Gastrointestinal stromal tumor (GIST) is the most common human sarcoma, frequently characterized by an oncogenic mutation in the KIT or PDGFRA gene. We performed RNA sequencing of 75 human GIST tumors from 75 patients, comprising what we believe to be the largest cohort of GISTs sequenced to date, in order to discover differences in the immune infiltrates of KIT- and PDGFRA-mutant GIST. Through bioinformatics, immunohistochemistry, and flow cytometry, we found that in PDGFRA-mutant GISTs, immune cells were more numerous and had higher cytolytic activity than in KIT-mutant GISTs. PDGFRA-mutant GISTs expressed many chemokines, such as CXCL14, at a significantly higher level when compared with KIT-mutant GISTs and exhibited more diverse driver-derived neoepitope:HLA binding, both of which may contribute to PDGFRA-mutant GIST immunogenicity. Through machine learning, we generated gene expression–based immune profiles capable of differentiating KIT- and PDGFRA-mutant GISTs, and identified additional immune features of high–PD-1– and –PD-L1–expressing tumors across all GIST mutational subtypes, which may provide insight into immunotherapeutic opportunities and limitations in GIST.
Gerardo A. Vitiello, Timothy G. Bowler, Mengyuan Liu, Benjamin D. Medina, Jennifer Q. Zhang, Nesteene J. Param, Jennifer K. Loo, Rachel L. Goldfeder, Frederic Chibon, Ferdinand Rossi, Shan Zeng, Ronald P. DeMatteo
Machine learning identifies an immune signature predictive of