@article{CéliaDupain2018,

title = {Discovery of new fusion transcripts in a cohort of pediatric solid cancers at relapse and relevance for personalized medicine},

author = {Célia Dupain, Anne C. Harttrampf, Yannick Boursin, Manuel Lebeurrier, Windy Rondof, Guillaume Robert-Siegwald, Pierre Khoueiry, Birgit Geoerger and Liliane Massaad-Massade},

editor = {Cell Press},

url = {https://www.sciencedirect.com/science/article/pii/S152500161830529X},

doi = {https://doi.org/10.1016/j.ymthe.2018.10.022},

year  = {2018},

date = {2018-11-02},

journal = {Molecular Therapy},

abstract = {We hypothetized that pediatric cancers would more likely harbor fusion transcripts. To dissect the complexity of the fusions landscape in recurrent solid pediatric cancers, we conducted a study on 48 patients with different relapsing/resistant malignancies. By analysing RNA-sequencing data with a new in-house pipeline for fusions detection named ChimComp, followed by verification by real time PCR, we identified and classified the most confident fusion transcripts (FT) according to their potential biological function and druggability. The majority of FT were predicted to affect key cancer pathways and described to be involved in oncogenesis. Contrary to previous descriptions, we found no significant correlation between the number of fusions and mutations, emphasizing the particularity to study pre-treated pediatric patients. A considerable proportion of FT containing tumor suppressor genes was detected, reflecting their importance in pediatric cancers. FT containing non-receptor tyrosine kinases occurred at low incidence and predominantly in brain tumors. Remarkably, more than 30% of patients presented a potentially druggable high-confidence fusion. In conclusion, we detected new oncogenic FT in relapsing pediatric cancer patients by establishing a robust pipeline that can be applied to other malignancies, to detect and prioritize experimental validation studies leading to the development of new therapeutic options.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}