Department of Mathematics, University of Oslo, Blindern, NO 0316 Oslo, Norway.
BMC Bioinformatics. 2009 Dec 13;10:413. doi: 10.1186/1471-2105-10-413.
Survival prediction from high-dimensional genomic data is an active field in today's medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions, but there is a lack of systematic studies on the topic. Also, for the widely used Cox regression model, it is not obvious how to handle such combined models.
We propose a way to combine classical clinical covariates with genomic data in a clinico-genomic prediction model based on the Cox regression model. The prediction model is obtained by a simultaneous use of both types of covariates, but applying dimension reduction only to the high-dimensional genomic variables. We describe how this can be done for seven well-known prediction methods: variable selection, unsupervised and supervised principal components regression and partial least squares regression, ridge regression, and the lasso. We further perform a systematic comparison of the performance of prediction models using clinical covariates only, genomic data only, or a combination of the two. The comparison is done using three survival data sets containing both clinical information and microarray gene expression data. Matlab code for the clinico-genomic prediction methods is available at http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/.
Based on our three data sets, the comparison shows that established clinical covariates will often lead to better predictions than what can be obtained from genomic data alone. In the cases where the genomic models are better than the clinical, ridge regression is used for dimension reduction. We also find that the clinico-genomic models tend to outperform the models based on only genomic data. Further, clinico-genomic models and the use of ridge regression gives for all three data sets better predictions than models based on the clinical covariates alone.
从高维基因组数据中进行生存预测是当今医学研究中的一个活跃领域。大多数提出的预测方法仅利用基因组数据，而不考虑通常可用且已知具有预测价值的既定临床协变量。最近的研究表明，结合临床和基因组信息可以提高预测效果，但在该主题上缺乏系统的研究。此外，对于广泛使用的 Cox 回归模型，如何处理此类组合模型并不明显。
我们提出了一种基于 Cox 回归模型的方法，可将经典临床协变量与基因组数据组合到临床基因组预测模型中。该预测模型通过同时使用两种类型的协变量来获得，但仅对高维基因组变量进行降维。我们描述了如何针对七种著名的预测方法来实现这一点：变量选择、无监督和有监督主成分回归和偏最小二乘回归、岭回归和套索。我们进一步使用仅包含临床信息和微阵列基因表达数据的三个生存数据集，对仅使用临床协变量、仅使用基因组数据或两者结合的预测模型的性能进行了系统比较。Matlab 代码可在 http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/ 获得。
根据我们的三个数据集，比较表明，既定的临床协变量通常会导致比仅从基因组数据获得的更好的预测。在基因组模型优于临床模型的情况下，使用岭回归进行降维。我们还发现，临床基因组模型往往比仅基于基因组数据的模型表现更好。此外，对于所有三个数据集，临床基因组模型和岭回归的使用都比仅基于临床协变量的模型提供了更好的预测。
