RT Journal Article
SR Electronic
T1 Identification and Ranking of Recurrent Neo-Epitopes in Cancer
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 389437
DO 10.1101/389437
A1 Eric Blanc
A1 Manuel Holtgrewe
A1 Arunraj Dhamodaran
A1 Clemens Messerschmidt
A1 Gerald Willimsky
A1 Thomas Blankenstein
A1 Dieter Beule
YR 2018
UL http://biorxiv.org/content/early/2018/08/10/389437.abstract
AB Neo-epitopes are emerging as attractive targets for cancer immunotherapy and new strategies for rapid identification of relevant candidates have become a priority. We propose a method for in silico selection of candidates which have a high potential for neo-antigen generation and are likely to appear in multiple patients. This is achieved by carefully screening 33 TCGA data sets for recurrent somatic amino acid exchanges and, for the 1,055 resulting recurrent variants, applying MHC class I binding prediction algorithms. A preliminary confirmation of epitope binding and recognition by CD8 T cells has been carried out for a couple of candidates in humanized mice. Recurrent neo-epitopes may be suitable to supplement existing personalized T cell treatment approaches with precision treatment options.