Purpose The objective of this study was to determine the incidence of the MYB-MFIB fusion in salivary adenoid cystic carcinoma (ACC), to establish the clinicopathological significance of the fusion and to analyze the expression of MYB in ACCs in the context of the MYB-NFIB fusion. Experimental Design We performed an extensive analysis involving 123 cancers of the salivary gland, including primary and metastatic ACCs, and non-ACC salivary carcinomas. MYB-NFIB fusions were identified by reverse transcription-PCR (RT-PCR) and sequencing of the RT-PCR products, and confirmed by fluorescence in situ hybridization. MYB RNA expression was determined by quantitative RT-PCR and protein expression was analyzed by immunohistochemistry. Results The MYB-NFIB fusion was detected in 28% primary and 35% metastatic ACCs, but not in any of the non-ACC salivary carcinomas analyzed. Different exons in both MYB and NFIB genes were involved in the fusions, resulting in expression of multiple chimeric variants. Notably, MYB was overexpressed in the vast majority of the ACCs, although MYB expression was significantly higher in tumors carrying the MYB-NFIB fusion. The presence of the MYB-NFIB fusion was significantly associated (p = 0.03) with patients older than 50 years of age. No correlation with other clinicopathological markers, factors and survival was found. Conclusions We conclude that the MYB-NFIB fusion characterizes a subset of ACCs and contributes to MYB overexpression. Additional mechanisms may be involved in MYB overexpression in ACCs lacking the MYB-NFIB fusion. These findings suggest that MYB may be a specific novel target for tumor intervention in patients with ACC.

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BackgroundMeasuring similarity between diseases plays an important role in disease-related molecular function research. Functional associations between disease-related genes and semantic associations between diseases are often used to identify pairs of similar diseases from different perspectives. Currently, it is still a challenge to exploit both of them to calculate disease similarity. Therefore, a new method (SemFunSim) that integrates semantic and functional association is proposed to address the issue.MethodsSemFunSim is designed as follows. First of all, FunSim (Functional similarity) is proposed to calculate disease similarity using disease-related gene sets in a weighted network of human gene function. Next, SemSim (Semantic Similarity) is devised to calculate disease similarity using the relationship between two diseases from Disease Ontology. Finally, FunSim and SemSim are integrated to measure disease similarity.ResultsThe high average AUC (area under the receiver operating characteristic curve) (96.37%) shows that SemFunSim achieves a high true positive rate and a low false positive rate. 79 of the top 100 pairs of similar diseases identified by SemFunSim are annotated in the Comparative Toxicogenomics Database (CTD) as being targeted by the same therapeutic compounds, while other methods we compared could identify 35 or less such pairs among the top 100. Moreover, when using our method on diseases without annotated compounds in CTD, we could confirm many of our predicted candidate compounds from literature. This indicates that SemFunSim is an effective method for drug repositioning.

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