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Abstract
BackgroundPancreatic adenocarcinoma (PAC) is one of the most intractable malignancies. In order to search for potential new therapeutic targets, we relied on computational methods aimed at identifying transcription factor binding sites (TFBSs) over-represented in the promoter regions of genes differentially expressed in PAC. Though many computational methods have been implemented to accomplish this, none has gained overall acceptance or produced proven novel targets in PAC. To this end we have developed DEMON, a novel method for motif detection.MethodologyDEMON relies on a hidden Markov model to score the appearance of sequence motifs, taking into account all potential sites in a promoter of potentially varying binding affinities. We demonstrate DEMON's accuracy on simulated and real data sets. Applying DEMON to PAC-related data sets identifies the RUNX family as highly enriched in PAC-related genes. Using a novel experimental paradigm to distinguish between normal and PAC cells, we find that RUNX3 mRNA (but not RUNX1 or RUNX2 mRNAs) exhibits time-dependent increases in normal but not in PAC cells. These increases are accompanied by changes in mRNA levels of putative RUNX gene targets.ConclusionsThe integrated application of DEMON and a novel differentiation system led to the identification of a single family member, RUNX3, which together with four of its putative targets showed a robust response to a differentiation stimulus in healthy cells, whereas this regulatory mechanism was absent in PAC cells, emphasizing RUNX3 as a promising target for further studies.
Highlights
Pancreatic adenocarcinoma (PAC) is one of the most aggressive cancers
The integrated application of Detecting Enriched MOtifs in co-regulated geNes (DEMON) and a novel differentiation system led to the identification of a single family member, RUNX3, which together with four of its putative targets showed a robust response to a differentiation stimulus in healthy cells, whereas this regulatory mechanism was absent in PAC cells, emphasizing RUNX3 as a promising target for further studies
Given a target set of promoters of co-regulated genes and a set of known transcription factor binding sites (TFBSs) motifs, DEMON seeks motifs that appear in those promoters more frequently than expected by chance
Summary
Pancreatic adenocarcinoma (PAC) is one of the most aggressive cancers. 10th in incidence, it is the fourth leading cause of cancer deaths in the Western world. In the US, approximately 30,000 new cases are diagnosed each year and virtually the same number of PAC patients die each year of the disease[1,2]. This grim picture makes this cancer a worthy subject for searching for novel therapeutic targets. In order to search for potential new therapeutic targets, we relied on computational methods aimed at identifying transcription factor binding sites (TFBSs) over-represented in the promoter regions of genes differentially expressed in PAC. Though many computational methods have been implemented to accomplish this, none has gained overall acceptance or produced proven novel targets in PAC. To this end we have developed DEMON, a novel method for motif detection
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