Differential immune profiles distinguish the mutational subtypes of gastrointestinal stromal tumor

Vitiello, Gerardo A.; Bowler, Timothy G.; Liu, Mengyuan; Medina, Benjamin D.; Zhang, Jennifer Q.; Param, Nesteene J.; Loo, Jennifer K.; Goldfeder, Rachel L.; Chibon, Fréderic; Rossi, Ferdinand; Zeng, Shan; DeMatteo, Ronald P.

doi:10.1172/jci124108

Cited by 60 publications

(87 citation statements)

References 65 publications

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“…This is consistent with a previous study that demonstrated immune cells were more numerous and had higher cytolytic activity in PDGFRA-mutant GISTs compared to KIT-mutant GISTs. 56 Despite of the promising result, we still had some limitations in our study. Unfortunately, we did not have any results referring to the mechanism why TLS could influence the imatinib resistance and prognosis of GIST.…”

Section: Discussionmentioning

confidence: 86%

Tumor-associated tertiary lymphoid structure predicts postoperative outcomes in patients with primary gastrointestinal stromal tumors

et al. 2020

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Tumor-infiltrating tertiary lymphoid structures (TLS) are thought to have anti-tumor activity and are believed to indicate a favorable prognosis in cancer patients. However, the prognostic value of TLS in gastrointestinal stromal tumors (GIST) is unknown. We evaluated the prognostic value of TLS using two independent GIST cohorts. Pathological examinations identified TLS in 44.9% of patients in our discovery cohort (DC). TLS was significantly associated with smaller tumor size (P = .011), relatively well morphological classification (P < .001), lower NIH classification (P < .001), lower recurrence (P = .005), longer survival time (P < .001) and lower imatinib resistance (P = .006). Kaplan-Meier curves showed that TLS was remarkably associated with favorable survival (P = .0002) and recurrence (P = .0015) time. In addition, the presence of KIT mutations and the absence of TLS suggested worst prognosis both in terms of overall survival (OS) (P = .0029) and time to recurrence (TTR) (P = .0150), while the presence of PDGFRA mutations and TLS suggested optimal prognosis for OS and TTR. Multivariate analyzes demonstrated that TLS was an independent prognostic factor for OS (HR:0.180, P = .002) and TTR (HR:0.412, P = .023). These results were confirmed using our validation cohort. Multiplexed immunohistochemistry staining was used to determine the composition of TLS. Therapies designed to target TLS may be a novel therapeutic strategy for GIST patients with imatinib resistance.

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Section: Discussionmentioning

confidence: 86%

Tumor-associated tertiary lymphoid structure predicts postoperative outcomes in patients with primary gastrointestinal stromal tumors

et al. 2020

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“…In a previous study, the clinical model was used to predict the biological behaviors of GISTs (25), and genotypes have been shown to be closely related to certain clinical characteristics (26)(27)(28). However, the relationship between some clinical characteristics and the KIT 11 mutation remains controversial (26,27,29,30). Most of the previous studies showed that GISTs with the KIT exon 11 mutation usually had high percentages of mitotic count and/or poor prognosis (26,27,30).…”

Section: Discussionmentioning

confidence: 99%

Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

Liu¹,

Wang²,

Liu³

et al. 2020

Preprint

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Background Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors.Methods We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase (A-phase), venous phase (V-phase), delayed phase (D-phase), and combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristic (ROC).Results The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval (CI), 0.640–0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397–0.790), 0.715 (95% CI, 0.506–0.873), and 0.679 (95% CI, 0.468–0.847), respectively, with the validation set.Conclusion The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

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“…Moreover, such inferences can be further refined using previously acquired information such as the gene expression profiles of immune cells, as detailed in reports using the CIBERSORT algorithm 12 . These approaches have become more important as immune checkpoint inhibitors have recently shown considerable clinical success 13 Moreover, viral genome‐capture sequencing has been used to identify viral integration sites as genomic hallmarks of human papillomavirus (HPV)‐positive CES genomes 14 …”

Section: Introductionmentioning

confidence: 99%

Genomic, transcriptomic, and viral integration profiles associated with recurrent/metastatic progression in high‐risk human papillomavirus cervical carcinomas

Liu

Lee

et al. 2020

Cancer Medicine

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Acquisition of recurrent/metastatic potential by a tumor cell defines a critical step in malignant progression. However, understanding of metastatic progression at the molecular level is scarce for cervical carcinomas (CES). In this study, we performed genomic, transcriptomic, and viral profiling of five pairs of primary (CES‐P) and matched recurrent/metastatic tumors (CES‐R/M) with high risk human papillomavirus. Whole exome sequencing revealed mutation features of CES‐R/M including elevated mutation burdens and prevalent copy number alterations compared to their matched CES‐P. A relative deficit of APOBEC‐related mutation signatures accompanying the transcriptional downregulation of APOBEC3A was observed for CES‐R/M. Mutations in genes encoding epigenetic regulators were commonly observed as CES‐R/M‐specific alterations. Immunoprofiling and gene set analysis revealed CES‐Ps were enriched with transcripts representing activated anticancer immunity such as interferon‐gamma pathway, while CES‐R/M exhibited upregulation of genes involved in epithelial‐mesenchymal transition and angiogenesis. Viral capture sequencing revealed that integration sites remained enriched in viral E1 protein domain during malignant progression. Moreover, we found transcriptional upregulation of POSTN and downregulation of APOBEC3A were associated with unfavorable clinical outcomes in CES. Comprehensive genomic and transcriptomic profiling of a rare cohort including CES‐R/M identified metastases‐specific features to advance the molecular understanding into CES metastatic progression with potential clinical implications.

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Differential immune profiles distinguish the mutational subtypes of gastrointestinal stromal tumor

Cited by 60 publications

References 65 publications

Tumor-associated tertiary lymphoid structure predicts postoperative outcomes in patients with primary gastrointestinal stromal tumors

Tumor-associated tertiary lymphoid structure predicts postoperative outcomes in patients with primary gastrointestinal stromal tumors

Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study

Genomic, transcriptomic, and viral integration profiles associated with recurrent/metastatic progression in high‐risk human papillomavirus cervical carcinomas

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