Differential immune profiles distinguish the mutational subtypes of gastrointestinal stromal tumor
Gerardo A. Vitiello, … , Shan Zeng, Ronald P. DeMatteo
Gerardo A. Vitiello, … , Shan Zeng, Ronald P. DeMatteo
Published February 14, 2019
Citation Information: J Clin Invest. 2019;129(5):1863-1877. https://doi.org/10.1172/JCI124108.
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Research Article Immunology Oncology

Differential immune profiles distinguish the mutational subtypes of gastrointestinal stromal tumor

Abstract

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.

Authors

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

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Figure 6

Machine learning identifies an immune signature predictive of KIT- and PDGFRA-mutant GIST.

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Machine learning identifies an immune signature predictive of KIT- and P...
(A) Random forest modeling with 5-fold cross-validation of KIT- and PDGFRA-mutant GIST specimens (training set created by partitioning 80% of KIT and PDGFRA samples from Supplemental Table 3, n = 50). Confusion matrix (right) indicates assessment of model fit to training set. OOB, out-of-bag. (B) Distribution of top 6 features identified by random forest modeling. *Adjusted P < 0.05 from DSeq2. (C) Predictive capacity of model on remaining KIT- and PDGFRA-mutant GIST testing set (n = 11) and the CINSARC cohort (n = 12). Accuracy (Acc), sensitivity, specificity, and P value[Acc >no information rate (NIR)] of the model are shown, calculated by caret package for R. Bars indicate mean + SEM. (D) Random forest modeling with 5-fold cross-validation of UPG KIT- and UPG PDGFRA-mutant GIST specimens (training set created by partitioning 80% of UPG KIT and UPG PDGFRA samples from Supplemental Table 4, n = 18). Confusion matrix (right) indicates assessment of model fit to training set. (E) Distribution of top 6 features identified by random forest modeling. *Adjusted P < 0.05 from DSeq2. (F) Predictive capacity of model on remaining UPG KIT- and UPG PDGFRA-mutant GIST testing set (n = 4) and the CINSARC cohort (n = 12). Accuracy, sensitivity, specificity, and P value[Acc >NIR] of the model are shown, calculated by caret package for R. For B and E, all data points are shown, with boxes defining the interquartile range and whiskers extending to the lowest and highest data points.