Sensitive detection of pathway perturbations in cancers

Abstract

BackgroundThe normal functioning of a living cell is characterized by complex interaction networks involving many different types of molecules. Associations detected between diseases and perturbations in well-defined pathways within such interaction networks have the potential to illuminate the molecular mechanisms underlying disease progression and response to treatment.ResultsIn this paper, we present a computational method that compares expression profiles of genes in cancer samples to samples from normal tissues in order to detect perturbations of pre-defined pathways in the cancer. In contrast to many previous methods, our scoring function approach explicitly takes into account the interactions between the gene products in a pathway. Moreover, we compute the sub-pathway that has the highest score, as opposed to merely computing the score for the entire pathway. We use a permutation test to assess the statistical significance of the most perturbed sub-pathway. We apply our method to 20 pathways in the Netpath database and to the Global Cancer Map of gene expression in 18 cancers. We demonstrate that our method yields more sensitive results than alternatives that do not consider interactions or measure the perturbation of a pathway as a whole. We perform a sensitivity analysis to show that our approach is robust to modest changes in the input data. Our method confirms numerous well-known connections between pathways and cancers.ConclusionsOur results indicate that integrating differential gene expression with the interaction structure in a pathway is a powerful approach for detecting links between a cancer and the pathways perturbed in it. Our results also suggest that even well-studied pathways may be perturbed only partially in any given cancer. Further analysis of cancer-specific sub-pathways may shed new light on the similarities and differences between cancers.

Unraveling Verapamil’s Multidimensional Role in Diabetes Therapy: From β-Cell Regeneration to Cholecystokinin Induction in Zebrafish and MIN6 Cell-Line Models

“…Functional classification and enrichment analysis were based on GO annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [ 25 ]. To detect the pathways differentially perturbed in verapamil-treated cells as compared to untreated cells, a computational method was used to integrate differential gene expression into predefined pathways, as previously described [ 32 ]. Briefly, P = (G, I) was graphed, where “P” is the pathway, “G” is their gene sets, and “I “depicts the interactions between these genes.…”

“…The fold change (treatment vs. control) data were entered, and the pathways perturbed by verapamil treatment were identified. The Liptak–Stouffer z-score was calculated as the perturbation of each subgraph, and the most perturbed subpathway was computed using the algorithm, as described elsewhere [ 32 ]. Bonferroni corrections were applied to compute p -values of the most perturbed pathways for more stringent proteomic and transcriptomic results.…”

Unraveling Verapamil's Multidimensional Role in Diabetes Therapy: From β-Cell Regeneration to Cholecystokinin Induction

“…Functional classification and enrichment analysis were based on GO annotation and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database [25]. To detect the pathways differentially perturbed in verapamil-treated cells as compared to untreated cells, a computational method was used to integrate differential gene expression into predefined pathways as previously described [32]. Briefly, P = (G, I) was graphed, where "P" is pathway, "G" is their gene sets, and "I "depicts the interactions between these genes.…”

“…The fold change (treatment vs. control) data were entered and the pathways perturbed by verapamil treatment were identified. Liptak-Stouffer z-score was calculated as the perturbation of each subgraph and the most perturbed sub-pathway was computed using the algorithm as described elsewhere [32]. Bonferroni corrections were applied to compute p-values of the most perturbed pathways for more stringent proteomic and transcriptomic results.…”