limitless Arity Multiple-testing Procedure (LAMP), a p-value correction technique for statistical tests for combination of markers. LAMP cannot handle survival data statistics, and hence we extended LAMP for the log-rank test, making it more appropriate for clinical data, with newly introduced theoretical lower bound of the p-value.
We applied the proposed method to gene combination detection for cancer and obtained gene interactions with statistically significant log-rank p-values. Gene combinations with orders of up to 32 genes were detected by our algorithm, and effects of some genes in these combinations are also supported by existing literature.
The novel approach for detecting prognostic markers presented here can identify statistically significant markers with no limitations on the order of interaction. Furthermore, it can be applied to different types of genomic data, provided that binarization is possible.
生存分析方法已广泛应用于健康和医学的不同领域，涵盖各种感兴趣的事件和目标疾病。它们可用于提供个体生存时间与感兴趣因素之间的关系，使其在诸如癌症等疾病的生物标志物搜索中很有用。然而，一些疾病进展可能非常不可预测，因为传统方法未能考虑多标记相互作用。候选标记数量的指数增长需要在多重检验校正中使用大的校正因子，从而掩盖了显著性。
我们通过采用最近开发的无限元多重检验程序（LAMP）来解决测试影响生存的标记组合的问题，LAMP是一种用于标记组合统计检验的p值校正技术。LAMP无法处理生存数据统计，因此我们将LAMP扩展用于对数秩检验，使其更适合临床数据，并新引入了p值的理论下限。
我们将所提出的方法应用于癌症的基因组合检测，并获得了具有统计学显著对数秩p值的基因相互作用。我们的算法检测到了多达32个基因的基因组合，现有文献也支持这些组合中一些基因的作用。
本文提出的检测预后标志物的新方法可以识别具有统计学显著性的标志物，且对相互作用的阶数没有限制。此外，只要可以进行二值化，它就可以应用于不同类型的基因组数据。
