Intron-Retention Neoantigen Load Predicts Favorable Prognosis in Pancreatic Cancer

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Date
2022
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American English
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American Society of Clinical Oncology
Abstract

Purpose: High tumor mutation burden (TMB) in many cancer types is associated with the production of tumor-specific neoantigens, a favorable outcome and response to immune checkpoint blockade (ICB) therapy. Besides mutation-derived neoantigens, aberrant intron retention also produces tumor neopeptides that could trigger an immune response. The relationship between intron-retention-derived tumor neoantigens (IR-neoAg) and clinical outcomes in pancreatic cancer remains uncertain. Here, we quantify IR-neoAg in pancreatic cancer and evaluate whether IR-neoAg load might serve as a biomarker for selecting patients who may benefit from ICB therapy.

Methods: We developed a computational approach to estimate patient-specific IR-neoAg load from transcriptome data available in The Cancer Genome Atlas pancreatic cancer cohort. Associations between IR-neoAg load and patient overall survival were evaluated using Kaplan-Meier estimates and Cox regression. Differential expression of immune checkpoint and HLA-I genes was evaluated in tumors with high IR-neoAg load.

Results: High IR-neoAg load predicted better overall survival in pancreatic cancer, although no association was found for TMB. IR-neoAg load remained a significant prognostic factor after adjusting for patient age, sex, tumor stage and grade, and TMB. Moreover, pancreatic tumors with both high IR-neoAg load and high HLA-I gene expression had similar gene expression profiles as other tumor types that showed response to anti-programmed cell death protein 1 therapy.

Conclusion: IR-neoAg load is associated with favorable survival in pancreatic cancer. These findings provide strong evidence for considering IR-neoAgs when selecting patients who might benefit from ICB therapy.

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Dong C, Reiter JL, Dong E, et al. Intron-Retention Neoantigen Load Predicts Favorable Prognosis in Pancreatic Cancer. JCO Clin Cancer Inform. 2022;6:e2100124. doi:10.1200/CCI.21.00124
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JCO Clinical Cancer Informatics
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PMC
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Article
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