Integrating Biological Knowledge with Gene Expression Profiles for Survival Prediction of Cancer

Due to the large variability in survival times between cancer patients and the plethora of genes on microarrays unrelated to outcome, building accurate prediction models that are easy to interpret remains a challenge. In this paper, we propose a general strategy for improving performance and interpretability of prediction models by integrating gene expression data with prior biological knowledge. First, we link gene identifiers in expression dataset with gene annotation databases such as Gene Ontology (GO). Then we construct supergenes for each gene category by summarizing information from genes related to outcome using a modified principal component analysis (PCA) method. Finally, instead of using genes as predictors, we use these supergenes representing information from each gene category as predictors to predict survival outcome. In addition to identifying gene categories associated with outcome, the proposed approach also carries out additional within-category selection to select important genes within each gene set. We show, using two real breast cancer microarray datasets, that the prediction models constructed based on gene sets (or pathway) information outperform the prediction models based on expression values of single genes, with improved prediction accuracy and interpretability.