Robust Selection of Cancer Survival Signatures from High-Throughput Genomic Data Using Two-Fold Subsampling

Identifying relevant signatures for clinical patient outcome is a fundamental task in high-throughput studies. Signatures, composed of features such as mRNAs, miRNAs, SNPs or other molecular variables, are often non-overlapping, even though they have been identified from similar experiments considering samples with the same type of disease. The lack of a consensus is mostly due to the fact that sample sizes are far smaller than the numbers of candidate features to be considered, and therefore signature selection suffers from large variation. We propose a robust signature selection method that enhances the selection stability of penalized regression algorithms for predicting survival risk. Our method is based on an aggregation of multiple, possibly unstable, signatures obtained with the preconditioned lasso algorithm applied to random (internal) subsamples of a given cohort data, where the aggregated signature is shrunken by a simple thresholding strategy. The resulting method, RS-PL, is conceptually simple and easy to apply, relying on parameters automatically tuned by cross validation. Robust signature selection using RS-PL operates within an (external) subsampling framework to estimate the selection probabilities of features in multiple trials of RS-PL. These probabilities are used for identifying reliable features to be included in a signature. Our method was evaluated on microarray data sets from neuroblastoma, lung adenocarcinoma, and breast cancer patients, extracting robust and relevant signatures for predicting survival risk. Signatures obtained by our method achieved high prediction performance and robustness, consistently over the three data sets. Genes with high selection probability in our robust signatures have been reported as cancer-relevant. The ordering of predictor coefficients associated with signatures was well-preserved across multiple trials of RS-PL, demonstrating the capability of our method for identifying a transferable consensus signature. The software is available as an R package rsig at CRAN (http://cran.r-project.org).在高通量研究领域，挖掘与临床患者结局相关的特征签名（signature）是一项核心研究任务。由mRNA、miRNA、单核苷酸多态性（SNP）或其他分子变量构成的特征签名，尽管均来自针对同一疾病类型样本的相似实验，但彼此之间往往互不重叠。造成这一共识缺失的核心原因在于：样本量远小于待筛选的候选特征总数，因此特征签名的选择过程存在显著波动。为此，我们提出一种鲁棒性特征签名选择方法，可提升用于预测生存风险的惩罚回归算法的选择稳定性。该方法的核心思路是对多个可能不稳定的特征签名进行聚合：针对给定队列数据的随机（内部）子样本，使用预处理套索（preconditioned lasso）算法生成特征签名，再通过简单的阈值化策略对聚合后的特征签名进行收缩。由此得到的RS-PL方法概念简洁、易于实现，其参数可通过交叉验证自动完成调优。基于RS-PL的鲁棒性特征签名选择在（外部）子采样框架下运行：在多次RS-PL试验中估计各特征的选择概率，并以此筛选出可纳入特征签名的可靠特征。我们在神经母细胞瘤、肺腺癌及乳腺癌患者的微阵列（microarray）数据集上对该方法进行了验证，成功提取出可用于预测生存风险的鲁棒且具有临床相关性的特征签名。在上述三个数据集上，我们的方法生成的特征签名均展现出优异的预测性能与鲁棒性，且结果保持高度一致。在我们的鲁棒特征签名中选择概率较高的基因，已有研究证实其与癌症发生发展密切相关。在多次RS-PL试验中，与特征签名相关的预测系数排序保持高度一致，这表明我们的方法能够识别出可迁移的共识特征签名。该软件以R包rsig的形式发布于CRAN（http://cran.r-project.org）。