Gene set optimization for cancer transcriptomics using sparse principal component analysis

Abstract

A common approach for exploring pathway dysregulation in cancer involves the gene set or pathway analysis of tumor transcriptomic data. Unfortunately, the effectiveness of cancer gene set testing is limited by the fact that most gene set collections model gene activity in normal tissue, which can differ significantly from gene activity found within tumors. To address this challenge, we have developed a bioinformatics approach based on sparse principal component analysis (PCA) for optimizing existing gene set collections to reflect the pattern of gene activity in dysplastic tissue and have used this technique to optimize the Molecular Signatures Database (MSigDB) Hallmark collection for 21 solid human cancers profiled via bulk RNA-seq by The Tumor Genome Atlas (TCGA). Demonstrating the biological utility of our approach, the average survival association of gene set members is improved after optimization for nearly all cancer types and Hallmark gene sets.

Figure 1:

Proportion of Hallmark gene set annotations retained after optimization on the bulk RNA-seq data for each TCGA cohort using 1 to 3 sparse PCs.

Figure 2:

Proportions of the Hallmark gene sets by outcome class after optimization on the bulk RNA-seq data for each TCGA cohort using different numbers of PCs.

Figure 3:

Proportions of the Hallmark gene sets by outcome class after optimization on the bulk RNA-seq data from distinct TCGA cohorts.