MedCommCancer BiologyPeer-reviewed journal article
Harnessing Tumor-Specific Transcript Diversity Uncovers a Shared Neoantigen Reservoir for Pancreatic Ductal Adenocarcinoma
Jingjing Zhao, Qiaojuan Li, Peng LiniD, Yu YangiD, Hongwu Yu, Yifan Wen, Wenqian YuiD, Huiyi HeiD, Sichen Tao, Feifei Zhang, Yan LiiD, Zhixiang HuiD, Jing Xie, Zhen CheniD, Shenglin HuangiD
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is refractory to immunotherapy due to its immunologically cold microenvironment and the scarcity of mutation-derived neoantigens. Here, we introduce NeoAPP, a computational tool designed to systematically decode neoantigens arising from tumor-specific transcripts (TSTs) generated by transcriptional dysregulation. Multi-cohort transcriptomic profiling of 413 PDAC samples using NeoAPP reveal a median of 351 neoantigens per sample derived from 56 neoantigen-encoding TSTs (neoTSTs), surpassing mutation-derived counterparts in both abundance and patient coverage. Mechanistic analyses show that non-canonical splicing junction and transposable element activation drive neoantigen generation, while FOXA2-regulated promoter usage constitutes a potential major source of neoTSTs. Tumor-derived neoTSTs are also detected in extracellular vesicles and cancer-associated fibroblasts, implicating stromal crosstalk in immune modulation. Vaccination with neoTSTs induce CD8+ T cell responses in HLA-A*02:01/A*11:01 transgenic mice and suppressed tumor growth in syngeneic PDAC models. Collectively, this work establishes TST-derived neoantigens as a dominant and therapeutically actionable antigen reservoir in PDAC, advancing a transcriptome-guided framework for neoantigen discovery with potential to overcome immune resistance in low-mutation cancers. Abstract Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/705024v1_ufig1.gif" ALT="Figure 1"> View larger version (71K): org.highwire.dtl.DTLVardef@82ff31org.highwire.dtl.DTLVardef@401342org.highwire.dtl.DTLVardef@b0771corg.highwire.dtl.DTLVardef@15c03ef_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- research focus: cell type annotation
- approach: deep learning
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- top 46%
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