PP085 Study of microRNAs’ expression profile in head and neck cancer

ObjectivemiRNAs’ expression profile is emerging among the best markers for diagnosis, staging and treatment of cancer. The present project aims at characterizing miRNAs signatures with prognostic, predictive or diagnostic power in HNSCC and at identifying possible correlations with TP53 status.MethodsPatients with histologically proven HNSCC who underwent surgical treatment without any previous chemo/radio-therapeutic treatment were included in the study. Three biopsies, from the tumor itself, peri-tumoral tissue and normal tissue, were obtained for each patient and submitted to RNA and genomic DNA extraction.ResultsTP53 status was assessed in 55 patients, by direct sequencing of exons 5–8, and 23 out of these 55 showed TP53 mutations. Three tumor samples presented double or triple mutations. TP53 status was also evaluated in the peri-tumoral and normal counterparts and a wild-type TP53 sequence was found in all cases.Additionally, cDNA cloning and subsequent sequencing from samples carrying double and triple TP53 mutations showed the existence of different allelic populations, suggesting that these tumors are composed by TP53-heterogeneous cell types.microRNAs’ expression profiling was performed on 55 patients using the Agilent platform. Nineteen miRNAs are differentially regulated in the HNSCC samples when compared with their normal tissue counterparts, while eighteen miRNAs are significantly altered in their expression when compared with their peri-tumoral tissue counterparts (using p-value <10−3 and fold change >2 as cut-off).Integration between microRNAs’ expression data and TP53 status evidenced four microRNAs whose alteration in HNSCC correlates with TP53 inactivation.ConclusionThe data obtained by TP53 cloning suggest the presence of different clonal populations in the tumor. Microarray analysis indicates that microRNAs are strongly modulated between HNSCC tumor and non-tumor samples and many of the altered microRNAs in our data. Ongoing analyses are integrating microRNAs’ expression data with clinical information in order to evaluate the predictive/prognostic power of the modulated microRNAs.