Comparison of Approaches to Transcriptomic Analysis in Multi-Sampled Tumors

Intratumoral heterogeneity is a well-documented feature of human cancers associated with outcome and treatment resistance. However, a heterogeneous tumor transcriptome contributes an unknown level of variability to analyses of differentially expressed genes that may contribute to phenotypes of interest, including treatment response. Although current clinical practice and the vast majority of research studies use a single sample from each patient, decreasing costs in sequencing technologies and computing costs have made repeated-measures analyses increasingly economical. Repeatedly sampling the same tumor increases the statistical power of differentially expressed gene analysis that is indispensable towards downstream analysis and also increases ones understanding of within-tumor variance that may affect conclusions. Here, we compared five different methods for analyzing gene expression profiles derived from repeated sampling of human prostate tumors in two separate cohorts of patients. We also benchmarked the sensitivity of generalized linear models to linear mixed models for identifying differentially expressed genes contributing to relevant prostate cancer pathways based on a ground truth model.