Over the last two decades, various pathway analysis methods have been proposed to investigate complex biological interactions with omics data. Topology based (TB) pathway analysis techniques are generally considered to have better performance than non-topology-based methods. However, these methods score an entire pathway as a unit, where the relevance of individual routes within the pathway is lost. In this paper, a novel route-based pathway analysis framework is discussed that can effectively process entire cohorts of gene expression data sets and identify significant pathway routes in the given cohort. The framework begins with identifying all possible transcription factor (TF) centric routes from KEGG signaling pathways. For each route in a pathway, activity scores and p-values are calculated for samples in the given cohort. Overall route activity in a cohort is assessed in terms of two summary metrics, “Proportion of Significance” (PS) and “Average Route Score” (ARS). Case studies of two human cancer cohorts from The Cancer Genome Atlas (TCGA) repository are presented.