Higher number of known pancreatic cancer mutations highlighted by whole-transcripto- me and whole-exome sequencing predicts cli- nical outcome in early stage patients

Abstract

Context. Despite pancreatic cancer (PC) genomic char- acterization, true advances in the development of prog- nosis classification and new therapeutic strategies have yet to come. Objective. We aimed to better understand genomic al- terations of invasive phenotype in order to improve pa- tient selection for treatment options. Methods. We analyzed 37 PC samples by either whole transcriptome or exome sequencing performed at 75- 100 bpx2 on an Illumina platform, matched with normal DNA to identify somatic events. Calls of single nucleo- tide variants and InDels were annotated with 1000 ge- nomes allele frequencies, dbSNP149 rsIDs, Exac and EVS using Oncotator. We detected the pathogenicity of the emerging variants by previous knowledge and bio- informatic mutation-prediction tools. Results. We highlighted 43 recurrently altered genes involving several pathways including chromatine re- modelling and DNA damage repair. The analysis limited on early stage patients (40% of samples) showed how a poor prognosis was significantly associated with a higher number of known PC mutations (pValue=0.047). The subgroup with better overall survival (>25 months) harbors an average of 24 events instead of the one with an overall survival <25 months, that presents an average of 40 mutations. Conclusions. Our data show how a complex genetic profile in early stage could be responsible of more ag- gressiveness, suggesting an urgently need of an innova- tive approach to classify this disease