Profiling XXXX gene mutations in @entity43 biopsies by targeted next-generation sequencing: a retrospective study.

@entity1 @entity43 @entity5255 @entity5 @entity876 @entity10412 @entity902 @entity14583
BACKGROUND: The majority of @entity1 with @entity43 (EOC) relapse after initially responding to @entity902 -based chemotherapy, and develop resistance. The genomic features involved in drug resistance are unknown. To unravel some of these features, we investigated the mutational profile of genes involved in pathways related to drug sensitivity in a cohort of matched @entity5 obtained at first surgery (Ft-S) and second surgery (Sd-S). @entity1 AND METHODS: Matched biopsies (33) taken at Ft-S and Sd-S were selected from the 'Pandora' @entity5 tissue collection. DNA libraries for 65 genes were generated using the TruSeq Custom Amplicon kit and sequenced on MiSeq (Illumina). Data were analyzed using a high-performance cluster computing platform (Cloud4CARE project) and independently validated. RESULTS: A total of 2270 somatic mutations were identified (89.85% base substitutions 8.19% indels, and 1.92% unknown). Homologous recombination (HR) genes and @entity876 were mutated in the majority of Ft-S, while ATM, ATR, @entity14583 and @entity5255 were mutated in the entire dataset. Only 2% of mutations were conserved between matched Ft-S and Sd-S. Mutations detected at second surgery clustered @entity1 in two groups characterized by different mutational profiles in genes associated with HR, @entity10412 , miRNA biogenesis and signal transduction. CONCLUSIONS: There was a low level of concordance between Ft-S and Sd-S in terms of mutations in genes involved in key processes of @entity5 growth and drug resistance. This result suggests the importance of future longitudinal analyses to improve the clinical management of relapsed EOC.
